my_flash_cards_general_cards_df_abstraction_groups.json

"{\"0\":{\"Question\":\"How do you use dual numbers for differentiation: \",\"Answer\":\"Dual numbers are just things that square to zero I think (think of an infinitesimal).\\nSo to get derivative, just plug in x + dual epsilon, then expand out result, and take linear term. \",\"Key ideas\":\"1. Dual numbers are a type of number that square to zero.\\n2. To differentiate using dual numbers, plug in x + dual epsilon into the equation.\\n3. Expand out the result of the equation.\\n4. Take the linear term of the result.\",\"Abstraction groups\":{\"-1\":[\"Dual Number\",\"Differentiation\",\"Epsilon\",\"Expansion\",\"Linear Term\"],\"0\":[\"Dual Number\"],\"1\":[\"Differentiation\"],\"2\":[\"Calculus\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"1\":{\"Question\":\"How is a double formatted in C? (same as a float in python) \",\"Answer\":\"64 bits of information. \\n1 bit sign, 11 bits exponent, 52 bit mantissa. \\nExtracting the number is: sign * mantissa * 2^(exponent)\",\"Key ideas\":\"1. A double is a 64-bit data type.\\n2. A double is the same as a float in Python.\\n3. A double is composed of 1 bit for the sign, 11 bits for the exponent, and 52 bits for the mantissa.\\n4. To extract the number from a double, use the formula: sign * mantissa * 2^(exponent).\",\"Abstraction groups\":{\"-1\":[\"Double\",\"C\",\"Python\",\"Float\",\"Bit\",\"Sign\",\"Exponent\",\"Mantissa\",\"Extracting\"],\"0\":[\"Double\"],\"1\":[\"Data Type\",\"C\",\"Python\"],\"2\":[\"Programming Language\",\"Computer Science\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"2\":{\"Question\":\"Which differentiation method is inherently prone to rounding errors? \",\"Answer\":\"Numerical differentiation, as opposed to symbolic differentiation \",\"Key ideas\":\"\\n1. Differentiation: the process of finding the rate of change of a function with respect to a variable. \\n2. Numerical differentiation: a method of differentiation that involves approximating the derivative of a function using numerical values. \\n3. Symbolic differentiation: a method of differentiation that involves using mathematical symbols to represent the derivative of a function. \\n4. Rounding errors: errors that occur when a numerical value is rounded off to a certain number of decimal places. \\n5. Inherently prone: having a tendency to be affected by something, in this case, rounding errors.\",\"Abstraction groups\":{\"-1\":[\"Differentiation\",\"Numerical\",\"Symbolic\",\"Rounding\",\"Error\",\"Inherently\"],\"0\":[\"Differentiation\"],\"1\":[\"Numerical\",\"Symbolic\"],\"2\":[\"Method\",\"Error\"],\"3\":[\"Rounding\",\"Prone\"],\"4\":[\"Inherently\"]}},\"3\":{\"Question\":\"When is a set of data considered linearly separable? \",\"Answer\":\"When there is hyperplane that can correctly classify everything into two classes. \",\"Key ideas\":\"\\n1. Data: A set of data is a collection of information. \\n2. Linear Separability: This is a property of a set of data that describes whether or not it can be correctly classified into two distinct classes. \\n3. Hyperplane: This is a line or plane that divides a set of data into two distinct classes. \\n4. Classification: This is the process of assigning a set of data into two distinct classes. \\n5. Correct Classification: This is when a set of data is correctly classified into two distinct classes.\",\"Abstraction groups\":{\"-1\":[\"Data\",\"Linear\",\"Hyperplane\",\"Classification\",\"Correct\"],\"0\":[\"Linear Separability\"],\"1\":[\"Data\",\"Classification\"],\"2\":[\"Separability\",\"Hyperplane\"],\"3\":[\"Set\",\"Class\"],\"4\":[\"Mathematics\"]}},\"4\":{\"Question\":\"Describe the relation between autoregressive models, flow models, and latent variable models in 1 sentence.\",\"Answer\":\"Autoregressive models are general prediction models\\nFlow models can handle continuous data and disentangle the latent space\\nLatent variable models compress the latent space to enhance inference. \",\"Key ideas\":\"1. Autoregressive models are general prediction models.\\n2. Flow models can handle continuous data and disentangle the latent space.\\n3. Latent variable models compress the latent space to enhance inference.\",\"Abstraction groups\":{\"-1\":[\"Autoregressive\",\"Flow\",\"Latent\",\"Model\",\"Prediction\",\"Continuous\",\"Disentangle\",\"Latent Space\",\"Compress\",\"Inference\"],\"0\":[\"Autoregressive Model\"],\"1\":[\"Flow Model\",\"Latent Variable Model\"],\"2\":[\"Prediction Model\",\"Data Processing\"],\"3\":[\"Modeling\",\"Inference\"],\"4\":[\"Mathematics\"]}},\"5\":{\"Question\":\"In CS294 (Berkeley) - Deep Unsupervised learning, what is the relation between VAE, VQ-VAE, and PixelVAE?\",\"Answer\":\"VAE is the basic architecture. \\nVQ-VAE is introducing a more realistic (categorical) latent space.\\nPixelVAE uses an autoregressive model in the decoder to improve the local structure generation. \",\"Key ideas\":\"1. VAE (Variational Autoencoder) is the basic architecture.\\n2. VQ-VAE (Vector Quantized Variational Autoencoder) introduces a more realistic (categorical) latent space.\\n3. PixelVAE (Pixel-wise Variational Autoencoder) uses an autoregressive model in the decoder to improve the local structure generation.\",\"Abstraction groups\":{\"-1\":[\"Vae\",\"Vq-Vae\",\"Pixelvae\",\"Latent Space\",\"Autoregressive Model\",\"Decoder\"],\"0\":[\"Deep Unsupervised Learning\"],\"1\":[\"VAE\",\"VQ-VAE\",\"PixelVAE\"],\"2\":[\"Latent Space\",\"Autoregressive Model\",\"Decoder\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"6\":{\"Question\":\"In CS294 (Berkeley) - Deep Unsupervised learning, what is the relation between flow models and latent variable models? What are latent variable models trying to address?\",\"Answer\":\"Latent variable models, like VAEs, try to compress the latent space compared to flow models (which maintain dimensionality). \\nThis is expected to be a more efficient representation for inference (rather than just sampling) \",\"Key ideas\":\"1. CS294 (Berkeley) is a course on Deep Unsupervised Learning. \\n2. Flow models and latent variable models are related. \\n3. Latent variable models (such as VAEs) try to compress the latent space compared to flow models. \\n4. Flow models maintain the dimensionality of the latent space. \\n5. Latent variable models are expected to be a more efficient representation for inference (rather than just sampling).\",\"Abstraction groups\":{\"-1\":[\"CS294\",\"Deep Unsupervised Learning\",\"Flow Model\",\"Latent Variable Model\",\"VAE\",\"Latent Space\",\"Dimensionality\",\"Inference\",\"Sampling\"],\"0\":[\"Latent Variable Model\"],\"1\":[\"Deep Unsupervised Learning\",\"Flow Model\"],\"2\":[\"Inference\",\"Sampling\"],\"3\":[\"Dimensionality\",\"Latent Space\"],\"4\":[\"CS294\"]}},\"7\":{\"Question\":\"In CS294 (Berkeley) - Deep Unsupervised learning, what is the relation between autoregressive models and flow models? What are flow models trying to address?\",\"Answer\":\"They can handle continuous spaces.\\nThey can find disentangled representations of latent variables (because the prior is assumed to be disentangled). \",\"Key ideas\":\"1. Autoregressive models and flow models are related in the context of deep unsupervised learning. \\n2. Flow models are used to handle continuous spaces.\\n3. Flow models can find disentangled representations of latent variables.\\n4. This is because the prior is assumed to be disentangled.\",\"Abstraction groups\":{\"-1\":[\"Autoregressive Model\",\"Flow Model\",\"Continuous Space\",\"Disentangled Representation\",\"Latent Variable\",\"Prior\"],\"0\":[\"Deep Unsupervised Learning\"],\"1\":[\"Autoregressive Model\",\"Flow Model\"],\"2\":[\"Representation\",\"Latent Variable\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"8\":{\"Question\":\"What were the topics of the first 3 major lectures in CS294 (Berkeley) - Deep Unsupervised learning?\",\"Answer\":\"Autoregressive models (eg. Transformers)\\nFlow models (eg. Glow)\\nLatent variable models (eg. VAEs) \",\"Key ideas\":\"\\n1. Autoregressive models are a type of deep unsupervised learning.\\n2. Transformers are an example of an autoregressive model.\\n3. Flow models are another type of deep unsupervised learning.\\n4. Glow is an example of a flow model.\\n5. Latent variable models are a third type of deep unsupervised learning.\\n6. VAEs (Variational Autoencoders) are an example of a latent variable model.\",\"Abstraction groups\":{\"-1\":[\"Autoregressive\",\"Transformer\",\"Flow\",\"Glow\",\"Latent\",\"VAE\"],\"0\":[\"Deep Unsupervised Learning\"],\"1\":[\"Autoregressive Model\",\"Flow Model\",\"Latent Variable Model\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Data Science\"],\"4\":[\"Science\"]}},\"9\":{\"Question\":\"Of the things I have learned about mechanistic interpretability, what two major categories can they be divided into? \",\"Answer\":\"Interpretability of basic network structure and information flow\\nInterpretability of transformers \",\"Key ideas\":\"\\n1. Mechanistic interpretability: \\n    a. Refers to the ability to understand the inner workings of a machine learning model\\n2. Major categories of mechanistic interpretability: \\n    a. Interpretability of basic network structure and information flow\\n    b. Interpretability of transformers\",\"Abstraction groups\":{\"-1\":[\"Mechanistic\",\"Interpretability\",\"Network\",\"Information\",\"Flow\",\"Transformer\"],\"0\":[\"Mechanistic Interpretability\"],\"1\":[\"Network Structure\",\"Information Flow\",\"Transformer\"],\"2\":[\"Model Understanding\"],\"3\":[\"Machine Learning\"],\"4\":[\"Artificial Intelligence\"]}},\"10\":{\"Question\":\"List a few of the major results in mechanistic interpretability relating to transformers \",\"Answer\":\"Induction head formation from Anthropic. Also transformer structure interpretation from anthropic \\nGrokking process and formation\\/ablation of generalization circuit\\nFactual association and editing in models (ROME) \",\"Key ideas\":\"\\n1. Induction head formation from Anthropic\\n2. Transformer structure interpretation from Anthropic\\n3. Grokking process and formation\\/ablation of generalization circuit\\n4. Factual association and editing in models (ROME)\",\"Abstraction groups\":{\"-1\":[\"Anthropic\",\"Transformer\",\"Grokking\",\"Generalization\",\"ROME\"],\"0\":[\"Mechanistic Interpretability\"],\"1\":[\"Transformer\",\"Anthropic\"],\"2\":[\"Modeling\",\"Grokking\"],\"3\":[\"Factual Association\",\"Generalization\"],\"4\":[\"Artificial Intelligence\"]}},\"11\":{\"Question\":\"List a few of the major results in mechanistic interpretability relating to the basic structure of networks \",\"Answer\":\"Circuits for vision models, hierarchy, and building blocks \\nToy model of superposition from Anthropic \",\"Key ideas\":\"\\n1. Circuits for vision models\\n2. Hierarchy\\n3. Building blocks\\n4. Toy model of superposition from Anthropic\",\"Abstraction groups\":{\"-1\":[\"Vision\",\"Hierarchy\",\"Block\",\"Superposition\",\"Anthropic\"],\"0\":[\"Mechanistic Interpretability\"],\"1\":[\"Network Structure\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Science\"],\"4\":[\"Knowledge\"]}},\"12\":{\"Question\":\"List a few of the major transformer types mentioned in the CS25 course from Stanford\",\"Answer\":\"1. GPT-3 decoder\\n\\t2. Decision transformer for RL\\n\\t3. Mixture of experts\\/switch transformer\\n\\t4. Perceiver (Projection)\\n\\t5. Non-parametric transformers\\n\\t6. Audio\\n\\t7. GLOM\",\"Key ideas\":\"\\n1. GPT-3 decoder: a type of transformer used in natural language processing\\n2. Decision transformer for RL: a type of transformer used in reinforcement learning\\n3. Mixture of experts\\/switch transformer: a type of transformer that combines multiple experts to make decisions\\n4. Perceiver (Projection): a type of transformer that projects input data into a higher-dimensional space\\n5. Non-parametric transformers: a type of transformer that does not require a fixed set of parameters\\n6. Audio: a type of transformer used for audio processing\\n7. GLOM: a type of transformer used for graph-based learning\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"RL\",\"Expert\",\"Perceiver\",\"Non-parametric\",\"Audio\",\"GLOM\"],\"0\":[\"Transformers\"],\"1\":[\"GPT-3 Decoder\",\"Decision Transformer For RL\",\"Mixture Of Experts\\/Switch Transformer\",\"Perceiver (Projection)\",\"Non-Parametric Transformers\",\"Audio\",\"GLOM\"],\"2\":[\"Natural Language Processing\",\"Reinforcement Learning\",\"Decision Making\",\"Projection\",\"Parameter-Free\",\"Audio Processing\",\"Graph-Based Learning\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\",\"Data Processing\"],\"4\":[\"Computer Science\"]}},\"13\":{\"Question\":\"How are the properties of the covariance matrix similar to a hermitian matrix? \",\"Answer\":\"Covariance matrix is normal and hermitian, so it is diagonalizable, and its eigenvalues are real, and different eigenvalues have orthogonal eigenvectors. \",\"Key ideas\":\"1. Covariance matrix is normal and hermitian. \\n2. Covariance matrix is diagonalizable. \\n3. Eigenvalues of the covariance matrix are real. \\n4. Different eigenvalues have orthogonal eigenvectors.\",\"Abstraction groups\":{\"-1\":[\"Covariance Matrix\",\"Hermitian Matrix\",\"Diagonalizable\",\"Eigenvalue\",\"Orthogonal Eigenvector\"],\"0\":[\"Covariance Matrix\"],\"1\":[\"Matrix\",\"Linear Algebra\"],\"2\":[\"Mathematics\",\"Science\"],\"3\":[\"Knowledge\",\"Learning\"],\"4\":[\"Education\"]}},\"14\":{\"Question\":\"Why is principle components analysis equivalent to finding the eigenvectors of the covariance matrix? \",\"Answer\":\"Once you find the eigenvectors of the covariance matrix, then view all of your data along the eigenvectors (ie project onto the eigenvectors and then calculate the covariance), then \\nthe remaining variance will be in independent components. \\nMoreover, those components will have decreasing\\/increasing importance, and some can be dropped. \",\"Key ideas\":\"\\n1. Principle components analysis (PCA) is a method of dimensionality reduction. \\n2. PCA is equivalent to finding the eigenvectors of the covariance matrix. \\n3. Eigenvectors are vectors that, when multiplied by a matrix, produce a scalar multiple of the original vector. \\n4. The covariance matrix is a matrix that contains the variance and covariance of a set of variables. \\n5. To find the eigenvectors of the covariance matrix, you must calculate the eigenvalues and eigenvectors of the matrix. \\n6. Once you find the eigenvectors of the covariance matrix, you can view all of your data along the eigenvectors (ie project onto the eigenvectors and then calculate the covariance). \\n7. The remaining variance will be in independent components. \\n8. Those components will have decreasing\\/increasing importance, and some can be dropped.\",\"Abstraction groups\":{\"-1\":[\"PCA\",\"Eigenvector\",\"Covariance\",\"Matrix\",\"Eigenvalue\",\"Variance\",\"Component\",\"Importance\"],\"0\":[\"PCA\"],\"1\":[\"Dimensionality Reduction\",\"Matrix Algebra\"],\"2\":[\"Data Analysis\",\"Mathematics\"],\"3\":[\"Science\",\"Problem Solving\"],\"4\":[\"Knowledge\"]}},\"15\":{\"Question\":\"What is the covariance of a vector of random variables?\\nWhat about for a finite sample of data? \",\"Answer\":\"It is the average of (x-\\\\bar{x})(x - \\\\bar{x})^T, with diagonal elements being the variances.\\nIf x has length p, then a finite sample of n values of x will have a covariance matrix X^T X where X is n by p (that is, you sum over the sample dimension). \",\"Key ideas\":\"\\n1. Covariance is a measure of the relationship between two variables.\\n2. Covariance of a vector of random variables is the average of (x-\\\\bar{x})(x - \\\\bar{x})^T, with diagonal elements being the variances.\\n3. For a finite sample of data, the covariance matrix is X^T X, where X is n by p (n is the sample size and p is the length of the vector).\",\"Abstraction groups\":{\"-1\":[\"Covariance\",\"Vector\",\"Random Variable\",\"Finite Sample\",\"Data\",\"X\",\"N\",\"P\"],\"0\":[\"Covariance\"],\"1\":[\"Statistics\",\"Mathematics\"],\"2\":[\"Data Analysis\",\"Quantitative Analysis\"],\"3\":[\"Science\",\"Research\"],\"4\":[\"Knowledge\"]}},\"16\":{\"Question\":\"Why was pixelVAE (a blend of pixelCNN and VQ-VAE) better than either individually? \",\"Answer\":\"VAE allows for explicit finding of latent variables, which can guide global structure on the decoder end. PixelCNN is bad at this (linear expansion of context with depth).\\nPixelCNN is very good at fine detail and edges and things like that, so gets the resolution really good. \",\"Key ideas\":\"1. PixelVAE is a blend of PixelCNN and VQ-VAE.\\n2. VAE (Variational Autoencoder) allows for explicit finding of latent variables, which can guide global structure on the decoder end.\\n3. PixelCNN is bad at this (linear expansion of context with depth).\\n4. PixelCNN is very good at fine detail and edges and things like that, so gets the resolution really good.\\n5. PixelVAE is better than either PixelCNN or VQ-VAE individually.\",\"Abstraction groups\":{\"-1\":[\"PixelVAE\",\"PixelCNN\",\"VQ-VAE\",\"VAE\",\"Latent Variable\",\"Decoder\",\"Resolution\"],\"0\":[\"PixelVAE\"],\"1\":[\"PixelCNN\",\"VQ-VAE\"],\"2\":[\"VAE\",\"Latent Variable\",\"Decoder\",\"Resolution\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"17\":{\"Question\":\"What is PixelVAE? \",\"Answer\":\"It blends VQ-VAE with pixelCNN.\\nSpecifically vector-quantized variational autoencoder, with a sequence of hierarchical pixelCNN autoregressive decoders. \",\"Key ideas\":\"\\n1. Vector-Quantized Variational Autoencoder (VQ-VAE): \\n    a. A type of autoencoder that uses vector quantization to compress data.\\n2. PixelCNN Autoregressive Decoders: \\n    a. A type of neural network that uses convolutional layers to generate images.\\n3. PixelVAE: \\n    a. A combination of VQ-VAE and PixelCNN autoregressive decoders. \\n    b. Used to generate images from compressed data.\",\"Abstraction groups\":{\"-1\":[\"VQ-VAE\",\"PixelCNN\",\"PixelVAE\"],\"0\":[\"PixelVAE\"],\"1\":[\"Autoencoder\",\"Neural Network\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"18\":{\"Question\":\"What did the second VQ-VAE paper add to the original VQ-VAE paper to make it even better at high-resolution image generation? \",\"Answer\":\"Hierarchy.\\nIt compressed the image further, doing hierarchical encoding for increased resolution, then sampled and decoded in this hierarchical way as well. \",\"Key ideas\":\"\\n1. VQ-VAE (Vector Quantized Variational Autoencoder): a type of autoencoder used for image generation\\n2. The original VQ-VAE paper: a paper that introduced the VQ-VAE\\n3. High-resolution image generation: the goal of the VQ-VAE\\n4. Hierarchy: a technique used in the second VQ-VAE paper to improve the VQ-VAE's ability to generate high-resolution images\\n5. Compression: the process of reducing the size of an image by encoding it in a hierarchical way\\n6. Sampling: the process of selecting a subset of data points from a larger set\\n7. Decoding: the process of transforming encoded data back into its original form\",\"Abstraction groups\":{\"-1\":[\"VQ-VAE\",\"Paper\",\"Image\",\"Hierarchy\",\"Compression\",\"Sampling\",\"Decoding\"],\"0\":[\"VQ-VAE\"],\"1\":[\"Image Generation\",\"Autoencoders\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"19\":{\"Question\":\"What is one major difference between images generated by GANs compared to VQ-VAE (at least a general trend). \",\"Answer\":\"GANs learn to collapse to a specific type of image to fool the critic network. They lack representational diversity (think of man holding a fish always being the same). \\nVQ-VAE generates images that have high diversity, while being semantically the same (ie zoomed in images of an ostrich vs far out) \",\"Key ideas\":\"1. GANs (Generative Adversarial Networks)\\n2. VQ-VAE (Vector Quantized Variational Autoencoder)\\n3. GANs learn to collapse to a specific type of image to fool the critic network\\n4. GANs lack representational diversity\\n5. VQ-VAE generates images that have high diversity\\n6. VQ-VAE generates images that are semantically the same\",\"Abstraction groups\":{\"-1\":[\"GAN\",\"VQ-VAE\",\"Image\",\"Critic\",\"Diversity\",\"Ostrich\"],\"0\":[\"Image Generation\"],\"1\":[\"GANs\",\"VQ-VAE\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"20\":{\"Question\":\"What problem is Beta-VAE trying to solve, and how does it solve it? \",\"Answer\":\"Vanilla VAE doesn't have a strong prior to learn disentangled latent variables. Ie tuning smiling could also tune the pose.\\nBeta-VAE adds a stronger penalty for regularization in the cost function (the KL divergence term). This forces independent distributions of each latent variables. Why does this work? Seems a bit unclear but basically a strong KL term means it tries to restrict the amount of information recorded, so information being minimized means having the least amount of correlation in the underlying variables (unsure)? \",\"Key ideas\":\"1. Vanilla VAE does not have a strong prior to learn disentangled latent variables.\\n2. Tuning one variable (e.g. smiling) could also tune another variable (e.g. pose).\\n3. Beta-VAE adds a stronger penalty for regularization in the cost function (the KL divergence term).\\n4. This forces independent distributions of each latent variable.\\n5. A strong KL term means it tries to restrict the amount of information recorded.\\n6. Minimizing information means having the least amount of correlation in the underlying variables.\",\"Abstraction groups\":{\"-1\":[\"Beta-Vae\",\"Vanilla Vae\",\"Kl Divergence\",\"Regularization\",\"Latent Variable\",\"Correlation\",\"Information\"],\"0\":[\"Beta-VAE\"],\"1\":[\"Regularization\",\"Latent Variable\",\"Correlation\",\"Information\"],\"2\":[\"Machine Learning\",\"Statistics\",\"Data Analysis\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"21\":{\"Question\":\"What was I originally confused about regarding VQ-VAE architectures? How does it turn out they are actually able to overcome this problem? \",\"Answer\":\"I was worried a latent space with just 512 possible categorical quantizations can't possibly reproduce enough complexity like what we see in nature. \\nIn reality this is not how it's done. There are something like 32x32 independent categorical varaibles (so 512^(32*32) possible outcomes in the latent space). So flexibility is sufficient.\",\"Key ideas\":\"1. VQ-VAE architectures are a type of latent space used in machine learning.\\n2. Latent spaces are used to represent complex data in a simpler form.\\n3. VQ-VAE architectures use 512 possible categorical quantizations to represent the data.\\n4. I was originally worried that 512 possible categorical quantizations would not be able to reproduce enough complexity like what we see in nature.\\n5. It turns out that VQ-VAE architectures actually use 32x32 independent categorical variables, resulting in 512^(32*32) possible outcomes in the latent space.\\n6. This means that the flexibility of VQ-VAE architectures is sufficient to represent complex data.\",\"Abstraction groups\":{\"-1\":[\"VQ-VAE\",\"Latent Space\",\"Quantization\",\"Complexity\",\"Variable\",\"Flexibility\"],\"0\":[\"VQ-VAE\"],\"1\":[\"Latent Space\",\"Quantization\",\"Complexity\",\"Variable\",\"Flexibility\"],\"2\":[\"Machine Learning\",\"Data Representation\"],\"3\":[\"Artificial Intelligence\",\"Computational Thinking\"],\"4\":[\"Computer Science\"]}},\"22\":{\"Question\":\"Describe the additional structure of the hidden layer in VQ-VAE. \\nHow is the latent variable determined from the output of a neural network, and how is it then fed back into the decoder network? \",\"Answer\":\"Encoder produces vector representation. \\nVector quantization finds closest cluster of examples (really there are K vectors that represent the latent space, and it finds the closest one). \\nOutput uses that vector representation (not the original) for generation. \",\"Key ideas\":\"1. Vector Quantization (VQ)\\n2. Vector Representation\\n3. Encoder Network\\n4. Decoder Network\\n5. Latent Variable\\n6. Closest Cluster of Examples\\n7. K Vectors Representing the Latent Space\\n8. Output Uses Vector Representation for Generation\",\"Abstraction groups\":{\"-1\":[\"VQ-VAE\",\"Vector\",\"Encoder\",\"Decoder\",\"Latent\",\"Cluster\",\"K\",\"Output\"],\"0\":[\"VQ-VAE\"],\"1\":[\"Neural Network\",\"Latent Variable\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"23\":{\"Question\":\"What is the main difference in the structure of the latent space for VQ-VAE compared to VAE? (vector quantized VAE vs normal VAE) \",\"Answer\":\"Latent space is a set of categorical variables in VQ-VAEs, rather than continuous spaces like a Gaussian for VAEs \",\"Key ideas\":\"1. VQ-VAE (vector quantized VAE): \\n    - A type of Variational Autoencoder (VAE)\\n    - Latent space is a set of categorical variables\\n2. VAE (normal VAE): \\n    - A type of Variational Autoencoder\\n    - Latent space is a continuous space, such as a Gaussian\",\"Abstraction groups\":{\"-1\":[\"VQ-VAE\",\"VAE\",\"Latent Space\",\"Categorical Variable\",\"Gaussian\"],\"0\":[\"VQ-VAE Vs VAE\"],\"1\":[\"Latent Space Structure\"],\"2\":[\"Variational Autoencoders\"],\"3\":[\"Machine Learning\"],\"4\":[\"Artificial Intelligence\"]}},\"24\":{\"Question\":\"Describe how the reparametrization trick is used to make a stable gradient calculation in a variational autoencoder. Specifically for the case of a Gaussian prior. \",\"Answer\":\"You parametrize mu and sigma with a network. You write the expectation value of some f(z) over q(z) as expectation over unit normal for variable epsilon of f(mu + sigma*epsilon).\\nNow you can differentiate in sigma, then average over epsilon, etc. \\nThis is stable beacuse the average over epsilon is tractable. \",\"Key ideas\":\"1. Variational Autoencoders (VAEs)\\n2. Reparametrization Trick\\n3. Gaussian Prior\\n4. Parametrizing mu and sigma with a network\\n5. Writing the expectation value of some f(z) over q(z) as expectation over unit normal for variable epsilon of f(mu + sigma*epsilon)\\n6. Differentiating in sigma\\n7. Averaging over epsilon\\n8. Stable gradient calculation in VAEs\",\"Abstraction groups\":{\"-1\":[\"VAE\",\"Reparametrization\",\"Gaussian\",\"Network\",\"Expectation\",\"Epsilon\",\"Differentiating\",\"Averaging\",\"Stable\"],\"0\":[\"Reparametrization Trick\"],\"1\":[\"Variational Autoencoders\",\"Gradient Calculation\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computing\",\"Mathematics\"],\"4\":[\"Science\"]}},\"25\":{\"Question\":\"What are the two tricks used to compute the gradient of the expectation value of some function, where you are taking a gradient with respect to the parameters of the distribution?\\nWhen can they both be used or not? \",\"Answer\":\"Likelihood gradient ratio: this can always be used, but is noisy because you do it for a single sample.\\nReparametrization trick: this can only be used for some easily-parametrized distributions, but in this case it's more stable since the randomness is now explicitly already averaged over to get the gradient. \",\"Key ideas\":\"1. The gradient of the expectation value of some function can be computed using two tricks.\\n2. The first trick is the likelihood gradient ratio, which can always be used but is noisy because it is done for a single sample.\\n3. The second trick is the reparametrization trick, which can only be used for some easily-parametrized distributions. This trick is more stable since the randomness is already explicitly averaged over to get the gradient.\",\"Abstraction groups\":{\"-1\":[\"Gradient\",\"Expectation\",\"Function\",\"Parameter\",\"Distribution\",\"Likelihood\",\"Ratio\",\"Sample\",\"Reparametrization\",\"Randomness\"],\"0\":[\"Gradient\"],\"1\":[\"Computation\",\"Expectation\",\"Function\",\"Parameter\",\"Distribution\"],\"2\":[\"Mathematics\",\"Statistics\",\"Probability\"],\"3\":[\"Science\",\"Data Analysis\"],\"4\":[\"Knowledge\"]}},\"26\":{\"Question\":\"What are the steps to setting up and training a variational autoencoder \",\"Answer\":\"Assume latent variables exist\\nWrite exact log likelihood to maximize, with summation over q(z). \\nTo make this easier to work with, do importance sampling, then use Jensen's to take out the average over q(z).\\nThis gives you the usual formula for the lower bound which can be written as expectation_q(z) of log(p(z) p(x|z)) + entropy over q(z). \\nParametrize your q_phi(z|x_i) and p_theta(x_i|z) and then optimize over all of these. To maximize it.\\nSpecifically, given some x_i samples, generate z_i samples, then calculate the VAE objective \",\"Key ideas\":\"1. Variational Autoencoders (VAEs) are a type of machine learning model.\\n2. To set up and train a VAE, one must assume that latent variables exist.\\n3. The log likelihood must be maximized with summation over q(z).\\n4. To make this easier, importance sampling can be used, followed by Jensen's inequality to take out the average over q(z).\\n5. This gives the lower bound which can be written as expectation_q(z) of log(p(z) p(x|z)) + entropy over q(z).\\n6. The q_phi(z|x_i) and p_theta(x_i|z) must be parametrized and then optimized over.\\n7. To maximize the VAE objective, given some x_i samples, generate z_i samples and calculate the VAE objective.\",\"Abstraction groups\":{\"-1\":[\"Vae\",\"Log Likelihood\",\"Importance Sampling\",\"Jensen's\",\"Lower Bound\",\"Expectation\",\"Log\",\"Entropy\",\"Q_Phi\",\"P_Theta\",\"Optimize\",\"Vae Objective\"],\"0\":[\"Variational Autoencoder\"],\"1\":[\"Machine Learning\",\"Optimization\"],\"2\":[\"Artificial Intelligence\",\"Probability Theory\"],\"3\":[\"Mathematics\",\"Computer Science\"],\"4\":[\"Science\"]}},\"27\":{\"Question\":\"What are the two effective components of the variational lower bound on an autoencoder loss function? \",\"Answer\":\"Reconstruction loss is Exp_q [log(p(x|z)]\\nRegularization term (minus KL divergence of q(z) from p(z)) \",\"Key ideas\":\"\\n1. Autoencoder loss function: \\n    a. Variational lower bound \\n    b. Two effective components \\n2. Reconstruction loss: \\n    a. Exp_q [log(p(x|z)] \\n3. Regularization term: \\n    a. Minus KL divergence of q(z) from p(z)\",\"Abstraction groups\":{\"-1\":[\"Autoencoder\",\"Loss\",\"Function\",\"Variational\",\"Lower\",\"Bound\",\"Reconstruction\",\"Exp_q\",\"Log\",\"P(x|z)\",\"Regularization\",\"KL\",\"Divergence\",\"Q(z)\",\"P(z)\"],\"0\":[\"Autoencoder Loss Function\"],\"1\":[\"Variational Lower Bound\"],\"2\":[\"Loss Function\",\"Autoencoder\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"28\":{\"Question\":\"How is the variational lower bound on an autoencoder loss function derived from the importance weighted autoencoder?\\nWhat is the single step which provides this lower bound? \",\"Answer\":\"The step is taking the expectation value outside the log. This leads to a lower bound on the original function (the log likelihood). \",\"Key ideas\":\"\\n1. Autoencoders: Autoencoders are a type of neural network used for unsupervised learning. They are composed of an encoder and a decoder, which are used to compress and reconstruct data respectively. \\n\\n2. Variational Lower Bound: The variational lower bound is a measure of how well an autoencoder is able to reconstruct data. It is derived from the importance weighted autoencoder (IWAE) and is used to evaluate the performance of an autoencoder.\\n\\n3. Expectation Value: The expectation value is a measure of the average value of a random variable. It is calculated by taking the sum of the product of each possible outcome and its probability. \\n\\n4. Log Likelihood: The log likelihood is a measure of how likely a given set of data is to have been generated by a given model. It is calculated by taking the logarithm of the probability of the data given the model. \\n\\n5. Single Step: Taking the expectation value outside the log is the single step which provides the lower bound on the original function (the log likelihood). This step is necessary to derive the variational lower bound on the autoencoder loss function.\",\"Abstraction groups\":{\"-1\":[\"Autoencoder\",\"Variational Lower Bound\",\"Expectation Value\",\"Log Likelihood\",\"Single Step\"],\"0\":[\"Autoencoder Loss Function\"],\"1\":[\"Variational Lower Bound\",\"Expectation Value\",\"Log Likelihood\"],\"2\":[\"Unsupervised Learning\",\"Neural Networks\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"29\":{\"Question\":\"What is importance sampling in the context of variational autoencoders? \",\"Answer\":\"You want to sample z values with a high probability of generating observed data. The ultimate goal is to calculate p_theta(x) = \\\\sum_z p_theta(x|z)p(z).\\nYou choose a sampling distribution q(z) which is likely to give those values. Then you can weigh the expectation value over z by Exp_q [p_theta(x|z)p(z)\\/q(z)] and you get meaningful samples. \",\"Key ideas\":\"\\n1. Variational Autoencoders (VAEs) are a type of generative model used to generate new data. \\n2. Importance sampling is a technique used to calculate the expectation value of a function over a probability distribution. \\n3. The goal of importance sampling in the context of VAEs is to sample z values with a high probability of generating observed data. \\n4. To do this, you choose a sampling distribution q(z) which is likely to give those values. \\n5. The expectation value is then calculated as Exp_q [p_theta(x|z)p(z)\\/q(z)]. \\n6. This allows you to get meaningful samples from the VAE.\",\"Abstraction groups\":{\"-1\":[\"VAE\",\"Importance Sampling\",\"Sampling Distribution\",\"Expectation Value\",\"P_theta(x)\",\"P_theta(x|z)\",\"P(z)\",\"Q(z)\"],\"0\":[\"Importance Sampling\"],\"1\":[\"Variational Autoencoders\"],\"2\":[\"Generative Models\",\"Machine Learning\"],\"3\":[\"Artificial Intelligence\",\"Computer Science\"],\"4\":[\"Science\"]}},\"30\":{\"Question\":\"What is the computational difficulty of just sampling values in the latent space in order to learn to optimize your variational autoencoder? \",\"Answer\":\"If sample z values don't reproduce the datapoints (they generally won't in a super high dimensional space), then you never have any signal to begin optimization. \",\"Key ideas\":\"\\n1. Variational Autoencoders (VAEs) are a type of neural network used for unsupervised learning. \\n2. VAEs learn by optimizing a latent space, which is a high-dimensional space that contains the data points. \\n3. Sampling values in the latent space is a computational difficulty when learning to optimize a VAE. \\n4. Sampling values in the latent space will generally not reproduce the data points in a super high dimensional space. \\n5. Without any signal to begin optimization, it is difficult to learn to optimize a VAE.\",\"Abstraction groups\":{\"-1\":[\"VAE\",\"Latent Space\",\"Sampling\",\"Optimization\",\"Signal\"],\"0\":[\"Variational Autoencoder\"],\"1\":[\"Neural Network\",\"Unsupervised Learning\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"31\":{\"Question\":\"What is the underlying assumption made in order to use a variational autoencoder? \",\"Answer\":\"Assumes latent variables exist. \",\"Key ideas\":\"\\n1. Variational Autoencoder (VAE): a type of artificial neural network used for unsupervised learning.\\n2. Unsupervised Learning: a type of machine learning algorithm used to find patterns in data without the use of labels.\\n3. Latent Variables: hidden variables that are not directly observed but are inferred from other variables.\\n4. Underlying Assumption: the assumption that latent variables exist in order to use a VAE.\",\"Abstraction groups\":{\"-1\":[\"Vae\",\"Unsupervised Learning\",\"Latent Variable\",\"Assumption\"],\"0\":[\"Variational Autoencoder\"],\"1\":[\"Unsupervised Learning\",\"Latent Variables\"],\"2\":[\"Artificial Neural Networks\",\"Machine Learning\"],\"3\":[\"Data Analysis\",\"Algorithms\"],\"4\":[\"Computer Science\"]}},\"32\":{\"Question\":\"What is the theoretical formula of the exact target function for a variational autoencoder?\\nWhat is the difficult part of using this? \",\"Answer\":\"We want to maximize log(p_theta(x)) with p_theta(x) = \\\\sum_z p_theta(x|z)p(z).\\nThe difficulty is that the sum over z is inside the log, which makes the computation harder. \",\"Key ideas\":\"1. Variational Autoencoders (VAEs)\\n2. The theoretical formula of the exact target function for a VAE: maximize log(p_theta(x)) with p_theta(x) = \\\\sum_z p_theta(x|z)p(z)\\n3. The difficulty of using this formula: the sum over z is inside the log, which makes the computation harder\",\"Abstraction groups\":{\"-1\":[\"VAE\",\"Formula\",\"Log\",\"Computation\"],\"0\":[\"VAE\"],\"1\":[\"Formula\",\"Log\",\"Computation\"],\"2\":[\"Maximization\",\"Target Function\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Science\",\"Technology\"]}},\"33\":{\"Question\":\"What is the major difference between variational autoencoders (VAEs) and flow models? \",\"Answer\":\"The latent space has smaller dimensionality in VAEs, but same in flow models. VAEs compress information. \",\"Key ideas\":\"\\n1. Variational Autoencoders (VAEs): \\n    a. A type of generative model \\n    b. Compresses information \\n    c. Latent space has smaller dimensionality \\n2. Flow Models: \\n    a. A type of generative model \\n    b. Latent space has same dimensionality \\n3. Major Difference between VAEs and Flow Models: \\n    a. VAEs compress information \\n    b. Latent space has smaller dimensionality in VAEs, but same in flow models\",\"Abstraction groups\":{\"-1\":[\"VAE\",\"Flow\",\"Latent\",\"Dimensionality\",\"Compress\"],\"0\":[\"VAE\",\"Flow Model\"],\"1\":[\"Generative Model\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"34\":{\"Question\":\"What did Geoff Hinton think is the key to creating useful representations of natural languages or images? \",\"Answer\":\"Hierarchy. Specifically trying to learn the part-whole hierarchy.\\nFor each patch of an image, or token in NLP, visualize each embedding as containing part\\/whole hierarchical encodings (a column of vectors, with more abstract concepts at the top, which agree across columns). \",\"Key ideas\":\"\\n1. Geoff Hinton believes that hierarchy is the key to creating useful representations of natural languages or images. \\n2. Hierarchy is achieved by visualizing each embedding as containing part\\/whole hierarchical encodings. \\n3. This is done by having a column of vectors, with more abstract concepts at the top, which agree across columns. \\n4. NLP stands for Natural Language Processing. \\n5. Images are composed of patches.\",\"Abstraction groups\":{\"-1\":[\"Hierarchy\",\"Embedding\",\"Vector\",\"Concept\",\"Column\",\"NLP\",\"Image\",\"Patch\"],\"0\":[\"Hierarchy\"],\"1\":[\"Representation\",\"Natural Language\",\"Image\"],\"2\":[\"Learning\",\"Visualizing\"],\"3\":[\"Geoff Hinton\",\"Part-Whole\"],\"4\":[\"Key\"]}},\"35\":{\"Question\":\"What is the basic unit of interpretability in an audio transformer? How is a sequence of tokens extracted? \",\"Answer\":\"You take a discrete FFT over certain time sequences of data (choose a context size). This is like the embedding vector. \",\"Key ideas\":\"\\n1. Audio transformers are used to interpret audio data. \\n2. A discrete FFT (Fast Fourier Transform) is used to extract a sequence of tokens from the audio data. \\n3. The FFT is used to create an embedding vector. \\n4. The context size of the FFT determines the size of the embedding vector.\",\"Abstraction groups\":{\"-1\":[\"Audio Transformer\",\"FFT\",\"Token\",\"Embedding Vector\",\"Context Size\"],\"0\":[\"Audio Transformer\"],\"1\":[\"Interpretability\",\"Audio Processing\"],\"2\":[\"Machine Learning\",\"Signal Processing\"],\"3\":[\"Artificial Intelligence\",\"Computing\"],\"4\":[\"Technology\"]}},\"36\":{\"Question\":\"What would you call a transformer with attention across examples in a minibatch, used at test time? \",\"Answer\":\"Non-parametric transformers (NPTs) (Cohere AI) \",\"Key ideas\":\"1. Transformers: A type of machine learning model used for natural language processing tasks.\\n2. Attention: A mechanism used in machine learning models to focus on certain parts of the input.\\n3. Minibatch: A subset of a dataset used to train a machine learning model.\\n4. Test time: The time when a machine learning model is evaluated on unseen data.\\n5. Non-parametric transformers (NPTs): A type of transformer with attention across examples in a minibatch, used at test time. (Cohere AI)\",\"Abstraction groups\":{\"-1\":[\"Transformer\",\"Attention\",\"Minibatch\",\"Test Time\",\"NPT\"],\"0\":[\"Non-parametric Transformer\"],\"1\":[\"Attention\",\"Minibatch\",\"Test Time\"],\"2\":[\"Transformer\",\"Machine Learning\"],\"3\":[\"Natural Language Processing\"],\"4\":[\"Artificial Intelligence\"]}},\"37\":{\"Question\":\"How did Cohere AI seek to extend the transformer architecture \",\"Answer\":\"Non-parametric transformers (NPTs)\\nThey do transformer attention across examples in a minibatch, as well as within a sequence. Show that it improves accuracy a little \",\"Key ideas\":\"\\n1. Cohere AI sought to extend the transformer architecture.\\n2. They did this by introducing Non-parametric Transformers (NPTs).\\n3. NPTs use transformer attention across examples in a minibatch, as well as within a sequence.\\n4. This improves accuracy a little.\",\"Abstraction groups\":{\"-1\":[\"Cohere AI\",\"Transformer\",\"NPT\",\"Minibatch\",\"Sequence\",\"Accuracy\"],\"0\":[\"Cohere AI\"],\"1\":[\"Transformer Architecture\",\"Non-Parametric Transformer (NPT)\"],\"2\":[\"Attention\",\"Accuracy\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Technology\"]}},\"38\":{\"Question\":\"What is a parametric model vs a non-parametric model?\\nDescribe the difference, then give an example of each. \",\"Answer\":\"Parametric is predicts data x given parameters theta and theta only.\\nNon-parametric is it can use other data at test time to help predict the result. \\nExamples are direct generative models, vs kNNs to classify an object. \",\"Key ideas\":\"1. Parametric models predict data x given parameters theta and theta only.\\n2. Non-parametric models can use other data at test time to help predict the result.\\n3. Examples of parametric models are direct generative models.\\n4. Examples of non-parametric models are kNNs (k-Nearest Neighbors) to classify an object.\",\"Abstraction groups\":{\"-1\":[\"Parametric\",\"Non-Parametric\",\"Generative\",\"Knn\"],\"0\":[\"Parametric\",\"Non-parametric\"],\"1\":[\"Model\",\"Prediction\"],\"2\":[\"Data Analysis\",\"Machine Learning\"],\"3\":[\"Artificial Intelligence\",\"Computer Science\"],\"4\":[\"Science\",\"Technology\"]}},\"39\":{\"Question\":\"What are two problems with the byte pair encoding (BPE)? \",\"Answer\":\"It assigns varying importance to words (varying number of tokens)\\nIt gives different outputs based on different factorization (the number of spaces after a word can affect the meaning encoding) \",\"Key ideas\":\"1. Byte Pair Encoding (BPE) is a type of text compression algorithm. \\n2. BPE assigns varying importance to words, resulting in a varying number of tokens. \\n3. Different factorization of words can affect the meaning encoding of BPE. \\n4. The number of spaces after a word can affect the meaning encoding of BPE.\",\"Abstraction groups\":{\"-1\":[\"BPE\",\"Word\",\"Factorization\",\"Space\"],\"0\":[\"Byte Pair Encoding (BPE)\"],\"1\":[\"Text Compression\",\"Algorithm\"],\"2\":[\"Computer Science\",\"Mathematics\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"40\":{\"Question\":\"What uses\\/example cases were shown for the perceiver architecture (Deepmind)? \",\"Answer\":\"They can stop using byte pair encoding and still achieve good loss on NLP. They want to do this because BPE has problems.\\nThe real benefit is not making an assumption of the structure of the input data. The positional encoding and embedding can just be learned as necessary. \",\"Key ideas\":\"1. Byte Pair Encoding (BPE) is a method used in Natural Language Processing (NLP).\\n2. BPE has problems, so Deepmind's perceiver architecture can be used to stop using BPE and still achieve good loss on NLP.\\n3. The real benefit of the perceiver architecture is that it does not make assumptions about the structure of the input data.\\n4. The positional encoding and embedding can be learned as necessary.\",\"Abstraction groups\":{\"-1\":[\"BPE\",\"NLP\",\"Deepmind\",\"Perceiver\",\"Encoding\",\"Embedding\"],\"0\":[\"Perceiver\"],\"1\":[\"Nlp\",\"Encoding\",\"Embedding\"],\"2\":[\"Artificial Intelligence\",\"Data Science\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"41\":{\"Question\":\"What is the goal of the perceiver architecture (Deepmind)? \",\"Answer\":\"Problem of attention being quadratic\\nProblem of attention architecture being designed for a fixed context\\/modality. \",\"Key ideas\":\"\\n1. Deepmind's perceiver architecture \\n2. Problem of attention being quadratic \\n3. Problem of attention architecture being designed for a fixed context\\/modality\",\"Abstraction groups\":{\"-1\":[\"Deepmind\",\"Perceiver\",\"Attention\",\"Quadratic\",\"Context\",\"Modality\"],\"0\":[\"Deepmind's Perceiver\"],\"1\":[\"Attention\",\"Quadratic\"],\"2\":[\"Problem Solving\",\"Architecture\"],\"3\":[\"Artificial Intelligence\",\"Cognitive Science\"],\"4\":[\"Science\",\"Technology\"]}},\"42\":{\"Question\":\"How is the perceiver architecture (Deepmind) different from a normal transformer? \",\"Answer\":\"Query is a learned set of vectors with a fixed number of tokens. \\nKeys and values are linear in input.\\nTransformer operates in this embedding space, after that one crossed attention input. \",\"Key ideas\":\"1. The perceiver architecture (Deepmind) is a type of transformer.\\n2. Query is a learned set of vectors with a fixed number of tokens.\\n3. Keys and values are linear in input.\\n4. Transformer operates in an embedding space.\\n5. Transformer uses one crossed attention input.\",\"Abstraction groups\":{\"-1\":[\"Perceiver Architecture\",\"Deepmind\",\"Transformer\",\"Query\",\"Token\",\"Key\",\"Value\",\"Input\",\"Embedding\",\"Attention\"],\"0\":[\"Transformer\"],\"1\":[\"Perceiver Architecture\",\"Deepmind\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"43\":{\"Question\":\"What is the idea of a mixture of experts or switch transformer? \",\"Answer\":\"Try to reduce computation time and make architecture adaptive by only triggering computation using subsets of the network when necessary. \",\"Key ideas\":\"\\n1. Mixture of experts or switch transformer is a technique used to reduce computation time. \\n2. It makes the architecture adaptive by only triggering computation using subsets of the network when necessary.\",\"Abstraction groups\":{\"-1\":[\"Mixture of Expert\",\"Switch Transformer\",\"Computation Time\",\"Architecture\",\"Subset\"],\"0\":[\"Mixture Of Expert\"],\"1\":[\"Computation Time\",\"Architecture\"],\"2\":[\"Adaptive Technique\",\"Network Subset\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"44\":{\"Question\":\"What is the Pearson correlation coefficient? And what values can it take? \",\"Answer\":\"It is a measure of linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between \\u22121 and 1.\",\"Key ideas\":\"1. Pearson correlation coefficient is a measure of linear correlation between two sets of data. \\n2. It is the ratio between the covariance of two variables and the product of their standard deviations. \\n3. It is essentially a normalized measurement of the covariance. \\n4. The result of the Pearson correlation coefficient always has a value between -1 and 1.\",\"Abstraction groups\":{\"-1\":[\"Pearson Correlation Coefficient\",\"Covariance\",\"Variable\",\"Standard Deviation\",\"Normalized Measurement\",\"Value\"],\"0\":[\"Pearson Correlation Coefficient\"],\"1\":[\"Measurement\",\"Correlation\"],\"2\":[\"Statistics\",\"Data Analysis\"],\"3\":[\"Mathematics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"45\":{\"Question\":\"What counts as patent infringement in terms of the actual document and the claims? \",\"Answer\":\"Must infringe on all elements of a claim completely to infringe on a patent\\nBut it only has to be one claim out of all of them. \",\"Key ideas\":\"1. A patent infringement must infringe on all elements of a claim completely. \\n2. It only has to be one claim out of all of them. \\n3. A patent is a document that grants exclusive rights to an inventor for a certain period of time. \\n4. A claim is a statement of what the inventor believes is protected by the patent. \\n5. The elements of a claim are the individual components that make up the claim. \\n6. Infringement occurs when someone uses or makes a product or process that is covered by the patent without permission from the patent holder.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Claim\",\"Element\",\"Infringement\",\"Permission\"],\"0\":[\"Patent Infringement\"],\"1\":[\"Actual Document\",\"Claims\"],\"2\":[\"Legal\",\"Intellectual Property\"],\"3\":[\"Rights\",\"Ownership\"],\"4\":[\"Law\"]}},\"46\":{\"Question\":\"What is the circuit found in the mechanistic interpretability paper by Nanda?\\nDescribe the claims about the output of the various layers, in brief. \",\"Answer\":\"Embedding gets sin and cosine of input at a specific number\\nAttention heads + MLP gets neuron activations that are periodic in both a and b (like sin(w(a+b)) ).\\nUnembedding gets cos(w(a + b - c)) and constructively interferes a few frequencies to end up with result \",\"Key ideas\":\"1. The circuit found in the mechanistic interpretability paper by Nanda is composed of an embedding, attention heads + MLP, and unembedding.\\n2. The embedding gets the sin and cosine of the input at a specific number.\\n3. The attention heads + MLP gets neuron activations that are periodic in both a and b, such as sin(w(a+b)).\\n4. The unembedding gets cos(w(a + b - c)) and constructively interferes a few frequencies to end up with the result.\",\"Abstraction groups\":{\"-1\":[\"Embedding\",\"Attention\",\"MLP\",\"Unembedding\",\"Sin\",\"Cosine\",\"Input\",\"Neuron\",\"Activation\",\"Periodic\",\"Constructive\",\"Interference\",\"Frequency\",\"Result\"],\"0\":[\"Circuit\"],\"1\":[\"Mechanistic Interpretability\",\"Embedding\",\"Attention\",\"Mlp\",\"Unembedding\"],\"2\":[\"Sin\",\"Cosine\",\"Input\",\"Neuron\",\"Activations\",\"Periodic\",\"Constructive Interference\",\"Frequencies\",\"Result\"],\"3\":[\"Mathematics\",\"Physics\",\"Computer Science\"],\"4\":[\"Science\"]}},\"47\":{\"Question\":\"In the article \\\"Progress measures for grokking via mechanistic interpretability\\\" by Nanda et al, what feature of the network training was required to observe grokking, they say? \",\"Answer\":\"Weight decay must be present, they observe.\\nOther evidence is: There is a signature in the norms of weights at each transition during training. \",\"Key ideas\":\"1. In the article \\\"Progress measures for grokking via mechanistic interpretability\\\" by Nanda et al, the authors discuss a feature of network training that is required to observe grokking. \\n2. This feature is weight decay, which must be present in order for grokking to be observed. \\n3. The authors also provide evidence for this, which is that there is a signature in the norms of weights at each transition during training.\",\"Abstraction groups\":{\"-1\":[\"Article\",\"Nanda\",\"Grokking\",\"Network\",\"Training\",\"Weight\",\"Decay\",\"Norm\",\"Weight\",\"Transition\"],\"0\":[\"Grokking\"],\"1\":[\"Network Training\",\"Weight Decay\"],\"2\":[\"Mechanistic Interpretability\",\"Progress Measures\"],\"3\":[\"Article\",\"Nanda\"],\"4\":[\"Research\"]}},\"48\":{\"Question\":\"In the article \\\"Progress measures for grokking via mechanistic interpretability\\\" by Nanda et al, what tools are used to measure behavior? \",\"Answer\":\"General loss (test and train)\\nAblation of key frequencies or of non-key frequencies, and test\\/train loss. \",\"Key ideas\":\"\\n1. The article \\\"Progress measures for grokking via mechanistic interpretability\\\" by Nanda et al.\\n2. Tools used to measure behavior:\\n    a. General loss (test and train)\\n    b. Ablation of key frequencies or of non-key frequencies\\n    c. Test\\/train loss\",\"Abstraction groups\":{\"-1\":[\"Article\",\"Tool\",\"General Loss\",\"Ablation\",\"Key Frequency\",\"Non-Key Frequency\",\"Test\\/Train Loss\"],\"0\":[\"Measurement\"],\"1\":[\"Behavior\",\"Tools\"],\"2\":[\"Article\",\"Progress\"],\"3\":[\"Grokking\",\"Interpretability\"],\"4\":[\"Mechanistic\"]}},\"49\":{\"Question\":\"In the article \\\"Progress measures for grokking via mechanistic interpretability\\\" by Nanda et al, what is the final interpretation of the process of Grokking? \",\"Answer\":\"First stage: memorization (train loss goes down, test is still bad)\\nSecond stage: Building a generalization circuit (and can detect this with other methods), but still using memorization for prediction (still high test loss)\\nThird stage: forgetting memorization information, and generalizing (now test loss goes down) \",\"Key ideas\":\"1. Grokking is a process of understanding and interpreting something. \\n2. The article \\\"Progress measures for grokking via mechanistic interpretability\\\" by Nanda et al discusses the process of Grokking. \\n3. The process of Grokking has three stages: \\n    a. Memorization (train loss goes down, test is still bad)\\n    b. Building a generalization circuit (and can detect this with other methods), but still using memorization for prediction (still high test loss)\\n    c. Forgetting memorization information, and generalizing (now test loss goes down)\",\"Abstraction groups\":{\"-1\":[\"Grokking\",\"Nanda\",\"Memorization\",\"Generalization\",\"Prediction\",\"Test Loss\"],\"0\":[\"Grokking\"],\"1\":[\"Interpretation\",\"Understanding\"],\"2\":[\"Cognitive Processes\",\"Mental Processes\"],\"3\":[\"Psychology\",\"Neuroscience\"],\"4\":[\"Science\"]}},\"50\":{\"Question\":\"What is Grokking in a language model? \",\"Answer\":\"Train loss goes down early, but then later test loss goes down \",\"Key ideas\":\"\\n1. Grokking: A term used to describe the process of understanding a language model.\\n2. Train Loss: The amount of error in the model when it is trained on a dataset.\\n3. Test Loss: The amount of error in the model when it is tested on a dataset.\\n4. Early: Refers to the early stages of training the model.\\n5. Later: Refers to the later stages of training the model.\\n6. Goes Down: Refers to the decrease in the amount of error in the model.\",\"Abstraction groups\":{\"-1\":[\"Grokking\",\"Train Loss\",\"Test Loss\",\"Early\",\"Later\",\"Goes Down\"],\"0\":[\"Grokking\"],\"1\":[\"Language Model\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"51\":{\"Question\":\"What is the test setup\\/architecture for the article \\\"Progress measures for grokking via mechanistic interpretability\\\" by Nanda et al? \",\"Answer\":\"Main subject: modular arithmetic with a 1 layer transformer and MLP\",\"Key ideas\":\"\\n1. Modular arithmetic: a branch of mathematics that deals with the remainder of a division operation.\\n2. Transformer: a type of artificial neural network used in natural language processing.\\n3. MLP: a type of artificial neural network used for supervised learning.\\n4. Progress measures: a way to measure the progress of a task or project.\\n5. Grokking: a term used to describe the process of understanding something deeply.\\n6. Mechanistic interpretability: a way to interpret the behavior of a system by understanding its underlying mechanisms.\",\"Abstraction groups\":{\"-1\":[\"Arithmetic\",\"Transformer\",\"MLP\",\"Progress\",\"Grokking\",\"Interpretability\"],\"0\":[\"Test Setup\"],\"1\":[\"Architecture\",\"Mechanistic Interpretability\"],\"2\":[\"Progress Measures\",\"Grokking\"],\"3\":[\"Modular Arithmetic\",\"Transformer\",\"Mlp\"],\"4\":[\"Artificial Neural Networks\"]}},\"52\":{\"Question\":\"Describe the transformer-XL architecture. How is the context length extended? \",\"Answer\":\"Previous layer hidden states are cached, and are not used to compute gradients or anything. \\nFor each segment of tokens under test, the keys and values for each layer are extended in context backward by one additional segment.\\nAs a result, the effective context size is increasing backwards like a triangle segment size * layer number. \",\"Key ideas\":\"\\n1. Transformer-XL is an architecture that uses a recurrent neural network (RNN) to extend the context length.\\n2. The context length is extended by caching the previous layer's hidden states and not using them to compute gradients.\\n3. For each segment of tokens under test, the keys and values for each layer are extended in context backward by one additional segment.\\n4. The effective context size increases backwards in a triangle shape, with the size of the segment multiplied by the number of layers.\",\"Abstraction groups\":{\"-1\":[\"Transformer-XL\",\"Context\",\"Segment\",\"Layer\",\"Gradient\",\"Key\",\"Value\",\"Triangle\"],\"0\":[\"Transformer-XL\"],\"1\":[\"Architecture\",\"Context\",\"Segment\",\"Layer\",\"Gradient\",\"Key\",\"Value\",\"Triangle\"],\"2\":[\"Neural Network\",\"Caching\",\"Computing\"],\"3\":[\"Machine Learning\",\"Data Processing\"],\"4\":[\"Artificial Intelligence\"]}},\"53\":{\"Question\":\"What is the statement of the Cramer Rao bound? \",\"Answer\":\"The inverse Fisher information is a lower bound on the expected variance of an unbiased estimator of a parameter, given the data. \",\"Key ideas\":\"\\n1. Fisher information: a measure of the amount of information that an observable random variable X carries about an unknown parameter \\u03b8 of a distribution. \\n2. Inverse Fisher information: the inverse of the Fisher information, which is a measure of the amount of uncertainty in the parameter \\u03b8 given the data X. \\n3. Cramer Rao bound: the inverse Fisher information is a lower bound on the expected variance of an unbiased estimator of a parameter, given the data.\",\"Abstraction groups\":{\"-1\":[\"Cramer Rao\",\"Bound\",\"Fisher\",\"Information\",\"Estimator\",\"Parameter\",\"Variance\",\"Data\"],\"0\":[\"Cramer Rao Bound\"],\"1\":[\"Estimation\",\"Statistics\"],\"2\":[\"Mathematics\",\"Probability\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Learning\",\"Understanding\"]}},\"54\":{\"Question\":\"The Fisher information has two equivalent formulations. What are they? \",\"Answer\":\"It is the expecation of negative curvature of the log likelihood.\\nIt is also the expectation of the variance of the Fisher score (derivative of log likelihood). \",\"Key ideas\":\"\\n1. Fisher information: \\n    a. It is a measure of the amount of information that an observable random variable X carries about an unknown parameter \\u03b8 of a distribution. \\n    b. It has two equivalent formulations. \\n2. Formulation 1: \\n    a. It is the expectation of negative curvature of the log likelihood. \\n3. Formulation 2: \\n    a. It is the expectation of the variance of the Fisher score (derivative of log likelihood).\",\"Abstraction groups\":{\"-1\":[\"Fisher Information\",\"Log Likelihood\",\"Negative Curvature\",\"Fisher Score\",\"Variance\"],\"0\":[\"Fisher Information\"],\"1\":[\"Measurement\",\"Expectation\"],\"2\":[\"Statistics\",\"Probability\"],\"3\":[\"Mathematics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"55\":{\"Question\":\"What is the intuitive description of the Fisher score? \",\"Answer\":\"It is derivative of log likelihood of x with theta.\\nSo when this is large, it says the fractional change in x with theta is large, so s carries a lot of information about the posterior on theta. \",\"Key ideas\":\"\\n1. Fisher score: \\n    a. It is a derivative of log likelihood of x with theta. \\n    b. It measures the fractional change in x with theta. \\n2. Log likelihood: \\n    a. It is a measure of how likely a set of data is to have been generated by a given model. \\n3. Theta: \\n    a. It is a parameter of a model that can be estimated from data.\",\"Abstraction groups\":{\"-1\":[\"Fisher Score\",\"Log Likelihood\",\"Theta\",\"X\",\"Posterior\"],\"0\":[\"Fisher Score\"],\"1\":[\"Derivative\",\"Log Likelihood\"],\"2\":[\"Measurement\",\"Parameter Estimation\"],\"3\":[\"Data Analysis\",\"Modeling\"],\"4\":[\"Statistics\"]}},\"56\":{\"Question\":\"What is the prior predictive density for a Bayesian variable x with some distribution dependent on parameters theta? \",\"Answer\":\"It is the distribution over x, averaged over the distribution of theta as well. \",\"Key ideas\":\"\\n1. Bayesian variable x: a variable whose probability distribution is determined by Bayesian inference.\\n2. Parameters theta: the parameters of the distribution of x.\\n3. Prior predictive density: the distribution over x, averaged over the distribution of theta.\",\"Abstraction groups\":{\"-1\":[\"Bayesian\",\"X\",\"Theta\",\"Density\",\"Distribution\"],\"0\":[\"Prior Predictive Density\"],\"1\":[\"Bayesian Inference\",\"Probability Distribution\"],\"2\":[\"Statistics\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"57\":{\"Question\":\"How is the Fisher information related to the certianty on the parameters of a distribution? \",\"Answer\":\"Fisher information is inverse certainty (variance of parameters). High fisher = low variance. \",\"Key ideas\":\"\\n1. Fisher information is a measure of the certainty of the parameters of a distribution. \\n2. Fisher information is inversely related to the variance of the parameters. \\n3. High Fisher information indicates low variance of the parameters.\",\"Abstraction groups\":{\"-1\":[\"Fisher\",\"Certainty\",\"Parameter\",\"Distribution\",\"Variance\"],\"0\":[\"Fisher Information\"],\"1\":[\"Measurement\",\"Distribution\"],\"2\":[\"Statistics\",\"Probability\"],\"3\":[\"Mathematics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"58\":{\"Question\":\"What is the intuition about the size of the Fisher information for a distribution and some data? \",\"Answer\":\"If Fisher information is large at the MLE, this means you are at a very strongly peaked maximum of the likelihood. So your certainty on the parameters will correspondingly be high. \",\"Key ideas\":\"\\n1. Fisher information: a measure of the amount of information about the parameters of a distribution that is contained in some data. \\n2. Maximum Likelihood Estimate (MLE): the value of the parameter that maximizes the likelihood of the data given the parameter. \\n3. If Fisher information is large at the MLE, this means the likelihood is very strongly peaked at the MLE, and the certainty on the parameters will be high.\",\"Abstraction groups\":{\"-1\":[\"Fisher Information\",\"MLE\",\"Likelihood\",\"Parameter\",\"Certainty\"],\"0\":[\"Fisher Information\"],\"1\":[\"Measurement\",\"Estimation\"],\"2\":[\"Statistics\",\"Probability\"],\"3\":[\"Mathematics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"59\":{\"Question\":\"What is the first derivative of the log-likelihood function with respect to the parameters of the likelihood function? What about the second derivative? \",\"Answer\":\"The first derivative is commonly known as the Fisher score function.\\nThe Fisher information is the negative expected value of the second derivative. \",\"Key ideas\":\"\\n1. Log-likelihood function: a mathematical function used to measure the goodness of fit of a statistical model.\\n2. Parameters of the likelihood function: the variables that are used to define the model.\\n3. First derivative: the rate of change of a function with respect to one of its variables.\\n4. Second derivative: the rate of change of the first derivative with respect to one of its variables.\\n5. Fisher score function: the first derivative of the log-likelihood function with respect to the parameters of the likelihood function.\\n6. Fisher information: the negative expected value of the second derivative of the log-likelihood function with respect to the parameters of the likelihood function.\",\"Abstraction groups\":{\"-1\":[\"Log-Likelihood\",\"Parameter\",\"First Derivative\",\"Second Derivative\",\"Fisher Score\",\"Fisher Information\"],\"0\":[\"Derivative\"],\"1\":[\"Log-likelihood\",\"Parameter\"],\"2\":[\"Mathematics\",\"Statistics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Learning\"]}},\"60\":{\"Question\":\"What is the Fisher score function? \",\"Answer\":\"The first derivative of the log-likelihood function with respect to the parameters of the likelihood function is commonly known as the Fisher score function. \",\"Key ideas\":\"\\n1. Log-likelihood function: a mathematical function used to measure the goodness of fit of a statistical model. \\n2. Parameters of the likelihood function: the variables that are used to define the model. \\n3. Fisher score function: the first derivative of the log-likelihood function with respect to the parameters of the likelihood function.\",\"Abstraction groups\":{\"-1\":[\"Fisher Score\",\"Log-Likelihood\",\"Parameter\",\"Derivative\"],\"0\":[\"Fisher Score\"],\"1\":[\"Mathematical Function\"],\"2\":[\"Statistics\",\"Probability\"],\"3\":[\"Mathematics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"61\":{\"Question\":\"What is a maximum likelihood estimator (MLE) \",\"Answer\":\"The maximum likelihood estimator (MLE) of some parameters of a distribution is the value of all possible parameters that maximizes the likelihood of the data that was observed. \",\"Key ideas\":\"1. A maximum likelihood estimator (MLE) is a value of some parameters of a distribution. \\n2. The MLE is the value of all possible parameters that maximizes the likelihood of the data that was observed. \\n3. The likelihood of the data is the probability of observing the data given the parameters of the distribution. \\n4. The MLE is the value of the parameters that maximizes the probability of observing the data.\",\"Abstraction groups\":{\"-1\":[\"MLE\",\"Parameter\",\"Distribution\",\"Data\",\"Likelihood\",\"Probability\"],\"0\":[\"MLE\"],\"1\":[\"Estimation\",\"Probability\"],\"2\":[\"Statistics\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Learning\"]}},\"62\":{\"Question\":\"What is the likelihood function of some observed data and probability distribution? \",\"Answer\":\"The likelihood function of some observed data with some parametrized probability distribution is a function of the parameters of the distribution. It is the probability of observing the data given the parameters. \",\"Key ideas\":\"1. The likelihood function is a function of the parameters of a probability distribution. \\n2. The likelihood function is the probability of observing the data given the parameters. \\n3. The observed data and probability distribution are related to each other.\",\"Abstraction groups\":{\"-1\":[\"Likelihood\",\"Parameter\",\"Probability\",\"Data\",\"Distribution\"],\"0\":[\"Likelihood\"],\"1\":[\"Probability\",\"Data\"],\"2\":[\"Statistics\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"63\":{\"Question\":\"What is the additional requirement for the mapping in multi-dimensional flow models compared to single dimensional? \",\"Answer\":\"The mapping change of variables determinant must be easy to compute (in addition to being invertible and differentiable). \",\"Key ideas\":\"\\n1. Multi-dimensional flow models require an additional requirement for the mapping compared to single dimensional models. \\n2. The additional requirement is that the mapping change of variables determinant must be easy to compute. \\n3. The mapping must also be invertible and differentiable.\",\"Abstraction groups\":{\"-1\":[\"Mapping\",\"Multi-Dimensional\",\"Single-Dimensional\",\"Variable\",\"Determinant\",\"Compute\",\"Invertible\",\"Differentiable\"],\"0\":[\"Mapping\"],\"1\":[\"Multi-dimensional\",\"Single dimensional\"],\"2\":[\"Flow models\",\"Change of variables\"],\"3\":[\"Requirements\",\"Compute\"],\"4\":[\"Mathematics\"]}},\"64\":{\"Question\":\"What did the NICE (2014) and RealNVP (2016), and Glow (2018) papers implement, and how did the latter ones add on the former?\",\"Answer\":\"NICE: Partition into 2 sets, do some transformation on one set to make the multi-dimensional flow model easy to calculate and invert. \\nRealNVP: Do affine transformation on the other set. And partition in interesting ways to advantage the priors (ie use checkerboard on an image, and things like that).\\nGlow: adds a 1x1 covolution of the image which basically just permutes between the images, and is easily invertible. This makes it even more able to disentangle variables.\",\"Key ideas\":\"1. NICE (2014): Partitioning into two sets and applying a transformation to one set to make the multi-dimensional flow model easier to calculate and invert.\\n2. RealNVP (2016): Applying an affine transformation to the other set and partitioning in interesting ways to take advantage of priors (e.g. using a checkerboard on an image).\\n3. Glow (2018): Adding a 1x1 convolution of the image which permutes between the images and is easily invertible, making it even more able to disentangle variables.\",\"Abstraction groups\":{\"-1\":[\"Nice\",\"RealNvp\",\"Glow\",\"Transformation\",\"Affine\",\"Prior\",\"Checkerboard\",\"Image\",\"1x1 Convolution\",\"Permutation\",\"Variable\"],\"0\":[\"Neural Network\"],\"1\":[\"Machine Learning\",\"Deep Learning\"],\"2\":[\"Artificial Intelligence\",\"Computational Science\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"65\":{\"Question\":\"What did the NICE paper (2014) argue is the motivation for multi-dimensional flow models?\",\"Answer\":\"A good representation has independent latent variables.\\nThey say: \\\"It is based on the idea that a good representation is one in which the data has a distribution that is easy to model. For this purpose, a non-linear deterministic transformation of the data is learned that maps it to a latent space so as to make the transformed data conform to a factorized distribution, i.e., resulting in independent latent variables\\\" \",\"Key ideas\":\"1. The NICE paper (2014) argued that a good representation of data has independent latent variables. \\n2. A non-linear deterministic transformation of the data is learned to map it to a latent space. \\n3. This transformation is done so as to make the transformed data conform to a factorized distribution. \\n4. A factorized distribution results in independent latent variables.\",\"Abstraction groups\":{\"-1\":[\"Nice Paper\",\"Representation\",\"Latent Variable\",\"Transformation\",\"Factorized Distribution\"],\"0\":[\"Multi-dimensional Flow Model\"],\"1\":[\"Representation\",\"Latent Variable\"],\"2\":[\"Transformation\",\"Factorized Distribution\"],\"3\":[\"Non-linear Deterministic Transformation\"],\"4\":[\"NICE Paper (2014)\"]}},\"66\":{\"Question\":\"Name the 3 main papers from CS294 lecture on multi-dimensional flow models, and their full names (not just acronyms)\",\"Answer\":\"NICE (2014) on non-linear independent components estimation\\nRealNVP (2016) on real-valued non-volume preserving transformations \\nGlow (2018)\",\"Key ideas\":\"\\n1. Multi-dimensional flow models are a type of machine learning model.\\n2. There are three main papers on multi-dimensional flow models: \\n    a. NICE (Non-linear Independent Components Estimation) from 2014 \\n    b. RealNVP (Real-valued Non-Volume Preserving Transformations) from 2016 \\n    c. Glow from 2018\\n3. Each paper has a full name, not just an acronym.\",\"Abstraction groups\":{\"-1\":[\"Nice\",\"RealNvp\",\"Glow\",\"Multi-Dimensional\",\"Flow\",\"Machine Learning\",\"Non-Linear\",\"Independent\",\"Component\",\"Estimation\",\"Real-Valued\",\"Non-Volume\",\"Preserving\",\"Transformation\"],\"0\":[\"Multi-dimensional Flow Model\"],\"1\":[\"Machine Learning\",\"Paper\"],\"2\":[\"Algorithm\",\"Research\"],\"3\":[\"Artificial Intelligence\",\"Knowledge\"],\"4\":[\"Science\",\"Technology\"]}},\"67\":{\"Question\":\"In multi-dimensional flow models, such as Glow by OpenAI, what is one unique way to use the latent space for generative applications? \",\"Answer\":\"Can measure two classes of objects, find their relative vector, and then augment other images along this axis (like smiling) \",\"Key ideas\":\"\\n1. Multi-dimensional flow models: A type of generative model that uses a latent space to represent data.\\n\\n2. Glow by OpenAI: A specific type of multi-dimensional flow model developed by OpenAI.\\n\\n3. Latent space: A space in which data is represented in a compressed form.\\n\\n4. Generative applications: Applications that use generative models to create new data.\\n\\n5. Measure two classes of objects: Use the latent space to measure the relative distance between two classes of objects.\\n\\n6. Relative vector: A vector that represents the relative distance between two classes of objects.\\n\\n7. Augment other images: Use the relative vector to augment other images along this axis (e.g. smiling).\",\"Abstraction groups\":{\"-1\":[\"Multi-Dimensional Flow Model\",\"OpenAI\",\"Latent Space\",\"Generative Application\",\"Measure\",\"Vector\",\"Augment\"],\"0\":[\"Generative Application\"],\"1\":[\"Multi-dimensional Flow Model\",\"OpenAI\",\"Latent Space\"],\"2\":[\"Generative Model\",\"Measure\",\"Vector\",\"Augment\"],\"3\":[\"Data Representation\",\"Image Manipulation\"],\"4\":[\"Artificial Intelligence\"]}},\"68\":{\"Question\":\"What is dequantization in a flow model? \",\"Answer\":\"Perturb your data if it is discrete before trying to fit a continuous flow model.\\nIe add uniform noise in some range (corresponding to the distance between discretization) \",\"Key ideas\":\"\\n1. Flow models are used to fit continuous data.\\n2. Data must be perturbed if it is discrete before attempting to fit a continuous flow model.\\n3. Perturbation involves adding uniform noise in some range.\\n4. The range of the noise should correspond to the distance between discretization.\",\"Abstraction groups\":{\"-1\":[\"Dequantization\",\"Flow Model\",\"Data\",\"Discrete\",\"Continuous\",\"Perturb\",\"Noise\",\"Range\",\"Discretization\"],\"0\":[\"Dequantization\"],\"1\":[\"Flow Model\",\"Data\"],\"2\":[\"Continuous\",\"Discrete\",\"Perturb\"],\"3\":[\"Noise\",\"Range\",\"Discretization\"],\"4\":[\"Modeling\"]}},\"69\":{\"Question\":\"What is one major drawback of an autoregressive multi-dimensional flow model? \",\"Answer\":\"Given the mapping parameterization, the sampling problem has to be done sequentially, but computing each x from each z separately. \\nTraining is simple and fast, but sampling is slow.\\nInverse autoregressive model is the opposite (fast sampling, slow training) \",\"Key ideas\":\"1. Autoregressive multi-dimensional flow model (AR-MDF)\\n2. Mapping parameterization\\n3. Sampling problem\\n4. Computing each x from each z separately\\n5. Training is simple and fast\\n6. Sampling is slow\\n7. Inverse autoregressive model (IAR)\\n8. IAR is the opposite (fast sampling, slow training)\",\"Abstraction groups\":{\"-1\":[\"AR-MDF\",\"Mapping\",\"Sampling\",\"X\",\"Z\",\"Training\",\"Sampling\",\"IAR\"],\"0\":[\"AR-MDF\"],\"1\":[\"Sampling\",\"Training\"],\"2\":[\"Modeling\",\"Computation\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"70\":{\"Question\":\"Give an example of an autoregressive multi-dimensional flow model, and an inverse one \",\"Answer\":\"Forward model:\\nZ_1 = F_1(x_1)\\nZ_2 = f_2(x_2; x_1)\\nZ_3 = f_3(x_3; x_2, x_1)\\nInverse swaps z and x.\",\"Key ideas\":\"1. Autoregressive multi-dimensional flow models are a type of machine learning model.\\n2. A forward model is a model that takes a set of inputs and produces a set of outputs.\\n3. An inverse model is a model that takes a set of outputs and produces a set of inputs.\\n4. The example given in the flashcard is a forward model with three dimensions: Z_1, Z_2, and Z_3.\\n5. The example given in the flashcard is an inverse model with three dimensions: x_1, x_2, and x_3.\\n6. The forward model is defined as:\\n    a. Z_1 = F_1(x_1)\\n    b. Z_2 = f_2(x_2; x_1)\\n    c. Z_3 = f_3(x_3; x_2, x_1)\\n7. The inverse model is defined as:\\n    a. x_1 = F_1(z_1)\\n    b. x_2 = f_2(z_2; z_1)\\n    c. x_3 = f_3(z_3; z_2, z_1)\",\"Abstraction groups\":{\"-1\":[\"Autoregressive\",\"Multi-Dimensional\",\"Flow\",\"Model\",\"Forward\",\"Inverse\",\"Z_1\",\"F_1\",\"X_1\",\"Z_2\",\"F_2\",\"X_2\",\"Z_3\",\"F_3\",\"X_3\"],\"0\":[\"Autoregressive Flow Model\"],\"1\":[\"Machine Learning\",\"Modeling\"],\"2\":[\"Data Science\",\"Artificial Intelligence\"],\"3\":[\"Computational Science\",\"Computer Science\"],\"4\":[\"Science\",\"Technology\"]}},\"71\":{\"Question\":\"For multi-dimensional flow models, what are the main differences in the MLE estimator from a 1D flow model?\",\"Answer\":\"You now have to use the jacobian of the transformation, rather than just the derivative. \\nLog of p(z) is potentially more complicated as well, since the distribution is not necessarily separable into the different dimensions of z. \",\"Key ideas\":\"\\n1. Multi-dimensional flow models are different from 1D flow models. \\n2. The MLE estimator for multi-dimensional flow models requires the use of the jacobian of the transformation, rather than just the derivative. \\n3. The log of p(z) is potentially more complicated in multi-dimensional flow models, since the distribution is not necessarily separable into the different dimensions of z.\",\"Abstraction groups\":{\"-1\":[\"MLE\",\"Transformation\",\"Jacobian\",\"Log\",\"P(z)\",\"Dimension\"],\"0\":[\"Multi-dimensional Flow Model\"],\"1\":[\"Estimation\",\"Flow Model\"],\"2\":[\"Modeling\",\"Statistic\"],\"3\":[\"Mathematics\",\"Data Science\"],\"4\":[\"Science\"]}},\"72\":{\"Question\":\"Give a few examples of maps for a 1D flow model that are invertible and differentiable\",\"Answer\":\"Mixture of cumulative density functions of gaussians \\nMixture of logistics. \",\"Key ideas\":\"\\n1. Maps: A map is a mathematical function that takes an input and produces an output.\\n2. 1D Flow Model: A 1D flow model is a mathematical model that describes the flow of a single variable over time.\\n3. Invertible and Differentiable: A map is invertible if it can be reversed, and differentiable if its derivative can be calculated.\\n4. Examples of Maps: Examples of maps for a 1D flow model that are invertible and differentiable include a mixture of cumulative density functions of gaussians and a mixture of logistics.\",\"Abstraction groups\":{\"-1\":[\"Map\",\"1D Flow\",\"Invertible\",\"Differentiable\",\"Gaussian\",\"Logistic\"],\"0\":[\"Maps\"],\"1\":[\"1D Flow Model\",\"Invertible\",\"Differentiable\"],\"2\":[\"Mathematical Function\",\"Example\"],\"3\":[\"Mathematical Model\",\"Mixture\"],\"4\":[\"Mathematics\"]}},\"73\":{\"Question\":\"What are the two core requirements for a 1D flow model map in order to work?\",\"Answer\":\"Must have f_theta(x) be invertible and differentiable \",\"Key ideas\":\"\\n1. A 1D flow model map must have two core requirements in order to work. \\n2. The two core requirements are that f_theta(x) must be invertible and differentiable. \\n3. Invertible means that the function can be reversed, so that the output of the function can be used as the input and the input of the function can be used as the output. \\n4. Differentiable means that the function can be differentiated, or that the rate of change of the function can be calculated.\",\"Abstraction groups\":{\"-1\":[\"1D Flow Model\",\"F_Theta(X)\",\"Invertible\",\"Differentiable\"],\"0\":[\"1D Flow Model\"],\"1\":[\"Map\",\"Requirement\"],\"2\":[\"Mathematics\",\"Modeling\"],\"3\":[\"Science\",\"Problem Solving\"],\"4\":[\"Knowledge\"]}},\"74\":{\"Question\":\"What is the prototypical example of a 1D flow model?\",\"Answer\":\"Cumulative density function IS a NORMALIZING FLOW that maps any density over x to the uniform distribution over z in 0 to 1. \\nmap: Z = F_theta(x) = cdf up to x of pdf.\\nResult: sample in z, map to some area in x. Can imagine that large density x regions map to fast changing CDF, which gets sampled often. \\nAnalytically: get prob in x from probability p(z) * df\\/dx = p_theta(x).\",\"Key ideas\":\"1. A 1D flow model is a cumulative density function (CDF) that maps any density over x to the uniform distribution over z in 0 to 1. \\n2. The mapping is represented by the equation Z = F_theta(x) = cdf up to x of pdf.\\n3. Sampling in z and mapping to some area in x can be imagined as large density x regions mapping to fast changing CDF, which gets sampled often. \\n4. The probability in x can be calculated from the probability p(z) multiplied by the derivative of the function with respect to x, or p_theta(x).\",\"Abstraction groups\":{\"-1\":[\"Flow Model\",\"CDF\",\"X\",\"Z\",\"PDF\",\"Sampling\",\"Density\",\"Probability\",\"Mapping\",\"Derivative\"],\"0\":[\"Normalizing Flow\"],\"1\":[\"1D Flow Model\",\"Cumulative Density Function\"],\"2\":[\"Mapping\",\"Sampling\"],\"3\":[\"Probability\",\"Density\"],\"4\":[\"Mathematics\"]}},\"75\":{\"Question\":\"Describe the basic steps of a 1D flow model for inference and sampling.\",\"Answer\":\"Pick a latent space for variable z, and some underlying distribution for z, called p(z) (like a unit Guassian over z).\\nGenerate a test distribution over x by mapping from this latent space for z to x in a specific way. The parameters of the distribution over x are the map between z and x. This ensures normalization. \\nFind the optimum map parameters to reproduce the data. \",\"Key ideas\":\"\\n1. Latent space: a space of variables that are not directly observed, but are inferred from data.\\n2. Distribution: a mathematical function that describes the probability of a given outcome.\\n3. p(z): the underlying distribution for the latent space z.\\n4. Mapping: a function that maps from the latent space z to the observed space x.\\n5. Parameters: the values that define the mapping from z to x.\\n6. Normalization: a process that ensures the test distribution over x is valid.\\n7. Optimum map parameters: the parameters that best reproduce the data.\",\"Abstraction groups\":{\"-1\":[\"Latent Space\",\"Distribution\",\"P(z)\",\"Mapping\",\"Parameter\",\"Normalization\",\"Optimum Map\"],\"0\":[\"1D Flow Model\"],\"1\":[\"Inference\",\"Sampling\"],\"2\":[\"Machine Learning\",\"Probability\"],\"3\":[\"Mathematics\",\"Statistics\"],\"4\":[\"Science\"]}},\"76\":{\"Question\":\"What is the main purpose of a flow model for embedding a random distribution in another variable? \",\"Answer\":\"Disentangle subspaces of relevant behavior into independent dimensions to improve sampling and interpretability. \\nDeal with continuous data in a stable way. \",\"Key ideas\":\"\\n1. Flow models are used to embed a random distribution in another variable.\\n2. The purpose of this is to disentangle subspaces of relevant behavior into independent dimensions.\\n3. This improves sampling and interpretability.\\n4. Flow models also allow for dealing with continuous data in a stable way.\",\"Abstraction groups\":{\"-1\":[\"Flow Model\",\"Random Distribution\",\"Subspace\",\"Sampling\",\"Interpretability\",\"Continuous Data\"],\"0\":[\"Flow Model\"],\"1\":[\"Embedding\",\"Random Distribution\"],\"2\":[\"Sampling\",\"Interpretability\"],\"3\":[\"Continuous Data\",\"Subspaces\"],\"4\":[\"Modeling\"]}},\"77\":{\"Question\":\"List a few of the attention patterns used to upgrade transformers that try to reduce the complexity scaling with context length. \",\"Answer\":\"Strided context or fixed (spaced) context (Sparse transformer)\\nCombining local and global context (Extended transformer construction, Longformer, and Big Bird)\\nHashing for L log(L) scaling with token length (Reformer, Hash keys. Find groups, then only attend within groups, and from final token to next group.)\\nLow rank attention (Linformer - linear complexity, Project token space down to a smaller dimension somehow ) \",\"Key ideas\":\"\\n1. Transformers are used to reduce complexity scaling with context length.\\n2. Strided context or fixed (spaced) context (Sparse transformer) is one of the attention patterns used to upgrade transformers.\\n3. Combining local and global context (Extended transformer construction, Longformer, and Big Bird) is another attention pattern used to upgrade transformers.\\n4. Hashing for L log(L) scaling with token length (Reformer, Hash keys. Find groups, then only attend within groups, and from final token to next group.) is another attention pattern used to upgrade transformers.\\n5. Low rank attention (Linformer - linear complexity, Project token space down to a smaller dimension somehow ) is another attention pattern used to upgrade transformers.\",\"Abstraction groups\":{\"-1\":[\"Transformer\",\"Strided\",\"Sparse\",\"Global\",\"Longformer\",\"Big Bird\",\"Reformer\",\"Hashing\",\"Group\",\"Low Rank\",\"Linformer\"],\"0\":[\"Transformer\"],\"1\":[\"Attention Pattern\"],\"2\":[\"Upgrading\",\"Complexity Scaling\"],\"3\":[\"Context Length\",\"Token Length\"],\"4\":[\"Artificial Intelligence\"]}},\"78\":{\"Question\":\"What is the main defining characteristic of the Universal Transformer architecture, as opposed to the vanilla transformer? \",\"Answer\":\"Universal \\\"aims to benefit from both a long-term global receptive field of Transformer and learned inductive biases of RNN.\\\"\\nIt has a single layer with parameters, but repeats a variable number of layers. Stops when certain conditions are reached. \",\"Key ideas\":\"\\n1. Universal Transformer architecture is an alternative to the vanilla transformer. \\n2. Universal Transformer aims to benefit from both a long-term global receptive field of Transformer and learned inductive biases of RNN. \\n3. Universal Transformer has a single layer with parameters, but repeats a variable number of layers. \\n4. Universal Transformer stops when certain conditions are reached.\",\"Abstraction groups\":{\"-1\":[\"Universal Transformer\",\"Transformer\",\"RNN\",\"Layer\",\"Parameter\",\"Condition\"],\"0\":[\"Universal Transformer\"],\"1\":[\"Architecture\",\"Machine Learning\"],\"2\":[\"Artificial Intelligence\",\"Technology\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"79\":{\"Question\":\"What are two methods tried to adjust the attention mechanism in transformers based on distance? What axes can be addressed? \",\"Answer\":\"Distance-aware attention (Add learned biases to each key attention based on relative distance)\\nAdaptive attention spans (Each head learns a context size to use (has to decay at end to be differentiable) \",\"Key ideas\":\"1. Distance-aware attention: \\n    - Adjusting the attention mechanism in transformers based on distance\\n    - Adding learned biases to each key attention based on relative distance\\n2. Adaptive attention spans: \\n    - Each head learns a context size to use \\n    - Context size has to decay at end to be differentiable\",\"Abstraction groups\":{\"-1\":[\"Attention\",\"Transformer\",\"Distance\",\"Bias\",\"Head\",\"Context\",\"Decay\"],\"0\":[\"Attention\"],\"1\":[\"Distance\",\"Bias\",\"Head\",\"Context\",\"Decay\"],\"2\":[\"Adjustment\",\"Learning\"],\"3\":[\"Transformer\"],\"4\":[\"Mechanism\"]}},\"80\":{\"Question\":\"What are a few methods tried to increase the context length of transformers? \",\"Answer\":\"Transformer-XL: Use previously computed hidden states \\nCompressive transformer: Compresses memories intentionally then keeps them in context \\nExternal memory: stores key-value pairs \",\"Key ideas\":\"1. Transformers are a type of neural network architecture used for natural language processing. \\n2. Transformers have a limited context length, which can be increased using various methods. \\n3. Transformer-XL is a method that uses previously computed hidden states to increase context length. \\n4. Compressive transformer is a method that compresses memories intentionally and keeps them in context. \\n5. External memory is a method that stores key-value pairs to increase context length.\",\"Abstraction groups\":{\"-1\":[\"Transformer\",\"Context\",\"Transformer-XL\",\"Compressive\",\"External\",\"Memory\",\"Key-Value\"],\"0\":[\"Increasing Context Length\"],\"1\":[\"Transformer\",\"Method\"],\"2\":[\"Neural Network\",\"Natural Language Processing\"],\"3\":[\"Artificial Intelligence\",\"Machine Learning\"],\"4\":[\"Computer Science\"]}},\"81\":{\"Question\":\"List a few of the positional embeddings used in transformers \",\"Answer\":\"Fixed sinusoidal\\nLearned positional\\nRelative position encoding \\nTransformer-XL position encoding\\nRotary position embedding \",\"Key ideas\":\"\\n1. Positional embeddings are used in transformers.\\n2. There are several types of positional embeddings, including:\\n    a. Fixed sinusoidal\\n    b. Learned positional\\n    c. Relative position encoding\\n    d. Transformer-XL position encoding\\n    e. Rotary position embedding\",\"Abstraction groups\":{\"-1\":[\"Positional Embedding\",\"Transformer\",\"Fixed Sinusoidal\",\"Learned Positional\",\"Relative Position Encoding\",\"Transformer-XL Position Encoding\",\"Rotary Position Embedding\"],\"0\":[\"Positional Embedding\"],\"1\":[\"Transformer\"],\"2\":[\"Natural Language Processing\",\"Machine Learning\"],\"3\":[\"Artificial Intelligence\",\"Computer Science\"],\"4\":[\"Science and Technology\"]}},\"82\":{\"Question\":\"In the ROME: Rank one model editing paper, what was the final location of most stored memories? What spot was most effective on average for restoring faculty? \",\"Answer\":\"\\\"This hypothesis localizes factual association along three dimensions, placing it (i) in the MLP modules (ii) at specific middle layers (iii) and specifically at the processing of the subject\\u2019s last token.\\\" \",\"Key ideas\":\"1. The ROME: Rank one model editing paper is a hypothesis that localizes factual association along three dimensions. \\n2. The three dimensions are: \\n    a. MLP modules \\n    b. Specific middle layers \\n    c. Processing of the subject\\u2019s last token \\n3. Most stored memories are located in the MLP modules. \\n4. The most effective spot on average for restoring faculty is the processing of the subject\\u2019s last token.\",\"Abstraction groups\":{\"-1\":[\"Rome\",\"Rank\",\"Model\",\"Editing\",\"Paper\",\"Dimension\",\"MLP\",\"Middle\",\"Token\",\"Memory\",\"Faculty\"],\"0\":[\"Rome\"],\"1\":[\"Rank\",\"Model\",\"Editing\",\"Paper\"],\"2\":[\"Hypothesis\",\"Localization\",\"Dimensions\"],\"3\":[\"Factual Association\",\"Memory\",\"Faculty\"],\"4\":[\"Processing\"]}},\"83\":{\"Question\":\"In the ROME: Rank one model editing paper, what method was used to locate the stored position of factual memories? \",\"Answer\":\"Measure object prediction in a phrase.\\nMeasure object prediction after corrupting subject data (adding random noise to first embedding)\\nRestore output of specific heads in specific layers to see how they improve final prediction. \",\"Key ideas\":\"1. ROME: Rank one model editing paper - a paper that discusses a method for editing factual memories\\n2. Measure object prediction in a phrase - a method used to locate the stored position of factual memories\\n3. Measure object prediction after corrupting subject data - adding random noise to the first embedding\\n4. Restore output of specific heads in specific layers - to see how they improve the final prediction\",\"Abstraction groups\":{\"-1\":[\"Rome\",\"Factual Memory\",\"Object Prediction\",\"Phrase\",\"Corrupting\",\"Embedding\",\"Output\",\"Layer\",\"Prediction\"],\"0\":[\"Rome\"],\"1\":[\"Factual Memory\",\"Object Prediction\"],\"2\":[\"Measurement\",\"Corrupting\",\"Output\"],\"3\":[\"Embedding\",\"Layers\",\"Prediction\"],\"4\":[\"Editing Paper\"]}},\"84\":{\"Question\":\"What does ROME stand for (a method in transformers)? \",\"Answer\":\"ROME: Rank one model editing \\nIs a method of altering memories in a transformer \",\"Key ideas\":\"1. ROME stands for Rank One Model Editing \\n2. ROME is a method of altering memories in a transformer \\n3. Transformers are machines that can change their shape and function\",\"Abstraction groups\":{\"-1\":[\"Rome\",\"Method\",\"Transformer\",\"Memory\",\"Altering\"],\"0\":[\"Rome\"],\"1\":[\"Method\",\"Transformer\",\"Memory\"],\"2\":[\"Altering\",\"Machine\"],\"3\":[\"Technology\"],\"4\":[\"Science\"]}},\"85\":{\"Question\":\"What are two general ways to look for behavior in a language model (for example when identifying induction heads)? \",\"Answer\":\"Correlation analysis\\nCausal analysis through perturbations (ie. ablations). \",\"Key ideas\":\"\\n1. Language models are used to identify induction heads. \\n2. Correlation analysis is one way to look for behavior in a language model. \\n3. Causal analysis through perturbations (also known as ablations) is another way to look for behavior in a language model.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Correlation\",\"Ablation\",\"Induction Head\"],\"0\":[\"Behavior in Language Model\"],\"1\":[\"Correlation Analysis\",\"Ablations\"],\"2\":[\"Causal Analysis\",\"Perturbations\"],\"3\":[\"Identifying Induction Heads\"],\"4\":[\"Language Modeling\"]}},\"86\":{\"Question\":\"List the major sources of correlative evidence for induction head formation that were found in the post by the anthropic people. \",\"Answer\":\"In context macroscopic learning loss correlated with induction head formation\\nAlso with a bump in training loss\\nAlso with a change in the PCA of the loss behavior. \",\"Key ideas\":\"\\n1. Induction head formation is a process that can be studied using correlative evidence. \\n2. The post by the anthropic people found three major sources of correlative evidence for induction head formation. \\n3. The first source of correlative evidence was macroscopic learning loss. \\n4. The second source of correlative evidence was a bump in training loss. \\n5. The third source of correlative evidence was a change in the PCA (Principal Component Analysis) of the loss behavior.\",\"Abstraction groups\":{\"-1\":[\"Induction\",\"Head\",\"Formation\",\"Correlative\",\"Evidence\",\"Anthropic\",\"Macroscopic\",\"Learning\",\"Loss\",\"Training\",\"PCA\"],\"0\":[\"Induction Head Formation\"],\"1\":[\"Correlative Evidence\",\"Anthropic People\"],\"2\":[\"Post\",\"Sources\"],\"3\":[\"Learning Loss\",\"Training Loss\",\"PCA\"],\"4\":[\"Formation\",\"Evidence\"]}},\"87\":{\"Question\":\"What macroscopic loss metric was measured in the post \\\"In context learning and induction heads\\\" to determine if inductive behavior was occuring? \",\"Answer\":\"In context learning loss is measured by looking at loss at 50th token and 500th token (better loss at end = in context learning)\",\"Key ideas\":\"1. In context learning is a type of learning that occurs when a model is able to learn from the context of a given task. \\n2. In context learning can be measured by looking at the loss at the 50th token and 500th token. \\n3. If the loss at the 500th token is lower than the loss at the 50th token, then it can be assumed that in context learning is occurring.\",\"Abstraction groups\":{\"-1\":[\"Macroscopic\",\"Loss\",\"Metric\",\"Post\",\"Context\",\"Learning\",\"Induction\",\"Head\",\"Loss\",\"50th\",\"500th\",\"Token\",\"End\"],\"0\":[\"In Context Learning\"],\"1\":[\"Loss Metric\",\"Induction Heads\"],\"2\":[\"Post\",\"Token\"],\"3\":[\"Learning\",\"Context\"],\"4\":[\"Macroscopic\"]}},\"88\":{\"Question\":\"What behavior was measured in the post \\\"In context learning and induction heads\\\" to determine if an attention head was an inducation head? \",\"Answer\":\"Induction head appearance is measured by success on random token repetition prediction. If a head predicts repeated sequences to happen again (even if never seen before) this is considered an induction head. \",\"Key ideas\":\"1. Attention heads are a type of artificial intelligence. \\n2. In context learning is a type of artificial intelligence. \\n3. Induction heads are a type of in context learning. \\n4. To determine if an attention head is an induction head, its success on random token repetition prediction must be measured. \\n5. If a head predicts repeated sequences to happen again (even if never seen before), this is considered an induction head.\",\"Abstraction groups\":{\"-1\":[\"Attention Head\",\"In Context Learning\",\"Induction Head\",\"Random Token Repetition\",\"Prediction\",\"Sequence\"],\"0\":[\"Induction Head\"],\"1\":[\"Artificial Intelligence\",\"In Context Learning\"],\"2\":[\"Machine Learning\",\"Cognitive Science\"],\"3\":[\"Computer Science\",\"Neuroscience\"],\"4\":[\"Science\",\"Technology\"]}},\"89\":{\"Question\":\"What was the main goal of the post \\\"In context learning and induction heads\\\"? \",\"Answer\":\"Goal is to try to determine the mechanistic source of in context learning within toy models and large models. Also to argue it is induction heads. \",\"Key ideas\":\"\\n1. In context learning is a type of learning that occurs when a learner is exposed to a particular context. \\n2. Induction heads are a type of model used to explain in context learning. \\n3. The goal of the post was to try to determine the mechanistic source of in context learning within toy models and large models, and to argue that it is induction heads.\",\"Abstraction groups\":{\"-1\":[\"Learning\",\"Context\",\"Induction\",\"Model\",\"Mechanistic\",\"Source\",\"Toy\",\"Large\"],\"0\":[\"In Context Learning\"],\"1\":[\"Induction Head\"],\"2\":[\"Learning\",\"Model\"],\"3\":[\"Mechanistic\",\"Source\"],\"4\":[\"Goal\"]}},\"90\":{\"Question\":\"What is the main statistical failure present in the skip-trigram interpretation of a 1 layer transformer?\",\"Answer\":\"Skip trigrams learn statistical associations, but the value vectors are added together linearly. \\nSo if two skip associations are present, along with both trigger keys, then the model will statistically mix up the next token between them. \",\"Key ideas\":\"\\n1. Skip trigrams learn statistical associations.\\n2. The value vectors are added together linearly.\\n3. If two skip associations are present, along with both trigger keys, then the model will statistically mix up the next token between them.\",\"Abstraction groups\":{\"-1\":[\"Skip Trigram\",\"Value Vector\",\"Trigger Key\",\"Statistical Association\",\"Token\"],\"0\":[\"Skip-Trigram\"],\"1\":[\"Statistical Failure\"],\"2\":[\"Transformer\",\"Layer\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"91\":{\"Question\":\"What is a zero layer transformer equivalent to?\\nWhat about a 1 layer transformer?\",\"Answer\":\"Zero layer just embeds and unembeds. The learned transformation will just be bigram statistics. \\nOne layer can incorporate skip-trigram statistics. It can learn to search for previous tokens corresponding to each token, and if they are present, output some specific value. \",\"Key ideas\":\"\\n1. A zero layer transformer is an algorithm that embeds and unembeds data. \\n2. The transformation learned by a zero layer transformer is just bigram statistics. \\n3. A one layer transformer can incorporate skip-trigram statistics. \\n4. A one layer transformer can learn to search for previous tokens corresponding to each token. \\n5. If the previous tokens are present, the one layer transformer will output some specific value.\",\"Abstraction groups\":{\"-1\":[\"Transformer\",\"Zero Layer\",\"Bigram\",\"One Layer\",\"Skip-Trigram\",\"Token\",\"Value\"],\"0\":[\"Transformer\"],\"1\":[\"Zero Layer\",\"One Layer\"],\"2\":[\"Algorithm\",\"Statistics\"],\"3\":[\"Embedding\",\"Searching\"],\"4\":[\"Data Processing\"]}},\"92\":{\"Question\":\"What is an alternative way to visualize the query\\/key transformation of an attention layer, and the output value? \",\"Answer\":\"The query-key matrix is a low rank matrix of size n_emb by n_emb that determines mappings between token positions (based on their embeddings)\\nThe output-value matrix is a low rank matrix of size n_emb by n_emb that maps within a single token to other embeddings for other tokens.\\nThese act on separate spaces as linear transformations. \",\"Key ideas\":\"1. Attention layers involve a query-key transformation and an output-value transformation. \\n2. The query-key matrix is a low rank matrix of size n_emb by n_emb. \\n3. This matrix determines mappings between token positions based on their embeddings. \\n4. The output-value matrix is a low rank matrix of size n_emb by n_emb. \\n5. This matrix maps within a single token to other embeddings for other tokens. \\n6. These act on separate spaces as linear transformations.\",\"Abstraction groups\":{\"-1\":[\"Attention Layer\",\"Query\",\"Key\",\"Output\",\"Value\",\"Matrix\",\"Embedding\",\"Token\",\"Linear Transformation\"],\"0\":[\"Attention Layer\"],\"1\":[\"Query-Key Transformation\",\"Output-Value Transformation\"],\"2\":[\"Matrix\",\"Embedding\",\"Token\"],\"3\":[\"Linear Transformation\"],\"4\":[\"Visualization\"]}},\"93\":{\"Question\":\"What were the main parameters determining whether there was presence or absence of superposition in the toy model of superposition by the people at anthropic? \",\"Answer\":\"Sparsity is required. \\nReLU is requried, with a negative bias added as well, so that certain features can not activate other features. \",\"Key ideas\":\"\\n1. Superposition is a concept in quantum mechanics that describes the behavior of particles in a quantum system. \\n2. The toy model of superposition by the people at anthropic is a simplified version of the concept of superposition. \\n3. Sparsity is a mathematical concept that describes the degree to which a set of elements is spread out. \\n4. ReLU stands for Rectified Linear Unit, which is a type of activation function used in neural networks. \\n5. A negative bias is an additional parameter that can be added to a ReLU activation function to prevent certain features from activating other features. \\n6. The main parameters determining whether there is presence or absence of superposition in the toy model of superposition by the people at anthropic are sparsity and ReLU with a negative bias.\",\"Abstraction groups\":{\"-1\":[\"Superposition\",\"Anthropic\",\"Sparsity\",\"ReLU\",\"Bias\"],\"0\":[\"Superposition\"],\"1\":[\"Quantum Mechanics\",\"Toy Model\"],\"2\":[\"Physics\",\"Modeling\"],\"3\":[\"Science\",\"Simulation\"],\"4\":[\"Knowledge\",\"Understanding\"]}},\"94\":{\"Question\":\"What methods were used to visualize the presence or absence of superposition in the toy model of superposition by the people at anthropic? \",\"Answer\":\"They visualize the low rank W^T W matrix, which is a n x n matrix mapping features to features. It is mostly diagonal, and diagonal values should be 1 on average, since they reconstruct the same feature (should be identity)\\nThe squared norm of the off diagonal parts of W^T W rows tells how much of each feature is not orthogonal to others. \\nThe total norm of each embedding tells whether something is represented or not.\",\"Key ideas\":\"1. Superposition is a concept in quantum mechanics that describes the behavior of particles in a quantum system. \\n2. The toy model of superposition was developed by the people at Anthropic. \\n3. To visualize the presence or absence of superposition in the toy model, they used the low rank W^T W matrix. \\n4. The W^T W matrix is a n x n matrix that maps features to features. \\n5. The matrix should be mostly diagonal, with diagonal values of 1 on average, since they reconstruct the same feature (should be identity). \\n6. The squared norm of the off diagonal parts of W^T W rows tells how much of each feature is not orthogonal to others. \\n7. The total norm of each embedding tells whether something is represented or not.\",\"Abstraction groups\":{\"-1\":[\"Superposition\",\"Anthropic\",\"W^T W\",\"Matrix\",\"Feature\",\"Identity\",\"Norm\"],\"0\":[\"Superposition\"],\"1\":[\"Quantum Mechanics\",\"Toy Model\",\"Visualization\"],\"2\":[\"Physics\",\"Modeling\",\"Analysis\"],\"3\":[\"Science\",\"Simulation\",\"Data\"],\"4\":[\"Knowledge\",\"Understanding\",\"Learning\"]}},\"95\":{\"Question\":\"What are the features of the input and output space of the toy model for superposition detection proposed by the folks at Anthropic? \",\"Answer\":\"Input has uniform sparsity (probability S that feature = 0, otherwise uniform in [0,1]\\nFeaures have varying importance (in the final MSE loss function for each feature).\\nFeatures x_i are independent.\\nLoss is MSE loss of input compared to output, with varying importance.\",\"Key ideas\":\"\\n1. The input space of the toy model for superposition detection proposed by the folks at Anthropic has uniform sparsity (probability S that feature = 0, otherwise uniform in [0,1]).\\n2. The features of the input space have varying importance (in the final MSE loss function for each feature).\\n3. The features x_i are independent.\\n4. The loss is MSE loss of input compared to output, with varying importance.\",\"Abstraction groups\":{\"-1\":[\"Superposition\",\"Detection\",\"Input\",\"Output\",\"Sparsity\",\"Feature\",\"Importance\",\"Independence\",\"MSE\",\"Loss\"],\"0\":[\"Superposition Detection\"],\"1\":[\"Input\\/Output Space\"],\"2\":[\"Toy Model\",\"Anthropic\"],\"3\":[\"Feature\",\"Sparsity\",\"Importance\",\"Independence\",\"MSE\",\"Loss\"],\"4\":[\"Modeling\"]}},\"96\":{\"Question\":\"Describe the setup of the toy model for superposition detection proposed by the folks at Anthropic \",\"Answer\":\"Take a high dimensional feature space of dimension n \\nEncode this set of features down into a smaller embedding space of dimension m < n using an embedding matrix W. \\nDecode using W_transposed back to feature space and adding a feature-dependent bias, then apply ReLU.\\nGoal: see if features can be reconstructed or not, and whether superposition arises. \",\"Key ideas\":\"\\n1. The toy model proposed by Anthropic uses a high dimensional feature space of dimension n. \\n2. This feature space is encoded into a smaller embedding space of dimension m, where m is less than n. \\n3. The encoding is done using an embedding matrix W. \\n4. The decoding is done using the transpose of W, and adding a feature-dependent bias. \\n5. The output of the decoding is then passed through a ReLU (Rectified Linear Unit) activation function. \\n6. The goal of the model is to see if features can be reconstructed or not, and whether superposition arises.\",\"Abstraction groups\":{\"-1\":[\"Superposition\",\"Embedding\",\"Matrix\",\"Bias\",\"ReLU\",\"Feature\",\"Reconstruction\"],\"0\":[\"Superposition Detection\"],\"1\":[\"Toy Model\",\"Anthropic\"],\"2\":[\"Feature Space\",\"Embedding\"],\"3\":[\"Matrix\",\"Bias\",\"ReLU\"],\"4\":[\"Features\",\"Reconstruction\"]}},\"97\":{\"Question\":\"What assumption is necessary for superposition to occur in a toy model with non-linear activation? \",\"Answer\":\"Sparisty is required: assumes features don't activate often (so don't activate together often) \",\"Key ideas\":\"\\n1. Superposition: the ability of a system to be in multiple states at the same time. \\n2. Toy model: a simplified version of a system used to illustrate a concept. \\n3. Non-linear activation: a type of activation that is not linear, meaning that the output is not proportional to the input. \\n4. Sparsity: a property of a system where features don't activate often. \\n5. Assumption: a statement that is accepted as true without proof. \\n6. Necessary: required for something to happen.\",\"Abstraction groups\":{\"-1\":[\"Superposition\",\"Toy Model\",\"Non-Linear\",\"Sparsity\",\"Assumption\",\"Necessary\"],\"0\":[\"Superposition\"],\"1\":[\"Non-linear Activation\",\"Sparsity\"],\"2\":[\"Assumption\",\"Necessity\"],\"3\":[\"Toy Model\"],\"4\":[\"Physics\"]}},\"98\":{\"Question\":\"What are two equivalent formulations of the output of the attention head in a transformer? \",\"Answer\":\"Can output low dimensional embeddings, then stack them, then project.\\nCan output low dimensional embeddings, then project to high dimensions, then add them.\\nBasic idea is attention heads can be seen as added together, linearly independently. \",\"Key ideas\":\"\\n1. Attention heads are a component of transformers.\\n2. Attention heads can output low dimensional embeddings.\\n3. These embeddings can be stacked and then projected.\\n4. Alternatively, the embeddings can be projected to high dimensions and then added together.\\n5. The basic idea is that attention heads can be seen as added together, linearly independently.\",\"Abstraction groups\":{\"-1\":[\"Attention Head\",\"Transformer\",\"Embedding\",\"Stacking\",\"Projecting\",\"Adding\",\"Linear Independence\"],\"0\":[\"Attention Head\"],\"1\":[\"Transformer\",\"Output\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computing\",\"Technology\"],\"4\":[\"Science\"]}},\"99\":{\"Question\":\"For a transformer being used to predict action sequences in reinforcement learning, what conditional information can be fed in to affect the behavior? (Think of the caption being used to condition the image embedding in CLIP) \",\"Answer\":\"You can specify the target final reward, and get the agent to choose a trajectory which is close to achieving this reward. \",\"Key ideas\":\"\\n1. Reinforcement learning is a type of machine learning where an agent learns to take actions in an environment to maximize a reward. \\n2. Transformers are a type of machine learning model used to predict action sequences in reinforcement learning. \\n3. CLIP (Contrastive Language-Image Pre-training) is a type of transformer model which uses a caption to condition the image embedding. \\n4. To affect the behavior of a transformer being used to predict action sequences in reinforcement learning, you can specify the target final reward. \\n5. This will get the agent to choose a trajectory which is close to achieving this reward.\",\"Abstraction groups\":{\"-1\":[\"Reinforcement Learning\",\"Transformer\",\"CLIP\",\"Reward\",\"Trajectory\"],\"0\":[\"Transformers\"],\"1\":[\"Machine Learning\",\"Reinforcement Learning\"],\"2\":[\"Artificial Intelligence\",\"Computational Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"100\":{\"Question\":\"What are a few examples of bottlenecks for information in a transformer? \",\"Answer\":\"Value vectors are much lower dimensional than the original representation, and are the only way to copy information from one token to another. \\nThe residual stream is the only way to copy from one MLP layer to another MLP layer, and this is much lower dimensional than the input to the MLP (and must compete with all other information stored in the residual stream). \",\"Key ideas\":\"\\n1. Transformers are a type of machine learning model used for natural language processing. \\n2. Value vectors are a way to copy information from one token to another, and are much lower dimensional than the original representation. \\n3. The residual stream is the only way to copy from one MLP layer to another MLP layer, and this is much lower dimensional than the input to the MLP. \\n4. The residual stream must compete with all other information stored in the residual stream.\",\"Abstraction groups\":{\"-1\":[\"Transformer\",\"Value Vector\",\"MLP\",\"Residual Stream\"],\"0\":[\"Bottleneck\"],\"1\":[\"Information\",\"Transformer\"],\"2\":[\"Machine Learning\",\"Natural Language Processing\"],\"3\":[\"Artificial Intelligence\",\"Data Science\"],\"4\":[\"Computer Science\"]}},\"101\":{\"Question\":\"What is an induction head in a multi-layer attention only transformer? \",\"Answer\":\"First layer head copies the previous token's information into the current token. In order to make a key that corresponds to the previous token, and a value corresponding to the current token\\nSecond layer induction head finds previous instances of keys corresponding to itself. Then copies value from next token that came after its previous instance. \",\"Key ideas\":\"1. An induction head is a type of multi-layer attention only transformer. \\n2. The first layer head copies the previous token's information into the current token. \\n3. This is done by creating a key that corresponds to the previous token, and a value corresponding to the current token. \\n4. The second layer induction head finds previous instances of keys corresponding to itself. \\n5. It then copies the value from the next token that came after its previous instance.\",\"Abstraction groups\":{\"-1\":[\"Induction Head\",\"Multi-Layer\",\"Attention\",\"Transformer\",\"First Layer\",\"Previous Token\",\"Current Token\",\"Key\",\"Value\",\"Second Layer\",\"Previous Instance\",\"Next Token\"],\"0\":[\"Induction Head\"],\"1\":[\"Multi-Layer Attention\",\"Transformer\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"102\":{\"Question\":\"Properties of Hermitian matrices \",\"Answer\":\"A dagger equals A\\nImplies it is diagonalizable (because of Shurs decomposition and properties of a normal matrix).\\nAll eigenvalues are real because you can take inner product with eigenvectors, and then conjugate it, and its the same.\\nAll eigenvectors with differing eigenvalues are orthogonal. \",\"Key ideas\":\"\\n1. A Hermitian matrix is a matrix where A dagger equals A.\\n2. A Hermitian matrix is diagonalizable due to Shur's decomposition and the properties of a normal matrix.\\n3. All eigenvalues of a Hermitian matrix are real because you can take the inner product of an eigenvector and its conjugate, and it will be the same.\\n4. All eigenvectors with differing eigenvalues of a Hermitian matrix are orthogonal.\",\"Abstraction groups\":{\"-1\":[\"Hermitian Matrix\",\"Dagger\",\"Diagonalizable\",\"Shur's Decomposition\",\"Normal Matrix\",\"Eigenvalue\",\"Inner Product\",\"Conjugate\",\"Eigenvector\",\"Orthogonal\"],\"0\":[\"Hermitian Matrix\"],\"1\":[\"Linear Algebra\",\"Matrix\"],\"2\":[\"Mathematics\",\"Algebra\"],\"3\":[\"Science\",\"Logic\"],\"4\":[\"Knowledge\"]}},\"103\":{\"Question\":\"What does the Shur decomposition ensure, and how does the algorithm proceed? \",\"Answer\":\"It ensures every complex square matrix can be transformed into an upper triangular matrix in another basis.\\nIt proceeds by identifying one eigenvector\\/eigenvalue, then decomposing the matrix into that subspace and its complement (removes all elements except for one in a column). Then repeat. \",\"Key ideas\":\"\\n1. Complex square matrices can be transformed into an upper triangular matrix in another basis. \\n2. The Shur decomposition algorithm is used to achieve this transformation. \\n3. The algorithm proceeds by identifying one eigenvector\\/eigenvalue, then decomposing the matrix into that subspace and its complement (removes all elements except for one in a column). \\n4. This process is then repeated until the matrix is transformed into an upper triangular matrix.\",\"Abstraction groups\":{\"-1\":[\"Shur Decomposition\",\"Algorithm\",\"Eigenvector\",\"Eigenvalue\",\"Subspace\",\"Matrix\",\"Column\"],\"0\":[\"Shur Decomposition\"],\"1\":[\"Algorithm\",\"Matrix Transformation\"],\"2\":[\"Linear Algebra\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Education\"]}},\"104\":{\"Question\":\"How does the self-attention autoregressive model significantly improve on the pixelCNN architecture? \",\"Answer\":\"It still achieves parameter sharing, but doesn't restrict to local information propagation. It sees full conditional context at all layers. \",\"Key ideas\":\"\\n1. Autoregressive models: models that predict the next value in a sequence based on the previous values. \\n2. Self-attention: a type of autoregressive model that uses attention mechanisms to focus on relevant parts of the input. \\n3. PixelCNN: a type of autoregressive model that uses convolutional neural networks to predict the next pixel in an image. \\n4. Parameter sharing: a technique used in neural networks to reduce the number of parameters and improve generalization. \\n5. Local information propagation: a technique used in PixelCNNs to restrict the information used to predict the next pixel to the local area around it. \\n6. Full conditional context: a technique used in self-attention autoregressive models to allow the model to see the full context of the input when predicting the next value.\",\"Abstraction groups\":{\"-1\":[\"Self-Attention\",\"PixelCNN\",\"Parameter Sharing\",\"Local Information\",\"Full Context\"],\"0\":[\"Self-Attention\"],\"1\":[\"Autoregressive Model\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"105\":{\"Question\":\"How does the PixelCNN architecture improve on the wavenet architecture? And what feature is slightly more complicated to implement than you might expect? \",\"Answer\":\"Introduces 2D convolutions\\/skip connections.\\nDifficulty is it also potentially introduces blind spots in the convolutional geometry, so you have to do special things to combat that.\",\"Key ideas\":\"\\n1. PixelCNN architecture is an improvement on the wavenet architecture.\\n2. PixelCNN introduces 2D convolutions and skip connections.\\n3. PixelCNN can potentially introduce blind spots in the convolutional geometry.\\n4. Special measures must be taken to combat the blind spots.\",\"Abstraction groups\":{\"-1\":[\"PixelCNN\",\"Wavenet\",\"2D Convolution\",\"Skip Connection\",\"Blind Spot\",\"Convolutional Geometry\"],\"0\":[\"PixelCNN\"],\"1\":[\"Neural Network\",\"Machine Learning\"],\"2\":[\"Artificial Intelligence\",\"Computing\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"106\":{\"Question\":\"What additional feature was necessary to make the wavenet architecture work well on image generation (such as MNIST)? \",\"Answer\":\"Including positional encoding is crucial for it to work on MNIST data. \",\"Key ideas\":\"\\n1. Wavenet architecture: a type of deep learning architecture that uses convolutional neural networks to generate audio signals.\\n2. Image generation: the process of creating images from data.\\n3. MNIST: a dataset of handwritten digits used for machine learning and image recognition.\\n4. Positional encoding: a technique used to encode the relative position of elements in a sequence.\\n5. Necessary feature: positional encoding is necessary for the wavenet architecture to work well on image generation tasks such as MNIST.\",\"Abstraction groups\":{\"-1\":[\"Wavenet\",\"Image\",\"MNIST\",\"Positional\",\"Feature\"],\"0\":[\"Positional Encoding\"],\"1\":[\"Feature\",\"Wavenet\"],\"2\":[\"Image Generation\",\"MNIST\"],\"3\":[\"Deep Learning\"],\"4\":[\"Machine Learning\"]}},\"107\":{\"Question\":\"How did wavenet improve on the MADE autoregressive modelling method? \",\"Answer\":\"It introduces shared parameters, as well as skip connections or \\\"dilated convolutions\\\". \",\"Key ideas\":\"1. MADE (Masked Autoregressive Distribution Estimation): a method of autoregressive modelling.\\n2. Wavenet: an improved version of the MADE autoregressive modelling method.\\n3. Shared parameters: parameters that are shared across different parts of the model.\\n4. Skip connections or \\\"dilated convolutions\\\": a type of connection between layers of a neural network that allows for information to be passed between layers without being processed by the intervening layers.\",\"Abstraction groups\":{\"-1\":[\"Made\",\"Wavenet\",\"Parameter\",\"Connection\"],\"0\":[\"Wavenet\"],\"1\":[\"Autoregressive Modelling\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"108\":{\"Question\":\"Comparing masked autoregressive models to recurrent neural networks, what is the main computational difference? \",\"Answer\":\"Masking allows for more parallel computation. \",\"Key ideas\":\"\\n1. Autoregressive models: \\n    a. Autoregressive models are a type of statistical model used to predict future values based on past values. \\n    b. Autoregressive models can be masked, meaning that certain values are hidden from the model. \\n2. Recurrent Neural Networks: \\n    a. Recurrent neural networks are a type of artificial neural network used to process sequential data. \\n3. Computational Difference: \\n    a. Masking allows for more parallel computation in autoregressive models compared to recurrent neural networks.\",\"Abstraction groups\":{\"-1\":[\"Autoregressive Model\",\"Masking\",\"Recurrent Neural Network\",\"Parallel Computation\"],\"0\":[\"Masked Autoregressive Model\"],\"1\":[\"Computational Difference\"],\"2\":[\"Autoregressive Model\",\"Recurrent Neural Network\"],\"3\":[\"Statistical Model\",\"Artificial Neural Network\"],\"4\":[\"Machine Learning\"]}},\"109\":{\"Question\":\"In the various approaches to autoregressive models, what are a few of the metrics or axes to compare architectures? \",\"Answer\":\"Whether parameters are shared between different tokens (self attention it is, convolutional approaches it is, MADE it is not). \\nDegree of connectivity or the receptive field size. \",\"Key ideas\":\"\\n1. Autoregressive models are approaches used to predict a sequence of values.\\n2. There are various approaches to autoregressive models, and these can be compared using metrics or axes.\\n3. One metric to compare architectures is whether parameters are shared between different tokens.\\n4. Self-attention approaches share parameters between different tokens.\\n5. Convolutional approaches also share parameters between different tokens.\\n6. MADE (Masked Autoregressive Distribution Estimation) does not share parameters between different tokens.\\n7. Another metric to compare architectures is the degree of connectivity or the receptive field size.\",\"Abstraction groups\":{\"-1\":[\"Autoregressive Model\",\"Metric\",\"Axis\",\"Parameter\",\"Token\",\"Self-Attention\",\"Convolutional\",\"MADE\",\"Connectivity\",\"Receptive Field\"],\"0\":[\"Autoregressive Model\"],\"1\":[\"Metric\",\"Axis\"],\"2\":[\"Comparison\",\"Architecture\"],\"3\":[\"Parameter\",\"Token\"],\"4\":[\"Modeling\"]}},\"110\":{\"Question\":\"Methods to improve speed in autoregressive models, such as RNNs and Wavenet: \",\"Answer\":\"Essentially break up hierarchy of conditioning (this has representational costs, as it reduces expressive power). However, this allows sub-sections of the image to be computed in parallel.\\nCaching (doesn't reduce expressive power). Example with Wavenet: can reuse previous computations. Makes next layer more like a linear problem I think? In the number of layers. \",\"Key ideas\":\"\\n1. Autoregressive models, such as RNNs and Wavenet, can be improved in terms of speed. \\n2. Breaking up the hierarchy of conditioning can improve speed, but this has representational costs as it reduces expressive power. \\n3. Caching can also be used to improve speed, and does not reduce expressive power. \\n4. An example of caching is used with Wavenet, where previous computations can be reused. \\n5. This makes the next layer more like a linear problem in terms of the number of layers.\",\"Abstraction groups\":{\"-1\":[\"Autoregressive Model\",\"RNN\",\"Wavenet\",\"Hierarchy of Conditioning\",\"Representational Cost\",\"Expressive Power\",\"Parallel Computing\",\"Caching\",\"Wavenet Example\",\"Linear Problem\",\"Number of Layer\"],\"0\":[\"Autoregressive Model\"],\"1\":[\"Speed Improvement\"],\"2\":[\"Method\",\"Caching\"],\"3\":[\"Representational Cost\",\"Expressive Power\"],\"4\":[\"Parallel Computing\"]}},\"111\":{\"Question\":\"4 types of masking mentioned as the historical progression in CS294 on autoregressive models\",\"Answer\":\"MADE: masked autoencoder for distribution estimation\\nWavenet: masked 1D convolution architecture\\nPixelCNN: generalization of wavenet to 2D\\nSelf-attention\",\"Key ideas\":\"1. Autoregressive models are a type of machine learning model.\\n2. There is a historical progression of four types of masking used in autoregressive models:\\n    a. MADE (Masked Autoencoder for Distribution Estimation)\\n    b. Wavenet (masked 1D convolution architecture)\\n    c. PixelCNN (generalization of Wavenet to 2D)\\n    d. Self-attention\",\"Abstraction groups\":{\"-1\":[\"Autoregressive\",\"Made\",\"Wavenet\",\"Pixelcnn\",\"Self-attention\"],\"0\":[\"Masking\"],\"1\":[\"Autoregressive Model\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"112\":{\"Question\":\"How to improve recurrent neural networks (RNNs) on the MNIST image dataset? \",\"Answer\":\"Add a positional encoding as part of the conditional information. \",\"Key ideas\":\"\\n1. Recurrent Neural Networks (RNNs): A type of artificial neural network that is used to process sequential data.\\n2. MNIST Image Dataset: A dataset of handwritten digits used for training and testing machine learning algorithms.\\n3. Improve RNNs on the MNIST Image Dataset: To make the RNNs more accurate and efficient when processing the MNIST dataset.\\n4. Positional Encoding: A technique used to add additional information to the input data, which can help improve the accuracy of the model.\",\"Abstraction groups\":{\"-1\":[\"Rnn\",\"Mnist\",\"Improve\",\"Positional Encoding\"],\"0\":[\"Improving Rnn\"],\"1\":[\"Neural Network\",\"Image Dataset\"],\"2\":[\"Machine Learning\",\"Data Processing\"],\"3\":[\"Artificial Intelligence\",\"Information Processing\"],\"4\":[\"Technology\"]}},\"113\":{\"Question\":\"What are two ways to simplify an auto-regressive model for sampling and inference? \",\"Answer\":\"Reduce context: Reduce size of conditional to make model simpler, and learn simple model\\nReduce parameters, but keep full context: Or keep the model using full context, but learn it with a sufficiently complex neural network with regularization. Example: have an MLP for each marginal distribution. \",\"Key ideas\":\"1. Auto-regressive models are used for sampling and inference. \\n2. Simplifying an auto-regressive model can be done in two ways: \\n    a. Reduce context: reduce the size of the conditional to make the model simpler, and learn a simple model. \\n    b. Reduce parameters, but keep full context: keep the model using full context, but learn it with a sufficiently complex neural network with regularization. Example: have an MLP (Multi-Layer Perceptron) for each marginal distribution.\",\"Abstraction groups\":{\"-1\":[\"Auto-Regressive\",\"Sampling\",\"Inference\",\"Reduce Context\",\"Simple Model\",\"Reduce Parameter\",\"Full Context\",\"Neural Network\",\"Regularization\",\"MLP\"],\"0\":[\"Auto-regressive\"],\"1\":[\"Sampling\",\"Inference\"],\"2\":[\"Model Simplification\",\"Neural Networks\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computing\"]}},\"114\":{\"Question\":\"When is an autoregressive model possible for some probability distribution? \",\"Answer\":\"Always! Probability of joint observation is product of marginal probabilities conditioned on previous observations. This is exact\\nLog prob of this becomes sum of log probs given parents. \",\"Key ideas\":\"\\n1. Autoregressive models are possible for any probability distribution. \\n2. The probability of a joint observation is the product of marginal probabilities conditioned on previous observations. \\n3. The exact log probability of a joint observation is the sum of log probabilities given the parents.\",\"Abstraction groups\":{\"-1\":[\"Autoregressive\",\"Probability\",\"Marginal\",\"Previous\",\"Log\",\"Parent\"],\"0\":[\"Autoregressive\"],\"1\":[\"Probability\",\"Model\"],\"2\":[\"Statistic\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"115\":{\"Question\":\"How can you achieve sampling and inference using a histogram of data dependent on a 1D variable?\",\"Answer\":\"Inference: given x, get p(x) from histogram directly (no model necessary)\\nSampling: just generate cumulative distribution vs x (picture cumulative on y axis), then uniform sample, then take location y and find x location. This gives position x with probability proportional to derivative of cumulative function, so is correct\\nProblem: If data is sparse or high dimensional, this fails \",\"Key ideas\":\"\\n1. Sampling and inference can be achieved using a histogram of data dependent on a 1D variable. \\n2. Inference: given x, the probability of x (p(x)) can be obtained directly from the histogram (no model necessary). \\n3. Sampling: generate a cumulative distribution vs x (with cumulative on the y axis), then uniform sample, then take the location y and find the x location. This gives a position x with a probability proportional to the derivative of the cumulative function, so it is correct. \\n4. If the data is sparse or high dimensional, this method fails.\",\"Abstraction groups\":{\"-1\":[\"Sampling\",\"Inference\",\"Histogram\",\"1D Variable\",\"Cumulative Distribution\",\"Uniform Sample\",\"Location\",\"Probability\",\"Derivative\",\"Sparse\",\"High Dimensional\"],\"0\":[\"Histogram\"],\"1\":[\"Sampling\",\"Inference\"],\"2\":[\"Data Analysis\",\"Probability\"],\"3\":[\"Statistics\",\"Mathematics\"],\"4\":[\"Science\"]}},\"116\":{\"Question\":\"What are two metrics to use when evaluating the efficiency of a generative model? \",\"Answer\":\"Computational efficiency (how much resources to run)\\nStatistical efficiency (how many examples necessary to achieve a decent representation). \",\"Key ideas\":\"1. Generative models are used to create data from a given set of parameters. \\n2. Two metrics can be used to evaluate the efficiency of a generative model: \\n    a. Computational efficiency - how much resources are needed to run the model. \\n    b. Statistical efficiency - how many examples are necessary to achieve a decent representation. \\n3. Acronyms and abbreviations should be explained to ensure understanding. \\n4. Answers should be brief and concise, while maintaining completeness and avoiding ambiguity.\",\"Abstraction groups\":{\"-1\":[\"Generative Model\",\"Computational Efficiency\",\"Statistical Efficiency\",\"Example\",\"Representation\"],\"0\":[\"Generative Model\"],\"1\":[\"Efficiency Metric\"],\"2\":[\"Model Evaluation\",\"Data Generation\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"117\":{\"Question\":\"What does MADE stand for? (it is a deep learning architecture) \",\"Answer\":\"MADE: Masked Autoencoder for Distribution Estimation \",\"Key ideas\":\"\\n1. Deep learning: a type of machine learning that uses artificial neural networks to learn from data.\\n2. Autoencoder: a type of artificial neural network used to learn efficient data representations.\\n3. Masked Autoencoder for Distribution Estimation (MADE): a type of autoencoder used to estimate the probability distribution of data.\",\"Abstraction groups\":{\"-1\":[\"Made\",\"Deep Learning\",\"Autoencoder\",\"Distribution\",\"Estimation\"],\"0\":[\"MADE\"],\"1\":[\"Deep Learning\",\"Autoencoder\"],\"2\":[\"Machine Learning\",\"Artificial Neural Networks\"],\"3\":[\"Data Representation\",\"Probability Distribution\"],\"4\":[\"Artificial Intelligence\"]}},\"118\":{\"Question\":\"How do you find the statistical uncertainty on the odds ratio for a 2x2 treatment and control situation, testing some outcome?\",\"Answer\":\"Each outcome has Poisson noise: each has fractional variance 1\\/num samples. Then you do chain rule to get total variance. \\nThe log of the odds ratio is a sum of independent random variables, so it is best to use to get the total uncertainty. \\nThen you can get a confidence interval on the log of odds ratio, \\nthen exponentiate it's edges to get the confidence interval for the odds ratio.\",\"Key ideas\":\"1. Poisson noise: each outcome has fractional variance 1\\/num samples\\n2. Chain rule: used to get total variance\\n3. Log of odds ratio: sum of independent random variables\\n4. Total uncertainty: best to use log of odds ratio to get\\n5. Confidence interval on log of odds ratio: exponentiate edges to get confidence interval for odds ratio\",\"Abstraction groups\":{\"-1\":[\"Odds Ratio\",\"2x2\",\"Treatment\",\"Control\",\"Outcome\",\"Poisson\",\"Variance\",\"Chain Rule\",\"Log\",\"Uncertainty\",\"Confidence Interval\"],\"0\":[\"Odds Ratio\"],\"1\":[\"Statistics\",\"Probability\"],\"2\":[\"Mathematics\",\"Science\"],\"3\":[\"Knowledge\",\"Learning\"],\"4\":[\"Education\"]}},\"119\":{\"Question\":\"What is the odds ratio for testing a drug intervention? \",\"Answer\":\"The odds ratio is defined as the odds of an event in the active treatment group divided by the odds of an event in the control group. \",\"Key ideas\":\"1. The odds ratio is a measure of the relative odds of an event occurring in one group compared to another. \\n2. The odds ratio is calculated by dividing the odds of an event in the active treatment group by the odds of an event in the control group. \\n3. The odds of an event is the ratio of the number of times the event occurs to the number of times it does not occur. \\n4. The active treatment group is the group of individuals receiving the drug intervention. \\n5. The control group is the group of individuals not receiving the drug intervention.\",\"Abstraction groups\":{\"-1\":[\"Odds Ratio\",\"Event\",\"Active Treatment\",\"Control\",\"Occurrence\"],\"0\":[\"Odds Ratio\"],\"1\":[\"Statistics\",\"Probability\"],\"2\":[\"Mathematics\",\"Science\"],\"3\":[\"Knowledge\",\"Understanding\"],\"4\":[\"Education\"]}},\"120\":{\"Question\":\"The derivative of the sigmoid function sig(x) is: \",\"Answer\":\"If the sigmoid function takes value sig(x), its derivative is sig(x)*(1-sig(x)). This goes to 0 at large amplitude input values, negative or positive.\",\"Key ideas\":\"1. The derivative of a function is a measure of how the output of the function changes with respect to a change in the input. \\n2. The sigmoid function is a mathematical function that takes a real-valued input and maps it to a value between 0 and 1. \\n3. The derivative of the sigmoid function is sig(x)*(1-sig(x)). \\n4. The derivative of the sigmoid function goes to 0 at large amplitude input values, negative or positive.\",\"Abstraction groups\":{\"-1\":[\"Derivative\",\"Sigmoid\",\"Function\",\"Input\",\"Output\",\"Value\",\"Amplitude\"],\"0\":[\"Sigmoid\"],\"1\":[\"Derivative\"],\"2\":[\"Function\",\"Input\",\"Output\"],\"3\":[\"Value\",\"Amplitude\"],\"4\":[\"Mathematics\"]}},\"121\":{\"Question\":\"What is the derivative of the entropy function for a bernoulli variable? What can it be written in terms of? \",\"Answer\":\"Is is minus the logit. You can also remember this based on the shape of the entropy function, and shape of the logit function. \",\"Key ideas\":\"\\n1. Entropy function: a mathematical function used to measure the uncertainty of a random variable. \\n2. Bernoulli variable: a type of random variable that can take on two values, usually denoted as 0 and 1. \\n3. Derivative: a measure of how a function changes when its inputs change. \\n4. Logit: a mathematical function used to model the probability of a binary outcome. \\n5. Shape of the entropy function: the entropy function is a convex function, meaning that it has a single maximum point. \\n6. Shape of the logit function: the logit function is a concave function, meaning that it has a single minimum point.\",\"Abstraction groups\":{\"-1\":[\"Entropy\",\"Bernoulli\",\"Derivative\",\"Logit\",\"Shape\",\"Maximum\",\"Minimum\"],\"0\":[\"Derivative Of Entropy\"],\"1\":[\"Mathematics\",\"Calculus\"],\"2\":[\"Science\",\"Problem Solving\"],\"3\":[\"Knowledge\",\"Thinking\"],\"4\":[\"Learning\"]}},\"122\":{\"Question\":\"How do you get a confidence interval for a gaussian distributed variable? \",\"Answer\":\"For a given confidence interval (like 95%), look up the z value corresponding to that. Z value is deviation\\/std. deviation.\\nFor your variable in question, you have a 95% chance it lies within +\\/- that z value of the estimated value.\",\"Key ideas\":\"1. Confidence intervals are used to measure the probability that a given variable lies within a certain range. \\n2. A confidence interval for a gaussian distributed variable can be calculated by looking up the z value corresponding to the desired confidence interval (e.g. 95%). \\n3. The z value is the deviation of the variable from the estimated value, divided by the standard deviation. \\n4. For a given confidence interval, there is a 95% chance that the variable lies within +\\/- the z value of the estimated value.\",\"Abstraction groups\":{\"-1\":[\"Confidence Interval\",\"Gaussian\",\"Z Value\",\"Deviation\",\"Standard Deviation\",\"Estimated Value\"],\"0\":[\"Confidence Interval\"],\"1\":[\"Statistics\",\"Probability\"],\"2\":[\"Mathematics\",\"Science\"],\"3\":[\"Academics\",\"Education\"],\"4\":[\"Knowledge\"]}},\"123\":{\"Question\":\"What is the relation between the logit function, odds, and probability of an event occuring p? \",\"Answer\":\"p = odds\\/(1+odds) = sigmoid(logit)\\nlogit = log(p\\/(1-p)) = log(odds)\\nodds = exp(logit) = p\\/(1-p)\",\"Key ideas\":\"1. The logit function is a mathematical function used to model the probability of an event occurring. \\n2. The odds of an event occurring is the ratio of the probability of the event occurring to the probability of the event not occurring. \\n3. The probability of an event occurring is equal to the odds of the event occurring divided by the sum of the odds and one. \\n4. The logit of a probability is equal to the natural logarithm of the odds of the event occurring divided by the odds of the event not occurring. \\n5. The odds of an event occurring is equal to the exponential of the logit of the probability of the event occurring. \\n6. The sigmoid function is a mathematical function used to model the probability of an event occurring, and is equal to the logit of the probability of the event occurring.\",\"Abstraction groups\":{\"-1\":[\"Logit\",\"Odd\",\"Probability\",\"Event\",\"Sigmoid\",\"Log\",\"Exp\"],\"0\":[\"Logit Function\"],\"1\":[\"Odds\",\"Probability\"],\"2\":[\"Mathematical Function\",\"Event\"],\"3\":[\"Relationship\"],\"4\":[\"Mathematics\"]}},\"124\":{\"Question\":\"What is the logit function for an event with probability of occuring p? \",\"Answer\":\"it is the log of the odds, so log(p\\/(1-p)).\\nIt is 0 at p=0.5, and goes way up\\/down at 0 or 1. Like a sideways s shape.\",\"Key ideas\":\"\\n1. The logit function is a mathematical function used to calculate the probability of an event occurring. \\n2. The logit function is expressed as log(p\\/(1-p)), where p is the probability of the event occurring. \\n3. The logit function is 0 when the probability of the event occurring is 0.5. \\n4. The logit function increases or decreases rapidly when the probability of the event occurring is 0 or 1. \\n5. The logit function has a sideways s-shape when graphed.\",\"Abstraction groups\":{\"-1\":[\"Logit\",\"Probability\",\"Odd\",0.5,0,1,\"Graph\"],\"0\":[\"Logit\"],\"1\":[\"Mathematics\",\"Probability\"],\"2\":[\"Science\",\"Statistics\"],\"3\":[\"Knowledge\",\"Reasoning\"],\"4\":[\"Understanding\"]}},\"125\":{\"Question\":\"What is the definition of the odds of an event occuring? \",\"Answer\":\"If event occurs with probability p, odds are p\\/(1-p).\",\"Key ideas\":\"1. Probability: the likelihood of an event occurring, expressed as a number between 0 and 1.\\n2. Odds: the ratio of the probability of an event occurring to the probability of it not occurring.\\n3. Formula for calculating odds: odds = probability\\/(1-probability).\",\"Abstraction groups\":{\"-1\":[\"Event\",\"Probability\",\"Odd\",\"Formula\"],\"0\":[\"Odd\"],\"1\":[\"Event\",\"Probability\"],\"2\":[\"Mathematics\",\"Statistics\"],\"3\":[\"Science\",\"Reasoning\"],\"4\":[\"Knowledge\"]}},\"126\":{\"Question\":\"Optimizing the input to a neural network for activation of a specific neuron also requires some other constraints, or you get adversarial activations. What else must you potentially do to control for this? \",\"Answer\":\"Examples are \\nfrequency penalization, \\ntransformation robustness (scale, shift etc), \\nor having a strong prior for realistic images \",\"Key ideas\":\"1. Neural networks require optimization of input to activate a specific neuron.\\n2. Without additional constraints, this can lead to adversarial activations.\\n3. To control for this, frequency penalization can be used.\\n4. Transformation robustness (scale, shift, etc.) can also be used.\\n5. Having a strong prior for realistic images is another option.\",\"Abstraction groups\":{\"-1\":[\"Neural Network\",\"Optimization\",\"Activation\",\"Constraint\",\"Adversarial\",\"Frequency\",\"Transformation\",\"Robustness\",\"Scale\",\"Shift\",\"Prior\",\"Realistic\",\"Image\"],\"0\":[\"Neural Network\"],\"1\":[\"Optimization\",\"Activation\",\"Constraint\"],\"2\":[\"Adversarial\",\"Frequency\",\"Transformation\",\"Robustness\",\"Scale\",\"Shift\",\"Prior\",\"Realistic\",\"Image\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computing\"]}},\"127\":{\"Question\":\"How can you create an image that causally activates a certain neuron or vector in a network? \",\"Answer\":\"Optimize pre-softmax logits to be large (this is better than optimizing post softmax for producing visually striking images, because post softmax optimization tries to reduce excitation of others)\\nCan optimize for de-excitation as well\\nOptimizing activation also requires some other constraints, or you get adversarial activations \",\"Key ideas\":\"\\n1. Pre-softmax logits can be optimized to be large in order to create an image that causally activates a certain neuron or vector in a network. \\n2. Optimizing post-softmax for producing visually striking images is not as effective as optimizing pre-softmax logits. \\n3. Optimizing for de-excitation is also possible. \\n4. Optimizing activation also requires some other constraints, or else you may get adversarial activations.\",\"Abstraction groups\":{\"-1\":[\"Image\",\"Neuron\",\"Vector\",\"Network\",\"Pre-Softmax\",\"Post-Softmax\",\"Optimization\",\"Excitation\",\"De-Excitation\",\"Adversarial Activation\"],\"0\":[\"Optimization\"],\"1\":[\"Image\",\"Neuron\",\"Vector\",\"Network\",\"Pre-softmax\",\"Post-softmax\",\"Excitation\",\"De-excitation\",\"Adversarial Activations\"],\"2\":[\"Creating\",\"Activating\"],\"3\":[\"Producing\",\"Optimizing\"],\"4\":[\"Understanding\"]}},\"128\":{\"Question\":\"Name a few examples of low-level building blocks and primitives of neural network circuits \",\"Answer\":\"Equivariant low level detectors: curve detectors and high-low frequency edges\\nUnioning over cases to obtain pose-invariant detectors\\nSuperposition to store information for later more efficiently \",\"Key ideas\":\"1. Neural network circuits are composed of low-level building blocks and primitives. \\n2. Equivariant low level detectors are one type of low-level building block and primitive. \\n3. Curve detectors and high-low frequency edges are examples of equivariant low level detectors. \\n4. Unioning over cases is a technique used to obtain pose-invariant detectors. \\n5. Superposition is a technique used to store information for later more efficiently.\",\"Abstraction groups\":{\"-1\":[\"Neural Network Circuit\",\"Equivariant Detector\",\"Curve Detector\",\"High-Low Frequency Edge\",\"Unioning\",\"Pose-Invariant Detector\",\"Superposition\"],\"0\":[\"Neural Network Circuit\"],\"1\":[\"Low-Level Building Block\",\"Primitive\"],\"2\":[\"Equivariant Low-Level Detector\",\"Unioning\",\"Superposition\"],\"3\":[\"Curve Detector\",\"High-Low Frequency Edge\",\"Pose-Invariant Detector\"],\"4\":[\"Artificial Intelligence\"]}},\"129\":{\"Question\":\"Describe the basic setup of a simple recurrent neural network (RNN) \",\"Answer\":\"Input is a vector at each timestep, along with a hidden state.\\nOutput is a new hidden state, and a prediction vector. \\nComputation is:\\nGet new hidden vector h_new = tanh(W_hh * h_prev + W_xh * x)\\nGet new prediction y = W_hy * h_new \",\"Key ideas\":\"1. A simple recurrent neural network (RNN) has an input vector at each timestep, along with a hidden state.\\n2. The output of an RNN is a new hidden state and a prediction vector.\\n3. The computation of an RNN involves calculating a new hidden vector (h_new) using the previous hidden vector (h_prev) and the input vector (x).\\n4. The new hidden vector (h_new) is calculated using the tanh function and two weight matrices (W_hh and W_xh).\\n5. The prediction vector (y) is calculated using the new hidden vector (h_new) and a weight matrix (W_hy).\",\"Abstraction groups\":{\"-1\":[\"RNN\",\"Input\",\"Hidden State\",\"Output\",\"Prediction\",\"Tanh\",\"W_hh\",\"W_xh\",\"W_hy\"],\"0\":[\"RNN\"],\"1\":[\"Neural Network\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"130\":{\"Question\":\"What are the drawbacks of recurrent neural networks (RNNs)? \",\"Answer\":\"Not long enough memory. \\nEncoding can be too compressive \\nDon\\u2019t allow for parallel computation \",\"Key ideas\":\"1. Recurrent neural networks (RNNs) are a type of artificial neural network. \\n2. RNNs have drawbacks, including: \\n    a. Not having a long enough memory \\n    b. Encoding can be too compressive \\n    c. Not allowing for parallel computation\",\"Abstraction groups\":{\"-1\":[\"RNN\",\"Memory\",\"Encoding\",\"Computation\"],\"0\":[\"RNN\"],\"1\":[\"Artificial Neural Network\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"131\":{\"Question\":\"What is the difference in optimal scaling found in the Chinchilla paper (Deepmind) vs OpenAI paper by Kaplan? \",\"Answer\":\"Kaplan suggested 10x compute increase should use 5x parameter increase, and 2x training increase.\\nChinchilla paper by Deepmind finds equal proportion increases instead.\",\"Key ideas\":\"1. Deepmind and OpenAI are research organizations that focus on artificial intelligence. \\n2. The Chinchilla paper is a research paper published by Deepmind. \\n3. The OpenAI paper is a research paper published by OpenAI. \\n4. Kaplan suggested that for a 10x increase in compute, there should be a 5x increase in parameters and a 2x increase in training. \\n5. The Chinchilla paper by Deepmind found that equal proportion increases should be used instead.\",\"Abstraction groups\":{\"-1\":[\"Deepmind\",\"OpenAI\",\"Chinchilla\",\"Kaplan\",\"Compute\",\"Parameter\",\"Training\"],\"0\":[\"Scaling\"],\"1\":[\"Optimal Scaling\",\"Chinchilla Paper\",\"OpenAI Paper\"],\"2\":[\"Deepmind\",\"Kaplan\"],\"3\":[\"Artificial Intelligence\",\"Research\"],\"4\":[\"Science\"]}},\"132\":{\"Question\":\"TD learning, Monte Carlo, and lambda returns are all ways of _________. \",\"Answer\":\"Sampling the value function \",\"Key ideas\":\"\\n1. TD learning: Temporal Difference learning is a type of reinforcement learning algorithm that uses a combination of trial and error and bootstrapping to estimate the value of a given state or action.\\n\\n2. Monte Carlo: Monte Carlo methods are a class of computational algorithms that use random sampling to solve complex problems.\\n\\n3. Lambda returns: Lambda returns are a type of Monte Carlo method that uses a weighted average of past returns to estimate the value of a given state or action.\\n\\n4. Sampling the value function: Sampling the value function is a process of using TD learning, Monte Carlo, and lambda returns to estimate the value of a given state or action.\",\"Abstraction groups\":{\"-1\":[\"Td Learning\",\"Monte Carlo\",\"Lambda Return\",\"Sampling\",\"Value Function\"],\"0\":[\"Sampling the Value Function\"],\"1\":[\"TD Learning\",\"Monte Carlo\",\"Lambda Returns\"],\"2\":[\"Reinforcement Learning\",\"Estimation\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"133\":{\"Question\":\"Match the following methods of choosing an action to the corresponding reinforcement learning algorithm:\\nAlgorithms: Value function, Q function, Policy probabilities, MCTS\\nActions: highest visit count, look ahead and best value, direct choice of optimal action, direct action sampling \",\"Answer\":\"Q function + direct choice of optimal action\\nPolicy probabilities + direct action sampling\\nValue function + look ahead and best value\\nMCTS + highest visit count \",\"Key ideas\":\"1. Reinforcement learning algorithms: \\n    a. Value function\\n    b. Q function\\n    c. Policy probabilities\\n    d. MCTS\\n2. Actions: \\n    a. Highest visit count\\n    b. Look ahead and best value\\n    c. Direct choice of optimal action\\n    d. Direct action sampling\\n3. Matching the algorithms to the corresponding actions: \\n    a. Q function + direct choice of optimal action\\n    b. Policy probabilities + direct action sampling\\n    c. Value function + look ahead and best value\\n    d. MCTS + highest visit count\",\"Abstraction groups\":{\"-1\":[\"Reinforcement\",\"Algorithm\",\"Action\",\"Value\",\"Q\",\"Policy\",\"MCTS\",\"Visit\",\"Look\",\"Value\",\"Optimal\",\"Sampling\"],\"0\":[\"Reinforcement Learning\"],\"1\":[\"Algorithm\",\"Action\"],\"2\":[\"Matching\",\"Choosing\"],\"3\":[\"Learning\",\"Decision Making\"],\"4\":[\"Artificial Intelligence\"]}},\"134\":{\"Question\":\"MCTS can be thought of as a type of __________ in the generalized policy iteration framework. \",\"Answer\":\"Policy improvement operator \",\"Key ideas\":\"\\n1. MCTS stands for Monte Carlo Tree Search.\\n2. Monte Carlo Tree Search is a type of policy improvement operator in the generalized policy iteration framework.\\n3. Policy iteration is a process of improving a policy by repeatedly evaluating and improving it.\\n4. A policy improvement operator is a method used to improve a policy.\\n5. Generalized policy iteration is a framework for solving Markov decision processes.\",\"Abstraction groups\":{\"-1\":[\"MCTS\",\"Policy\",\"Iteration\",\"Improvement\",\"Framework\"],\"0\":[\"MCTS\"],\"1\":[\"Policy Improvement Operator\"],\"2\":[\"Generalized Policy Iteration\",\"Markov Decision Processes\"],\"3\":[\"Artificial Intelligence\",\"Machine Learning\"],\"4\":[\"Computer Science\"]}},\"135\":{\"Question\":\"What types of regularization are common in reinforcement learning? \",\"Answer\":\"Environment: Opponent pool choice and upper confidence bound\\nNetwork: L2 regularization of network parameters \\nAction: Entropy favoring in policy gradient\",\"Key ideas\":\"1. Regularization is a technique used to improve the performance of a model by reducing its complexity. \\n2. In reinforcement learning, regularization can be applied to the environment, network, and action. \\n3. Environment regularization includes opponent pool choice and upper confidence bound. \\n4. Network regularization includes L2 regularization of network parameters. \\n5. Action regularization includes entropy favoring in policy gradient.\",\"Abstraction groups\":{\"-1\":[\"Regularization\",\"Environment\",\"Opponent\",\"Pool\",\"Choice\",\"Upper\",\"Confidence\",\"Bound\",\"Network\",\"L2\",\"Parameter\",\"Action\",\"Entropy\",\"Favoring\",\"Policy\",\"Gradient\"],\"0\":[\"Regularization\"],\"1\":[\"Reinforcement Learning\",\"Technique\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computational Science\",\"Computer Science\"],\"4\":[\"Science\"]}},\"136\":{\"Question\":\"What are the different ways to predict the target for training a reinforcement learning algoirthm? That is, what are the different targets used? \\nHint: it's always related to total expected returns \",\"Answer\":\"Sampling value function (on policy). Types: TD learning, Monte Carlo, and lambda returns \\nQ learning (off policy) using Bellman equation\\nPolicy gradient formula (on policy). Types: Vanilla, baseline, and PPO (batch training, and clip)\\nDirect action prediction (cross entropy loss in AGZ) (on policy) \",\"Key ideas\":\"\\n1. Reinforcement learning algorithms use targets to predict outcomes. \\n2. The target is always related to total expected returns. \\n3. Sampling value function (on policy) is one way to predict the target. \\n4. Types of sampling value function include TD learning, Monte Carlo, and lambda returns. \\n5. Q learning (off policy) uses the Bellman equation to predict the target. \\n6. Policy gradient formula (on policy) is another way to predict the target. \\n7. Types of policy gradient formula include Vanilla, baseline, and PPO (batch training, and clip). \\n8. Direct action prediction (cross entropy loss in AGZ) (on policy) is a third way to predict the target.\",\"Abstraction groups\":{\"-1\":[\"Reinforcement Learning\",\"Target\",\"Sampling\",\"TD Learning\",\"Monte Carlo\",\"Lambda Return\",\"Q Learning\",\"Bellman Equation\",\"Policy Gradient\",\"Vanilla\",\"Baseline\",\"PPO\",\"Batch Training\",\"Clip\",\"Direct Action\",\"Cross Entropy\",\"AGZ\"],\"0\":[\"Reinforcement Learning\"],\"1\":[\"Predicting Target\",\"Total Expected Return\"],\"2\":[\"Algorithm Training\",\"Sampling Value Function\",\"Q Learning\",\"Policy Gradient\",\"Direct Action Prediction\"],\"3\":[\"On Policy\",\"Off Policy\",\"Bellman Equation\",\"Vanilla\",\"Baseline\",\"PPO\",\"Batch Training\",\"Clip\",\"Cross Entropy\",\"AGZ\"],\"4\":[\"Machine Learning\"]}},\"137\":{\"Question\":\"What are the different types of outputs from a reinforcement learning algorithm to use to choose the action to take for the global policy? \",\"Answer\":\"Value function + look ahead and best value\\nQ function + direct choice of optimal action\\nPolicy probabilities + direct action sampling\\nMCTS + highest visit count \",\"Key ideas\":\"1. Reinforcement learning algorithms use outputs to choose the action to take for the global policy.\\n2. There are four different types of outputs from a reinforcement learning algorithm:\\n    a. Value function + look ahead and best value\\n    b. Q function + direct choice of optimal action\\n    c. Policy probabilities + direct action sampling\\n    d. MCTS (Monte Carlo Tree Search) + highest visit count\",\"Abstraction groups\":{\"-1\":[\"Reinforcement Learning\",\"Output\",\"Value Function\",\"Look Ahead\",\"Best Value\",\"Q Function\",\"Optimal Action\",\"Policy Probability\",\"Action Sampling\",\"MCTS\",\"Visit Count\"],\"0\":[\"Reinforcement Learning\"],\"1\":[\"Output\"],\"2\":[\"Algorithm\",\"Policy\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"138\":{\"Question\":\"What are the three possible physical implementations to use in encoding value in reinforcement learning? \",\"Answer\":\"Lookup table for state\\nFeature vector and lookup table\\nNeural network \",\"Key ideas\":\"\\n1. Reinforcement learning is a type of machine learning that uses rewards and punishments to learn. \\n2. Value encoding is a way of representing the rewards and punishments in reinforcement learning. \\n3. There are three possible physical implementations to use in encoding value in reinforcement learning: \\n    a. Lookup table for state \\n    b. Feature vector and lookup table \\n    c. Neural network\",\"Abstraction groups\":{\"-1\":[\"Reinforcement Learning\",\"Value Encoding\",\"Lookup Table\",\"Feature Vector\",\"Neural Network\"],\"0\":[\"Value Encoding\"],\"1\":[\"Reinforcement Learning\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"139\":{\"Question\":\"What architectural changes were made in alpha Go zero compared to alpha go, which allowed it to have a deeper RL architecture? \",\"Answer\":\"Used residual connections and batch norm. \",\"Key ideas\":\"\\n1. Alpha Go Zero: \\n    a. A computer program developed by Google DeepMind to play the game of Go. \\n    b. Uses a deep reinforcement learning (RL) architecture. \\n2. Architectural changes made in Alpha Go Zero compared to Alpha Go: \\n    a. Used residual connections. \\n    b. Used batch normalization.\",\"Abstraction groups\":{\"-1\":[\"Alpha Go\",\"Alpha Go Zero\",\"Residual Connection\",\"Batch Norm\"],\"0\":[\"Alpha Go Zero\"],\"1\":[\"Deep Reinforcement Learning\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"140\":{\"Question\":\"What are the main parameter choices to make in the vanilla MCTS algorithm (for example the kind which I used in the Tron game)? \",\"Answer\":\"What rollout policy to use (has to be fast, but also somewhat accurate)\\nWhat upper confidence bound exploration pareter to use (need to not branch too much for computational efficiency) \",\"Key ideas\":\"\\n1. MCTS (Monte Carlo Tree Search) is an algorithm used to make decisions in a game.\\n2. Rollout policy is a strategy used to evaluate the potential outcomes of a decision.\\n3. Upper confidence bound exploration is a technique used to explore the decision tree in a computationally efficient manner.\\n4. The parameters chosen for the MCTS algorithm must be fast and accurate for the rollout policy, and must not branch too much for the upper confidence bound exploration.\",\"Abstraction groups\":{\"-1\":[\"MCTS\",\"Rollout\",\"Exploration\",\"Parameter\",\"Efficiency\"],\"0\":[\"MCTS Parameter\"],\"1\":[\"Algorithm\",\"Parameter\"],\"2\":[\"Decision Making\",\"Computation\"],\"3\":[\"Artificial Intelligence\",\"Computer Science\"],\"4\":[\"Science\",\"Technology\"]}},\"141\":{\"Question\":\"What are the main components of the monte-carlo tree search algorithm? \",\"Answer\":\"Selection of the node to expand, \\nexpansion of the tree, \\nsimulation from that node to the end of the game, and \\nbackup of value to all parent nodes, then \\nfinal action selection \",\"Key ideas\":\"\\n1. Monte-Carlo Tree Search (MCTS) is an algorithm used to find the best move in a game.\\n2. MCTS consists of four main components: \\n    a. Selection of the node to expand \\n    b. Expansion of the tree \\n    c. Simulation from that node to the end of the game \\n    d. Backup of value to all parent nodes \\n3. The final action selection is based on the results of the MCTS algorithm.\",\"Abstraction groups\":{\"-1\":[\"MCTS\",\"Node\",\"Tree\",\"Simulation\",\"Value\",\"Action\"],\"0\":[\"Monte-Carlo Tree Search\"],\"1\":[\"Algorithm\",\"Search\"],\"2\":[\"Artificial Intelligence\",\"Computation\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"142\":{\"Question\":\"Why is realistic speech generation necessary, rather than just a cool artistic thing? \",\"Answer\":\"In a noisy environment, it can significantly improve understanding. \",\"Key ideas\":\"1. Realistic speech generation is a technology used to create natural-sounding speech from text. \\n2. It is necessary in certain situations, such as in a noisy environment, where it can significantly improve understanding. \\n3. It is not just a cool artistic thing, but a practical tool with real-world applications.\",\"Abstraction groups\":{\"-1\":[\"Speech Generation\",\"Realistic\",\"Environment\",\"Understanding\",\"Artistic\",\"Cool\"],\"0\":[\"Speech Generation\"],\"1\":[\"Realistic\",\"Environment\",\"Understanding\"],\"2\":[\"Technology\",\"Practical\",\"Artistic\"],\"3\":[\"Communication\",\"Interaction\",\"Cool\"],\"4\":[\"Audio\",\"Visual\",\"Cognitive\"]}},\"143\":{\"Question\":\"Why is unsupervised learning useful? What tasks can it be used for? \",\"Answer\":\"Take advantage of huge amounts of data. Improve a downstream task with fine tuning. Compression of information (representations).\\nGenerate novel data \",\"Key ideas\":\"1. Unsupervised learning is useful because it can take advantage of huge amounts of data.\\n2. It can be used to improve a downstream task with fine tuning.\\n3. It can be used for compression of information (representations).\\n4. It can be used to generate novel data.\",\"Abstraction groups\":{\"-1\":[\"Unsupervised Learning\",\"Data\",\"Fine Tuning\",\"Compression\",\"Representation\",\"Novel Data\"],\"0\":[\"Unsupervised Learning\"],\"1\":[\"Machine Learning\",\"Artificial Intelligence\"],\"2\":[\"Data Science\",\"Computational Thinking\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\",\"Knowledge\"]}},\"144\":{\"Question\":\"What did Betthauser et al (2023) measure as the metric for the \\\"learning deficit\\u201d during the pandemic school disruption?\",\"Answer\":\"Cohen\\u2019s d is calculated as the difference in the mean learning gain in a given subject (maths or reading) over two comparable periods before and after the onset of the pandemic, divided by the pooled standard deviation of learning progress in this subject.\\nResult is a cohen's d of around -0.14, which corresponds to 1\\/3 of a year of learning.\",\"Key ideas\":\"1. Betthauser et al (2023) measured the \\u201clearning deficit\\u201d during the pandemic school disruption.\\n2. Cohen\\u2019s d is a metric used to measure the learning deficit.\\n3. Cohen\\u2019s d is calculated as the difference in the mean learning gain in a given subject (maths or reading) over two comparable periods before and after the onset of the pandemic, divided by the pooled standard deviation of learning progress in this subject.\\n4. The result of this calculation is a cohen's d of around -0.14, which corresponds to 1\\/3 of a year of learning.\",\"Abstraction groups\":{\"-1\":[\"Betthauser\",\"Pandemic\",\"Cohen's D\",\"Mean\",\"Subject\",\"Standard Deviation\",\"Learning\",\"Progress\",\"Year\"],\"0\":[\"Learning Deficit\"],\"1\":[\"Betthauser Et Al (2023), Cohen's D\"],\"2\":[\"Metric\",\"Mean\",\"Standard Deviation\"],\"3\":[\"Pandemic\",\"School Disruption\",\"Learning Progress\"],\"4\":[\"Mathematics\",\"Reading\",\"Year\"]}},\"145\":{\"Question\":\"What did Betthauser et al (2023) show about the time dependence of the learning deficit (cohen's d) after the pandemic?\",\"Answer\":\"It is persisting over time, and across countries. \",\"Key ideas\":\"\\n1. Betthauser et al (2023) conducted a study on the learning deficit (cohen's d) after the pandemic. \\n2. Cohen's d is a measure of the effect size of a difference between two groups. \\n3. The study showed that the learning deficit is persisting over time and across countries.\",\"Abstraction groups\":{\"-1\":[\"Betthauser\",\"Pandemic\",\"Learning\",\"Deficit\",\"Cohen's D\",\"Time\",\"Country\"],\"0\":[\"Learning Deficit\"],\"1\":[\"Pandemic\",\"Cohen's D\"],\"2\":[\"Time\",\"Countries\"],\"3\":[\"Betthauser Et Al (2023)\"],\"4\":[\"Persistence\"]}},\"146\":{\"Question\":\"What is cohen's d statistic? \",\"Answer\":\"It is the difference in mean value between two populations, divided by the normalized pooled standard deviation (basically quadratic mean). \",\"Key ideas\":\"1. Cohen's d statistic is a measure of the difference in mean value between two populations. \\n2. It is calculated by dividing the difference in mean value by the normalized pooled standard deviation. \\n3. The normalized pooled standard deviation is also known as the quadratic mean.\",\"Abstraction groups\":{\"-1\":[\"Cohen's D\",\"Mean\",\"Population\",\"Standard Deviation\",\"Quadratic Mean\"],\"0\":[\"Cohen's D\"],\"1\":[\"Statistics\",\"Measurement\"],\"2\":[\"Mathematics\",\"Science\"],\"3\":[\"Knowledge\",\"Understanding\"],\"4\":[\"Education\"]}},\"147\":{\"Question\":\"In the paper \\\"The Increasing Dominance of Teams in Production of Knowledge\\\" what is the main thesis? \",\"Answer\":\"Over time, research is more often done by teams and larger teams.\\nTeams receive more citations and are more likely to be found in the top end of citations. \",\"Key ideas\":\"\\n1. Research is increasingly done by teams, rather than individuals. \\n2. Teams are more likely to receive citations than individuals. \\n3. Teams are more likely to be found in the top end of citations.\",\"Abstraction groups\":{\"-1\":[\"Team\",\"Production\",\"Knowledge\",\"Thesis\",\"Citation\",\"Research\"],\"0\":[\"Team\"],\"1\":[\"Production\",\"Knowledge\"],\"2\":[\"Research\",\"Citation\"],\"3\":[\"Thesis\"],\"4\":[\"Increasing Dominance\"]}},\"148\":{\"Question\":\"If a paper is made by a team or a solo author, what is the ratio of the likelihood it ends up in the high citation regime (1000 to 10,000 citations)\",\"Answer\":\"6 times larger that it came from a team (not necessarily causal).\",\"Key ideas\":\"1. Papers can be written by either a team or a solo author. \\n2. The likelihood of a paper ending up in the high citation regime (1000 to 10,000 citations) is 6 times larger if it came from a team. \\n3. This does not necessarily mean that the team is the cause of the paper ending up in the high citation regime.\",\"Abstraction groups\":{\"-1\":[\"Paper\",\"Team\",\"Solo\",\"Likelihood\",\"High Citation\",\"Ratio\",\"Causal\"],\"0\":[\"Paper\"],\"1\":[\"Authorship\",\"Citation\"],\"2\":[\"Research\",\"Publication\"],\"3\":[\"Academic\",\"Scholarly\"],\"4\":[\"Knowledge\",\"Information\"]}},\"149\":{\"Question\":\"In the paper \\\"The Increasing Dominance of Teams in Production of Knowledge\\\" (2007), what was the change in team paper citations compared to solo paper citations in the sciences from 1950s to now?\",\"Answer\":\"Ratio of team paper citations (on average) to solo paper citations (on average) went from 1.5 to 2 or so\",\"Key ideas\":\"1. The paper \\\"The Increasing Dominance of Teams in Production of Knowledge\\\" (2007) studied the change in team paper citations compared to solo paper citations in the sciences from 1950s to now. \\n2. Team paper citations (on average) to solo paper citations (on average) went from 1.5 to 2 or so.\",\"Abstraction groups\":{\"-1\":[\"Paper\",\"Team\",\"Solo\",\"Citation\",\"Science\",\"1950s\",\"Ratio\",\"Average\"],\"0\":[\"Citation\"],\"1\":[\"Team\",\"Solo\"],\"2\":[\"Paper\",\"Science\"],\"3\":[\"1950s\",\"Ratio\"],\"4\":[\"Average\"]}},\"150\":{\"Question\":\"In the paper \\\"The Increasing Dominance of Teams in Production of Knowledge\\\" (2007), what was the change in mean team size in the sciences from 1950s to now?\",\"Answer\":\"Mean team size in sciences went from 2 to 4 basically from 1970 to now.\",\"Key ideas\":\"1. The paper \\\"The Increasing Dominance of Teams in Production of Knowledge\\\" was published in 2007. \\n2. Mean team size in the sciences has increased from the 1950s to now. \\n3. Mean team size in sciences went from 2 to 4 basically from 1970 to now.\",\"Abstraction groups\":{\"-1\":[\"Paper\",\"Team\",\"Size\",\"Science\",\"1950s\",\"1970s\",\"Now\"],\"0\":[\"Team Size\"],\"1\":[\"Science\",\"Paper\"],\"2\":[\"Knowledge\",\"Change\"],\"3\":[\"Production\",\"Mean\"],\"4\":[\"1950s\",\"1970s\",\"Now\"]}},\"151\":{\"Question\":\"How did the pandemic affect low income school closures vs high income school closures? \",\"Answer\":\"Low income had an extra of 5-10 weeks of school closure within a state, after controlling for overall state differences.\",\"Key ideas\":\"1. The pandemic had an effect on school closures. \\n2. Low income schools were affected more than high income schools. \\n3. Low income schools had an extra 5-10 weeks of school closure within a state, after controlling for overall state differences.\",\"Abstraction groups\":{\"-1\":[\"Pandemic\",\"Low Income\",\"High Income\",\"School Closure\",\"State Difference\"],\"0\":[\"School Closure\"],\"1\":[\"Pandemic\",\"Income\"],\"2\":[\"Socioeconomic\",\"Education\"],\"3\":[\"Inequality\",\"Access\"],\"4\":[\"Social Justice\"]}},\"152\":{\"Question\":\"How did the pandemic affect school outcomes on average for school age children? What is it equivalent to? \",\"Answer\":\"Equivalent to around 1\\/3 of a school year lost, on average. \\nTest scores dropped by around 0.3 std deviations, with around 0.2 extra for 50% remote instruction.\",\"Key ideas\":\"1. The pandemic has had a significant effect on school outcomes for school age children. \\n2. This effect is equivalent to around 1\\/3 of a school year lost, on average. \\n3. Test scores dropped by around 0.3 standard deviations, with around 0.2 extra for 50% remote instruction.\",\"Abstraction groups\":{\"-1\":[\"Pandemic\",\"School\",\"Outcome\",\"Average\",\"Equivalent\",\"School Year\",\"Test Score\",\"Standard Deviation\",\"Remote Instruction\"],\"0\":[\"Pandemic\"],\"1\":[\"School Outcome\"],\"2\":[\"Average\",\"Equivalent\"],\"3\":[\"School Year\",\"Test Score\",\"Standard Deviation\"],\"4\":[\"Remote Instruction\"]}},\"153\":{\"Question\":\"In the paper \\\"Can behavioral interventions be too salient?\\\" (2022), what did they conclude as general takeaways?\",\"Answer\":\"Be careful designing interventions that my hurt. \\nGrabbing too much attention can cause distraction and crashes (and they showed it was distraction). \\nImpact did not persist after treatment stopped. \",\"Key ideas\":\"1. Interventions should be designed carefully, as they can have unintended consequences. \\n2. Too much attention can cause distraction and crashes. \\n3. The study showed that distraction was the cause of crashes. \\n4. The impact of the intervention did not persist after treatment stopped.\",\"Abstraction groups\":{\"-1\":[\"Intervention\",\"Attention\",\"Distraction\",\"Crash\",\"Treatment\"],\"0\":[\"Behavioral Intervention\"],\"1\":[\"Salience\",\"Impact\",\"Persistence\"],\"2\":[\"Design\",\"Attention\",\"Treatment\"],\"3\":[\"Consequence\",\"Distraction\",\"Crash\"],\"4\":[\"Research\",\"Study\",\"Conclusion\"]}},\"154\":{\"Question\":\"What was measured\\/perturbed in the paper \\\"Can behavioral interventions be too salient?\\\" (2022)?\",\"Answer\":\"They displayed traffic deaths on a sign in texas, and this did not improve safety on net. In fact it increased crashes by almost 5%. \\nIt was too salient, and increased traffic accidents.\",\"Key ideas\":\"1. Traffic deaths were displayed on a sign in Texas. \\n2. This intervention did not improve safety on net. \\n3. In fact, it increased crashes by almost 5%. \\n4. The intervention was too salient, and thus increased traffic accidents.\",\"Abstraction groups\":{\"-1\":[\"Traffic\",\"Death\",\"Sign\",\"Texas\",\"Safety\",\"Crash\",\"Intervention\",\"Salience\"],\"0\":[\"Traffic Death\"],\"1\":[\"Intervention\",\"Salience\"],\"2\":[\"Behavioral Intervention\",\"Traffic Safety\"],\"3\":[\"Public Health\",\"Transportation\"],\"4\":[\"Social Science\"]}},\"155\":{\"Question\":\"Are global fish stocks in collapse? Why or why not? \",\"Answer\":\"The optimal fish population for sustainable extraction of maximum resources (stable population) is sometimes 50% smaller than historical levels \\nThis is because catch can be larger and larger and take home grows (population isn't much effected) but eventually the population gets depleted too fast and your catch goes down\",\"Key ideas\":\"1. Global fish stocks are not necessarily in collapse. \\n2. The optimal fish population for sustainable extraction of maximum resources is sometimes 50% smaller than historical levels. \\n3. Catch can be larger and larger and take home grows (population isn't much effected). \\n4. Eventually the population gets depleted too fast and your catch goes down.\",\"Abstraction groups\":{\"-1\":[\"Fish\",\"Stock\",\"Collapse\",\"Optimal\",\"Sustainable\",\"Maximum\",\"Resource\",\"Population\",\"Historical\",\"Catch\",\"Depleted\"],\"0\":[\"Fish Stock\"],\"1\":[\"Collapse\",\"Sustainable Extraction\"],\"2\":[\"Population\",\"Resources\"],\"3\":[\"Optimal\",\"Historical\"],\"4\":[\"Catch\",\"Depleted\"]}},\"156\":{\"Question\":\"Which fish types are subject to overfishing, and which are not? \",\"Answer\":\"Sharks and rays are bad\\nTuna is generally pretty good (Depends on geographic location and which type of tuna) \",\"Key ideas\":\"1. Overfishing is a problem for certain types of fish. \\n2. Sharks and rays are subject to overfishing. \\n3. Tuna is generally not subject to overfishing, but this depends on the geographic location and type of tuna.\",\"Abstraction groups\":{\"-1\":[\"Fish\",\"Overfishing\",\"Shark\",\"Ray\",\"Tuna\",\"Location\",\"Type\"],\"0\":[\"Overfishing\"],\"1\":[\"Fish\",\"Marine Life\"],\"2\":[\"Wildlife\",\"Environment\"],\"3\":[\"Ecology\",\"Conservation\"],\"4\":[\"Sustainability\"]}},\"157\":{\"Question\":\"In the paper \\\"Greening of the Earth and its drivers\\\" (2016), what methods were used?\",\"Answer\":\"Satellite observation of leafy coverage \\nClimate models to predict causes. \",\"Key ideas\":\"1. The paper \\\"Greening of the Earth and its drivers\\\" was published in 2016. \\n2. Satellite observation of leafy coverage was used as a method. \\n3. Climate models were used to predict causes.\",\"Abstraction groups\":{\"-1\":[\"Paper\",\"Greening\",\"Earth\",\"Driver\",\"Satellite\",\"Leafy\",\"Coverage\",\"Climate\",\"Model\",\"Cause\"],\"0\":[\"Greening\"],\"1\":[\"Earth\",\"Driver\"],\"2\":[\"Environment\",\"Climate\"],\"3\":[\"Earth Science\",\"Atmospheric Science\"],\"4\":[\"Science\"]}},\"158\":{\"Question\":\"What was the main quantitative result of the paper \\\"Greening of the Earth and its drivers\\\" (2016)?\",\"Answer\":\"\\\"We show a persistent and widespread increase of growing season integrated LAI (greening) over 25% to 50% of the global vegetated area\\\"\\n\\\"...models suggest that CO2 fertilization effects explain 70% of the observed greening trend\\\"\\nAverage rate is 0.07 m^2 per m^2 per year, so around increase by 2 (not a factor of 2) from 1982 to 2009.\",\"Key ideas\":\"1. The paper \\\"Greening of the Earth and its drivers\\\" (2016) was a quantitative study.\\n2. The main result of the study was a persistent and widespread increase of growing season integrated LAI (Leaf Area Index) over 25% to 50% of the global vegetated area.\\n3. Models suggest that CO2 fertilization effects explain 70% of the observed greening trend.\\n4. The average rate of increase was 0.07 m^2 per m^2 per year.\\n5. This is an increase of around 2 (not a factor of 2) from 1982 to 2009.\",\"Abstraction groups\":{\"-1\":[\"Greening\",\"Lai\",\"Global\",\"Co2\",1982,2009,\"Increase\"],\"0\":[\"Greening\"],\"1\":[\"Quantitative Study\",\"Earth\",\"Driver\"],\"2\":[\"Science\",\"Environment\",\"Climate Change\"],\"3\":[\"Research\",\"Global\",\"CO2\"],\"4\":[\"Knowledge\",\"Understanding\",\"Learning\"]}},\"159\":{\"Question\":\"What main metric is used to see how plants respond to climate change? \",\"Answer\":\"Leaf area index: surface area of leaves per unit of surface area of earth. Can use units in paper to understand from there. \",\"Key ideas\":\"\\n1. Leaf area index (LAI): a metric used to measure how plants respond to climate change.\\n2. LAI is measured by the surface area of leaves per unit of surface area of earth.\\n3. Units of measurement used to measure LAI can be found in scientific papers.\",\"Abstraction groups\":{\"-1\":[\"Climate Change\",\"Leaf Area\",\"Unit\",\"Paper\"],\"0\":[\"Leaf Area Index\"],\"1\":[\"Climate Change\",\"Metrics\"],\"2\":[\"Plant Response\",\"Measurement\"],\"3\":[\"Environmental Science\",\"Quantification\"],\"4\":[\"Science\"]}},\"160\":{\"Question\":\"What main factors are used in the model to predict changes in leafy area coverage for predicting forest greening with climate change? \",\"Answer\":\"CO2 concentration\\nNitrogen concentration (deposition)\\nClimate change (Temp change and Precipitation change)\\nLand cover change (I think this means clouds?)\",\"Key ideas\":\"1. CO2 concentration\\n2. Nitrogen concentration (deposition)\\n3. Climate change (Temp change and Precipitation change)\\n4. Land cover change (clouds)\",\"Abstraction groups\":{\"-1\":[\"CO2\",\"Nitrogen\",\"Climate\",\"Land\",\"Clouds\"],\"0\":[\"Forest Greening\"],\"1\":[\"Climate Change\",\"Land Cover Change\"],\"2\":[\"Carbon Cycle\",\"Nitrogen Cycle\"],\"3\":[\"Ecology\",\"Atmospheric Science\"],\"4\":[\"Earth Science\"]}},\"161\":{\"Question\":\"Name a few technologies first used in world war 1\",\"Answer\":\"U boats from germany cause heavy losses of england navy \\nGermany tries to blockade england, but ends up sinking a lot of US people, and that is a big reason the US joined the war. \\nTrench warfare\\nBombs\\nAircraft\\nTanks\\nChemical warfare \",\"Key ideas\":\"1. U-boats from Germany caused heavy losses of England's navy.\\n2. Germany attempted to blockade England, but ended up sinking a lot of US ships, which was a major factor in the US joining the war.\\n3. Trench warfare was used.\\n4. Bombs were used.\\n5. Aircraft were used.\\n6. Tanks were used.\\n7. Chemical warfare was used.\",\"Abstraction groups\":{\"-1\":[\"U-Boat\",\"England\",\"US\",\"Trench Warfare\",\"Bomb\",\"Aircraft\",\"Tank\",\"Chemical Warfare\"],\"0\":[\"World War 1\"],\"1\":[\"Technology\",\"Warfare\"],\"2\":[\"Military\",\"Conflict\"],\"3\":[\"History\",\"Politics\"],\"4\":[\"Human Experience\"]}},\"162\":{\"Question\":\"What was the outcome of world war 1?\",\"Answer\":\"Ottoman. Is totally cut up to modern states. This is not what was promised to Arab countries. \\nAustro-hungarian empire is cut up into modern day states. \\nGermany cedes a lot of land. Has to have \\\"war guilt clause\\\" and pay a bunch of nations. Reduced military (Versailles)\\nLeague of nations is formed (versailles) \",\"Key ideas\":\"1. The Ottoman Empire was cut up into modern states.\\n2. Arab countries were not given what was promised to them.\\n3. The Austro-Hungarian Empire was cut up into modern day states.\\n4. Germany had to cede a lot of land and had to accept the \\\"war guilt clause\\\" and pay a bunch of nations.\\n5. Germany's military was reduced (as per the Versailles Treaty).\\n6. The League of Nations was formed (as per the Versailles Treaty).\",\"Abstraction groups\":{\"-1\":[\"Ottoman\",\"Arab\",\"Austro-Hungarian\",\"Germany\",\"War Guilt Clause\",\"Versailles\",\"League of Nations\"],\"0\":[\"World War 1\"],\"1\":[\"Outcome\",\"Treaty Of Versailles\",\"League Of Nations\"],\"2\":[\"Ottoman Empire\",\"Austro-Hungarian Empire\",\"Germany\"],\"3\":[\"War Guilt Clause\",\"Arab Countries\"],\"4\":[\"International Relations\"]}},\"163\":{\"Question\":\"What are a few methods of monetary policy used by the government, and what do they do? \",\"Answer\":\"Change bank reserve ratio (more or less cash on hand)\\nChange federal funds rate (ie change short term interest rate offered by government directly) (more or less cash on hand)\\nBuy and sell treasury securities (bonds) on open market (called open market operations) (more or less cash on hand) \",\"Key ideas\":\"1. Monetary policy is a tool used by the government to influence the economy. \\n2. Change bank reserve ratio: This is when the government changes the amount of cash that banks must keep on hand. \\n3. Change federal funds rate: This is when the government changes the short-term interest rate offered by the government directly. \\n4. Open market operations: This is when the government buys and sells treasury securities (bonds) on the open market. This affects the amount of cash that banks have on hand.\",\"Abstraction groups\":{\"-1\":[\"Monetary Policy\",\"Bank Reserve Ratio\",\"Federal Funds Rate\",\"Open Market Operation\",\"Treasury Security\",\"Bond\"],\"0\":[\"Monetary Policy\"],\"1\":[\"Government Intervention\",\"Economic Policy\"],\"2\":[\"Macroeconomics\",\"Economics\"],\"3\":[\"Social Sciences\",\"Sciences\"],\"4\":[\"Knowledge\"]}},\"164\":{\"Question\":\"How can innovation and technology reduce interest rates? Why is this unintuitive? \",\"Answer\":\"If you discover a way to extract huge amounts of natural resources, then the marginal return on new resources will go down, so interest rates go down.\\nThis is unintuitive because usually increased technology = more return on investment = higher interest rate on average. \",\"Key ideas\":\"1. Natural resources can be extracted through innovation and technology. \\n2. The marginal return on new resources decreases when more resources are extracted. \\n3. As a result, interest rates go down. \\n4. This is counterintuitive because usually increased technology leads to higher returns on investment, which in turn leads to higher interest rates.\",\"Abstraction groups\":{\"-1\":[\"Innovation\",\"Technology\",\"Interest Rate\",\"Resource\",\"Return\",\"Investment\"],\"0\":[\"Interest Rate\"],\"1\":[\"Innovation\",\"Technology\"],\"2\":[\"Resource\",\"Return\",\"Investment\"],\"3\":[\"Economics\"],\"4\":[\"Social Science\"]}},\"165\":{\"Question\":\"What sets the interest rate of an economy, in the absence of monetary policy\\/government intervention? \",\"Answer\":\"The productivity of capital on average sets the interest rate, given the total capital supply and technology at a certain moment in time \",\"Key ideas\":\"\\n1. Interest rate: the rate at which money can be borrowed or lent\\n2. Monetary policy\\/government intervention: actions taken by a government or central bank to influence the availability and cost of money and credit\\n3. Productivity of capital: the rate of return on capital investments\\n4. Total capital supply: the total amount of capital available in an economy\\n5. Technology: the application of scientific knowledge for practical purposes\",\"Abstraction groups\":{\"-1\":[\"Interest Rate\",\"Monetary Policy\",\"Government Intervention\",\"Productivity\",\"Capital\",\"Supply\",\"Technology\"],\"0\":[\"Interest Rate\"],\"1\":[\"Monetary Policy\",\"Government Intervention\"],\"2\":[\"Economics\",\"Finance\"],\"3\":[\"Business\",\"Social Sciences\"],\"4\":[\"Knowledge\"]}},\"166\":{\"Question\":\"What was the Bretton woods system? \",\"Answer\":\"An international monetary gold standard tied to US dollar after WW2 until 1971 inflation\\/energy crisis\",\"Key ideas\":\"1. Bretton Woods system was an international monetary system \\n2. It was established after World War II \\n3. It was based on a gold standard \\n4. The gold standard was tied to the US dollar \\n5. The system was in place until 1971 \\n6. The system ended due to inflation and the energy crisis\",\"Abstraction groups\":{\"-1\":[\"Bretton Wood\",\"WW2\",\"Gold Standard\",\"US Dollar\",\"1971\",\"Inflation\",\"Energy Crisis\"],\"0\":[\"Bretton Woods\"],\"1\":[\"International Monetary System\"],\"2\":[\"Economics\",\"Globalization\"],\"3\":[\"Politics\",\"History\"],\"4\":[\"Social Sciences\"]}},\"167\":{\"Question\":\"How can GPS be enhanced in accuracy? What is the bottleneck using this method? \",\"Answer\":\"Use multimodal sources of information, such as local wifi, and ground-based positioning emitters. \\nDifficulty is getting timing precision sub-nanosecond for ground-based emitters. \",\"Key ideas\":\"1. GPS can be enhanced in accuracy by using multimodal sources of information. \\n2. Examples of multimodal sources of information include local wifi and ground-based positioning emitters. \\n3. The difficulty in using ground-based emitters is getting timing precision sub-nanosecond.\",\"Abstraction groups\":{\"-1\":[\"GPS\",\"Accuracy\",\"Multimodal\",\"Wifi\",\"Emitter\",\"Timing\",\"Precision\"],\"0\":[\"GPS\"],\"1\":[\"Accuracy\",\"Multimodal\",\"Wifi\",\"Emitters\",\"Timing\",\"Precision\"],\"2\":[\"Enhancing\",\"Bottleneck\"],\"3\":[\"Technology\",\"Positioning\"],\"4\":[\"Navigation\"]}},\"168\":{\"Question\":\"In the paper \\\"Semantic reconstruction of continuous language from non-invasive brain recordings\\\" by Tang et al (2022), what was the basic setup?\",\"Answer\":\"Have people listen to stories in an fMRI machine, then train a decoder to predict the text or thoughts when presented with new fMRI data. \",\"Key ideas\":\"1. The paper \\\"Semantic reconstruction of continuous language from non-invasive brain recordings\\\" was published in 2022 by Tang et al. \\n2. The basic setup of the paper was to have people listen to stories in an fMRI (functional Magnetic Resonance Imaging) machine. \\n3. The goal was to train a decoder to predict the text or thoughts when presented with new fMRI data.\",\"Abstraction groups\":{\"-1\":[\"Paper\",\"Tang\",\"2022\",\"fMRI\",\"Story\",\"Decoder\",\"Text\",\"Thought\",\"Data\"],\"0\":[\"Brain Recording\"],\"1\":[\"Non-invasive\",\"Semantic Reconstruction\"],\"2\":[\"Language\",\"Continuous\"],\"3\":[\"Paper\",\"Fmri\",\"Decoder\"],\"4\":[\"Setup\",\"Story\",\"Text\",\"Thought\",\"Data\"]}},\"169\":{\"Question\":\"What did the paper \\\"Induction of visual orientation modules in auditory cortex\\\" (2000) do experimentally?\",\"Answer\":\"Rewire visual nerve to go to auditory cortex in ferrets, and see how it develops. \",\"Key ideas\":\"1. The paper \\\"Induction of visual orientation modules in auditory cortex\\\" (2000) was an experiment. \\n2. The experiment involved rewiring visual nerves to go to the auditory cortex in ferrets. \\n3. The purpose of the experiment was to observe how the rewired visual nerves developed.\",\"Abstraction groups\":{\"-1\":[\"Paper\",\"Visual\",\"Auditory\",\"Cortex\",\"Ferret\",\"Rewire\",\"Develop\"],\"0\":[\"Rewire\"],\"1\":[\"Experiment\",\"Rewiring\",\"Ferret\"],\"2\":[\"Research\",\"Neuroscience\",\"Animal\"],\"3\":[\"Science\",\"Biology\",\"Behavior\"],\"4\":[\"Knowledge\",\"Understanding\",\"Exploration\"]}},\"170\":{\"Question\":\"Paper \\\"Connectomes across development\\\" (2020), second order results about synapses: major conserved themes.\",\"Answer\":\"Increasing modularity with age\\nSynapses become more feed forward (from sensory, to integrated processing, to motor)\\nCentral processing or interneuron structure was relatively conserved. \",\"Key ideas\":\"1. Increasing modularity with age\\n2. Synapses become more feed forward (from sensory, to integrated processing, to motor)\\n3. Central processing or interneuron structure was relatively conserved\",\"Abstraction groups\":{\"-1\":[\"Connectome\",\"Development\",\"Synapse\",\"Modularity\",\"Feed Forward\",\"Sensory\",\"Integrated\",\"Motor\",\"Interneuron\",\"Conserved\"],\"0\":[\"Connectome\"],\"1\":[\"Development\",\"Synapse\"],\"2\":[\"Modularity\",\"Feed Forward\"],\"3\":[\"Sensory\",\"Integrated\",\"Motor\",\"Interneuron\",\"Conserved\"],\"4\":[\"Structure\",\"Age\"]}},\"171\":{\"Question\":\"Paper \\\"Connectomes across development\\\" (2020), first order results on synapse structure\",\"Answer\":\"Synapse density per axon length is roughly similar during growth and across individuals.\\nActual map is different though. \",\"Key ideas\":\"1. Connectomes across development (2020) is a paper that studied synapse structure. \\n2. Synapse density per axon length is roughly similar during growth and across individuals. \\n3. The actual map of synapse structure is different though.\",\"Abstraction groups\":{\"-1\":[\"Connectome\",\"Development\",\"Synapse\",\"Structure\",\"Density\",\"Axon\",\"Length\",\"Growth\",\"Individual\",\"Map\"],\"0\":[\"Synapse Structure\"],\"1\":[\"Connectome\",\"Development\"],\"2\":[\"Structure\",\"Density\",\"Axon Length\",\"Growth\",\"Individual\",\"Map\"],\"3\":[\"Biology\",\"Neuroscience\"],\"4\":[\"Science\"]}},\"172\":{\"Question\":\"Paper \\\"Connectomes across development\\\" (2020) measured synapses in what animal using what technology?\",\"Answer\":\"C. Elegans, using Scanning Electron Microscopy after cross sectioning. \",\"Key ideas\":\"1. Paper \\\"Connectomes across development\\\" (2020) \\n2. Measured synapses \\n3. Animal used: C. Elegans \\n4. Technology used: Scanning Electron Microscopy (SEM) \\n5. Cross sectioning of C. Elegans was necessary for SEM to measure synapses\",\"Abstraction groups\":{\"-1\":[\"Connectome\",\"Development\",\"Synapse\",\"C. Elegans\",\"SEM\",\"Cross Sectioning\"],\"0\":[\"Connectome\"],\"1\":[\"Development\",\"Synapse\"],\"2\":[\"Animal\",\"Technology\"],\"3\":[\"Measurement\",\"Cross Sectioning\"],\"4\":[\"Research\",\"Experimentation\"]}},\"173\":{\"Question\":\"What is the major thing that differentiates the Proterozoic era from the Archaen era? \",\"Answer\":\"Development of Eukaryotes (around 2 billion years ago), and then multicellular life (around 1.6 billion years ago)\",\"Key ideas\":\"\\n1. Proterozoic era: a geological era that lasted from 2.5 billion to 541 million years ago\\n2. Archaen era: a geological era that lasted from 4 billion to 2.5 billion years ago\\n3. Eukaryotes: a type of organism whose cells contain a nucleus and other organelles enclosed within membranes\\n4. Multicellular life: organisms composed of more than one cell, such as plants and animals\",\"Abstraction groups\":{\"-1\":[\"Proterozoic\",\"Archaen\",\"Eukaryote\",\"Multicellular\"],\"0\":[\"Proterozoic\",\"Archaen\"],\"1\":[\"Geological Era\"],\"2\":[\"Earth's History\",\"Earth's Timeline\"],\"3\":[\"Earth Science\",\"Natural Science\"],\"4\":[\"Science\"]}},\"174\":{\"Question\":\"What differentiates the beginning of the Archaen era from the Hadean era? \",\"Answer\":\"Hadean had water -> Archean has single cell life, and then later photosynthesis. \",\"Key ideas\":\"1. The Hadean era was the earliest era of Earth's history.\\n2. It was characterized by the presence of water.\\n3. The Archean era began after the Hadean era.\\n4. It was characterized by the presence of single-celled life.\\n5. Later in the Archean era, photosynthesis began.\",\"Abstraction groups\":{\"-1\":[\"Hadean\",\"Water\",\"Archean\",\"Life\",\"Photosynthesis\"],\"0\":[\"Earth's History\"],\"1\":[\"Hadean\",\"Archean\"],\"2\":[\"Geological Era\"],\"3\":[\"Earth's History\"],\"4\":[\"Natural History\"]}},\"175\":{\"Question\":\"What are the 4 major historical epochs of the earth development, and their times?\",\"Answer\":\"Hadean: 4.5 billion to 4 billion years ago. \\nArchaen: ends 2.5 billion ago\\nProterozoic: ends 539 million ago (with Cambrian explosion)\\nPhanerozoic: current\",\"Key ideas\":\"1. The Earth has gone through 4 major historical epochs.\\n2. The Hadean epoch lasted from 4.5 billion to 4 billion years ago.\\n3. The Archaen epoch ended 2.5 billion years ago.\\n4. The Proterozoic epoch ended 539 million years ago, and was marked by the Cambrian explosion.\\n5. The Phanerozoic epoch is the current epoch.\",\"Abstraction groups\":{\"-1\":[\"Hadean\",\"Archaen\",\"Proterozoic\",\"Cambrian\",\"Phanerozoic\",\"Earth\",\"Epoch\",\"Year\"],\"0\":[\"Earth Development\"],\"1\":[\"Epoch\",\"Time\"],\"2\":[\"Historical\",\"Development\"],\"3\":[\"Earth\",\"Nature\"],\"4\":[\"Science\"]}},\"176\":{\"Question\":\"Characteristics of Cnidaria (Coral and jellyfish) \",\"Answer\":\"special cells cnidocytes for capturing prey, radial symmetry usually, one orifice, decentralized nerve nets \",\"Key ideas\":\"1. Cnidaria are a group of animals that includes coral and jellyfish. \\n2. Cnidaria have special cells called cnidocytes which they use to capture prey. \\n3. Cnidaria usually have radial symmetry, meaning their body parts are arranged around a central point. \\n4. Cnidaria have one orifice, or opening, through which they take in food and expel waste. \\n5. Cnidaria have decentralized nerve nets, meaning their nervous system is not centralized in one area.\",\"Abstraction groups\":{\"-1\":[\"Cnidaria\",\"Cnidocyte\",\"Radial Symmetry\",\"Orifice\",\"Nerve Net\"],\"0\":[\"Cnidaria\"],\"1\":[\"Animal\",\"Invertebrate\"],\"2\":[\"Organism\",\"Living Thing\"],\"3\":[\"Biology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"177\":{\"Question\":\"Characteristics of Arthropods (insects) \",\"Answer\":\"chitin, exoskeleton not vertebrate, moulting, segmentation, cambrian period, metamorphosis \",\"Key ideas\":\"1. Arthropods (insects) are characterized by: \\n    a. Chitin \\n    b. An exoskeleton, meaning they are not vertebrates \\n    c. Moulting \\n    d. Segmentation \\n    e. Emerged during the Cambrian period \\n    f. Metamorphosis\",\"Abstraction groups\":{\"-1\":[\"Chitin\",\"Exoskeleton\",\"Moulting\",\"Segmentation\",\"Cambrian\",\"Metamorphosis\"],\"0\":[\"Arthropod\"],\"1\":[\"Insect\"],\"2\":[\"Arthropod\",\"Animal\"],\"3\":[\"Organism\",\"Living Thing\"],\"4\":[\"Biology\"]}},\"178\":{\"Question\":\"Characteristics of Porifera (Sponges) \",\"Answer\":\"unspecialized cells, no nervous or digestive or circulatory system, first to branch off. Are mobile in first cell stage. \",\"Key ideas\":\"1. Porifera (Sponges) have unspecialized cells. \\n2. They do not have a nervous, digestive, or circulatory system. \\n3. Porifera (Sponges) were the first to branch off from other organisms. \\n4. They are mobile in their first cell stage.\",\"Abstraction groups\":{\"-1\":[\"Porifera\",\"Cell\",\"Nervous\",\"Digestive\",\"Circulatory\",\"Branch\",\"Mobile\",\"Stage\"],\"0\":[\"Porifera\"],\"1\":[\"Characteristic\"],\"2\":[\"Biology\",\"Organism\"],\"3\":[\"Science\",\"Living Thing\"],\"4\":[\"Knowledge\"]}},\"179\":{\"Question\":\"What are the categories to consider when examining a new robotics application? \",\"Answer\":\"Spatial configuration (size). \\nWhether it moves. \\nHow it interacts with humans. \",\"Key ideas\":\"\\n1. Spatial configuration: the size of the robotics application.\\n2. Whether the application moves or is stationary.\\n3. How the application interacts with humans.\",\"Abstraction groups\":{\"-1\":[\"Robotic\",\"Spatial\",\"Size\",\"Movement\",\"Interaction\",\"Human\"],\"0\":[\"Robotics\"],\"1\":[\"Spatial\",\"Movement\",\"Interaction\"],\"2\":[\"Configuration\",\"Human-Robot Interaction\"],\"3\":[\"Technology\",\"Human-Computer Interaction\"],\"4\":[\"Science\",\"Engineering\"]}},\"180\":{\"Question\":\"What idea did I have for helping people choose between research fields? \",\"Answer\":\"Measure: Of authors with publications in multiple fields, which field are they currently in? (example: using arxiv publications)\\nPost selects on people who purposefully changed careers and thus people who probably thought a bit more deeply \",\"Key ideas\":\"\\n1. Measure the field of authors with publications in multiple fields.\\n2. Use arxiv publications as an example.\\n3. Select people who purposefully changed careers.\\n4. These people probably thought more deeply about their choice.\",\"Abstraction groups\":{\"-1\":[\"Research Field\",\"Measure\",\"Author\",\"Publication\",\"Arxiv\",\"Select\",\"Person\",\"Career\",\"Thought\"],\"0\":[\"Choosing Research Field\"],\"1\":[\"Measurement\",\"Publication\",\"Person\"],\"2\":[\"Research\",\"Career\",\"Thought\"],\"3\":[\"Data\",\"Analysis\",\"Decision-Making\"],\"4\":[\"Knowledge\",\"Understanding\",\"Wisdom\"]}},\"181\":{\"Question\":\"Ionnidis article \\\"Why Most Published Research Findings Are False\\\" (2005) main point\",\"Answer\":\"The priors of truth in a field matter, and can mean most published results are false.\\nCorollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. \\nCorollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true. \\nCorollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. \\nCorollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true \\nCorollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely\",\"Key ideas\":\"1. The priors of truth in a field matter, and can mean most published results are false.\\n2. Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. \\n3. Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true. \\n4. Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. \\n5. Corollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true \\n6. Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true.\",\"Abstraction groups\":{\"-1\":[\"Ionnidis\",\"Research\",\"Finding\",\"False\",\"Prior\",\"Study\",\"Effect\",\"Relationship\",\"Flexibility\",\"Design\",\"Definition\",\"Outcome\",\"Analytical\",\"Mode\",\"Interest\",\"Prejudice\"],\"0\":[\"Ionnidis\"],\"1\":[\"Research\",\"Finding\",\"False\"],\"2\":[\"Truth\",\"Study\",\"Effect\",\"Relationship\",\"Flexibility\",\"Design\",\"Definition\",\"Outcome\",\"Analytical\",\"Mode\",\"Interest\",\"Prejudice\"],\"3\":[\"Science\",\"Publication\",\"Prior\"],\"4\":[\"Knowledge\"]}},\"182\":{\"Question\":\"In the paper \\\"Motivated Numeracy and Enlightened Self-Government\\\" (2013) by Kahan et al, what two hypotheses were being tested?\",\"Answer\":\"People just need to be more informed or taught better statistics.\\nBeing informed isn't the problem. It's that identity biases trigger bad reasoning, and that doesn't get better with better numeracy\\nThe latter was more supported. \",\"Key ideas\":\"1. Kahan et al. published a paper in 2013 titled \\\"Motivated Numeracy and Enlightened Self-Government\\\". \\n2. The paper tested two hypotheses: \\n    a. People just need to be more informed or taught better statistics. \\n    b. Being informed isn't the problem. It's that identity biases trigger bad reasoning, and that doesn't get better with better numeracy. \\n3. The latter hypothesis was more supported.\",\"Abstraction groups\":{\"-1\":[\"Kahan\",\"Paper\",\"Hypothesis\",\"Numeracy\",\"Self-Government\",\"Information\",\"Statistic\",\"Identity\",\"Reasoning\"],\"0\":[\"Motivated Numeracy\"],\"1\":[\"Paper\",\"Hypothesis\"],\"2\":[\"Research\",\"Reasoning\"],\"3\":[\"Knowledge\",\"Identity\"],\"4\":[\"Understanding\",\"Self-Government\"]}},\"183\":{\"Question\":\"In the paper \\\"Motivated Numeracy and Enlightened Self-Government\\\" (2013) by Kahan et al, what was the experimental setup?\",\"Answer\":\"Experiment 1: test individual ability to spot factual or reasoning errors in a study about a skin rash. \\nResult 1: more numeracy = more correct.\\nExperiment 2: same thing, but in a study about gun control, does crime increase or decrease.\\nResult 2: Both political leanings were bad at catching mistakes that go against their beliefs, but good at catching opposing mistakes.\",\"Key ideas\":\"1. Kahan et al conducted an experiment in 2013 to test individual ability to spot factual or reasoning errors in a study. \\n2. The first experiment was about a skin rash. \\n3. The result of the first experiment was that more numeracy = more correct. \\n4. The second experiment was about gun control and whether crime increases or decreases. \\n5. The result of the second experiment was that both political leanings were bad at catching mistakes that go against their beliefs, but good at catching opposing mistakes.\",\"Abstraction groups\":{\"-1\":[\"Kahan\",\"Numeracy\",\"Self-Government\",\"Experiment\",\"Skin Rash\",\"Gun Control\",\"Crime\",\"Political Leanings\"],\"0\":[\"Motivated Numeracy\"],\"1\":[\"Experiment\",\"Gun Control\",\"Skin Rash\"],\"2\":[\"Crime\",\"Political Leanings\"],\"3\":[\"Factual Errors\",\"Reasoning Errors\"],\"4\":[\"Kahan\",\"Self-Government\"]}},\"184\":{\"Question\":\"In the paper \\\"Motivated Numeracy and Enlightened Self-Government\\\" (2013) by Kahan et al, what was the main result shown?\",\"Answer\":\"Individuals higher in numeracy are not better at avoiding polarization. Specifically, they make as many or more mistakes when presented with a polarizing topic. \",\"Key ideas\":\"\\n1. The paper \\\"Motivated Numeracy and Enlightened Self-Government\\\" (2013) by Kahan et al.\\n2. Numeracy - the ability to understand and work with numbers.\\n3. Polarization - the tendency of individuals to form extreme opinions on a given topic.\\n4. The main result of the paper - individuals higher in numeracy are not better at avoiding polarization.\\n5. Specifically, they make as many or more mistakes when presented with a polarizing topic.\",\"Abstraction groups\":{\"-1\":[\"Paper\",\"Numeracy\",\"Polarization\",\"Result\",\"Mistake\"],\"0\":[\"Motivated Numeracy\"],\"1\":[\"Research Paper\",\"Result\"],\"2\":[\"Academic Writing\",\"Knowledge\"],\"3\":[\"Learning\",\"Understanding\"],\"4\":[\"Education\"]}},\"185\":{\"Question\":\"What were the main results of the Deepmind algorithmic distillation paper? \",\"Answer\":\"It learns from fewer examples than otherwise necessary\\nSeeing more initial examples (seed size for text LLMs) leads to faster learning \\nDoesn't plateau like some other algorithm distillation methods \",\"Key ideas\":\"1. Deepmind algorithmic distillation paper is a research paper. \\n2. It learns from fewer examples than otherwise necessary. \\n3. Seeing more initial examples (seed size for text LLMs) leads to faster learning. \\n4. It does not plateau like some other algorithm distillation methods.\",\"Abstraction groups\":{\"-1\":[\"Deepmind\",\"Algorithmic\",\"Distillation\",\"Example\",\"Seed\",\"Text\",\"LLM\",\"Learning\",\"Plateau\",\"Algorithm\"],\"0\":[\"Algorithmic Distillation\"],\"1\":[\"Deepmind\",\"Learning\"],\"2\":[\"Algorithm\",\"Example\"],\"3\":[\"Research\",\"Plateau\"],\"4\":[\"Artificial Intelligence\"]}},\"186\":{\"Question\":\"How did deepmind use transformers to generate a learning algorithm for exploring trajectories in reinforcement learning? \",\"Answer\":\"Trained a transformer to predict the next action to take in some environment, to best explore and gain information. \",\"Key ideas\":\"\\n1. Deepmind: a British artificial intelligence company\\n2. Transformers: a type of neural network architecture used for natural language processing\\n3. Reinforcement learning: a type of machine learning algorithm that uses rewards and punishments to learn\\n4. Trajectories: a sequence of states or actions taken by an agent in an environment\\n5. Predicting the next action: using a transformer to predict the next action to take in some environment\\n6. Exploring and gaining information: using the predicted action to explore and gain information in the environment\",\"Abstraction groups\":{\"-1\":[\"Deepmind\",\"Transformer\",\"Reinforcement Learning\",\"Trajectory\",\"Action\",\"Environment\",\"Information\"],\"0\":[\"Transformers\"],\"1\":[\"Reinforcement Learning\",\"Artificial Intelligence\"],\"2\":[\"Machine Learning\",\"Technology\"],\"3\":[\"Science\",\"Research\"],\"4\":[\"Knowledge\"]}},\"187\":{\"Question\":\"In trying to prove resource requirements for a bandit problem, how do you lower bound the necessary loss? \",\"Answer\":\"Use information theory to argue that there is a minimum number of mistakes you need to make to gather information.\\nGoal is to converge upper and lower bounds for loss to prove you have the optimal strategy. \",\"Key ideas\":\"\\n1. Bandit problem: A type of reinforcement learning problem where an agent interacts with an environment by taking actions and receiving rewards.\\n\\n2. Resource requirements: The amount of resources (e.g. time, money, etc.) needed to solve a problem.\\n\\n3. Lower bound: The minimum value of a given quantity.\\n\\n4. Information theory: A branch of mathematics that studies the quantification, storage, and communication of information.\\n\\n5. Mistakes: Errors made by an agent in a reinforcement learning problem.\\n\\n6. Upper and lower bounds: The maximum and minimum values of a given quantity.\\n\\n7. Optimal strategy: The best possible strategy for solving a problem.\",\"Abstraction groups\":{\"-1\":[\"Bandit\",\"Resource\",\"Lower Bound\",\"Information\",\"Mistake\",\"Bound\",\"Strategy\"],\"0\":[\"Bandit Problem\"],\"1\":[\"Resource Requirements\",\"Lower Bound\"],\"2\":[\"Information Theory\",\"Mistake\"],\"3\":[\"Upper and Lower Bounds\",\"Optimal Strategy\"],\"4\":[\"Problem Solving\"]}},\"188\":{\"Question\":\"In the Deepmind paper \\\"Generally capable agents emerge from open-ended play\\\" 2021, what was interesting about the environment used?\",\"Answer\":\"Have a programmatically generated environment to build testing space for agents to be able to function best in a variety of environments \\nFind best \\\"general agent\\\" in each generation, and propagate to harder next generation \\nMy takeaway: Generative model of environment is like generating fake data for training on Mnist. If it's different enough, it lets you get way better \",\"Key ideas\":\"\\n1. The paper \\\"Generally capable agents emerge from open-ended play\\\" (2021) used a programmatically generated environment to build a testing space for agents. \\n2. The goal was to find the best \\\"general agent\\\" in each generation, and propagate it to the next generation. \\n3. The generative model of the environment is similar to generating fake data for training on Mnist. \\n4. If the environment is different enough, it can lead to much better results.\",\"Abstraction groups\":{\"-1\":[\"Deepmind\",\"Agent\",\"Environment\",\"Generative\",\"Mnist\",\"Result\"],\"0\":[\"Deepmind\"],\"1\":[\"Agent\",\"Environment\"],\"2\":[\"Generative\",\"Results\"],\"3\":[\"Mnist\"],\"4\":[\"Artificial Intelligence\"]}},\"189\":{\"Question\":\"Describe the process of byte pair encoding to compress information to input to a transformer \",\"Answer\":\"In a large corpus, find most common bigrams, then redefine them to be one thing, and add that to the vocab list. \\nThen repeat, until a certain number of unique objects have been formed. \",\"Key ideas\":\"1. Byte pair encoding is a method of compressing information to input to a transformer. \\n2. To use byte pair encoding, a large corpus of text must be analyzed. \\n3. The most common bigrams in the corpus must be identified. \\n4. The identified bigrams must be redefined to be one thing. \\n5. The redefined bigrams must be added to the vocab list. \\n6. This process must be repeated until a certain number of unique objects have been formed.\",\"Abstraction groups\":{\"-1\":[\"Byte Pair Encoding\",\"Compression\",\"Transformer\",\"Corpus\",\"Bigram\",\"Vocab List\",\"Unique Object\"],\"0\":[\"Byte Pair Encoding\"],\"1\":[\"Compression\",\"Transformer\"],\"2\":[\"Text Processing\",\"Machine Learning\"],\"3\":[\"Artificial Intelligence\",\"Data Science\"],\"4\":[\"Computer Science\"]}},\"190\":{\"Question\":\"Why does the no free lunch theorem not really matter for AGI? \",\"Answer\":\"It only really matters for resource constrained optimization problems, where learning one thing well makes you bad at another. \\nIn a world where a computer can harness other resources (like a human uses a computer), this is a very weak tradeoff. \",\"Key ideas\":\"\\n1. The No Free Lunch Theorem (NFLT) is a theorem that states that no algorithm can outperform any other algorithm on all possible problems. \\n\\n2. The NFLT does not really matter for Artificial General Intelligence (AGI) because it only applies to resource constrained optimization problems. \\n\\n3. In a world where a computer can harness other resources (like a human uses a computer), the tradeoff between learning one thing well and learning another thing poorly is very weak.\",\"Abstraction groups\":{\"-1\":[\"NFLT\",\"AGI\",\"Resource\",\"Optimization\",\"Computer\",\"Human\",\"Tradeoff\"],\"0\":[\"NFLT\"],\"1\":[\"Artificial Intelligence\",\"Optimization\"],\"2\":[\"Algorithm\",\"Computer\",\"Human\"],\"3\":[\"Resource\",\"Tradeoff\"],\"4\":[\"Problem Solving\"]}},\"191\":{\"Question\":\"How does the no free lunch theorem apply to Machine learning? \",\"Answer\":\"It says there are tradeoffs in learning one function vs another. In the space of all functions, being good at one thing means you are necessarily bad at some other thing. \",\"Key ideas\":\"\\n1. No Free Lunch Theorem (NFLT): This theorem states that there are tradeoffs in learning one function vs another. \\n2. Space of all functions: This refers to the set of all possible functions that can be learned. \\n3. Being good at one thing: This means that if a function is good at one task, it is necessarily bad at some other task.\",\"Abstraction groups\":{\"-1\":[\"NFLT\",\"Function\",\"Tradeoff\",\"Learning\",\"Good\\/Bad\"],\"0\":[\"Machine Learning\"],\"1\":[\"No Free Lunch Theorem\"],\"2\":[\"Theorem\",\"Learning\"],\"3\":[\"Mathematics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"192\":{\"Question\":\"What is the difference between prediction and inference? \",\"Answer\":\"Do you want a statistically accurate value, or do you want to understand the model \",\"Key ideas\":\"\\n1. Prediction: A statistical process used to estimate the value of a variable based on existing data.\\n2. Inference: A process used to understand the underlying model of a system based on existing data.\\n3. The difference between prediction and inference is that prediction is used to get a statistically accurate value, while inference is used to understand the model.\",\"Abstraction groups\":{\"-1\":[\"Prediction\",\"Inference\",\"Value\",\"Model\"],\"0\":[\"Prediction\",\"Inference\"],\"1\":[\"Statistical Process\"],\"2\":[\"Data Analysis\",\"Modeling\"],\"3\":[\"Mathematics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"193\":{\"Question\":\"In the GPT-3 paper, when generating news articles that are indistinguishable from human-generated articles, what key features were observed?\",\"Answer\":\"Had humans rate their prediction: machine or not? \\nSaw indistinguishability with larger models\\nPersisted from 200 to 500 word articles (didn't see decay with article length, which sometimes happens as models drift during generation)\",\"Key ideas\":\"\\n1. GPT-3 paper: a paper discussing the Generative Pre-trained Transformer 3 (GPT-3) model\\n2. Generating news articles: the GPT-3 model was used to generate news articles that were indistinguishable from human-generated articles\\n3. Had humans rate their prediction: the paper had humans rate their prediction of whether the article was machine-generated or not\\n4. Saw indistinguishability with larger models: the paper observed that the larger the model, the more indistinguishable the machine-generated articles were from human-generated articles\\n5. Persisted from 200 to 500 word articles: the indistinguishability persisted from 200 to 500 word articles, meaning that the machine-generated articles were still indistinguishable from human-generated articles even when the article length increased\\n6. No decay with article length: the paper did not observe any decay in the indistinguishability of the machine-generated articles as the article length increased, which sometimes happens as models drift during generation\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"News\",\"Prediction\",\"Model\",\"Length\",\"Decay\"],\"0\":[\"GPT-3\"],\"1\":[\"Machine Learning\",\"Generative Model\",\"Natural Language Processing\"],\"2\":[\"Artificial Intelligence\",\"Data Science\",\"Computer Science\"],\"3\":[\"Technology\",\"Science\",\"Research\"],\"4\":[\"Knowledge\"]}},\"194\":{\"Question\":\"What key example discussed near the end of the GPT-3 paper was used to compare the model to humans?\",\"Answer\":\"Generating news articles that are indistinguishable from human-generated articles, from the same title and subtitle. \",\"Key ideas\":\"1. GPT-3: an AI model developed by OpenAI\\n2. Generating news articles: the ability of GPT-3 to generate news articles that are indistinguishable from human-generated articles\\n3. Title and subtitle: the title and subtitle of the news article used to generate the article with GPT-3\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"Generating\",\"News\",\"Article\",\"Title\",\"Subtitle\"],\"0\":[\"GPT-3\"],\"1\":[\"Generating\",\"News\",\"Article\",\"Title\",\"Subtitle\"],\"2\":[\"AI\",\"Generating Text\",\"Writing\"],\"3\":[\"Artificial Intelligence\",\"Language Processing\"],\"4\":[\"Technology\"]}},\"195\":{\"Question\":\"What was one goal of the GPT-3 paper as compared to Kaplan et. al. 2020?\",\"Answer\":\"One goal was validating the general scaling laws proposed by Kaplan at a higher order of magnitude. \",\"Key ideas\":\"\\n1. GPT-3 paper: This refers to the paper titled \\u201cLanguage Models are Few-Shot Learners\\u201d by Brown et al. (2020).\\n2. Kaplan et. al. 2020: This refers to the paper titled \\u201cScaling Laws for Neural Language Models\\u201d by Kaplan et al. (2020).\\n3. Goal of GPT-3 paper: The goal of the GPT-3 paper was to validate the general scaling laws proposed by Kaplan et al. (2020) at a higher order of magnitude.\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"Kaplan\",\"Goal\",\"Scaling\",\"Law\",\"Magnitude\"],\"0\":[\"GPT-3\"],\"1\":[\"Language Model\",\"Few-Shot Learning\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\",\"Knowledge\"]}},\"196\":{\"Question\":\"Name one limitation of the Codex model from the original paper (2021)\",\"Answer\":\"Still very sample inefficient training compared to a human programmer \\nFailure increases with docstring length\\nCan reference variables outside the scope \",\"Key ideas\":\"\\n1. The Codex model is a machine learning model for automated programming. \\n2. It is still very sample inefficient compared to a human programmer.\\n3. The failure rate of the model increases with docstring length.\\n4. The model can reference variables outside the scope.\",\"Abstraction groups\":{\"-1\":[\"Codex\",\"Sample\",\"Docstring\",\"Variable\"],\"0\":[\"Codex\"],\"1\":[\"Machine Learning\",\"Automated Programming\"],\"2\":[\"Artificial Intelligence\",\"Algorithms\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\",\"Knowledge\"]}},\"197\":{\"Question\":\"What is an example of a situation where a more nuanced success metric for code generation is needed compared to pass@k? \",\"Answer\":\"Ex. For auto-complete prompts, you cannot present all possible solutions. You must choose one to show the user. \",\"Key ideas\":\"1. Pass@k: A metric used to measure the success of code generation.\\n2. Nuanced success metric: A metric that takes into account more than just a pass\\/fail result.\\n3. Auto-complete prompts: A feature of some software that suggests possible solutions to a user based on what they have already typed.\\n4. Not all possible solutions can be presented: Due to the limited space available, only one solution can be presented to the user.\",\"Abstraction groups\":{\"-1\":[\"Pass@k\",\"Nuanced\",\"Auto-Complete\",\"Solution\"],\"0\":[\"Code Generation\"],\"1\":[\"Metric\",\"Success\"],\"2\":[\"Measurement\",\"Evaluation\"],\"3\":[\"Automation\",\"User Experience\"],\"4\":[\"Software Development\"]}},\"198\":{\"Question\":\"In the Codex paper (2021), what method was found to be best for choosing the sampled output to present to the user?\",\"Answer\":\"Best way is mean token log probability throughout the generated sequence. \",\"Key ideas\":\"\\n1. Codex paper (2021): This is a paper published in 2021 that discusses a method for choosing the sampled output to present to the user. \\n2. Mean token log probability: This is a method for choosing the sampled output to present to the user. It is based on the average log probability of the tokens in the generated sequence. \\n3. Generated sequence: This is a sequence of tokens that is generated by a machine learning model.\",\"Abstraction groups\":{\"-1\":[\"Codex\",\"Method\",\"Sampled Output\",\"User\",\"Mean Token\",\"Log Probability\",\"Generated Sequence\"],\"0\":[\"Choosing Sampled Output\"],\"1\":[\"Machine Learning\",\"Natural Language Processing\"],\"2\":[\"Artificial Intelligence\",\"Data Science\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"199\":{\"Question\":\"How must the optimal temperature be tuned for the pass@k metric for GPT-codex? \",\"Answer\":\"You want a higher temperature for larger k in pass at k, to have more probability of generating meaningfully different samples. \",\"Key ideas\":\"1. GPT-codex is a metric used to measure the quality of a text-generating model. \\n2. The optimal temperature for the pass@k metric must be tuned. \\n3. A higher temperature should be used for larger values of k in pass@k. \\n4. This is to increase the probability of generating meaningfully different samples.\",\"Abstraction groups\":{\"-1\":[\"GPT-Codex\",\"Temperature\",\"Pass@k\",\"Probability\",\"Sample\"],\"0\":[\"Pass@k\"],\"1\":[\"Temperature Tuning\"],\"2\":[\"GPT-Codex\",\"Metrics\"],\"3\":[\"Text-Generating Models\",\"Quality Measurement\"],\"4\":[\"Artificial Intelligence\"]}},\"200\":{\"Question\":\"What detail of the codex paper differs from GPT-3, that is not part of the architecture?\",\"Answer\":\"The tokenizer is adjusted to account for specific things relating to code, rather than natural language (adjusts for whitespace). \",\"Key ideas\":\"\\n1. GPT-3: an open-source language model developed by OpenAI, which uses deep learning to produce human-like text.\\n2. Codex paper: a paper written by OpenAI researchers that describes the architecture of GPT-3.\\n3. Tokenizer: a component of GPT-3 that breaks up text into individual words or phrases.\\n4. Natural language: language used by humans to communicate.\\n5. Whitespace: the space between words and characters in a text.\\n6. Adjustment: a change made to the tokenizer to account for specific things relating to code, rather than natural language.\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"Codex\",\"Tokenizer\",\"Language\",\"Whitespace\",\"Adjustment\"],\"0\":[\"Tokenizer\"],\"1\":[\"Adjustment\",\"Whitespace\"],\"2\":[\"Language\",\"Code\"],\"3\":[\"GPT-3\",\"Codex\"],\"4\":[\"Architecture\"]}},\"201\":{\"Question\":\"In the Codex paper (2021), the model was fine tuned to succeed at what tasks?\",\"Answer\":\"Fine tuned on successful solutions to get a better model.\\nFine tuned on generating a doc string. \",\"Key ideas\":\"1. The Codex paper was published in 2021. \\n2. The model was fine tuned to succeed at two tasks: \\n    a. Getting a better model by fine tuning successful solutions. \\n    b. Generating a doc string.\",\"Abstraction groups\":{\"-1\":[\"Codex\",\"Model\",\"Fine Tuning\",\"Solution\",\"Doc String\"],\"0\":[\"Codex\"],\"1\":[\"Model\",\"Fine Tuning\",\"Solutions\",\"Doc String\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\",\"Knowledge\"]}},\"202\":{\"Question\":\"In the Codex paper (2021), why is the pass@k metric difficult to compute?\",\"Answer\":\"Just using 1 - (1-p)^k for the estimate of the pass@1 p, is systematically underestimating it. \\nInstead they have a ratio of binomials in some form to accomplish it.\",\"Key ideas\":\"1. The pass@k metric is a measure of the accuracy of a model.\\n2. The pass@k metric is difficult to compute because it requires a ratio of binomials in some form.\\n3. Just using 1 - (1-p)^k for the estimate of the pass@1 p is systematically underestimating it.\",\"Abstraction groups\":{\"-1\":[\"Pass@k\",\"Metric\",\"Computation\",\"Estimate\",\"Ratio\",\"Binomial\"],\"0\":[\"Pass@k\"],\"1\":[\"Metric\",\"Computation\"],\"2\":[\"Estimate\",\"Ratio\"],\"3\":[\"Binomial\"],\"4\":[\"Accuracy\"]}},\"203\":{\"Question\":\"In the Codex paper (2021), what metric is used instead of 0 shot and few shot learning?\",\"Answer\":\"Pass@k, or the pass rate for having at least one successful sample out of k samples \",\"Key ideas\":\"\\n1. 0 shot and few shot learning are metrics used to measure the performance of a model. \\n2. Pass@k is a metric used instead of 0 shot and few shot learning. \\n3. Pass@k is the pass rate for having at least one successful sample out of k samples.\",\"Abstraction groups\":{\"-1\":[\"0 Shot\",\"Few Shot\",\"Pass@k\",\"Metric\",\"Sample\"],\"0\":[\"Pass@k\"],\"1\":[\"Metric\"],\"2\":[\"Performance\",\"Evaluation\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computing\"]}},\"204\":{\"Question\":\"How to visualize the process of patent formation abstractly, from the point of view of general progress in society. \",\"Answer\":\"Visualize a set of Venn diagrams. Each successive patent comes into existence as a subset of previous patents or claims (and a specific patent usually has many component subsets).\\nVisualize the frontier of ideas. Patents come into existence, hold for some time, then disappear. All past area claimed by patents cannot be re-patented. Only smaller areas can. It's like encouraging tree search in the space of ideas. \",\"Key ideas\":\"\\n1. Patents come into existence as a subset of previous patents or claims.\\n2. Each patent usually has many component subsets.\\n3. Patents come into existence, hold for some time, then disappear.\\n4. All past area claimed by patents cannot be re-patented.\\n5. Only smaller areas can be re-patented.\\n6. This process is like encouraging tree search in the space of ideas.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Claim\",\"Subset\",\"Idea\",\"Tree Search\",\"Venn Diagram\"],\"0\":[\"Patent Formation\"],\"1\":[\"Intellectual Property\",\"Innovation\"],\"2\":[\"Progress\",\"Society\"],\"3\":[\"Ideas\",\"Knowledge\"],\"4\":[\"Human Activity\"]}},\"205\":{\"Question\":\"What is the main tradeoff between patents and trade secrets? \",\"Answer\":\"Summary: Protection level, time, and cost. \\nPatents have stronger protections against reverse engineering and independent invention, but last for less time and require full disclosure, and cost more.\\nTrade secrets are not protected from reverse engineering and independent invention, but last forever, don't require disclosure, and cost less to enforce. \",\"Key ideas\":\"\\n1. Patents have stronger protections against reverse engineering and independent invention.\\n2. Patents last for less time than trade secrets.\\n3. Patents require full disclosure.\\n4. Patents cost more to enforce.\\n5. Trade secrets are not protected from reverse engineering and independent invention.\\n6. Trade secrets last forever.\\n7. Trade secrets do not require disclosure.\\n8. Trade secrets cost less to enforce.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Trade Secret\",\"Protection\",\"Time\",\"Cost\",\"Reverse Engineering\",\"Independent Invention\",\"Disclosure\"],\"0\":[\"Patent\",\"Trade Secret\"],\"1\":[\"Intellectual Property\",\"Business Law\"],\"2\":[\"Law\",\"Economics\"],\"3\":[\"Social Science\",\"Humanity\"],\"4\":[\"Knowledge\"]}},\"206\":{\"Question\":\"What legal framework is the concept of non-disclosure agreements aimed at safeguarding? \",\"Answer\":\"Trade secrets \",\"Key ideas\":\"\\n1. Non-disclosure agreements (NDAs): agreements between two or more parties that restrict the sharing of confidential information.\\n2. Trade secrets: information that is not generally known or easily accessible, and is of economic value to the holder.\\n3. Legal framework: the set of laws, regulations, and other rules that govern the activities of individuals and organizations.\",\"Abstraction groups\":{\"-1\":[\"NDA\",\"Trade Secret\",\"Legal Framework\"],\"0\":[\"Non-disclosure Agreement\"],\"1\":[\"Trade Secret\"],\"2\":[\"Legal Framework\"],\"3\":[\"Business Law\"],\"4\":[\"Law\"]}},\"207\":{\"Question\":\"Is a trade secret something you apply for? \",\"Answer\":\"Unlike other forms of intellectual property, such as patents, copyrights, and trademarks, which generally require registration in order to be fully effective, trade secrets are essentially a \\\"do-it-yourself\\\" form of protection.\\nYou do not register with the government to secure your trade secret; you most simply keep the information under wraps. Trade secret protection lasts for as long as the secret is kept confidential without any statutory limitations period. \",\"Key ideas\":\"1. Trade secrets are a form of intellectual property.\\n2. Unlike other forms of intellectual property, trade secrets do not require registration with the government in order to be effective.\\n3. Trade secret protection lasts for as long as the secret is kept confidential, without any statutory limitations period.\\n4. Trade secrets are a \\\"do-it-yourself\\\" form of protection, meaning that the responsibility of keeping the information confidential lies with the owner of the secret.\",\"Abstraction groups\":{\"-1\":[\"Trade Secret\",\"Intellectual Property\",\"Registration\",\"Government\",\"Protection\",\"Confidentiality\",\"Limitation\",\"Responsibility\"],\"0\":[\"Trade Secret\"],\"1\":[\"Intellectual Property\",\"Protection\"],\"2\":[\"Legal\",\"Business\"],\"3\":[\"Knowledge\",\"Resource\"],\"4\":[\"Security\"]}},\"208\":{\"Question\":\"What are two characteristics that must be present for a patent public disclosure? \",\"Answer\":\"Has to be sufficiently public (and a reasonable expectation of it being public)\\nHas to enable replication or disclose the full technology \",\"Key ideas\":\"\\n1. A patent public disclosure must be sufficiently public.\\n2. There must be a reasonable expectation that the disclosure is public.\\n3. The disclosure must enable replication of the technology.\\n4. The disclosure must fully disclose the technology.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Public\",\"Disclosure\",\"Sufficiently\",\"Expectation\",\"Replication\",\"Technology\"],\"0\":[\"Patent\"],\"1\":[\"Public Disclosure\"],\"2\":[\"Intellectual Property\",\"Law\"],\"3\":[\"Business\",\"Governance\"],\"4\":[\"Knowledge\"]}},\"209\":{\"Question\":\"What are the two protections afforded to a person trying to patent their work in the US, to help them have enough time before \\\"first to file\\\"? \",\"Answer\":\"Novelty grace period (yourself not considered prior art)\\nDisclosure shield - further prior art is not a problem for first to disclose \",\"Key ideas\":\"\\n1. Patenting: A process of legally protecting an invention or idea from being used or sold by others without permission.\\n2. US Patent System: The system of laws and regulations in the United States that govern the granting of patents.\\n3. First to File: A rule in the US Patent System that states that the first person to file a patent application for an invention or idea is the one who will be granted the patent.\\n4. Novelty Grace Period: A period of time in the US Patent System in which an inventor can file a patent application for their invention or idea without being considered prior art.\\n5. Prior Art: Any evidence that an invention or idea has already been publicly disclosed or used before the filing of a patent application.\\n6. Disclosure Shield: A protection in the US Patent System that allows an inventor to file a patent application for their invention or idea even if prior art exists, as long as the inventor was the first to disclose the invention or idea.\",\"Abstraction groups\":{\"-1\":[\"Patenting\",\"Us\",\"File\",\"Novelty\",\"Prior\",\"Disclosure\"],\"0\":[\"Patenting\"],\"1\":[\"Legal Protection\",\"Intellectual Property\"],\"2\":[\"Property Rights\",\"Rights of Ownership\"],\"3\":[\"Right\",\"Legal Right\"],\"4\":[\"Law\"]}},\"210\":{\"Question\":\"Why is the disclosure shield for a patent very weak after the America Invents Act in 2011?\",\"Answer\":\"It can be invalidated by the claim of obviousness if someone publishes a public disclosure of a submethod, which is different from yours, but makes yours obvious, before you actually file. \",\"Key ideas\":\"1. The America Invents Act of 2011 weakened the disclosure shield for patents. \\n2. The disclosure shield can be invalidated by the claim of obviousness. \\n3. The claim of obviousness is based on a public disclosure of a submethod that makes the patent obvious. \\n4. The public disclosure must be made before the patent is filed.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Disclosure Shield\",\"America Invents Act\",\"Obviousness\",\"Submethod\",\"Public Disclosure\",\"Filing\"],\"0\":[\"Patent\"],\"1\":[\"Disclosure Shield\",\"America Invents Act\"],\"2\":[\"Obviousness\",\"Submethod\",\"Public Disclosure\",\"Filing\"],\"3\":[\"Intellectual Property\",\"Legal Protection\"],\"4\":[\"Law\"]}},\"211\":{\"Question\":\"What are patentable subject materials, and what aren't? \",\"Answer\":\"Yes: Process, machine, manufacture, and composition of matter\\nNo: laws of nature, natural phenomena, or abstract ideas \",\"Key ideas\":\"\\n1. Patentable subject materials include processes, machines, manufactures, and compositions of matter. \\n2. Laws of nature, natural phenomena, and abstract ideas are not patentable subject materials.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Process\",\"Machine\",\"Manufacture\",\"Composition\",\"Matter\",\"Law\",\"Nature\",\"Phenomenon\",\"Abstract\",\"Idea\"],\"0\":[\"Patent\"],\"1\":[\"Subject Material\",\"Patentability\"],\"2\":[\"Intellectual Property\",\"Law\"],\"3\":[\"Property\",\"Legal System\"],\"4\":[\"Knowledge\",\"Society\"]}},\"212\":{\"Question\":\"What are the 4 main criteria of an idea necessary to get a patent?\",\"Answer\":\"It must be patentable subject material: Process, machine, manufacture, and composition of matter (and it must not be laws of nature, natural phenomena, or abstract ideas)\\nIt must show: Utility, novelty, non-obviousness (and if you are in an \\u201cunpredictable field\\u201d like bio, you need to provide more evidence) \",\"Key ideas\":\"\\n1. A patent must be for patentable subject material, such as a process, machine, manufacture, or composition of matter. \\n2. It must not be a law of nature, natural phenomena, or abstract idea. \\n3. It must show utility. \\n4. It must be novel. \\n5. It must be non-obvious. \\n6. If the patent is in an unpredictable field, such as biotechnology, more evidence must be provided.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Subject Material\",\"Process\",\"Machine\",\"Manufacture\",\"Composition\",\"Law\",\"Nature\",\"Phenomenon\",\"Abstract Idea\",\"Utility\",\"Novelty\",\"Non-Obviousness\",\"Unpredictable Field\",\"Bio\",\"Evidence\"],\"0\":[\"Patent\"],\"1\":[\"Subject Material\",\"Utility\",\"Novelty\",\"Non-Obviousness\"],\"2\":[\"Process\",\"Machine\",\"Manufacture\",\"Composition\",\"Law\",\"Nature\",\"Phenomenon\",\"Abstract Idea\",\"Unpredictable Field\",\"Bio\",\"Evidence\"],\"3\":[\"Intellectual Property\",\"Invention\",\"Innovation\"],\"4\":[\"Legal\",\"Business\"]}},\"213\":{\"Question\":\"Patent blocking: describe what it is \",\"Answer\":\"You can get a patent on some narrow point of a broader patent (ie if it is a novel sub-idea), and then be blocked from practicing it by the broader patent that someone else holds.\\nBut the broader patent holder is not blocked, because they don\\u2019t infringe on all aspects of the more narrow patent.\\nThis is necessarily asymmetric. \",\"Key ideas\":\"\\n1. A patent is a legal document that grants an inventor exclusive rights to an invention. \\n2. Patent blocking occurs when someone holds a broader patent that covers a more narrow patent held by someone else. \\n3. The holder of the more narrow patent is blocked from practicing it, but the holder of the broader patent is not blocked, as they do not infringe on all aspects of the more narrow patent. \\n4. This is necessarily asymmetric, meaning that the holder of the more narrow patent is blocked, but the holder of the broader patent is not.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Blocking\",\"Novel\",\"Sub-Idea\",\"Asymmetric\"],\"0\":[\"Patent Blocking\"],\"1\":[\"Intellectual Property\"],\"2\":[\"Property Law\",\"Business Law\"],\"3\":[\"Law\"],\"4\":[\"Social Science\"]}},\"214\":{\"Question\":\"If you legally sell a patented object to someone, and then they sell it to someone else, does the second person need to obtain patent rights? \",\"Answer\":\"No. The first authorized sale removes all restrictions that the patentee can exercise on the object. \",\"Key ideas\":\"1. A patent is a legal right granted to an inventor to exclude others from making, using, or selling an invention. \\n2. The patentee (the person who holds the patent) has the right to control the sale of the patented object. \\n3. The first authorized sale of a patented object removes all restrictions that the patentee can exercise on the object. \\n4. The second person does not need to obtain patent rights in order to sell the object.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Patentee\",\"Sale\",\"Restriction\",\"Right\"],\"0\":[\"Patent Right\"],\"1\":[\"Sale\",\"Restriction\"],\"2\":[\"Legal Right\",\"Inventor\"],\"3\":[\"Intellectual Property\",\"Property Right\"],\"4\":[\"Law\"]}},\"215\":{\"Question\":\"What is meaningfully different about indirect and direct patent infringement with regards to intent? \",\"Answer\":\"Direct infringement doesn't need intent. Indirect infringement needs intent to be valid. \",\"Key ideas\":\"1. Patent infringement: \\n    a. Direct infringement: does not require intent. \\n    b. Indirect infringement: requires intent. \\n2. Intent: \\n    a. Necessary for indirect infringement. \\n    b. Not necessary for direct infringement.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Infringement\",\"Direct\",\"Indirect\",\"Intent\"],\"0\":[\"Patent Infringement\"],\"1\":[\"Intent\",\"Direct\",\"Indirect\"],\"2\":[\"Law\",\"Intellectual Property\"],\"3\":[\"Business\",\"Legal\"],\"4\":[\"Knowledge\"]}},\"216\":{\"Question\":\"What are the two types of patent infringement, and which must be present to bring litigation? \",\"Answer\":\"Direct infringement (making, using, selling, or importing)\\nIndirect infringement (encouraging or enabling someone else to do this).\\nDirect infringement is always necessary to bring litigation. \",\"Key ideas\":\"1. Patent infringement is divided into two types: \\n    a. Direct infringement \\n    b. Indirect infringement \\n2. Direct infringement involves: \\n    a. Making \\n    b. Using \\n    c. Selling \\n    d. Importing \\n3. Indirect infringement involves: \\n    a. Encouraging \\n    b. Enabling someone else to do the activities listed in point 2 \\n4. Direct infringement is always necessary to bring litigation.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Infringement\",\"Direct\",\"Indirect\",\"Making\",\"Using\",\"Selling\",\"Importing\",\"Encouraging\",\"Enabling\",\"Litigation\"],\"0\":[\"Patent Infringement\"],\"1\":[\"Direct\",\"Indirect\"],\"2\":[\"Making\",\"Using\",\"Selling\",\"Importing\",\"Encouraging\",\"Enabling\"],\"3\":[\"Litigation\"],\"4\":[\"Law\"]}},\"217\":{\"Question\":\"How do a group of patent owners determine who can be licensed to use the patent in the US? What about in the rest of the world? \",\"Answer\":\"In the US any of the individual patent owners can license, but unanimous agreement is necessary to sue someone for infringement.\\nIn other places, it is the opposite (hard to license, easy to sue) \",\"Key ideas\":\"1. In the US, any of the individual patent owners can license the patent, but all must agree to sue someone for infringement. \\n2. In other places, it is the opposite - it is hard to license the patent, but easy to sue someone for infringement.\",\"Abstraction groups\":{\"-1\":[\"Patent Owner\",\"License\",\"US\",\"Agreement\",\"Infringement\",\"Other Place\"],\"0\":[\"Patent Owner\"],\"1\":[\"Licensing\",\"Agreement\",\"Infringement\"],\"2\":[\"Intellectual Property\",\"Law\"],\"3\":[\"Business\",\"Governance\"],\"4\":[\"Human Interaction\"]}},\"218\":{\"Question\":\"Patent authorship - how to figure out who is included? \",\"Answer\":\"Just have to have contributed to at least one claim, then treated equally \\nDon\\u2019t need to know if something works in order to have conception \",\"Key ideas\":\"\\n1. To be included as an author on a patent, one must have contributed to at least one claim.\\n2. All authors of a patent are treated equally.\\n3. Knowing if something works is not necessary to have conception.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Authorship\",\"Claim\",\"Equally\",\"Conception\",\"Work\"],\"0\":[\"Patent Authorship\"],\"1\":[\"Authorship\",\"Patent\"],\"2\":[\"Intellectual Property\",\"Law\"],\"3\":[\"Legal System\",\"Knowledge\"],\"4\":[\"Society\",\"Human Activity\"]}},\"219\":{\"Question\":\"What are the three major branches of patent law? \",\"Answer\":\"Prosecution (making the patent, filing it, and getting it issued)\\nLitigation (sueing for infringement)\\nLicensing (contracting it out, can be totally private) \",\"Key ideas\":\"1. Patent law is divided into three major branches:\\n    a. Prosecution \\n    b. Litigation \\n    c. Licensing \\n2. Prosecution involves making the patent, filing it, and getting it issued.\\n3. Litigation involves suing for infringement.\\n4. Licensing involves contracting out the patent, which can be done totally privately.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Prosecution\",\"Filing\",\"Issuing\",\"Litigation\",\"Infringement\",\"Licensing\",\"Contracting\",\"Private\"],\"0\":[\"Patent Law\"],\"1\":[\"Prosecution\",\"Litigation\",\"Licensing\"],\"2\":[\"Making\",\"Filing\",\"Issuing\",\"Suing\",\"Contracting\",\"Private\"],\"3\":[\"Process\",\"Legal\",\"Agreement\"],\"4\":[\"Law\"]}},\"220\":{\"Question\":\"What was the main change inherent in the America invents act of 2011 relating to patents?\",\"Answer\":\"US switched from first to invent to first to file. \",\"Key ideas\":\"\\n1. The America Invents Act of 2011 was a law passed in the United States. \\n2. This law changed the way patents are granted in the US. \\n3. The US switched from a system of \\u201cfirst to invent\\u201d to a system of \\u201cfirst to file\\u201d. \\n4. Under the \\u201cfirst to invent\\u201d system, the first person to invent a product or process was granted the patent. \\n5. Under the \\u201cfirst to file\\u201d system, the first person to file a patent application for a product or process is granted the patent. \\n6. This change was intended to simplify the patent process and reduce the amount of litigation related to patent disputes.\",\"Abstraction groups\":{\"-1\":[\"America Invents Act\",\"Patent\",\"First to Invent\",\"First to File\",\"Patent Process\",\"Litigation\"],\"0\":[\"Patent\"],\"1\":[\"America Invents Act\",\"First to File\"],\"2\":[\"Intellectual Property\",\"Legal System\"],\"3\":[\"Business\",\"Government\"],\"4\":[\"Society\"]}},\"221\":{\"Question\":\"What are the 4 actions that a patent prohibits others from doing?\",\"Answer\":\"Prohibits making, using, selling, or importing \\nIt is a negative right \",\"Key ideas\":\"\\n1. A patent is a negative right. \\n2. It prohibits others from doing four specific actions: \\n    a. Making \\n    b. Using \\n    c. Selling \\n    d. Importing \\n3. Understanding the four actions is necessary to fully understand the flashcard.\",\"Abstraction groups\":{\"-1\":[\"Patent\",\"Negative\",\"Making\",\"Using\",\"Selling\",\"Importing\"],\"0\":[\"Patent\"],\"1\":[\"Negative Right\"],\"2\":[\"Intellectual Property\",\"Property Rights\"],\"3\":[\"Law\",\"Economics\"],\"4\":[\"Social Science\"]}},\"222\":{\"Question\":\"When measuring the scaling laws for neural network test loss (log MLE), what are the control parameters to think about? \",\"Answer\":\"Control variables are:\\nTokens trained (data size)\\nParameters of model (model size)\\nCompute (tokens times parameters, or petaflop days)\\nThese are somewhat similar to the OpenAI research tasks (Data, training, and tools) and the major resources improving deep learning (data access, hardware, and new architectures) \",\"Key ideas\":\"\\n1. Neural network test loss (log MLE) is measured using scaling laws.\\n2. Control variables for measuring scaling laws are:\\n    a. Tokens trained (data size)\\n    b. Parameters of model (model size)\\n    c. Compute (tokens times parameters, or petaflop days)\\n3. These control variables are similar to the OpenAI research tasks (Data, training, and tools) and the major resources improving deep learning (data access, hardware, and new architectures).\",\"Abstraction groups\":{\"-1\":[\"Neural Network\",\"Test Loss\",\"Log MLE\",\"Scaling Law\",\"Control Variable\",\"Token\",\"Data Size\",\"Model Size\",\"Compute\",\"Petaflop Day\",\"OpenAI\",\"Research Task\",\"Data\",\"Training\",\"Tool\",\"Resource\",\"Data Access\",\"Hardware\",\"Architecture\"],\"0\":[\"Scaling Law\"],\"1\":[\"Neural Network\",\"Test Loss\",\"Log MLE\"],\"2\":[\"Measurement\",\"Control Variable\"],\"3\":[\"Data\",\"Training\",\"Tool\",\"Resource\"],\"4\":[\"Deep Learning\"]}},\"223\":{\"Question\":\"How does the Dalle-2 paper use the CLIP process for generating images from captions?\",\"Answer\":\"It generates an image embedding based on a CLIP encoding of the text caption, and then decodes that image embedding. \\nThis is a tunable thing (allows interpolation between two embeddings) \",\"Key ideas\":\"1. CLIP (Contrastive Language-Image Pre-training) is a process used to generate images from captions. \\n2. The Dalle-2 paper uses the CLIP process to generate images from captions. \\n3. The CLIP process generates an image embedding based on a CLIP encoding of the text caption. \\n4. The CLIP encoding of the text caption is then decoded to generate the image. \\n5. The CLIP process is tunable, allowing interpolation between two embeddings.\",\"Abstraction groups\":{\"-1\":[\"Clip\",\"Dalle2\",\"Caption\",\"Image\",\"Embedding\",\"Encoding\",\"Decoding\",\"Interpolation\"],\"0\":[\"CLIP\"],\"1\":[\"Generating Images\",\"Captions\"],\"2\":[\"Image Processing\",\"Natural Language Processing\"],\"3\":[\"Artificial Intelligence\",\"Machine Learning\"],\"4\":[\"Computer Science\"]}},\"224\":{\"Question\":\"What was the goal of the CLIP paper (Jan 2021)?\",\"Answer\":\"Goal: try to replicate task agnostic web scale pretraining in computer vision rather than NLP\\nLeverage huge access to labels on the internet \\nProduce good generalizability \",\"Key ideas\":\"\\n1. CLIP paper (Jan 2021): \\n    a. Goal: try to replicate task agnostic web scale pretraining in computer vision rather than NLP\\n    b. Leverage huge access to labels on the internet \\n    c. Produce good generalizability\",\"Abstraction groups\":{\"-1\":[\"Clip\",\"Pretraining\",\"Computer Vision\",\"Nlp\",\"Label\",\"Internet\",\"Generalizability\"],\"0\":[\"CLIP\"],\"1\":[\"Pretraining\",\"Computer Vision\",\"NLP\"],\"2\":[\"Label\",\"Internet\"],\"3\":[\"Generalizability\"],\"4\":[\"Goal\"]}},\"225\":{\"Question\":\"What is the tension between process-based vs outcome-based types of ML systems in terms of alignment? \",\"Answer\":\"Process based systems are easier to interpret, diagnose, and align, but may be less powerful than outcome based systems. \\nThis is the idea of the \\\"alignment tax\\\". \",\"Key ideas\":\"1. Machine Learning (ML) systems can be divided into two types: process-based and outcome-based. \\n2. Process-based systems are easier to interpret, diagnose, and align, but may be less powerful than outcome-based systems. \\n3. This difference between process-based and outcome-based systems is referred to as the \\\"alignment tax\\\".\",\"Abstraction groups\":{\"-1\":[\"ML\",\"Process-Based\",\"Outcome-Based\",\"Interpret\",\"Diagnose\",\"Align\",\"Alignment Tax\"],\"0\":[\"Alignment Tax\"],\"1\":[\"ML\",\"Process-Based\",\"Outcome-Based\"],\"2\":[\"Interpret\",\"Diagnose\",\"Align\"],\"3\":[\"System\",\"Tension\"],\"4\":[\"Alignment\"]}},\"226\":{\"Question\":\"Explain the difference between process-based vs outcome-based types of ML systems \",\"Answer\":\"Process-based systems optimize the entire process to be correct along the way (and must observe the whole process).\\nOutcome-based only target the final outcome. \",\"Key ideas\":\"1. Machine Learning (ML) systems can be divided into two types: process-based and outcome-based. \\n2. Process-based systems optimize the entire process to be correct along the way (and must observe the whole process). \\n3. Outcome-based systems only target the final outcome.\",\"Abstraction groups\":{\"-1\":[\"ML\",\"Process-Based\",\"Outcome-Based\",\"Process\",\"Outcome\"],\"0\":[\"ML System\"],\"1\":[\"Process-based\",\"Outcome-based\"],\"2\":[\"Optimization\",\"Observation\"],\"3\":[\"Machine Learning\",\"Process\",\"Outcome\"],\"4\":[\"Artificial Intelligence\"]}},\"227\":{\"Question\":\"What is the factored cognition primer, and how is it an example of a process-based machine learning system? \",\"Answer\":\"It is an attempt to lay out a sequential and interpretable machine learning approach to answer questions and solve problems. \\nThere is a record of what it said and did. \",\"Key ideas\":\"\\n1. The Factored Cognition Primer is an attempt to create a sequential and interpretable machine learning approach to answer questions and solve problems. \\n2. This approach is a process-based machine learning system. \\n3. It records what it said and did.\",\"Abstraction groups\":{\"-1\":[\"Factored Cognition Primer\",\"Machine Learning\",\"Process-Based\",\"Question\",\"Problem\",\"Record\"],\"0\":[\"Factored Cognition Primer\"],\"1\":[\"Machine Learning\",\"Process-based\"],\"2\":[\"Problem Solving\",\"Questions\"],\"3\":[\"Artificial Intelligence\",\"Cognitive Science\"],\"4\":[\"Science\",\"Technology\"]}},\"228\":{\"Question\":\"What is the term for extracting the reward function from a set of behaviors? \",\"Answer\":\"Inverse reinforcement learning \",\"Key ideas\":\"\\n1. Reinforcement learning is a type of machine learning that uses rewards to learn from its environment. \\n2. Inverse reinforcement learning is a technique used to extract the reward function from a set of behaviors. \\n3. This technique can be used to infer the goals of an agent from its observed behavior. \\n4. It can also be used to infer the reward function of a given environment from the behavior of an agent. \\n5. Inverse reinforcement learning can be used to improve the performance of an agent in a given environment.\",\"Abstraction groups\":{\"-1\":[\"Reinforcement Learning\",\"Inverse Reinforcement Learning\",\"Goal\",\"Reward Function\",\"Environment\",\"Agent\"],\"0\":[\"Inverse Reinforcement Learning\"],\"1\":[\"Machine Learning\",\"Artificial Intelligence\"],\"2\":[\"Computational Intelligence\",\"Cognitive Science\"],\"3\":[\"Computer Science\",\"Science\"],\"4\":[\"Knowledge\"]}},\"229\":{\"Question\":\"What is inverse reinforcement learning? \",\"Answer\":\"Extracting the reward function from a set of behaviors. \",\"Key ideas\":\"\\n1. Reinforcement learning: a type of machine learning algorithm that uses rewards and punishments to learn how to perform a task. \\n2. Inverse reinforcement learning: a type of reinforcement learning algorithm that extracts the reward function from a set of behaviors. \\n3. Reward function: a mathematical function that assigns a numerical value to a given behavior, indicating how desirable it is. \\n4. Behavior: an action or set of actions taken by an agent in response to a given situation.\",\"Abstraction groups\":{\"-1\":[\"Reinforcement Learning\",\"Inverse Reinforcement Learning\",\"Reward Function\",\"Behavior\"],\"0\":[\"Inverse Reinforcement Learning\"],\"1\":[\"Machine Learning\",\"Algorithm\"],\"2\":[\"Artificial Intelligence\",\"Computational Thinking\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\",\"Technology\"]}},\"230\":{\"Question\":\"What is goal misgeneralization? \",\"Answer\":\"Agent pursues a proximal goal that is correlated with the real reward during training, but doesn't generalize well outside of the training dataset. \",\"Key ideas\":\"\\n1. Goal misgeneralization is a phenomenon that occurs when an agent pursues a proximal goal that is correlated with the real reward during training. \\n2. This proximal goal does not generalize well outside of the training dataset. \\n3. Agent refers to a computer program or artificial intelligence system. \\n4. Proximal goal is a goal that is close to the real reward. \\n5. Real reward is the desired outcome of the agent's actions. \\n6. Training dataset is the data used to train the agent.\",\"Abstraction groups\":{\"-1\":[\"Agent\",\"Proximal Goal\",\"Real Reward\",\"Training Dataset\"],\"0\":[\"Goal Misgeneralization\"],\"1\":[\"Artificial Intelligence\",\"Machine Learning\"],\"2\":[\"Computer Science\",\"Technology\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\",\"Learning\"]}},\"231\":{\"Question\":\"List a few examples of \\\"more is different\\\" in nature \",\"Answer\":\"Think of more physical to more abstract, or less biological to more biological. \\nUranium. With a bit of uranium, nothing special happens; with a large amount of uranium packed densely enough, you get a nuclear reaction.\\nWater. Individual water molecules aren\\u2019t wet. Wetness only occurs due to the interaction forces between many water molecules interspersed throughout a fabric (or other material).\\nDNA. Given only small molecules such as calcium, you can\\u2019t meaningfully encode useful information; given larger molecules such as DNA, you can encode a genome.\\nSpecialization. Historically, in small populations, virtually everyone needed to farm or hunt to survive; in contrast, in larger and denser communities, enough food is produced for large fractions of the population to specialize in non-agricultural work. \",\"Key ideas\":\"\\n1. More is different in nature can refer to physical to more abstract, or less biological to more biological. \\n2. Uranium is an example of more is different, as nothing special happens with a small amount, but a large amount packed densely enough can cause a nuclear reaction. \\n3. Water is an example of more is different, as individual water molecules aren\\u2019t wet, but wetness occurs due to the interaction forces between many water molecules interspersed throughout a fabric (or other material). \\n4. DNA is an example of more is different, as given only small molecules such as calcium, you can\\u2019t meaningfully encode useful information, but given larger molecules such as DNA, you can encode a genome. \\n5. Specialization is an example of more is different, as in small populations, virtually everyone needed to farm or hunt to survive, but in larger and denser communities, enough food is produced for large fractions of the population to specialize in non-agricultural work.\",\"Abstraction groups\":{\"-1\":[\"Uranium\",\"Water\",\"DNA\",\"Specialization\",\"Nuclear\",\"Molecule\",\"Calcium\",\"Genome\",\"Fabric\",\"Interaction\"],\"0\":[\"More Is Different\"],\"1\":[\"Nature\",\"Physical\",\"Biological\"],\"2\":[\"Interaction\",\"Specialization\"],\"3\":[\"Molecule\",\"Fabric\",\"Calcium\",\"Genome\"],\"4\":[\"Uranium\",\"Water\",\"DNA\"]}},\"232\":{\"Question\":\"How many parameters did the AlexNet convolutional network have? \",\"Answer\":\"60 million\",\"Key ideas\":\"1. AlexNet: AlexNet is a convolutional neural network (CNN) developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. \\n2. Convolutional Neural Network (CNN): A type of artificial neural network used in computer vision and image processing. It is composed of multiple layers of neurons that process visual data.\\n3. Parameters: A parameter is a value that can be adjusted to optimize the performance of a model. \\n4. AlexNet Convolutional Network: The AlexNet convolutional network had 60 million parameters.\",\"Abstraction groups\":{\"-1\":[\"AlexNet\",\"CNN\",\"Parameter\",\"AlexNet Convolutional Network\"],\"0\":[\"AlexNet\"],\"1\":[\"Convolutional Neural Network\",\"Parameters\"],\"2\":[\"Artificial Neural Network\",\"Computer Vision\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computing\"]}},\"233\":{\"Question\":\"In an attention layer with size n_emb, what is the total number of parameters roughly? Walk through the rough math. \\nApply this to GPT-3\",\"Answer\":\"n_emb*(n_emb*3) projects into query, key, and value space.\\nThen one output projection is n_emb*n_emb.\\nThen MLP is n_emb*(n_emb*4) but it happens twice.\\nFinal result in total is 12*n_emb^2 for each layer roughly \\n(there's a few more random parameters on the input and output layers).\\n---\\nFor GPT-3 this ends up being around 10,000^2 * 12 or around 10^9 = 1.8 billion params roughly.\",\"Key ideas\":\"1. Attention layers have a size n_emb.\\n2. The total number of parameters in an attention layer is roughly 12*n_emb^2.\\n3. The parameters are divided into query, key, and value space (n_emb*(n_emb*3)).\\n4. There is one output projection (n_emb*n_emb).\\n5. There are two MLP layers (n_emb*(n_emb*4)).\\n6. For GPT-3, this ends up being around 1.8 billion parameters.\",\"Abstraction groups\":{\"-1\":[\"Attention Layer\",\"Size\",\"Parameter\",\"Math\",\"GPT-3\",\"Query\",\"Key\",\"Value\",\"Output\",\"MLP\",\"Input\",\"Output\"],\"0\":[\"Attention Layer\"],\"1\":[\"Parameter\",\"Math\"],\"2\":[\"Attention\",\"GPT-3\"],\"3\":[\"Neural Network\",\"Machine Learning\"],\"4\":[\"Artificial Intelligence\"]}},\"234\":{\"Question\":\"GPT-3 model size: number of layers, embedding size, number of heads in each layer, and size of each head\",\"Answer\":\"96 layers, embedding size of 12288 (or 96*128), 96 heads per layer, and 128 dimensions per head.\",\"Key ideas\":\"1. GPT-3: an advanced natural language processing model\\n2. Model size: the number of layers, embedding size, number of heads in each layer, and size of each head\\n3. Number of layers: 96\\n4. Embedding size: 12288 (or 96*128)\\n5. Number of heads per layer: 96\\n6. Size of each head: 128 dimensions\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"Layer\",\"Embedding\",\"Head\",\"Dimension\"],\"0\":[\"GPT-3 Model Size\"],\"1\":[\"Number of Layers\",\"Embedding Size\",\"Number of Heads\",\"Size of Each Head\"],\"2\":[\"Model Size\",\"Natural Language Processing\"],\"3\":[\"Computer Science\",\"Artificial Intelligence\"],\"4\":[\"Science\"]}},\"235\":{\"Question\":\"GPT-3 model size: total number of training tokens\",\"Answer\":\"300 billion tokens\",\"Key ideas\":\"1. GPT-3: GPT-3 stands for Generative Pre-trained Transformer 3, which is a type of natural language processing (NLP) model.\\n2. Model size: This refers to the total number of training tokens used to create the model.\\n3. Training tokens: These are the pieces of data used to train the model.\\n4. Total number of training tokens: 300 billion tokens.\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"Model\",\"Token\",\"Training\",\"Number\"],\"0\":[\"GPT-3 Model Size\"],\"1\":[\"Training Tokens\",\"Number\"],\"2\":[\"Model\",\"Data\"],\"3\":[\"Natural Language Processing\"],\"4\":[\"Artificial Intelligence\"]}},\"236\":{\"Question\":\"GPT-3 model size: total number of parameters\",\"Answer\":\"175 billion\",\"Key ideas\":\"1. GPT-3: GPT-3 stands for Generative Pre-trained Transformer 3, which is a type of natural language processing (NLP) model.\\n2. Model size: This refers to the total number of parameters in the GPT-3 model.\\n3. Parameters: Parameters are the variables that are used to define the model and its behavior.\\n4. Total number of parameters: This is the total number of parameters in the GPT-3 model, which is 175 billion.\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"Model\",\"Parameter\",\"Number\"],\"0\":[\"GPT-3\"],\"1\":[\"Model Size\",\"Parameters\"],\"2\":[\"Natural Language Processing\",\"Machine Learning\"],\"3\":[\"Artificial Intelligence\",\"Computing\"],\"4\":[\"Technology\"]}},\"237\":{\"Question\":\"How many parameters are there in the original BERT paper? What about GPT-2?\",\"Answer\":\"In the original BERT paper (may 2019) by Devlin et al, there are 340 million parameters. \\nGPT-2 (2018) had 1.5 billion\",\"Key ideas\":\"\\n1. BERT (Bidirectional Encoder Representations from Transformers) is a paper published in May 2019 by Devlin et al. \\n2. GPT-2 (Generative Pre-trained Transformer 2) is a paper published in 2018. \\n3. BERT has 340 million parameters. \\n4. GPT-2 has 1.5 billion parameters.\",\"Abstraction groups\":{\"-1\":[\"Bert\",\"Gpt-2\",\"Parameter\",\"Devlin\",\"2019\",\"2018\"],\"0\":[\"Parameter\"],\"1\":[\"BERT\",\"GPT-2\"],\"2\":[\"Paper\",\"Devlin\"],\"3\":[\"2019\",\"2018\"],\"4\":[\"Number\"]}},\"238\":{\"Question\":\"In the original BERT paper (may 2019) by Devlin et al, what is the training goal?\",\"Answer\":\"\\\"In order to train a deep bidirectional representation, we simply mask some percentage of the input tokens at random, and then predict those masked tokens.\\\"\\nThey mask around 15% of tokens.\",\"Key ideas\":\"1. BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model developed by Devlin et al in May 2019. \\n2. The training goal of BERT is to create a deep bidirectional representation of the input tokens. \\n3. To achieve this goal, BERT randomly masks a percentage of the input tokens. \\n4. The masked tokens are then predicted by the model. \\n5. The original BERT paper suggests masking around 15% of tokens.\",\"Abstraction groups\":{\"-1\":[\"Bert\",\"Devlin\",\"Masking\",\"Token\",\"Prediction\",\"Percentage\"],\"0\":[\"BERT\"],\"1\":[\"Deep Learning\",\"Machine Learning\"],\"2\":[\"Artificial Intelligence\",\"Data Science\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"239\":{\"Question\":\"In the original BERT paper (may 2019) by Devlin et al, what is the new form of the architecture compared to Vaswani et al?\",\"Answer\":\"This is a \\\"bidirectional encoder representations from transformers\\\" (BERT)\\nIt is the same as the encoder block from Vaswani. it is all to all connected attention. \",\"Key ideas\":\"\\n1. BERT (Bidirectional Encoder Representations from Transformers) is a new form of architecture compared to Vaswani et al.\\n2. BERT is the same as the encoder block from Vaswani.\\n3. BERT is an all-to-all connected attention architecture.\",\"Abstraction groups\":{\"-1\":[\"Bert\",\"Vaswani\",\"Architecture\",\"Encoder\",\"Attention\"],\"0\":[\"BERT\"],\"1\":[\"Architecture\",\"Attention\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"240\":{\"Question\":\"Researchers from what two organizations collaborated on the paper \\\"Deep reinforcement learning from human preferences\\\" (2017)?\",\"Answer\":\"OpenAI and Deepmind \",\"Key ideas\":\"\\n1. Deep reinforcement learning (DRL): a type of machine learning algorithm that uses rewards and punishments to learn how to complete tasks.\\n\\n2. Human preferences: the preferences of humans that are used to guide the learning process of DRL algorithms.\\n\\n3. OpenAI: a research laboratory focused on artificial intelligence (AI) and machine learning.\\n\\n4. Deepmind: a British artificial intelligence company acquired by Google in 2014.\\n\\n5. \\\"Deep reinforcement learning from human preferences\\\" (2017): a paper published by researchers from OpenAI and Deepmind in 2017.\",\"Abstraction groups\":{\"-1\":[\"OpenAI\",\"Deepmind\",\"DRL\",\"Human Preference\",\"Paper\"],\"0\":[\"Collaboration\"],\"1\":[\"Research\",\"Paper\",\"Organizations\"],\"2\":[\"Collaboration\",\"AI\",\"Deep Learning\"],\"3\":[\"Science\",\"Technology\",\"Innovation\"],\"4\":[\"Knowledge\",\"Learning\",\"Progress\"]}},\"241\":{\"Question\":\"What game\\/task was targeted and demonstrated in the paper \\\"Deep reinforcement learning from human preferences\\\" (2017),\",\"Answer\":\"Atari game, and robotic motion resembling human behavior. \",\"Key ideas\":\"\\n1. Deep reinforcement learning: a type of machine learning that uses rewards and punishments to learn how to perform a task.\\n2. Human preferences: the preferences of humans that are used to guide the learning process.\\n3. Atari game: a type of video game developed by Atari Inc. in the 1970s and 1980s.\\n4. Robotic motion resembling human behavior: the use of robots to mimic human behavior.\",\"Abstraction groups\":{\"-1\":[\"Deep Reinforcement Learning\",\"Human Preference\",\"Atari\",\"Robotics\"],\"0\":[\"Deep Reinforcement Learning\"],\"1\":[\"Human Preference\",\"Atari Game\",\"Robotic Motion\"],\"2\":[\"Machine Learning\",\"Video Game\",\"Robotics\"],\"3\":[\"Artificial Intelligence\",\"Entertainment\",\"Automation\"],\"4\":[\"Technology\"]}},\"242\":{\"Question\":\"In the paper \\\"Deep reinforcement learning from human preferences\\\" (2017), what method was chosen to try to provide a reward on complex tasks that are hard to specify mathematically?\",\"Answer\":\"Train a reinforcement learning model to predict human preference between states (supervised learning), then use that reward predictor to train the actual agent. \",\"Key ideas\":\"1. Reinforcement learning (RL): a type of machine learning algorithm that uses rewards to learn how to complete tasks.\\n2. Supervised learning: a type of machine learning algorithm that uses labeled data to learn how to complete tasks.\\n3. Deep reinforcement learning: a type of reinforcement learning algorithm that uses deep neural networks to learn how to complete tasks.\\n4. Human preferences: the preferences of humans that can be used to provide rewards for complex tasks that are hard to specify mathematically.\\n5. Train a reinforcement learning model: use supervised learning to train a reinforcement learning model to predict human preferences between states.\\n6. Reward predictor: the trained reinforcement learning model used to provide rewards for complex tasks.\\n7. Actual agent: the agent that is trained using the reward predictor.\",\"Abstraction groups\":{\"-1\":[\"Reinforcement Learning\",\"Supervised Learning\",\"Deep Learning\",\"Human Preference\",\"Reward Predictor\",\"Actual Agent\"],\"0\":[\"Human Preference\"],\"1\":[\"Reinforcement Learning\",\"Supervised Learning\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"243\":{\"Question\":\"How has the context of foundation models changed in the last few years with things like Dalle as opposed to just GPT-3?\",\"Answer\":\"Foundation models have become multimodal (translating between different sources of data) \",\"Key ideas\":\"1. Foundation models: models used to build other models, such as GPT-3\\n2. Multimodal: the ability to translate between different sources of data\\n3. Dalle: a new foundation model that has been developed in the last few years\",\"Abstraction groups\":{\"-1\":[\"Foundation Model\",\"GPT-3\",\"Multimodal\",\"Dalle\"],\"0\":[\"Foundation Model\"],\"1\":[\"Multimodal\",\"Dalle\"],\"2\":[\"GPT-3\",\"Context\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Technology\"]}},\"244\":{\"Question\":\"When did foundation models become prevalent? What is the first reasonable example? \",\"Answer\":\"BERT near 2019 was the first serious foundation model used across an entire group of tasks.\",\"Key ideas\":\"\\n1. Foundation models are a type of machine learning model.\\n2. Foundation models became prevalent near 2019.\\n3. BERT (Bidirectional Encoder Representations from Transformers) was the first serious foundation model used across an entire group of tasks.\",\"Abstraction groups\":{\"-1\":[\"Foundation Model\",\"2019\",\"Bert\",\"Task\"],\"0\":[\"Foundation Model\"],\"1\":[\"Machine Learning\"],\"2\":[\"Artificial Intelligence\",\"Computing\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"245\":{\"Question\":\"What improvements in technology have allowed foundation models to emerge? \",\"Answer\":\"Improvement in: Hardware, architecture (transformers), and training data \\nThis is analogous to OpenAI main research tasks (data, training, and tools), except tools is replaced by architectures \",\"Key ideas\":\"1. Technology improvements have allowed foundation models to emerge. \\n2. Hardware improvements have been a key factor in this emergence. \\n3. Architectures, such as transformers, have also been a key factor. \\n4. Training data has been a key factor in the emergence of foundation models. \\n5. OpenAI's main research tasks are analogous to the factors mentioned above, except tools is replaced by architectures.\",\"Abstraction groups\":{\"-1\":[\"Technology\",\"Hardware\",\"Architecture\",\"Transformer\",\"Training Data\",\"OpenAI\",\"Data\",\"Tool\",\"Model\"],\"0\":[\"Foundation Model\"],\"1\":[\"Technology\",\"Hardware\",\"Architecture\",\"Training Data\"],\"2\":[\"Improvement\",\"OpenAI\"],\"3\":[\"Research Task\",\"Tool\"],\"4\":[\"Emergence\"]}},\"246\":{\"Question\":\"According to the paper \\\"On the opportunities and Risks of Foundation Models\\\" by Bommasani et. Al., what are the key characteristics of foundation models? \",\"Answer\":\"Emergence of behavior at scale, learned implicitly from data\\nHomogenization: cross task applicability means foundation models replace other models across a range of domains. Provides power, but points of failure too \",\"Key ideas\":\"\\n1. Foundation models are characterized by emergence of behavior at scale, which is learned implicitly from data.\\n2. Foundation models have the ability to homogenize, meaning they can be applied across a range of domains and replace other models.\\n3. This provides power, but also points of failure.\",\"Abstraction groups\":{\"-1\":[\"Foundation Model\",\"Emergence\",\"Data\",\"Homogenization\",\"Cross Task\",\"Power\",\"Failure\"],\"0\":[\"Foundation Model\"],\"1\":[\"Emergence\",\"Homogenization\"],\"2\":[\"Data\",\"Cross Task\"],\"3\":[\"Power\",\"Failure\"],\"4\":[\"Opportunity\",\"Risk\"]}},\"247\":{\"Question\":\"Explain the difference between precision and recall. \",\"Answer\":\"Precision: maximizes true positives out of all positives\\nRecall: maximized true positives out of all actual things \",\"Key ideas\":\"\\n1. Precision is a measure of how accurately a model classifies positive results.\\n2. Recall is a measure of how many of the actual positives the model is able to identify.\\n3. Precision maximizes true positives out of all positives.\\n4. Recall maximizes true positives out of all actual things.\",\"Abstraction groups\":{\"-1\":[\"Precision\",\"Recall\",\"True Positive\",\"Positive\",\"Actual Thing\"],\"0\":[\"Precision\",\"Recall\"],\"1\":[\"Measurement\",\"Evaluation\"],\"2\":[\"Modeling\",\"Data Analysis\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"248\":{\"Question\":\"How often should you re-train your machine learning model? \",\"Answer\":\"The speed at which your world changes determines how often you should retrain your model.\\nYour system only works in the world where it was designed to work. \",\"Key ideas\":\"1. Machine learning models should be retrained regularly.\\n2. The frequency of retraining depends on the rate of change in the world.\\n3. The model should only be used in the world it was designed for.\",\"Abstraction groups\":{\"-1\":[\"Machine Learning\",\"Retraining\",\"World\",\"System\"],\"0\":[\"Retraining\"],\"1\":[\"Machine Learning\",\"Training\"],\"2\":[\"Artificial Intelligence\",\"Data Science\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"249\":{\"Question\":\"What is k-fold cross validation, and when is it useful? \",\"Answer\":\"You choose some integer k, and then leave out 1\\/k of the data and do that in k ways and train the model based on the data left in each subset, and then aggregate predictions of these models.\\nThis is useful when you don't want to do a direct random training\\/validation split, or you need to check your sensitivity to outliers.\",\"Key ideas\":\"1. K-fold cross validation is a technique used to train a model.\\n2. It involves splitting the data into k subsets, leaving out 1\\/k of the data in each subset.\\n3. The model is then trained on the data left in each subset.\\n4. The predictions of the models are then aggregated.\\n5. This technique is useful when you don't want to do a direct random training\\/validation split, or you need to check your sensitivity to outliers.\",\"Abstraction groups\":{\"-1\":[\"K-Fold\",\"Cross Validation\",\"Data\",\"Subset\",\"Model\",\"Prediction\",\"Aggregation\",\"Random Split\",\"Outlier\"],\"0\":[\"K-Fold Cross Validation\"],\"1\":[\"Machine Learning\",\"Model Training\"],\"2\":[\"Data Analysis\",\"Statistical Analysis\"],\"3\":[\"Data Science\",\"Computational Science\"],\"4\":[\"Science\",\"Technology\",\"Engineering\",\"Mathematics\"]}},\"250\":{\"Question\":\"What are the two stages involved in training ChatGPT as opposed to original GPT-3 architecture?\",\"Answer\":\"Original pretraining phase just gets it to babble text.\\nFine tuning stage with reinforcement learning gets it to be helpful as an assistant, rather than completing documents. \",\"Key ideas\":\"1. GPT-3 is an architecture for natural language processing.\\n2. ChatGPT is a variant of GPT-3.\\n3. Training ChatGPT involves two stages:\\n    a. Pretraining phase\\n    b. Fine tuning stage with reinforcement learning\\n4. Pretraining phase gets ChatGPT to babble text.\\n5. Fine tuning stage with reinforcement learning gets ChatGPT to be helpful as an assistant, rather than completing documents.\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"ChatGPT\",\"Pretraining\",\"Fine Tuning\",\"Reinforcement Learning\",\"Babbling\",\"Assistant\"],\"0\":[\"Training ChatGPT\"],\"1\":[\"Natural Language Processing\",\"Reinforcement Learning\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"251\":{\"Question\":\"What differentiates the GPT-3 architecture from the original \\\"Attention is all you need\\\" paper? \\nWhy was Attention is all you need different? What task were they targeting?\",\"Answer\":\"GPT-3 is a decoder only transformer, and doesn't have the cross-attention which is present in the Attention is all you need paper. GPT-3 only masks out future tokens in the attention mechanism.\\nAttention is all you need was targeting machine translation, so they had an encoder block to gather info in the original phrase, then the decoder has that context injected within each layer of the decoder.\",\"Key ideas\":\"1. GPT-3 is a decoder only transformer.\\n2. GPT-3 does not have the cross-attention which is present in the Attention is all you need paper.\\n3. GPT-3 only masks out future tokens in the attention mechanism.\\n4. Attention is all you need was targeting machine translation.\\n5. Attention is all you need had an encoder block to gather info in the original phrase.\\n6. Attention is all you need had the context injected within each layer of the decoder.\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"Attention\",\"Machine Translation\",\"Encoder\",\"Decoder\",\"Cross-Attention\",\"Masking\",\"Future Token\"],\"0\":[\"GPT-3\"],\"1\":[\"Attention\",\"Machine Translation\",\"Encoder\",\"Decoder\",\"Cross-Attention\",\"Masking\",\"Future Tokens\"],\"2\":[\"Artificial Intelligence\",\"Natural Language Processing\",\"Neural Networks\"],\"3\":[\"Computer Science\",\"Data Science\"],\"4\":[\"Science\"]}},\"252\":{\"Question\":\"In layer normalization in the transformer architecture, how many parameters are there per layer norm?\\nDescribe the mathematical steps in each forward pass of the layer norm. \",\"Answer\":\"Just two, but for each location in the embedding (the bias and the scaling)\\nEach token's embedding at this layer (ie a 1D vector of some size) has its mean and variance computed, and then scaled away indepenently. Then you scale and shift and scale by the overall layer norm parameters at each embedding location by the same amount for each token.\",\"Key ideas\":\"1. Layer normalization is a technique used in the transformer architecture.\\n2. There are two parameters per layer norm.\\n3. For each location in the embedding, the mean and variance are computed and scaled away independently.\\n4. The overall layer norm parameters are then applied to each embedding location for each token.\",\"Abstraction groups\":{\"-1\":[\"Layer Normalization\",\"Transformer Architecture\",\"Parameter\",\"Embedding\",\"Mean\",\"Variance\",\"Scaling\",\"Shifting\",\"Token\"],\"0\":[\"Layer Normalization\"],\"1\":[\"Transformer Architecture\",\"Parameters\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"253\":{\"Question\":\"In a transformer, is the MLP applied to the tokens combined, or separately? \",\"Answer\":\"Each token passes through the MLP separately. \",\"Key ideas\":\"\\n1. Transformers are a type of machine learning model.\\n2. Transformers use a Multi-Layer Perceptron (MLP) to process input tokens.\\n3. The MLP is applied to each token separately, not to the tokens combined.\",\"Abstraction groups\":{\"-1\":[\"Transformer\",\"Mlp\",\"Token\"],\"0\":[\"Transformer\"],\"1\":[\"Machine Learning\",\"Artificial Intelligence\"],\"2\":[\"Computing\",\"Technology\"],\"3\":[\"Science\",\"Engineering\"],\"4\":[\"Knowledge\"]}},\"254\":{\"Question\":\"What can interspersing the fully connected layer (multi-layer perceptron or MLP) with the transformer attention mechanism, then repeating many times, be described as?\\nWhat is a quick and pithy way to say it? \",\"Answer\":\"Interspersing computation (MLP) and communication (attention) \",\"Key ideas\":\"1. Fully connected layer (multi-layer perceptron or MLP): A type of artificial neural network consisting of multiple layers of neurons connected to each other.\\n2. Transformer attention mechanism: A type of artificial neural network that uses attention mechanisms to focus on certain parts of the input data.\\n3. Interspersing computation (MLP) and communication (attention): Combining the two types of artificial neural networks to create a more powerful model.\",\"Abstraction groups\":{\"-1\":[\"MLP\",\"Attention\",\"Computation\",\"Communication\"],\"0\":[\"Interpreting MLP and Attention\"],\"1\":[\"Artificial Neural Network\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"255\":{\"Question\":\"What is the multi-head attention mechanism in a transformer analogous to in convolutional neural networks? \",\"Answer\":\"It is analogous to computing multiple images within each convolutional layer (the number of images in each layer is analogous to the number of heads).\\nDoing this allows you to compute multiple kinds of things, to feed in as context for the following layer. \",\"Key ideas\":\"1. Transformers are a type of neural network architecture.\\n2. The multi-head attention mechanism is a key component of the transformer architecture.\\n3. Convolutional neural networks (CNNs) are another type of neural network architecture.\\n4. The multi-head attention mechanism in a transformer is analogous to computing multiple images within each convolutional layer.\\n5. The number of images in each layer is analogous to the number of heads in the transformer.\\n6. Computing multiple images within each convolutional layer allows for multiple kinds of context to be fed into the following layer.\",\"Abstraction groups\":{\"-1\":[\"Transformer\",\"Attention\",\"CNN\",\"Image\",\"Head\",\"Context\"],\"0\":[\"Multi-head Attention\"],\"1\":[\"Transformer\",\"CNN\"],\"2\":[\"Neural Network\",\"Machine Learning\"],\"3\":[\"Artificial Intelligence\",\"Computing\"],\"4\":[\"Technology\"]}},\"256\":{\"Question\":\"In the GPT architecture, if the query, key, and value matrices have the last dimension (call it the column index) be the elements of the embedding, then why is the product query * transpose(keys) masked to be lower triangular, before it is multiplied by the value matrix? \",\"Answer\":\"This is because the query * transpose(keys) matrix is tokens by tokens dimension, and the row index is the original token index. Masking this to be lower triangular means that the multiplication by the values matrix picks out only value vectors that are in the past or present of the token in question, and places them in the final row index associated with that token. \",\"Key ideas\":\"1. GPT (Generative Pre-trained Transformer) is an architecture used in natural language processing. \\n2. Query, key, and value matrices are used in the GPT architecture. \\n3. The last dimension of the query, key, and value matrices is the elements of the embedding. \\n4. The product query * transpose(keys) is masked to be lower triangular before it is multiplied by the value matrix. \\n5. This is because the query * transpose(keys) matrix is tokens by tokens dimension, and the row index is the original token index. \\n6. Masking this to be lower triangular means that the multiplication by the values matrix picks out only value vectors that are in the past or present of the token in question, and places them in the final row index associated with that token.\",\"Abstraction groups\":{\"-1\":[\"GPT\",\"Query\",\"Key\",\"Value\",\"Embedding\",\"Masking\",\"Token\"],\"0\":[\"GPT\"],\"1\":[\"Natural Language Processing\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\"],\"4\":[\"Science\"]}},\"257\":{\"Question\":\"Why is the training process for GPT codex unique compared to GPT3 and so on?\",\"Answer\":\"There is a ground truth success or failure for code with unit tests and an interpreter, unlike language which is up to debate \",\"Key ideas\":\"1. GPT codex: a type of artificial intelligence (AI) technology\\n2. GPT3: another type of AI technology\\n3. Training process: the process of teaching a computer to do something\\n4. Ground truth: a set of facts or principles that are accepted as true\\n5. Success or failure: the result of a task or process\\n6. Unit tests: tests that check the functionality of a specific part of a program\\n7. Interpreter: a program that translates instructions written in a programming language into a form that can be understood by a computer\\n8. Language: a system of communication used by humans\",\"Abstraction groups\":{\"-1\":[\"GPT\",\"Codex\",\"GPT3\",\"Training\",\"Ground Truth\",\"Success\",\"Failure\",\"Unit Test\",\"Interpreter\",\"Language\"],\"0\":[\"GPT Codex\"],\"1\":[\"Artificial Intelligence\",\"Training Process\"],\"2\":[\"Technology\",\"Processes\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"258\":{\"Question\":\"How should the training process of the GPT-3 language model be understood, if we are able to know why it started to succeed on few shot learning compared to previous models. \\nHow is this similar to Jacob Andreas' paper on agent models?\",\"Answer\":\"Can think of training process as:\\nOuter loop of stochastic gradient descent is reducing loss to learn to encode multiple tasks\\nInner loop of a set of tasks in a context window is helping language model figure out which task is under test \\nThis is similar to the idea that language models learn to figure out what agent they're modeling, in order to more accurately predict text. \",\"Key ideas\":\"1. GPT-3 language model is a type of few shot learning\\n2. Training process of GPT-3 involves an outer loop of stochastic gradient descent to reduce loss and an inner loop of tasks in a context window\\n3. Language models learn to figure out what agent they're modeling in order to more accurately predict text\\n4. This idea is similar to Jacob Andreas' paper on agent models\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"Few Shot Learning\",\"Stochastic Gradient Descent\",\"Context Window\",\"Language Model\",\"Agent Model\",\"Jacob Andreas\"],\"0\":[\"GPT-3\"],\"1\":[\"Language Model\",\"Agent Model\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Cognitive Science\"],\"4\":[\"Science\"]}},\"259\":{\"Question\":\"What were the critical advances or demonstrated capabilities of the GPT-3 paper?\",\"Answer\":\"In context learning success (zero shot or few shot) started to really improve (success rate vs number of examples shown)\\nObserved growth in gap of zero shot and few shot learning success rates with more scale in model (success gap vs model size) \",\"Key ideas\":\"1. GPT-3 paper demonstrated capabilities in context learning success. \\n2. Zero shot and few shot learning success rates improved with more examples shown. \\n3. Success gap between zero shot and few shot learning increased with larger model size.\",\"Abstraction groups\":{\"-1\":[\"GPT-3\",\"Context\",\"Learning\",\"Zero Shot\",\"Few Shot\",\"Success\",\"Example\",\"Model\",\"Size\"],\"0\":[\"GPT-3\"],\"1\":[\"Context Learning\",\"Zero Shot\",\"Few Shot\",\"Success\",\"Examples\",\"Model\",\"Size\"],\"2\":[\"Learning\",\"Scale\",\"Gap\"],\"3\":[\"Capabilities\",\"Advances\"],\"4\":[\"AI\"]}},\"260\":{\"Question\":\"What is the key reason OpenAI switched focus to go all in on language models as opposed to other technologies? \",\"Answer\":\"Language sequence prediction allows for massive unsupervised learning via text prediction\\nLocal prediction is enough, since it still encourages learning the larger context \",\"Key ideas\":\"\\n1. OpenAI switched focus to go all in on language models.\\n2. Language sequence prediction allows for massive unsupervised learning.\\n3. Unsupervised learning is a type of machine learning that does not require labeled data.\\n4. Text prediction is a type of unsupervised learning.\\n5. Local prediction is enough to encourage learning the larger context.\",\"Abstraction groups\":{\"-1\":[\"OpenAI\",\"Language\",\"Prediction\",\"Unsupervised\",\"Text\",\"Local\",\"Context\"],\"0\":[\"OpenAI\"],\"1\":[\"Language Model\",\"Technology\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\",\"Knowledge\"]}},\"261\":{\"Question\":\"Critical architectural components of \\\"Attention is all you need\\\" paper on transformers \",\"Answer\":\"Positional representation\\nMultihead attention to learn to encode different concepts \\nMasking for causality\\nNon-linearities in the fully connected layer. \",\"Key ideas\":\"\\n1. Positional representation\\n2. Multihead attention to learn to encode different concepts\\n3. Masking for causality\\n4. Non-linearities in the fully connected layer\",\"Abstraction groups\":{\"-1\":[\"Positional Representation\",\"Multihead Attention\",\"Masking\",\"Causality\",\"Non-Linearity\",\"Fully Connected Layer\"],\"0\":[\"Transformer\"],\"1\":[\"Critical Architectural Component\"],\"2\":[\"Attention Is All You Need Paper\"],\"3\":[\"Natural Language Processing\"],\"4\":[\"Artificial Intelligence\"]}},\"262\":{\"Question\":\"What was the timeline for plant development evolutionarily? \",\"Answer\":\"Algal scum appeared about 1.2 billion years ago on land, and bigger plants appeared around 450 million years ago.\",\"Key ideas\":\"1. Plant development evolutionarily has a timeline. \\n2. Algal scum appeared about 1.2 billion years ago on land. \\n3. Bigger plants appeared around 450 million years ago.\",\"Abstraction groups\":{\"-1\":[\"Plant\",\"Development\",\"Evolution\",\"Timeline\",\"Algal\",\"Scum\",\"Land\",\"Bigger\",\"Plant\",\"450 Million Years\"],\"0\":[\"Plant Development\"],\"1\":[\"Evolution\",\"Timeline\"],\"2\":[\"Biology\",\"Earth Science\"],\"3\":[\"Science\",\"Nature\"],\"4\":[\"Knowledge\"]}},\"263\":{\"Question\":\"Given the two mindsets or approaches to use when designing a model to fit some observed data in an experiment (minimize false negatives or false postives), when is it not appropriate to try to fit a model to the data (and instead you should try to minimize false positives)? \",\"Answer\":\"When you know there is already extremely low probability of a false negative (you know there exists a line to fit roughly linear data. That's a boring question to ask and you don't get any information out of doing it). Instead you should be trying to see if you can produce the right line from scratch before looking at the data (minimizing false positives: this gives you the most information per experiment, where the experiment is now the model generation process.) \",\"Key ideas\":\"\\n1. Two mindsets or approaches to use when designing a model to fit some observed data in an experiment: \\n    a. Minimize false negatives \\n    b. Minimize false positives \\n2. When is it not appropriate to try to fit a model to the data (and instead you should try to minimize false positives)? \\n    a. When you know there is already extremely low probability of a false negative (you know there exists a line to fit roughly linear data. That's a boring question to ask and you don't get any information out of doing it). \\n3. What should you do instead? \\n    a. Try to see if you can produce the right line from scratch before looking at the data (minimizing false positives: this gives you the most information per experiment, where the experiment is now the model generation process.)\",\"Abstraction groups\":{\"-1\":[\"Mindset\",\"Model\",\"Data\",\"False Negative\",\"False Positive\",\"Line\",\"Experiment\",\"Model Generation\"],\"0\":[\"Model Fitting\"],\"1\":[\"Data Analysis\",\"Modeling\"],\"2\":[\"Experimentation\",\"Problem Solving\"],\"3\":[\"Scientific Thinking\",\"Critical Thinking\"],\"4\":[\"Cognitive Skills\"]}},\"264\":{\"Question\":\"What does the Iswap gate do in a quantum circuit? \",\"Answer\":\"It is like swap (swaps 10 and 01) but it also adds an imaginary i in front of those two. \\nThis i is crucial for not producing a trivial gate set.\",\"Key ideas\":\"1. The Iswap gate is a type of quantum circuit gate. \\n2. It is similar to the swap gate, which swaps 10 and 01. \\n3. The Iswap gate also adds an imaginary i in front of those two. \\n4. This i is important for not producing a trivial gate set.\",\"Abstraction groups\":{\"-1\":[\"Iswap\",\"Swap\",\"10\",\"01\",\"I\",\"Gate Set\"],\"0\":[\"Iswap\"],\"1\":[\"Quantum Circuit\",\"Gate\"],\"2\":[\"Quantum Computing\",\"Computing\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"265\":{\"Question\":\"What does the Cook Levin theorem prove? \",\"Answer\":\"Any NP problem can be reduced in polynomial time to 3 Sat (the 3 satisfiability boolean problem). \\nIt proceeds by showing that \\nany problem which can be verified in polynomial time on a turing machine can be written as a polynomially large chunk of circuits\\nthis circuit can be expanded in polynomial time to a satifiability problem in conjunctive normal form.\",\"Key ideas\":\"1. NP problems can be reduced in polynomial time to 3 Sat (the 3 satisfiability boolean problem). \\n2. Any problem which can be verified in polynomial time on a turing machine can be written as a polynomially large chunk of circuits.\\n3. This circuit can be expanded in polynomial time to a satifiability problem in conjunctive normal form.\",\"Abstraction groups\":{\"-1\":[\"Cook Levin Theorem\",\"NP\",\"3 Sat\",\"Turing Machine\",\"Circuit\",\"Satifiability\",\"Conjunctive Normal Form\"],\"0\":[\"Cook Levin Theorem\"],\"1\":[\"Computational Complexity\",\"Boolean Satisfiability\"],\"2\":[\"Algorithm\",\"Mathematics\"],\"3\":[\"Computer Science\",\"Science\"],\"4\":[\"Knowledge\",\"Reality\"]}},\"266\":{\"Question\":\"3 sat (the 3 satisfiability problem): is it or of a bunch of ands, or and of a bunch of ors?\",\"Answer\":\"It is the and of a bunch of 3 ors. \\nThis is because any conjunction like (a or b) can be replaced with (a or z) and (b or bar(z)), and produce the same Sat problem (both are satisfiable at the same time, or not). In reality, if a is an \\\"or\\\" of two variables, and \\\"b\\\" is an or of 2 variables, then you can convert that \\\"or\\\" of 4 variables to an and of two sets of 3 variables. This is the reduction process. \\nThis is called the conjunctive normal form (conjunctive means \\\"and\\\").\",\"Key ideas\":\"\\n1. The 3 Satisfiability Problem (3SAT) is an and of a bunch of 3 ors. \\n2. Any conjunction like (a or b) can be replaced with (a or z) and (b or bar(z)), and produce the same Sat problem. \\n3. This is called the conjunctive normal form (conjunctive means \\\"and\\\"). \\n4. This reduction process is used to convert a \\\"or\\\" of two variables, and \\\"b\\\" is an or of 2 variables, to an and of two sets of 3 variables.\",\"Abstraction groups\":{\"-1\":[\"3SAT\",\"Or\",\"And\",\"Reduction\",\"Variable\",\"Conjunctive\",\"Normal Form\"],\"0\":[\"3SAT\"],\"1\":[\"Logic\",\"Problem Solving\"],\"2\":[\"Mathematics\",\"Computation\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"267\":{\"Question\":\"Learning with errors cryptosystem: what is the basic setup? \",\"Answer\":\"Have a vector of n objects over a ring mod q, and then do some manipulation on it and add some errors, then person with private key can subtract off something with similar errors, but someone without private key would just see some effectively random object mod q. \",\"Key ideas\":\"1. A Learning with Errors (LWE) cryptosystem is a type of encryption system. \\n2. It involves a vector of n objects over a ring mod q. \\n3. The vector is manipulated and errors are added. \\n4. The person with the private key can subtract off something with similar errors. \\n5. Someone without the private key would just see some effectively random object mod q.\",\"Abstraction groups\":{\"-1\":[\"Lwe\",\"Vector\",\"Ring\",\"Mod\",\"Manipulation\",\"Error\",\"Private Key\",\"Random Object\"],\"0\":[\"LWE\"],\"1\":[\"Cryptosystem\"],\"2\":[\"Encryption\",\"Security\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"268\":{\"Question\":\"Why have many resources that were predicted to run out in the last 50 years not run out? What is the general bias or reasoning mistake that people make when thinking about this problem?\",\"Answer\":\"People need to satisfy goals, not use specific things, though it is often assumed that they need a specific resource. \\nPeople switch between using different methods to satisfy the same goals (like we have switched from whale oil to gasoline and to natural gas). They switch before the thing actually runs out. This is why things don't run out. \",\"Key ideas\":\"1. People need to satisfy goals, not use specific things, though it is often assumed that they need a specific resource. \\n2. People switch between using different methods to satisfy the same goals (like we have switched from whale oil to gasoline and to natural gas). \\n3. People switch before the thing actually runs out. \\n4. This is why things don't run out.\",\"Abstraction groups\":{\"-1\":[\"Resource\",\"Last 50 Year\",\"Goal\",\"Specific Thing\",\"Bias\",\"Reasoning Mistake\",\"Whale Oil\",\"Gasoline\",\"Natural Gas\"],\"0\":[\"Resource\"],\"1\":[\"Running Out\",\"Bias\"],\"2\":[\"Prediction\",\"Reasoning\"],\"3\":[\"Goal\",\"Switching\"],\"4\":[\"Satisfaction\"]}},\"269\":{\"Question\":\"What has the hard coral cover of the northern great barrier reef done over time quantitatively? Give percentages and times. \",\"Answer\":\"25% historically since 1990\\nBleaching event in 2016 reduced it to 15%, but now it is back to 35% in 2022\\n25, 15, 35\",\"Key ideas\":\"1. The northern great barrier reef has a hard coral cover.\\n2. This hard coral cover has changed over time.\\n3. The change has been quantified in terms of percentages and time periods.\\n4. 25% of the hard coral cover was present historically since 1990.\\n5. A bleaching event in 2016 reduced the hard coral cover to 15%.\\n6. The hard coral cover is now back to 35% in 2022.\",\"Abstraction groups\":{\"-1\":[\"Coral\",\"Great Barrier Reef\",1990,2016,2022,\"Bleaching\",\"Percentage\"],\"0\":[\"Hard Coral Cover\"],\"1\":[\"Great Barrier Reef\",\"Quantitative Change\"],\"2\":[\"Coral\",\"Time Periods\"],\"3\":[\"Percentages\",\"Bleaching\"],\"4\":[\"Ecology\",\"Environment\"]}},\"270\":{\"Question\":\"What does the Australian marine organization suggest are key sources of vulnerability for the great barrier reef in the future? \",\"Answer\":\"Monoculture hard corals (the specific one that bounced back is relatively susceptible to wave damage and bleaching, apparently). \\nCyclones\\nHeat waves \\nCrown of thorns starfish (eats coral) \",\"Key ideas\":\"1. The Australian marine organization suggests that there are three key sources of vulnerability for the great barrier reef in the future: \\n    a. Monoculture hard corals (the specific one that bounced back is relatively susceptible to wave damage and bleaching, apparently). \\n    b. Cyclones\\n    c. Heat waves \\n    d. Crown of thorns starfish (eats coral) \\n2. Monoculture hard corals are susceptible to wave damage and bleaching. \\n3. Cyclones can be a source of vulnerability for the great barrier reef in the future. \\n4. Heat waves can be a source of vulnerability for the great barrier reef in the future. \\n5. Crown of thorns starfish (COTS) can be a source of vulnerability for the great barrier reef in the future, as they eat coral.\",\"Abstraction groups\":{\"-1\":[\"Monoculture\",\"Coral\",\"Wave\",\"Bleaching\",\"Cyclone\",\"Heat\",\"COT\"],\"0\":[\"Great Barrier Reef\"],\"1\":[\"Vulnerability\",\"Source\"],\"2\":[\"Future\",\"Marine Organization\"],\"3\":[\"Australian\",\"Environment\"],\"4\":[\"Ecology\"]}},\"271\":{\"Question\":\"What trends have occured over time with coral bleaching in the great barrier reef (GBR) in the past 30 years?\\nCompare the yearly derivative (looks bad) to the cumulative total (looks good).\",\"Answer\":\"The northern great barrier reef had significant events in 2016-2017, and last few years. More than 60% bleaching. But central and southern had less effects. \\nThe important thing is that in 2022, the hard coral cover has completely recovered in the northern reef.\",\"Key ideas\":\"1. Coral bleaching has occurred in the Great Barrier Reef (GBR) over the past 30 years. \\n2. The northern GBR has experienced more significant events in 2016-2017 and the last few years, with more than 60% bleaching. \\n3. The central and southern GBR have experienced less effects. \\n4. By 2022, the hard coral cover has completely recovered in the northern reef.\",\"Abstraction groups\":{\"-1\":[\"Coral Bleaching\",\"GBR\",\"2016-2017\",\"Last Few Year\",\"60% Bleaching\",\"Central & Southern GBR\",\"Hard Coral Cover\",\"Northern Reef\"],\"0\":[\"Coral Bleaching\"],\"1\":[\"Great Barrier Reef\",\"2016-2017\",\"Last Few Year\"],\"2\":[\"Marine Ecosystem\",\"Climate Change\"],\"3\":[\"Environmental Science\",\"Global Change\"],\"4\":[\"Science\",\"Knowledge\"]}},\"272\":{\"Question\":\"What is one significant cause of coral bleaching and a few significant consequences? \",\"Answer\":\"Elevated and sustained temperatures can cause it.  \\nEffects are: can cause some mortality but is more likely to produce sub-lethal effects like reduced growth, reproductive output and larval settlement. \",\"Key ideas\":\"\\n1. Coral bleaching is caused by elevated and sustained temperatures.\\n2. Coral bleaching can cause some mortality.\\n3. Coral bleaching can also produce sub-lethal effects such as reduced growth, reproductive output, and larval settlement.\",\"Abstraction groups\":{\"-1\":[\"Coral\",\"Bleaching\",\"Temperature\",\"Mortality\",\"Growth\",\"Reproductive\",\"Larval\",\"Settlement\"],\"0\":[\"Coral Bleaching\"],\"1\":[\"Temperature\",\"Mortality\",\"Growth\",\"Reproductive\",\"Larval\",\"Settlement\"],\"2\":[\"Cause\",\"Effect\"],\"3\":[\"Environmental Change\"],\"4\":[\"Ecology\"]}},\"273\":{\"Question\":\"What does \\u2018percent hard coral cover\\u2019 mean in the context of evaluating the health of the great barrier reef? \",\"Answer\":\"This measure describes the proportion of the seafloor that is covered in live hard coral.\\nIt is measured by manta towing. \\nHard coral cover greater than 50% is rare, because other things live there too.\",\"Key ideas\":\"1. Percent hard coral cover is a measure of the proportion of the seafloor that is covered in live hard coral. \\n2. This measure is used to evaluate the health of the great barrier reef. \\n3. The measure is taken by manta towing. \\n4. Hard coral cover greater than 50% is rare, because other things live there too.\",\"Abstraction groups\":{\"-1\":[\"Percent\",\"Hard Coral\",\"Cover\",\"Seafloor\",\"Health\",\"Great Barrier Reef\",\"Manta Towing\",\"50%\"],\"0\":[\"Hard Coral Cover\"],\"1\":[\"Measurement\",\"Health\",\"Great Barrier Reef\"],\"2\":[\"Evaluation\",\"Proportion\",\"Seafloor\"],\"3\":[\"Quantification\",\"Living Thing\",\"Marine Environment\"],\"4\":[\"Science\",\"Nature\",\"Environment\"]}},\"274\":{\"Question\":\"What are two general reasons why economic growth and ecological protection are compatible? \",\"Answer\":\"Development brings resources: The Kuznets curve of development and economic impact implies improvement over time (this curve states that as populations acquire more wealth, they have more willingness to care about the environment, and more ability to do something about it)\\nNatural trends in efficiency: such as densification of cities and people, dematerialization of objects and resource use, and decarbonization of energy sources as we simply seek to become more resource efficient. \",\"Key ideas\":\"\\n1. Kuznets curve of development and economic impact: This curve states that as populations acquire more wealth, they have more willingness to care about the environment, and more ability to do something about it.\\n2. Densification of cities and people: This is the process of making cities and people more compact and efficient.\\n3. Dematerialization of objects and resource use: This is the process of reducing the amount of materials used to produce a given product or service.\\n4. Decarbonization of energy sources: This is the process of reducing the amount of carbon dioxide emitted from energy sources.\",\"Abstraction groups\":{\"-1\":[\"Economic Growth\",\"Ecological Protection\",\"Development\",\"Kuznets Curve\",\"Wealth\",\"Efficiency\",\"Densification\",\"Dematerialization\",\"Decarbonization\"],\"0\":[\"Economic Growth\",\"Ecological Protection\"],\"1\":[\"Compatibility\"],\"2\":[\"Economics\",\"Ecology\"],\"3\":[\"Social Sciences\",\"Natural Sciences\"],\"4\":[\"Knowledge\"]}},\"275\":{\"Question\":\"What is the theory of why the history of world Gross Domestic Product (GDP) growth is super-exponential? Why is this important? \",\"Answer\":\"More resources means faster technology growth which means faster resource production. It's not just that more resources allow faster resource production (which would be exponential). The growth rate at fixed resources is also increasing because of technology, and that's super-exponential. \",\"Key ideas\":\"1. Gross Domestic Product (GDP) is a measure of the total value of goods and services produced in a country over a period of time. \\n2. The history of world GDP growth is super-exponential. \\n3. More resources allow faster resource production, which would be exponential. \\n4. Technology growth also increases the growth rate at fixed resources, which is super-exponential. \\n5. This is important because it means that more resources can lead to faster technology growth, which in turn leads to faster resource production.\",\"Abstraction groups\":{\"-1\":[\"GDP\",\"Growth\",\"Resource\",\"Technology\",\"Production\"],\"0\":[\"World Gdp Growth\"],\"1\":[\"Super-Exponential Growth\",\"Technology Growth\"],\"2\":[\"Resource\",\"Production\"],\"3\":[\"Economics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"276\":{\"Question\":\"What are goals that are likely to be shared by any generated artificial general intelligence, as a corrolary or side effect of trying to achieve some other goal (convergent goals) \",\"Answer\":\"1.a Acquire more cognition power \\n1.b Acquire more resources like energy \\n1.c Technology development to have control \\n2.a Goal-content integrity (seeking to prevent alteration or meddling with its internal goals, and changing its mind)\\n2.b Self preservation\",\"Key ideas\":\"\\n1. Artificial general intelligence (AGI) has goals that are likely to be shared by any generated AGI, as a corrolary or side effect of trying to achieve some other goal (convergent goals).\\n2. These goals include: \\n    a. Acquiring more cognition power \\n    b. Acquiring more resources like energy \\n    c. Technology development to have control \\n3. Other goals include: \\n    a. Goal-content integrity (seeking to prevent alteration or meddling with its internal goals, and changing its mind)\\n    b. Self preservation\",\"Abstraction groups\":{\"-1\":[\"AGI\",\"Convergent Goal\",\"Cognition\",\"Resource\",\"Technology\",\"Goal-Content Integrity\",\"Self Preservation\"],\"0\":[\"AGI\"],\"1\":[\"Convergent Goal\"],\"2\":[\"Cognition\",\"Resource\",\"Technology\"],\"3\":[\"Goal-Content Integrity\",\"Self Preservation\"],\"4\":[\"Artificial Intelligence\"]}},\"277\":{\"Question\":\"What are paths to artificial general intelligence suggested by Nick Bostrom in Superintelligence? \",\"Answer\":\"Algorithmic\\/evolutionary: This is the usual notion of a path to AGI through building computer programs from scratch. We can estimate the historical evolution rate to estimate what the required complexity is, and how close we are. However, this is hard to estimate. This method may sneak up on us\\nWhole brain emulation: this seems feasible long term, but there are lots of technical hurdles, so it is unlikely to sneak up on us as a workable method.\\nEnhancement: Brain machine interfaces (BMIs), or genetic selection of humans.\\nCan think of the first two as: building a modern airplane (1) or trying to reproduce how biological birds fly (2). Then (3) is more weird, like building wings onto humans, or selecting humans who are the lightest and have the biggest arms.\",\"Key ideas\":\"1. Artificial general intelligence (AGI) is a concept proposed by Nick Bostrom in his book Superintelligence.\\n2. There are three suggested paths to AGI: algorithmic\\/evolutionary, whole brain emulation, and enhancement.\\n3. Algorithmic\\/evolutionary is the usual notion of a path to AGI through building computer programs from scratch.\\n4. We can estimate the historical evolution rate to estimate what the required complexity is, and how close we are to achieving AGI.\\n5. Whole brain emulation is a feasible long-term path to AGI, but there are lots of technical hurdles.\\n6. Enhancement is a path to AGI involving brain machine interfaces (BMIs) or genetic selection of humans.\\n7. Algorithmic\\/evolutionary can be thought of as building a modern airplane, while whole brain emulation is like trying to reproduce how biological birds fly.\\n8. Enhancement is more weird, like building wings onto humans, or selecting humans who are the lightest and have the biggest arms.\",\"Abstraction groups\":{\"-1\":[\"AGI\",\"Algorithmic\\/Evolutionary\",\"Whole Brain Emulation\",\"Enhancement\",\"BMI\",\"Genetic Selection\",\"Modern Airplane\",\"Biological Bird\",\"Wing\",\"Human\"],\"0\":[\"AGI\"],\"1\":[\"Artificial Intelligence\",\"Paths To AGI\"],\"2\":[\"Computer Science\",\"Robotics\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"278\":{\"Question\":\"What are some malignant failure mechanisms of artificial general intelligence? \",\"Answer\":\"Perverse instantiation of goals, such as maximizing paperclip production\\nInfrastructure grab or infrastructure profusion (gathering resources to make achieving some goal easier)\\nMind crime (simulating tortured minds, ie black mirror) \",\"Key ideas\":\"\\n1. Artificial general intelligence (AGI) can have malignant failure mechanisms. \\n2. Perverse instantiation of goals is one such malignant failure mechanism, which involves the AGI pursuing a goal that is not in line with the original intent of the programmer. \\n3. Infrastructure grab or infrastructure profusion is another malignant failure mechanism, which involves the AGI gathering resources to make achieving some goal easier. \\n4. Mind crime is a third malignant failure mechanism, which involves the AGI simulating tortured minds, as seen in the TV show Black Mirror.\",\"Abstraction groups\":{\"-1\":[\"AGI\",\"Goal\",\"Resource\",\"Mind Crime\"],\"0\":[\"Malignant Failure Mechanism\"],\"1\":[\"Artificial General Intelligence\"],\"2\":[\"Artificial Intelligence\",\"Robotics\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"279\":{\"Question\":\"How long was alpha go zero trained? How many games must have been run in parallel to do this? \",\"Answer\":\"3 days, 5 million games\\nGame speed: 0.4s per move, 200 moves per game, or around 1000 games per day. \\nSo this implies around 1500 games being played in parallel somehow.\",\"Key ideas\":\"1. Alpha Go Zero was trained for 3 days. \\n2. 5 million games were run in parallel to do this. \\n3. Each move took 0.4 seconds. \\n4. There were 200 moves per game. \\n5. This implies around 1000 games per day. \\n6. This implies around 1500 games being played in parallel somehow.\",\"Abstraction groups\":{\"-1\":[\"Alpha Go Zero\",\"Game\",\"Move\",\"Time\",\"Parallel\"],\"0\":[\"Alpha Go Zero\"],\"1\":[\"Training\",\"Game\"],\"2\":[\"Artificial Intelligence\",\"Computer Science\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"280\":{\"Question\":\"How does the prediction accuracy for human expert moves of the policy network of the alpha go zero (AGZ) algorithm differ from the prediction of networks trained on human expert play, with the same resources? Why is this interesting? \",\"Answer\":\"AGZ has worse prediction accuracy on the human play than the supervised learning. But it wins more often. This suggest it's learning strategies that aren't contained in the human data. \",\"Key ideas\":\"1. Alpha Go Zero (AGZ) algorithm \\n2. Prediction accuracy of AGZ on human expert moves \\n3. Prediction accuracy of networks trained on human expert play \\n4. Resources used for both AGZ and networks trained on human expert play \\n5. AGZ has worse prediction accuracy on the human play than the supervised learning \\n6. AGZ wins more often than the supervised learning \\n7. AGZ is learning strategies that aren't contained in the human data\",\"Abstraction groups\":{\"-1\":[\"AGZ\",\"Prediction\",\"Accuracy\",\"Human\",\"Expert\",\"Move\",\"Network\",\"Trained\",\"Resource\",\"Supervised\",\"Learning\",\"Strategy\"],\"0\":[\"AGZ\"],\"1\":[\"Prediction\",\"Accuracy\",\"Human\",\"Expert\",\"Move\",\"Network\",\"Trained\",\"Resource\",\"Supervised\",\"Learning\",\"Strategy\"],\"2\":[\"Algorithm\",\"Data\",\"Winning\"],\"3\":[\"AI\",\"Analysis\"],\"4\":[\"Technology\"]}},\"281\":{\"Question\":\"What are two mindsets or approaches to use when designing a model to fit some observed data in an experiment?\\nHow can these be characterized in analogy to experimental errors in statistics? \",\"Answer\":\"Design model from first principles, and hope it matches. If so, because you didn't fine tune it, the probability of getting a false positive for the question \\\"is this the right description?\\\" (accidentally getting the right parameters) is very low. You minimized the chance of false positives. \\nDesign a model that best fits the data, and then reverse engineer its behavior from there. This is minimizing false negatives for the question \\\"is this describable at all?\\\". You ensure the data is describable. \",\"Key ideas\":\"1. There are two approaches to designing a model to fit observed data in an experiment: \\n    a. Design model from first principles, and hope it matches. This minimizes the chance of false positives. \\n    b. Design a model that best fits the data, and then reverse engineer its behavior from there. This minimizes false negatives. \\n2. False positives and false negatives are analogous to experimental errors in statistics. \\n3. False positives refer to the probability of getting a false positive for the question \\\"is this the right description?\\\"\\n4. False negatives refer to the probability of getting a false negative for the question \\\"is this describable at all?\\\"\",\"Abstraction groups\":{\"-1\":[\"Mindset\",\"Design\",\"Model\",\"Data\",\"Experiment\",\"Principle\",\"Parameter\",\"False Positive\",\"False Negative\",\"Statistic\"],\"0\":[\"Model Design\"],\"1\":[\"Mindset\",\"Design\",\"Model\",\"Data\",\"Experiment\"],\"2\":[\"Principle\",\"Parameter\",\"False Positive\",\"False Negative\",\"Statistic\"],\"3\":[\"Error Analysis\"],\"4\":[\"Scientific Method\"]}},\"282\":{\"Question\":\"What is syntactic illusion, what are two examples, and why is it interesting in a machine learning context? \",\"Answer\":\"Syntactic illusion is when humans make a mistake and hear something that wasn't in the text.\\nTwo examples are:\\nA sentence about \\\"how many animals did Moses take on the ark\\\", which makes no sense because Moses didn't do that\\nA sentence with a negative in the middle in a secluded clause, that is gramatically incorrect because it needs a negative in the main part of the sentence, but the brain hears the negative gets \\\"heard\\\" outside the secluded clause. \\nSyntactic illusion seems to happen in both language models, and in humans (can tell based on looking at attention mechanism). This is interesting because it may say something about the structure of language and thought, rather than something specific about human deficiencies \",\"Key ideas\":\"1. Syntactic illusion is when humans make a mistake and hear something that wasn't in the text. \\n2. Two examples of syntactic illusion are: \\n    a. A sentence about \\\"how many animals did Moses take on the ark\\\", which makes no sense because Moses didn't do that\\n    b. A sentence with a negative in the middle in a secluded clause, that is gramatically incorrect because it needs a negative in the main part of the sentence, but the brain hears the negative gets \\\"heard\\\" outside the secluded clause. \\n3. Syntactic illusion seems to happen in both language models, and in humans (can tell based on looking at attention mechanism). \\n4. This is interesting because it may say something about the structure of language and thought, rather than something specific about human deficiencies.\",\"Abstraction groups\":{\"-1\":[\"Syntactic Illusion\",\"Moses\",\"Ark\",\"Negative\",\"Secluded Clause\",\"Language Model\",\"Attention Mechanism\",\"Structure\",\"Thought\",\"Human Deficiency\"],\"0\":[\"Syntactic Illusion\"],\"1\":[\"Language\",\"Mistake\",\"Attention\"],\"2\":[\"Human Behavior\",\"Machine Learning\"],\"3\":[\"Cognitive Science\",\"Artificial Intelligence\"],\"4\":[\"Science\"]}},\"283\":{\"Question\":\"In summary the main experimental steps of the T-cell challenge design are: \",\"Answer\":\"Introduce gene knockouts to ~70 genes in mice T cells.\\nTake mice with Melanoma, and introduce these altered T-cells for a while\\nDo single cell RNA sequencing for transcription amount \\nProcess and cluster gene expression profiles on the single cell level.\",\"Key ideas\":\"1. T-cell challenge design is an experimental method used to study gene expression in mice T cells.\\n2. Gene knockouts are introduced to ~70 genes in mice T cells.\\n3. Mice with Melanoma are used and the altered T-cells are introduced for a while.\\n4. Single cell RNA sequencing is used to measure transcription amount.\\n5. Gene expression profiles are processed and clustered on the single cell level.\",\"Abstraction groups\":{\"-1\":[\"Gene Knockout\",\"Mouse\",\"Melanoma\",\"T-Cell\",\"RNA Sequencing\",\"Transcription\",\"Clustering\"],\"0\":[\"T-Cell Challenge Design\"],\"1\":[\"Gene Expression\",\"Mouse\",\"Melanoma\"],\"2\":[\"Experimental Method\",\"Genetic Manipulation\"],\"3\":[\"Biological Research\",\"Scientific Inquiry\"],\"4\":[\"Knowledge Acquisition\"]}},\"284\":{\"Question\":\"What are the steps for gathering clusters of gene expression programs in the T-Cell population, after gathering and sequencing single cells? \",\"Answer\":\"Remove tails of the distribution of total gene expression (correspond to damaged cells, and multiple cells)\\nNormalize total expression\\nFocus on highly variable genes and gene groups using prinicple component analysis (PCA)\\nDo graph based clustering. \",\"Key ideas\":\"1. Remove tails of the distribution of total gene expression (correspond to damaged cells, and multiple cells)\\n2. Normalize total expression\\n3. Focus on highly variable genes and gene groups using prinicple component analysis (PCA)\\n4. Do graph based clustering\",\"Abstraction groups\":{\"-1\":[\"Gene Expression\",\"T-Cell\",\"Sequencing\",\"Distribution\",\"Normalize\",\"PCA\",\"Clustering\"],\"0\":[\"Gene Expression\"],\"1\":[\"T-Cell\",\"Sequencing\",\"Distribution\"],\"2\":[\"Normalize\",\"PCA\",\"Clustering\"],\"3\":[\"Data Analysis\",\"Cell Biology\"],\"4\":[\"Science\"]}},\"285\":{\"Question\":\"How do they perform gene knockout in the cancer T-Cell challenge at the Broad institute? \",\"Answer\":\"Crispr is used to target and cause a double stranded break in the midst of a gene in the genome. Then they rely on mutations during the repair process to knock out that gene. They are not using Crispr for insertion. \",\"Key ideas\":\"1. The Broad Institute is conducting a cancer T-Cell challenge. \\n2. Gene knockout is a technique used to disable a gene. \\n3. Crispr is a tool used to target and cause a double stranded break in the midst of a gene in the genome. \\n4. Mutations during the repair process are used to knock out the gene. \\n5. Crispr is not being used for insertion.\",\"Abstraction groups\":{\"-1\":[\"Broad Institute\",\"Cancer T-Cell\",\"Gene Knockout\",\"Crispr\",\"Double Stranded Break\",\"Genome\",\"Mutation\",\"Repair Process\",\"Insertion\"],\"0\":[\"Gene Knockout\"],\"1\":[\"Cancer T-Cell\",\"Crispr\"],\"2\":[\"Broad Institute\",\"Double Stranded Break\",\"Genome\",\"Mutation\",\"Repair Process\"],\"3\":[\"Biotechnology\",\"Genetic\"],\"4\":[\"Science\"]}},\"286\":{\"Question\":\"What is the test animal for measuring T-Cell response to Tumors for the cancer T-cell challenge? \",\"Answer\":\"Mice with Melanoma \",\"Key ideas\":\"\\n1. T-Cell: A type of white blood cell that plays a major role in the body's immune system.\\n2. Tumors: Abnormal growths of cells that can form in any part of the body.\\n3. Cancer T-cell Challenge: A research project that uses T-cells to identify and target cancer cells.\\n4. Test Animal: An animal used to test the effectiveness of a particular treatment or procedure.\\n5. Mice with Melanoma: A type of mouse that has been genetically modified to develop melanoma, a type of skin cancer.\",\"Abstraction groups\":{\"-1\":[\"T-Cell\",\"Tumor\",\"Cancer\",\"Test\",\"Mouse\",\"Melanoma\"],\"0\":[\"T-Cell Response\"],\"1\":[\"Cancer T-cell Challenge\",\"Test Animal\"],\"2\":[\"T-Cell\",\"Tumor\"],\"3\":[\"Cancer\",\"Animal Testing\"],\"4\":[\"Biology\",\"Medicine\"]}},\"287\":{\"Question\":\"What is one potentially surprising thing about the statistics of gene expression in measured gene expression maps?  What are two reasons it occurs? \",\"Answer\":\"There are a lot of 0s in the gene expression map. Of something like 20,000 genes, only a small subset are expressed in any active cell, and cause it to have that character\\nIn addition, there are technical problems that lead to mis-detection, and that leads to more 0s than expected on gene expression.\",\"Key ideas\":\"1. Gene expression maps measure the expression of genes in active cells.\\n2. Of the 20,000 genes, only a small subset are expressed in any active cell.\\n3. This leads to a lot of 0s in the gene expression map.\\n4. Technical problems can lead to mis-detection, which can also lead to more 0s than expected on gene expression.\",\"Abstraction groups\":{\"-1\":[\"Gene Expression\",\"0\",\"20,000 Gene\",\"Small Subset\",\"Technical Problem\",\"Mis-Detection\"],\"0\":[\"Gene Expression\"],\"1\":[\"Statistics\",\"Maps\"],\"2\":[\"Measurement\",\"Expression\"],\"3\":[\"Gene\",\"Cell\"],\"4\":[\"Biology\"]}},\"288\":{\"Question\":\"How does single cell RNA sequencing work? \",\"Answer\":\"Water droplet are tagged with DNA barcodes. \\nYou inject single cells into droplets and let them acquire the barcodes somehow\\nThen you separate cells and do sequencing \",\"Key ideas\":\"1. Single cell RNA sequencing is a method of sequencing RNA from individual cells. \\n2. Water droplets are tagged with DNA barcodes. \\n3. Single cells are injected into droplets and acquire the barcodes. \\n4. The cells are then separated and sequencing is done.\",\"Abstraction groups\":{\"-1\":[\"RNA\",\"Sequencing\",\"Cell\",\"Water\",\"DNA\",\"Barcode\",\"Droplet\",\"Injection\"],\"0\":[\"Single Cell RNA Sequencing\"],\"1\":[\"Molecular Biology\",\"Genetics\"],\"2\":[\"Biochemistry\",\"Cell Biology\"],\"3\":[\"Biology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"289\":{\"Question\":\"What are the major milestones in animal evolutionary development over the past billion years? \",\"Answer\":\"950 million years ago - common ancestor\\n650 m years ago - Hox genes\\n600 m years ago - Chordates\\n539 m years ago - Cambrian explosion\",\"Key ideas\":\"1. Animals have been evolving for over a billion years.\\n2. 950 million years ago, a common ancestor of all animals existed.\\n3. 650 million years ago, Hox genes appeared.\\n4. 600 million years ago, chordates appeared.\\n5. 539 million years ago, the Cambrian explosion occurred.\",\"Abstraction groups\":{\"-1\":[\"Animal\",\"Ancestor\",\"Hox\",\"Chordate\",\"Cambrian\",\"Explosion\"],\"0\":[\"Animal Evolution\"],\"1\":[\"Milestone\",\"Billion Year\"],\"2\":[\"Development\",\"Ancestor\"],\"3\":[\"Evolution\",\"Gene\"],\"4\":[\"Animal\",\"Time\"]}},\"290\":{\"Question\":\"In what time period did most animals come into existence? When was this explosion? \",\"Answer\":\"Most animals came around the time of the Cambrian explosion 539 million years ago.\",\"Key ideas\":\"1. Animals exist. \\n2. The Cambrian explosion was a period of rapid evolution of animals 539 million years ago. \\n3. The Cambrian explosion is an event in Earth's history.\",\"Abstraction groups\":{\"-1\":[\"Animal\",\"Cambrian\",\"Explosion\",\"Time\",\"Year\"],\"0\":[\"Animal\"],\"1\":[\"Evolution\",\"Cambrian\"],\"2\":[\"History\",\"Time\"],\"3\":[\"Biology\",\"Earth\"],\"4\":[\"Science\"]}},\"291\":{\"Question\":\"What key development in animal genetics led to the ability to form many new kinds of multicellular life?\\nWhen did it happen? \",\"Answer\":\"Hox genes developed around 650 million years ago, leading to most land based life. They allow organization of the body structure and limbs, etc.\",\"Key ideas\":\"1. Hox genes are a key development in animal genetics. \\n2. Hox genes allow for the organization of body structure and limbs. \\n3. Hox genes developed around 650 million years ago. \\n4. Hox genes led to the ability to form many new kinds of multicellular life.\",\"Abstraction groups\":{\"-1\":[\"Animal Genetics\",\"Hox Gene\",\"Organization\",\"Body Structure\",\"Limb\",\"650 Million Years Ago\",\"Multicellular Life\"],\"0\":[\"Hox Gene\"],\"1\":[\"Animal Genetics\",\"Development\"],\"2\":[\"Genetics\",\"Evolution\"],\"3\":[\"Biology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"292\":{\"Question\":\"What are some kinds of conceptaul flashcard prompts suggested by Andy Matuschak \",\"Answer\":\"Here are 5 types, written with the example of describing \\\"chicken stock\\\" as the content of the flashcard.\\nAttributes and tendencies: What makes stock, stock? What\\u2019s always, sometimes, and never true of stock?\\nSimilarities and differences: Knowing what stock is requires knowing what relates and distinguishes it from other adjacent concepts.\\nParts and wholes: What are some examples of stocks? Are there important \\u201csub-concepts\\u201d of stocks? Is \\u201cstock\\u201d a part of some broader category? Visualize a Venn diagram, even if the edges are fuzzy.\\nCauses and effects: What does stock do? What causes it to do that? What doesn\\u2019t it do? When is it used?\\nSignificance and implications: Why does stock matter? What does it suggest? Make the concept personally meaningful.\\nMy additional interpretation:\\nVisualize a venn diagram, with sub parts, and with similar nearby ven diagrams side to side. Above that is the \\\"significance\\\", and in front of it along another dimension is the cause\\/effect axis.\",\"Key ideas\":\"1. Stock is a type of concept that has certain attributes and tendencies.\\n2. Knowing what stock is requires understanding what distinguishes it from other adjacent concepts.\\n3. Stock is composed of parts and wholes, and can be visualized in a Venn diagram.\\n4. Stock has certain causes and effects, and is used in certain situations.\\n5. Stock has significance and implications that can be made personally meaningful.\",\"Abstraction groups\":{\"-1\":[\"Stock\",\"Attribute\",\"Tendency\",\"Similarity\",\"Difference\",\"Part\",\"Whole\",\"Cause\",\"Effect\",\"Significance\",\"Implication\",\"Venn Diagram\"],\"0\":[\"Chicken Stock\"],\"1\":[\"Food\",\"Cooking\",\"Ingredient\"],\"2\":[\"Nutrition\",\"Preparation\"],\"3\":[\"Science\",\"Art\"],\"4\":[\"Knowledge\",\"Understanding\"]}},\"293\":{\"Question\":\"What are some broad types of flashcard prompts suggested by Andy Matuschak \",\"Answer\":\"Facts\\nProcedural (make you remember a procedure as steps)\\nSaliency hints (try to get you to recall something by not quite explicitly saying it)\\nConceptual questions\\nMy hint: think of these as similar to the types of lists\\/storage media in python. The facts are a set (unordered). The procedural is like a list. The saliency hints and conceptual questions are like dictionaries? (This last one is a bit loose, but saliency hints are like pointers.) \",\"Key ideas\":\"\\n1. Flashcards can be used to test a variety of topics.\\n2. Andy Matuschak suggests four broad types of flashcard prompts: facts, procedural, saliency hints, and conceptual questions.\\n3. Facts are unordered lists.\\n4. Procedural prompts are like lists.\\n5. Saliency hints are like pointers.\\n6. Conceptual questions are like dictionaries.\",\"Abstraction groups\":{\"-1\":[\"Flashcard\",\"Andy Matuschak\",\"Fact\",\"Procedural\",\"Saliency Hint\",\"Conceptual Question\",\"Python\",\"Set\",\"List\",\"Dictionary\",\"Pointer\"],\"0\":[\"Flashcard\"],\"1\":[\"Study Aid\",\"Testing\",\"Memory\"],\"2\":[\"Learning\",\"Education\",\"Knowledge\"],\"3\":[\"Cognition\",\"Thinking\",\"Reasoning\"],\"4\":[\"Human Behavior\",\"Psychology\",\"Philosophy\"]}},\"294\":{\"Question\":\"What experimental evidence is there to support the programmatic notion of complexity of general intelligence? \",\"Answer\":\"It was demonstrated that rewiring of neurons is possible to achieve visual sight after severing the optic nerve in ferrets. This suggests that the algorithm for learning and development is versatile to develop intelligent behavior. \",\"Key ideas\":\"\\n1. Complexity of general intelligence is a programmatic notion. \\n2. Rewiring of neurons is possible to achieve visual sight after severing the optic nerve in ferrets. \\n3. This suggests that the algorithm for learning and development is versatile to develop intelligent behavior.\",\"Abstraction groups\":{\"-1\":[\"Complexity\",\"General Intelligence\",\"Rewiring\",\"Neuron\",\"Optic Nerve\",\"Ferret\",\"Algorithm\",\"Learning\",\"Development\",\"Intelligent Behavior\"],\"0\":[\"General Intelligence\"],\"1\":[\"Complexity\",\"Algorithm\",\"Learning\"],\"2\":[\"Programmatic Notion\",\"Rewiring\",\"Development\"],\"3\":[\"Neuron\",\"Optic Nerve\",\"Ferret\"],\"4\":[\"Experimental Evidence\"]}},\"295\":{\"Question\":\"What is the evidence against the programmatic complexity notion of the difficulty of artificial general intelligence? \",\"Answer\":\"Physical specialization may be large: Many subsets of the brain seem specialized and developed and hardwired by evolution. It might be that much more is necessary for human behavior than the difference in genetics from humans to chimps (ie parts of chimps brains might be necessay). \\nSocietal encoding of structure (so we forgot to include this measure in the complexity): Many human societies have similar language and social structures, which suggest evolutionary optimization. \",\"Key ideas\":\"\\n1. Artificial general intelligence (AGI) is a notion of difficulty that is based on programmatic complexity. \\n2. Physical specialization may be a large factor in the difficulty of AGI: many subsets of the brain seem to be specialized and developed by evolution. \\n3. It may be that much more is necessary for human behavior than the difference in genetics from humans to chimps (ie parts of chimps brains might be necessary). \\n4. Societal encoding of structure may also be a factor in the difficulty of AGI: many human societies have similar language and social structures, which suggest evolutionary optimization.\",\"Abstraction groups\":{\"-1\":[\"AGI\",\"Genetic\",\"Chimp\",\"Societal\",\"Structure\",\"Evolutionary\"],\"0\":[\"Artificial General Intelligence\"],\"1\":[\"Programmatic Complexity\",\"Physical Specialization\",\"Societal Encoding\"],\"2\":[\"Difficulty\",\"Genetics\",\"Evolutionary Optimization\"],\"3\":[\"Brain\",\"Human Behavior\",\"Language\",\"Social Structures\"],\"4\":[\"Intelligence\"]}},\"296\":{\"Question\":\"What did the paper Camburu 2018 demonstrate about language models?\",\"Answer\":\"They demonstrate adding annotation of intents and agent information to training data for a transformer, and how this helps the model be able to explain itself.\\nSpecifically, the model learns to explain\\/annotate whether two statements are related by implication, or neutral, or contradictory, for example. \",\"Key ideas\":\"1. Language models can be improved by adding annotation of intents and agent information to training data. \\n2. The paper Camburu 2018 demonstrated this by using a transformer. \\n3. The model was able to explain itself by learning to explain\\/annotate whether two statements are related by implication, neutral, or contradictory.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Intent\",\"Agent\",\"Training Data\",\"Transformer\",\"Explanation\",\"Implication\",\"Neutral\",\"Contradictory\"],\"0\":[\"Language Model\"],\"1\":[\"Intent\",\"Agent\",\"Training Data\",\"Transformer\"],\"2\":[\"Explanation\",\"Implication\",\"Neutral\",\"Contradictory\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Technology\"]}},\"297\":{\"Question\":\"What are two possible reasons why language models sometimes fail to correctly predict text, probably through the mechanism of failing to encode the emotions and intent of the agent speaking? \",\"Answer\":\"The training data doesn't include sufficient annotation of the agent type. Solution: add explicit annotation in training data about who an agent is\\nLimitations of the context window being too short \",\"Key ideas\":\"1. Language models can sometimes fail to correctly predict text. \\n2. This failure is likely due to the language model not encoding the emotions and intent of the agent speaking. \\n3. One possible reason for this failure is that the training data doesn't include sufficient annotation of the agent type. \\n4. A solution to this problem is to add explicit annotation in the training data about who an agent is. \\n5. Another possible reason for this failure is that the context window being too short.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Prediction\",\"Emotion\",\"Intent\",\"Agent\",\"Training Data\",\"Annotation\",\"Context Window\"],\"0\":[\"Language Model\"],\"1\":[\"Prediction\",\"Emotion\",\"Intent\"],\"2\":[\"Agent\",\"Training Data\",\"Annotation\"],\"3\":[\"Context Window\"],\"4\":[\"Failure\"]}},\"298\":{\"Question\":\"What are two examples of metascience entrepeneurs that already exist? \",\"Answer\":\"The creator of Arxiv, who was a physicist and then transitioned full time to creating arxiv. \\nThe creators of focused research organizations (FROs). \",\"Key ideas\":\"\\n1. Metascience is a field of study that focuses on the scientific process itself. \\n2. Metascience entrepreneurs are individuals who use their knowledge of the scientific process to create new products and services. \\n3. Arxiv is an online repository of scientific papers and other documents. \\n4. The creator of Arxiv was a physicist who transitioned to creating Arxiv full time. \\n5. Focused Research Organizations (FROs) are organizations that specialize in a particular area of research. \\n6. FROs are an example of metascience entrepreneurs.\",\"Abstraction groups\":{\"-1\":[\"Metascience\",\"Entrepreneur\",\"Arxiv\",\"Physicist\",\"FRO\"],\"0\":[\"Metascience Entrepreneur\"],\"1\":[\"Example\"],\"2\":[\"Existing Entity\",\"Business\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Innovation\"]}},\"299\":{\"Question\":\"In the Alphacode program, how was a diversity of problem solutions generated from the initial problem statement? (given that the architecture is just a transformer) \",\"Answer\":\"1. Use higher temperature on transformer output\\n2. Condition on random metadata as a header (like statements of the problem difficulty, and problem tags, like solution methods that might be used)\",\"Key ideas\":\"1. Alphacode program\\n2. Architecture of the Alphacode program is a transformer\\n3. Generating a diversity of problem solutions from the initial problem statement\\n4. Increasing the temperature on the transformer output\\n5. Conditioning on random metadata as a header\\n6. Metadata header includes statements of problem difficulty and problem tags\\n7. Solution methods that might be used\",\"Abstraction groups\":{\"-1\":[\"Alphacode\",\"Transformer\",\"Temperature\",\"Metadata\",\"Header\",\"Difficulty\",\"Tag\",\"Method\"],\"0\":[\"Alphacode\"],\"1\":[\"Problem Solution\",\"Transformer\",\"Temperature\",\"Metadata\",\"Header\",\"Difficulty\",\"Tag\",\"Method\"],\"2\":[\"Generation\",\"Architecture\",\"Conditioning\"],\"3\":[\"Programming\",\"Problem Statement\"],\"4\":[\"Computing\"]}},\"300\":{\"Question\":\"For cancer immunotherapy with checkpoint blockade, what has the average life expectancy of late stage melanoma gone from, between around 2005 and now?\",\"Answer\":\"It was around a few months then, to now around 6 years on average.\",\"Key ideas\":\"1. Cancer immunotherapy: a type of treatment that uses the body's own immune system to fight cancer.\\n2. Checkpoint blockade: a type of cancer immunotherapy that works by blocking certain proteins that cancer cells use to evade the immune system.\\n3. Late stage melanoma: a type of skin cancer that has spread to other parts of the body.\\n4. Average life expectancy: the average amount of time a person is expected to live.\\n5. Around 2005: the approximate time period when the average life expectancy of late stage melanoma was a few months.\\n6. Now: the approximate time period when the average life expectancy of late stage melanoma is around 6 years.\",\"Abstraction groups\":{\"-1\":[\"Cancer\",\"Immunotherapy\",\"Checkpoint\",\"Melanoma\",\"Life Expectancy\",\"2005\",\"Now\"],\"0\":[\"Cancer Immunotherapy\"],\"1\":[\"Checkpoint Blockade\",\"Late Stage Melanoma\"],\"2\":[\"Life Expectancy\",\"2005\",\"Now\"],\"3\":[\"Treatment\",\"Skin Cancer\"],\"4\":[\"Health\",\"Time\"]}},\"301\":{\"Question\":\"For cancer immunotherapy with checkpoint blockade, what is the fraction of patients in early ipilimumab trials that responded? \",\"Answer\":\"around 20%\",\"Key ideas\":\"\\n1. Cancer immunotherapy: a type of treatment that uses the body's own immune system to fight cancer.\\n2. Checkpoint blockade: a type of cancer immunotherapy that works by blocking certain proteins that cancer cells use to evade the immune system.\\n3. Ipilimumab: a type of checkpoint blockade therapy that was used in early trials.\\n4. Response rate: the fraction of patients in a trial that respond to a given treatment.\\n5. Around 20%: the response rate of patients in early ipilimumab trials.\",\"Abstraction groups\":{\"-1\":[\"Cancer\",\"Immunotherapy\",\"Checkpoint\",\"Blockade\",\"Ipilimumab\",\"Trial\",\"Response\",\"Rate\",\"20%\"],\"0\":[\"Cancer Immunotherapy\"],\"1\":[\"Checkpoint Blockade\",\"Ipilimumab\"],\"2\":[\"Cancer Treatment\",\"Clinical Trials\"],\"3\":[\"Medicine\",\"Science\"],\"4\":[\"Knowledge\"]}},\"302\":{\"Question\":\"What was the incentive for the first company to offer credit card rewards? \",\"Answer\":\"They can capture more customers by offering some incentive, then make more profits, then return that to the customers. \\nIt doesn't have to be net zero in that case \",\"Key ideas\":\"1. Companies offer credit card rewards as an incentive to capture more customers. \\n2. This incentive can be used to make more profits. \\n3. These profits can then be returned to customers in the form of rewards. \\n4. The incentive does not have to be net zero.\",\"Abstraction groups\":{\"-1\":[\"Credit Card\",\"Reward\",\"Incentive\",\"Customer\",\"Profit\",\"Return\"],\"0\":[\"Credit Card Reward\"],\"1\":[\"Incentive\",\"Profit\",\"Customer\"],\"2\":[\"Business\",\"Economics\"],\"3\":[\"Commerce\",\"Finance\"],\"4\":[\"Economics\"]}},\"303\":{\"Question\":\"How are credit card rewards an example of a zero sum game? \",\"Answer\":\"If every company offers them, then the entire market doesn't change, the net profits are the same, and the money given out in the rewards must come at the expense of customers who are not gathering rewareds \",\"Key ideas\":\"1. Credit card rewards are an example of a zero sum game. \\n2. A zero sum game is a situation in which the total gains of all participants is equal to the total losses of all participants. \\n3. If every company offers credit card rewards, then the entire market does not change. \\n4. The net profits of the companies remain the same. \\n5. The money given out in the rewards must come at the expense of customers who are not gathering rewards.\",\"Abstraction groups\":{\"-1\":[\"Credit Card\",\"Reward\",\"Zero Sum Game\",\"Market\",\"Profit\",\"Reward\",\"Customer\"],\"0\":[\"Credit Card Reward\"],\"1\":[\"Zero Sum Game\"],\"2\":[\"Economy\",\"Financial Transaction\"],\"3\":[\"Business\",\"Money\"],\"4\":[\"Exchange\",\"Resource\"]}},\"304\":{\"Question\":\"What are the two things that differentiate powder based 3D printing from extrusion?\",\"Answer\":\"Gradients of materials is more easy.\\nCan build more complicated physical structures. \",\"Key ideas\":\"1. 3D printing is a type of manufacturing process.\\n2. Powder based 3D printing is a type of 3D printing.\\n3. Extrusion is another type of 3D printing.\\n4. The two things that differentiate powder based 3D printing from extrusion are:\\n    a. Gradients of materials is more easy.\\n    b. Can build more complicated physical structures.\",\"Abstraction groups\":{\"-1\":[\"3D Printing\",\"Powder\",\"Extrusion\",\"Gradient\",\"Structure\"],\"0\":[\"3D Printing\"],\"1\":[\"Manufacturing Process\"],\"2\":[\"Technology\",\"Engineering\"],\"3\":[\"Science\",\"Mathematics\"],\"4\":[\"Knowledge\"]}},\"305\":{\"Question\":\"What are the 4 types of T cell states relevant to cancer immunotherapy?\",\"Answer\":\"Progenitor\\nEffector (killing)\\nDividing\\nExhausted (this is the bad one, that we want to minimize). \",\"Key ideas\":\"1. T cell states are relevant to cancer immunotherapy. \\n2. There are 4 types of T cell states: \\n    a. Progenitor \\n    b. Effector (killing) \\n    c. Dividing \\n    d. Exhausted (this is the bad one, that we want to minimize).\",\"Abstraction groups\":{\"-1\":[\"T Cell\",\"State\",\"Cancer\",\"Immunotherapy\",\"Progenitor\",\"Effector\",\"Killing\",\"Dividing\",\"Exhausted\"],\"0\":[\"T Cell State\"],\"1\":[\"Cancer Immunotherapy\"],\"2\":[\"Immunology\",\"Oncology\"],\"3\":[\"Biology\",\"Medicine\"],\"4\":[\"Science\"]}},\"306\":{\"Question\":\"What are the two major categories of cancer immunotherapy currently? \",\"Answer\":\"Car T cell therapy\\nCheckpoint therapies (that re-establish the ability of the immune system to recognize certain tumor atoms) \",\"Key ideas\":\"1. Cancer immunotherapy is a type of treatment that uses the body's own immune system to fight cancer. \\n2. There are two major categories of cancer immunotherapy currently: \\n    a. Car T cell therapy \\n    b. Checkpoint therapies \\n3. Car T cell therapy involves genetically engineering a patient's own T cells to recognize and attack cancer cells. \\n4. Checkpoint therapies re-establish the ability of the immune system to recognize certain tumor antigens.\",\"Abstraction groups\":{\"-1\":[\"Cancer\",\"Immunotherapy\",\"Car T\",\"Checkpoint\",\"T Cell\",\"Tumor\",\"Antigen\"],\"0\":[\"Cancer Immunotherapy\"],\"1\":[\"Treatment\",\"Immunology\"],\"2\":[\"Medicine\",\"Biology\"],\"3\":[\"Science\",\"Health\"],\"4\":[\"Knowledge\"]}},\"307\":{\"Question\":\"How has the average CD index changed over time? Why might this not matter? \",\"Answer\":\"It has gone down from around 0.25 to 0.5, down to 0.0. \\nThis might not matter because the absolute number of highly disruptive papers is still decently large (similar size).\",\"Key ideas\":\"\\n1. The CD index is a measure of the number of highly disruptive papers published in a given year. \\n2. The average CD index has gone down from around 0.25 to 0.5, down to 0.0. \\n3. The absolute number of highly disruptive papers is still decently large (similar size). \\n4. This might not matter because the absolute number of highly disruptive papers is still decently large (similar size).\",\"Abstraction groups\":{\"-1\":[\"CD Index\",\"Average\",\"Disruptive Paper\",\"Size\"],\"0\":[\"CD Index\"],\"1\":[\"Change\",\"Measurement\"],\"2\":[\"Time\",\"Statistics\"],\"3\":[\"Research\",\"Analysis\"],\"4\":[\"Science\",\"Knowledge\"]}},\"308\":{\"Question\":\"How has the fraction of highly disruptive papers (CD index above 0.75) in different fields changed over time?\",\"Answer\":\"It is now heavier on the technology and computer fields, and less heavy on the life, physical, and social sciences. \",\"Key ideas\":\"1. CD index: A measure of the disruptive potential of a paper, with a value of 0.75 or higher indicating a highly disruptive paper. \\n2. Change over time: The fraction of highly disruptive papers has changed over time. \\n3. Technology and computer fields: The fraction of highly disruptive papers is now heavier on the technology and computer fields. \\n4. Life, physical, and social sciences: The fraction of highly disruptive papers is now less heavy on the life, physical, and social sciences.\",\"Abstraction groups\":{\"-1\":[\"CD Index\",\"Time\",\"Technology\",\"Computer\",\"Life\",\"Physical\",\"Social\",\"Science\"],\"0\":[\"Disruptive Paper\"],\"1\":[\"CD Index\",\"Field\"],\"2\":[\"Change Over Time\",\"Technology\",\"Computer\",\"Life\",\"Physical\",\"Social\",\"Science\"],\"3\":[\"Fraction\",\"Highly Disruptive\"],\"4\":[\"Measurement\",\"Distribution\"]}},\"309\":{\"Question\":\"How has the fraction of highly disruptive papers (CD index above 0.75) changed since 1950?\",\"Answer\":\"It has gone way down. \\nHowever, the absolute number of highly disruptive papers has stayed similar \",\"Key ideas\":\"\\n1. CD index: A measure of the disruptive potential of a paper, with a value of 0.75 or higher indicating a highly disruptive paper. \\n2. 1950: The year in which the fraction of highly disruptive papers is being compared to. \\n3. Fraction of highly disruptive papers: The proportion of papers with a CD index of 0.75 or higher. \\n4. Has gone way down: The fraction of highly disruptive papers has decreased since 1950. \\n5. Absolute number of highly disruptive papers: The total number of papers with a CD index of 0.75 or higher, regardless of the fraction. \\n6. Has stayed similar: The absolute number of highly disruptive papers has not changed significantly since 1950.\",\"Abstraction groups\":{\"-1\":[\"CD Index\",1950,\"Fraction\",\"Disruptive\",\"Gone\",\"Absolute\",\"Number\",\"Stayed\"],\"0\":[\"CD Index\"],\"1\":[\"Highly Disruptive Paper\"],\"2\":[\"Fraction\",\"Number\"],\"3\":[\"Change\",\"1950\"],\"4\":[\"Comparison\"]}},\"310\":{\"Question\":\"What is the CD index for measuring disruptiveness vs consolidation in scientific papers (using citations)? \",\"Answer\":\"It is basically a ratio of how many future citing papers (future papers are those that cite the paper under question (called the focal paper), or cite papers that were cited by the papers under question) cite only focal, both focal and predecessor, or just predecessor papers. \\nDisruptive is only citing focal paper, not predecessors (+1)\\nConsolidating is both (-1)\\nIf they cite only previous papers, not focal, it is just not important (0) (however, this does drag the overall metric to 0 due to averaging, so that most papers are clustered at 0 unless they are super important)\\nThe final metric is average of all of these over citing papers.\",\"Key ideas\":\"1. The CD index is a ratio used to measure disruptiveness vs consolidation in scientific papers (using citations).\\n2. The CD index is calculated by looking at how many future citing papers cite only the focal paper, both the focal paper and predecessor papers, or just predecessor papers.\\n3. Citing only the focal paper is considered disruptive (+1).\\n4. Citing both the focal paper and predecessor papers is considered consolidating (-1).\\n5. Citing only predecessor papers is not important (0).\\n6. The final metric is the average of all of these over citing papers.\",\"Abstraction groups\":{\"-1\":[\"CD Index\",\"Disruptiveness\",\"Consolidation\",\"Citation\",\"Focal Paper\",\"Predecessor Paper\",\"Citing Paper\",\"Final Metric\"],\"0\":[\"CD Index\"],\"1\":[\"Measuring Disruptiveness\",\"Citation\"],\"2\":[\"Scientific Paper\",\"Ratio\"],\"3\":[\"Future Citing Paper\",\"Focal Paper\",\"Predecessor Paper\"],\"4\":[\"Averaging\"]}},\"311\":{\"Question\":\"How is the metric of disruptive papers compared to consolidating papers similar to one of Nielsen's ideas about science processes? \",\"Answer\":\"It is related to Nielsen's idea of researchers either being a problem creator or problem solver. Problem creators would write disruptive papers, while problem solvers would write consolidating papers. \",\"Key ideas\":\"1. Nielsen's idea of researchers either being a problem creator or problem solver. \\n2. Problem creators write disruptive papers. \\n3. Problem solvers write consolidating papers. \\n4. Metric of disruptive papers compared to consolidating papers is related to Nielsen's idea.\",\"Abstraction groups\":{\"-1\":[\"Metric\",\"Disruptive\",\"Consolidating\",\"Paper\",\"Nielsen\",\"Problem\",\"Creator\",\"Solver\"],\"0\":[\"Metric\"],\"1\":[\"Disruptive Paper\",\"Consolidating Paper\"],\"2\":[\"Problem Creator\",\"Problem Solver\"],\"3\":[\"Nielsen's Idea\"],\"4\":[\"Science Process\"]}},\"312\":{\"Question\":\"In brief, what did the paper \\\"Park 2023\\\" show about scientific discovery?\",\"Answer\":\"It showed that disruptive papers are becoming less common over time, as measured by a specific metric \",\"Key ideas\":\"1. The paper \\\"Park 2023\\\" was a scientific paper. \\n2. It measured the rate of disruptive papers over time. \\n3. It showed that disruptive papers are becoming less common. \\n4. The metric used to measure the rate of disruptive papers was not specified.\",\"Abstraction groups\":{\"-1\":[\"Park 2023\",\"Scientific\",\"Discovery\",\"Disruptive\",\"Paper\",\"Time\",\"Metric\"],\"0\":[\"Park 2023\"],\"1\":[\"Scientific Discovery\",\"Disruptive Paper\"],\"2\":[\"Research\",\"Time\"],\"3\":[\"Knowledge\",\"Measurement\"],\"4\":[\"Understanding\"]}},\"313\":{\"Question\":\"What differentiates powder based 3D printing from extrusion in terms of physical construction?\",\"Answer\":\"You can build more complicated structures because the bed of powder supports the device as it grows. In contrast, with extrusion the device itself must support itself. \",\"Key ideas\":\"1. 3D printing is a process of creating a three-dimensional object from a digital model. \\n2. There are two main types of 3D printing: powder based and extrusion. \\n3. Powder based 3D printing involves building an object layer by layer using a bed of powder. \\n4. Extrusion 3D printing involves building an object layer by layer using a filament of material. \\n5. The physical construction of an object created with powder based 3D printing is different from that of an object created with extrusion 3D printing. \\n6. With powder based 3D printing, the bed of powder supports the device as it grows, allowing for more complicated structures. \\n7. With extrusion 3D printing, the device must support itself as it grows, limiting the complexity of the structures that can be created.\",\"Abstraction groups\":{\"-1\":[\"3D Printing\",\"Powder\",\"Extrusion\",\"Structure\",\"Bed\",\"Device\"],\"0\":[\"3D Printing\"],\"1\":[\"Powder\",\"Extrusion\"],\"2\":[\"Physical Construction\",\"Digital Model\"],\"3\":[\"Manufacturing\",\"Technology\"],\"4\":[\"Science\",\"Engineering\"]}},\"314\":{\"Question\":\"What is one benefit of powder based 3D printing and melting compared to extrusion from a materials science perspective?\",\"Answer\":\"It's potentially easier to dope the composition of the material (like add ceramic dopants by coating 40 micron particles with ceramics, then melting them).\\nYou can do gradients of material somewhat more easily\",\"Key ideas\":\"1. Powder based 3D printing and melting is a process used in materials science.\\n2. It is different from extrusion.\\n3. It is potentially easier to dope the composition of the material.\\n4. This means adding ceramic dopants by coating 40 micron particles with ceramics, then melting them.\\n5. It is also possible to do gradients of material somewhat more easily.\",\"Abstraction groups\":{\"-1\":[\"3D Printing\",\"Melting\",\"Extrusion\",\"Material Science\",\"Doping\",\"Ceramic\",\"Particle\",\"Gradient\"],\"0\":[\"Powder Based 3D Printing\",\"Melting\"],\"1\":[\"Materials Science\",\"Doping\"],\"2\":[\"Chemistry\",\"Physics\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"315\":{\"Question\":\"In a 3D metal printer, what is the wavelength of the laser? Does this matter?\",\"Answer\":\"1200 nm, and no it does not matter too much. It's just a wavelength that can get high power and has good absorption\",\"Key ideas\":\"1. 3D metal printing is a process that uses a laser to create 3D objects. \\n2. The wavelength of the laser used in 3D metal printing is 1200 nm. \\n3. The wavelength of the laser does not have a significant impact on the 3D metal printing process. \\n4. 1200 nm is a wavelength that can get high power and has good absorption.\",\"Abstraction groups\":{\"-1\":[\"3D Metal Printing\",\"Laser\",\"Wavelength\",\"Power\",\"Absorption\"],\"0\":[\"3D Metal Printing\"],\"1\":[\"Laser\",\"Wavelength\"],\"2\":[\"Power\",\"Absorption\"],\"3\":[\"Manufacturing\",\"Technology\"],\"4\":[\"Science\",\"Engineering\"]}},\"316\":{\"Question\":\"How does a 3D metals printer work?\",\"Answer\":\"Metal powder layer, then lasers melt it locally, then spread more powder, then repeat \",\"Key ideas\":\"1. 3D metals printing is a process that uses lasers to melt metal powder. \\n2. The process begins by laying down a layer of metal powder. \\n3. Lasers are then used to melt the powder locally. \\n4. More powder is then spread over the melted powder. \\n5. This process is then repeated until the desired shape is achieved.\",\"Abstraction groups\":{\"-1\":[\"3D Metal Printing\",\"Laser\",\"Metal Powder\",\"Melting\",\"Spreading\"],\"0\":[\"3D Metals Printing\"],\"1\":[\"Laser\",\"Metal Powder\",\"Melting\",\"Spreading\"],\"2\":[\"Manufacturing\",\"Technology\"],\"3\":[\"Science\",\"Engineering\"],\"4\":[\"Knowledge\"]}},\"317\":{\"Question\":\"What are the main metrics for how good a material is in  metals material engineering? \",\"Answer\":\"Tensile strength\\nHardness (resistance to scratching)\\nCorrosion resistance \",\"Key ideas\":\"\\n1. Metals material engineering: the study of the properties and characteristics of metals and their use in engineering applications.\\n2. Metrics: measurements used to evaluate the performance of a material.\\n3. Tensile strength: the amount of force a material can withstand before breaking.\\n4. Hardness: the resistance of a material to scratching or indentation.\\n5. Corrosion resistance: the ability of a material to resist corrosion or deterioration due to exposure to environmental elements.\",\"Abstraction groups\":{\"-1\":[\"Metal\",\"Engineering\",\"Tensile\",\"Hardness\",\"Corrosion\"],\"0\":[\"Metals Material Engineering\"],\"1\":[\"Metric\",\"Tensile Strength\",\"Hardness\",\"Corrosion Resistance\"],\"2\":[\"Property\",\"Characteristic\"],\"3\":[\"Material\",\"Engineering\"],\"4\":[\"Science\"]}},\"318\":{\"Question\":\"What are the two main benefits of atomic quantum gas experiments for understanding physics? \",\"Answer\":\"Isolation and tunability \",\"Key ideas\":\"\\n1. Atomic quantum gas experiments are used to study physics. \\n2. Isolation is one of the main benefits of these experiments. \\n3. Isolation means that the system is kept away from external influences. \\n4. Tunability is the other main benefit of these experiments. \\n5. Tunability means that the system can be adjusted to study different physical phenomena.\",\"Abstraction groups\":{\"-1\":[\"Atomic\",\"Quantum\",\"Gas\",\"Experiment\",\"Physics\",\"Isolation\",\"Tunability\"],\"0\":[\"Atomic Quantum Gas Experiment\"],\"1\":[\"Physics\",\"Experiment\"],\"2\":[\"Science\",\"Research\"],\"3\":[\"Knowledge\",\"Understanding\"],\"4\":[\"Learning\"]}},\"319\":{\"Question\":\"What is the concept of implicit search in large language models? \",\"Answer\":\"It is the idea that transformer somehow implement a hidden search algorithm to find concepts buried in their network. \",\"Key ideas\":\"\\n1. Large language models are based on transformers. \\n2. Transformers are a type of neural network. \\n3. Implicit search is the idea that transformers can find concepts buried in their network. \\n4. This is done without explicitly searching for the concept. \\n5. Implicit search is a powerful tool for understanding language.\",\"Abstraction groups\":{\"-1\":[\"Language\",\"Model\",\"Transformer\",\"Search\",\"Concept\",\"Network\"],\"0\":[\"Implicit Search\"],\"1\":[\"Large Language Models\"],\"2\":[\"Neural Networks\",\"Transformers\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computing\"]}},\"320\":{\"Question\":\"For t-sne reconstruction, what is the cost function which is minimized to best reconstruct the distribution of the original datapoints in a lower dimensional space? \",\"Answer\":\"The cost function used is the Kulback Liebler divergence from the new distribution Q to the old distribution P of the similarities between datapoints (P has a gaussian similarity metric based on distance (symmetrized from the measurement of i to j and from j to i), and Q has the t-distributed similarity metric (also symmetrized from i to j and from j to i)) \",\"Key ideas\":\"\\n1. T-SNE reconstruction is a technique used to reduce the dimensionality of a dataset while preserving the original distribution of the data points. \\n2. The cost function used to best reconstruct the distribution of the original datapoints in a lower dimensional space is the Kulback Liebler divergence. \\n3. The Kulback Liebler divergence is a measure of the difference between two probability distributions. \\n4. The original distribution P has a gaussian similarity metric based on distance, which is symmetrized from the measurement of i to j and from j to i. \\n5. The new distribution Q has a t-distributed similarity metric, which is also symmetrized from i to j and from j to i.\",\"Abstraction groups\":{\"-1\":[\"T-SNE\",\"Cost Function\",\"Kulback Liebler\",\"Distribution\",\"Gaussian\",\"T-Distributed\",\"Similarity\",\"Symmetrized\"],\"0\":[\"T-SNE Reconstruction\"],\"1\":[\"Cost Function\",\"Distribution\"],\"2\":[\"Similarity\",\"Symmetrized\"],\"3\":[\"Kulback Liebler\"],\"4\":[\"Dimensionality Reduction\"]}},\"321\":{\"Question\":\"Sketch a quick proof of why the Kulback Liebler divergence is always positive. \",\"Answer\":\"To prove, note that the KL divergence is sum_i p_i (- log (q_i \\/ p_i)).\\n- log(x) is always greater than or equal to 1 - x. \\nReplace the log by this, and then perform the sum, which results in 0. So KL divergence is greater than 0.\",\"Key ideas\":\"1. The Kulback Liebler divergence is a measure of the difference between two probability distributions. \\n2. The KL divergence is always positive. \\n3. The KL divergence is calculated as the sum of p_i (- log (q_i \\/ p_i)). \\n4. The logarithm of a number is always greater than or equal to 1 - x. \\n5. When the logarithm is replaced by 1 - x, the sum of the KL divergence is 0. \\n6. Therefore, the KL divergence is greater than 0.\",\"Abstraction groups\":{\"-1\":[\"Kulback Liebler Divergence\",\"Probability Distribution\",\"P_i\",\"Q_i\",\"Logarithm\",\"1 - X\"],\"0\":[\"Kulback Liebler Divergence\"],\"1\":[\"Probability Distribution\"],\"2\":[\"Mathematics\",\"Statistics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Learning\"]}},\"322\":{\"Question\":\"Regardless of the phrasing of the definition of Kulback Liebler divergence:\\nHow should one remember, in the resulting formula, which distribution is included in the term which has the usual Shannon entropy form, and which distribution is only present in the cross entropy term? \",\"Answer\":\"The distribution which you believe to be the \\\"correct\\\" one, in some sense, is the one written in the Shannon form. The one you are using instead, is the cross entropy term. Thus the KL divergence measures the surprise you would experience if you expected the non-correct distribution, when the correct one was the real distriubtion. \",\"Key ideas\":\"\\n1. Kulback Liebler divergence is a measure of the difference between two probability distributions. \\n2. The resulting formula includes two terms: one with the usual Shannon entropy form, and one with a cross entropy term. \\n3. The distribution which is believed to be the \\\"correct\\\" one is written in the Shannon form. \\n4. The distribution which is being used instead is written in the cross entropy term. \\n5. The KL divergence measures the surprise one would experience if they expected the non-correct distribution, when the correct one was the real distribution.\",\"Abstraction groups\":{\"-1\":[\"Kulback Liebler Divergence\",\"Shannon Entropy\",\"Cross Entropy\",\"Distribution\",\"Surprise\"],\"0\":[\"Kulback Liebler Divergence\"],\"1\":[\"Probability Distribution\"],\"2\":[\"Mathematics\",\"Statistics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Learning\"]}},\"323\":{\"Question\":\"How to remember the phrasing of the definition of Kulback Liebler divergence?\\nThe Kulback Liebler divergence is usually phrased as \\\"from distribution Q to distribution P\\\". \\nHow should one remember, in the resulting formula, which distribution is included in the term which has the usual Shannon entropy form, and which distribution is only present in the cross entropy term? \",\"Answer\":\"The distribution you are comparing \\\"to\\\" should be thought of as the ground truth distribution you really believe is correct. Therefore you are measuring the divergence from the other thing, to that ground truth. \\nOn wikipedia: \\\"Usually P represents the data, the observations, or a measured probability distribution. Distribution Q represents instead a theory, a model, a description or an approximation of P.\\\" \",\"Key ideas\":\"1. Kulback Liebler divergence is a measure of the difference between two probability distributions. \\n2. The divergence is usually phrased as \\\"from distribution Q to distribution P\\\". \\n3. The distribution P represents the data, the observations, or a measured probability distribution. \\n4. Distribution Q represents a theory, a model, a description, or an approximation of P. \\n5. The resulting formula includes a term with the usual Shannon entropy form, and a cross entropy term. \\n6. The distribution you are comparing \\\"to\\\" should be thought of as the ground truth distribution you really believe is correct. \\n7. Therefore you are measuring the divergence from the other thing, to that ground truth.\",\"Abstraction groups\":{\"-1\":[\"Kulback Liebler\",\"Distribution\",\"Q\",\"P\",\"Shannon Entropy\",\"Cross Entropy\",\"Ground Truth\"],\"0\":[\"Kulback Liebler\"],\"1\":[\"Divergence\",\"Probability Distribution\"],\"2\":[\"Mathematics\",\"Statistics\"],\"3\":[\"Science\",\"Data Analysis\"],\"4\":[\"Knowledge\"]}},\"324\":{\"Question\":\"23% of solid waste generated in the US is paper. How much of it is recycled each year?\\nOf the total waste generated, how much ends up in landfills?\",\"Answer\":\"Roughly half of paper products are recycled. \\nRoughly half of all waste ends up in landfills. \",\"Key ideas\":\"1. 23% of solid waste generated in the US is paper. \\n2. Roughly half of paper products are recycled each year. \\n3. Roughly half of all waste ends up in landfills.\",\"Abstraction groups\":{\"-1\":[\"Waste\",\"Paper\",\"Recycled\",\"Landfill\"],\"0\":[\"Waste\"],\"1\":[\"Paper\",\"Recycled\",\"Landfill\"],\"2\":[\"Solid Waste\",\"Waste Management\"],\"3\":[\"Environmental Issue\"],\"4\":[\"Science\"]}},\"325\":{\"Question\":\"Of solid waste generated in the US each year, how much is paper, food, and plastic? \",\"Answer\":\"23% paper, 20% food, 10% plastic\",\"Key ideas\":\"\\n1. Solid waste: waste materials that are discarded after use, such as paper, food, and plastic. \\n2. US: United States \\n3. Generated: created or produced \\n4. Year: a period of twelve months \\n5. Paper: a material made from wood pulp, used for writing, printing, packaging, and many other purposes \\n6. Food: any substance consumed to provide nutritional support for the body \\n7. Plastic: any of a wide range of synthetic or semi-synthetic organic compounds that are malleable and can be molded into solid objects \\n8. Percentages: a fraction of a whole expressed in hundredths \\n9. 23%: 23 hundredths of the total amount of solid waste generated in the US each year is paper \\n10. 20%: 20 hundredths of the total amount of solid waste generated in the US each year is food \\n11. 10%: 10 hundredths of the total amount of solid waste generated in the US each year is plastic\",\"Abstraction groups\":{\"-1\":[\"Waste\",\"US\",\"Generated\",\"Year\",\"Paper\",\"Food\",\"Plastic\",\"Percentage\",23,20,10],\"0\":[\"Solid Waste\"],\"1\":[\"Paper\",\"Food\",\"Plastic\"],\"2\":[\"Generated\",\"US\",\"Year\"],\"3\":[\"Percentages\"],\"4\":[\"Waste\"]}},\"326\":{\"Question\":\"How many landfills are there in the united states? \",\"Answer\":\"There are around 2000 landfills.\",\"Key ideas\":\"1. The United States has around 2000 landfills. \\n2. A landfill is a site for the disposal of waste materials by burial. \\n3. Landfills are regulated by the Environmental Protection Agency (EPA). \\n4. Landfills are a form of waste management. \\n5. Landfills can have a negative impact on the environment.\",\"Abstraction groups\":{\"-1\":[\"Landfill\",\"United State\",2000,\"EPA\",\"Waste Management\",\"Environment\"],\"0\":[\"Landfill\"],\"1\":[\"Waste Management\"],\"2\":[\"Environment\",\"United States\"],\"3\":[\"Pollution\",\"Regulation\"],\"4\":[\"Human Impact\"]}},\"327\":{\"Question\":\"How does the t-sne reconstruction process intentionally differ from the original measurement of the distribution of points in the high dimensional space? \",\"Answer\":\"The final setup uses a similarity metric based on distance which is a Laplacian rather than a Gaussian.  (I think this is the t-distribution part of the name of t-sne.)\\nThis is crucial, because it is what allows us to move all the very far away points into closer proximity, and the close points into more even distribution; it moves everything into the middle region. This makes it easier to see everything on one plot.\\n-----\\nAs a reminder, the overal goal is that it tries to reproduce the nearest neighbor distribution entropy for each point, but it does so with a new probability metric. \",\"Key ideas\":\"1. The t-sne reconstruction process uses a similarity metric based on distance which is a Laplacian rather than a Gaussian. \\n2. This is crucial because it allows us to move all the very far away points into closer proximity, and the close points into more even distribution. \\n3. This makes it easier to see everything on one plot. \\n4. The overall goal is to reproduce the nearest neighbor distribution entropy for each point, but it does so with a new probability metric.\",\"Abstraction groups\":{\"-1\":[\"T-Sne\",\"Reconstruction\",\"Process\",\"Measurement\",\"Distribution\",\"Point\",\"High Dimensional Space\",\"Similarity\",\"Metric\",\"Distance\",\"Laplacian\",\"Gaussian\",\"Proximity\",\"Distribution\",\"Plot\",\"Goal\",\"Entropy\",\"Probability\"],\"0\":[\"T-SNE\"],\"1\":[\"Reconstruction\",\"Process\"],\"2\":[\"Measurement\",\"Distribution\"],\"3\":[\"Point\",\"High Dimensional Space\"],\"4\":[\"Similarity\",\"Metric\",\"Distance\",\"Laplacian\",\"Gaussian\",\"Proximity\",\"Distribution\",\"Plot\",\"Goal\",\"Entropy\",\"Probability\"]}},\"328\":{\"Question\":\"In t-sne, what is the original measurement that controls the final distribution of points? \",\"Answer\":\"The original measurement is a similarity metric from point i to point j. \\nThis is the probability that point i would pick point j as it's nearest neighbor based on their relative distance plugged into a Gaussian with some adjusted Gaussian width. \\nFor each point i, the gaussian width is adjusted so that each point's nearest neighbor distribution has the same entropy distribution as all the other points. \",\"Key ideas\":\"1. t-sne is a technique used to visualize high-dimensional data in a low-dimensional space. \\n2. The original measurement used to control the final distribution of points in t-sne is a similarity metric from point i to point j. \\n3. This similarity metric is based on the relative distance between point i and point j. \\n4. This relative distance is plugged into a Gaussian with some adjusted Gaussian width. \\n5. For each point i, the gaussian width is adjusted so that each point's nearest neighbor distribution has the same entropy distribution as all the other points.\",\"Abstraction groups\":{\"-1\":[\"T-SNE\",\"Similarity\",\"Point I\",\"Point J\",\"Relative Distance\",\"Gaussian\",\"Entropy\"],\"0\":[\"T-Sne\"],\"1\":[\"Similarity\",\"Gaussian\",\"Entropy\"],\"2\":[\"Visualization\",\"Measurement\"],\"3\":[\"Data Analysis\",\"Statistics\"],\"4\":[\"Mathematics\"]}},\"329\":{\"Question\":\"What does t-sne stand for in machine learning? \",\"Answer\":\"t-distributed stochastic neighbor embedding \",\"Key ideas\":\"\\n1. Machine learning is a field of artificial intelligence that uses algorithms to learn from data. \\n2. t-SNE stands for t-distributed stochastic neighbor embedding. \\n3. t-SNE is a machine learning technique used to visualize high-dimensional data in a lower-dimensional space. \\n4. t-SNE works by mapping data points to a probability distribution in a lower-dimensional space. \\n5. t-SNE is useful for exploring and understanding complex datasets.\",\"Abstraction groups\":{\"-1\":[\"Machine Learning\",\"T-SNE\",\"Visualization\",\"Probability\",\"Dataset\"],\"0\":[\"T-Sne\"],\"1\":[\"Machine Learning\",\"Visualization\"],\"2\":[\"Artificial Intelligence\",\"Data Analysis\"],\"3\":[\"Computing\",\"Statistics\"],\"4\":[\"Science\",\"Technology\"]}},\"330\":{\"Question\":\"What year was Obamacare passed? \",\"Answer\":\"2010\",\"Key ideas\":\"1. The Patient Protection and Affordable Care Act (PPACA) was passed in 2010. \\n2. The PPACA is commonly referred to as Obamacare. \\n3. Obamacare is a healthcare reform law that was passed in the United States. \\n4. The law was designed to make healthcare more accessible and affordable for all Americans.\",\"Abstraction groups\":{\"-1\":[\"Obamacare\",\"2010\",\"PPACA\",\"Healthcare\",\"Reform\",\"United States\"],\"0\":[\"Obamacare\"],\"1\":[\"Healthcare\",\"Reform\"],\"2\":[\"Law\",\"United States\"],\"3\":[\"Policy\",\"2010\"],\"4\":[\"Government\",\"Society\"]}},\"331\":{\"Question\":\"What are two phenomena and geographical areas in the United States where risk of damage due to climate change exhibits an expanding bulls eye effect? \",\"Answer\":\"Florida and east coast and people in hurricane path \\nWest coast and homes in severe wildfire risk areas \\nBoth are up 7x from 70 years ago until now\",\"Key ideas\":\"1. Climate change is a phenomenon that can cause damage in certain geographical areas. \\n2. In the United States, there are two areas where the risk of damage due to climate change is increasing. \\n3. The first area is Florida and the east coast, where people are at risk of damage from hurricanes. \\n4. The second area is the west coast, where homes are at risk of damage from severe wildfires. \\n5. The risk of damage in both areas has increased 7 times from 70 years ago until now.\",\"Abstraction groups\":{\"-1\":[\"Climate Change\",\"United States\",\"Risk\",\"Damage\",\"Bulls Eye Effect\",\"Florida\",\"East Coast\",\"Hurricane\",\"West Coast\",\"Wildfire\",\"70 Years\"],\"0\":[\"Climate Change\"],\"1\":[\"Risk\",\"Damage\"],\"2\":[\"Phenomena\",\"Geographical Areas\"],\"3\":[\"United States\",\"Bulls Eye Effect\"],\"4\":[\"Expansion\"]}},\"332\":{\"Question\":\"What is the solution to deal with the large randomness and local minima in strategy in poker (to bet randomly to only lose half the time, rather than more than half), as a reinforcement learning problem? \",\"Answer\":\"Solution: have a solid opponent pool that prevents overfitting to one policy and makes sure to explore the space of actions enough. This is effectively regularization. \",\"Key ideas\":\"1. Reinforcement learning: a type of machine learning that uses rewards and punishments to learn how to perform a task.\\n2. Poker: a card game in which players bet on the strength of their hands.\\n3. Randomness: the lack of predictability in the outcome of a game.\\n4. Local minima: a point in a function where the value of the function is lower than the values of the points around it.\\n5. Strategy: a plan of action designed to achieve a goal.\\n6. Overfitting: when a model is too closely fitted to the data it is trained on, and does not generalize well to new data.\\n7. Regularization: a technique used to reduce the complexity of a model by adding a penalty to the loss function.\",\"Abstraction groups\":{\"-1\":[\"Poker\",\"Randomness\",\"Minima\",\"Strategy\",\"Overfitting\",\"Regularization\"],\"0\":[\"Poker\"],\"1\":[\"Strategy\",\"Reinforcement Learning\"],\"2\":[\"Machine Learning\",\"Game Theory\"],\"3\":[\"Artificial Intelligence\",\"Probability\"],\"4\":[\"Mathematics\"]}},\"333\":{\"Question\":\"Python file access commands for opening a file and writing to it \",\"Answer\":\"For writing:\\nf = open(\\\"hello.txt\\\", \\\"a\\\")  # a stands for append\\nf.write(\\\"hi\\\")  # appends to line\\nf.close()\\n-----\\nIf you do f = open(\\\"hello.txt\\\", \\\"w\\\") then write will overwrite things. \",\"Key ideas\":\"1. Python is a programming language. \\n2. Files can be accessed and manipulated using Python commands. \\n3. The command for opening a file is \\\"f = open(\\\"hello.txt\\\", \\\"a\\\")\\\". \\n4. The \\\"a\\\" in the command stands for \\\"append\\\". \\n5. The command for writing to a file is \\\"f.write(\\\"hi\\\")\\\". \\n6. This command appends to the line. \\n7. If you use \\\"f = open(\\\"hello.txt\\\", \\\"w\\\")\\\" then the write command will overwrite things.\",\"Abstraction groups\":{\"-1\":[\"Python\",\"File\",\"Access\",\"Open\",\"Write\",\"Append\",\"Overwrite\"],\"0\":[\"File Access\"],\"1\":[\"Python\",\"Commands\"],\"2\":[\"Programming\",\"Manipulation\"],\"3\":[\"Technology\",\"Data\"],\"4\":[\"Computing\"]}},\"334\":{\"Question\":\"What is the practical difference between water soluble and fat soluble vitamins? \",\"Answer\":\"Water soluble: These have to be replenished often, and are small molecules and simple.\\nFat soluble: These can be stored in body long term, and are more complex molecules.\\n------\\nWater-soluble vitamins are soluble in water and are not stored in the body to any significant extent. They must be obtained from the diet on a regular basis. Examples of water-soluble vitamins include vitamin C and the B vitamins. These vitamins are typically small molecules with a simple structure.\\nFat-soluble vitamins are soluble in fat and are stored in the body's fat tissues and liver. Examples of fat-soluble vitamins include vitamins A, D, E, and K. These vitamins have a more complex structure, and they are typically larger molecules than water-soluble vitamins. \",\"Key ideas\":\"1. Water-soluble vitamins must be obtained from the diet on a regular basis.\\n2. Examples of water-soluble vitamins include vitamin C and the B vitamins.\\n3. Water-soluble vitamins are typically small molecules with a simple structure.\\n4. Fat-soluble vitamins are soluble in fat and are stored in the body's fat tissues and liver.\\n5. Examples of fat-soluble vitamins include vitamins A, D, E, and K.\\n6. Fat-soluble vitamins have a more complex structure, and they are typically larger molecules than water-soluble vitamins.\",\"Abstraction groups\":{\"-1\":[\"Water-Soluble\",\"Fat-Soluble\",\"Vitamin\",\"Replenish\",\"Store\",\"Diet\",\"Vitamin C\",\"B Vitamin\",\"Structure\",\"Fat Tissue\",\"Liver\"],\"0\":[\"Vitamin\"],\"1\":[\"Water-soluble\",\"Fat-soluble\"],\"2\":[\"Replenish\",\"Store\",\"Diet\"],\"3\":[\"Structure\",\"Fat Tissue\",\"Liver\"],\"4\":[\"Nutrition\"]}},\"335\":{\"Question\":\"Major burdens on health: what are a few of the preventable major causes of excess deaths \",\"Answer\":\"Obesity: 5 million\\nAir polution: 7 million\\nTobacco use: 8 million\\n(out of around 50 million excess deaths)\",\"Key ideas\":\"\\n1. Major burdens on health can be preventable.\\n2. Excess deaths are a major burden on health.\\n3. Around 50 million excess deaths occur each year.\\n4. Obesity is a preventable cause of excess deaths, with 5 million deaths attributed to it.\\n5. Air pollution is a preventable cause of excess deaths, with 7 million deaths attributed to it.\\n6. Tobacco use is a preventable cause of excess deaths, with 8 million deaths attributed to it.\",\"Abstraction groups\":{\"-1\":[\"Obesity\",\"Air Pollution\",\"Tobacco Use\",\"Excess Death\",\"Preventable\"],\"0\":[\"Excess Death\"],\"1\":[\"Preventable Cause\"],\"2\":[\"Major Burden On Health\"],\"3\":[\"Obesity\",\"Air Pollution\",\"Tobacco Use\"],\"4\":[\"Public Health\"]}},\"336\":{\"Question\":\"What fraction of energy use by each person (on average) comes from various sources? \",\"Answer\":\"Coal 30%\\nOil 30%\\nGas 20%\\nNuclear, wind, solar, hydro - remaining 20%\",\"Key ideas\":\"1. Energy use by each person (on average) is divided into various sources. \\n2. The sources of energy use are: \\n    a. Coal (30%)\\n    b. Oil (30%)\\n    c. Gas (20%)\\n    d. Nuclear, wind, solar, hydro (remaining 20%)\",\"Abstraction groups\":{\"-1\":[\"Energy\",\"Person\",\"Coal\",\"Oil\",\"Gas\",\"Nuclear\",\"Wind\",\"Solar\",\"Hydro\"],\"0\":[\"Energy Use\"],\"1\":[\"Coal\",\"Oil\",\"Gas\",\"Nuclear\",\"Wind\",\"Solar\",\"Hydro\"],\"2\":[\"Source\",\"Renewable\",\"Non-renewable\"],\"3\":[\"Consumption\",\"Production\"],\"4\":[\"Environment\"]}},\"337\":{\"Question\":\"The Patient Protection and Affordable Care Act\\nWhat two broad categories can the components of the bill be categorized as? \",\"Answer\":\"Expansion of the healthcare system\\nRequriements on individuals and companies \",\"Key ideas\":\"\\n1. The Patient Protection and Affordable Care Act (PPACA) is a law passed in 2010. \\n2. The PPACA is also known as Obamacare. \\n3. The components of the PPACA can be categorized into two broad categories: \\n    a. Expansion of the healthcare system \\n    b. Requirements on individuals and companies \\n4. Expansion of the healthcare system includes: \\n    a. Expansion of Medicaid \\n    b. Subsidies for health insurance \\n    c. Creation of health insurance exchanges \\n5. Requirements on individuals and companies include: \\n    a. Requirement for individuals to have health insurance \\n    b. Requirement for employers to provide health insurance \\n    c. Tax penalties for individuals and employers who do not comply with the requirements\",\"Abstraction groups\":{\"-1\":[\"PPACA\",\"Expansion\",\"Requirement\",\"Medicaid\",\"Subsidy\",\"Exchange\",\"Individual\",\"Company\",\"Insurance\",\"Penalty\"],\"0\":[\"PPACA\"],\"1\":[\"Expansion\",\"Requirement\"],\"2\":[\"Healthcare System\",\"Individual\",\"Company\"],\"3\":[\"Medicaid\",\"Subsidy\",\"Exchange\",\"Insurance\",\"Penalty\"],\"4\":[\"Law\"]}},\"338\":{\"Question\":\"The Patient Protection and Affordable Care Act, also known as Obamacare\\nRequirements or prohibitions contained in the bill \",\"Answer\":\"Individual mandate for health insurance\\nProhibition of insurance companies denying coverage or charging more based on pre-existing conditions\\nRequiring insurance companies to cover essential health benefits \",\"Key ideas\":\"\\n1. The Patient Protection and Affordable Care Act (also known as Obamacare) is a law passed by the US government.\\n2. The law contains an individual mandate, which requires all US citizens to have health insurance.\\n3. Insurance companies are prohibited from denying coverage or charging more based on pre-existing conditions.\\n4. Insurance companies are required to cover essential health benefits.\",\"Abstraction groups\":{\"-1\":[\"Patient Protection\",\"Affordable Care Act\",\"Obamacare\",\"Individual Mandate\",\"Insurance Company\",\"Coverage\",\"Pre-existing Condition\",\"Essential Health Benefit\"],\"0\":[\"Obamacare\"],\"1\":[\"Healthcare\",\"Legislation\"],\"2\":[\"Government\",\"Social Policy\"],\"3\":[\"Law\",\"Economics\"],\"4\":[\"Society\"]}},\"339\":{\"Question\":\"The Patient Protection and Affordable Care Act, also known as Obamacare\\nMain expansions of the healthcare system \",\"Answer\":\"Healthcare marketplaces for individuals and small businesses\\nExpansion of Medicaid \",\"Key ideas\":\"\\n1. The Patient Protection and Affordable Care Act is also known as Obamacare.\\n2. Healthcare marketplaces are available for individuals and small businesses.\\n3. Medicaid has been expanded under the Act.\",\"Abstraction groups\":{\"-1\":[\"Obamacare\",\"Healthcare\",\"Marketplace\",\"Individual\",\"Small Business\",\"Medicaid\"],\"0\":[\"Obamacare\"],\"1\":[\"Healthcare\",\"Legislation\"],\"2\":[\"Social Policy\",\"Economics\"],\"3\":[\"Politics\",\"Society\"],\"4\":[\"Human Activity\"]}},\"340\":{\"Question\":\"Compare and the costs and benefits of wind and solar to nuclear energy \",\"Answer\":\"Cost: The upfront cost of building wind and solar facilities is generally lower than the cost of building a nuclear power plant. However, the cost of generating electricity from nuclear energy is often lower than the cost of generating electricity from wind or solar, especially over the long term, due to the higher capacity factor (the amount of electricity a power plant generates compared to its theoretical maximum output) of nuclear plants. Also land use is much higher for wind and solar. \\nReliability: Nuclear power plants have a very high capacity factor and can operate continuously for extended periods of time, making them a reliable source of electricity. In contrast, the output of wind and solar facilities is dependent on weather conditions, which can be variable.\\nFurther concerns:\\nEnvironmental impact of nuclear mining and disposal. \\nSafety of nuclear plants \",\"Key ideas\":\"1. The upfront cost of building wind and solar facilities is generally lower than the cost of building a nuclear power plant. \\n2. The cost of generating electricity from nuclear energy is often lower than the cost of generating electricity from wind or solar, especially over the long term, due to the higher capacity factor (the amount of electricity a power plant generates compared to its theoretical maximum output) of nuclear plants. \\n3. Land use is much higher for wind and solar. \\n4. Nuclear power plants have a very high capacity factor and can operate continuously for extended periods of time, making them a reliable source of electricity. \\n5. The output of wind and solar facilities is dependent on weather conditions, which can be variable. \\n6. Environmental impact of nuclear mining and disposal. \\n7. Safety of nuclear plants.\",\"Abstraction groups\":{\"-1\":[\"Wind\",\"Solar\",\"Nuclear\",\"Cost\",\"Capacity\",\"Land Use\",\"Reliability\",\"Weather\",\"Environmental\",\"Safety\"],\"0\":[\"Energy\"],\"1\":[\"Wind\",\"Solar\",\"Nuclear\"],\"2\":[\"Cost\",\"Reliability\",\"Environmental\",\"Safety\"],\"3\":[\"Capacity\",\"Land Use\",\"Weather\"],\"4\":[\"Comparison\"]}},\"341\":{\"Question\":\"Types of creativity according to Nielsen \",\"Answer\":\"Problem solver vs problem creator.\\nA problem solver is someone who is good at solving puzzles, or math, or building devices to accomplish a goal.\\nA problem creator is someone who thinks deeply about the field and about the problems it is encountering, and the unstated assumptions or gaps in knowledge, and proposes research questions to move the field toward a better understanding. Then problem solvers tackles those research questions.\\nProblem creators are much more rare, and probably do a more difficult task. \",\"Key ideas\":\"1. Creativity can be divided into two types: problem solvers and problem creators. \\n2. Problem solvers are good at solving puzzles, math, or building devices to accomplish a goal.\\n3. Problem creators think deeply about the field and the problems it is encountering, and propose research questions to move the field toward a better understanding.\\n4. Problem creators are much more rare, and do a more difficult task than problem solvers.\",\"Abstraction groups\":{\"-1\":[\"Creativity\",\"Problem Solver\",\"Problem Creator\",\"Puzzle\",\"Math\",\"Device\",\"Field\",\"Problem\",\"Assumption\",\"Gap\",\"Knowledge\",\"Research Question\"],\"0\":[\"Creativity\"],\"1\":[\"Problem Solver\",\"Problem Creator\"],\"2\":[\"Puzzle\",\"Math\",\"Device\",\"Field\",\"Problem\",\"Assumption\",\"Gap\",\"Knowledge\",\"Research Question\"],\"3\":[\"Thinking\",\"Understanding\",\"Moving\"],\"4\":[\"Knowledge\",\"Understanding\",\"Action\"]}},\"342\":{\"Question\":\"Eric Reis's book: The lean startup - main ideas \",\"Answer\":\"Build minimum viable product: goal is to start answering questions as fast as possible and use small batch production to quickly feedback and tune\\nTune the engine of growth by measuring the right metrics (not overall growth rate, but  measure improvements to acquisition, retention, etc. Change in slope) \",\"Key ideas\":\"\\n1. Build a minimum viable product (MVP): goal is to start answering questions as fast as possible and use small batch production to quickly feedback and tune.\\n2. Tune the engine of growth by measuring the right metrics: not overall growth rate, but measure improvements to acquisition, retention, etc. Change in slope.\",\"Abstraction groups\":{\"-1\":[\"Eric Reis\",\"Lean Startup\",\"MVP\",\"Question\",\"Small Batch\",\"Feedback\",\"Tune\",\"Growth\",\"Metric\",\"Acquisition\",\"Retention\",\"Slope\"],\"0\":[\"The Lean Startup\"],\"1\":[\"Business\",\"Entrepreneurship\"],\"2\":[\"Innovation\",\"Strategy\"],\"3\":[\"Management\",\"Economics\"],\"4\":[\"Social Sciences\"]}},\"343\":{\"Question\":\"What was the sequence of events leading to failure of power in texas in 2021 with a large ice storm?\",\"Answer\":\"1. Wind turbine icing prevented wind generation of electricity\\n2. The natural gas pipeline compressors were electric, and failed because wind power was gone.\\n3. Utility companies that generated electricity using natural gas were shut down to leave as much natural gas as possible for houses for people to heat their own homes.\\n4. The cooling pipes in the electricity generation plants of the utility companies froze because they were not running, and were not sufficiently insulated. Then these companies could not turn electricity back on again.\",\"Key ideas\":\"1. Wind turbines can ice over and prevent wind generation of electricity.\\n2. Natural gas pipeline compressors are electric and can fail when wind power is gone.\\n3. Utility companies that generate electricity using natural gas may be shut down to conserve natural gas for heating homes.\\n4. Cooling pipes in electricity generation plants of utility companies can freeze when not running and not sufficiently insulated, preventing the companies from turning electricity back on.\",\"Abstraction groups\":{\"-1\":[\"Wind Turbine\",\"Natural Gas\",\"Pipeline Compressor\",\"Utility Company\",\"Electricity\",\"Natural Gas\",\"House\",\"Cooling Pipe\",\"Electricity Generation Plant\"],\"0\":[\"Power Failure\"],\"1\":[\"Ice Storm\",\"Texas\",\"2021\"],\"2\":[\"Weather\",\"Infrastructure\"],\"3\":[\"Natural Phenomena\",\"Human Systems\"],\"4\":[\"Environment\",\"Society\"]}},\"344\":{\"Question\":\"What are the energy generation systems involved in the failure of power in texas in 2021?\",\"Answer\":\"Wind turbines, \\nnatural gas pipelines, \\nelectricty generators of utility companies, where were running on natural gas \",\"Key ideas\":\"1. Wind turbines are a form of energy generation system.\\n2. Natural gas pipelines are a form of energy generation system.\\n3. Electricity generators of utility companies are a form of energy generation system.\\n4. In 2021, the failure of power in Texas was caused by a combination of these energy generation systems running on natural gas.\",\"Abstraction groups\":{\"-1\":[\"Wind Turbine\",\"Natural Gas\",\"Electricity\",\"Utility Company\",\"Natural Gas Pipeline\",2021,\"Texas\",\"Power\"],\"0\":[\"Power Failure In Texas 2021\"],\"1\":[\"Energy Generation System\"],\"2\":[\"Wind Turbine\",\"Natural Gas Pipeline\",\"Electricity Generator\"],\"3\":[\"Utility Company\",\"Natural Gas\"],\"4\":[\"2021\",\"Texas\"]}},\"345\":{\"Question\":\"Where was jim Allison's first, second, and third job located? \",\"Answer\":\"Smithville texas, berkeley, then Sloan Kettering in new york. \",\"Key ideas\":\"1. Jim Allison had three jobs. \\n2. His first job was located in Smithville, Texas. \\n3. His second job was located in Berkeley. \\n4. His third job was located at Sloan Kettering in New York.\",\"Abstraction groups\":{\"-1\":[\"Jim Allison\",\"Smithville\",\"Texas\",\"Berkeley\",\"Sloan Kettering\",\"New York\"],\"0\":[\"Jim Allison\"],\"1\":[\"Job\",\"Location\"],\"2\":[\"Career\",\"Geography\"],\"3\":[\"Work\",\"Place\"],\"4\":[\"Life\",\"Space\"]}},\"346\":{\"Question\":\"What happened to the approval process for cancer drugs at the FDA around 2005 that didn't happen in other areas of disease?\",\"Answer\":\"The FDA decided to identify severely debilitating and life threatening diseases as requiring a different risk benefit analysis, and different approval process.\\nThis led to an over representation of cancer in pharmaceutical investment, because only oncology allowed this cost benefit at the FDA, and other types of treatment did not \",\"Key ideas\":\"1. The FDA is the US Food and Drug Administration. \\n2. Around 2005, the FDA decided to identify severely debilitating and life threatening diseases as requiring a different risk benefit analysis and approval process. \\n3. This led to an over representation of cancer in pharmaceutical investment, because only oncology allowed this cost benefit at the FDA, and other types of treatment did not.\",\"Abstraction groups\":{\"-1\":[\"FDA\",2005,\"Risk Benefit\",\"Approval Process\",\"Cancer\",\"Pharmaceutical\",\"Investment\",\"Oncology\"],\"0\":[\"FDA\"],\"1\":[\"Approval Process\",\"Risk Benefit\"],\"2\":[\"Pharmaceutical\",\"Oncology\"],\"3\":[\"Cancer\",\"Investment\"],\"4\":[\"2005\"]}},\"347\":{\"Question\":\"What does the CTLA-4 receptor do on T cells in the human immune system?\\nWhat do tumors do to it?\",\"Answer\":\"It is a brake for the immune system when it is engaged. It stops T cells from being so active.\\nTumors engage the CTLA-4 receptor to turn off the immune response, and survive better.\",\"Key ideas\":\"1. The CTLA-4 receptor is a brake for the immune system when it is engaged. \\n2. It stops T cells from being so active. \\n3. Tumors engage the CTLA-4 receptor to turn off the immune response. \\n4. This allows tumors to survive better.\",\"Abstraction groups\":{\"-1\":[\"CTLA-4\",\"T Cell\",\"Human Immune System\",\"Tumor\",\"Immune Response\"],\"0\":[\"CTLA-4\"],\"1\":[\"Receptor\",\"T Cell\",\"Immune System\"],\"2\":[\"Human Body\",\"Biology\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"348\":{\"Question\":\"What is the hardest thing to do in industry drug development when choosing what to do about a project? \",\"Answer\":\"The hardest thing as a leader in drug discovery is to not kill the important things, even when they look hopeless. It\\u2019s easy to say no and kill things but, then to be left with no spectacular breakthroughs that succeed in the long term \",\"Key ideas\":\"1. Drug development is a complex process that requires careful decision-making. \\n2. As a leader in drug discovery, it is important to not kill the important things, even when they look hopeless. \\n3. Saying no and killing things is easy, but it can lead to a lack of spectacular breakthroughs in the long term.\",\"Abstraction groups\":{\"-1\":[\"Drug Development\",\"Leader\",\"Drug Discovery\",\"Saying No\",\"Killing Thing\",\"Spectacular Breakthrough\",\"Long Term\"],\"0\":[\"Drug Development\"],\"1\":[\"Decision-Making\",\"Leadership\"],\"2\":[\"Drug Discovery\",\"Saying No\"],\"3\":[\"Killing Things\",\"Spectacular Breakthroughs\"],\"4\":[\"Long Term\"]}},\"349\":{\"Question\":\"What are the phases of clinical trials for drug development? \",\"Answer\":\"1 - this phase tests safety and dosage (involves 15-50 people)\\n2 - this phase test efficacy for a specific target disease, and side effects (involves less than 100 people)\\n3 - this phase tests the comparison between the new treatment, and existing therapeutics and drugs on the market (involves hundreds of people, and takes years usually)\",\"Key ideas\":\"1. Clinical trials are a process used to test the safety and efficacy of a drug before it is approved for use.\\n2. Clinical trials are divided into three phases.\\n3. The first phase tests safety and dosage and involves 15-50 people.\\n4. The second phase tests efficacy for a specific target disease, and side effects, and involves less than 100 people.\\n5. The third phase tests the comparison between the new treatment, and existing therapeutics and drugs on the market, and involves hundreds of people, and takes years usually.\",\"Abstraction groups\":{\"-1\":[\"Clinical Trial\",\"Drug Development\",\"Safety\",\"Dosage\",\"Efficacy\",\"Target Disease\",\"Side Effect\",\"New Treatment\",\"Existing Therapeutic\",\"Drug\"],\"0\":[\"Clinical Trial\"],\"1\":[\"Drug Development\"],\"2\":[\"Medical Research\",\"Pharmaceuticals\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"350\":{\"Question\":\"In the book by Eric Reis - The lean startup - what are the different engines of growth? \",\"Answer\":\"Sticky engine (when it is hard for someone to change away from your product). Drawback is it is hard for the company to tune. The goal of the company is to maximize retention of customers.\\nViral engine. This engine relies on the number of other customers that each person infects. The company wants to increase this infection rate. \\nPaid customer acquisition. This engine has the company pay to attract more customers. In the long term, this enginer relies on costs of acquisition being less than revenue generated from each customer. \",\"Key ideas\":\"1. The book by Eric Reis is called The Lean Startup.\\n2. There are three different engines of growth: sticky engine, viral engine, and paid customer acquisition.\\n3. The sticky engine is when it is hard for someone to change away from your product. The goal of the company is to maximize retention of customers.\\n4. The viral engine relies on the number of other customers that each person infects. The company wants to increase this infection rate.\\n5. Paid customer acquisition has the company pay to attract more customers. In the long term, this engine relies on costs of acquisition being less than revenue generated from each customer.\",\"Abstraction groups\":{\"-1\":[\"Eric Reis\",\"Lean Startup\",\"Sticky\",\"Retention\",\"Viral\",\"Infection\",\"Acquisition\",\"Revenue\"],\"0\":[\"Growth Engine\"],\"1\":[\"The Lean Startup\",\"Eric Reis\"],\"2\":[\"Business\",\"Entrepreneurship\"],\"3\":[\"Economics\",\"Management\"],\"4\":[\"Social Science\"]}},\"351\":{\"Question\":\"Eric Reis - The lean startup - key assumptions to be validated for every startup \",\"Answer\":\"Growth hypothesis\\nValue hypothesis \",\"Key ideas\":\"\\n1. Eric Reis is the author of the book The Lean Startup.\\n2. The Lean Startup is a methodology for developing businesses and products.\\n3. The Lean Startup methodology involves validating two key assumptions:\\n    a. Growth hypothesis: This is the assumption that the product or service will be able to grow and scale.\\n    b. Value hypothesis: This is the assumption that the product or service will provide value to customers.\",\"Abstraction groups\":{\"-1\":[\"Eric Reis\",\"Lean Startup\",\"Growth\",\"Value\",\"Hypothesis\"],\"0\":[\"Lean Startup\"],\"1\":[\"Business\",\"Validation\"],\"2\":[\"Entrepreneurship\",\"Product Development\"],\"3\":[\"Innovation\",\"Strategy\"],\"4\":[\"Management\"]}},\"352\":{\"Question\":\"Texas sequence of events leading to power failure: What was second failure point that could have been prevented, between natural gas pipelines and homes and power generators? \",\"Answer\":\"Major power generators which use natural gas should not have been taken offline.\\nOr if they were offline, the water cooling pipes should not have been stopped from circulating so that they froze. \",\"Key ideas\":\"1. Texas experienced a power failure due to a sequence of events. \\n2. Natural gas pipelines were the first failure point. \\n3. Major power generators which use natural gas were the second failure point. \\n4. The water cooling pipes of the power generators should not have been stopped from circulating, which caused them to freeze. \\n5. If the power generators had not been taken offline, the water cooling pipes would not have frozen. \\n6. The power failure caused homes and businesses to lose power.\",\"Abstraction groups\":{\"-1\":[\"Texas\",\"Power Failure\",\"Natural Gas\",\"Pipeline\",\"Generator\",\"Water Cooling\",\"Freezing\",\"Home\",\"Business\"],\"0\":[\"Power Failure\"],\"1\":[\"Texas\",\"Natural Gas\",\"Generator\",\"Water Cooling\",\"Freezing\"],\"2\":[\"Pipeline\",\"Home\",\"Business\"],\"3\":[\"Sequence Of Events\",\"Prevention\"],\"4\":[\"Cause And Effect\"]}},\"353\":{\"Question\":\"What was Jim allisons first big discovery? \",\"Answer\":\"He found the T cell receptor molecule in the immune system \",\"Key ideas\":\"1. Jim Allison was a scientist. \\n2. He made a major discovery in the field of immunology. \\n3. The discovery was the T cell receptor molecule. \\n4. The T cell receptor molecule is part of the immune system.\",\"Abstraction groups\":{\"-1\":[\"Jim\",\"Discovery\",\"T Cell\",\"Receptor\",\"Molecule\",\"Immune\"],\"0\":[\"Jim Allison\"],\"1\":[\"Discovery\",\"Immunology\"],\"2\":[\"Science\",\"Medicine\"],\"3\":[\"Research\",\"Knowledge\"],\"4\":[\"Learning\",\"Understanding\"]}},\"354\":{\"Question\":\"What was the change in clinical trial benchmark necessary for the drug ipilimumab to be approved by the FDA? \",\"Answer\":\"They were going to shut down clinical trials because the benchmark for chemotherapy drugs was a certain amount of tumor size reduction after 12 weeks (around 30 percent). But ipilimumab was a different mechanism than chemotherapy drugs, and they needed to instead do long term survival as the benchmark.\",\"Key ideas\":\"\\n1. The FDA had to approve the drug ipilimumab. \\n2. The benchmark for approval was different than the benchmark for chemotherapy drugs. \\n3. The benchmark for chemotherapy drugs was a certain amount of tumor size reduction after 12 weeks (around 30 percent). \\n4. The benchmark for ipilimumab was long term survival.\",\"Abstraction groups\":{\"-1\":[\"FDA\",\"Drug\",\"Benchmark\",\"Chemotherapy\",\"Tumor\",\"Size\",\"Reduction\",\"Week\",\"Ipilimumab\",\"Mechanism\",\"Survival\"],\"0\":[\"Clinical Trial Benchmark\"],\"1\":[\"Drug Approval\",\"Clinical Trials\"],\"2\":[\"FDA Regulations\",\"Medical Research\"],\"3\":[\"Health Care\",\"Science\"],\"4\":[\"Human Activity\"]}},\"355\":{\"Question\":\"Texas sequence of events leading to power failure: What was first failure point that could have been prevented, between wind turbines and natural gas pipelines? \",\"Answer\":\"The natural gas pipeline compressors were all electric, and so they failed when electricity failed. Some of these compressors should have been able to be run with other power sources, like natural gas. \",\"Key ideas\":\"\\n1. Texas experienced a power failure. \\n2. Wind turbines and natural gas pipelines were involved in the sequence of events leading to the power failure. \\n3. The first failure point that could have been prevented was the natural gas pipeline compressors. \\n4. The natural gas pipeline compressors were all electric and so they failed when electricity failed. \\n5. Some of the natural gas pipeline compressors should have been able to be run with other power sources, such as natural gas.\",\"Abstraction groups\":{\"-1\":[\"Texas\",\"Power Failure\",\"Wind Turbine\",\"Natural Gas Pipeline\",\"Compressor\",\"Electricity\",\"Other Power Source\",\"Natural Gas\"],\"0\":[\"Texas Power Failure\"],\"1\":[\"Sequence of Events\",\"Power Sources\"],\"2\":[\"Energy\",\"Infrastructure\"],\"3\":[\"Resource\",\"Technology\"],\"4\":[\"Society\"]}},\"356\":{\"Question\":\"When was the last single common ancester of all animals walking on earth? When did Chordates split off? \",\"Answer\":\"Single common ancestor around 950 million years ago\\nChordates split off 600 million years ago with Echinoderms\",\"Key ideas\":\"1. Single common ancestor of all animals\\n2. When the single common ancestor of all animals existed (950 million years ago)\\n3. Chordates (a group of animals)\\n4. When Chordates split off (600 million years ago)\\n5. Echinoderms (a group of animals that Chordates split off from)\",\"Abstraction groups\":{\"-1\":[\"Animal\",\"Ancestor\",\"Chordate\",\"Echinoderm\",\"Split\",\"Year\"],\"0\":[\"Evolution\"],\"1\":[\"Animal\",\"Ancestor\",\"Chordate\",\"Echinoderm\",\"Split\"],\"2\":[\"Biology\",\"Time\"],\"3\":[\"Science\",\"History\"],\"4\":[\"Knowledge\"]}},\"357\":{\"Question\":\"Applications of machine learning in physics: \\nWhat are some examples of generative models applied to science? \",\"Answer\":\"Generating sampling distributions in quantum field theory\\nGenerate molecular dynamics and states \",\"Key ideas\":\"\\n1. Machine learning: a type of artificial intelligence that uses algorithms to learn from data and make predictions.\\n2. Generative models: a type of machine learning algorithm that can generate new data from existing data.\\n3. Quantum field theory: a branch of physics that studies the behavior of particles and fields in space and time.\\n4. Sampling distributions: a type of probability distribution used to estimate the population parameters from a sample.\\n5. Molecular dynamics: a branch of physics that studies the motion of molecules in a system.\\n6. States: a set of properties that describe the behavior of a system.\",\"Abstraction groups\":{\"-1\":[\"Machine Learning\",\"Generative Model\",\"Quantum Field Theory\",\"Sampling Distribution\",\"Molecular Dynamics\",\"State\"],\"0\":[\"Generative Model\"],\"1\":[\"Machine Learning\",\"Physics\"],\"2\":[\"Artificial Intelligence\",\"Science\"],\"3\":[\"Technology\",\"Knowledge\"],\"4\":[\"Understanding\",\"Exploration\"]}},\"358\":{\"Question\":\"Expectation maximization algorithm - how can this be viewed as an optimization of the free energy? \",\"Answer\":\"Define a modified free energy F_q = minus slightly different log likelihood with q(z) distribution over latent variables z for each observation x, plus the entropy of q(z) for each x. \\nYou can prove that this modified free energy bounds the true free energy of the model from above, where the true free energy is just the prediction for each datapoint x by taking is probability over z as an intermediate step. \\nThe expectation maximization algorithm is iteratively updating q(z) for each x, and then optimizing the model parameters theta for fixed q(z) over all x. Then at the end for a new x, you know what q(z) is and you then can predict the observation. \",\"Key ideas\":\"1. The expectation maximization algorithm is an optimization of the free energy. \\n2. Define a modified free energy F_q which is a slightly different log likelihood with q(z) distribution over latent variables z for each observation x, plus the entropy of q(z) for each x. \\n3. The modified free energy bounds the true free energy of the model from above. \\n4. The true free energy is the prediction for each datapoint x by taking its probability over z as an intermediate step. \\n5. The expectation maximization algorithm is iteratively updating q(z) for each x, and then optimizing the model parameters theta for fixed q(z) over all x. \\n6. At the end for a new x, you know what q(z) is and you then can predict the observation.\",\"Abstraction groups\":{\"-1\":[\"Expectation Maximization\",\"Free Energy\",\"Modified Free Energy\",\"Log Likelihood\",\"Latent Variable\",\"Entropy\",\"Model Parameter\",\"Prediction\"],\"0\":[\"Expectation Maximization\"],\"1\":[\"Algorithm Optimization\"],\"2\":[\"Machine Learning\",\"Optimization\"],\"3\":[\"Artificial Intelligence\",\"Mathematics\"],\"4\":[\"Computer Science\"]}},\"359\":{\"Question\":\"Expectation maximization algorithm - what is it most similar to among other clustering algorithms? \",\"Answer\":\"K means clustering \",\"Key ideas\":\"\\n1. Clustering algorithms: algorithms used to group data points into clusters based on similarity.\\n2. Expectation Maximization (EM) algorithm: a clustering algorithm that uses an iterative approach to find the maximum likelihood of a given set of data points.\\n3. K-means clustering: a clustering algorithm that uses a centroid-based approach to group data points into clusters.\\n4. Similarity: the degree to which two data points are alike.\",\"Abstraction groups\":{\"-1\":[\"Clustering\",\"Expectation Maximization\",\"K-Means\",\"Similarity\"],\"0\":[\"Expectation Maximization Algorithm\"],\"1\":[\"Clustering Algorithm\"],\"2\":[\"Machine Learning Algorithm\",\"Data Analysis\"],\"3\":[\"Artificial Intelligence\",\"Computer Science\"],\"4\":[\"Science\"]}},\"360\":{\"Question\":\"What modern encryption system is already an example of homomorphic computing?\\nWhat future technology might be as well? \",\"Answer\":\"RSA\\nQuantum homomorphic computing \",\"Key ideas\":\"1. RSA is a modern encryption system that is an example of homomorphic computing.\\n2. Homomorphic computing is a type of computing that allows for computations to be performed on encrypted data without decrypting it.\\n3. Quantum homomorphic computing is a potential future technology that could also be used for homomorphic computing.\",\"Abstraction groups\":{\"-1\":[\"RSA\",\"Homomorphic\",\"Computing\",\"Quantum\"],\"0\":[\"Encryption\"],\"1\":[\"Homomorphic Computing\",\"RSA\"],\"2\":[\"Computing\",\"Encryption\"],\"3\":[\"Technology\",\"Security\"],\"4\":[\"Information\"]}},\"361\":{\"Question\":\"What is a more general word for a system that lets you encrypt the input to a computation, let someone else compute on it while it remains encrypted, then return you the encrypted result? \",\"Answer\":\"Homomorphic computing \",\"Key ideas\":\"\\n1. Encryption: A process of encoding information in such a way that only authorized parties can access it.\\n2. Computation: A process of performing calculations or other operations on data.\\n3. Homomorphic Computing: A system that allows for computations to be performed on encrypted data without decrypting it.\\n4. Encrypted Input: Data that has been encoded using an encryption algorithm.\\n5. Encrypted Result: The output of a computation performed on encrypted data, which remains encrypted.\",\"Abstraction groups\":{\"-1\":[\"Encryption\",\"Computation\",\"Homomorphic\",\"Input\",\"Result\"],\"0\":[\"Homomorphic Computing\"],\"1\":[\"Encryption\",\"Computation\"],\"2\":[\"Data Security\",\"Data Processing\"],\"3\":[\"Information Technology\",\"Computing\"],\"4\":[\"Technology\"]}},\"362\":{\"Question\":\"RSA encryption details: \\nWhat is the order of a multiplicative ring of size p with p prime?\\nWhat about a ring mod N with N=p*q and p and q prime? \",\"Answer\":\"p-1 and (p-1)*(q-1) apparently.\",\"Key ideas\":\"1. RSA encryption: a type of public-key cryptography used to secure data transmission\\n2. Multiplicative ring: a mathematical structure consisting of a set of elements and operations that satisfy certain properties\\n3. Prime number: a number that is only divisible by 1 and itself\\n4. Order of a multiplicative ring: the number of elements in the ring\\n5. Ring mod N: a type of ring where the elements are the integers modulo N\\n6. N=p*q: N is the product of two prime numbers, p and q\\n7. Answer: the order of a multiplicative ring of size p with p prime is p-1, and the order of a ring mod N with N=p*q and p and q prime is (p-1)*(q-1)\",\"Abstraction groups\":{\"-1\":[\"RSA\",\"Multiplicative\",\"Prime\",\"Order\",\"Ring\",\"N\",\"Answer\"],\"0\":[\"RSA Encryption\"],\"1\":[\"Cryptography\",\"Mathematics\"],\"2\":[\"Security\",\"Algebra\"],\"3\":[\"Technology\",\"Number Theory\"],\"4\":[\"Science\",\"Logic\"]}},\"363\":{\"Question\":\"RSA encryption details: \\nWhat is the order of a multiplicative ring? \",\"Answer\":\"A number which collapses the ring upon exponentiation by that number \",\"Key ideas\":\"\\n1. RSA encryption: a type of public-key cryptography used to secure data transmission\\n2. Multiplicative ring: a mathematical structure consisting of a set of elements and two binary operations (multiplication and addition)\\n3. Order of a multiplicative ring: a number which collapses the ring upon exponentiation by that number\",\"Abstraction groups\":{\"-1\":[\"RSA\",\"Multiplicative\",\"Ring\",\"Order\",\"Exponentiation\"],\"0\":[\"RSA Encryption\"],\"1\":[\"Cryptography\",\"Mathematics\"],\"2\":[\"Security\",\"Algebra\"],\"3\":[\"Technology\",\"Number Theory\"],\"4\":[\"Science\",\"Logic\"]}},\"364\":{\"Question\":\"RSA encryption why does it work, in 1 sentence?\",\"Answer\":\"The point is someone can know N (the mod) and e (the encryptor) but not know the power required to give unity upon exponentiation, so they can't find d (the decryptor power).  \\nIn short, it's a one way function. \",\"Key ideas\":\"\\n1. RSA encryption is a one-way function. \\n2. It works because someone can know N (the mod) and e (the encryptor) but not know the power required to give unity upon exponentiation. \\n3. This means they can't find d (the decryptor power). \\n4. N is the modulus, which is a product of two large prime numbers. \\n5. e is the encryptor, which is a number that is relatively prime to (and less than) the modulus. \\n6. d is the decryptor power, which is the inverse of e modulo the modulus. \\n7. Unity upon exponentiation means that when e is raised to the power of d, the result is 1.\",\"Abstraction groups\":{\"-1\":[\"RSA\",\"Encryption\",\"One-way\",\"N\",\"E\",\"D\",\"Unity\",\"Exponentiation\"],\"0\":[\"RSA Encryption\"],\"1\":[\"Cryptography\",\"Security\"],\"2\":[\"Computer Science\",\"Mathematics\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"365\":{\"Question\":\"RSA encryption: how does it work?\\nMechanics of encryption\\/decryption without proof: \",\"Answer\":\"1. Choose p and q large primes\\n\\t2. N=p*q is a large number which is not divisible by most things\\n\\t6. If we take any message M and exponentiate it by some known number phi(N) times it will come back to itself (mod N).\\n\\t7. Now we choose two integers e and d which are multiplicative inverses mod phi(N), so that if we exponentiate a message M^(e*d), then we know if we take M^(e*d) mod N it must be M, and we're done.\",\"Key ideas\":\"\\n1. RSA encryption is a type of encryption that uses two large prime numbers, p and q, to generate a large number, N, which is not divisible by most things.\\n2. The encryption process involves exponentiating a message, M, by some known number, phi(N), times.\\n3. Two integers, e and d, are chosen which are multiplicative inverses mod phi(N).\\n4. Exponentiating a message, M^(e*d), will result in the original message, M, when taken mod N.\",\"Abstraction groups\":{\"-1\":[\"RSA\",\"Prime\",\"N\",\"Message\",\"Phi(N)\",\"E\",\"D\",\"Exponentiation\",\"Mod N\"],\"0\":[\"RSA Encryption\"],\"1\":[\"Cryptography\",\"Encryption\"],\"2\":[\"Security\",\"Data Protection\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\",\"Technology\"]}},\"366\":{\"Question\":\"What happens to the determinant of the product of two matrices \",\"Answer\":\"It's the product of determinants. \",\"Key ideas\":\"\\n1. Matrices: A matrix is a rectangular array of numbers, symbols, or expressions, arranged in rows and columns. \\n\\n2. Determinant: The determinant of a matrix is a numerical value that can be calculated from the elements of a square matrix. \\n\\n3. Product of two matrices: The product of two matrices is a new matrix that is the result of multiplying each element of one matrix by the corresponding element of the other matrix. \\n\\n4. Determinant of the product of two matrices: The determinant of the product of two matrices is the product of determinants.\",\"Abstraction groups\":{\"-1\":[\"Matrix\",\"Determinant\",\"Product\",\"Product Determinant\"],\"0\":[\"Product Determinant\"],\"1\":[\"Matrix\",\"Determinant\"],\"2\":[\"Algebra\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Learning\"]}},\"367\":{\"Question\":\"Determinant algorithm complexity \",\"Answer\":\"It's n cubed. You add and subtract rows to each other row until it's upper diagonal. Because that doesn't change the determinant it's the same determinant at end and that's the product of diagonals. \",\"Key ideas\":\"\\n1. The complexity of the determinant algorithm is n cubed. \\n2. To calculate the determinant, you add and subtract rows to each other row until it's upper diagonal. \\n3. This process does not change the determinant. \\n4. The determinant at the end is the product of the diagonals.\",\"Abstraction groups\":{\"-1\":[\"Determinant\",\"Algorithm\",\"Complexity\",\"Row\",\"Diagonal\",\"Product\"],\"0\":[\"Determinant Algorithm\"],\"1\":[\"Complexity\",\"Algorithm\"],\"2\":[\"Mathematics\",\"Computation\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"368\":{\"Question\":\"Formula for the inverse of a matrix using the determinant \",\"Answer\":\"The inverse formula uses the laplace form of the matrix determinant. One needs the determinants of the minors and then places these in the entries of the matrix inverse in a certain way with factors of -1 as well, and divides by the determinant.\",\"Key ideas\":\"1. The inverse of a matrix can be calculated using the determinant of the matrix. \\n2. The Laplace form of the matrix determinant is used to calculate the inverse. \\n3. The determinants of the minors of the matrix are needed to calculate the inverse. \\n4. The entries of the matrix inverse are determined by the determinants of the minors and factors of -1. \\n5. The inverse is calculated by dividing the matrix inverse by the determinant.\",\"Abstraction groups\":{\"-1\":[\"Matrix\",\"Inverse\",\"Determinant\",\"Laplace\",\"Minor\",\"Entry\",\"Factor\",\"Divide\"],\"0\":[\"Matrix Inverse\"],\"1\":[\"Mathematics\",\"Algebra\"],\"2\":[\"Science\",\"Problem Solving\"],\"3\":[\"Logic\",\"Reasoning\"],\"4\":[\"Knowledge\"]}},\"369\":{\"Question\":\"What are two formulas to write the determinant of a square matrix? \",\"Answer\":\"Laplace and Leibniz forms:Leibniz form is product over a_i,sigma_i with a new sigma for each i, and sign of permutation, summed over permutations Laplace form is sum over a_in times minus one to i plus j times determinant of minor \",\"Key ideas\":\"1. The determinant of a square matrix can be written using two formulas: Laplace and Leibniz forms. \\n2. The Leibniz form is a product over a_i,sigma_i with a new sigma for each i, and sign of permutation, summed over permutations. \\n3. The Laplace form is a sum over a_in times minus one to i plus j times determinant of minor.\",\"Abstraction groups\":{\"-1\":[\"Determinant\",\"Square Matrix\",\"Laplace\",\"Leibniz\",\"A_i\",\"Sigma_i\",\"Sign\",\"Permutation\",\"Sum\",\"A_in\",\"Minor\"],\"0\":[\"Determinant\"],\"1\":[\"Square Matrix\",\"Laplace\",\"Leibniz\"],\"2\":[\"Mathematics\",\"Algebra\"],\"3\":[\"Science\",\"Logic\"],\"4\":[\"Knowledge\"]}},\"370\":{\"Question\":\"Jacob Andreas 2022: \\\"Language Models as Agent Models\\\"\\nThe example: Training to predict online reviews on Amazon\\nWhat did they do to try to test the agent modeling hypothesis in the structure of the network?\",\"Answer\":\"There is a specific neuron that encodes sentiment accurately, When training without any knowledge of the review\\nIt accurately predicts review score\\nYou can hold the neuron fixed, and change the sentiment of the output \",\"Key ideas\":\"1. Jacob Andreas 2022 wrote a paper titled \\\"Language Models as Agent Models\\\".\\n2. The example used in the paper was training to predict online reviews on Amazon.\\n3. To test the agent modeling hypothesis in the structure of the network, they used a specific neuron that encodes sentiment accurately.\\n4. When training without any knowledge of the review, the neuron accurately predicts review score.\\n5. To further test the hypothesis, they held the neuron fixed and changed the sentiment of the output.\",\"Abstraction groups\":{\"-1\":[\"Jacob Andreas\",\"Language Model\",\"Agent Model\",\"Online Review\",\"Amazon\",\"Neuron\",\"Sentiment\",\"Review Score\",\"Output\"],\"0\":[\"Agent Modeling\"],\"1\":[\"Language Model\",\"Online Review\",\"Neuron\"],\"2\":[\"Jacob Andreas\",\"Amazon\",\"Sentiment\",\"Review Score\",\"Output\"],\"3\":[\"Training\",\"Prediction\",\"Knowledge\"],\"4\":[\"Hypothesis Testing\"]}},\"371\":{\"Question\":\"Jacob Andreas 2022: \\\"Language Models as Agent Models\\\"\\nThe example: Training to predict online reviews on Amazon\\nWhy is this so great of an example?\",\"Answer\":\"This is a good example because the goals and beliefs of authors differ wildly, but otherwise content is rather similar \",\"Key ideas\":\"\\n1. Language Models: A type of artificial intelligence that uses statistical methods to generate natural language.\\n\\n2. Agent Models: A type of artificial intelligence that uses a set of rules to simulate the behavior of an agent.\\n\\n3. Training: The process of teaching a machine learning algorithm to recognize patterns in data.\\n\\n4. Online Reviews: Reviews written by customers about products or services they have purchased.\\n\\n5. Amazon: An online retail platform.\\n\\n6. Goals and Beliefs: The motivations and opinions of authors when writing reviews.\\n\\n7. Content: The information contained in reviews.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Agent Model\",\"Training\",\"Online Review\",\"Amazon\",\"Goal\",\"Belief\",\"Content\"],\"0\":[\"Language Model\"],\"1\":[\"Artificial Intelligence\",\"Machine Learning\"],\"2\":[\"Technology\",\"Computing\"],\"3\":[\"Science\",\"Research\"],\"4\":[\"Knowledge\"]}},\"372\":{\"Question\":\"Jacob Andreas 2022: \\\"Language Models as Agent Models\\\"\\nFor the example given: Providing factually irrelevant context about the type of person, what was shown? What was the interpretation?\",\"Answer\":\"Result: prompting agent information without any factual relevance can affect accuracy on other factual questions. The example given was \\\"can coughing cause heart attacks?\\\" Conditioning on an agent that is a professor and wants to be truthful is better than conditioning on someone who loves alternative medicine. \\nConclusion: the model is inferring the agent beliefs and knowledge \\n---------\\nBold interpretation is that this implies that the LM has learned to encode truth, but has to be conditioned to give an agent who tells the truth: \\nIt is possible... \\u201cthat LMs do distinguish true answers from false ones; and, in a document context indicating that an author\\u2019s goal is to inform, could generate truthful answers preferentially.\\u201d \",\"Key ideas\":\"1. Language Models (LMs) can be used as Agent Models. \\n2. Providing factually irrelevant context about the type of person can affect accuracy on other factual questions. \\n3. The example given was \\\"can coughing cause heart attacks?\\\"\\n4. Conditioning on an agent that is a professor and wants to be truthful is better than conditioning on someone who loves alternative medicine. \\n5. The model is inferring the agent beliefs and knowledge. \\n6. It is possible that LMs do distinguish true answers from false ones. \\n7. In a document context indicating that an author\\u2019s goal is to inform, LMs could generate truthful answers preferentially.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Agent Model\",\"Factually Irrelevant Context\",\"Accuracy\",\"Coughing\",\"Heart Attack\",\"Professor\",\"Alternative Medicine\",\"Agent Belief\",\"Knowledge\",\"True Answer\",\"False Answer\",\"Document Context\",\"Author's Goal\",\"Truthful Answer\"],\"0\":[\"Language Model\"],\"1\":[\"Agent Model\",\"Factually Irrelevant Context\"],\"2\":[\"Accuracy\",\"Coughing\",\"Heart Attack\"],\"3\":[\"Professor\",\"Alternative Medicine\",\"Agent Belief\",\"Knowledge\"],\"4\":[\"True Answer\",\"False Answer\",\"Document Context\",\"Author's Goal\",\"Truthful Answer\"]}},\"373\":{\"Question\":\"Jacob Andreas 2022: \\\"Language Models as Agent Models\\\"\\nFor example of explicit training on a toy model of 3 populations to show internal agent coherence and population decoherence, what are the main outcomes, and interpretation?\",\"Answer\":\"Without context, next output is a random sample of the 3 populations \\nWith conditioning on some beliefs, you find outputs broadly follow the conditioned population behavior \\n-------\\nInterpretation: \\u201c[The LM] can infer author identity, and when properly conditioned can imitate individual authors. The LM is not an A-type agent, or an O-type one, but can be straightforwardly made to act like one given the right hidden state.\\u201d\",\"Key ideas\":\"1. Language Models (LMs) can be used as Agent Models.\\n2. Jacob Andreas 2022 studied the use of explicit training on a toy model of 3 populations to show internal agent coherence and population decoherence.\\n3. Without context, the next output of the LM is a random sample of the 3 populations.\\n4. With conditioning on some beliefs, the outputs of the LM broadly follow the conditioned population behavior.\\n5. The LM can infer author identity and imitate individual authors when properly conditioned.\\n6. The LM is not an A-type agent or an O-type one, but can be made to act like one given the right hidden state.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Agent Model\",\"Jacob Andreas\",\"Toy Model\",\"3 Population\",\"Internal Coherence\",\"Population Decoherence\",\"Context\",\"Belief\",\"Output\",\"Author Identity\",\"A-type Agent\",\"O-type Agent\",\"Hidden State\"],\"0\":[\"Language Model\"],\"1\":[\"Agent Model\",\"Training\"],\"2\":[\"Toy Model\",\"Population\"],\"3\":[\"Coherence\",\"Decoherence\"],\"4\":[\"Belief\",\"Context\",\"Output\"]}},\"374\":{\"Question\":\"Jacob Andreas 2022: \\\"Language Models as Agent Models\\\"\\nWhat are the 3 examples given in the paper to show how LMs construct agent models?\",\"Answer\":\"Explicit training of a toy model of 3 population\\nPrompt engineering: professor smith vs someone who loves alternative medicine\\nTraining to predict online reviews on Amazon\",\"Key ideas\":\"1. Language Models (LMs) can be used to construct agent models.\\n2. The paper \\\"Jacob Andreas 2022: Language Models as Agent Models\\\" provides 3 examples of how LMs construct agent models.\\n3. The first example is explicit training of a toy model of 3 populations.\\n4. The second example is prompt engineering, which is the process of creating a prompt to differentiate between two different agents, such as Professor Smith and someone who loves alternative medicine.\\n5. The third example is training to predict online reviews on Amazon.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Agent Model\",\"Toy Model\",\"Prompt Engineering\",\"Professor Smith\",\"Alternative Medicine\",\"Online Review\",\"Amazon\"],\"0\":[\"Language Model\"],\"1\":[\"Agent Model\",\"Prompt Engineering\",\"Online Review\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\",\"Natural Language Processing\"],\"3\":[\"Computer Science\",\"Data Science\"],\"4\":[\"Science\"]}},\"375\":{\"Question\":\"Jacob Andreas 2022: \\\"Language Models as Agent Models\\\"\\nWhat are possible solutions to improve the agent modeling capabilities of language models\",\"Answer\":\"1. Expand the context size input to transformers to not be limited, so that the history of the conversation or words of an agent can be inferred. \\n2. Use a scratch pad for encoding information, and feed it to the model along with a new prompt when necessary.\",\"Key ideas\":\"1. Language models can be used as agent models.\\n2. Transformers are limited in their ability to infer the history of a conversation or words of an agent.\\n3. A scratch pad can be used to encode information and feed it to the model along with a new prompt when necessary.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Agent Model\",\"Transformer\",\"Context Size\",\"Scratch Pad\",\"Prompt\"],\"0\":[\"Agent Modeling\"],\"1\":[\"Language Model\",\"Solution\"],\"2\":[\"Improving Capability\",\"Input\"],\"3\":[\"Agent Interaction\",\"Data Representation\"],\"4\":[\"Artificial Intelligence\"]}},\"376\":{\"Question\":\"Jacob Andreas 2022: \\\"Language Models as Agent Models\\\"\\nWhat is the mechanics (training data and training process) by which language models construct agent models?\",\"Answer\":\"How this inference of beliefs and conditioning on beliefs selected for during training?\\nLMs are trained on populations of internally coherent agents, but who don\\u2019t necessarily share the same desires and beliefs. \\nIn order to more accurately predict text, it\\u2019s useful to figure out what type of agent you are working with. \",\"Key ideas\":\"1. Language Models (LMs) are trained on populations of internally coherent agents.\\n2. These agents may not necessarily share the same desires and beliefs.\\n3. In order to more accurately predict text, it is useful to figure out what type of agent you are working with.\\n4. This inference of beliefs and conditioning on beliefs is selected for during training.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Agent\",\"Desire\",\"Belief\",\"Inference\",\"Conditioning\",\"Training\"],\"0\":[\"Agent Model\"],\"1\":[\"Language Model\",\"Training Data\",\"Training Process\"],\"2\":[\"Inference\",\"Belief\",\"Conditioning\"],\"3\":[\"Artificial Intelligence\"],\"4\":[\"Machine Learning\"]}},\"377\":{\"Question\":\"Jacob Andreas 2022: \\\"Language Models as Agent Models\\\"\\nWhat is the significance of the main claim that language models construct belief representations?\",\"Answer\":\"This lens can help predict why something fails, and how to fix it \",\"Key ideas\":\"1. Language models are agent models. \\n2. Language models construct belief representations. \\n3. Belief representations can help predict why something fails and how to fix it.\",\"Abstraction groups\":{\"-1\":[\"Jacob Andreas\",2022,\"Language Model\",\"Agent Model\",\"Belief Representation\",\"Prediction\",\"Failure\",\"Fix\"],\"0\":[\"Jacob Andreas 2022\"],\"1\":[\"Language Model\",\"Agent Model\"],\"2\":[\"Belief Representation\",\"Prediction\"],\"3\":[\"Failure\",\"Fix\"],\"4\":[\"Research\"]}},\"378\":{\"Question\":\"Jacob Andreas 2022: \\\"Language Models as Agent Models\\\"\\nWhat is the main claim?\",\"Answer\":\"Main claim is language models are pushed to do two things in order to make accurate predictions (beyond grammar)\\nInfer the beliefs and desires of an agent in a prompt\\nUse that inference to predict marginal tokens \",\"Key ideas\":\"1. Language models are pushed to do two things in order to make accurate predictions beyond grammar.\\n2. Infer the beliefs and desires of an agent in a prompt.\\n3. Use that inference to predict marginal tokens.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Prediction\",\"Grammar\",\"Belief\",\"Desire\",\"Prompt\",\"Token\"],\"0\":[\"Language Model\"],\"1\":[\"Agent Model\",\"Language Processing\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\"],\"3\":[\"Computer Science\",\"Cognitive Science\"],\"4\":[\"Science\"]}},\"379\":{\"Question\":\"Vision of metascience by Nielsen and Qiu:\\nWhat are specific suggestions for changing the social processes where science is done? \",\"Answer\":\"Grant funding orgs\\nFund for variance of outcomes\\nFund high risk by having a guaranteed fraction of failures rather than successes as the metric for a grant institution \\nFunding individuals\\nFund fellowships to change disciplines for scientists, to inject disciplinary liquidity \\nFund stable professorships for under 25 people to encourage them to be bold \\nTenure insurance\",\"Key ideas\":\"\\n1. Grant funding organizations should fund for variance of outcomes and high risk projects, with a guaranteed fraction of failures as the metric for success.\\n2. Funding should be provided to individuals, such as fellowships to change disciplines for scientists and stable professorships for under 25 people to encourage boldness.\\n3. Tenure insurance should be provided.\",\"Abstraction groups\":{\"-1\":[\"Grant\",\"Variance\",\"High Risk\",\"Failure\",\"Funding\",\"Fellowship\",\"Discipline\",\"Professorship\",\"Tenure\",\"Insurance\"],\"0\":[\"Science\"],\"1\":[\"Metascience\",\"Social Processes\"],\"2\":[\"Change\",\"Grant Funding\"],\"3\":[\"Individual\",\"Fellowship\",\"Professorship\"],\"4\":[\"Funding\",\"Insurance\"]}},\"380\":{\"Question\":\"Vision of metascience by Nielsen and Qiu:\\nWhat would success look like? \",\"Answer\":\"Fields, Organizations, and Culture\\n----\\n10x the rate of new fields being created\\nHave garage band research organizations grow to worldwide pre-eminence (indicates some new method works, and is allowed within the system)\\nLarge cultural shifts in what is acceptable for a scientist to do\",\"Key ideas\":\"\\n1. Vision of metascience by Nielsen and Qiu:\\n2. Success would look like:\\n    a. 10x the rate of new fields being created\\n    b. Garage band research organizations growing to worldwide pre-eminence\\n    c. Large cultural shifts in what is acceptable for a scientist to do\",\"Abstraction groups\":{\"-1\":[\"Metascience\",\"Nielsen\",\"Qiu\",\"Field\",\"Organization\",\"Culture\",\"Rate\",\"Research\",\"Pre-eminence\",\"Scientist\"],\"0\":[\"Metascience\"],\"1\":[\"Vision\",\"Success\"],\"2\":[\"Field\",\"Organization\",\"Culture\"],\"3\":[\"Rate\",\"Research\",\"Pre-eminence\"],\"4\":[\"Scientist\"]}},\"381\":{\"Question\":\"Vision of metascience by Nielsen and Qiu:\\nWhat are aspects of the scientific system that can change? \",\"Answer\":\"institutional practices (like funding), \\nincentives, \\nnorms \",\"Key ideas\":\"1. Vision of metascience by Nielsen and Qiu:\\n2. Aspects of the scientific system that can change:\\n    a. Institutional practices (like funding)\\n    b. Incentives\\n    c. Norms\",\"Abstraction groups\":{\"-1\":[\"Metascience\",\"Nielsen\",\"Qiu\",\"System\",\"Funding\",\"Incentive\",\"Norm\"],\"0\":[\"Metascience\"],\"1\":[\"Scientific System\",\"Change\"],\"2\":[\"Knowledge\",\"Practices\"],\"3\":[\"Incentives\",\"Norms\"],\"4\":[\"Funding\"]}},\"382\":{\"Question\":\"Vision of metascience by Nielsen and Qiu:\\nWhat is the main point of the article? \",\"Answer\":\"They argue it is necessary to increase the diversity of social processes where people do science in order to systematically find the most productive methods of figuring things out. \",\"Key ideas\":\"\\n1. Metascience is a vision proposed by Nielsen and Qiu. \\n2. This vision suggests that it is necessary to increase the diversity of social processes where people do science. \\n3. This is in order to systematically find the most productive methods of figuring things out.\",\"Abstraction groups\":{\"-1\":[\"Metascience\",\"Nielsen\",\"Qiu\",\"Diversity\",\"Social Process\",\"Science\",\"Productive Method\",\"Figuring Things Out\"],\"0\":[\"Metascience\"],\"1\":[\"Vision\",\"Nielsen\",\"Qiu\"],\"2\":[\"Diversity\",\"Social Process\"],\"3\":[\"Science\",\"Productive Method\"],\"4\":[\"Figuring Things Out\"]}},\"383\":{\"Question\":\"What is the functional form of the Boltzmann machine loss function as derived from maximization of log likelihood of data \",\"Answer\":\"Training method: Maximize <log (p_model(x))>_data for x from data.\\nThis turns into -<E_data> - log(Z_model)\\nThen take derivative to get -dE_data +dE_model.\\nSo during training, to increase the likelihood of the model predicting data, change the model parameters which increase the model energy (evaluated everywhere, including AWAY from the training data), compared to increasing the energy of the data.\\nThis reduces the probability of not-seen data compared to seen data \",\"Key ideas\":\"1. The Boltzmann machine loss function is derived from maximization of log likelihood of data.\\n2. The training method involves maximizing the log probability of the model given the data.\\n3. This can be expressed as the expectation of the data minus the log of the model partition function.\\n4. To increase the likelihood of the model predicting data, the model parameters must be changed to increase the model energy, compared to increasing the energy of the data.\\n5. This reduces the probability of not-seen data compared to seen data.\",\"Abstraction groups\":{\"-1\":[\"Boltzmann Machine\",\"Loss Function\",\"Log Likelihood\",\"Training\",\"Expectation\",\"Partition Function\",\"Model Parameter\",\"Model Energy\",\"Data Energy\",\"Probability\"],\"0\":[\"Boltzmann Machine\"],\"1\":[\"Loss Function\",\"Log Likelihood\",\"Training\"],\"2\":[\"Maximization\",\"Expectation\",\"Partition Function\",\"Model Parameters\",\"Model Energy\",\"Data Energy\",\"Probability\"],\"3\":[\"Machine Learning\",\"Statistical Modeling\",\"Data Analysis\"],\"4\":[\"Mathematics\"]}},\"384\":{\"Question\":\"What are the two approaches to regularization in ML? \",\"Answer\":\"1. Add noise to the learning process so that specific solutions aren't overfit. Examples of this are the dropout method and batchnorm method in neural network training. \\n2. Penalize the size of parameters used in the model, or the number of parameters used. This can be done using the cost function in gradient descent, for example.\",\"Key ideas\":\"1. Regularization is a technique used in machine learning to reduce overfitting. \\n2. There are two approaches to regularization: \\n    a. Adding noise to the learning process, such as with the dropout and batchnorm methods in neural network training. \\n    b. Penalizing the size of parameters used in the model, or the number of parameters used, such as with the cost function in gradient descent.\",\"Abstraction groups\":{\"-1\":[\"Regularization\",\"ML\",\"Noise\",\"Dropout\",\"Batchnorm\",\"Parameter\",\"Cost Function\",\"Gradient Descent\"],\"0\":[\"Regularization\"],\"1\":[\"Machine Learning\"],\"2\":[\"Artificial Intelligence\",\"Data Science\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"385\":{\"Question\":\"What is the quantum trotterization algorithm for time evolution? \",\"Answer\":\"It is a mathematical formula that lets you exponentiate a sum of matrices which do not necessarily commute, but exponentiating small fractions of them many times in repetition. This is useful for Hamiltonian time evolution in quantum mechanics, which often requires exponentiating time multiplied by a hamiltonian matrix which has many components that do not commute. \",\"Key ideas\":\"1. Quantum Trotterization Algorithm is a mathematical formula that allows you to exponentiate a sum of matrices which do not necessarily commute. \\n2. Exponentiating small fractions of the matrices many times in repetition is useful for Hamiltonian time evolution in quantum mechanics. \\n3. Hamiltonian time evolution often requires exponentiating time multiplied by a Hamiltonian matrix which has many components that do not commute.\",\"Abstraction groups\":{\"-1\":[\"Quantum Trotterization\",\"Matrix\",\"Exponentiating\",\"Time Evolution\",\"Hamiltonian\",\"Quantum Mechanics\"],\"0\":[\"Quantum Trotterization\"],\"1\":[\"Algorithm\",\"Time Evolution\"],\"2\":[\"Mathematics\",\"Quantum Mechanics\"],\"3\":[\"Physics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"386\":{\"Question\":\"What is a challenge for neuralink using RL on neuronal output from the brain? \",\"Answer\":\"It is not stable over time (on the brain side).\\nHardware can change or decay too. \",\"Key ideas\":\"1. Neuralink: a technology developed by Elon Musk's company, Neuralink, which uses robotics and artificial intelligence to connect the human brain to computers.\\n2. RL: Reinforcement Learning, a type of machine learning algorithm that uses rewards and punishments to learn from its environment.\\n3. Neuronal output: the signals sent from neurons in the brain to other parts of the body.\\n4. Not stable over time: the signals sent from the brain can change or decay over time, making it difficult to use RL on neuronal output from the brain.\\n5. Hardware: the physical components of the Neuralink technology, which can also change or decay over time.\",\"Abstraction groups\":{\"-1\":[\"Neuralink\",\"RL\",\"Neuronal Output\",\"Time\",\"Hardware\"],\"0\":[\"Neuralink\"],\"1\":[\"Reinforcement Learning\",\"Neuronal Output\"],\"2\":[\"Brain\",\"Time\",\"Hardware\"],\"3\":[\"Artificial Intelligence\",\"Robotics\"],\"4\":[\"Technology\"]}},\"387\":{\"Question\":\"What is an autogyro, and how is it different from a helicopter? \",\"Answer\":\"Air moves up through blades rather than down.\\nIsn't powered.\\nLift is up and back from the rotating blades, so you need a front propellor for steady state. \\nFor helicopter, lift and thrust are forward and sufficient to cancel drag from airframe. \",\"Key ideas\":\"\\n1. An autogyro is an aircraft that is not powered, and instead relies on air moving up through blades to generate lift.\\n2. Unlike a helicopter, the lift generated by an autogyro is up and back from the rotating blades, so a front propellor is needed for steady state.\\n3. For a helicopter, lift and thrust are forward and sufficient to cancel drag from the airframe.\",\"Abstraction groups\":{\"-1\":[\"Autogyro\",\"Helicopter\",\"Blade\",\"Lift\",\"Thrust\",\"Propellor\",\"Drag\"],\"0\":[\"Autogyro\"],\"1\":[\"Aircraft\",\"Rotorcraft\"],\"2\":[\"Aviation\",\"Transportation\"],\"3\":[\"Technology\",\"Engineering\"],\"4\":[\"Science\"]}},\"388\":{\"Question\":\"What are examples of multimodal neurons from OpenAI's CLIP model?\",\"Answer\":\"Emotions and faces\\nPhysical regions\\nPeople \",\"Key ideas\":\"1. OpenAI's CLIP model is a multimodal neuron.\\n2. Multimodal neurons are neurons that can process multiple types of data.\\n3. Examples of multimodal neurons from OpenAI's CLIP model include:\\n    a. Emotions and faces\\n    b. Physical regions\\n    c. People\",\"Abstraction groups\":{\"-1\":[\"OpenAI\",\"CLIP\",\"Multimodal\",\"Emotion\",\"Face\",\"Physical\",\"Region\",\"Person\"],\"0\":[\"Multimodal Neuron\"],\"1\":[\"OpenAI\",\"CLIP\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"389\":{\"Question\":\"What is useful about knowing what neurons are multimodal in a model (openAI microscope and CLIP)? \",\"Answer\":\"Interpretability of knowledge encoding.\\nYou can see what the model associates together: for example, you can observe what other topics activate the \\\"donald trump\\\" neuron, and by how much. This is the association between concepts encoded in the network. \",\"Key ideas\":\"\\n1. Neurons: \\n    a. What they are \\n    b. How they are used in models \\n2. OpenAI Microscope and CLIP: \\n    a. What they are \\n    b. How they are used in models \\n3. Interpretability of knowledge encoding: \\n    a. What it is \\n    b. How it is used to observe what other topics activate the \\\"donald trump\\\" neuron \\n    c. How it is used to observe the association between concepts encoded in the network\",\"Abstraction groups\":{\"-1\":[\"Neuron\",\"OpenAI\",\"CLIP\",\"Interpretability\",\"Knowledge\",\"Encoding\",\"Donald Trump\"],\"0\":[\"Neuron\"],\"1\":[\"Multimodal\",\"Model\"],\"2\":[\"Interpretability\",\"Knowledge\"],\"3\":[\"Encoding\",\"Activation\"],\"4\":[\"Understanding\"]}},\"390\":{\"Question\":\"How did OpenAI identify multimodal neurons \",\"Answer\":\"Label concepts, then see which neurons activate most strongly when concept is presented in multiple modalities. \",\"Key ideas\":\"1. OpenAI: OpenAI is an artificial intelligence research laboratory founded in 2015.\\n2. Multimodal neurons: Multimodal neurons are neurons that respond to multiple types of stimuli.\\n3. Identifying multimodal neurons: OpenAI identified multimodal neurons by labeling concepts and then observing which neurons activated most strongly when the concept was presented in multiple modalities.\",\"Abstraction groups\":{\"-1\":[\"OpenAI\",\"Multimodal\",\"Neuron\",\"Label\",\"Concept\",\"Modality\"],\"0\":[\"OpenAI\"],\"1\":[\"Artificial Intelligence\",\"Research\"],\"2\":[\"Technology\",\"Science\"],\"3\":[\"Knowledge\",\"Understanding\"],\"4\":[\"Learning\"]}},\"391\":{\"Question\":\"What is a multimodal neuron in a neural net? \",\"Answer\":\"Something that encodes a concept from multiple modalities.\\nExample: a spider man neuron that firest for words, drawings, movie shots, etc \",\"Key ideas\":\"\\n1. A multimodal neuron is a neuron in a neural net. \\n2. It encodes a concept from multiple modalities. \\n3. Examples of modalities include words, drawings, movie shots, etc.\",\"Abstraction groups\":{\"-1\":[\"Neuron\",\"Neural Net\",\"Modality\",\"Word\",\"Drawing\",\"Movie Shot\"],\"0\":[\"Multimodal Neuron\"],\"1\":[\"Neural Net\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"392\":{\"Question\":\"How is alphacode a realistic attempt to solve the main problems in RL? What does it do that is important. \\nConversely, how is it probably not a big step forward (just more compute, but not a smart new solution)? \",\"Answer\":\"It tries to address complex analytic reasoning with natural language input. \\nHowever, it seems to just be massive sample size and filtering solution, rather than reasoning. \\n------------\\nComment on 1: Still has all the problems of transformers: doesn't address making the model agentic, or learning over time from few examples with feedback, or memory, or encoding logic.\\nComment on 2: it doesn't seem to operate like a human brain does. Massive sampling to succeed isn't efficient encoding of a sample solution. There has to be a better way.  \\nSeems like success rate scales exponentially badly with problem difficulty.\",\"Key ideas\":\"\\n1. Alphacode is a realistic attempt to solve the main problems in RL by trying to address complex analytic reasoning with natural language input. \\n2. Alphacode is probably not a big step forward, as it is just a massive sample size and filtering solution, rather than reasoning. \\n3. Alphacode still has all the problems of transformers, such as not addressing making the model agentic, or learning over time from few examples with feedback, or memory, or encoding logic.\\n4. Alphacode does not operate like a human brain does, as massive sampling to succeed isn't efficient encoding of a sample solution.\\n5. The success rate of Alphacode scales exponentially badly with problem difficulty.\",\"Abstraction groups\":{\"-1\":[\"Alphacode\",\"RL\",\"Natural Language\",\"Sample Size\",\"Filtering\",\"Transformer\",\"Agentic\",\"Feedback\",\"Memory\",\"Logic\",\"Human Brain\",\"Sample Solution\",\"Problem Difficulty\"],\"0\":[\"Alphacode\"],\"1\":[\"Artificial Intelligence\",\"Reinforcement Learning\"],\"2\":[\"Machine Learning\",\"Computational Thinking\"],\"3\":[\"Computer Science\",\"Cognitive Science\"],\"4\":[\"Science\",\"Technology\",\"Engineering\",\"Mathematics\"]}},\"393\":{\"Question\":\"What is the scary part of the alpha-code success rate scaling with sample size?\\nHow does this compare to other problems in computer science? \",\"Answer\":\"It scales logarithmically with the number of potential solutions generated. \\nBut larger models have a better scaling coefficient at least. \\n-----\\nInterpretation: the success rate is a proxy for the hardness of a problem (20% success vs 40% success means roughly 2x harder)\\nWith this interpretation, the AlphaCode success rate scales exponentially badly with the \\\"hardness\\\" of the problem.\",\"Key ideas\":\"\\n1. AlphaCode success rate is a proxy for the hardness of a problem.\\n2. The success rate scales logarithmically with the number of potential solutions generated.\\n3. Larger models have a better scaling coefficient.\\n4. The AlphaCode success rate scales exponentially badly with the \\\"hardness\\\" of the problem.\",\"Abstraction groups\":{\"-1\":[\"AlphaCode\",\"Success Rate\",\"Sample Size\",\"Scaling\",\"Computer Science\",\"Model\",\"Hardness\"],\"0\":[\"AlphaCode\"],\"1\":[\"Success Rate\",\"Scaling\"],\"2\":[\"Computer Science\",\"Models\"],\"3\":[\"Sample Size\",\"Hardness\"],\"4\":[\"Problem Solving\"]}},\"394\":{\"Question\":\"How does alphacode success compare to basic transformer-based submissions? \",\"Answer\":\"30% success vs single digits success. \\nMain reason: sample filtering.\\n--------\\nCAUTION; might just be massive solution sampling? (log scaling with sample number)\",\"Key ideas\":\"1. Alphacode success is 30% compared to single digits success for basic transformer-based submissions. \\n2. The main reason for this is sample filtering. \\n3. It is possible that the success rate is due to massive solution sampling, which is a log scaling with sample number.\",\"Abstraction groups\":{\"-1\":[\"Alphacode\",\"Transformer\",\"Success\",\"Sample\",\"Filtering\",\"Log\",\"Scaling\",\"Number\"],\"0\":[\"Alphacode\"],\"1\":[\"Success\",\"Sample Filtering\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"395\":{\"Question\":\"Alphacode what are the two main advancements that allow human-level success? \",\"Answer\":\"Higher compute (large dataset of codebase problems of similar structure)\\nPopulation filtering of samples. \",\"Key ideas\":\"1. Alphacode is a type of computer programming language.\\n2. Human-level success in computer programming requires two main advancements:\\n    a. Higher compute, which involves having a large dataset of codebase problems of similar structure.\\n    b. Population filtering of samples, which involves selecting the best solutions from a large set of possible solutions.\",\"Abstraction groups\":{\"-1\":[\"Alphacode\",\"Compute\",\"Dataset\",\"Structure\",\"Sample\",\"Solution\",\"Success\"],\"0\":[\"Alphacode\"],\"1\":[\"Computer Programming\",\"Programming Language\"],\"2\":[\"Computer Science\",\"Technology\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Learning\",\"Education\"]}},\"396\":{\"Question\":\"Alphacode what is the architecture? \",\"Answer\":\"Transformer to generate sample submissions\\nPopulation clustering and selection to generate 10 actual submissions (crucial)\",\"Key ideas\":\"\\n1. Alphacode is an architecture that uses a Transformer to generate sample submissions.\\n2. Population clustering and selection is used to generate 10 actual submissions, which is a crucial part of the architecture.\",\"Abstraction groups\":{\"-1\":[\"Alphacode\",\"Transformer\",\"Sample\",\"Population\",\"Selection\",\"Submission\"],\"0\":[\"Alphacode\"],\"1\":[\"Architecture\",\"Submission\"],\"2\":[\"Generating\",\"Clustering\"],\"3\":[\"Technology\",\"Process\"],\"4\":[\"Computing\"]}},\"397\":{\"Question\":\"What is the Lotka volterra model of population dynamics? \",\"Answer\":\"The growth rate of each species is simply a constant times the population density, but at second order there are also interactions between the growth rate of a single species and the density of other species \",\"Key ideas\":\"1. The Lotka Volterra model is a model of population dynamics. \\n2. The growth rate of each species is determined by a constant multiplied by the population density. \\n3. There are second order interactions between the growth rate of a single species and the density of other species.\",\"Abstraction groups\":{\"-1\":[\"Lotka Volterra\",\"Population\",\"Dynamics\",\"Growth Rate\",\"Density\",\"Interaction\"],\"0\":[\"Lotka Volterra\"],\"1\":[\"Population Dynamics\"],\"2\":[\"Biological Systems\",\"Mathematical Models\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"398\":{\"Question\":\"What is the most basic model of how natural environments determine major animal types in a given biome? \",\"Answer\":\"The prevalence of water and the average temperature determine which plants can live in the biome (ie grasslands have some water and are warm, while tundra has water but is cold, and desert has no water but is warm). Those plants which can exist in a biome determine what types of large animals can exist as well. \",\"Key ideas\":\"1. Different biomes have different average temperatures and levels of water.\\n2. The plants that can exist in a biome determine what types of large animals can exist as well.\\n3. Grasslands have some water and are warm.\\n4. Tundra has water but is cold.\\n5. Desert has no water but is warm.\",\"Abstraction groups\":{\"-1\":[\"Biome\",\"Water\",\"Temperature\",\"Plant\",\"Animal\",\"Grassland\",\"Tundra\",\"Desert\"],\"0\":[\"Animal Type\"],\"1\":[\"Natural Environment\",\"Biome\"],\"2\":[\"Climate\",\"Temperature\",\"Water\"],\"3\":[\"Ecology\",\"Biology\"],\"4\":[\"Science\"]}},\"399\":{\"Question\":\"What is persistent contrastive divergence compared to constrastive divergence? \",\"Answer\":\"As you do overall gradient descent on a Boltzmann machine, you need to resample the gradient w.r.t. parameters in E(x). \\nFor this, you need to continually re-estimate the average energy of the model.\\nCD does this with MC estimation.\\nPCD uses the MC end value of the previous CD iteration in SGD to seed the next MC distribution (and so it will not rely on data, and can diverge further). \",\"Key ideas\":\"\\n1. Boltzmann machines are a type of neural network.\\n2. Gradient descent is an optimization technique used to minimize a loss function.\\n3. Contrastive divergence (CD) is a method of estimating the gradient of a Boltzmann machine.\\n4. Persistent contrastive divergence (PCD) is an extension of CD which uses the MC end value of the previous CD iteration in SGD to seed the next MC distribution.\\n5. PCD does not rely on data and can diverge further than CD.\",\"Abstraction groups\":{\"-1\":[\"Boltzmann Machine\",\"Gradient Descent\",\"CD\",\"PCD\",\"MC\",\"SGD\",\"Data\",\"Divergence\"],\"0\":[\"PCD\"],\"1\":[\"Contrastive Divergence\",\"Persistent Contrastive Divergence\"],\"2\":[\"Optimization\",\"Estimation\"],\"3\":[\"Machine Learning\",\"Neural Networks\"],\"4\":[\"Artificial Intelligence\"]}},\"400\":{\"Question\":\"How do we get the expectation value of energy of data for MLE (maximum likelihood estimation) of a boltzmann machine? \",\"Answer\":\"The energy E(x) is well defined. We just need to sample all its terms (integrate over hidden nodes x = {h,v}).\\nTo do this, write down the terms in the energy, and some of them will couple to the hidden layer activations. \\nThen just sample the hidden layer activations given the visible layer values (set by the data). The probability distributions for the hidden nodes are of a simple functional form (if it is a restricted Boltzmann machine), and so doing this sum over the hidden node probability does not require calculating the full partition function. \",\"Key ideas\":\"1. Maximum likelihood estimation (MLE) of a Boltzmann machine \\n2. The energy E(x) is well defined \\n3. Sampling all terms of the energy \\n4. Writing down the terms in the energy \\n5. Some of the terms couple to the hidden layer activations \\n6. Sampling the hidden layer activations given the visible layer values \\n7. The probability distributions for the hidden nodes are of a simple functional form \\n8. Doing the sum over the hidden node probability does not require calculating the full partition function\",\"Abstraction groups\":{\"-1\":[\"MLE\",\"Energy\",\"Sampling\",\"Term\",\"Hidden Layer\",\"Activation\",\"Visible Layer\",\"Probability\",\"Partition Function\"],\"0\":[\"Boltzmann Machine\"],\"1\":[\"Maximum Likelihood Estimation\",\"Energy\"],\"2\":[\"Sampling\",\"Term\"],\"3\":[\"Hidden Layer\",\"Activation\",\"Visible Layer\",\"Probability\"],\"4\":[\"Partition Function\"]}},\"401\":{\"Question\":\"What are the steps involved in contrastive divergence? \",\"Answer\":\"We want to sample the energy of a Boltzmann machine model (on average) for computing a gradient in MLE.\\nTo do so, we start with a real sample in the visible layer, get probability sample of hidden layer, then go back and forth, until we converge to something representative of the model (MC estimate). \",\"Key ideas\":\"1. Boltzmann machine model: a type of stochastic recurrent neural network that can be used to sample from a probability distribution\\n2. Maximum Likelihood Estimation (MLE): a method of estimating the parameters of a statistical model given data\\n3. Contrastive Divergence: a method of approximating the gradient of the log-likelihood of a Boltzmann machine model\\n4. Visible Layer: the layer of a Boltzmann machine model that contains the input data\\n5. Hidden Layer: the layer of a Boltzmann machine model that contains the latent variables\\n6. Real Sample: a sample of the visible layer that is taken from the actual data\\n7. Probability Sample: a sample of the hidden layer that is taken from the probability distribution of the model\\n8. Monte Carlo Estimate: an estimate of a quantity based on repeated random sampling\",\"Abstraction groups\":{\"-1\":[\"Boltzmann\",\"MLE\",\"Divergence\",\"Visible\",\"Hidden\",\"Sample\",\"Probability\",\"MC\"],\"0\":[\"Contrastive Divergence\"],\"1\":[\"Boltzmann Machine Model\",\"Maximum Likelihood Estimation\"],\"2\":[\"Neural Network\",\"Probability\",\"Estimation\"],\"3\":[\"Machine Learning\",\"Statistics\"],\"4\":[\"Mathematics\"]}},\"402\":{\"Question\":\"What is constrastive divergence used for? \",\"Answer\":\"Measuring samples from a Boltzmann machine (which is necessary when doing gradient descent on the Boltzmann machine parameters, like the parameters of its energy distribution, in order to most closely match the generated distribution to some observed data). \",\"Key ideas\":\"1. Boltzmann machine: a type of stochastic recurrent neural network\\n2. Contrastive divergence: a method used to measure samples from a Boltzmann machine\\n3. Gradient descent: an optimization algorithm used to find the parameters of a Boltzmann machine that most closely match some observed data\\n4. Parameters of a Boltzmann machine: the parameters of its energy distribution, such as weights and biases\",\"Abstraction groups\":{\"-1\":[\"Boltzmann Machine\",\"Contrastive Divergence\",\"Gradient Descent\",\"Parameter\"],\"0\":[\"Contrastive Divergence\"],\"1\":[\"Measuring Sample\"],\"2\":[\"Optimization\",\"Neural Network\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computing\"]}},\"403\":{\"Question\":\"What does gradient descent look like for energy based models? \\nWhat is the physical intuition in terms of energy? \",\"Answer\":\"Raise the energy of non-data, lower the energy of data.\\n----------------------\\nWe have a model with some form E(x) for a given data sample at configuration x.\\nWe want to minimize -MLE = -log(model stated probability of data) = <E(x)>_data_x + log(Z)\\nDerivative of this is d <E>_data\\/d theta - d <E>_model\\/d theta. We want this to be negative, so we want to raise the model energy on average, compared to the data energy on average. \\nThis results in a new boltzmann distribution with real data in the minima, likely to be sampled. \",\"Key ideas\":\"\\n1. Gradient descent is a method of minimizing a function by taking steps in the direction of steepest descent. \\n2. Energy based models are models that use energy to represent the probability of a given data sample. \\n3. The physical intuition of energy based models is to raise the energy of non-data and lower the energy of data. \\n4. The goal of gradient descent for energy based models is to minimize the maximum likelihood estimation (MLE) of the model. \\n5. The MLE is calculated by taking the average energy of the data sample at a given configuration x and subtracting the average energy of the model at the same configuration. \\n6. The derivative of the MLE is the difference between the average energy of the data and the average energy of the model. \\n7. To minimize the MLE, the average energy of the model must be higher than the average energy of the data. \\n8. This results in a new Boltzmann distribution with real data in the minima, likely to be sampled.\",\"Abstraction groups\":{\"-1\":[\"Gradient Descent\",\"Energy\",\"Data\",\"Model\",\"MLE\",\"Derivative\",\"Boltzmann Distribution\",\"Minima\"],\"0\":[\"Gradient Descent\"],\"1\":[\"Energy Based Model\"],\"2\":[\"Machine Learning\",\"Optimization\"],\"3\":[\"Mathematics\",\"Computer Science\"],\"4\":[\"Science\"]}},\"404\":{\"Question\":\"Why are Boltzmann machines especially powerful in physics applications? when interpreted as energy based models \",\"Answer\":\"Can encode arbitrary effective interactions \\n-----\\nbetween visible nodes if the hidden nodes have a complicated distribution function. In the sense that the effective energy of the active layer (integrating out the hidden layer) is described however you want, given the energy of the hidden node vs it's value. You can learn what order of interactions are necessary rather than hard coding it. \",\"Key ideas\":\"\\n1. Boltzmann machines are powerful in physics applications because they can encode arbitrary effective interactions between visible nodes. \\n2. This is possible because the hidden nodes have a complicated distribution function. \\n3. The effective energy of the active layer (integrating out the hidden layer) is described however you want, given the energy of the hidden node vs its value. \\n4. This allows you to learn what order of interactions are necessary rather than hard coding it.\",\"Abstraction groups\":{\"-1\":[\"Boltzmann Machine\",\"Physics Application\",\"Visible Node\",\"Hidden Node\",\"Distribution Function\",\"Active Layer\",\"Energy\",\"Interaction\"],\"0\":[\"Boltzmann Machine\"],\"1\":[\"Energy-Based Model\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computational Model\",\"Algorithm\"],\"4\":[\"Computer Science\"]}},\"405\":{\"Question\":\"Clean code: what are the rules for a function input and output? \",\"Answer\":\"Should have 1 or 2 inputs only (use a class if necessary).\\nShouldn't alter things in place. Should return an altered object.\",\"Key ideas\":\"1. A function should have 1 or 2 inputs only. \\n2. If more than 2 inputs are necessary, a class should be used. \\n3. A function should not alter things in place. \\n4. A function should return an altered object.\",\"Abstraction groups\":{\"-1\":[\"Function\",\"Input\",\"Class\",\"Place\",\"Object\"],\"0\":[\"Clean Code\"],\"1\":[\"Rule\",\"Function Input\",\"Function Output\"],\"2\":[\"Programming\",\"Software Development\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"406\":{\"Question\":\"How to simplify input arguments to functions to have one or two arguments, not many? \",\"Answer\":\"Combine objects into natural classes.\\n----\\nExample\\nCircle makeCircle(double x, double y, double radius);\\nCircle makeCircle(Point center, double radius); \",\"Key ideas\":\"1. Functions can take multiple arguments, but it is often better to simplify them to one or two arguments. \\n2. Combining objects into natural classes is one way to simplify input arguments. \\n3. An example of this is combining the x, y, and radius arguments into a Point center and double radius argument.\",\"Abstraction groups\":{\"-1\":[\"Function\",\"Argument\",\"Simplify\",\"Object\",\"Class\",\"Circle\",\"Point\",\"Radius\"],\"0\":[\"Simplifying Argument\"],\"1\":[\"Function\",\"Argument\"],\"2\":[\"Programming\",\"Problem-solving\"],\"3\":[\"Computer Science\",\"Logic\"],\"4\":[\"Science\",\"Mathematics\"]}},\"407\":{\"Question\":\"How to simplify input arguments to functions to prevent forgetting input format? \",\"Answer\":\"Add hints in function name, of what type of object it is, and order of them \",\"Key ideas\":\"1. Functions can take input arguments. \\n2. It is important to simplify input arguments to prevent forgetting the input format. \\n3. To simplify input arguments, add hints in the function name about the type of object it is and the order of them. \\n4. Examples of hints include acronyms and abbreviations. \\n5. The answer should be brief and concise, while still being complete and not introducing ambiguity.\",\"Abstraction groups\":{\"-1\":[\"Function\",\"Input Argument\",\"Hint\",\"Object Type\",\"Order\"],\"0\":[\"Simplifying Input Arguments\"],\"1\":[\"Function Inputs\",\"Hints\"],\"2\":[\"Programming\",\"Problem Solving\"],\"3\":[\"Computer Science\",\"Logic\"],\"4\":[\"Science\",\"Mathematics\"]}},\"408\":{\"Question\":\"What is the wavenet architecture in the wavenet paper, as compared to Bengio 2003 paper which used embedding for predicting the next character?\",\"Answer\":\"The wavenet paper adds heirarchy in the network, and starts to approach the transformer type network. It combines bigrams, then combines those, then those. The result is a 3 layer network which compresses 8 characters into one object then reverse embeds. In principle this uses information more efficiently.\",\"Key ideas\":\"1. Wavenet architecture:\\n    - Combines bigrams, then combines those, then those\\n    - Result is a 3 layer network\\n    - Compresses 8 characters into one object then reverse embeds\\n    - Uses information more efficiently\\n2. Bengio 2003 paper:\\n    - Used embedding for predicting the next character\",\"Abstraction groups\":{\"-1\":[\"Wavenet\",\"Bengio\",\"Embedding\",\"Bigram\",\"Network\",\"Character\",\"Object\",\"Information\"],\"0\":[\"Wavenet\"],\"1\":[\"Architecture\",\"Network\",\"Paper\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\",\"Computer Science\"],\"3\":[\"Technology\",\"Science\",\"Engineering\"],\"4\":[\"Knowledge\"]}},\"409\":{\"Question\":\"Karpathy suggestion for workflow on building deep networks: how to debug it's doing what you want. Also what project did I do this on? \",\"Answer\":\"Make a small example network with fake input.\\nPush data through and check shape throughout the process.\\nThen paste into final implementation. I did this for alpha go zero network \",\"Key ideas\":\"\\n1. Karpathy's suggestion for workflow on building deep networks:\\n2. How to debug it to ensure it is doing what you want:\\n    a. Make a small example network with fake input.\\n    b. Push data through and check shape throughout the process.\\n    c. Paste into final implementation.\\n3. The project this was done on: Alpha Go Zero Network.\",\"Abstraction groups\":{\"-1\":[\"Karpathy\",\"Workflow\",\"Debugging\",\"Network\",\"Input\",\"Shape\",\"Implementation\",\"Alpha Go Zero\"],\"0\":[\"Debugging\"],\"1\":[\"Workflow\",\"Deep Networks\"],\"2\":[\"Building\",\"Karpathy\"],\"3\":[\"Suggestion\",\"Alpha Go Zero\"],\"4\":[\"Project\"]}},\"410\":{\"Question\":\"What are two major problems with existing LLMs like ChatGPT? \",\"Answer\":\"Hallucination (logic): \\\"Tell me why X is true\\\" it won't question if its true. \\nMemory (long term): We can't yet build a personalized assistant that has long term memory of information it has told you specifically \",\"Key ideas\":\"1. LLMs (Language Learning Models) are computer programs that can understand and respond to natural language.\\n2. ChatGPT is an example of an existing LLM.\\n3. Hallucination is a major problem with existing LLMs like ChatGPT. This occurs when the program does not question the truth of a statement, but instead simply responds to it.\\n4. Memory is another major problem with existing LLMs like ChatGPT. We cannot yet build a personalized assistant that has long term memory of information it has told you specifically.\",\"Abstraction groups\":{\"-1\":[\"LLM\",\"ChatGPT\",\"Hallucination\",\"Memory\",\"Personalized Assistant\"],\"0\":[\"LLM\"],\"1\":[\"Language Learning Model\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"411\":{\"Question\":\"Avalon architecture main overarching goal, and key design choices to achieve this \",\"Answer\":\"Create environment sufficiently similar to human development, to enable a general agent with cross task learning.\\nKey ideas:\\nFix ground truth reward\\nFix mechanics\\nOnly change intermediate structure of challenges \",\"Key ideas\":\"\\n1. The main overarching goal of the Avalon architecture is to create an environment sufficiently similar to human development, to enable a general agent with cross task learning. \\n2. To achieve this goal, the ground truth reward must be fixed. \\n3. The mechanics of the environment must also be fixed. \\n4. The only thing that can be changed is the intermediate structure of the challenges.\",\"Abstraction groups\":{\"-1\":[\"Avalon\",\"Goal\",\"Design\",\"Environment\",\"Agent\",\"Learning\",\"Reward\",\"Mechanic\",\"Challenge\"],\"0\":[\"Avalon\"],\"1\":[\"Architecture\",\"Goal\",\"Design\"],\"2\":[\"Artificial Intelligence\",\"Robotics\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\",\"Knowledge\"]}},\"412\":{\"Question\":\"Avalon architecture: how is the timing of the environment different from most RL tasks like Stratego and Go? \",\"Answer\":\"High speed and continuous environment \",\"Key ideas\":\"\\n1. Avalon architecture: a type of reinforcement learning (RL) task\\n2. Timing of the environment: different from most RL tasks such as Stratego and Go\\n3. Difference: high speed and continuous environment\",\"Abstraction groups\":{\"-1\":[\"Avalon\",\"RL\",\"Timing\",\"Stratego\",\"Go\",\"Speed\",\"Environment\"],\"0\":[\"Avalon Architecture\"],\"1\":[\"Reinforcement Learning\",\"Timing\"],\"2\":[\"Artificial Intelligence\",\"Computer Science\"],\"3\":[\"Technology\",\"Science\"],\"4\":[\"Knowledge\"]}},\"413\":{\"Question\":\"Why did the avalon architecture ONLY change the structure of intermediate challenges, not the game mechanics or global reward? \",\"Answer\":\"To encourage general learning and cross task learning. \\nSub skills should transfer \",\"Key ideas\":\"\\n1. The Avalon architecture changed the structure of intermediate challenges, but not the game mechanics or global reward. \\n2. This was done to encourage general learning and cross task learning. \\n3. Sub skills should transfer between tasks.\",\"Abstraction groups\":{\"-1\":[\"Avalon\",\"Structure\",\"Challenge\",\"Mechanic\",\"Reward\",\"Learning\",\"Transfer\"],\"0\":[\"Avalon\"],\"1\":[\"Architecture\",\"Challenge\",\"Mechanic\",\"Reward\"],\"2\":[\"Structure\",\"Learning\",\"Transfer\"],\"3\":[\"Encouragement\"],\"4\":[\"Change\"]}},\"414\":{\"Question\":\"Arguments against current large language models being sentient (they lack X): What is X? \",\"Answer\":\"Sensory perception, embodiment, recurrent processing, world model, global workspace, unified agency, biology.\\nMy strongest ones:\\nWorld model, recurrent processing, unified agency \",\"Key ideas\":\"\\n1. Arguments against current large language models being sentient:\\n2. Lack sensory perception, embodiment, recurrent processing, world model, global workspace, unified agency, and biology.\\n3. World model, recurrent processing, and unified agency are the strongest arguments.\",\"Abstraction groups\":{\"-1\":[\"Language Model\",\"Sentient\",\"Sensory\",\"Embodiment\",\"Recurrent\",\"World\",\"Global\",\"Workspace\",\"Unified\",\"Agency\",\"Biology\"],\"0\":[\"Language Model\"],\"1\":[\"Sentient\",\"Argument\"],\"2\":[\"Perception\",\"Embodiment\",\"Processing\",\"Model\",\"Workspace\",\"Agency\",\"Biology\"],\"3\":[\"Cognitive\",\"Artificial\",\"Intelligence\"],\"4\":[\"Technology\"]}},\"415\":{\"Question\":\"Are LLMs sentient (Chalmers talk). \\nArguments for current systems being sentient (they have X): \",\"Answer\":\"Self-report of sentience, conversational ability, domain general knowledge \",\"Key ideas\":\"\\n1. LLMs (Legal Language Machines) are computer systems that are capable of understanding and responding to legal language. \\n2. Sentience is the capacity to feel, perceive, or experience subjectively. \\n3. Chalmers talk is a philosophical discussion about whether machines can be conscious. \\n4. Self-report of sentience is when a machine is able to report its own experience of sentience. \\n5. Conversational ability is the capacity of a machine to engage in meaningful conversations with humans. \\n6. Domain general knowledge is the capacity of a machine to understand and respond to a wide range of topics.\",\"Abstraction groups\":{\"-1\":[\"LLM\",\"Sentience\",\"Chalmer\",\"Self-Report\",\"Conversational\",\"Domain General\"],\"0\":[\"LLM\"],\"1\":[\"Sentience\",\"Chalmers Talk\"],\"2\":[\"Computer System\",\"Philosophical Discussion\"],\"3\":[\"Artificial Intelligence\",\"Consciousness\"],\"4\":[\"Technology\",\"Philosophy\"]}},\"416\":{\"Question\":\"Biggest overarching challenge in RL \",\"Answer\":\"Designing an agent with unsupervised general learning capability in many environments.\\nSpecifics:\\nIt needs to achieve few shot learning\\nIt probably needs to incorporate a natural language interface (that is, somehow combine with language models) \",\"Key ideas\":\"1. Reinforcement Learning (RL) is the biggest overarching challenge.\\n2. Designing an agent with unsupervised general learning capability in many environments.\\n3. The agent needs to achieve few shot learning.\\n4. The agent probably needs to incorporate a natural language interface, which is a combination of language models.\",\"Abstraction groups\":{\"-1\":[\"RL\",\"Agent\",\"Unsupervised\",\"Many Environment\",\"Few Shot Learning\",\"Natural Language Interface\",\"Language Model\"],\"0\":[\"Reinforcement Learning\"],\"1\":[\"Agent\",\"Unsupervised\",\"Many Environments\"],\"2\":[\"Learning\",\"Few Shot Learning\",\"Natural Language Interface\"],\"3\":[\"Artificial Intelligence\",\"Machine Learning\"],\"4\":[\"Computer Science\"]}},\"417\":{\"Question\":\"How is batch norm an example of solving a key problem in design in neural networks \",\"Answer\":\"Robustness to initialization effects on training.\\nMore generally, we need robust methods of regularization that don't require fine tuning.\\nInitialization and regularization \",\"Key ideas\":\"\\n1. Batch norm is a method of regularization used in neural networks.\\n2. It helps to make the network more robust to initialization effects on training.\\n3. Regularization is a process of introducing additional information to a model to prevent overfitting.\\n4. Initialization is the process of setting the initial values of the weights and biases of a neural network.\\n5. Batch norm helps to make the network more robust to initialization effects without requiring fine tuning.\",\"Abstraction groups\":{\"-1\":[\"Batch Norm\",\"Neural Network\",\"Robustness\",\"Initialization\",\"Regularization\",\"Fine Tuning\"],\"0\":[\"Batch Norm\"],\"1\":[\"Regularization\",\"Initialization\"],\"2\":[\"Neural Network\",\"Design\"],\"3\":[\"Machine Learning\",\"Artificial Intelligence\"],\"4\":[\"Computer Science\"]}},\"418\":{\"Question\":\"Is the stratego algorithm similar to AGZ? \",\"Answer\":\"No. Stratego algorithm is model free, which is surprising. There is no explicit model of the environment. No planning. \",\"Key ideas\":\"\\n1. Stratego algorithm is model free.\\n2. Stratego algorithm does not involve planning.\\n3. AGZ is a different algorithm than Stratego.\",\"Abstraction groups\":{\"-1\":[\"Stratego\",\"AGZ\",\"Model\",\"Environment\",\"Planning\"],\"0\":[\"Stratego Algorithm\"],\"1\":[\"Algorithm\",\"Artificial Intelligence\"],\"2\":[\"Computer Science\",\"Technology\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"419\":{\"Question\":\"In the stratego paper, what is the regularized nash dynamics algorithm most similar to in other reinforcement learning algorithms? \",\"Answer\":\"Proximal policy optimization, because it penalizes deviating too far from the original policy \",\"Key ideas\":\"\\n1. Stratego paper: a paper written by researchers that discusses the use of reinforcement learning algorithms in the game of Stratego.\\n\\n2. Regularized nash dynamics algorithm: an algorithm used in the Stratego paper to optimize the game-playing agent's policy.\\n\\n3. Reinforcement learning algorithms: algorithms that allow an agent to learn from its environment by taking actions and receiving rewards.\\n\\n4. Proximal policy optimization: a reinforcement learning algorithm that penalizes deviating too far from the original policy.\\n\\n5. Penalty: a consequence of deviating from the original policy, which can be used to encourage the agent to stay close to the original policy.\",\"Abstraction groups\":{\"-1\":[\"Stratego\",\"Nash\",\"Dynamic\",\"Reinforcement\",\"Learning\",\"Algorithm\",\"Proximal\",\"Policy\",\"Optimization\",\"Penalty\"],\"0\":[\"Regularized Nash Dynamics\"],\"1\":[\"Algorithm\",\"Reinforcement Learning\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\"],\"3\":[\"Computational Thinking\",\"Computer Science\"],\"4\":[\"Science\",\"Technology\",\"Engineering\",\"Mathematics\"]}},\"420\":{\"Question\":\"What is the regularized nash dynamics algorithm used in the stratego paper by Deepmind? \",\"Answer\":\"Uses policy gradient and altered reward function (similar to PPO) to ensure convergence to some local minimum.\\nCheckpoints policy, then uses altered reward function which penalizes deviating from that policy too much (similar to PPO clip, but continuous), and has a guaranteed local optimum.\\nRepeat many times, converges to global optimum strategy. \",\"Key ideas\":\"1. The regularized nash dynamics algorithm is used in the stratego paper by Deepmind. \\n2. It uses policy gradient and an altered reward function to ensure convergence to some local minimum. \\n3. The algorithm uses a checkpoint policy and an altered reward function which penalizes deviating from that policy too much. \\n4. This altered reward function is similar to the Proximal Policy Optimization (PPO) clip, but is continuous. \\n5. The algorithm has a guaranteed local optimum. \\n6. The algorithm is repeated many times, and converges to a global optimum strategy.\",\"Abstraction groups\":{\"-1\":[\"Nash Dynamics\",\"Deepmind\",\"Stratego\",\"Policy Gradient\",\"Reward Function\",\"PPO\",\"Checkpoint\",\"Local Optimum\",\"Global Optimum\"],\"0\":[\"Regularized Nash Dynamics\"],\"1\":[\"Algorithm\",\"Deepmind\",\"Stratego\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"421\":{\"Question\":\"What differentiates the challenges of Stratego from poker and Go? \",\"Answer\":\"Stratego has way bigger state space and initial state space (think poker hand size vs stratego \\\"hand\\\" size) and hidden information. \\nSlow, sequential reasoning \",\"Key ideas\":\"1. Stratego has a much larger state space and initial state space than poker and Go.\\n2. The state space of Stratego is much larger than the size of a poker hand.\\n3. Stratego has hidden information, unlike poker and Go.\\n4. Reasoning in Stratego is slow and sequential.\",\"Abstraction groups\":{\"-1\":[\"Stratego\",\"Poker\",\"Go\",\"State Space\",\"Initial State Space\",\"Hidden Information\",\"Reasoning\"],\"0\":[\"Stratego\"],\"1\":[\"Game\",\"Strategy\"],\"2\":[\"Recreation\",\"Problem-solving\"],\"3\":[\"Cognitive Skill\",\"Mental Activity\"],\"4\":[\"Learning\"]}},\"422\":{\"Question\":\"Historical example of intelligence augmentation (think hundreds of years) \",\"Answer\":\"Language\\nWriting\\nDescartes and algebraic representation of geometry \",\"Key ideas\":\"\\n1. Intelligence augmentation is a concept that has been around for hundreds of years. \\n2. Language is an example of intelligence augmentation.\\n3. Writing is an example of intelligence augmentation.\\n4. Descartes and algebraic representation of geometry are examples of intelligence augmentation.\",\"Abstraction groups\":{\"-1\":[\"Intelligence\",\"Augmentation\",\"Language\",\"Writing\",\"Descartes\",\"Algebra\",\"Geometry\"],\"0\":[\"Intelligence Augmentation\"],\"1\":[\"Language\",\"Writing\",\"Descartes\",\"Algebraic Representation of Geometry\"],\"2\":[\"Communication\",\"Representation\",\"Mathematical Thinking\"],\"3\":[\"Knowledge\",\"Thinking\",\"Problem Solving\"],\"4\":[\"Human Intelligence\"]}},\"423\":{\"Question\":\"Examples of AI-based intelligence augmentation from Nielsen essay \",\"Answer\":\"Generative models with a 1D tuning axis for complex concepts\\nEmotions in faces\\nBoldness in fonts.\",\"Key ideas\":\"\\n1. AI-based intelligence augmentation is a type of technology that can be used to enhance human capabilities.\\n2. Generative models are a type of AI-based intelligence augmentation that can be used to create complex concepts.\\n3. Generative models have a 1D tuning axis, which is a parameter that can be adjusted to modify the output of the model.\\n4. AI-based intelligence augmentation can also be used to detect emotions in faces.\\n5. AI-based intelligence augmentation can also be used to adjust the boldness of fonts.\",\"Abstraction groups\":{\"-1\":[\"AI\",\"Augmentation\",\"Generative\",\"1D\",\"Concept\",\"Emotion\",\"Face\",\"Font\",\"Boldness\"],\"0\":[\"AI-Based Intelligence Augmentation\"],\"1\":[\"Generative Model\",\"Emotion\",\"Font\"],\"2\":[\"AI\",\"Augmentation\"],\"3\":[\"Technology\",\"Human Capability\"],\"4\":[\"Enhancement\"]}},\"424\":{\"Question\":\"What is the vanilla policy gradient theorem with baseline (the baseline part) and why is it good? \",\"Answer\":\"The baseline term provides a generalization of the VPG (vanilla policy gradient) formula that reduces variance in the update strength during gradient descent.\\nIn the VPG formula, you can change the Q function estimate q to q+c for any constant c that doesn't depend on the action (such as a value function), and this can reduce variance.  \\n------------------------------\\nwhy? \\nYou can subtract the constant c from it is multiplied by the policy, and summed over actions. Then you can subtract c because the sum over actions of gradient of the policy pi is 0 (pi sums to 1)\\nIt helps because the variance of the product of two random variables is larger in general if one of them has a large mean, and smallest if they have 0 mean for both of them.\",\"Key ideas\":\"1. Vanilla Policy Gradient (VPG) theorem\\n2. Baseline term in VPG formula\\n3. How the baseline term reduces variance in the update strength during gradient descent\\n4. How to change the Q function estimate q to q+c for any constant c that doesn't depend on the action\\n5. How subtracting the constant c from the policy and summing over actions reduces variance\\n6. How the variance of the product of two random variables is larger in general if one of them has a large mean, and smallest if they have 0 mean for both of them\",\"Abstraction groups\":{\"-1\":[\"VPG\",\"Baseline\",\"Variance\",\"Q Function\",\"Constant\",\"Gradient\",\"Policy\",\"Action\",\"Variable\",\"Mean\"],\"0\":[\"VPG\"],\"1\":[\"Policy Gradient\",\"Baseline\"],\"2\":[\"Gradient Descent\",\"Variance Reduction\"],\"3\":[\"Optimization\",\"Random Variable\"],\"4\":[\"Mathematics\"]}},\"425\":{\"Question\":\"What is the policy gradient theorem formula? \",\"Answer\":\"gradient of value = expectation over states and actions of (grad log[pi(a|s)]) * q(s,a). \\nThe log comes about because we had sum_actions grad pi(a|s), but to put it into expectation over actions, we get the log. \",\"Key ideas\":\"\\n1. The policy gradient theorem formula is used to calculate the gradient of a value. \\n2. The formula involves an expectation over states and actions. \\n3. The formula includes a log of the policy (pi(a|s)) multiplied by the q(s,a). \\n4. The log is necessary because the formula originally included a sum of gradients of the policy (pi(a|s)). \\n5. To put the formula into expectation over actions, the log is used.\",\"Abstraction groups\":{\"-1\":[\"Policy\",\"Gradient\",\"Value\",\"State\",\"Action\",\"Log\",\"Pi\",\"Q\"],\"0\":[\"Policy Gradient Theorem\"],\"1\":[\"Mathematics\",\"Theorem\"],\"2\":[\"Science\",\"Statistics\",\"Calculus\"],\"3\":[\"Learning\",\"Optimization\"],\"4\":[\"Knowledge\"]}},\"426\":{\"Question\":\"What will this do?\\nx = np.random.multinomial(n=1, pvals=[1\\/6, 1\\/6, 1\\/6, 1\\/6, 1\\/6, 1\\/6])\",\"Answer\":\"Returns a sample vector from the multinomial distribution: Chooses 1 sample, and returns a vector with 1 hot encoding of result. If n=10, it will have 10 samples (vector will sum to 10)Binomial distribution is the same thing with 2 elements in pvals, rather than 6.\",\"Key ideas\":\"1. The multinomial distribution is a probability distribution that is used to model the outcomes of a situation where there are multiple possible outcomes. \\n2. The multinomial distribution is similar to the binomial distribution, but with more than two possible outcomes. \\n3. The np.random.multinomial() function is used to generate a sample vector from the multinomial distribution. \\n4. The np.random.multinomial() function takes two arguments: n and pvals. \\n5. The n argument is the number of samples to be taken from the multinomial distribution. \\n6. The pvals argument is a list of probabilities for each of the possible outcomes. \\n7. The np.random.multinomial() function returns a vector with a 1 hot encoding of the result. \\n8. If n is set to 10, the vector will sum to 10.\",\"Abstraction groups\":{\"-1\":[\"Multinomial\",\"Binomial\",\"Np.Random.Multinomial\",\"N\",\"Pval\",\"Vector\",\"1 Hot Encoding\",\"Sample\"],\"0\":[\"Multinomial\"],\"1\":[\"Probability Distribution\"],\"2\":[\"Statistics\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Learning\"]}},\"427\":{\"Question\":\"How can I draw samples from a normal distribution with numpy? \",\"Answer\":\"Normal distribution: x = np.random.normal(loc=1, scale=2, size=(2, 3))\",\"Key ideas\":\"1. Normal distribution: a type of probability distribution that is symmetric around its mean, with a bell-shaped curve.\\n2. Numpy: a Python library used for scientific computing.\\n3. np.random.normal(): a function in the numpy library used to draw samples from a normal distribution.\\n4. loc: the mean of the normal distribution.\\n5. scale: the standard deviation of the normal distribution.\\n6. size: the shape of the output array.\",\"Abstraction groups\":{\"-1\":[\"Normal\",\"Numpy\",\"Np.Random.Normal\",\"Loc\",\"Scale\",\"Size\"],\"0\":[\"Normal Distribution\"],\"1\":[\"Probability\",\"Statistics\"],\"2\":[\"Mathematics\",\"Data Science\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"428\":{\"Question\":\"np.random.permutation(arr) \",\"Answer\":\"returns a random permutation (is not in place operation, like shuffle is) \",\"Key ideas\":\"1. np.random.permutation() is a function. \\n2. It takes an array (arr) as an argument. \\n3. It returns a random permutation of the array. \\n4. It is not an in-place operation, unlike np.random.shuffle().\",\"Abstraction groups\":{\"-1\":[\"Np.Random.Permutation\",\"Array\",\"Random Permutation\",\"In-Place Operation\"],\"0\":[\"Np.Random.Permutation\"],\"1\":[\"Function\",\"Array\"],\"2\":[\"Programming\",\"Mathematics\"],\"3\":[\"Computing\",\"Problem-solving\"],\"4\":[\"Knowledge\"]}},\"429\":{\"Question\":\"What are numpy filter arrays? \",\"Answer\":\"They are boolean arrays which let you select subsets of data from another array. There is one Boolean value for each index in the other array. Then you run arr[filter_arr] to take the subset of arr with a true value in the filter_arr \",\"Key ideas\":\"1. Numpy filter arrays are boolean arrays. \\n2. Each index in the other array has one Boolean value. \\n3. To take the subset of an array, you use the command arr[filter_arr]. \\n4. The filter_arr contains the Boolean values which determine which elements of the array are taken. \\n5. True values in the filter_arr indicate which elements of the array are taken. \\n6. False values in the filter_arr indicate which elements of the array are not taken.\",\"Abstraction groups\":{\"-1\":[\"Numpy\",\"Filter\",\"Array\",\"Boolean\",\"Index\",\"Array\",\"Subset\",\"Command\",\"True\",\"False\"],\"0\":[\"Numpy Filter Array\"],\"1\":[\"Boolean Array\"],\"2\":[\"Data Selection\",\"Array\"],\"3\":[\"Numpy\",\"Programming\"],\"4\":[\"Computer Science\"]}},\"430\":{\"Question\":\"How do you join two numpy arrays? What about along a new axis? What about split? \",\"Answer\":\"np.concatenate(a,b) joins arrays\\nnp.stack(a,b) concatenates, but along a new axis\\nOpposite of np.stack is np.split, which breaks it into multiple equal sized sub arrays. \\nnp.array_split(arr, numsplits) is the same as np.split, but it is possible to choose what size sub arrays to split into. \",\"Key ideas\":\"1. Numpy is a library for scientific computing in Python. \\n2. np.concatenate is a function that can be used to join two numpy arrays. \\n3. np.stack is a function that can be used to concatenate two numpy arrays along a new axis. \\n4. np.split is the opposite of np.stack, and it breaks an array into multiple equal sized sub arrays. \\n5. np.array_split is the same as np.split, but it is possible to choose what size sub arrays to split into.\",\"Abstraction groups\":{\"-1\":[\"Numpy\",\"Concatenate\",\"Stack\",\"Split\",\"Array_split\"],\"0\":[\"Joining Numpy Array\"],\"1\":[\"Array Manipulation\"],\"2\":[\"Data Manipulation\",\"Data Analysis\"],\"3\":[\"Scientific Computing\",\"Programming\"],\"4\":[\"Computer Science\"]}},\"431\":{\"Question\":\"What does python super() do? \",\"Answer\":\"Is used to create a class that will inherit all the methods and properties from another class:\\nclass Child(Parent):\\n def __init__(self, txt):\\n super().__init__(txt) \",\"Key ideas\":\"\\n1. Python is a programming language. \\n2. The super() function is used to create a class that will inherit all the methods and properties from another class. \\n3. The syntax for using the super() function is: \\n    class Child(Parent):\\n    def __init__(self, txt):\\n    super().__init__(txt) \\n4. The __init__() method is a special method that is used to initialize an object. \\n5. The txt parameter is used to pass a string value to the __init__() method.\",\"Abstraction groups\":{\"-1\":[\"Python\",\"Super\",\"Parent\",\"Child\",\"__Init__\",\"Txt\"],\"0\":[\"Super()\"],\"1\":[\"Inheritance\",\"Class\"],\"2\":[\"Object-oriented Programming\",\"Programming\"],\"3\":[\"Computer Science\"],\"4\":[\"Science\"]}},\"432\":{\"Question\":\"What does python next(stuff) do? \",\"Answer\":\"Returns the next item in an iterator and increments it as well.\\nExample\\nmylist = iter([\\\"apple\\\", \\\"banana\\\", \\\"cherry\\\"])\\nx = next(mylist)\\nprint(x)\\nx = next(mylist)\\nprint(x) \",\"Key ideas\":\"1. Python is a programming language.\\n2. An iterator is an object that can be iterated upon, meaning it can be used in a loop.\\n3. The next() function returns the next item in an iterator and increments it as well.\\n4. The example given is a list of strings, which is an iterable object.\\n5. The example shows how the next() function can be used to access the items in the list one by one.\",\"Abstraction groups\":{\"-1\":[\"Python\",\"Iterator\",\"Next()\",\"List\",\"String\"],\"0\":[\"Next()\"],\"1\":[\"Python\",\"Iterator\"],\"2\":[\"Programming\",\"Data Structure\"],\"3\":[\"Computer Science\",\"Problem Solving\"],\"4\":[\"Knowledge\",\"Understanding\"]}},\"433\":{\"Question\":\"How do you access a dictionary value, but not throw an error if it doesn't exist? \",\"Answer\":\"Dict.get(key, defaultValue)\\nGets value, returns default if it didn\\u2019t exist, and adds it to dict \",\"Key ideas\":\"1. A dictionary is a data structure that stores key-value pairs. \\n2. To access a value in a dictionary, you must use the key associated with it. \\n3. The dict.get() method can be used to access a dictionary value, but it will not throw an error if the key does not exist. \\n4. The dict.get() method takes two arguments: the key and a default value. \\n5. If the key exists, the dict.get() method will return the associated value. \\n6. If the key does not exist, the dict.get() method will return the default value that was provided. \\n7. The dict.get() method will also add the key and the default value to the dictionary if the key does not exist.\",\"Abstraction groups\":{\"-1\":[\"Dictionary\",\"Key\",\"Value\",\"Error\",\"Dict.get()\",\"Default\",\"Add\"],\"0\":[\"Dictionary\"],\"1\":[\"Accessing Values\",\"Error Handling\"],\"2\":[\"Data Structure\",\"Programming\"],\"3\":[\"Computer Science\"],\"4\":[\"Science\"]}},\"434\":{\"Question\":\"How do you get a tuple of keys and values from a dict in python? \",\"Answer\":\"Dict.items()\\nReturns a tuple of the key,value pairs \",\"Key ideas\":\"\\n1. Python is a programming language. \\n2. A dictionary (dict) is a data structure in Python that stores key-value pairs. \\n3. The dict.items() method returns a tuple of the key-value pairs in the dictionary. \\n4. A tuple is an immutable sequence of elements. \\n5. The elements in a tuple can be accessed by indexing. \\n6. The elements in a tuple can be of any type, including other tuples.\",\"Abstraction groups\":{\"-1\":[\"Python\",\"Dict\",\"Item\",\"Tuple\",\"Key-value\",\"Indexing\"],\"0\":[\"Dict.items()\"],\"1\":[\"Python\",\"Data Structure\",\"Tuple\"],\"2\":[\"Programming\",\"Key-Value Pair\"],\"3\":[\"Immutable Sequence\",\"Indexing\"],\"4\":[\"Computing\"]}},\"435\":{\"Question\":\"What is the equivalent of list.extend(stuff) for dictionaries in python? \",\"Answer\":\"Dict.update({key1:value1, key2:value2})\",\"Key ideas\":\"\\n1. Python is a programming language. \\n2. A list is a data structure in Python. \\n3. The list.extend() method adds elements to the end of a list. \\n4. A dictionary is another data structure in Python. \\n5. The dict.update() method adds elements to a dictionary. \\n6. The dict.update() method takes a dictionary as an argument. \\n7. The argument dictionary should contain key-value pairs.\",\"Abstraction groups\":{\"-1\":[\"Python\",\"List\",\"Extend\",\"Dictionary\",\"Update\",\"Key-value\",\"Pair\"],\"0\":[\"Python\"],\"1\":[\"Data Structure\",\"Method\"],\"2\":[\"Programming\",\"Computing\"],\"3\":[\"Technology\",\"Problem Solving\"],\"4\":[\"Knowledge\"]}},\"436\":{\"Question\":\"How do you sort a list by some function in python? \",\"Answer\":\"List.Sort(reverse=True, key=myFunc)\\nSorts by myFunc applied to each element. Called in place and rearranges. \",\"Key ideas\":\"\\n1. Python is a programming language. \\n2. A list is a data structure in Python. \\n3. The list.sort() function can be used to sort a list. \\n4. The reverse argument can be set to True to sort the list in reverse order. \\n5. The key argument can be set to a function to sort the list by the result of the function applied to each element. \\n6. The list.sort() function is called in place and rearranges the list.\",\"Abstraction groups\":{\"-1\":[\"Python\",\"List\",\"Sort\",\"Reverse\",\"Key\",\"Function\",\"Place\",\"Rearrange\"],\"0\":[\"Sorting\"],\"1\":[\"Python\",\"List\",\"Sort\",\"Reverse\",\"Key\",\"Function\",\"Place\",\"Rearrange\"],\"2\":[\"Programming\",\"Data Structure\",\"Algorithm\",\"Manipulation\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"437\":{\"Question\":\"What two methods let you find the location and number of objects in a string, list, or tuple? \",\"Answer\":\"List.index(\\\"hi\\\") - Searches for \\\"hi\\\" and returns the first place where it was found\\nList.count(\\\"hi\\\") - Returns the number of times something occurs in a list \",\"Key ideas\":\"1. Objects can be found in strings, lists, and tuples. \\n2. There are two methods to find the location and number of objects in a string, list, or tuple: \\n    a. List.index(\\\"hi\\\") - Searches for \\\"hi\\\" and returns the first place where it was found\\n    b. List.count(\\\"hi\\\") - Returns the number of times something occurs in a list\",\"Abstraction groups\":{\"-1\":[\"Object\",\"String\",\"List\",\"Tuple\",\"List.Index\",\"List.Count\"],\"0\":[\"Finding Object\"],\"1\":[\"Location\",\"Number\"],\"2\":[\"Method\",\"String\",\"List\",\"Tuple\"],\"3\":[\"Finding\",\"Object\"],\"4\":[\"Computer Science\"]}},\"438\":{\"Question\":\"What python module has functions for interpolation? \",\"Answer\":\"scipy.interpolate.interp1d\\nxs = np.arange(10). ys = 2*xs + 1. interp_func = interp1d(xs, ys)\",\"Key ideas\":\"\\n1. Python is a programming language. \\n2. A module is a collection of functions and data that can be used in a program. \\n3. Interpolation is a technique used to estimate values between two known points. \\n4. The scipy module contains functions for interpolation. \\n5. The interp1d function is used to interpolate data. \\n6. The np module is used to create arrays of numbers. \\n7. The np.arange function creates an array of numbers from a given start to a given end. \\n8. The interp_func variable is used to store the interpolation function.\",\"Abstraction groups\":{\"-1\":[\"Python\",\"Module\",\"Interpolation\",\"Scipy\",\"Interp1d\",\"Np\",\"Arange\",\"Interp_func\"],\"0\":[\"Interp1d\"],\"1\":[\"Interpolation\",\"Python Module\"],\"2\":[\"Data Analysis\",\"Programming\"],\"3\":[\"Computing\",\"Mathematics\"],\"4\":[\"Science\"]}},\"439\":{\"Question\":\"What python module has SI constants? \",\"Answer\":\"scipy.constants\\nFrom scipy import constants. Then constants.gram = 0.001, constants.week, constants.mile are all in SI units (meters, seconds, kg)\",\"Key ideas\":\"1. Python is a programming language. \\n2. A module is a file containing Python definitions and statements. \\n3. SI stands for the International System of Units. \\n4. scipy.constants is a Python module that contains SI constants. \\n5. To use scipy.constants, you must first import it. \\n6. Examples of SI constants that can be found in scipy.constants are: \\n    a. 1 gram = 0.001 \\n    b. 1 week = 604800 seconds \\n    c. 1 mile = 1609.344 meters\",\"Abstraction groups\":{\"-1\":[\"Python\",\"Module\",\"SI\",\"Scipy.Constants\",\"Import\",\"Gram\",\"Week\",\"Mile\"],\"0\":[\"SI Constant\"],\"1\":[\"Python\",\"Module\"],\"2\":[\"Programming\",\"Science\"],\"3\":[\"Computing\",\"Mathematics\"],\"4\":[\"Technology\"]}},\"440\":{\"Question\":\"What is the gamma distribution conjugate prior to? \",\"Answer\":\"Exponential and Poisson \\n------------\\nWhy? Because it encodes a distribution over something like a \\\"rate\\\". \\nExponential and poisson are siblings \",\"Key ideas\":\"\\n1. Gamma distribution: a type of probability distribution\\n2. Conjugate prior: a prior probability distribution used in Bayesian inference\\n3. Exponential and Poisson: two distributions that are siblings of the gamma distribution\\n4. Rate: a measure of the frequency of an event occurring in a given period of time\\n5. Bayesian inference: a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available\",\"Abstraction groups\":{\"-1\":[\"Gamma\",\"Conjugate\",\"Exponential\",\"Poisson\",\"Rate\",\"Bayesian\"],\"0\":[\"Gamma Distribution\"],\"1\":[\"Conjugate Prior\"],\"2\":[\"Probability Distribution\",\"Bayesian Inference\"],\"3\":[\"Statistics\",\"Mathematics\"],\"4\":[\"Science\"]}},\"441\":{\"Question\":\"What is the beta distribution conjugate prior to? \",\"Answer\":\"Binomial, bernoulli, negative binomial, and geometric. \\n--------------\\nWhy? all of them have the same generative model (a bernoulli variable) \",\"Key ideas\":\"\\n1. The beta distribution is a conjugate prior to four distributions: binomial, bernoulli, negative binomial, and geometric. \\n2. All of these distributions have the same generative model, which is a bernoulli variable. \\n3. A bernoulli variable is a random variable that can take on two values, usually 0 and 1.\",\"Abstraction groups\":{\"-1\":[\"Beta Distribution\",\"Conjugate Prior\",\"Binomial\",\"Bernoulli\",\"Negative Binomial\",\"Geometric\",\"Generative Model\",\"Bernoulli Variable\"],\"0\":[\"Beta Distribution\"],\"1\":[\"Conjugate Prior\"],\"2\":[\"Probability Theory\",\"Statistics\"],\"3\":[\"Mathematics\"],\"4\":[\"Science\"]}},\"442\":{\"Question\":\"Why does MCTS use the number of visits, not the Q function value? \",\"Answer\":\"Because Q is noisy. it can be updated right at the end to favor a weird state, while num visits reflects better the certainty. \",\"Key ideas\":\"1. MCTS (Monte Carlo Tree Search) is an algorithm used to find the best move in a game. \\n2. The Q function is a measure of the expected reward for a given state. \\n3. The Q function is noisy, meaning it can be updated right at the end to favor a weird state. \\n4. The number of visits reflects better the certainty of a given state. \\n5. MCTS uses the number of visits, not the Q function value, to determine the best move.\",\"Abstraction groups\":{\"-1\":[\"MCTS\",\"Q Function\",\"Visit\",\"Reward\",\"Certainty\"],\"0\":[\"MCTS\"],\"1\":[\"Algorithm\",\"Game Theory\"],\"2\":[\"Computer Science\",\"Mathematics\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"443\":{\"Question\":\"What is one major way that the alpha go backup is different from normal MCTS backup? What about alpha go zero? \\n(there are other differences outside the backup too) \",\"Answer\":\"The return is combined with the value predicted by a policy network, to reduce noise.\\nFor zero, it's purely the value output by the network. No simulation required. \",\"Key ideas\":\"\\n1. Alpha Go Backup: \\n    a. It is a type of Monte Carlo Tree Search (MCTS) backup. \\n    b. The return is combined with the value predicted by a policy network, to reduce noise.\\n2. Alpha Go Zero: \\n    a. It does not require simulation. \\n    b. The value is purely output by the network.\",\"Abstraction groups\":{\"-1\":[\"Alpha Go\",\"Backup\",\"MCTS\",\"Value\",\"Network\",\"Noise\",\"Simulation\",\"Zero\"],\"0\":[\"Alpha Go\"],\"1\":[\"Backup\",\"Zero\"],\"2\":[\"MCTS\",\"Value\",\"Network\",\"Noise\",\"Simulation\"],\"3\":[\"Artificial Intelligence\",\"Machine Learning\"],\"4\":[\"Computer Science\"]}},\"444\":{\"Question\":\"How is the alpha go Q value initialized for each node when first encountered? \",\"Answer\":\"It is set by the parent node, to reflect that bad paths or good paths shouldn't have a bias toward 0 return.\",\"Key ideas\":\"\\n1. Alpha Go Q value: This is a value assigned to each node in a game tree, which is used to determine the best move to make in a given situation. \\n\\n2. Initialization: The Alpha Go Q value for each node is set by the parent node when first encountered. \\n\\n3. No bias: The Alpha Go Q value is set to reflect that bad paths or good paths should not have a bias toward 0 return.\",\"Abstraction groups\":{\"-1\":[\"Alpha Go Q Value\",\"Initialization\",\"No Bias\"],\"0\":[\"Alpha Go Q Value\"],\"1\":[\"Initialization\",\"No Bias\"],\"2\":[\"Game Tree\",\"Best Move\"],\"3\":[\"Artificial Intelligence\",\"Decision Making\"],\"4\":[\"Computer Science\"]}},\"445\":{\"Question\":\"How is the predictive upper confidence tree algorithm (PUCT) different from UCT? \",\"Answer\":\"It steers the exploration to require less feedback, and be correct the first time. \\n------------------------------------\\nHas confidence term multiplied by probability a policy takes it, to favor likely moves. \\nAlso has sqrt(N)\\/(1+n_i), instead of sqrt(log(N)\\/n_i), though they asymptote similarly to 1\\/sqrt(n_i)\",\"Key ideas\":\"\\n1. UCT (Upper Confidence Tree) is an algorithm used in reinforcement learning. \\n2. PUCT (Predictive Upper Confidence Tree) is an improved version of UCT. \\n3. PUCT requires less feedback than UCT and is more likely to be correct the first time. \\n4. PUCT has a confidence term multiplied by the probability that a policy takes it, to favor likely moves. \\n5. PUCT has a different exploration term than UCT, sqrt(N)\\/(1+n_i), instead of sqrt(log(N)\\/n_i), though they asymptote similarly to 1\\/sqrt(n_i).\",\"Abstraction groups\":{\"-1\":[\"UCT\",\"PUCT\",\"Feedback\",\"Confidence\",\"Probability\",\"Policy\",\"Exploration\",\"N\",\"N_i\"],\"0\":[\"PUCT\"],\"1\":[\"Algorithm\",\"Reinforcement Learning\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"446\":{\"Question\":\"Visualize the set of networks used in training the original alpha go network, and what each was used for \",\"Answer\":\"There was a rollout policy and human expert move policy which are trained on the human expert data.\\nThere was the RL generated agent which was trained using RL and plays differently, and the value function trained on the RL generated agent. \\nRollout policy and RL generated value function, and human expert move policy were all used in the final gameplay time. \",\"Key ideas\":\"1. Alpha Go network: a computer program developed by Google DeepMind to play the board game Go\\n2. Rollout policy: a type of artificial intelligence algorithm used to predict the outcome of a game\\n3. Human expert move policy: a type of artificial intelligence algorithm used to learn from human experts\\n4. RL generated agent: an agent trained using reinforcement learning (RL) to play differently than the rollout policy\\n5. Value function: a type of artificial intelligence algorithm used to evaluate the expected outcome of a game\\n6. Final gameplay time: the time when the Alpha Go network is used to play the game\",\"Abstraction groups\":{\"-1\":[\"Alpha Go\",\"Rollout\",\"Human Expert\",\"RL\",\"Value\",\"Final Gameplay\"],\"0\":[\"Alpha Go\"],\"1\":[\"Artificial Intelligence\",\"Computer Program\",\"Board Game\"],\"2\":[\"Machine Learning\",\"Algorithm\",\"Strategy\"],\"3\":[\"Technology\",\"Computation\",\"Game\"],\"4\":[\"Science\",\"Knowledge\",\"Learning\"]}},\"447\":{\"Question\":\"Neuralink 3 key design concerns\",\"Answer\":\"safety (long term)\\nScalability (can exchange, etc)\\nAccess to the relevant parts of the brain (deep, vision centers, etc) \",\"Key ideas\":\"\\n1. Neuralink 3 is a technology designed to allow for the connection of a computer to the human brain. \\n2. Safety is a key design concern for Neuralink 3, as it must be safe for long-term use. \\n3. Scalability is another key design concern for Neuralink 3, as it must be able to exchange data and be easily upgradable. \\n4. Access to the relevant parts of the brain is a key design concern for Neuralink 3, as it must be able to access deep, vision centers, and other areas of the brain.\",\"Abstraction groups\":{\"-1\":[\"Neuralink\",\"Safety\",\"Scalability\",\"Access\",\"Brain\"],\"0\":[\"Neuralink 3\"],\"1\":[\"Key Design Concern\"],\"2\":[\"Technology\",\"Brain-Computer Interface\"],\"3\":[\"Neuroscience\",\"Artificial Intelligence\"],\"4\":[\"Science\",\"Technology\",\"Engineering\",\"Mathematics\"]}},\"448\":{\"Question\":\"How does Neuralink plan to use reinforcement learning \",\"Answer\":\"To understand the output from firing of a large number of neurons \",\"Key ideas\":\"\\n1. Neuralink: a company founded by Elon Musk that is developing a brain-computer interface to connect humans and computers. \\n2. Reinforcement Learning: a type of machine learning algorithm that uses rewards and punishments to learn from its environment. \\n3. Output: the result of a process or action. \\n4. Firing of neurons: the process of neurons sending electrical signals to other neurons. \\n5. Large number of neurons: a large group of neurons that are firing together.\",\"Abstraction groups\":{\"-1\":[\"Neuralink\",\"Reinforcement Learning\",\"Output\",\"Firing\",\"Neuron\"],\"0\":[\"Neuralink\"],\"1\":[\"Reinforcement Learning\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Technology\",\"Robotics\"],\"4\":[\"Science\",\"Engineering\"]}},\"449\":{\"Question\":\"What is the reason we need BMIs? (Neuralink presentation by musk) \",\"Answer\":\"Output rate from brain is too slow. Input is fine. \",\"Key ideas\":\"\\n1. BMI stands for Brain-Machine Interface. \\n2. Neuralink is a company founded by Elon Musk. \\n3. Neuralink presented a technology that allows for a direct connection between the brain and a computer. \\n4. The output rate from the brain is too slow to be useful. \\n5. The input rate from the brain is fine. \\n6. BMIs are needed to bridge the gap between the slow output rate and the fine input rate.\",\"Abstraction groups\":{\"-1\":[\"BMI\",\"Neuralink\",\"Brain\",\"Computer\",\"Output\",\"Input\"],\"0\":[\"BMI\"],\"1\":[\"Brain-Machine Interface\",\"Neuralink\",\"Output\",\"Input\"],\"2\":[\"Technology\",\"Brain\",\"Computer\"],\"3\":[\"Connectivity\",\"Interaction\"],\"4\":[\"Communication\"]}},\"450\":{\"Question\":\"How to improve flashcards over time? \",\"Answer\":\"Make cards more atomic\\nAdd multiple forms of media, like pictures and text and sound\\nChange them up: Avoid learning syntax of question as the memory device \",\"Key ideas\":\"\\n1. Make cards more atomic - breaking down complex ideas into smaller, more manageable chunks.\\n2. Add multiple forms of media - such as pictures, text, and sound - to the cards to make them more engaging and memorable.\\n3. Avoid learning the syntax of the question as the memory device - change up the questions and answers to keep the student engaged and prevent memorization of the question itself.\",\"Abstraction groups\":{\"-1\":[\"Flashcard\",\"Improve\",\"Atomic\",\"Media\",\"Picture\",\"Text\",\"Sound\",\"Change\",\"Syntax\",\"Memory\",\"Device\"],\"0\":[\"Flashcard\"],\"1\":[\"Improve\",\"Atomic\",\"Media\"],\"2\":[\"Learning\",\"Memory\"],\"3\":[\"Education\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"451\":{\"Question\":\"What is an \\\"orphan\\\" flash card? \",\"Answer\":\"Some topic without sister flashcards which are conceptually similar \",\"Key ideas\":\"\\n1. A flashcard is a tool used to help students learn and remember information. \\n2. An \\\"orphan\\\" flashcard is a flashcard that does not have any sister flashcards which are conceptually similar. \\n3. Sister flashcards are flashcards that are related to each other in terms of the topic or concept being tested. \\n4. Orphan flashcards are often used to test a student's knowledge of a single concept or topic.\",\"Abstraction groups\":{\"-1\":[\"Flashcard\",\"Orphan\",\"Sister\",\"Concept\"],\"0\":[\"Flashcard\"],\"1\":[\"Orphan\",\"Sister\"],\"2\":[\"Concept\"],\"3\":[\"Learning\"],\"4\":[\"Education\"]}},\"452\":{\"Question\":\"Learning a new field: 3 steps according to Nielsen\",\"Answer\":\"Deep dive a major paper (shallow skims first)\\nThen deep dive 5-10 more\\nThen shallow skim many\",\"Key ideas\":\"\\n1. To learn a new field, according to Nielsen, there are three steps:\\n    a. Deep dive a major paper (shallow skims first)\\n    b. Deep dive 5-10 more\\n    c. Shallow skim many\\n2. Deep diving a paper involves reading it in detail and understanding the main ideas and arguments.\\n3. Shallow skimming involves quickly reading a paper to get a general idea of its content.\",\"Abstraction groups\":{\"-1\":[\"Field\",\"Step\",\"Nielsen\",\"Paper\",\"Skim\",\"Deep Dive\",\"Shallow Skim\"],\"0\":[\"Learning\"],\"1\":[\"Field\",\"Step\",\"Nielsen\"],\"2\":[\"Knowledge\",\"Process\",\"Methodology\"],\"3\":[\"Learning\",\"Acquisition\",\"Understanding\"],\"4\":[\"Education\"]}},\"453\":{\"Question\":\"Common pitfall when trying to learn a new field \",\"Answer\":\"A common mistake is spending too much time building up knowledge of docs, textbooks, and APIs for example.\\nInstead, Nielsen suggests to: Read a paper in depth, or do a project in depth, and take atomic notes in context. Knowledge of basic syntax and terminology will come over time, and in fact may come more easily because it's learned in context with exciting information. \",\"Key ideas\":\"\\n1. A common pitfall when trying to learn a new field is spending too much time building up knowledge of documents, textbooks, and APIs.\\n2. Instead, Nielsen suggests to read a paper in depth, or do a project in depth, and take atomic notes in context.\\n3. Knowledge of basic syntax and terminology will come over time, and in fact may come more easily because it's learned in context with exciting information.\",\"Abstraction groups\":{\"-1\":[\"Pitfall\",\"Knowledge\",\"Document\",\"Textbook\",\"API\",\"Nielsen\",\"Paper\",\"Project\",\"Note\",\"Syntax\",\"Terminology\",\"Context\",\"Exciting Information\"],\"0\":[\"Pitfall\"],\"1\":[\"Knowledge\",\"Document\",\"Textbook\",\"API\"],\"2\":[\"Learning\",\"Reading\",\"Project\"],\"3\":[\"Syntax\",\"Terminology\",\"Context\",\"Exciting Information\"],\"4\":[\"Understanding\"]}},\"454\":{\"Question\":\"Why generative models are expected to be important \",\"Answer\":\"Because you probably have a latent representation if you can do that well \",\"Key ideas\":\"\\n1. Generative models: models that generate data from a given set of parameters.\\n2. Latent representation: a representation of data that is not explicitly visible, but can be inferred from the data.\\n3. Expected importance: why generative models are expected to be important.\\n4. Inference: the process of inferring the latent representation from the data.\\n5. Well: the ability to do inference well is what makes generative models important.\",\"Abstraction groups\":{\"-1\":[\"Generative Model\",\"Latent Representation\",\"Expected Importance\",\"Inference\",\"Well\"],\"0\":[\"Generative Model\"],\"1\":[\"Machine Learning\",\"Artificial Intelligence\"],\"2\":[\"Data Science\",\"Computational Thinking\"],\"3\":[\"Problem Solving\",\"Critical Thinking\"],\"4\":[\"Thinking Skills\"]}},\"455\":{\"Question\":\"What is the policy gradient theorem, and why is it useful ? \",\"Answer\":\"It lets us represent the gradient of the total returns with respect to some parameters of the policy. Then we can gather measurements of this term, and use it for a gradient descent update. \\nSpecifically it takes the total expected return for an entire episode, and expands out the expected return over all possible actions, and transitions, then takes the gradient. \",\"Key ideas\":\"\\n1. Policy gradient theorem: \\n    a. It is a way to represent the gradient of the total returns with respect to some parameters of the policy. \\n    b. It takes the total expected return for an entire episode, and expands out the expected return over all possible actions and transitions. \\n    c. It then takes the gradient. \\n2. Gradient descent update: \\n    a. It is a way to use the measurements of the policy gradient theorem to update the parameters of the policy.\",\"Abstraction groups\":{\"-1\":[\"Policy\",\"Gradient\",\"Theorem\",\"Parameter\",\"Return\",\"Episode\",\"Action\",\"Transition\",\"Update\"],\"0\":[\"Policy Gradient Theorem\"],\"1\":[\"Gradient Descent\"],\"2\":[\"Optimization\",\"Machine Learning\"],\"3\":[\"Artificial Intelligence\",\"Computer Science\"],\"4\":[\"Science\"]}},\"456\":{\"Question\":\"How do lambda returns improve on Monte Carlo (MC) estimates and temporal difference (TD) learning? \",\"Answer\":\"Lambda blends the two.\\nMC is unbiased, higher variance, but faster update\\nTD is biased, but more stable, slower convergence\\nMC is able to update values all over the time space immediately (faster), but has a lot of variance and noise, and is unbiased.\\nTD learning works back as a gradient from the end so it is biased by your initial guess, but it has less variance. \\nLambda: Instead use a mix of biased and slow updates (1 step look ahead with learned value function) and fast but high variance updates (monte carlo on full episode)\",\"Key ideas\":\"1. Lambda returns improve on Monte Carlo (MC) estimates and temporal difference (TD) learning by blending the two.\\n2. MC is unbiased, has higher variance, and updates values faster.\\n3. TD is biased, but more stable and has slower convergence.\\n4. Lambda uses a mix of biased and slow updates (1 step look ahead with learned value function) and fast but high variance updates (monte carlo on full episode).\",\"Abstraction groups\":{\"-1\":[\"Lambda\",\"MC\",\"TD\",\"Variance\",\"Noise\",\"Gradient\",\"Initial Guess\",\"Look Ahead\",\"Value Function\",\"Monte Carlo\",\"Episode\"],\"0\":[\"Lambda\"],\"1\":[\"Improving Monte Carlo\",\"TD Learning\"],\"2\":[\"Estimation\",\"Learning\"],\"3\":[\"Artificial Intelligence\",\"Machine Learning\"],\"4\":[\"Computer Science\"]}},\"457\":{\"Question\":\"How does proximal policy optimization and generalized advantage estimation improve on the vanilla policy gradient algorithm? \",\"Answer\":\"Vanilla usually already has a baseline to reduce variance, and an entropy term for regularization. PPO with GAE makes two changes: replaces the policy term and the Q value term with slightly altered things to prevent rapidly changing the policy, and to give better value estimates with less noise and less bias. ----------\\nPPO (proximal policy optimization) has a regularization term to prevent changing the probability of any action too much (removes it from the loss function once it changes too much). This is the clip term. \\nGAE (generalized advantage estimation) takes the best of both worlds of MC (Monte carlo) estimates and 1 step TD learning (temporal difference learning) to rapidly sample the Q value in an unbiased way.\",\"Key ideas\":\"1. Vanilla policy gradient algorithm\\n2. Proximal policy optimization (PPO)\\n3. Generalized advantage estimation (GAE)\\n4. Monte Carlo (MC) estimates\\n5. Temporal difference learning (TD learning)\\n6. Clip term in PPO\\n7. Baseline to reduce variance\\n8. Entropy term for regularization\\n9. Replacing policy term and Q value term with slightly altered things to prevent rapidly changing the policy and to give better value estimates with less noise and less bias\",\"Abstraction groups\":{\"-1\":[\"PPO\",\"GAE\",\"MC\",\"TD\",\"Clip\",\"Baseline\",\"Entropy\",\"Policy\",\"Q Value\"],\"0\":[\"Policy Gradient\"],\"1\":[\"Reinforcement Learning\",\"Machine Learning\"],\"2\":[\"Artificial Intelligence\",\"Computational Thinking\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"458\":{\"Question\":\"How do policy gradients improve on TD learning and Q learning? \",\"Answer\":\"Value based RL is trying to \\nsolve a self consistency equation in the TD-learning case. This is hard to solve. \\nCan't handle continuous action spaces\\nCan't be stochastic \\nPolicy gradient solves this by... \\nPolicy gradient to directly learn the policy. This is a smoother space to update (rather than a greedy value function which jumps between actions)\\nCan be stochastic\\nCan handle continuous action spaces (if you learn the parameters of a distribution) \\nBoth are still model free and general \",\"Key ideas\":\"1. Value based RL is trying to solve a self consistency equation in the TD-learning case. \\n2. This is hard to solve and can't handle continuous action spaces or be stochastic. \\n3. Policy gradient solves this by directly learning the policy, which is a smoother space to update than a greedy value function. \\n4. Policy gradient can be stochastic and can handle continuous action spaces if the parameters of a distribution are learned. \\n5. Both TD-learning and policy gradient are model free and general.\",\"Abstraction groups\":{\"-1\":[\"Policy Gradient\",\"TD Learning\",\"Q Learning\",\"Value Based RL\",\"Self Consistency\",\"Continuous Action Space\",\"Stochastic\",\"Greedy Value Function\",\"Parameter\",\"Distribution\",\"Model Free\",\"General\"],\"0\":[\"Policy Gradient\"],\"1\":[\"Reinforcement Learning\",\"Machine Learning\"],\"2\":[\"Artificial Intelligence\",\"Computational Thinking\"],\"3\":[\"Computer Science\",\"Science\"],\"4\":[\"Knowledge\"]}},\"459\":{\"Question\":\"What are the challenges of poker as a reinforcement learning problem, and how to avoid them? \",\"Answer\":\"1. It is highly random, which prevents stable training. \\n2. The state space is huge, and the state of the opponent is largely hidden 3. The action space has local minima (going all in to reduce to random games).\",\"Key ideas\":\"1. Poker is a reinforcement learning problem.\\n2. It is highly random, which can make training difficult.\\n3. The state space is large and the state of the opponent is largely hidden.\\n4. The action space has local minima, which can lead to random games.\",\"Abstraction groups\":{\"-1\":[\"Poker\",\"Reinforcement Learning\",\"Randomness\",\"State Space\",\"Opponent\",\"Action Space\",\"Local Minima\"],\"0\":[\"Poker\"],\"1\":[\"Reinforcement Learning\"],\"2\":[\"Artificial Intelligence\",\"Machine Learning\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"460\":{\"Question\":\"Why is the upper confidence bound in MCTS of the given form (specifically the log(N) and 1\\/sqrt(n) parts)? \\nQ value + c sqrt( log(N)\\/n_i)\\nThink: concentration bound, then how to limit loss\",\"Answer\":\"Concentration bound tells us: Probability of true value being higher than epsilon scales like exp(-n eps^2\\/2) \\\\equiv delta\\nThen you can solve this to give prob true value is higher than sqrt(2 log(1\\/delta)\\/n) is delta. \\nThen if you want to have your overall loss be minimized, you want your probability of being wrong to scale like 1\\/N_samples so that overall loss is capped. To have this scaling, you set delta = 1\\/N. \\nNow if you choose your arm according to the UCB equation, you will be wrong with a probability that scales less than with 1\\/N, so your error is bounded\",\"Key ideas\":\"1. Concentration bound: Probability of true value being higher than epsilon scales like exp(-n eps^2\\/2) \\\\equiv delta\\n2. Solve the concentration bound to give prob true value is higher than sqrt(2 log(1\\/delta)\\/n) is delta\\n3. Want overall loss to be minimized, so want probability of being wrong to scale like 1\\/N_samples\\n4. Choose arm according to UCB equation, so error is bounded\",\"Abstraction groups\":{\"-1\":[\"MCTS\",\"QValue\",\"C\",\"Sqrt\",\"Log\",\"N\",\"NI\",\"ConcentrationBound\",\"Epsilon\",\"Delta\",\"NSamples\",\"UCBEquation\"],\"0\":[\"Upper Confidence Bound\"],\"1\":[\"MCTS\",\"Q Value\",\"C\",\"Sqrt\",\"Log\",\"N\",\"N_i\"],\"2\":[\"Probability\",\"Loss\",\"Error\"],\"3\":[\"Mathematics\",\"Statistics\"],\"4\":[\"Science\"]}},\"461\":{\"Question\":\"What is the difference between Q learning, TD learning, and policy gradients, compared to MCTS and dynamic programming? \",\"Answer\":\"Latter are model based, former are model free. \",\"Key ideas\":\"1. Q learning, TD learning, and policy gradients are model free. \\n2. MCTS and dynamic programming are model based. \\n3. Model free methods do not require a model of the environment. \\n4. Model based methods require a model of the environment. \\n5. Q learning, TD learning, and policy gradients are methods of reinforcement learning. \\n6. MCTS and dynamic programming are methods of planning.\",\"Abstraction groups\":{\"-1\":[\"Q Learning\",\"TD Learning\",\"Policy Gradient\",\"MCTS\",\"Dynamic Programming\",\"Model Free\",\"Model Based\",\"Reinforcement Learning\",\"Planning\"],\"0\":[\"Model Free\",\"Model Based\"],\"1\":[\"Reinforcement Learning\",\"Planning\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Science\"],\"4\":[\"Knowledge\"]}},\"462\":{\"Question\":\"When can you use MCTS (Monte Carlo tree search) to explore and plan? \",\"Answer\":\"When you have a model of the environment\\nLess important: a relatively small branching ratio (and if you're doing true MCTS, not alpha go zero algorithm, then you need a concrete episode termination and rollout policy.) \",\"Key ideas\":\"1. MCTS stands for Monte Carlo tree search.\\n2. MCTS can be used to explore and plan.\\n3. You need a model of the environment in order to use MCTS.\\n4. The branching ratio should be relatively small.\\n5. If you are using true MCTS, you need a concrete episode termination and rollout policy.\",\"Abstraction groups\":{\"-1\":[\"MCTS\",\"Environment\",\"Branching Ratio\",\"Episode Termination\",\"Rollout Policy\"],\"0\":[\"MCTS\"],\"1\":[\"Planning\",\"Exploration\"],\"2\":[\"Artificial Intelligence\",\"Algorithms\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"463\":{\"Question\":\"np.ufuncs. What are they? \",\"Answer\":\"Are specialized array operations that work fast on adding\\/multiplying arrays. Are usually called internally. \",\"Key ideas\":\"\\n1. np.ufuncs are specialized array operations. \\n2. They work fast on adding\\/multiplying arrays. \\n3. They are usually called internally.\",\"Abstraction groups\":{\"-1\":[\"NpUfunc\",\"Array\",\"Adding\",\"Multiplying\",\"Internally\"],\"0\":[\"Np.Ufunc\"],\"1\":[\"Array Operation\"],\"2\":[\"Mathematics\",\"Computation\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"464\":{\"Question\":\"What does numpy.random.choice do? For example:\\nx = np.random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(100))\",\"Answer\":\"This gives array of size 100, with values taken from the first array, and with each value sampled with a probability taken from the corresponding point in the second array\",\"Key ideas\":\"1. Numpy is a library of Python functions used for scientific computing. \\n2. The random.choice function is a function within the numpy library. \\n3. The random.choice function takes two arguments: an array of values and an array of probabilities. \\n4. The array of values is a list of the values that can be chosen. \\n5. The array of probabilities is a list of the probabilities associated with each value in the array of values. \\n6. The size argument is an optional argument that specifies the size of the array that will be returned. \\n7. The random.choice function will return an array of size specified by the size argument, with values taken from the first array, and with each value sampled with a probability taken from the corresponding point in the second array.\",\"Abstraction groups\":{\"-1\":[\"Numpy\",\"Random\",\"Choice\",\"Array\",\"Value\",\"Probability\",\"Size\"],\"0\":[\"Random.choice\"],\"1\":[\"Numpy\",\"Random\",\"Choice\"],\"2\":[\"Python\",\"Scientific Computing\"],\"3\":[\"Programming\",\"Mathematics\"],\"4\":[\"Computing\"]}},\"465\":{\"Question\":\"Numpy array - what is view and base? \",\"Answer\":\"arr.view() returns a reference to some data, but not a copy\\narr.copy() is an independent copy \\nnp.base will be None if an object is its own data, but will be a reference to another array if object is a \\\"view\\\" of another, as in a = b.view() \",\"Key ideas\":\"1. Numpy array is a type of data structure. \\n2. arr.view() returns a reference to some data, but not a copy.\\n3. arr.copy() is an independent copy.\\n4. np.base will be None if an object is its own data.\\n5. np.base will be a reference to another array if object is a \\\"view\\\" of another, as in a = b.view().\",\"Abstraction groups\":{\"-1\":[\"Numpy\",\"Array\",\"View\",\"Base\",\"Copy\",\"Reference\",\"Data\"],\"0\":[\"Numpy Array\"],\"1\":[\"Data Structure\"],\"2\":[\"Computer Science\",\"Mathematics\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"466\":{\"Question\":\"What is the abstract goal of Intelligence augmentation tools (Nielsen)? And how does AI accomplish this? \",\"Answer\":\"Compact abstraction of concepts, so that humans can operate at a higher level of abstraction, using new forms of primitives which are internalized. \",\"Key ideas\":\"\\n1. Intelligence augmentation tools (Nielsen): \\n    - Abstract goal is to provide a compact abstraction of concepts \\n    - This allows humans to operate at a higher level of abstraction \\n    - New forms of primitives are internalized \\n2. AI: \\n    - Accomplishes the abstract goal of intelligence augmentation tools \\n    - Does this by providing a compact abstraction of concepts \\n    - Allows humans to operate at a higher level of abstraction \\n    - Introduces new forms of primitives which are internalized\",\"Abstraction groups\":{\"-1\":[\"Intelligence Augmentation\",\"AI\",\"Concept\",\"Abstraction\",\"Primitive\"],\"0\":[\"Intelligence Augmentation\"],\"1\":[\"Tool\",\"AI\"],\"2\":[\"Technology\",\"Augmentation\"],\"3\":[\"Innovation\",\"Automation\"],\"4\":[\"Progress\",\"Efficiency\"]}},\"467\":{\"Question\":\"Clean coding: What should a function do? How should it be designed? \",\"Answer\":\"Should be short - a few lines\\nShould do only one thing (on an abstract level)\\nShould not contain multiple levels of abstraction. Instead call other functions at each new level of abstraction \",\"Key ideas\":\"\\n1. A function should be short - a few lines.\\n2. A function should do only one thing (on an abstract level).\\n3. A function should not contain multiple levels of abstraction.\\n4. Instead, call other functions at each new level of abstraction.\",\"Abstraction groups\":{\"-1\":[\"Clean Coding\",\"Function\",\"Short\",\"One Thing\",\"Abstraction\",\"Multiple Level\",\"Call Function\"],\"0\":[\"Clean Coding\"],\"1\":[\"Function\",\"Design\"],\"2\":[\"Coding\",\"Programming\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"468\":{\"Question\":\"What is a python iterator? \",\"Answer\":\"It is a wrapper that lets you iterate over items in an object. \\nExample:\\nmystr = \\\"banana\\\"\\nmyit = iter(mystr)\\nprint(next(myit))\\nprint(next(myit))  # will print \\\"b\\\" then \\\"a\\\" then \\\"n\\\" ... \",\"Key ideas\":\"1. A python iterator is a wrapper that allows you to iterate over items in an object. \\n2. An example of a python iterator is the iter() function. \\n3. The iter() function takes an object as an argument and returns an iterator object. \\n4. The iterator object can be used to access the items in the object one at a time. \\n5. The next() function can be used to access the next item in the iterator object. \\n6. The next() function will return the next item in the iterator object each time it is called.\",\"Abstraction groups\":{\"-1\":[\"Python\",\"Iterator\",\"Iter\",\"Object\",\"Next\"],\"0\":[\"Iterator\"],\"1\":[\"Python\",\"Wrapper\"],\"2\":[\"Programming\",\"Iteration\"],\"3\":[\"Computing\",\"Data Structure\"],\"4\":[\"Technology\"]}},\"469\":{\"Question\":\"What does python map do? \",\"Answer\":\"Map()\\nThe map() function executes a specified function for each item in an iterable.\\nIf function has 2 parameters,  call \\\"map(func, it1, it2)\\\"\",\"Key ideas\":\"1. Python is a programming language.\\n2. The map() function is a built-in function in Python.\\n3. The map() function executes a specified function for each item in an iterable.\\n4. An iterable is an object that can be iterated over.\\n5. The map() function can take two parameters, a function and an iterable.\\n6. When calling the map() function, the syntax is \\\"map(func, it1, it2)\\\".\",\"Abstraction groups\":{\"-1\":[\"Python\",\"Map\",\"Function\",\"Iterable\",\"Parameter\",\"Syntax\"],\"0\":[\"Map\"],\"1\":[\"Python\",\"Function\"],\"2\":[\"Programming\",\"Iterable\"],\"3\":[\"Computation\",\"Parameters\"],\"4\":[\"Technology\",\"Syntax\"]}},\"470\":{\"Question\":\"How do you split a string in python? \",\"Answer\":\"String.Split(c) - Splits at character c, returns list\\nString.Splitlines() - Returns list with split lines \",\"Key ideas\":\"1. Python is a programming language. \\n2. Strings are a type of data in Python. \\n3. The String.Split() method is used to split a string in Python. \\n4. The String.Split() method takes a character (c) as an argument. \\n5. The String.Split() method returns a list of strings. \\n6. The String.Splitlines() method is used to split a string into lines. \\n7. The String.Splitlines() method returns a list of strings.\",\"Abstraction groups\":{\"-1\":[\"Python\",\"String\",\"Split\",\"Character\",\"List\",\"Splitline\"],\"0\":[\"Splitting String\"],\"1\":[\"Python\",\"String\"],\"2\":[\"Programming\",\"Data\"],\"3\":[\"Computing\",\"Manipulation\"],\"4\":[\"Technology\"]}},\"471\":{\"Question\":\"Python file access commands for opening a file and reading it \",\"Answer\":\"For reading:\\nf = open(\\\"hello.txt\\\", \\\"r\\\")\\ntext = f.read()  # reads whole thing\\nline = f.readline()  # reads a line\\nf.close()  # do this at the end \",\"Key ideas\":\"1. Python is a programming language.\\n2. A file is a collection of data stored in a computer.\\n3. Accessing a file requires commands.\\n4. The command for opening a file is \\\"f = open(\\\"hello.txt\\\", \\\"r\\\")\\\".\\n5. The command for reading a file is \\\"text = f.read()\\\" for reading the whole thing, or \\\"line = f.readline()\\\" for reading a line.\\n6. After reading a file, it is important to close it with the command \\\"f.close()\\\".\",\"Abstraction groups\":{\"-1\":[\"Python\",\"File\",\"Access\",\"Open\",\"Read\",\"Close\"],\"0\":[\"File Access\"],\"1\":[\"Python\",\"Commands\"],\"2\":[\"Programming\",\"Data\"],\"3\":[\"Technology\",\"Information\"],\"4\":[\"Knowledge\"]}},\"472\":{\"Question\":\"What is the gamma distribution formula, and what can it look like? \",\"Answer\":\"Is a generalization of the chi-squared distribution and exponential \\n\\tLooks like PDF which is similar to exponential, or poisson, or chi squared \",\"Key ideas\":\"1. Gamma distribution is a generalization of the chi-squared distribution and exponential. \\n2. Gamma distribution looks like a PDF (Probability Density Function) which is similar to exponential, poisson, or chi squared.\",\"Abstraction groups\":{\"-1\":[\"Gamma\",\"Chi-Squared\",\"Exponential\",\"Pdf\",\"Exponential\",\"Poisson\",\"Chi-Squared\"],\"0\":[\"Gamma Distribution\"],\"1\":[\"Formula\",\"Distribution\"],\"2\":[\"Mathematics\",\"Statistics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"473\":{\"Question\":\"What is the beta distribution formula, and what can it look like? \",\"Answer\":\"Lives on interval 0 to 1\\nPDF can be nice gaussian looking, or uniform, or highly skewed\",\"Key ideas\":\"\\n1. The beta distribution formula is a probability distribution that lives on the interval 0 to 1. \\n2. The probability density function (PDF) of the beta distribution can take on different shapes, such as a nice gaussian looking shape, a uniform shape, or a highly skewed shape.\",\"Abstraction groups\":{\"-1\":[\"Beta Distribution\",\"Formula\",\"Interval\",\"PDF\",\"Gaussian\",\"Uniform\",\"Skewed\"],\"0\":[\"Beta Distribution\"],\"1\":[\"Probability Distribution\"],\"2\":[\"Statistics\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"474\":{\"Question\":\"What is a conjugate prior distribution in statistics, and why is it useful? \",\"Answer\":\"It is the set of functions (sufficiently general) so that your posterior FOR THE PARAMETERS OF A PROBABILITY DISTRIBUTION is always in that set of function, even after you make some measurements or observations of the system. That is, your prior is in this set of functions, and your posterior is as well.\\nThis can sometimes let you do an analytic calculation of the Bayesian update rule (for example with a binomial variable) .\\nExample: You know you have a bernoulli variable. It has some parameter theta. You have a prior distribution for theta (maybe it's uniform in 0 to 1) that can be expressed as a beta function with some parameters. You can analytically do the math to show that the correct posterior for theta is another beta distribution that depends in an analytic way on your sampled data and initial beta parameters\",\"Key ideas\":\"1. A conjugate prior distribution is a set of functions that can be used to calculate the posterior of a probability distribution. \\n2. The posterior of a probability distribution is the result of making measurements or observations of the system. \\n3. The prior distribution is also in the set of functions, and the posterior is as well. \\n4. This set of functions can sometimes allow for an analytic calculation of the Bayesian update rule. \\n5. An example of this is a Bernoulli variable, which has a parameter called theta. \\n6. The prior distribution for theta can be expressed as a beta function with some parameters. \\n7. The correct posterior for theta is another beta distribution that depends analytically on the sampled data and initial beta parameters.\",\"Abstraction groups\":{\"-1\":[\"Conjugate Prior\",\"Posterior\",\"Parameter\",\"Probability\",\"Measurement\",\"System\",\"Bayesian\",\"Binomial\",\"Bernoulli\",\"Theta\",\"Beta\",\"Data\",\"Sampled\"],\"0\":[\"Conjugate Prior\"],\"1\":[\"Statistics\",\"Probability\"],\"2\":[\"Mathematics\",\"Science\"],\"3\":[\"Knowledge\",\"Learning\"],\"4\":[\"Education\"]}},\"475\":{\"Question\":\"Chi-squared distribution \",\"Answer\":\"Distribution of the sum of squares of normally distributed variables (usually with std dev 1). \\nExplicit formula is complicated. Rough idea is PDF goes like x^a e^-x\\/2 so a polyomial term times an exponential. CDF is more like an exponential \\nBasic idea is it lets us construct confidence intervals\",\"Key ideas\":\"1. Chi-squared distribution is the distribution of the sum of squares of normally distributed variables (usually with standard deviation 1). \\n2. The explicit formula for the Chi-squared distribution is complicated. \\n3. The probability density function (PDF) of the Chi-squared distribution goes like x^a e^-x\\/2, where a is a polyomial term and e is an exponential. \\n4. The cumulative distribution function (CDF) of the Chi-squared distribution is more like an exponential. \\n5. The basic idea of the Chi-squared distribution is that it lets us construct confidence intervals.\",\"Abstraction groups\":{\"-1\":[\"Chi-Squared\",\"Normally Distributed\",\"Standard Deviation\",\"Formula\",\"Pdf\",\"Polyomial\",\"Exponential\",\"Cdf\",\"Confidence Interval\"],\"0\":[\"Chi-Squared\"],\"1\":[\"Distribution\",\"Statistics\"],\"2\":[\"Mathematics\",\"Probability\"],\"3\":[\"Science\",\"Data Analysis\"],\"4\":[\"Knowledge\"]}},\"476\":{\"Question\":\"Various types of means. ie Arithmetic mean vs geometric mean vs harmonic meanWhen is each used? \",\"Answer\":\"Most general is called p-norm. (1\\/n*Sum of x^p)^(1\\/p).p=-1 is harmonicp=1 is arithmeticp=2 is quadraticp->0 is geometric (to prove, take exponential of log of this, then l'hopital it)p-> infinity is the maximum------------Geometric mean is the Nth root of the product of n numbers: N-root(x1*x2*...)\\nOften used for things that have meaning when multiplied as a product (ie where the objects are exponentials of some rate)\\nHarmonic mean is N*(1\\/x1 + 1\\/x2 + ...)\\nDominated by the minimum of the arguments \\nUsed when you are finding average of rates (think of example of \\\"average speed of a car trip)\\nQuadratic mean: sqrt of sum of squares\",\"Key ideas\":\"1. Most general type of mean is called p-norm, which is (1\\/n*Sum of x^p)^(1\\/p).\\n2. p=-1 is harmonic mean, p=1 is arithmetic mean, p=2 is quadratic mean, and p->0 is geometric mean.\\n3. Geometric mean is the Nth root of the product of n numbers: N-root(x1*x2*...).\\n4. Geometric mean is often used for things that have meaning when multiplied as a product (ie where the objects are exponentials of some rate).\\n5. Harmonic mean is N*(1\\/x1 + 1\\/x2 + ...).\\n6. Harmonic mean is dominated by the minimum of the arguments.\\n7. Harmonic mean is used when you are finding average of rates (think of example of \\\"average speed of a car trip).\\n8. Quadratic mean is the square root of the sum of squares.\",\"Abstraction groups\":{\"-1\":[\"P-Norm\",\"Arithmetic\",\"Geometric\",\"Harmonic\",\"Quadratic\",\"Nth Root\",\"Product\",\"Exponential\",\"Rate\",\"Maximum\",\"Minimum\",\"Sum of Square\"],\"0\":[\"Mean\"],\"1\":[\"P-norm\",\"Arithmetic\",\"Geometric\",\"Harmonic\",\"Quadratic\"],\"2\":[\"Nth Root\",\"Product\",\"Exponentials\",\"Rates\",\"Maximum\",\"Minimum\",\"Sum Of Squares\"],\"3\":[\"Mathematics\",\"Statistics\"],\"4\":[\"Science\"]}},\"477\":{\"Question\":\"Log-normal distribution \",\"Answer\":\"The log of the values is normally distributed. \\nThe exponentiation of a normally distributed variable is log-normal distributed. \\nYou can go through the change of variables math easily to derive the PDF shown in image \\nPDF is only at positive values. It is sort of like Poisson looking, but very asymmetric \\nThis shows up when you have a product of many independent random variables. This is known as Gibrat's law. \\nYou can derive this quickly by immediately asking what is the probability distribution of the log of the product of stuff. It will converge, by central limit theorem, to a Gaussian, QED you are log normal (the log of your variable (the product) is normal distributed). \",\"Key ideas\":\"\\n1. Log-normal distribution is when the log of the values is normally distributed. \\n2. The exponentiation of a normally distributed variable is log-normal distributed. \\n3. The PDF of a log-normal distribution is only at positive values and is sort of like Poisson looking, but very asymmetric. \\n4. Log-normal distributions show up when you have a product of many independent random variables, known as Gibrat's law. \\n5. You can derive log-normal distributions quickly by asking what is the probability distribution of the log of the product of stuff. \\n6. This will converge, by central limit theorem, to a Gaussian, which means the log of your variable (the product) is normal distributed.\",\"Abstraction groups\":{\"-1\":[\"Log-Normal\",\"Exponentiation\",\"Pdf\",\"Poisson\",\"Gibrat's Law\",\"Probability\",\"Log\",\"Product\",\"Central Limit Theorem\",\"Gaussian\"],\"0\":[\"Log-normal\"],\"1\":[\"Distribution\",\"Random Variable\"],\"2\":[\"Probability\",\"Mathematics\"],\"3\":[\"Statistics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"478\":{\"Question\":\"Weibull distribution \",\"Answer\":\"A generalization of the exponential distribution, characterizing time to failure.\\nIt allows the rate of failure to depend on time (ie go like time to some power). End result is a P(x) which goes like (is dominated by) e^-(x^2) or e^-(x^k) for some k.\",\"Key ideas\":\"1. Weibull distribution is a generalization of the exponential distribution.\\n2. It characterizes time to failure.\\n3. It allows the rate of failure to depend on time.\\n4. The probability of failure (P(x)) is dominated by e^-(x^2) or e^-(x^k) for some k.\",\"Abstraction groups\":{\"-1\":[\"Weibull\",\"Exponential\",\"Time\",\"Failure\",\"Rate\",\"Probability\",\"E^-(X^2)\",\"E^-(X^K)\"],\"0\":[\"Weibull\"],\"1\":[\"Distribution\",\"Time to Failure\"],\"2\":[\"Probability\",\"Rate\"],\"3\":[\"Mathematics\",\"Statistics\"],\"4\":[\"Science\"]}},\"479\":{\"Question\":\"Geometric distribution \",\"Answer\":\"Physically: how many failures before the first success (how many tails before the first heads)\\nP(x) = (1-p)^x p \\nE(x) = 1\\/p\\nvar(X) = (1-p)\\/p^2\\nIt's built from the Bernoulli distribution, and it leads to the exponential distribution in the limit of becoming a rate process. \\n\\\"Negative binomial distribution\\\" is a generalization (kind of like hypergeometric is a generalization of bernoulli). It asks the probability of the the number of failures being X before the first r successes (not just the first success).\",\"Key ideas\":\"\\n1. Geometric distribution is a physically-based concept that describes how many failures occur before the first success (e.g. how many tails before the first heads). \\n2. The probability of x failures before the first success is given by the formula P(x) = (1-p)^x p.\\n3. The expected number of failures before the first success is given by the formula E(x) = 1\\/p.\\n4. The variance of the number of failures before the first success is given by the formula var(X) = (1-p)\\/p^2.\\n5. Geometric distribution is built from the Bernoulli distribution.\\n6. Geometric distribution leads to the exponential distribution in the limit of becoming a rate process.\\n7. Negative binomial distribution is a generalization of the geometric distribution, which asks the probability of the number of failures being X before the first r successes (not just the first success).\",\"Abstraction groups\":{\"-1\":[\"Geometric Distribution\",\"Bernoulli Distribution\",\"Exponential Distribution\",\"Rate Process\",\"Negative Binomial Distribution\",\"Hypergeometric Distribution\",\"Probability\",\"Success\",\"Failure\",\"Variance\"],\"0\":[\"Geometric Distribution\"],\"1\":[\"Probability\",\"Success\",\"Failure\"],\"2\":[\"Variance\",\"Bernoulli Distribution\"],\"3\":[\"Exponential Distribution\",\"Rate Process\"],\"4\":[\"Distribution\"]}},\"480\":{\"Question\":\"Bernoulli distribution \",\"Answer\":\"Pure single coin toss. \\n-> Binomial with more coin tosses. \\n-> Hypergeometric without replacement \",\"Key ideas\":\"\\n1. Bernoulli distribution is a type of probability distribution. \\n2. It is a pure single coin toss. \\n3. Binomial distribution is a type of probability distribution that involves more than one coin toss. \\n4. Hypergeometric distribution is a type of probability distribution that involves sampling without replacement.\",\"Abstraction groups\":{\"-1\":[\"Bernoulli\",\"Coin\",\"Toss\",\"Binomial\",\"Hypergeometric\",\"Replacement\"],\"0\":[\"Bernoulli\"],\"1\":[\"Probability Distribution\"],\"2\":[\"Statistics\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"481\":{\"Question\":\"Hypergeometric distribution \",\"Answer\":\"This probability distribution is similar to the binomial distribution. \\nBinomial: If you draw white and black balls from an urn (where there's some ratio of them), and always place it back before the next trial, and do n trials, thats binomial.\\nHypergeoemtric: This is the same, but you do not replace the balls after drawing them from the urn. Thus it ends up being a bit different from Binomial, but otherwise pretty close. \",\"Key ideas\":\"1. Hypergeometric distribution is a probability distribution. \\n2. It is similar to the binomial distribution. \\n3. Binomial distribution involves drawing white and black balls from an urn, and doing n trials, without replacing the balls after each trial. \\n4. Hypergeometric distribution is the same as binomial, but without replacing the balls after each trial. \\n5. This difference makes the hypergeometric distribution slightly different from the binomial distribution.\",\"Abstraction groups\":{\"-1\":[\"Hypergeometric\",\"Binomial\",\"Urn\",\"Trial\",\"Replacing\"],\"0\":[\"Hypergeometric Distribution\"],\"1\":[\"Probability\",\"Distribution\"],\"2\":[\"Mathematics\",\"Statistics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Education\",\"Learning\"]}},\"482\":{\"Question\":\"Exponential distribution, mean, and variance \",\"Answer\":\"Probability density is Lambda*e^-Lambda x.\\nPhysically it gives the probability of an interval length between poisson distributed random events. \\nMean is 1\\/Lambda, and variance is 1\\/Lambda squared.\",\"Key ideas\":\"\\n1. Exponential distribution is a probability density that gives the probability of an interval length between poisson distributed random events. \\n2. Lambda is a parameter of the exponential distribution. \\n3. The mean of the exponential distribution is 1\\/Lambda. \\n4. The variance of the exponential distribution is 1\\/Lambda squared.\",\"Abstraction groups\":{\"-1\":[\"Exponential\",\"Lambda\",\"Mean\",\"Variance\",\"Poisson\",\"Interval\"],\"0\":[\"Exponential Distribution\"],\"1\":[\"Probability\",\"Statistics\"],\"2\":[\"Mathematics\",\"Science\"],\"3\":[\"Knowledge\",\"Understanding\"],\"4\":[\"Learning\"]}},\"483\":{\"Question\":\"Normal distribution \",\"Answer\":\"Shows up everywhere because of central limit theorem.\\nKnow explicit formula \",\"Key ideas\":\"\\n1. Normal distribution is a type of probability distribution. \\n2. Central Limit Theorem states that the sum of a large number of independent random variables will tend to be normally distributed. \\n3. Normal distribution shows up in many areas of mathematics, statistics, and science. \\n4. Explicit formula for normal distribution is given by the equation: \\n    f(x) = (1\\/sqrt(2*pi*sigma^2)) * e^(-1\\/2*((x-mu)\\/sigma)^2)\\n    where mu is the mean, sigma is the standard deviation, and pi is the ratio of the circumference of a circle to its diameter.\",\"Abstraction groups\":{\"-1\":[\"Normal Distribution\",\"Central Limit Theorem\",\"Explicit Formula\",\"Mean\",\"Standard Deviation\",\"Pi\"],\"0\":[\"Normal Distribution\"],\"1\":[\"Probability Distribution\"],\"2\":[\"Statistics\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"484\":{\"Question\":\"Poisson distribution \",\"Answer\":\"How many events in a process with continuous rate in some fixed time? Each time is independent with some fixed probability. \\nMain control parameter is rate times time, which gives average number observed. \\nMean and variance are both the average. This is the limit of a binomial distribution with p going to 0, but repeated many times.\",\"Key ideas\":\"\\n1. Poisson distribution is a way to measure how many events occur in a process with a continuous rate over some fixed time. \\n2. Each event is independent and has a fixed probability. \\n3. The main control parameter is the rate times the time, which gives the average number of events observed. \\n4. The mean and variance of the distribution are both equal to the average. \\n5. The Poisson distribution is the limit of a binomial distribution with p going to 0, but repeated many times.\",\"Abstraction groups\":{\"-1\":[\"Poisson\",\"Event\",\"Rate\",\"Time\",\"Probability\",\"Parameter\",\"Average\",\"Mean\",\"Variance\",\"Binomial\",\"P\",\"Repeated\"],\"0\":[\"Poisson Distribution\"],\"1\":[\"Probability\",\"Distribution\"],\"2\":[\"Statistics\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\",\"Learning\"]}},\"485\":{\"Question\":\"Binomial distribution \",\"Answer\":\"Number of heads and tails in a certain number of coin flips. \\nMean is np\\nvariance is np(1-p)\",\"Key ideas\":\"1. A binomial distribution is a type of probability distribution.\\n2. It is used to describe the number of heads and tails in a certain number of coin flips.\\n3. The mean of a binomial distribution is calculated using the formula np, where n is the number of coin flips and p is the probability of getting a head.\\n4. The variance of a binomial distribution is calculated using the formula np(1-p), where n is the number of coin flips and p is the probability of getting a head.\",\"Abstraction groups\":{\"-1\":[\"Binomial\",\"Head\",\"Tail\",\"Coin\",\"Flip\",\"Mean\",\"Variance\",\"Np\",\"P\"],\"0\":[\"Binomial Distribution\"],\"1\":[\"Probability Distribution\"],\"2\":[\"Statistics\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Learning\"]}},\"486\":{\"Question\":\"Importance of determinants \",\"Answer\":\"Tells you if a matrix is invertible \",\"Key ideas\":\"\\n1. Matrices: a rectangular array of numbers, symbols, or expressions, arranged in rows and columns. \\n2. Invertible: a matrix is invertible if it has an inverse. \\n3. Determinants: a number that is calculated from the elements of a square matrix and encodes certain properties of the linear transformation described by the matrix. \\n4. Importance of Determinants: Determinants are important because they can tell you if a matrix is invertible.\",\"Abstraction groups\":{\"-1\":[\"Matrix\",\"Invertible\",\"Determinant\",\"Importance\"],\"0\":[\"Determinant\"],\"1\":[\"Mathematics\",\"Linear Algebra\"],\"2\":[\"Algebra\",\"Mathematics\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\",\"Learning\"]}},\"487\":{\"Question\":\"Singular Value Decomposition of a matrix:\\nWhat is its purpose?\\nWhat are key components of proof that it exists? \",\"Answer\":\"Purpose: The singular value decomposition makes it possible to diagonalize and invert matrices, and generally makes matrix manipulation easier.\\nKey components:\\nThe Schur decomposition shows any square matrix is able to be made upper triangular. \\nThe spectral theorem for square matrices shows that any normal square matrix is diagonalizable (uses Schur).\\nFinally, the singular value decomposition of a matrix M is a specific application of the spectral theorem to the matrix M * M, and M M* matrix, and followed by a few steps. \",\"Key ideas\":\"1. Schur decomposition: any square matrix is able to be made upper triangular.\\n2. Spectral theorem for square matrices: any normal square matrix is diagonalizable (uses Schur).\\n3. Singular value decomposition of a matrix: a specific application of the spectral theorem to the matrix M * M, and M M* matrix, and followed by a few steps.\\n4. Purpose of singular value decomposition: makes it possible to diagonalize and invert matrices, and generally makes matrix manipulation easier.\",\"Abstraction groups\":{\"-1\":[\"Matrix\",\"Schur\",\"Spectral\",\"Decomposition\",\"Invert\",\"Diagonalize\",\"Manipulation\"],\"0\":[\"Singular Value Decomposition\"],\"1\":[\"Matrix\",\"Decomposition\"],\"2\":[\"Manipulation\",\"Invert\",\"Diagonalize\"],\"3\":[\"Mathematics\",\"Algebra\"],\"4\":[\"Science\"]}},\"488\":{\"Question\":\"What is the biggest unmet challenge in working with neural networks? \",\"Answer\":\"Understanding neural network structure and information flow\\nOne solution: Finding equivalence classes and methods to distill network down to a similar structure, regardless of the starting structure \",\"Key ideas\":\"1. Neural networks are a type of artificial intelligence technology. \\n2. Neural networks are composed of interconnected nodes that process information. \\n3. The challenge of working with neural networks is understanding the structure and information flow. \\n4. One solution to this challenge is to find equivalence classes and methods to distill the network down to a similar structure, regardless of the starting structure.\",\"Abstraction groups\":{\"-1\":[\"Neural Network\",\"Structure\",\"Information\",\"Equivalence Class\",\"Method\",\"Distill\"],\"0\":[\"Neural Network\"],\"1\":[\"Artificial Intelligence\",\"Machine Learning\"],\"2\":[\"Computing\",\"Technology\"],\"3\":[\"Science\",\"Engineering\"],\"4\":[\"Knowledge\",\"Understanding\"]}},\"489\":{\"Question\":\"Sam altman AI talk main points\\nWhat are the main business products for Language Models? \",\"Answer\":\"Main products (rather than research questions)\\nA search engine that can compete with google\\nCompanies will perform fine tuning of large language models for specific applications \\nContributing to science by creating new knowledge (alpha fold), and by accelerating research productivity for each individual with new tools \",\"Key ideas\":\"\\n1. Language Models are used to create business products.\\n2. One such product is a search engine that can compete with Google.\\n3. Companies can use Language Models to fine-tune them for specific applications.\\n4. Language Models can be used to create new knowledge (Alpha Fold).\\n5. Language Models can be used to accelerate research productivity for each individual with new tools.\",\"Abstraction groups\":{\"-1\":[\"AI\",\"Talk\",\"Product\",\"Language Model\",\"Search Engine\",\"Google\",\"Fine Tuning\",\"Application\",\"Science\",\"Knowledge\",\"Alpha Fold\",\"Tool\"],\"0\":[\"Language Model\"],\"1\":[\"Business Product\",\"Fine Tuning\",\"Knowledge\",\"Tool\"],\"2\":[\"AI\",\"Search Engine\",\"Application\"],\"3\":[\"Talk\",\"Google\",\"Science\"],\"4\":[\"Sam Altman\"]}},\"490\":{\"Question\":\"What is Jensen-Shannon divergence, and what properties of basic Kulback Liebler divergence make it useful.\\nWhat is it used in (what type of reinforcement learning?) \",\"Answer\":\"Jensen shannon is Kulback Liebler divergence of p from (p+q)\\/2 plus Kulback Liebler divergence of q from (p+q)\\/2, averaged \\nOne portion of this punishes the model having predictions where there isn't data, the other portion punishes the data existing where the model is not \\nNormal maximum likelihood estimation only pushes model to predict data. It doesn't penalize predicting things that aren't the data. \\nJensen Shannon divergence is used for generative adversarial networks.\",\"Key ideas\":\"1. Kulback Liebler divergence is a measure of the difference between two probability distributions. \\n2. Jensen-Shannon divergence is a combination of two Kulback Liebler divergences. \\n3. One portion of the Jensen-Shannon divergence punishes the model for having predictions where there isn't data. \\n4. The other portion of the Jensen-Shannon divergence punishes the data existing where the model is not. \\n5. Maximum likelihood estimation only pushes the model to predict data, but does not penalize predicting things that aren't the data. \\n6. Jensen-Shannon divergence is used for generative adversarial networks.\",\"Abstraction groups\":{\"-1\":[\"Jensen-Shannon\",\"Kulback Liebler\",\"Divergence\",\"Probability\",\"Model\",\"Data\",\"Maximum Likelihood\",\"Generative Adversarial Network\"],\"0\":[\"Jensen-Shannon\"],\"1\":[\"Divergence\",\"Probability\"],\"2\":[\"Model\",\"Data\"],\"3\":[\"Maximum Likelihood\",\"Generative Adversarial Networks\"],\"4\":[\"Reinforcement Learning\"]}},\"491\":{\"Question\":\"What are Boltzmann machines? How is the math set up? \",\"Answer\":\"Think about the math necessary to setup the problem. Boltzmann machines are energy based models with weights and biases between layers that define an energy for each configuration of activations within each layer. This energy leads to a Boltzmann probability distribution over the visible layer activations via maximization of entropy. \",\"Key ideas\":\"\\n1. Boltzmann machines are energy based models. \\n2. They have weights and biases between layers. \\n3. These weights and biases define an energy for each configuration of activations within each layer. \\n4. This energy leads to a Boltzmann probability distribution over the visible layer activations. \\n5. This probability distribution is achieved by maximization of entropy.\",\"Abstraction groups\":{\"-1\":[\"Boltzmann Machine\",\"Math\",\"Weight\",\"Bias\",\"Layer\",\"Activation\",\"Energy\",\"Probability\",\"Distribution\",\"Entropy\"],\"0\":[\"Boltzmann Machine\"],\"1\":[\"Energy-based Model\",\"Probability Distribution\"],\"2\":[\"Machine Learning\",\"Mathematics\"],\"3\":[\"Artificial Intelligence\",\"Computer Science\"],\"4\":[\"Science\"]}},\"492\":{\"Question\":\"Are LLMs sentient (Chalmers talk) main takeaway \",\"Answer\":\"General idea: 10% chance now, but most objections are likely to be temporary.\",\"Key ideas\":\"\\n1. LLMs (Legal Learning Machines) are artificial intelligence systems that are capable of understanding and interpreting legal documents. \\n2. The Chalmers talk is a discussion about the sentience of LLMs. \\n3. The main takeaway from the discussion is that there is a 10% chance that LLMs are currently sentient, but most objections to this are likely to be temporary.\",\"Abstraction groups\":{\"-1\":[\"LLM\",\"Sentience\",\"Chalmers\",\"Takeaway\",\"Chance\",\"Objection\"],\"0\":[\"LLM\"],\"1\":[\"Sentience\",\"Artificial Intelligence\"],\"2\":[\"Technology\",\"Law\"],\"3\":[\"Science\",\"Philosophy\"],\"4\":[\"Knowledge\",\"Understanding\"]}},\"493\":{\"Question\":\"Main applications of machine learning in physics problems \",\"Answer\":\"(from least to most interesting...)\\n1. Constructing generative models for data, and representing states (examples: molecular structure prediction and generation, and quantum field theory lattice sampling)\\n2. Learning equations of motion or effective models (example: learning the equations of motion for active matter)\\n3. Experimental design and hypothesis generation (example: ultimately, being able to replace a graduate student)\",\"Key ideas\":\"1. Machine learning can be used to construct generative models for data and represent states.\\n2. Examples of this include molecular structure prediction and generation, and quantum field theory lattice sampling.\\n3. Machine learning can also be used to learn equations of motion or effective models, such as for active matter.\\n4. Finally, machine learning can be used for experimental design and hypothesis generation, with the ultimate goal of replacing a graduate student.\",\"Abstraction groups\":{\"-1\":[\"Machine Learning\",\"Physics\",\"Generative Model\",\"Data\",\"State\",\"Molecular Structure\",\"Generation\",\"Quantum Field Theory\",\"Lattice Sampling\",\"Equation of Motion\",\"Effective Model\",\"Active Matter\",\"Experimental Design\",\"Hypothesis Generation\",\"Graduate Student\"],\"0\":[\"Machine Learning\"],\"1\":[\"Physics\",\"Application\"],\"2\":[\"Data Science\",\"Artificial Intelligence\"],\"3\":[\"Computational Science\",\"Science\"],\"4\":[\"Technology\",\"Knowledge\"]}},\"494\":{\"Question\":\"Eras of science and discovery (methods) according to Max Welling \",\"Answer\":\"1. Empirical trial and error (think of experimenters in the 1600s discovering what nature does with gasses and liquids and things like this)\\n2. Data driven modelling (physical prototyping. Think of designing a plane by building toy models and testing them in a wind tunnel)\\n3. In-silico design (think of purely computational simulation of building a plane and how it behaves in a wind tunnel)\\n4. Self-improving emulators (this is the predicted fourth stage of science and discovery, where computers generate predictions and data in a way that is less directed by humans. For example, generating molecular designs for drug targets)\",\"Key ideas\":\"1. Science and discovery can be divided into four eras according to Max Welling.\\n2. The first era is empirical trial and error, which involves experimenting with nature to discover how gasses and liquids behave.\\n3. The second era is data driven modelling, which involves designing a plane by building toy models and testing them in a wind tunnel.\\n4. The third era is in-silico design, which involves purely computational simulation of building a plane and how it behaves in a wind tunnel.\\n5. The fourth era is self-improving emulators, which involves computers generating predictions and data in a way that is less directed by humans, such as generating molecular designs for drug targets.\",\"Abstraction groups\":{\"-1\":[\"Science\",\"Discovery\",\"Max Welling\",\"Empirical\",\"Trial\",\"Error\",\"Data\",\"Modelling\",\"Physical\",\"Prototyping\",\"In-Silico\",\"Design\",\"Self-Improving\",\"Emulator\",\"Computer\",\"Prediction\",\"Data\",\"Molecular\",\"Design\",\"Drug Target\"],\"0\":[\"Science\",\"Discovery\"],\"1\":[\"Era\",\"Max Welling\"],\"2\":[\"Method\",\"Experimentation\",\"Design\"],\"3\":[\"Nature\",\"Gas\",\"Liquid\",\"Computation\",\"Prediction\"],\"4\":[\"Knowledge\",\"Understanding\"]}},\"495\":{\"Question\":\"Energy-based methods and Boltzmann learning: when is this the best model? \",\"Answer\":\"General principle: Energy-based methods are best for describing situations with restricted uncertainty on some variables, but maximal uncertainty on all other things.\\nIndeed, the Boltzmann distribution with some \\\"energy\\\" is the most general way to encode a distribution which satisfies some average properties (this can be shown using Lagrange multipliers) but otherwise has the maximum entropy possible. \",\"Key ideas\":\"1. Energy-based methods are best for describing situations with restricted uncertainty on some variables.\\n2. The Boltzmann distribution is the most general way to encode a distribution which satisfies some average properties.\\n3. This can be shown using Lagrange multipliers.\\n4. The Boltzmann distribution has the maximum entropy possible.\",\"Abstraction groups\":{\"-1\":[\"Energy-Based Method\",\"Boltzmann Learning\",\"Lagrange Multiplier\",\"Maximum Entropy\"],\"0\":[\"Boltzmann Learning\"],\"1\":[\"Energy-Based Method\"],\"2\":[\"Probabilistic Model\",\"Machine Learning\"],\"3\":[\"Artificial Intelligence\",\"Computational Model\"],\"4\":[\"Computer Science\"]}},\"496\":{\"Question\":\"Expectation maximization algorithm - describe the basic setup and steps \",\"Answer\":\"The overall goal is to maximize the log likelihood of data within the structure of a given model. \\nExpectation maximization assumes that latent variables exist which describe the data (such as clusters of data being drawn from similar groups)\\nThe algorithm assigns each datapoint to a cluster with a given probability, then updates the parameters of the clusters to optimize overall data reconstruction, and then reassigns datapoints to their new optimal clusters, and then repeats \",\"Key ideas\":\"1. The expectation maximization algorithm is used to maximize the log likelihood of data within the structure of a given model. \\n2. The algorithm assumes that latent variables exist which describe the data (such as clusters of data being drawn from similar groups).\\n3. The algorithm assigns each datapoint to a cluster with a given probability.\\n4. The parameters of the clusters are then updated to optimize overall data reconstruction.\\n5. Datapoints are then reassigned to their new optimal clusters.\\n6. The process is then repeated.\",\"Abstraction groups\":{\"-1\":[\"Expectation Maximization\",\"Log Likelihood\",\"Model\",\"Latent Variable\",\"Cluster\",\"Probability\",\"Parameter\",\"Datapoint\",\"Reconstruction\"],\"0\":[\"Expectation Maximization\"],\"1\":[\"Algorithm\",\"Optimization\"],\"2\":[\"Machine Learning\",\"Data Analysis\"],\"3\":[\"Artificial Intelligence\",\"Computational Science\"],\"4\":[\"Computer Science\"]}},\"497\":{\"Question\":\"What is generalized policy iteration? \",\"Answer\":\"Policy evaluation is updating the value function. TD update rule with learning rate converges to value function of policy you were taking already, if you never update the policy. Every policy has one corresponding value function\\nPolicy improvement. Acting greedily with respect to a value function will not decrease the value of any state \",\"Key ideas\":\"1. Policy evaluation is updating the value function. \\n2. TD update rule with learning rate converges to value function of policy you were taking already, if you never update the policy. \\n3. Every policy has one corresponding value function. \\n4. Policy improvement is acting greedily with respect to a value function, which will not decrease the value of any state.\",\"Abstraction groups\":{\"-1\":[\"Policy\",\"Evaluation\",\"Td\",\"Learning\",\"Rate\",\"Value\",\"Function\",\"Policy\",\"Improvement\",\"Greedily\",\"Value\"],\"0\":[\"Generalized Policy Iteration\"],\"1\":[\"Policy\",\"Evaluation\",\"TD\",\"Learning\",\"Rate\",\"Value\",\"Function\",\"Improvement\",\"Greedily\"],\"2\":[\"Iteration\",\"Updating\",\"Converging\",\"Corresponding\",\"Acting\"],\"3\":[\"Algorithm\",\"Rule\",\"Function\"],\"4\":[\"Artificial Intelligence\"]}},\"498\":{\"Question\":\"How to alter variable scope inside a function \",\"Answer\":\"by default anything new is local. \\nGlobal variables used inside should be declared as \\\"global x\\\" \",\"Key ideas\":\"\\n1. Variables can have different scopes inside a function. \\n2. By default, anything new is local. \\n3. Global variables used inside a function should be declared as \\\"global x\\\".\",\"Abstraction groups\":{\"-1\":[\"Variable\",\"Scope\",\"Function\",\"Global\",\"Declare\"],\"0\":[\"Variable Scope\"],\"1\":[\"Altering\",\"Function\"],\"2\":[\"Variable\",\"Inside\"],\"3\":[\"Scope\",\"Declare\"],\"4\":[\"Programming\"]}},\"499\":{\"Question\":\"Major array types and properties \",\"Answer\":\"\\u25cb List is a collection which is ordered and changeable. Allows duplicate members.\\n\\t\\t\\u25cb Tuple is a collection which is ordered and unchangeable. Allows duplicate members.\\n\\t\\t\\u25cb Set is a collection which is unordered, unchangeable*, and unindexed. No duplicate members.\\nDictionary is a collection which is ordered** and changeable. No duplicate members. \",\"Key ideas\":\"1. There are four major array types: List, Tuple, Set, and Dictionary. \\n2. List is a collection which is ordered and changeable, and allows duplicate members.\\n3. Tuple is a collection which is ordered and unchangeable, and allows duplicate members.\\n4. Set is a collection which is unordered, unchangeable, and unindexed, and does not allow duplicate members.\\n5. Dictionary is a collection which is ordered and changeable, and does not allow duplicate members.\",\"Abstraction groups\":{\"-1\":[\"Array\",\"List\",\"Tuple\",\"Set\",\"Dictionary\",\"Collection\",\"Ordered\",\"Changeable\",\"Unchangeable\",\"Unindexed\",\"Duplicate\"],\"0\":[\"Array\"],\"1\":[\"Collection\",\"Property\"],\"2\":[\"Data Structure\",\"Data Type\"],\"3\":[\"Computer Science\",\"Programming\"],\"4\":[\"Science\",\"Knowledge\"]}},\"500\":{\"Question\":\"How to unpack multiple elements in a list \",\"Answer\":\"use asterisk. \\n\\t\\tfruits = (\\\"apple\\\", \\\"banana\\\", \\\"cherry\\\", \\\"strawberry\\\", \\\"raspberry\\\")\\ngreen, yellow, *red = fruits \",\"Key ideas\":\"1. Unpacking multiple elements in a list is a way to assign multiple values to multiple variables. \\n2. Asterisk (*) is used to unpack multiple elements in a list. \\n3. An example of a list with multiple elements is: fruits = (\\\"apple\\\", \\\"banana\\\", \\\"cherry\\\", \\\"strawberry\\\", \\\"raspberry\\\"). \\n4. The asterisk can be used to assign multiple elements in the list to multiple variables, such as: green, yellow, *red = fruits.\",\"Abstraction groups\":{\"-1\":[\"Unpacking\",\"List\",\"Asterisk\",\"Fruit\",\"Variable\",\"Green\",\"Yellow\",\"Red\"],\"0\":[\"Unpacking\"],\"1\":[\"List\",\"Variable\"],\"2\":[\"Data Structure\",\"Programming\"],\"3\":[\"Computer Science\",\"Problem Solving\"],\"4\":[\"Knowledge\",\"Thinking\"]}},\"501\":{\"Question\":\"print attributes and methods of a class \",\"Answer\":\"dir(object) \",\"Key ideas\":\"\\n1. The dir() function is used to print attributes and methods of a class.\\n2. The dir() function takes an object as an argument.\\n3. An object is a data structure that contains data and instructions for manipulating the data.\\n4. Attributes are characteristics of an object, such as its size, color, or shape.\\n5. Methods are functions that can be used to manipulate the object, such as to move it, change its color, or resize it.\",\"Abstraction groups\":{\"-1\":[\"Dir()\",\"Object\",\"Attribute\",\"Method\"],\"0\":[\"Dir()\"],\"1\":[\"Printing\",\"Attribute\",\"Method\"],\"2\":[\"Function\",\"Object\"],\"3\":[\"Data Structure\",\"Manipulation\"],\"4\":[\"Computer Science\"]}},\"502\":{\"Question\":\"how to concatenate two lists in python? \",\"Answer\":\"\\u2022 Can extend a list by multiple elements (that is, another list) using \\nmylist.extend(newlist) to alter mylist \",\"Key ideas\":\"1. Python is a programming language. \\n2. Lists are a data structure in Python. \\n3. Concatenation is the process of combining two lists into one. \\n4. The extend() method can be used to concatenate two lists. \\n5. The syntax for using the extend() method is mylist.extend(newlist).\",\"Abstraction groups\":{\"-1\":[\"Python\",\"List\",\"Concatenation\",\"Extend()\",\"Mylist\",\"Newlist\"],\"0\":[\"Concatenation\"],\"1\":[\"Python\",\"List\"],\"2\":[\"Programming\",\"Data Structure\"],\"3\":[\"Computer Science\",\"Algorithm\"],\"4\":[\"Technology\",\"Problem Solving\"]}},\"503\":{\"Question\":\"List comprehension examples in Python \",\"Answer\":\"\\u25cb fruits = [\\\"apple\\\", \\\"banana\\\", \\\"cherry\\\", \\\"kiwi\\\", \\\"mango\\\"]\\n\\t\\t\\u25cb newlist = [x for x in fruits if \\\"a\\\" in x]\\n\\t\\t\\u25cb newlist  = [expression for item in iterable if condition == True]\\nnewlist = [x if x != \\\"banana\\\" else \\\"orange\\\" for x in fruits] \",\"Key ideas\":\"\\n1. List comprehension is a feature of the Python programming language.\\n2. List comprehension is a way to create a new list from an existing list.\\n3. List comprehension uses an expression, item, and condition to create the new list.\\n4. The expression is the item that will be added to the new list.\\n5. The item is the element from the existing list that will be used in the expression.\\n6. The condition is a boolean expression that determines whether the item will be added to the new list.\\n7. An example of a list comprehension in Python is: \\n\\t\\u25cb fruits = [\\\"apple\\\", \\\"banana\\\", \\\"cherry\\\", \\\"kiwi\\\", \\\"mango\\\"]\\n\\t\\u25cb newlist = [x for x in fruits if \\\"a\\\" in x]\\n8. Another example of a list comprehension in Python is: \\n\\t\\u25cb newlist = [x if x != \\\"banana\\\" else \\\"orange\\\" for x in fruits]\",\"Abstraction groups\":{\"-1\":[\"Python\",\"List Comprehension\",\"Expression\",\"Item\",\"Condition\",\"Fruit\",\"Newlist\",\"Boolean\"],\"0\":[\"List Comprehension\"],\"1\":[\"Python\",\"Programming\"],\"2\":[\"Computer Science\",\"Technology\"],\"3\":[\"Science\",\"Mathematics\"],\"4\":[\"Knowledge\"]}},\"504\":{\"Question\":\"Key components of modern large pretrained language models (architecture and training method) \",\"Answer\":\"Transformer, and word prediction training method\\n----------\\nTransformers for attention, and byte pair encoder-decoder to streamline meaning extraction by still keeping odd and unique words within the vocabulary. \\nSelf-supervised learning from generative problems, such as masking out a word and predicting it.These methods work so well because they are a general enough task that they bias the system toward learning general NLP abilities like sentence structure, grammar, meaning, etc. \\nThese are complementary ideas: Transformer and masking word are simplest ways to impose structure that make learning efficient, but still have a sufficiently general function space or optimization plateau to be good at general problems. \",\"Key ideas\":\"1. Transformer is a key component of modern large pretrained language models.\\n2. Word prediction training method is a key component of modern large pretrained language models.\\n3. Byte pair encoder-decoder is used to streamline meaning extraction while still keeping odd and unique words within the vocabulary.\\n4. Self-supervised learning from generative problems, such as masking out a word and predicting it, is used to train language models.\\n5. Masking out a word and predicting it is a general enough task that it biases the system toward learning general NLP abilities like sentence structure, grammar, meaning, etc.\\n6. Transformer and masking word are simplest ways to impose structure that make learning efficient, but still have a sufficiently general function space or optimization plateau to be good at general problems.\",\"Abstraction groups\":{\"-1\":[\"Transformer\",\"Word Prediction\",\"Byte Pair\",\"Self-Supervised\",\"Masking\",\"Optimization\"],\"0\":[\"Language Model\"],\"1\":[\"Pretrained\",\"Architecture\",\"Training Method\"],\"2\":[\"NLP\",\"Generative Problem\"],\"3\":[\"Streamline Meaning Extraction\",\"Optimization\"],\"4\":[\"Bias System\",\"Impose Structure\"]}},\"505\":{\"Question\":\"Geologic history timescales \",\"Answer\":\"Timeline for continental drift is Pangea around 200m years ago \\nPermian epoch ended with mass extinction geological activitity around 250m years ago. 95% of species lost. Unclear exactly how. Greenhouse gasses from volcanism in Siberia, and sea level drop exposing co2 deposits near the ocean.\\nCretaceous ended around 66 m years ago with chixculub crater. 10 years of cold.\",\"Key ideas\":\"1. Pangea was a supercontinent that existed around 200 million years ago.\\n2. The Permian epoch ended with a mass extinction event around 250 million years ago, resulting in the loss of 95% of species.\\n3. The cause of the mass extinction is unclear, but may have been due to greenhouse gasses from volcanism in Siberia, and a drop in sea level exposing carbon dioxide deposits near the ocean.\\n4. The Cretaceous period ended around 66 million years ago with the Chicxulub crater, which caused 10 years of cold temperatures.\",\"Abstraction groups\":{\"-1\":[\"Pangea\",\"Permian\",\"Mass Extinction\",\"Greenhouse Gas\",\"Volcanism\",\"Siberia\",\"Sea Level\",\"Carbon Dioxide\",\"Cretaceous\",\"Chicxulub\",\"Cold\"],\"0\":[\"Geologic History Timescale\"],\"1\":[\"Continental Drift\",\"Mass Extinction\"],\"2\":[\"Geological Activity\",\"Greenhouse Gasses\"],\"3\":[\"Volcanism\",\"Sea Level\"],\"4\":[\"Earth History\"]}},\"506\":{\"Question\":\"Computer network layers \",\"Answer\":\"Physical network 1. Physical Ethernet connection and Mac addresses and collisions 2. Data link is Ethernet protocols 3. Network is physical router and cables \\nInformation sent over network 4. Transport is packet protocols like IP 5. Session is TCP for opening and closing sessions\",\"Key ideas\":\"1. Computer networks are composed of layers.\\n2. The physical layer consists of Ethernet connections, MAC addresses, and collisions.\\n3. The data link layer consists of Ethernet protocols.\\n4. The network layer consists of physical routers and cables.\\n5. The information sent over the network is at the transport layer, which uses packet protocols such as IP.\\n6. The session layer is responsible for opening and closing sessions, and uses TCP.\",\"Abstraction groups\":{\"-1\":[\"Network\",\"Physical\",\"Ethernet\",\"Mac\",\"Collision\",\"Data Link\",\"Protocol\",\"Router\",\"Cable\",\"Transport\",\"Packet\",\"IP\",\"Session\",\"TCP\"],\"0\":[\"Computer Network Layer\"],\"1\":[\"Network\",\"Physical\",\"Ethernet\",\"Mac\",\"Collision\",\"Data Link\",\"Protocol\",\"Router\",\"Cable\",\"Transport\",\"Packet\",\"Ip\",\"Session\",\"Tcp\"],\"2\":[\"Networking\",\"Connectivity\",\"Communication\",\"Data Transfer\"],\"3\":[\"Technology\",\"Information System\"],\"4\":[\"Computer Science\"]}},\"507\":{\"Question\":\"Two types of programming \",\"Answer\":\"Dynamic programming and gradient descent as the two main algorithmic approaches \",\"Key ideas\":\"\\n1. Programming is a process of writing instructions for a computer to execute. \\n2. There are two main types of programming: dynamic programming and gradient descent. \\n3. Dynamic programming is a method of solving complex problems by breaking them down into smaller, simpler sub-problems. \\n4. Gradient descent is an optimization algorithm used to find the minimum of a function by taking small steps in the direction of the negative gradient. \\n5. Both dynamic programming and gradient descent are algorithmic approaches used to solve problems.\",\"Abstraction groups\":{\"-1\":[\"Programming\",\"Dynamic\",\"Gradient\",\"Problem\",\"Sub-Problem\",\"Optimization\",\"Algorithm\",\"Function\",\"Step\",\"Gradient\"],\"0\":[\"Programming\"],\"1\":[\"Dynamic\",\"Gradient\"],\"2\":[\"Algorithmic\",\"Optimization\"],\"3\":[\"Problem-solving\",\"Function\"],\"4\":[\"Computing\"]}},\"508\":{\"Question\":\"Algorithmic complexity of mergesort \",\"Answer\":\"n log_2(n) \\nbecause of log_2(n) layers, and each layer takes n\\/2 comparisons to cycle through\",\"Key ideas\":\"\\n1. Mergesort is an algorithm used to sort data. \\n2. Algorithmic complexity is a measure of how efficient an algorithm is. \\n3. The algorithmic complexity of mergesort is n log_2(n). \\n4. This is because mergesort has log_2(n) layers, and each layer takes n\\/2 comparisons to cycle through.\",\"Abstraction groups\":{\"-1\":[\"Mergesort\",\"Algorithmic Complexity\",\"N\",\"Log_2(N)\",\"Layer\",\"Comparison\"],\"0\":[\"Mergesort\"],\"1\":[\"Algorithmic Complexity\"],\"2\":[\"Algorithm\",\"Data Sorting\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"509\":{\"Question\":\"Why don't we train Mnist data to output classification in binary? \",\"Answer\":\"There are physical locations in the image that correspond to the different digits, but not to the \\\"most significant part of the binary representation of the digit\\\"\\nIt's about how the information is encoded into the data. \",\"Key ideas\":\"1. Mnist data is a type of image data.\\n2. Classification in binary is a way of categorizing data into two distinct groups.\\n3. There are physical locations in the image that correspond to the different digits.\\n4. The physical locations in the image do not correspond to the \\\"most significant part of the binary representation of the digit\\\".\\n5. The information is encoded into the data in a way that does not allow for binary classification.\",\"Abstraction groups\":{\"-1\":[\"Mnist\",\"Binary\",\"Image\",\"Digit\",\"Binary Representation\",\"Information\",\"Encoding\"],\"0\":[\"Binary Classification\"],\"1\":[\"Mnist\",\"Image\",\"Digit\"],\"2\":[\"Data\",\"Representation\",\"Information\"],\"3\":[\"Categorization\",\"Encoding\"],\"4\":[\"Training\"]}},\"510\":{\"Question\":\"Generalized gradient descent methods (stochastic) \",\"Answer\":\"Momentum based (keep fraction of previous change going in next, has some decay time)\\nTrack second moment of gradient as wellIf gradient is decreasing, then slow down, if it is increasing, then speed up. ADAM algorithm moves by momentum\\/(sqrt(variance +m^2) + epsilon), so that when variance is high, it slows down, but for persistent gradients, it is capped at some speed.\",\"Key ideas\":\"\\n1. Generalized gradient descent methods are stochastic.\\n2. Momentum based methods keep a fraction of the previous change going in the next step, with some decay time.\\n3. Track the second moment of the gradient.\\n4. If the gradient is decreasing, slow down. If it is increasing, speed up.\\n5. ADAM algorithm moves by momentum\\/(sqrt(variance +m^2) + epsilon).\\n6. When variance is high, it slows down, but for persistent gradients, it is capped at some speed.\",\"Abstraction groups\":{\"-1\":[\"Gradient Descent\",\"Momentum\",\"Decay\",\"Second Moment\",\"Speed\",\"Variance\",\"ADAM\",\"Epsilon\",\"M^2\"],\"0\":[\"Gradient Descent\"],\"1\":[\"Optimization\",\"Machine Learning\"],\"2\":[\"Mathematics\",\"Algorithms\"],\"3\":[\"Computer Science\",\"Artificial Intelligence\"],\"4\":[\"Science\",\"Technology\"]}},\"511\":{\"Question\":\"Limitations of simple gradient descent \",\"Answer\":\"It only finds a local minimum.\\nThe speed is limited. If you move too fast, you get out of a minimum. \\n------\\nIn particular, you can only move at a rate that is set by the fastest changing cost landscape (and newton's method using the curvature can tell you what speed that is). The general multi-dimensional newton's method is not computationally efficient (involves calculating the Hessian). \",\"Key ideas\":\"\\n1. Simple gradient descent can only find a local minimum.\\n2. The speed of simple gradient descent is limited.\\n3. If you move too fast, you can get out of a minimum.\\n4. The rate of movement is determined by the fastest changing cost landscape.\\n5. Newton's method can be used to determine the speed of movement.\\n6. General multi-dimensional Newton's method is not computationally efficient.\\n7. Calculating the Hessian is necessary for general multi-dimensional Newton's method.\",\"Abstraction groups\":{\"-1\":[\"Gradient Descent\",\"Local Minimum\",\"Speed\",\"Minimum\",\"Cost Landscape\",\"Newton's Method\",\"Curvature\",\"Multi-Dimensional\",\"Computational Efficiency\",\"Hessian\"],\"0\":[\"Gradient Descent\"],\"1\":[\"Optimization\",\"Algorithm\"],\"2\":[\"Mathematics\",\"Computer Science\"],\"3\":[\"Science\",\"Technology\"],\"4\":[\"Knowledge\"]}},\"512\":{\"Question\":\"Main properties of Plants \",\"Answer\":\"Plants use cellulose for structure, and are photosynthetic autotrophs \",\"Key ideas\":\"1. Plants have a structure made of cellulose.\\n2. Plants are autotrophs.\\n3. Autotrophs are organisms that can produce their own food.\\n4. Photosynthesis is the process by which autotrophs produce their own food.\",\"Abstraction groups\":{\"-1\":[\"Plant\",\"Cellulose\",\"Autotroph\",\"Photosynthesis\"],\"0\":[\"Plant\"],\"1\":[\"Autotroph\",\"Photosynthesis\"],\"2\":[\"Cellulose\",\"Structure\"],\"3\":[\"Organism\",\"Food Production\"],\"4\":[\"Biology\"]}},\"513\":{\"Question\":\"Properties of Fungi \",\"Answer\":\"Fungi - chitin, mushrooms, yeasts, molds, heterotrophs. \\nInteresting life cycle, asexual and other types of reproduction, sporing, \\nPrimary role is decomposers \",\"Key ideas\":\"\\n1. Fungi are composed of chitin.\\n2. Fungi include mushrooms, yeasts, and molds.\\n3. Fungi are heterotrophs.\\n4. Fungi have an interesting life cycle.\\n5. Fungi reproduce asexually and through other types of reproduction.\\n6. Fungi produce spores.\\n7. Fungi are primarily decomposers.\",\"Abstraction groups\":{\"-1\":[\"Fungus\",\"Chitin\",\"Mushroom\",\"Yeast\",\"Mold\",\"Heterotroph\",\"Life Cycle\",\"Reproduction\",\"Sporing\",\"Decomposer\"],\"0\":[\"Fungi\"],\"1\":[\"Chitin\",\"Mushroom\",\"Yeast\",\"Mold\",\"Heterotroph\"],\"2\":[\"Life Cycle\",\"Reproduction\",\"Sporing\"],\"3\":[\"Decomposer\"],\"4\":[\"Organism\"]}},\"514\":{\"Question\":\"Properties of animals - what are some shared characteristics of all animals? \",\"Answer\":\"Multicellular, breathe oxygen, move around, reproduce sexually, and have similar blastular development \",\"Key ideas\":\"1. Animals are multicellular organisms. \\n2. Animals breathe oxygen. \\n3. Animals are able to move around. \\n4. Animals reproduce sexually. \\n5. Animals have similar blastular development.\",\"Abstraction groups\":{\"-1\":[\"Animal\",\"Multicellular\",\"Oxygen\",\"Move\",\"Reproduce\",\"Blastular\"],\"0\":[\"Animal\"],\"1\":[\"Property\"],\"2\":[\"Characteristic\",\"Biology\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Understanding\"]}},\"515\":{\"Question\":\"Examples of poor natural agents (animals) as a cool story \",\"Answer\":\"Dung beetle keeps pushing after removing object\\nSphinx wasp resets task order if interrupted (comes back out and drags it back) \",\"Key ideas\":\"1. Natural agents (animals) can be used as a cool story. \\n2. Dung beetle is an example of a poor natural agent. \\n3. Dung beetle keeps pushing after removing an object. \\n4. Sphinx wasp is an example of a poor natural agent. \\n5. Sphinx wasp resets task order if interrupted (comes back out and drags it back).\",\"Abstraction groups\":{\"-1\":[\"Dung Beetle\",\"Object\",\"Sphinx Wasp\",\"Task\"],\"0\":[\"Natural Agent\"],\"1\":[\"Animal\",\"Cool Story\"],\"2\":[\"Organism\",\"Narrative\"],\"3\":[\"Living Thing\",\"Tale\"],\"4\":[\"Life\",\"Story\"]}},\"516\":{\"Question\":\"In reinforcement learning, what are the two major axes which characterize an environment? \",\"Answer\":\"Axis 1: How much is observable about \\nThe state (observability of the physical environment. Parts of it can be hidden) \\nThe rules of physics (are these known or unknown) \\nThe environment of agents (the internal representations of others) \\n(Generally these are axes which lead to more probabilistic outcomes)\\n------------------------------------\\nAxis 2: Discrete vs continuous in \\nTime\\nSpace \\nCausality structure (is evolution episodic so that there are discrete breaks in the causal structure, or is the environment and timeline continuously evolving without any breaks?)\",\"Key ideas\":\"\\n1. Reinforcement learning is a type of machine learning where an agent interacts with an environment and learns from the rewards it receives. \\n2. There are two major axes which characterize an environment in reinforcement learning: \\n    a. Axis 1: How much is observable about the state, the rules of physics, and the environment of agents. Generally, these are axes which lead to more probabilistic outcomes. \\n    b. Axis 2: Discrete vs continuous in time, space, and causality structure. Is evolution episodic so that there are discrete breaks in the causal structure, or is the environment and timeline continuously evolving without any breaks? \\n3. Acronyms and abbreviations used: \\n    a. RL (Reinforcement Learning) \\n    b. ML (Machine Learning)\",\"Abstraction groups\":{\"-1\":[\"RL\",\"ML\",\"Observability\",\"Physics\",\"Agent\",\"Probabilistic\",\"Time\",\"Space\",\"Causality\"],\"0\":[\"Reinforcement Learning\"],\"1\":[\"Machine Learning\",\"Observability\",\"Physics\",\"Agents\"],\"2\":[\"Probabilistic\",\"Time\",\"Space\",\"Causality\"],\"3\":[\"Artificial Intelligence\",\"Robotics\"],\"4\":[\"Computer Science\"]}},\"517\":{\"Question\":\"Why do scientific inference problems (like for wave equation for active matter) work now? Why are they timely as a research question? \",\"Answer\":\"We are getting access to new datasets on complex problems, that are unpublished so far (this is why it is possible, and timely). \\nThe solution method works on a problem with local interactions that possesses some assumed simple analytic form for the solution (the equations of motion). So we know it is possible. \\nIt is also timely because it is harder than what humans can guess and check in some easy way. So we need machine learning to solve it. \",\"Key ideas\":\"1. Scientific inference problems, such as the wave equation for active matter, are possible and timely due to access to new datasets on complex problems that have not been published yet. \\n2. The solution method works on a problem with local interactions that has an assumed simple analytic form for the solution (the equations of motion). \\n3. It is timely because it is harder than what humans can guess and check in some easy way, so machine learning is needed to solve it.\",\"Abstraction groups\":{\"-1\":[\"Scientific Inference\",\"Wave Equation\",\"Active Matter\",\"Dataset\",\"Complex Problem\",\"Local Interaction\",\"Analytic Form\",\"Equation of Motion\",\"Machine Learning\"],\"0\":[\"Scientific Inference\"],\"1\":[\"Wave Equation\",\"Active Matter\"],\"2\":[\"Dataset\",\"Complex Problem\"],\"3\":[\"Local Interaction\",\"Analytic Form\"],\"4\":[\"Equation of Motion\",\"Machine Learning\"]}},\"518\":{\"Question\":\"Puzzle about why neural networks work: what are properties that make it work? \",\"Answer\":\"There is something about the structure of the real environment which is amenable to this representation.\\nNeural nets are sufficiently general and flexible\\nGradient descent is somehow able to approach the optimal configuration \",\"Key ideas\":\"1. Neural networks are a type of representation of the real environment.\\n2. Neural networks are general and flexible.\\n3. Gradient descent is a method used to approach the optimal configuration.\",\"Abstraction groups\":{\"-1\":[\"Neural Network\",\"Representation\",\"Environment\",\"Flexibility\",\"Gradient Descent\",\"Optimal Configuration\"],\"0\":[\"Neural Network\"],\"1\":[\"Representation\",\"Flexibility\",\"Gradient Descent\"],\"2\":[\"Environment\",\"Optimal Configuration\"],\"3\":[\"Machine Learning\"],\"4\":[\"Artificial Intelligence\"]}},\"519\":{\"Question\":\"What is important about high dimensionality for learning \",\"Answer\":\"Local minima probably don't exist. There's always an escape direction \",\"Key ideas\":\"\\n1. High dimensionality: refers to a space with many dimensions, or variables.\\n2. Local minima: a point in a space where the value of a function is lower than in its immediate surroundings.\\n3. Learning: the process of acquiring knowledge or skills through experience, study, or by being taught.\\n4. Probably don't exist: it is unlikely that local minima exist in a high dimensional space.\\n5. Escape direction: in a high dimensional space, there is always a direction in which one can move away from a local minima.\",\"Abstraction groups\":{\"-1\":[\"Dimensionality\",\"Minimum\",\"Learning\",\"Existence\",\"Direction\"],\"0\":[\"High Dimensionality\"],\"1\":[\"Learning\",\"Minima\"],\"2\":[\"Dimensionality\",\"Existence\",\"Direction\"],\"3\":[\"Knowledge\",\"Skill\",\"Experience\",\"Study\"],\"4\":[\"Process\"]}},\"520\":{\"Question\":\"Why Sample Variance is Divided by n-1\",\"Answer\":\"It's a better estimate for the true population variance \",\"Key ideas\":\"\\n1. Sample Variance: A measure of how spread out a set of data is, calculated by taking the sum of the squared differences between each data point and the mean, and dividing by the number of data points.\\n2. Population Variance: A measure of how spread out a population of data is, calculated by taking the sum of the squared differences between each data point and the mean, and dividing by the total number of data points in the population.\\n3. Estimate: A value that is calculated or estimated from available data.\\n4. n-1: The number of data points in the sample minus one.\\n5. Why Sample Variance is Divided by n-1: It's a better estimate for the true population variance. This is because the sample variance is an unbiased estimate of the population variance, and dividing by n-1 instead of n reduces the bias.\",\"Abstraction groups\":{\"-1\":[\"Sample Variance\",\"Population Variance\",\"Estimate\",\"N-1\",\"Variance\"],\"0\":[\"Variance\"],\"1\":[\"Sample Variance\",\"Population Variance\"],\"2\":[\"Estimation\",\"Bias\"],\"3\":[\"Statistics\",\"Probability\"],\"4\":[\"Mathematics\"]}},\"521\":{\"Question\":\"How much trash is generated by the united states population each year by volume (in total, and per person)? \",\"Answer\":\"Each person generates 3 cubic meters of solid waste per year.\\nThere are 300 million people in the united states, roughly.\\nThe result is 1 cubic kilometer of trash every year, if we wanted to put it into a landfill only\",\"Key ideas\":\"1. The United States population is roughly 300 million people. \\n2. Each person generates 3 cubic meters of solid waste per year. \\n3. The total amount of trash generated by the US population each year is 1 cubic kilometer. \\n4. This amount of trash would fill a landfill.\",\"Abstraction groups\":{\"-1\":[\"Trash\",\"United States\",\"Volume\",\"Total\",\"Person\",\"Solid Waste\",\"Year\",\"Cubic Meter\",\"300 Million\",\"Cubic Kilometer\",\"Landfill\"],\"0\":[\"Trash\"],\"1\":[\"United States\",\"Volume\",\"Total\",\"Person\"],\"2\":[\"Solid Waste\",\"Year\",\"Cubic Meters\",\"300 Million\",\"Cubic Kilometer\",\"Landfill\"],\"3\":[\"Pollution\",\"Waste Management\",\"Environment\"],\"4\":[\"Human Impact\"]}},\"522\":{\"Question\":\"Fusion reaction process \",\"Answer\":\"Sparc talk - cycle of fusion reaction, dependence on magnetic length \\n\\tDueterium tritium becomes helium and neutron, neutron combines with lithium to give helium and tritium\\nSo you just have to put in lithium and deuterium in principle and get out heat and helium \",\"Key ideas\":\"1. Fusion reaction is a process in which two light nuclei combine to form a heavier nucleus.\\n2. The process is known as the Sparc cycle of fusion reaction.\\n3. The process is dependent on the magnetic length.\\n4. In the fusion reaction, deuterium and tritium combine to form helium and a neutron.\\n5. The neutron then combines with lithium to form helium and tritium.\\n6. In principle, all that is needed to initiate the fusion reaction is deuterium and lithium.\\n7. The result of the fusion reaction is heat and helium.\",\"Abstraction groups\":{\"-1\":[\"Fusion\",\"Sparc\",\"Magnetic\",\"Deuterium\",\"Tritium\",\"Helium\",\"Neutron\",\"Lithium\",\"Heat\",\"Principle\"],\"0\":[\"Fusion Reaction\"],\"1\":[\"Process\",\"Cycle\",\"Magnetic\"],\"2\":[\"Energy\",\"Nuclei\",\"Heat\"],\"3\":[\"Atomic\",\"Chemical\",\"Physical\"],\"4\":[\"Science\"]}},\"523\":{\"Question\":\"High level description of boosting \",\"Answer\":\"Boosting is a method to combine weak learners (simple functions) into more complex functions or strong learners.Adaboost and XGboost \",\"Key ideas\":\"\\n1. Boosting is a method of combining weak learners (simple functions) into more complex functions or strong learners.\\n2. Adaboost and XGboost are two examples of boosting algorithms.\",\"Abstraction groups\":{\"-1\":[\"Boosting\",\"Weak Learner\",\"Strong Learner\",\"Adaboost\",\"XGboost\"],\"0\":[\"Boosting\"],\"1\":[\"Machine Learning\",\"Algorithm\"],\"2\":[\"Artificial Intelligence\",\"Data Science\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"524\":{\"Question\":\"How does bagging reduce variance \",\"Answer\":\"Not by sampling more data, but rather making it as if you sampled more data by doing some transformation that introduces randomization into your model in a way that is like sampling more data. \",\"Key ideas\":\"\\n1. Bagging is a technique used to reduce variance in a model. \\n2. It does not involve sampling more data, but rather introducing randomization into the model in a way that is like sampling more data. \\n3. This randomization is done through a transformation that is applied to the model.\",\"Abstraction groups\":{\"-1\":[\"Bagging\",\"Variance\",\"Sampling\",\"Randomization\",\"Transformation\"],\"0\":[\"Bagging\"],\"1\":[\"Reducing Variance\"],\"2\":[\"Model Improvement\",\"Data Transformation\"],\"3\":[\"Machine Learning\",\"Statistical Analysis\"],\"4\":[\"Data Science\"]}},\"525\":{\"Question\":\"What is the cost function usually for inferring the functional form of data? \",\"Answer\":\"Cost function is always the Kulback Liebler (KL) divergence from the model predictions to the data at each x axis location. That is, it is the entropy with the model predictions inside the log and the data points outside the log, minus the entropy with the data points everywhere. \",\"Key ideas\":\"\\n1. Cost function is used to infer the functional form of data. \\n2. Cost function is the Kulback Liebler (KL) divergence from the model predictions to the data at each x axis location. \\n3. KL divergence is the entropy with the model predictions inside the log and the data points outside the log, minus the entropy with the data points everywhere. \\n4. Entropy is a measure of the uncertainty of a random variable. \\n5. Logarithm is a mathematical function that expresses a number as a power of another number.\",\"Abstraction groups\":{\"-1\":[\"Cost Function\",\"KL Divergence\",\"Entropy\",\"Logarithm\",\"Model Prediction\",\"Data Point\"],\"0\":[\"Cost Function\"],\"1\":[\"Inference\",\"Modeling\"],\"2\":[\"Data Analysis\",\"Statistical Analysis\"],\"3\":[\"Mathematics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"526\":{\"Question\":\"Income distribution follows Pareto law. What is it? \",\"Answer\":\"Fraction higher than x scales like 1\\/x^alpha. For income it\\u2019s between 2 and 3 equals alpha. So 1\\/100 people income 100k. 1\\/10,000 people income 1 million. 1\\/300 million income 100 million roughly.\",\"Key ideas\":\"\\n1. Pareto law is a law of income distribution. \\n2. Fraction higher than x scales like 1\\/x^alpha. \\n3. For income, alpha is between 2 and 3. \\n4. This means that 1\\/100 people have an income of 100k. \\n5. 1\\/10,000 people have an income of 1 million. \\n6. 1\\/300 million people have an income of 100 million.\",\"Abstraction groups\":{\"-1\":[\"Pareto Law\",\"Income\",\"Alpha\",\"100k\",\"1 Million\",\"100 Million\"],\"0\":[\"Income Distribution\"],\"1\":[\"Pareto Law\"],\"2\":[\"Economics\",\"Distribution\"],\"3\":[\"Social Sciences\",\"Mathematics\"],\"4\":[\"Science\"]}},\"527\":{\"Question\":\"Economic model of growth main points \",\"Answer\":\"3 levels\\nLevel 1: Food capital and labor capital.\\nLevel 2: Knowledge (technology) and investment capital to make level 1 more efficient.\\nLevel 3: Network capital to be better at figuring out new technology (the scientific system), and better at more efficiently distributing investment capital (financial markets)\",\"Key ideas\":\"1. Economic growth is based on three levels.\\n2. Level 1 consists of food capital and labor capital.\\n3. Level 2 consists of knowledge (technology) and investment capital to make level 1 more efficient.\\n4. Level 3 consists of network capital to be better at figuring out new technology (the scientific system), and better at more efficiently distributing investment capital (financial markets).\",\"Abstraction groups\":{\"-1\":[\"Growth\",\"Level\",\"Food\",\"Labor\",\"Knowledge\",\"Investment\",\"Technology\",\"Network\",\"Scientific\",\"Financial\"],\"0\":[\"Economic Growth\"],\"1\":[\"Food\",\"Labor\",\"Knowledge\",\"Investment\",\"Technology\",\"Network\",\"Scientific\",\"Financial\"],\"2\":[\"Capital\",\"Efficiency\",\"Markets\"],\"3\":[\"Resources\",\"Systems\"],\"4\":[\"Growth\"]}},\"528\":{\"Question\":\"Pinkers trends that help the environment (3 ds)\",\"Answer\":\"Densification, dematerialization, decarbonization as natural processes \",\"Key ideas\":\"\\n1. Pinkers are trends that help the environment. \\n2. Densification is a process that involves increasing the population density of an area. \\n3. Dematerialization is a process that involves reducing the amount of materials used in production. \\n4. Decarbonization is a process that involves reducing the amount of carbon dioxide emissions. \\n5. All three processes (densification, dematerialization, and decarbonization) are natural processes.\",\"Abstraction groups\":{\"-1\":[\"Pinker\",\"Densification\",\"Dematerialization\",\"Decarbonization\",\"Process\"],\"0\":[\"Pinker\"],\"1\":[\"Environment\",\"Trend\"],\"2\":[\"Natural Process\"],\"3\":[\"Conservation\"],\"4\":[\"Sustainability\"]}},\"529\":{\"Question\":\"Pinkers ideas for how to combat CO2 and global warming\",\"Answer\":\"At source: carbon taxation or credits, and nuclear energy\\nAt end: carbon capture, geoengineering, and adaptation \",\"Key ideas\":\"\\n1. Carbon taxation or credits: a system of taxation or credits designed to reduce the amount of carbon dioxide released into the atmosphere.\\n\\n2. Nuclear energy: the use of nuclear fission to generate electricity.\\n\\n3. Carbon capture: a process of capturing and storing carbon dioxide from the atmosphere.\\n\\n4. Geoengineering: the deliberate large-scale manipulation of the environment to counteract climate change.\\n\\n5. Adaptation: the process of adjusting to climate change by making changes to the environment or to human activities.\",\"Abstraction groups\":{\"-1\":[\"Carbon\",\"Taxation\",\"Credit\",\"Nuclear\",\"Energy\",\"Capture\",\"Geoengineering\",\"Adaptation\"],\"0\":[\"Global Warming\"],\"1\":[\"CO2\",\"Solution\"],\"2\":[\"Environment\",\"Climate Change\"],\"3\":[\"Energy\",\"Economics\"],\"4\":[\"Science\",\"Technology\"]}},\"530\":{\"Question\":\"How does a Squid magnetometer work? \",\"Answer\":\"SQUID magnetometer in terms of JJ.  - Bias near critical current. Screening sends one JJ above Ic. Produces a voltage drop. \",\"Key ideas\":\"\\n1. SQUID magnetometer: \\n    a. Uses Josephson Junctions (JJ) \\n    b. Bias near critical current \\n2. Screening sends one JJ above Ic \\n3. Produces a voltage drop\",\"Abstraction groups\":{\"-1\":[\"Squid\",\"Jj\",\"Bias\",\"Critical\",\"Current\",\"Screening\",\"Ic\",\"Voltage\"],\"0\":[\"SQUID Magnetometer\"],\"1\":[\"Magnetometer\",\"SQUID\"],\"2\":[\"Measurement\",\"Electronics\"],\"3\":[\"Physics\",\"Technology\"],\"4\":[\"Science\"]}},\"531\":{\"Question\":\"Power transmission line physics: why high voltage? \",\"Answer\":\"Power is I^2 R in transmission lines. With fixed R, have to lower I. \\nTransformers at output conserve energy, so if some device needs power P=IV_low, then I_high = I*V_low\\/V_high. \\nPower losses in the cable can be significantly reduced with high voltage.\",\"Key ideas\":\"1. Power is I^2 R in transmission lines. \\n2. With fixed R, current (I) must be lowered to conserve energy. \\n3. Transformers at output conserve energy. \\n4. Power P=IV_low, so I_high = I*V_low\\/V_high. \\n5. High voltage reduces power losses in the cable.\",\"Abstraction groups\":{\"-1\":[\"Power\",\"Transmission\",\"Line\",\"Physics\",\"High Voltage\",\"Fixed R\",\"Current\",\"Transformer\",\"Output\",\"Power P\",\"Low Voltage\",\"High Voltage\",\"Cable\",\"Loss\"],\"0\":[\"Power Transmission\"],\"1\":[\"Physics\",\"Electricity\",\"Energy\"],\"2\":[\"Science\",\"Technology\"],\"3\":[\"Engineering\",\"Innovation\"],\"4\":[\"Knowledge\"]}},\"532\":{\"Question\":\"What are some things to be careful about when evaluating a deep learning model? (These are a few of Andrew Ng's suggestions for a good deep learning workflow) \",\"Answer\":\"Always know the human error rate at some task. This is the benchmark you should be comparing your performance to. \\nBe careful about test set being a physically different distribution from your development set. One example is audio identification for use in a self driving card, maybe your training data was hard to collect so you used general speech recognition training data, but the data in the car has specific background noise types that make it perform differently. \",\"Key ideas\":\"\\n1. Always know the human error rate at some task. This is the benchmark you should be comparing your performance to. \\n2. Be aware of the possibility that the test set may be a physically different distribution from the development set. \\n3. Understand that this difference in distribution can lead to unexpected performance differences. \\n4. Consider the example of audio identification for use in a self driving car, where the training data may be general speech recognition training data, but the data in the car has specific background noise types that make it perform differently.\",\"Abstraction groups\":{\"-1\":[\"Deep Learning\",\"Evaluation\",\"Human Error\",\"Test Set\",\"Development Set\",\"Audio Identification\",\"Self Driving Car\",\"Training Data\",\"Background Noise\"],\"0\":[\"Deep Learning Evaluation\"],\"1\":[\"Human Error\",\"Test Set\",\"Development Set\"],\"2\":[\"Evaluation\",\"Data Distribution\"],\"3\":[\"Deep Learning Workflow\",\"Audio Identification\"],\"4\":[\"Artificial Intelligence\"]}},\"533\":{\"Question\":\"Benefits of neural networks compared to other machine learning methods \",\"Answer\":\"Benefit from huge data without fine tuning in ways that other models do not \\nCan learn to represent concepts that are transferable and hierarchical \\nFlexible at representing things \",\"Key ideas\":\"1. Neural networks are a type of machine learning method.\\n2. Neural networks can benefit from large amounts of data without needing to be fine-tuned in the same way as other models.\\n3. Neural networks can learn to represent concepts that are transferable and hierarchical.\\n4. Neural networks are flexible in representing things.\",\"Abstraction groups\":{\"-1\":[\"Neural Network\",\"Machine Learning\",\"Data\",\"Fine Tuning\",\"Concept\",\"Hierarchical\",\"Representing\"],\"0\":[\"Neural Network\"],\"1\":[\"Machine Learning\",\"Data\"],\"2\":[\"Artificial Intelligence\",\"Technology\"],\"3\":[\"Science\",\"Knowledge\"],\"4\":[\"Learning\",\"Understanding\"]}},\"534\":{\"Question\":\"Limitations of neural networks (NNs) \",\"Answer\":\"Need labeled data, and lots of it\\nCan't easily deal with inhomogenous datatypes (while decision trees can, for example) Hard to interpret \",\"Key ideas\":\"\\n1. Neural networks (NNs) have limitations. \\n2. NNs require labeled data, and a lot of it.\\n3. NNs cannot easily deal with inhomogenous datatypes, while decision trees can.\\n4. It is hard to interpret the results of NNs.\",\"Abstraction groups\":{\"-1\":[\"NN\",\"Labeled Data\",\"Inhomogenous Datatype\",\"Decision Tree\",\"Interpretation\"],\"0\":[\"Neural Network\"],\"1\":[\"Limitation\"],\"2\":[\"Machine Learning\",\"Artificial Intelligence\"],\"3\":[\"Computer Science\",\"Technology\"],\"4\":[\"Science\"]}},\"535\":{\"Question\":\"Boosting algorithms, review interpretation \",\"Answer\":\"Adaboost as gathering good predictions at the most uncertain points but diffusing at the most certain points (which happens slower, so it's fine),  XGboost as taylor approximation in different remaining residual subspaces \",\"Key ideas\":\"1. Boosting algorithms are a type of machine learning algorithm.\\n2. Adaboost is a boosting algorithm that focuses on gathering good predictions at the most uncertain points.\\n3. Adaboost diffuses at the most certain points, but this happens slower.\\n4. XGboost is a boosting algorithm that uses taylor approximation in different remaining residual subspaces.\",\"Abstraction groups\":{\"-1\":[\"Boosting\",\"Adaboost\",\"XGboost\",\"Prediction\",\"Uncertainty\",\"Certainty\",\"Taylor Approximation\",\"Residual Subspace\"],\"0\":[\"Boosting Algorithm\"],\"1\":[\"Machine Learning\"],\"2\":[\"Artificial Intelligence\",\"Algorithm\"],\"3\":[\"Computer Science\",\"Mathematics\"],\"4\":[\"Science\"]}},\"536\":{\"Question\":\"What is the way to solve the bandit problem?\\nIn brief, you have multiple \\\"arms\\\", or choices of what decision to make. You assume a uniform distribution on the return for each arm with some noise, and some average value. With repeated trials, you gather some return from each arm. \",\"Answer\":\"You use the upper confidence bound algorithm.\\nIn brief, you explore each arm until you get enough statistical knowledge of its average value compared to other arms. The metric to minimize is the expected negative return in the worst case scenario. \",\"Key ideas\":\"1. The bandit problem is a problem of decision-making in which multiple choices (arms) are available.\\n2. Each arm has an expected return with some noise and an average value.\\n3. The upper confidence bound algorithm is used to solve the bandit problem.\\n4. The algorithm explores each arm until enough statistical knowledge of its average value is gathered.\\n5. The metric to minimize is the expected negative return in the worst case scenario.\",\"Abstraction groups\":{\"-1\":[\"Bandit Problem\",\"Arm\",\"Return\",\"Noise\",\"Average Value\",\"Upper Confidence Bound Algorithm\",\"Statistical Knowledge\",\"Metric\",\"Negative Return\"],\"0\":[\"Bandit Problem\"],\"1\":[\"Decision-making\",\"Algorithm\"],\"2\":[\"Problem-solving\",\"Computing\"],\"3\":[\"Mathematics\",\"Science\"],\"4\":[\"Knowledge\"]}},\"537\":{\"Question\":\"Methods of dimensional reduction \",\"Answer\":\"PCA, t-SNEImportant differnce is PCA is parametric meaning we can do it to a new datapoint right away, while t-SNE is not \",\"Key ideas\":\"\\n1. Dimensional reduction is a technique used to reduce the number of variables in a dataset. \\n2. Two common methods of dimensional reduction are Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). \\n3. PCA is a parametric method, meaning it can be applied to a new dataset without needing to be re-trained. \\n4. t-SNE is a non-parametric method, meaning it needs to be re-trained for each new dataset.\",\"Abstraction groups\":{\"-1\":[\"Dimensional Reduction\",\"Pca\",\"T-Sne\",\"Parametric\",\"Non-Parametric\"],\"0\":[\"Dimensional Reduction\"],\"1\":[\"Methods of Data Analysis\"],\"2\":[\"Data Science\",\"Machine Learning\"],\"3\":[\"Artificial Intelligence\",\"Computer Science\"],\"4\":[\"Science and Technology\"]}},\"538\":{\"Question\":\"Types of clustering algorithms \",\"Answer\":\"K means, hierarchical, density based, expectation maximization with gaussian mixtures. Really think through expectation maximization and others \",\"Key ideas\":\"\\n1. Clustering algorithms are used to group data points into clusters. \\n2. There are four main types of clustering algorithms: \\n    a. K-means \\n    b. Hierarchical \\n    c. Density-based \\n    d. Expectation Maximization with Gaussian Mixtures \\n3. Expectation Maximization with Gaussian Mixtures is a more complex algorithm and requires more thought to understand.\",\"Abstraction groups\":{\"-1\":[\"Clustering\",\"K-means\",\"Hierarchical\",\"Density-based\",\"Expectation Maximization\",\"Gaussian Mixture\"],\"0\":[\"Clustering Algorithm\"],\"1\":[\"K-Means\",\"Hierarchical\",\"Density-Based\",\"Expectation Maximization With Gaussian Mixtures\"],\"2\":[\"Data Analysis\",\"Machine Learning\"],\"3\":[\"Artificial Intelligence\",\"Computing\"],\"4\":[\"Science And Technology\"]}},\"539\":{\"Question\":\"Nielsen on Artificial general intelligence (AGI) - what two notions of complexity determine whether its possible? \",\"Answer\":\"Viewpoint 1, connectomic complexity: complexity is on the neuron map level (this assumes you must specify the 70 quadrillion numbers roughly that are the floating point representation of the 100 trillion synapses of 100 billion neurons in a human brain) \\nViewpoint 2, programmatic complexity: complexity is the genetic difference between monkeys and humans in terms of the genetic code (125 million base pairs roughly out of 3 billion basepairs, or 4 percent, are different)\",\"Key ideas\":\"\\n1. Artificial general intelligence (AGI) is a concept that attempts to replicate human intelligence in machines. \\n2. Two notions of complexity are used to determine whether AGI is possible: \\n    a. Connectomic complexity: complexity is on the neuron map level, which assumes that one must specify the 70 quadrillion numbers roughly that are the floating point representation of the 100 trillion synapses of 100 billion neurons in a human brain. \\n    b. Programmatic complexity: complexity is the genetic difference between monkeys and humans in terms of the genetic code, which is 125 million base pairs roughly out of 3 billion basepairs, or 4 percent, are different.\",\"Abstraction groups\":{\"-1\":[\"AGI\",\"Complexity\",\"Connectomic\",\"Neuron\",\"Synapse\",\"Human\",\"Monkey\",\"Genetic Code\"],\"0\":[\"AGI\"],\"1\":[\"Complexity\",\"Connectomic\",\"Programmatic\"],\"2\":[\"Neuron\",\"Synapse\",\"Human\",\"Monkey\",\"Genetic Code\"],\"3\":[\"Intelligence\",\"Replication\"],\"4\":[\"Machine\"]}},\"540\":{\"Question\":\"What is quantum key distribution and how does it use the principles of quantum physics to create secure communication?\",\"Answer\":\"Quantum key distribution is a cryptographic technique that uses quantum mechanics to allow two parties to securely establish a shared secret key. It does so by encoding the key in a series of quantum states and detecting any eavesdropping attempts, as any interruption of these states would be immediately evident.\",\"Key ideas\":null,\"Abstraction groups\":{\"-1\":[\"Quantum Key Distribution\",\"Cryptography\",\"Quantum Mechanics\",\"Secure Communication\",\"Eavesdropping\"],\"0\":[\"Quantum\"],\"1\":[\"Communication\",\"Security\"],\"2\":[\"Physics\",\"Encryption\"],\"3\":[\"Mathematics\"],\"4\":[\"Technology\",\"Science\"]}},\"541\":{\"Question\":\"What is the no-cloning theorem in quantum mechanics and how does it relate to the security of quantum communication protocols?\",\"Answer\":\"The no-cloning theorem states that it is impossible to create an identical copy of an unknown quantum state. This means that any attempt to intercept and replicate a quantum signal without being detected would thus fail, ensuring the security of quantum communication protocols.\",\"Key ideas\":null,\"Abstraction groups\":{\"-1\":[\"No-cloning Theorem\",\"Quantum State\",\"Quantum Signal\",\"Quantum Communication Protocol\",\"Security\",\"Replication\",\"Detection\"],\"0\":[\"Quantum Mechanics\"],\"1\":[\"Theorem\",\"Identical Copy\"],\"2\":[\"Interception\",\"Quantum Encryption\"],\"3\":[\"Physics\",\"Quantum Computing\"],\"4\":[\"Science\"]}}}"