Merge pull request #451 from bilgeacun/dev

Proofreading the sustainability section - fixing typos

contents/sustainable_ai/sustainable_ai.qmd

@@ -47,7 +47,7 @@ There is a clear and present need for us to have open and honest conversations a
 
 Additionally, there is an increasing need for AI companies to scrutinize their contributions to climate change and environmental harm. Large tech firms are responsible for the cloud infrastructure, data center energy demands, and resource extraction required to power today's AI. Leadership should assess whether organizational values and policies promote sustainability, from hardware manufacturing through model training pipelines.
 
-Furthermore, more than voluntary self-regulation may be needed-- -governments may need to introduce new regulations aimed at sustainable AI standards and practices if we hope to curb the projected energy explosion of ever-larger models. Reported metrics like computing usage, carbon footprint, and efficiency benchmarks could hold organizations accountable.
+Furthermore, more than voluntary self-regulation may be needed---governments may need to introduce new regulations aimed at sustainable AI standards and practices if we hope to curb the projected energy explosion of ever-larger models. Reported metrics like computing usage, carbon footprint, and efficiency benchmarks could hold organizations accountable.
 
 Through ethical principles, company policies, and public rules, AI technologists and corporations have a profound duty to our planet to ensure the responsible and sustainable advancement of technology positioned to transform modern society radically. We owe it to future generations to get this right.
 
@@ -143,7 +143,7 @@ Recognizing the importance of energy-efficient cooling, there have been innovati
 
 The environmental impact of data centers is not only caused by the direct energy consumption of the data center itself [@siddik2021environmental]. Data center operation involves the supply of treated water to the data center and the discharge of wastewater from the data center. Water and wastewater facilities are major electricity consumers.
 
-Next to electricity usage, there are many more aspects to the environmental impacts of these data centers. The water usage of the data centers can lead to water scarcity issues, increased water treatment needs, and proper wastewater discharge infrastructure. Also, raw materials required for construction and network transmission considerably impact environmental t the environment, and components in data centers need to be upgraded and maintained. Where almost 50 percent of servers were refreshed within 3 years of usage, refresh cycles have shown to slow down [@davis2022uptime]. Still, this generates significant e-waste, which can be hard to recycle.
+Next to electricity usage, there are many more aspects to the environmental impacts of these data centers. The water usage of the data centers can lead to water scarcity issues, increased water treatment needs, and proper wastewater discharge infrastructure. Also, raw materials required for construction and network transmission considerably impact the environment, and components in data centers need to be upgraded and maintained. Where almost 50 percent of servers were refreshed within 3 years of usage, refresh cycles have shown to slow down [@davis2022uptime]. Still, this generates significant e-waste, which can be hard to recycle.
 
 ### Energy Optimization {#energy-optimization}
 
@@ -197,7 +197,7 @@ Innovation in energy storage solutions is required to enable constant use of ren
 
 ![Energy sources and generation capabilities. Source: [Energy Charts](https://www.energy-charts.info/?l=en&c=DE).](images/png/europe_energy_grid.png){#fig-energyprod}
 
-Additionally, the manufacturing and disposal of AI hardware add to the carbon footprint. Producing specialized computing devices, such as GPUs and CPUs, is energy- and resource-intensive. This phase often relies on energy sources that contribute to greenhouse gas emissions. The electronics industry's manufacturing process has been identified as one of the eight big supply chains responsible for more than 50 percent of global emissions [@challenge2021supply]. Furthermore, the end-of-life disposal of this hardware, which can lead to electronic waste, also has environmental implications. As mentioned, servers have a refresh cycle of roughly 3 to 5 years. Of this e-waste, currently [only 17.4 percent is properly collected and recycled.](https://www.genevaenvironmentnetwork.org/resources/updates/the-growing-environmental-risks-of-e-waste/). The carbon emissions of this e-waste has shown an increase of more than 50 percent between 2014 and 2020 [@singh2022disentangling].
+Additionally, the manufacturing and disposal of AI hardware add to the carbon footprint. Producing specialized computing devices, such as GPUs and CPUs, is energy- and resource-intensive. This phase often relies on energy sources that contribute to greenhouse gas emissions. The electronics industry's manufacturing process has been identified as one of the eight big supply chains responsible for more than 50 percent of global emissions [@challenge2021supply]. Furthermore, the end-of-life disposal of this hardware, which can lead to electronic waste, also has environmental implications. As mentioned, servers have a refresh cycle of roughly 3 to 5 years. Of this e-waste, currently [only 17.4 percent is properly collected and recycled](https://www.genevaenvironmentnetwork.org/resources/updates/the-growing-environmental-risks-of-e-waste/). The carbon emissions of this e-waste has shown an increase of more than 50 percent between 2014 and 2020 [@singh2022disentangling].
 
 As is clear from the above, a proper Life Cycle Analysis is necessary to portray all relevant aspects of the emissions caused by AI. Another method is carbon accounting, which quantifies the amount of carbon dioxide emissions directly and indirectly associated with AI operations. This measurement typically uses $\textrm{CO}_2$ equivalents, allowing for a standardized way of reporting and assessing emissions.
 
@@ -425,7 +425,7 @@ While strides are being made in sustainable AI infrastructure, challenges remain
 
 Access to the right frameworks and tools is essential to effectively implementing green AI practices. These resources are designed to assist developers and researchers in creating more energy-efficient and environmentally friendly AI systems. They range from software libraries optimized for low-power consumption to platforms that facilitate the development of sustainable AI applications.
 
-Several software libraries and development environments are specifically tailored for Green AI. These tools often include features for optimizing AI models to reduce their computational load and, consequently, their energy consumption. For example, libraries in PyTorch and TensorFlow that support model pruning, quantization, and efficient neural network architectures enable developers to build AI systems that require less processing power and energy. Additionally, open-source communities like the [Green Carbon Foundation](https://github.com/Green-Software-Foundation) are creating a centralized carbon intensity metric and building software for carbon-aware computing.
+Several software libraries and development environments are specifically tailored for Green AI. These tools often include features for optimizing AI models to reduce their computational load and, consequently, their energy consumption. For example, libraries in PyTorch and TensorFlow that support model pruning, quantization, and efficient neural network architectures enable developers to build AI systems that require less processing power and energy. Additionally, open-source communities like the [Green Software Foundation](https://github.com/Green-Software-Foundation) are creating a centralized carbon intensity metric and building software for carbon-aware computing.
 
 Energy monitoring tools are crucial for Green AI, as they allow developers to measure and analyze the energy consumption of their AI systems. By providing detailed insights into where and how energy is being used, these tools enable developers to make informed decisions about optimizing their models for better energy efficiency. This can involve adjustments in algorithm design, hardware selection, cloud computing software selection, or operational parameters. @fig-azuredashboard is a screenshot of an energy consumption dashboard provided by Microsoft's cloud services platform.
 
@@ -470,7 +470,7 @@ To curb emissions from their rapidly expanding AI workloads, Google engineers sy
 * **Mechanization:** By leveraging cloud computing systems tailored for high utilization over conventional on-premise data centers, energy costs are reduced by 1.4-2X. Google cites its data center's power usage effectiveness as outpacing industry averages.
 * **Map:** Choosing data center locations with low-carbon electricity reduces gross emissions by another 5-10X. Google provides real-time maps highlighting the percentage of renewable energy used by its facilities.
 
-Together, these practices created drastic compound efficiency gains. For example, optimizing the Transformer AI model on TPUs in a sustainable data center location cut energy use by 83. It lowered $\textrm{CO}_2$ emissions by a factor of 747.
+Together, these practices created drastic compound efficiency gains. For example, optimizing the Transformer AI model on TPUs in a sustainable data center location cut energy use by 83x. It lowered $\textrm{CO}_2$ emissions by a factor of 747.
 
 ### Significant Results {#significant-results}
 
@@ -490,7 +490,7 @@ One area of focus is showing how advances are often incorrectly viewed as increa
 
 Additionally, the analysis reveals that focusing sustainability efforts on data center and server-side optimization makes sense, given the dominant energy draw versus consumer devices. Though Google shrinks inference impacts across processors like mobile phones, priority rests on improving training cycles and data center renewables procurement for maximal effect.
 
-To that end, Google's progress in pooling computing inefficiently designed cloud facilities highlights the value of scale and centralization. As more workloads shift away from inefficient on-premise servers, internet giants' prioritization of renewable energy—with Google and Facebook matched 100% by renewables since 2017 and 2020, respectively—unlocks compounding emissions cuts.
+To that end, Google's progress in pooling computing inefficiently designed cloud facilities highlights the value of scale and centralization. As more workloads shift away from inefficient on-premise servers, internet giants' prioritization of renewable energy—with Google and Meta matched 100% by renewables since 2017 and 2020, respectively—unlocks compounding emissions cuts.
 
 Together, these efforts emphasize that while no resting on laurels is possible, Google's multipronged approach shows that AI efficiency improvements are only accelerating. Cross-domain initiatives around lifecycle assessment, carbon-conscious development patterns, transparency, and matching rising AI demand with clean electricity supply pave a path toward bending the curve further as adoption grows. The company's results compel the broader field towards replicating these integrated sustainability pursuits.
 
@@ -574,7 +574,7 @@ Another potential incentive program that is beginning to be explored is using go
 
 Complimentary to potential government action, voluntary self-governance mechanisms allow the AI community to pursue sustainability ends without top-down intervention:
 
-Renewables Commitments: Large AI practitioners like Google, Microsoft, Amazon, and Facebook have pledged to procure enough renewable electricity to match 100% of their energy demands. These commitments unlock compounding emissions cuts as compute scales up. Formalizing such programs incentivizes green data center regions. However, there are critiques on whether these pledges are enough [@monyei2018electrons].
+Renewables Commitments: Large AI practitioners like Google, Microsoft, Amazon, and Meta have pledged to procure enough renewable electricity to match 100% of their energy demands. These commitments unlock compounding emissions cuts as compute scales up. Formalizing such programs incentivizes green data center regions. However, there are critiques on whether these pledges are enough [@monyei2018electrons].
 
 Internal Carbon Prices: Some organizations use shadow prices on carbon emissions to represent environmental costs in capital allocation decisions between AI projects. If modeled effectively, theoretical charges on development carbon footprints steer funding toward efficient innovations rather than solely accuracy gains.