From 5dfca3f03ed974a3178353e24fb1a6aae19e51f2 Mon Sep 17 00:00:00 2001 From: jasonjabbour Date: Mon, 18 Nov 2024 02:26:29 -0500 Subject: [PATCH] motivate why TinyML --- contents/core/ai_for_good/ai_for_good.qmd | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/contents/core/ai_for_good/ai_for_good.qmd b/contents/core/ai_for_good/ai_for_good.qmd index d2228fdf..ab24e044 100644 --- a/contents/core/ai_for_good/ai_for_good.qmd +++ b/contents/core/ai_for_good/ai_for_good.qmd @@ -40,7 +40,11 @@ What is special about the SDGs is that they are a collection of interlinked obje A recent study [@vinuesa2020role] highlights the influence of AI on all aspects of sustainable development, particularly on the 17 Sustainable Development Goals (SDGs) and 169 targets internationally defined in the 2030 Agenda for Sustainable Development. The study shows that AI can act as an enabler for 134 targets through technological improvements, but it also highlights the challenges of AI on some targets. The study shows that AI can benefit 67 targets when considering AI and societal outcomes. Still, it also warns about the issues related to the implementation of AI in countries with different cultural values and wealth. -In our book's context, TinyML could help advance at least some of these SDG goals. +While all forms of AI and machine learning have the potential to contribute to the SDGs, the focus of this chapter is on TinyML because of its unique ability to address challenges faced in resource-limited settings. ML systems, particularly those reliant on cloud infrastructure, often require significant computational power, consistent internet connectivity, and substantial financial investment, which can limit their adoption in developing regions or remote areas. In contrast, TinyML enables localized, low-cost, and low-power solutions by running efficient machine learning models directly on microcontrollers. These qualities make it particularly effective for tackling issues such as agricultural monitoring, health diagnostics in underserved areas, and environmental conservation where infrastructure may be minimal. + +By focusing on TinyML, this chapter highlights a branch of AI that provides practical, localized solutions capable of functioning independently of the energy and connectivity demands typically associated with larger-scale ML deployments. TinyML aligns well with the SDG’s emphasis on sustainability and accessibility by offering scalable innovations that address global challenges in resource-constrained settings. + +In the context of this book, TinyML could contribute to advancing the following SDG goals: * **Goal 1 - No Poverty:** TinyML could help provide low-cost solutions for crop monitoring to improve agricultural yields in developing countries.