Why Machines Learn: The Elegant Math Behind Modern AI
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A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumour is cancerous, or deciding whether someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extra-solar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene. We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artifical and natural intelligence. Might the same math underpin them both? As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.
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I was sent an ARC by NetGalley.
I approached “Why Machines Learn” with an appreciation for the intricate dance between theoretical concepts and their practical applications. It delves deeply into the mathematical frameworks that have driven the remarkable advancements in ML and AI.
Ananthaswamy excels in breaking down complex mathematical ideas into digestible segments. From Rosenblatt's perceptrons to contemporary deep neural networks, the book navigates through decades of developments with clarity. The author's ability to explain linear algebra, calculus, and other foundational mathematical concepts is commendable, making these subjects accessible even to those without an extensive background in mathematics. This is crucial for a broader audience to appreciate the profound implications of these algorithms.
What I found to be one of the book's strongest points is its integration of the social and historical contexts within which these mathematical advancements occurred. By weaving narratives of key figures in AI, such as Geoffrey Hinton and others, Ananthaswamy provides a richer, more nuanced understanding of how these technologies evolved. This approach not only humanises the scientific endeavour but also highlights the collaborative nature of scientific progress.
The book does not shy away from discussing the real-world applications and ethical dilemmas posed by ML systems. Ananthaswamy explores how these algorithms impact critical areas like medical diagnostics, financial decisions, and criminal justice, prompting readers to consider both the capabilities and the limitations of AI. This balanced perspective is essential in an era where AI is increasingly intertwined with everyday life.
While “Why Machines Learn” is highly informative, there are areas where it could delve deeper. For instance, the mathematical discussions, while clear, sometimes gloss over the more intricate proofs and derivations that a mathematically sophisticated audience might crave. Including appendices or supplementary sections with detailed mathematical treatments could enhance the book's appeal to a technically proficient readership.
Additionally, the book could benefit from a more thorough exploration of cutting-edge topics such as quantum ML and the implications of AI for theoretical physics. Given the rapid pace of advancements in AI, a forward-looking chapter on speculative developments and future trends would have been a valuable addition.
