Anil Ananthaswamy (http://anilananthaswamy.com) is the author of more than 5 books. Why Machines Learn: The Elegant Math Behind Modern AI was a few days ago. It is the 55th book I completed reading in 2024.

Opinions expressed here are unbiased and entirely my own! I categorize this book as G.

The author covers the historical evolution of Machine Learning (ML). He covers the conception of artificial neurons and how they have been used. He discusses Bayesian reasoning and how important matrices are to ML. He proceeds to explain support vector machines. A great deal of time is spent on artificial neural networks. How they work, and how they are trained. Backpropagation is explained, and examples are given. Ananthaswamy describes how neural networks can be applied to image recognition and delves into deep neural networks.

I enjoyed the 11+ hours I spent reading this 476-page book on technology. I have read many books on technology, though this is the first I have written a review for. I picked this book from NetGalley because I am interested in AI. I taught a course on AI one semester when I was in the Computer Sciences faculty of St. Edward’s University and wanted to see how much had changed. While the author includes considerable math in the book, it does not get in the way of understanding the material. After reading this book I certainly now have a better understanding of how the current plethora of Large Language Model systems work. I like the chosen cover art. I give this book a rating of 4 out of 5.

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Why Machines Learn is a fascinating, thorough, and semi-accessible monograph on AI and how machine learning algorithms are changing -everything- in modern society, meticulously delineated by Anil Ananthaswamy. Due out 16th July 2024 from Penguin Random House on their Dutton imprint, it's 480 pages and available in hardcover, audio, and ebook formats. It's worth noting that the ebook format has a handy interactive table of contents as well as interactive links throughout. The ebook format's search function is also helpful for finding info and references (recommended).

The author does a very good job covering the background and development of machine learning and relates a lot of the human history and key players from the 50s to the current day. It's a timeline with an ever increasing pace and he draws a clear line from the creeping forward progress to the hurtling (scary) pace of the current day.

Oddly enough, the author doesn't speculate about the near (or far) future of AI and machine learning, and his insights would've been a valuable addition to the book. The specific mathematics included in the book were at an odd level as well; too simple for people conversant with the material, and probably too complex for non-math-inclined laypeople.

Although written in mostly accessible, non-rigorous language, it's meticulously annotated throughout, and the chapter notes will provide readers with many hours of further reading.

Four stars. Definitely a niche book, but well written and interesting. It would be an excellent choice for public library or post-secondary library acquisition, for home use, or possibly as adjunct/support text for more formal math/science history classes at the post-secondary level.

Disclosure: I received an ARC at no cost from the author/publisher for review purposes

I approached Ananthaswamy's "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.

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 endeavor 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.