Publishers Weekly
05/06/2024
This impenetrable primer from science writer Ananthaswamy (Through Two Doors at Once) unsuccessfully attempts to elucidate how AI works. He explains that it learns by scanning data for patterns and then makes predictions about what kinds of data are likely to appear in sequence. Unfortunately, the excruciatingly detailed breakdown of the roles played by probability, principal component analysis (“projecting high-dimensional data onto a much smaller number of axes to find the dimensions along which the data vary the most”), and eigenvectors (which are never satisfactorily defined) will sail over the heads of anyone without an advanced math degree. Biographical background on physicist John Hopfield, electrical engineer Bernhard Boser, and other pioneering contributors to machine learning does little to alleviate the labyrinthine discussions of their advances. There are some bright spots—as when Ananthaswamy discusses how statisticians deduced the authorship of the contested Federalist Papers by analyzing whether the writing more closely reflected the vocabulary of James Madison or Alexander Hamilton—but these highlights are few and far between, surrounded by bewildering equations and dense proofs for mathematical theorems. General readers will struggle to follow this. Agent: Peter Tallack, Curious Minds Agency. (July)
From the Publisher
Some books about the development of neural networks describe the underlying mathematics while others describe the social history. This book presents the mathematics in the context of the social history. It is a masterpiece. The author is very good at explaining the mathematics in a way that makes it available to people with only a rudimentary knowledge of the field, but he is also a very good writer who brings the social history to life.”
—Geoffrey Hinton, deep learning pioneer, Turing Award winner, former VP at Google, and Professor Emeritus at University of Toronto
“After just a few minutes of reading Why Machines Learn, you’ll feel your own synaptic weights getting updated. By the end you will have achieved your own version of deep learning—with deep pleasure and insight along the way.”
—Steven Strogatz, New York Times bestselling author of Infinite Powers and professor of mathematics at Cornell University
“If you were looking for a way to make sense of the AI revolution that is well underway, look no further. With this comprehensive yet engaging book, Anil Ananthaswamy puts it all into context, from the origin of the idea and its governing equations to its potential to transform medicine, quantum physics—and virtually every aspect of our life. An essential read for understanding both the possibilities and limitations of artificial intelligence.”
—Sabine Hossenfelder, physicist and New York Times bestselling author of Existential Physics: A Scientist's Guide to Life's Biggest Questions
“Why Machines Learn is a masterful work that explains—in clear, accessible, and entertaining fashion—the mathematics underlying modern machine learning, along with the colorful history of the field and its pioneering researchers. As AI has increasingly profound impacts in our world, this book will be an invaluable companion for anyone who wants a deep understanding of what’s under the hood of these often inscrutable machines.”
—Melanie Mitchell, author of Artificial Intelligence and Professor at the Santa Fe Institute
“Generative AI, with its foundations in machine learning, is as fundamental an advance as the creation of the microprocessor, the Internet, and the mobile phone. But almost no one, outside of a handful of specialists, understands how it works. Anil Ananthaswamy has removed the mystery by giving us a gentle, intuitive, and human-oriented introduction to the math that underpins this revolutionary development.”
—Peter E. Hart, AI pioneer, entrepreneur, and co-author of Pattern Classification
“Anil Ananthaswamy’s Why Machines Learn embarks on an exhilarating journey through the origins of contemporary machine learning. With a captivating narrative, the book delves into the lives of influential figures driving the AI revolution while simultaneously exploring the intricate mathematical formalism that underpins it. As Anil traces the roots and unravels the mysteries of modern AI, he gently introduces the underlying mathematics, rendering the complex subject matter accessible and exciting for readers of all backgrounds.”
—Björn Ommer, Professor at the Ludwig Maximilian University of Munich and leader of the original team behind Stable Diffusion
“An inspiring introduction to the mathematics of AI.”
—Arthur I. Miller, author of The Artist in the Machine: The World of AI-Powered Creativity
“[An] illuminating overview of how machine learning works.”
—Kirkus Reviews
Kirkus Reviews
2024-05-11
A study of the concepts that power AI.
In this demanding but rewarding book, Ananthaswamy, author of The Man Who Wasn’t There, “explains the elegant mathematics and algorithms [behind]…machine learning, a type of AI that involves building machines that can learn to discern patterns in data without being explicitly programmed to do so.” With astute reference to principles from the disciplines of math, computer science, physics, and neuroscience, the author guides readers through the conceptual frameworks involved in the creation of AI. While it would be helpful to come to the book with a strong background in math (especially statistics and calculus), clear and detailed illustrations help make it accessible to anyone willing to immerse themselves in the material. Ananthaswamy makes the power of AI obvious, and his engaging case studies explore its emerging abilities in the generation of new media—text, images, video, and music—and contributions to discoveries in areas such as drug development and the dynamics of gene expression. The author also provides a vivid picture of how AI will continue to transform everyday activities and, very soon, revolutionize our social and economic lives. Ananthaswamy demonstrates how a profound merging of human activities with machine processes is already far along and will soon accelerate strikingly. The author could have offered a little more insight about these coming changes, though the introduction and epilogue do touch on pressing questions about the various risks of emerging technologies and how they might be mitigated. Familiarizing ourselves with what is at stake, the author rightly notes, is now an urgent personal and public responsibility: “It is only when we understand the inevitability of learning machines that we will be prepared to tackle a future in which AI is ubiquitous, for good and for bad.”
A challenging and illuminating overview of how machine learning works.
