Why Machines Learn: The Elegant Math Behind Modern AI

by Ananthaswamy, Anil

• ISBN13: 9780593185766
• ISBN10: 0593185765
• eBook ISBN(s): 9780593185759
• Format: Paperback
• Copyright: 2025-08-26
• Publisher: Dutton
• More Book Details

Summary

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 tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extrasolar 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 artificial 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.

In a brand-new afterword exclusively in the paperback edition, Ananthaswamy dives into the Transformer architecture that makes large language models like ChatGPT possible and points to groundbreaking future directions enabled by the technology.

Author Biography

Anil Ananthaswamy is an award-winning science writer and a former staff writer and deputy news editor for New Scientist. He is the author of several popular science books, including The Man Who Wasn’t There, which was longlisted for the PEN/E. O. Wilson Literary Science Writing Award. He was a 2019-20 MIT Knight Science Journalism Fellow and the recipient of the Distinguished Alum Award, the highest award given by IIT Madras to its graduates, for his contributions to science writing.

Table of Contents

Prologue 1

CHAPTER 1
Desperately Seeking Patterns 7

CHAPTER 2
We Are All Just Numbers Here 26

CHAPTER 3
The Bottom of the Bowl 64

CHAPTER 4
In All Probability 95

CHAPTER 5
Birds of a Feather 144

CHAPTER 6
There’s Magic in Them Matrices 176

CHAPTER 7
The Great Kernel Rope Trick 206

CHAPTER 8
With a Little Help from Physics 242

CHAPTER 9
The Man Who Set Back Deep Learning (Not Really) 277

CHAPTER 10
The Algorithm that Put Paid to a Persistent Myth 302

CHAPTER 11
The Eyes of a Machine 346

CHAPTER 12
Terra Incognita 382

Epilogue 415

Acknowledgments 431
Notes 435
Index 455