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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 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.
Anil Ananthaswamy
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
