Why Machines Learn
The Elegant Math Behind Modern AI
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4.3 • 9 Ratings
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- $2.99
Publisher Description
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.
PUBLISHERS WEEKLY
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.
