How the Quest for the Ultimate Learning Machine Will Remake Our World
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Traditionally, the only way to make a computer execute a task is to write precise instructions: an algorithm. As the author notes in this enthusiastic but not dumbed-down introduction to machine learning, it is impossible to “write a program to tell a computer how to drive a car or decipher handwriting, but if we give enough examples to a computer running a learning algorithm, it will figure out how to do it on its own.”

The ultimate learning program, writes Domingos (Computer Science/Univ. of Washington), is the master algorithm, and the process is well underway to allow computers to function creatively. Data alone is not enough. Defeating the world’s greatest chess or Jeopardy players was a matter of brute force, but simpler computers running learning programs already beat talent scouts in baseball, connoisseurs in wine tasting, and doctors in disease diagnosis. Though lucid and consistently informative, Domingos’ explanation of how a variety of scientific schools approaches the master algorithm requires close attention from readers. Symbolists believe that intelligence emerges from manipulating symbols, just as mathematicians solve equations by replacing expressions with other expressions. Connectionists try to reverse-engineer the brain. Evolutionaries write programs that change in ways similar to natural selection. Bayesians know that all learned knowledge is uncertain, so they emphasize 18th-century English clergyman Thomas Bayes’ theorem, which can handle probabilistic inference. Finally, analogizers search for similarities in data and write code that combines them to make new predictions. “Armed with your new understanding of machine learning,” writes the author, “you’re in a much better position to think about issues like privacy and data sharing, the future of work, robot warfare, and the promise and peril of AI.”

With wit, vision, and scholarship, Domingos describes how these scientists are creating programs that allow a computer to teach itself. Readers unfamiliar with logic and computer theory will have a difficult time, but those who persist will discover fascinating insights.

Pub Date: Sept. 22nd, 2015
ISBN: 978-0465065707
Page count: 352pp
Publisher: Basic
Review Posted Online:
Kirkus Reviews Issue: July 1st, 2015


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