Review by Choice Review
Domingos (Univ. of Washington) aims to provide uninitiated readers with a conceptual model of machine learning, which is the process by which websites such as Amazon and Netflix learn a user's preferences and then make recommendations for that user. The author devotes one chapter to each of the five schools of thought in machine learning: symbolists, "connectionists," evolutionaries, Bayesians, and "analogizers" and discusses other disciplines that each group uses. Domingos explains that ideally, a master algorithm that would combine the advantages of all five approaches could be created, and that master algorithm would be able to derive all knowledge if given sufficient data. He also gives a short overview of how the human brain learns and how that learning process differs from machine learning. For example, Microsoft Windows will not get any faster with practice, but most humans do, in any learned activity. This interesting work assumes no prior knowledge of computer science or the disciplines that it uses, making it appropriate for general audiences and undergraduate students. Summing Up: Recommended. General readers and undergraduate students. --Miklos Bona, University of Florida
Copyright American Library Association, used with permission.
Review by Booklist Review
*Starred Review* In the amazing machine logic that helps Netflix users find movies, eHarmony clients find dates, and Amazon customers find books, Domingos sees merely the forerunner of a powerful Master Algorithm that may soon revolutionize our world, making computers and robots self-programming learners that no longer need human guidance. Himself a pioneer in the field, Domingos tutors nonspecialist readers in the workings of algorithms that render a computer capable of teaching itself how to perform a task with ever-increasing reliability. Without delving into the complexities, readers explore the thinking of the five different groups striving to perfect the three fundamental elements representation, evaluation, and optimization driving this process and consider ways that creative software engineers might fuse the strengths of these five groups' divergent perspectives into one all-potent universal algorithm. When finally forged, that algorithm, Domingos predicts, will so thoroughly transform the world that human soldiers will disappear from battlefields, children will grow up with robot nannies, and ordinary Janes and Joes will use computer-assisted DNA design to claim the movie-star bodies they crave. Skeptics may dismiss Domingos' vision as hopelessly utopian, and libertarians may brand it as dangerously intrusive. But no one will find it boring. An exhilarating venture into groundbreaking computer science.--Christensen, Bryce Copyright 2015 Booklist
From Booklist, Copyright (c) American Library Association. Used with permission.
Review by Kirkus Book Review
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. Copyright Kirkus Reviews, used with permission.
Copyright (c) Kirkus Reviews, used with permission.
