How Smart Machines Think

  • 5h 55m
  • Sean Gerrish
  • The MIT Press
  • 2018

Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs.

The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart.

Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now.

Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.

About the Author

Sean Gerrish is a software engineer and machine learning geek. He has worked as an engineer at Teza Technologies and as an engineering manager for machine learning and data science teams at Google. He holds a PhD in machine learning from Princeton University.

In this Book

  • Foreword
  • The Secret of the Automaton
  • Self-Driving Cars and the Darpa Grand Challenge
  • Keeping within the Lanes—Perception in Self-Driving Cars
  • Yielding at Intersections—The Brain of a Self-Driving Car
  • Netflix and the Recommendation-Engine Challenge
  • Ensembles of Teams—The Netflix Prize Winners
  • Teaching Computers by Giving Them Treats
  • How to Beat Atari Games by Using Neural Networks
  • Artificial Neural Networks’ View of the World
  • Looking under the Hood of Deep Neural Networks
  • Neural Networks That Can Hear, Speak, and Remember
  • Understanding Natural Language (and Jeopardy! Questions)
  • Mining the Best Jeopardy! Answer
  • Brute-Force Search Your Way to a Good Strategy
  • Expert-Level Play for the Game of Go
  • Real-Time Ai and Starcraft
  • Five Decades (Or More) from Now
  • Notes