How Smart Machines Think

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.

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How Smart Machines Think

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.

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How Smart Machines Think

How Smart Machines Think

How Smart Machines Think

How Smart Machines Think

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Overview

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.


Product Details

ISBN-13: 9780262537971
Publisher: MIT Press
Publication date: 10/22/2019
Series: The MIT Press
Edition description: Reprint
Pages: 312
Sales rank: 1,019,031
Product dimensions: 5.90(w) x 8.80(h) x 0.90(d)
Age Range: 18 Years

About the Author

Sean Gerrish is a software engineering manager at Google. He has worked in various capacities on machine learning and data science projects at his current company and in a previous role as a quantitative engineer at Teza Technologies. He holds a PhD in machine learning from Princeton University.

Table of Contents

Foreword Kevin Scott, CTO, Microsoft ix

Preface xi

Acknowledgments xiii

1 The Secret Of The Automaton 1

2 Self-Driving Cars And The Darpa Grand Challenge 9

3 Keeping Within The Lanes: Perception In Self-Driving Cars 23

4 Yielding At Intersections: The Brain Of A Self-Driving Car 37

5 Netflix And The Recommendation-Engine Challenge 57

6 Ensembles Of Teams: The Netflix Prize Winners 73

7 Teaching Computers By Giving Them Treats 89

8 How To Beat Atari Games By Using Neural Networks 107

9 Artificial Neural Networks' View Of The World 125

10 Looking Under The Hood Of Deep Neural Networks 145

11 Neural Networks That Can Hear, Speak, And Remember 157

12 Understanding Natural Language (And Jeopardy! Questions) 171

13 Mining The Best Jeopardy! Answer 187

14 Brute-Force Search Your Way To A Good Strategy 207

15 Expert-Level Play For The Game Of Go 229

16 Real-Time Ai And Starcraft 249

17 Five Decades (Or More) From Now 261

Notes 269

Index 295

What People are Saying About This

Endorsement

If you're curious about what made some of the recent AI successes possible, from winning at Go to self-driving cars, this fascinating book is for you.

Pedro Domingos, Professor of Computer Science, and Author of The Master Algorithm, The University of Washington

From the Publisher

How Smart Machines Think is an enjoyable and insightful 'look under the hood' at recent AI developments. Gerrish introduces complex and important concepts in terms that any reader can understand.

Ray Kurzweil, Author of New York Times bestsellers, How to Create a Mind: The Secret of Human Thought Revealed and The Singularity is Near: When Humans Transcend Biology

An excellent layman's introduction to contemporary AI and machine learning. Gerrish clearly explains the key ideas behind the winning entries in various recent high-profile competitions, such as the DARPA Grand Challenge for self-driving cars and the Jeopardy! Challenge for question-answering. In addition, he emphasizes the role of collaborative human effort in building these systems, both in terms of openly publishing basic research, as well as carrying out the relevant engineering. This more nuanced portrait of progress makes claims of AI autonomously taking over the world much less worrisome.

Kevin Murphy, Senior Staff Research Scientist, Google, and Author of Machine Learning: A Probabilistic Approach

If you're curious about what made some of the recent AI successes possible, from winning at Go to self-driving cars, this fascinating book is for you.

Pedro Domingos, Professor of Computer Science, and Author of The Master Algorithm, The University of Washington

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