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 thingswork? 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.
Author(s): Sean Gerrish, Kevin Scott
Publisher: The MIT Press
Year: 2018
Language: English
Pages: 313
Tags: Smart Machines, Think, AI
Contents......Page 8
Foreword......Page 10
Preface......Page 12
Acknowledgments......Page 14
The Flute Player......Page 16
Today’s Automata......Page 18
The Swing of a Pendulum......Page 19
Automata We’ll Discuss in this Book......Page 20
The $1 Million Race in the Desert......Page 24
How to Build a Self-Driving Car......Page 25
Planning a Path......Page 29
Path Search......Page 30
Navigation......Page 33
The Winner of the Grand Challenge......Page 34
A Failed Race......Page 36
The Second Grand Challenge......Page 38
Stanley’s Architecture......Page 40
Avoiding Obstacles......Page 42
Finding the Road’s Edges......Page 44
Seeing the Road......Page 46
Path Planning......Page 47
How Parts of Stanley’s Brain Talked to Each Other......Page 49
The Urban Challenge......Page 52
Perceptual Abstraction......Page 53
The Race......Page 56
Boss’s Higher-Level Reasoning Layer......Page 57
Getting Past Traffic Jams......Page 63
Three-Layer Architectures......Page 65
Classifying the Objects Seen by Self-Driving Cars......Page 69
Self-Driving Cars are Complicated Systems......Page 70
The Trajectory of Self-Driving Cars......Page 71
A Million-Dollar Grand Prize......Page 72
The Contenders......Page 73
How to Train a Classifier......Page 74
The Goals of the Competition......Page 77
A Giant Ratings Matrix......Page 78
Matrix Factorization......Page 82
The First Year Ends......Page 86
Closing the Gap between Contenders......Page 88
The End of the First Year......Page 89
Predictions Over Time......Page 92
Overfitting......Page 94
Model Blending......Page 95
The Second Year......Page 97
The Final Year......Page 98
After the Competition......Page 101
DeepMind Plays Atari......Page 104
Reinforcement Learning......Page 106
Instructions to the Agent......Page 108
Programming the Agent......Page 110
Nuggets of Experience......Page 114
Playing Atari with Reinforcement Learning......Page 118
Approximation, Not Perfection......Page 122
Neural Networks as Mathematical Functions......Page 124
The Architecture of an Atari-Playing Neural Network......Page 129
Digging Deeper into Neural Networks......Page 136
The Mystique of Artificial Intelligence......Page 140
The Automaton Chess Player, or the Turk......Page 141
Misdirection in Neural Networks......Page 143
Recognizing Objects in Images......Page 144
Overfitting......Page 146
ImageNet......Page 148
Convolutional Neural Networks......Page 150
Why “Deep” Networks?......Page 154
Data Bottlenecks......Page 158
Computer-Generated Images......Page 160
Squashing Functions......Page 161
ReLU Activation Functions......Page 163
Android Dreams......Page 166
What It Means for a Machine to “Understand”......Page 172
Deep Speech II......Page 173
Recurrent Neural Networks......Page 174
Generating Captions for Images......Page 179
Long Short-Term Memory......Page 182
Adversarial Data......Page 183
Publicity Stunt or Boon to AI Research?......Page 186
Challenges in Beating Jeopardy......Page 187
Long Lists of Facts......Page 188
The Jeopardy Challenge is Born......Page 190
DeepQA......Page 192
Question Analysis......Page 193
How Watson Interprets a Sentence......Page 195
The Basement Baseline......Page 202
Candidate Generation......Page 204
Searching for Answers......Page 205
Lightweight Filter......Page 208
Evidence Retrieval......Page 209
Scoring......Page 212
Aggregation and Ranking......Page 214
Tuning Watson......Page 217
Was Watson Intelligent?......Page 218
Search for Playing Games......Page 222
Sudoku......Page 223
The Size of the Tree......Page 227
Uncertainty in Games......Page 229
Claude Shannon......Page 233
Evaluation Functions......Page 234
Deep Blue......Page 238
Joining IBM......Page 239
Search and Neural Networks......Page 240
TD-Gammon......Page 241
Limitations of Search......Page 243
Computer Go......Page 244
The Game of Go......Page 246
Sample Moves to Build an Intuition......Page 248
The Hand of God......Page 253
Monte Carlo Tree Search......Page 256
One-Armed Bandits......Page 259
Did AlphaGo Need to Be So Complicated?......Page 261
Limitations of AlphaGo......Page 262
Building Better Gaming Bots......Page 264
StarCraft and AI......Page 265
Simplifying the Game......Page 267
Pragmatic StarCraft Bots......Page 269
OpenAI and DOTA 2......Page 271
The Future of StarCraft Bots......Page 274
The Fits and Starts of AI Development......Page 276
How to Replicate the Successes in this Book......Page 277
Where We Go Next......Page 280
Chapter 2: Self-Driving Cars and the DARPA Grand Challenge......Page 284
Chapter 3: Keeping within the Lanes......Page 286
Chapter 4: Yielding at Intersections......Page 288
Chapter 5: Netflix and the Recommendation-Engine Challenge......Page 290
Chapter 6: Ensembles of Teams......Page 292
Chapter 7: Teaching Computers by Giving Them Treats......Page 294
Chapter 9: Artificial Neural Networks’ View of the World......Page 295
Chapter 10: Looking Under the Hood of Deep Neural Networks......Page 297
Chapter 11: Neural Networks that Can Hear, Speak, and Remember......Page 299
Chapter 12: Understanding Natural Language (and Jeopardy! Questions)......Page 300
Chapter 13: Mining the Best Jeopardy! Answer......Page 302
Chapter 14: Brute-Force Search Your Way to a Good Strategy......Page 304
Chapter 15: Expert-Level Play for the Game of Go......Page 305
Chapter 16: Real-Time AI and StarCraft......Page 307
Chapter 17: Five Decades (or More) from Now......Page 309
Index......Page 310
