Algorithms Are Not Enough: Creating General Artificial Intelligence

by Herbert L. Roitblat

Overview

Why a new approach is needed in the quest for general artificial intelligence.

Since the inception of artificial intelligence, we have been warned about the imminent arrival of computational systems that can replicate human thought processes. Before we know it, computers will become so intelligent that humans will be lucky to kept as pets. And yet, although artificial intelligence has become increasingly sophisticated—with such achievements as driverless cars and humanless chess-playing—computer science has not yet created general artificial intelligence. In Algorithms Are Not Enough, Herbert Roitblat explains how artificial general intelligence may be possible and why a robopocalypse is neither imminent, nor likely.

Existing artificial intelligence, Roitblat shows, has been limited to solving path problems, in which the entire problem consists of navigating a path of choices—finding specific solutions to well-structured problems. Human problem-solving, on the other hand, includes problems that consist of ill-structured situations, including the design of problem-solving paths themselves. These are insight problems, and insight is an essential part of intelligence that has not been addressed by computer science. Roitblat draws on cognitive science, including psychology, philosophy, and history, to identify the essential features of intelligence needed to achieve general artificial intelligence.
Roitblat describes current computational approaches to intelligence, including the Turing Test, machine learning, and neural networks. He identifies building blocks of natural intelligence, including perception, analogy, ambiguity, common sense, and creativity. General intelligence can create new representations to solve new problems, but current computational intelligence cannot. The human brain, like the computer, uses algorithms; but general intelligence, he argues, is more than algorithmic processes.

Product Details

ISBN-13:	9780262044127
Publisher:	MIT Press
Publication date:	10/13/2020
Pages:	336
Sales rank:	499,001
Product dimensions:	6.10(w) x 9.10(h) x 1.30(d)

About the Author

Herbert L. Roitblat is Principal Data Scientist at Mimecast.

Table of Contents

Preface ix

1 Introduction: Intelligence, Artificial and Natural 1

The Invention of Human Intelligence 5

Computational Intelligence 8

Natural Intelligence 9

The General in General Intelligence 11

Specialized, General, and Superintelligence 13

Resources 18

2 Human Intelligence 21

Intelligence Testing 22

Problem Solving 25

Well-Formed Problems 25

Formal Problems 29

Insight Problems 36

Quirks of Human Intelligence 43

Conclusion 49

Resources 49

3 Physical Symbol Systems: The Symbolic Approach to Intelligence 53

Turing Machines and the Turing Test 54

The Dartmouth Summer Workshop (1956) 59

Representation 61

Definition of General Intelligence 74

Conclusion 76

Resources 77

4 Computational Intelligence and Machine Learning 81

Limits of Expert Systems 81

Probabilistic Reasoning 84

Machine Learning 86

Varieties of Machine Learning 88

Perceptrons and the Perceptron Learning Rule 93

Beginnings of Machine Learning 96

Reinforcement Learning 104

Summary: A Few Examples of Machine Learning Systems 106

Conclusion 107

Resources 107

5 Neural Network Approach to Artificial Intelligence 109

Neural Network Basics 112

Dolphin Biosonar: An Example 115

Whole Brain Hypothesis 122

Conclusion 128

Resources 129

6 Recent Advances in Artificial Intelligence 133

Watson 137

Siri and Her Relatives 140

AlphaGo 146

Self-Driving Cars 149

Poker 153

Conclusion 156

Resources 157

7 Building Blocks of Intelligence 161

Perception and Pattern Recognition 162

Gestalt Properties 164

Ambiguity 164

Intelligence and Language 167

Common Sense 174

Representing Common Sense 177

Resources 182

8 Expertise 185

Source of Expertise 192

IQ and Expertise 193

Fluid and Crystallized Intelligence 194

The Acquisition of Expertise 196

Resources 204

9 Intelligent Hacks and TRICS 207

Representations for General Intelligence 222

Conclusion 226

Resources 227

10 Algorithms: From People to Computers 229

Optimal Choices: Using Algorithms to Guide Human Behavior 237

Game Theory 251

Resources 253

11 The Coming Robopocalypse? 255

Superintelligence 257

Concerns about Superintelligence 259

Time to Interact with the World 266

Resources 275

12 General Intelligence 277

Defining Intelligence 278

Achieving General Intelligence 280

Beginning the Sketch of Artificial General Intelligence 282

More on the Stack of Hedgehogs 288

General Intelligence Is Not Algorithmic Optimization 291

Intelligence and TRICS 291

Transfer Learning 295

Intelligence Entails Risk 299

Creativity in General Intelligence 301

Growing General Intelligence 302

Whole Brain Emulation 303

Analogy 305

Other Limitations of the Current Paradigm 306

Metaleaming 309

Insight 310

A Sketch of Artificial General Intelligence 314

Resources 317

Index 321