How to Grow a Robot: Developing Human-Friendly, Social AI
How to develop robots that will be more like humans and less like computers, more social than machine-like, and more playful and less programmed.

Most robots are not very friendly. They vacuum the rug, mow the lawn, dispose of bombs, even perform surgery—but they aren't good conversationalists. It's difficult to make eye contact. If the future promises more human-robot collaboration in both work and play, wouldn't it be better if the robots were less mechanical and more social? In How to Grow a Robot, Mark Lee explores how robots can be more human-like, friendly, and engaging.

Developments in artificial intelligence—notably Deep Learning—are widely seen as the foundation on which our robot future will be built. These advances have already brought us self-driving cars and chess match–winning algorithms. But, Lee writes, we need robots that are perceptive, animated, and responsive—more like humans and less like computers, more social than machine-like, and more playful and less programmed. The way to achieve this, he argues, is to “grow” a robot so that it learns from experience—just as infants do.

After describing “what's wrong with artificial intelligence” (one key shortcoming: it's not embodied), Lee presents a different approach to building human-like robots: developmental robotics, inspired by developmental psychology and its accounts of early infant behavior. He describes his own experiments with the iCub humanoid robot and its development from newborn helplessness to ability levels equal to a nine-month-old, explaining how the iCub learns from its own experiences. AI robots are designed to know humans as objects; developmental robots will learn empathy. Developmental robots, with an internal model of “self,” will be better interactive partners with humans. That is the kind of future technology we should work toward.

1133420102
How to Grow a Robot: Developing Human-Friendly, Social AI
How to develop robots that will be more like humans and less like computers, more social than machine-like, and more playful and less programmed.

Most robots are not very friendly. They vacuum the rug, mow the lawn, dispose of bombs, even perform surgery—but they aren't good conversationalists. It's difficult to make eye contact. If the future promises more human-robot collaboration in both work and play, wouldn't it be better if the robots were less mechanical and more social? In How to Grow a Robot, Mark Lee explores how robots can be more human-like, friendly, and engaging.

Developments in artificial intelligence—notably Deep Learning—are widely seen as the foundation on which our robot future will be built. These advances have already brought us self-driving cars and chess match–winning algorithms. But, Lee writes, we need robots that are perceptive, animated, and responsive—more like humans and less like computers, more social than machine-like, and more playful and less programmed. The way to achieve this, he argues, is to “grow” a robot so that it learns from experience—just as infants do.

After describing “what's wrong with artificial intelligence” (one key shortcoming: it's not embodied), Lee presents a different approach to building human-like robots: developmental robotics, inspired by developmental psychology and its accounts of early infant behavior. He describes his own experiments with the iCub humanoid robot and its development from newborn helplessness to ability levels equal to a nine-month-old, explaining how the iCub learns from its own experiences. AI robots are designed to know humans as objects; developmental robots will learn empathy. Developmental robots, with an internal model of “self,” will be better interactive partners with humans. That is the kind of future technology we should work toward.

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How to Grow a Robot: Developing Human-Friendly, Social AI

How to Grow a Robot: Developing Human-Friendly, Social AI

by Mark H. Lee
How to Grow a Robot: Developing Human-Friendly, Social AI

How to Grow a Robot: Developing Human-Friendly, Social AI

by Mark H. Lee

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Overview

How to develop robots that will be more like humans and less like computers, more social than machine-like, and more playful and less programmed.

Most robots are not very friendly. They vacuum the rug, mow the lawn, dispose of bombs, even perform surgery—but they aren't good conversationalists. It's difficult to make eye contact. If the future promises more human-robot collaboration in both work and play, wouldn't it be better if the robots were less mechanical and more social? In How to Grow a Robot, Mark Lee explores how robots can be more human-like, friendly, and engaging.

Developments in artificial intelligence—notably Deep Learning—are widely seen as the foundation on which our robot future will be built. These advances have already brought us self-driving cars and chess match–winning algorithms. But, Lee writes, we need robots that are perceptive, animated, and responsive—more like humans and less like computers, more social than machine-like, and more playful and less programmed. The way to achieve this, he argues, is to “grow” a robot so that it learns from experience—just as infants do.

After describing “what's wrong with artificial intelligence” (one key shortcoming: it's not embodied), Lee presents a different approach to building human-like robots: developmental robotics, inspired by developmental psychology and its accounts of early infant behavior. He describes his own experiments with the iCub humanoid robot and its development from newborn helplessness to ability levels equal to a nine-month-old, explaining how the iCub learns from its own experiences. AI robots are designed to know humans as objects; developmental robots will learn empathy. Developmental robots, with an internal model of “self,” will be better interactive partners with humans. That is the kind of future technology we should work toward.


Product Details

ISBN-13: 9780262043731
Publisher: MIT Press
Publication date: 04/28/2020
Series: The MIT Press
Pages: 384
Product dimensions: 6.10(w) x 9.10(h) x 1.30(d)
Age Range: 18 Years

About the Author

Mark H. Lee is Professor of Computer Science at Aberystwyth University, Wales.

Table of Contents

Preface xiii

Acknowledgments xv

I What's Wrong with Artificial Intelligence? 1

1 The Nature of the Problem 3

Acting and Thinking 4

The Social Robot 6

The Role of Artificial Intelligence 7

Intelligence in General 7

Brains Need Bodies 9

The Structure and Theme of This Book 9

Coping with the Pace of Change 16

A Note on Jargon 18

2 Commercial Robots 19

Domestic Robots and Service Robots 20

Field Robotics 22

Robotic Road Vehicles 23

Medical Robots 26

Swarm Robotics 27

Entertainment Robots 29

Companion Robots 30

Humanlike Robots? 31

Observations 32

3 From Research Bench to Market 35

Bin Picking 38

Biorobotics 40

Care and Assistive Robots 40

Affective Computing 41

Humanoid Robots 42

Why Has Industrial Robotics Been So Successful? 46

The Current State of Robotics 50

Observations 53

4 A Tale of Brute Force 55

Searching through the Options 56

The World Chess Champion Is a Computer-So What? 58

Computer "Thinking" 62

The Outcome 63

Observations 65

5 Knowledge Versus Power 67

How Can Knowledge Be Stored for Utilization? 70

Common Sense Knowledge 72

Search Is a Standard Technique 74

Symbols and Numbers 75

Learning to Improve 75

Feature Engineering 77

Observations 78

6 A Little Vision and a Major Breakthrough 81

The End of Feature Engineering 86

What Happened? 91

Observations 92

7 The Rise of the Learning Machines 95

The Evolution of Machine Learning 96

Data Mining in Supermarkets 97

Learning Algorithms That Learn Algorithms 100

Discovering Patterns 101

Big Data 102

Statistics Is Important, but Misunderstood 104

The Revolution Continues-with Deep Zero 105

Observations 109

8 Deep Thought and Other Oracles 111

AI Is a Highly Focused Business 112

Task-Based AI 113

Machine Oracles 114

Knowledge Engineering 118

Social Conversation 121

Observations 124

9 Building Giant Brains 125

Brain-Building Projects 126

Whole Brain Emulation (WBE) 128

The Brain Is a Machine-So What? 130

Basic Artificial Neural Networks (ANNs) 133

Different Approaches: AI and Brain Science 134

More Advanced Networks 137

Predictive Coding and Autoencoders 138

Issues with ANNs 139

Simulation Problems for Robots 141

Observations 143

10 Bolting It All Together 145

The Complexity of Modular Interactions 146

How Can Computers Represent What They Know and Experience? 149

The Limitations of Task-Based AI 151

General AI 151

Master Algorithms 152

Biological Comparisons 154

Superintelligence (SI) 155

Integrating Deep Artificial Neural Networks (ANNs) 158

Observations for Part I 160

II Robots That Grow and Develop 167

11 Groundwork-Synthesis, Grounding, and Authenticity 169

The Classical Cybernetics Movement 171

Modern Cybernetics 174

Symbol Grounding 176

The New Robotics 177

Observations 179

12 The Developmental Approach-Grow Your Own Robot 181

The Role of Ontogeny: Growing Robots 184

Sequences, Stages, and Timelines 185

Constraints on Development 188

Start Small and Start Early 191

The Importance of Anatomy 193

The Amazing Complexity of the Human Body 195

Autonomy and Motivation 197

Play-Exploration and Discovery without Goals 198

An Architecture for Growth 201

Observations 206

13 Developmental Growth in the iCUB Humanoid Robot 207

iCub-A Humanoid Robot for Research 208

Managing the Constraints of Immaturity 210

Vision, Gazing, and Fixations 211

Motor and Visual Spaces 213

Object Perception 215

Experiment 1 Longitudinal Development 215

Experiment 2 The Generation of Play Behavior 217

How Does It Work? 221

III Where Do We Go From Here? 229

14 How Developmental Robots Will Develop 231

How Developmental Robots Behave 232

Taught, not Programmed 237

Knowing Oneself and Other Agents 239

Self-Awareness Is Common in Animals 241

Robot Selves 242

Consciousness 244

Communication 246

Developmental Characteristics 247

Will All This Happen? 248

We Must Get Out More … 250

Observations 251

15 How AI and AI-Robots are Developing 253

Task-Based AI 253

Human-Level AI (HLAI) 255

Deep AI 257

Robot Developments 259

Social Robots 260

Artificial Human Intelligence (AHI) 262

Observations 264

16 Understanding Future Technology 267

Rapid Growth-It's Not Really Exponential 268

Growth Patterns in the Twenty-First Century-So Far 270

Artificial General Intelligence (AGI) 272

Deep Networks, Learning, and Autonomous Learning 273

Are There Any Dead Certainties? 274

Trust, Validation, and Safety 278

The Product-Centred Viewpoint 279

The Crucial Rote of Humans 284

The Ethical Viewpoint 286

Lessons from Opaque and Unregulated Markets 288

Observations 290

17 Futurology and Science Fiction 293

Are We Smart Enough to Know How Smart Animals Are? 294

What Kind of World Do We Live In? 295

Futurology, Expert Opinion, and Meta-opinions 296

Threats on the Horizon? 300

Superintelligence and the Singularity 300

Transhumanism-Downloading the Brain 302

Imminent Threats 303

Toward Dystopia? 306

It's Not All Doom and Gloom! 309

Threats in Perspective 310

Final Remarks 310

Appendix: Principles for the Developmental Approach 313

Notes 319

Bibliography 339

Index 357

What People are Saying About This

From the Publisher

If we have learned anything over the past sixty years of AI research, it is that there is no single magic ingredient to make the dream of AI a reality. Perhaps more than any other scientific discipline, AI requires new directions and new ways of thinking to progress. This volume provides an energetic and thought-provoking survey of AI past and present, and a fascinating proposal for an emerging new direction—developmental robotics. Overall, the book is a welcome addition to the AI canon.

Michael Wooldridge, Professor of Computer Science at Oxford University

How to Grow a Robot cuts through the hype and charts a new path forward for robotics and AI. A must-read for anyone who craves a well-balanced understanding of the field.

Kate Darling, author of The New Breed: What Our History with Animals Reveals about Our Future with Machines

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