Artificial Intelligence

Alan Dix is Director of the Computational Foundry at Swansea University, a 30 million pound initiative to boost computational research in Wales with a strong focus on creating social and economic benefit. Previously Alan has worked in a mix of academic, commercial and government roles. Alan is principally known for his work in human-computer interaction, and is the author of one of the major international textbooks on HCI as well as of over 450 research publications from formal methods to intelligent interfaces and design creativity. Technically, he works equally happily with AI and machine learning alongside traditional mathematical and statistical techniques. He has a broad understanding of mathematical, computational and human issues, and he authored some of the earliest papers on gender and ethnic bias in black box-algorithms.

List of Figures xxv

Preface xxxv

Author Bio xxxvii

Chapter 1 ■ Introduction 1

1.1 WHAT IS ARTIFICIAL INTELLIGENCE? 1

1.1.1 How much like a human: strong vs. weak AI 1

1.1.2 Top-down or bottom-up: symbolic vs. sub-symbolic 2

1.1.3 A working definition 3

1.1.4 Human intelligence 3

1.1.5 Bottom up and top down 4

1.2 HUMANS AT THE HEART 4

1.3 A SHORT HISTORY OF ARTIFICIAL INTELLIGENCE 5

1.3.1 The development of AI 6

1.3.2 The physical symbol system hypothesis 8

1.3.3 Sub-symbolic spring 9

1.3.4 AI Renaissance 10

1.3.5 Moving onwards 11

1.4 STRUCTURE OF THIS BOOK – A LANDSCAPE OF AI 11

Section I Knowledge-Rich AI

Chapter 2 ■ Knowledge in AI 15

2.1 OVERVIEW 15

2.2 INTRODUCTION 15

2.3 REPRESENTING KNOWLEDGE 16

2.4 METRICS FOR ASSESSING KNOWLEDGE REPRESENTATION SCHEMES

19

2.5 LOGIC REPRESENTATIONS 20

2.6 PROCEDURAL REPRESENTATION 23

vii

viii ■ Contents

2.6.1 The database 23

2.6.2 The production rules 23

2.6.3 The interpreter 24

2.6.4 An example production system: making a loan 24

2.7 NETWORK REPRESENTATIONS 26

2.8 STRUCTURED REPRESENTATIONS 28

2.8.1 Frames 29

2.8.2 Scripts 29

2.9 GENERAL KNOWLEDGE 31

2.10 THE FRAME PROBLEM 32

2.11 KNOWLEDGE ELICITATION 33

2.12 SUMMARY 33

Chapter 3 ■ Reasoning 37

3.1 OVERVIEW 37

3.2 WHAT IS REASONING? 37

3.3 FORWARD AND BACKWARD REASONING 39

3.4 REASONING WITH UNCERTAINTY 40

3.4.1 Non-monotonic reasoning 40

3.4.2 Probabilistic reasoning 41

3.4.3 Certainty factors 43

3.4.4 Fuzzy reasoning 45

3.4.5 Reasoning by analogy 46

3.4.6 Case-based reasoning 46

3.5 REASONING OVER NETWORKS 48

3.6 CHANGING REPRESENTATIONS 51

3.7 SUMMARY 51

Chapter 4 ■ Search 53

4.1 INTRODUCTION 53

4.1.1 Types of problem 53

4.1.2 Structuring the search space 57

4.2 EXHAUSTIVE SEARCH AND SIMPLE PRUNING 63

4.2.1 Depth and breadth first search 63

4.2.2 Comparing depth and breadth first searches 65

4.2.3 Programming and space costs 67

4.2.4 Iterative deepening and broadening 68

Contents ■ ix

4.2.5 Finding the best solution – branch and bound 69

4.2.6 Graph search 70

4.3 HEURISTIC SEARCH 70

4.3.1 Hill climbing andbest first – goal-directed search 72

4.3.2 Finding the best solution – the A∗ algorithm 72

4.3.3 Inexact search 75

4.4 KNOWLEDGE-RICH SEARCH 77

4.4.1 Constraint satisfaction 78

4.5 SUMMARY 80

Section II Data and Learning

Chapter 5 ■ Machine learning 85

5.1 OVERVIEW 85

5.2 WHY DO WE WANT MACHINE LEARNING? 85

5.3 HOW MACHINES LEARN 87

5.3.1 Phases of machine learning 87

5.3.2 Rote learning and the importance of generalization 89

5.3.3 Inputs to training 90

5.3.4 Outputs of training 91

5.3.5 The training process 92

5.4 DEDUCTIVE LEARNING 93

5.5 INDUCTIVE LEARNING 94

5.5.1 Version spaces 95

5.5.2 Decision trees 99

5.5.2.1 Building a binary tree 99

5.5.2.2 More complex trees 102

5.5.3 Rule induction and credit assignment 103

5.6 EXPLANATION-BASED LEARNING 104

5.7 EXAMPLE: QUERY-BY-BROWSING 105

5.7.1 What the user sees 105

5.7.2 How it works 105

5.7.3 Problems 107

5.8 SUMMARY 107

Chapter 6 ■ Neural Networks 109

6.1 OVERVIEW 109

x ■ Contents

6.2 WHY USE NEURAL NETWORKS? 109

6.3 THE PERCEPTRON 110

6.3.1 The XOR problem 112

6.4 THE MULTI-LAYER PERCEPTRON 113

6.5 BACKPROPAGATION 114

6.5.1 Basic principle 115

6.5.2 Backprop for a single layer network 116

6.5.3 Backprop for hidden layers 117

6.6 ASSOCIATIVE MEMORIES 117

6.6.1 Boltzmann Machines 119

6.6.2 Kohonen self-organizing networks 121

6.7 LOWER-LEVEL MODELS 122

6.7.1 Cortical layers 122

6.7.2 Inhibition 123

6.7.3 Spiking neural networks 123

6.8 HYBRID ARCHITECTURES 124

6.8.1 Hybrid layers 124

6.8.2 Neurosymbolic AI 125

6.9 SUMMARY 126

Chapter 7 ■ Statistical and Numerical Techniques 129

7.1 OVERVIEW 129

7.2 LINEAR REGRESSION 129

7.3 VECTORS AND MATRICES 132

7.4 EIGENVALUES AND PRINCIPAL COMPONENTS 134

7.5 CLUSTERING AND K-MEANS 136

7.6 RANDOMNESS 138

7.6.1 Simple statistics 138

7.6.2 Distributions and long-tail data 140

7.6.3 Least squares 142

7.6.4 Monte Carlo techniques 142

7.7 NON-LINEAR FUNCTIONS FOR MACHINE LEARNING 144

7.7.1 Support Vector Machines 144

7.7.2 Reservoir Computing 145

7.7.3 Kolmogorov-Arnold Networks 146

7.8 SUMMARY 147

Contents ■ xi

Chapter 8 ■ Going Large: deep learning and big data 151

8.1 OVERVIEW 151

8.2 DEEP LEARNING 152

8.2.1 Why are many layers so difficult? 153

8.2.2 Architecture of the layers 153

8.3 GROWING THE DATA 156

8.3.1 Modifying real data 157

8.3.2 Virtual worlds 157

8.3.3 Self learning 157

8.4 DATA REDUCTION 158

8.4.1 Dimension reduction 159

8.4.1.1 Vector space techniques 159

8.4.1.2 Non-numeric features 160

8.4.2 Reduce total number of data items 161

8.4.2.1 Sampling 161

8.4.2.2 Aggregation 161

8.4.3 Segmentation 162

8.4.3.1 Class segmentation 162

8.4.3.2 Result recombination 162

8.4.3.3 Weakly-communicating partial analysis 163

8.5 PROCESSING BIG DATA 164

8.5.1 Why it is hard – distributed storage and computation 164

8.5.2 Principles behind MapReduce 165

8.5.3 MapReduce for the cloud 166

8.5.4 If it can go wrong – resilience for big processing 167

8.6 DATA AND ALGORITHMS AT SCALE 169

8.6.1 Big graphs 169

8.6.2 Time series and event streams 170

8.6.2.1 Multi-scale with mega-windows 170

8.6.2.2 Untangling streams 171

8.6.2.3 Real-time processing 171

8.7 SUMMARY 171

Chapter 9 ■ Making Sense of Machine Learning 175

9.1 OVERVIEW 175

9.2 THE MACHINE LEARNING PROCESS 175

xii ■ Contents

9.2.1 Training phase 176

9.2.2 Application phase 177

9.2.3 Validation phase 177

9.3 EVALUATION 178

9.3.1 Measures of effectiveness 178

9.3.2 Precision–recall trade-off 180

9.3.3 Data for evaluation 182

9.3.4 Multi-stage evaluation 182

9.4 THE FITNESS LANDSCAPE 183

9.4.1 Hill-climbing and gradient descent / ascent 183

9.4.2 Local maxima and minima 184

9.4.3 Plateau and ridge effects 185

9.4.4 Local structure 186

9.4.5 Approximating the landscape 186

9.4.6 Forms of fitness function 187

9.5 DEALING WITH COMPLEXITY 188

9.5.1 Degrees of freedom and dimension reduction 188

9.5.2 Constraints and dependent features 189

9.5.3 Continuity and learning 191

9.5.4 Multi-objective optimisation 193

9.5.5 Partially labelled data 194

9.6 SUMMARY 196

Chapter 10 ■Data Preparation 199

10.1 OVERVIEW 199

10.2 STAGES OF DATA PREPARATION 199

10.3 CREATING A DATASET 200

10.3.1 Extraction and gathering of data 200

10.3.2 Entity reconciliation and linking 201

10.3.3 Exception sets 202

10.4 MANIPULATION AND TRANSFORMATION OF DATA 202

10.4.1 Types of data value 203

10.4.2 Transforming to the right kind of data 204

10.5 NUMERICAL TRANSFORMATIONS 205

10.5.1 Information 205

10.5.2 Normalising data 207

Contents ■ xiii

10.5.3 Missing values – filling the gaps 207

10.5.4 Outliers – dealing with extremes 209

10.6 NON-NUMERIC TRANSFORMATIONS 211

10.6.1 Media data 211

10.6.2 Text 212

10.6.3 Structure transformation 214

10.7 AUTOMATION AND DOCUMENTATION 214

10.8 SUMMARY 216

Section III Specialised Areas

Chapter 11 ■Game playing 221

11.1 OVERVIEW 221

11.2 INTRODUCTION 221

11.3 CHARACTERISTICS OF GAME PLAYING 223

11.4 STANDARD GAMES 225

11.4.1 A simple game tree 225

11.4.2 Heuristics and minimax search 225

11.4.3 Horizon problems 227

11.4.4 Alpha–beta pruning 228

11.4.5 The imperfect opponent 229

11.5 NON-ZERO-SUM GAMES AND SIMULTANEOUS PLAY 229

11.5.1 The prisoner’s dilemma 230

11.5.2 Searching the game tree 230

11.5.3 No alpha–beta pruning 232

11.5.4 Pareto-optimality 232

11.5.5 Multi-party competition and co-operation 233

11.6 THE ADVERSARY IS LIFE! 233

11.7 PROBABILITY 235

11.8 NEURAL NETWORKS FOR GAMES 236

11.8.1 Where to use a neural network 236

11.8.2 Training data and self play 238

11.9 SUMMARY 238

Chapter 12 ■Computer vision 243

12.1 OVERVIEW 243

12.2 INTRODUCTION 243

xiv ■ Contents

12.2.1 Why computer vision is difficult 243

12.2.2 Phases of computer vision 244

12.3 DIGITIZATION AND SIGNAL PROCESSING 245

12.3.1 Digitizing images 245

12.3.2 Thresholding 246

12.3.3 Digital filters 248

12.3.3.1 Linear filters 249

12.3.3.2 Smoothing 249

12.3.3.3 Gaussian filters 251

12.3.3.4 Practical considerations 252

12.4 EDGE DETECTION 252

12.4.1 Identifying edge pixels 253

12.4.1.1 Gradient operators 253

12.4.1.2 Robert’s operator 253

12.4.1.3 Sobel’s operator 256

12.4.1.4 Laplacian operator 257

12.4.1.5 Successive refinement and Marr’s primal sketch 258

12.4.2 Edge following 259

12.5 REGION DETECTION 260

12.5.1 Region growing 261

12.5.2 The problem of texture 261

12.5.3 Representing regions – quadtrees 262

12.5.4 Computational problems 263

12.6 RECONSTRUCTING OBJECTS 263

12.6.1 Inferring three-dimensional features 263

12.6.1.1 Problems with labelling 266

12.6.2 Using properties of regions 267

12.7 IDENTIFYING OBJECTS 269

12.7.1 Using bitmaps 269

12.7.2 Using summary statistics 270

12.7.3 Using outlines 271

12.7.4 Using paths 272

12.8 FACIAL AND BODY RECOGNITION 273

12.9 NEURAL NETWORKS FOR IMAGES 276

12.9.1 Convolutional neural networks 276

12.9.2 Autoencoders 277

Contents ■ xv

12.10 GENERATIVE ADVERSARIAL NETWORKS 279

12.10.1 Generated data 279

12.10.2 Diffusion models 280

12.10.3 Bottom-up and top-down processing 281

12.11 MULTIPLE IMAGES 281

12.11.1 Stereo vision 282

12.11.2 Moving pictures 284

12.12 SUMMARY 285

Chapter 13 ■Natural language understanding 289

13.1 OVERVIEW 289

13.2 WHAT IS NATURAL LANGUAGE UNDERSTANDING? 289

13.3 WHY DO WE NEED NATURAL LANGUAGE UNDERSTANDING? 290

13.4 WHY IS NATURAL LANGUAGE UNDERSTANDING DIFFICULT? 290

13.5 AN EARLY ATTEMPT AT NATURAL LANGUAGE UNDERSTANDING:

SHRDLU 292

13.6 HOW DOES NATURAL LANGUAGE UNDERSTANDING WORK? 293

13.7 SYNTACTIC ANALYSIS 295

13.7.1 Grammars 296

13.7.2 An example: generating a grammar fragment 297

13.7.3 Transition networks 299

13.7.4 Context-sensitive grammars 302

13.7.5 Feature sets 303

13.7.6 Augmented transition networks 304

13.7.7 Taggers 304

13.8 SEMANTIC ANALYSIS 305

13.8.1 Semantic grammars 306

13.8.1.1 An example: a database query interpreter revisited 306

13.8.2 Case grammars 307

13.9 PRAGMATIC ANALYSIS 310

13.9.1 Speech acts 311

13.10 GRAMMAR-FREE APPROACHES 311

13.10.1 Template matching 311

13.10.2 Keyword matching 312

13.10.3 Predictive methods 312

13.10.4 Statistical methods 313

13.11 SUMMARY 314

xvi ■ Contents

13.12 SOLUTION TO SHRDLU PROBLEM 315

Chapter 14 ■Time Series and Sequential Data 317

14.1 OVERVIEW 317

14.2 GENERAL PROPERTIES 317

14.2.1 Kinds of temporal and sequential data 317

14.2.2 Looking through time 318

14.2.3 Processing temporal data 320

14.2.3.1 Windowing 320

14.2.3.2 Hidden state 321

14.2.3.3 Non-time domain transformations 321

14.3 PROBABILITY MODELS 322

14.3.1 Markov Model 323

14.3.2 Higher-order Markov Model 324

14.3.3 Hidden Markov Model 326

14.4 GRAMMAR AND PATTERN-BASED APPROACHES 327

14.4.1 Regular expressions 327

14.4.2 More complex grammars 328

14.5 NEURAL NETWORKS 329

14.5.1 Window-based methods 329

14.5.2 Recurrent Neural Networks 331

14.5.3 Long-term short-term memory networks 332

14.5.4 Transformer models 332

14.6 STATISTICAL AND NUMERICAL TECHNIQUES 332

14.6.1 Simple data cleaning techniques 333

14.6.2 Logarithmic transformations and exponential growth 334

14.6.3 ARMA models 335

14.6.4 Mixed statistics/ML models 336

14.7 MULTI-STAGE/SCALE 337

14.8 SUMMARY 339

Chapter 15 ■Planning and robotics 343

15.1 OVERVIEW 343

15.2 INTRODUCTION 343

15.2.1 Friend or foe? 343

15.2.2 Different kinds of robots 344

15.3 GLOBAL PLANNING 345

Contents ■ xvii

15.3.1 Planning actions – means–ends analysis 345

15.3.2 Planning routes – configuration spaces 348

15.4 LOCAL PLANNING 350

15.4.1 Local planning and obstacle avoidance 350

15.4.2 Finding out about the world 353

15.5 LIMBS, LEGS AND EYES 356

15.5.1 Limb control 356

15.5.2 Walking – on one, two or more legs 359

15.5.3 Active vision 361

15.6 PRACTICAL ROBOTICS 363

15.6.1 Controlling the environment 363

15.6.2 Safety and hierarchical control 364

15.7 SUMMARY 365

Chapter 16 ■Agents 369

16.1 OVERVIEW 369

16.2 SOFTWARE AGENTS 369

16.2.1 The rise of the agent 370

16.2.2 Triggering actions 371

16.2.3 Watching and learning 372

16.2.4 Searching for information 374

16.3 REINFORCEMENT LEARNING 376

16.3.1 Single step learning 376

16.3.2 Choices during learning 378

16.3.3 Intermittent rewards and credit assignment 379

16.4 COOPERATING AGENTS AND DISTRIBUTED AI 379

16.4.1 Blackboard architectures 380

16.4.2 Distributed control 382

16.5 LARGER COLLECTIVES 383

16.5.1 Emergent behaviour 383

16.5.2 Cellular automata 384

16.5.3 Artificial life 384

16.5.4 Swarm computing 385

16.5.5 Ensemble methods 386

16.6 SUMMARY 388

Chapter 17 ■Web scale reasoning 391

xviii ■ Contents

17.1 OVERVIEW 391

17.2 THE SEMANTIC WEB 391

17.2.1 Representing knowledge – RDF and triples 392

17.2.2 Ontologies 394

17.2.3 Asking questions – SPARQL 395

17.2.4 Talking about RDF – reification, named graphs and provenance

396

17.2.5 Linked data – connecting the Semantic Web 398

17.3 MINING THE WEB: SEARCH AND SEMANTICS 402

17.3.1 Search words and links 402

17.3.2 Explicit markup 403

17.3.3 External semantics 405

17.4 USING WEB DATA 408

17.4.1 Knowledge-rich applications 408

17.4.2 The surprising power of big data 409

17.5 THE HUMAN WEB 412

17.5.1 Recommender systems 412

17.5.2 Crowdsourcing and human computation 414

17.5.3 Social media as data 416

17.6 SUMMARY 417

Section IV Humans at the Heart

Chapter 18 ■Expert and decision support systems 421

18.1 OVERVIEW 421

18.2 INTRODUCTION – EXPERTS IN THE LOOP 421

18.3 EXPERT SYSTEMS 422

18.3.1 Uses of expert systems 423

18.3.2 Architecture of an expert system 425

18.3.3 Explanation facility 425

18.3.4 Dialogue and UI component 427

18.3.5 Examples of four expert systems 428

18.3.5.1 Example 1: MYCIN 428

18.3.5.2 Example 2: PROSPECTOR 429

18.3.5.3 Example 3: DENDRAL 429

18.3.5.4 Example 4: XCON 430

18.3.6 Building an expert system 430

Contents ■ xix

18.3.7 Limitations of expert systems 431

18.4 KNOWLEDGE ACQUISITION 431

18.4.1 Knowledge elicitation 432

18.4.1.1 Unstructured interviews. 432

18.4.1.2 Structured interviews. 433

18.4.1.3 Focused discussions. 433

18.4.1.4 Role reversal. 433

18.4.1.5 Think-aloud. 433

18.4.2 Knowledge Representation 434

18.4.2.1 Expert system shells 434

18.4.2.2 High-level programming languages 434

18.4.2.3 Ontologies 434

18.4.2.4 Selecting a tool 435

18.5 EXPERTS AND MACHINE LEARNING 436

18.5.1 Knowledge elicitation for ML 438

18.5.1.1 Acquiring tacit knowledge 438

18.5.1.2 Feature selection 438

18.5.1.3 Expert labelling 438

18.5.1.4 Iteration and interaction 439

18.5.2 Algorithmic choice, validation and explanation 439

18.6 DECISION SUPPORT SYSTEMS. 441

18.6.1 Visualisation 442

18.6.2 Data management and analysis 443

18.6.3 Visual Analytics 444

18.6.3.1 Visualisation in VA 445

18.6.3.2 Data management and analysis for VA 446

18.7 STEPPING BACK 447

18.7.1 Who is it about? 447

18.7.2 Why are we doing it? 447

18.7.3 Wider context 449

18.7.4 Cost–benefit balance 450

18.8 SUMMARY 451

Chapter 19 ■AI working with and for humans 455

19.1 OVERVIEW 455

19.2 INTRODUCTION 455

xx ■ Contents

19.3 LEVELS AND TYPES OF HUMAN CONTACT 457

19.3.1 Social scale 457

19.3.2 Visibility and embodiment 458

19.3.3 Intentionality 458

19.3.4 Who is in control 459

19.3.5 Levels of automation 460

19.4 ON A DEVICE – INTELLIGENT USER INTERFACES 462

19.4.1 Low-level input 462

19.4.2 Conversational user interfaces 462

19.4.3 Predicting what next 464

19.4.4 Finding and managing information 464

19.4.5 Helping with tasks 466

19.4.6 Adaptation and personalisation 467

19.4.7 Going small 468

19.5 IN THE WORLD – SMART ENVIRONMENTS 469

19.5.1 Configuration 470

19.5.2 Sensor fusion 470

19.5.3 Context and activity 472

19.5.4 Designing for uncertainty in sensor-rich smart environments 473

19.5.5 Dealing with hiddenness – a central heating controller 474

19.6 DESIGNING FOR AI–HUMAN INTERACTION 476

19.6.1 Appropriate intelligence – soft failure 476

19.6.2 Feedback – error detection and repair 477

19.6.3 Decisions and suggestions 478

19.6.4 Case study: OnCue – appropriate intelligence by design 480

19.7 TOWARDS HUMAN–MACHINE SYNERGY 481

19.7.1 Tuning AI algorithms for interaction 481

19.7.2 Tuning interaction for AI 482

19.8 SUMMARY 483

Chapter 20 ■When things go wrong 487

20.1 OVERVIEW 487

20.2 INTRODUCTION 487

20.3 WRONG ON PURPOSE? 488

20.3.1 Intentional bad use 488

20.3.2 Unintentional problems 489

Contents ■ xxi

20.4 GENERAL STRATEGIES 490

20.4.1 Transparency and trust 490

20.4.2 Algorithmic accountability 491

20.4.3 Levels of opacity 492

20.5 SOURCES OF ALGORITHMIC BIAS 493

20.5.1 What is bias? 493

20.5.2 Stages in machine learning 494

20.5.3 Bias in the training data 494

20.5.4 Bias in the objective function 497

20.5.5 Bias in the accurate result 498

20.5.6 Proxy measures 499

20.5.7 Input feature choice 500

20.5.8 Bias and human reasoning 500

20.5.9 Avoiding bias 501

20.6 PRIVACY 502

20.6.1 Anonymisation 502

20.6.2 Obfuscation 503

20.6.3 Aggregation 503

20.6.4 Adversarial privacy 504

20.6.5 Federated learning 504

20.7 COMMUNICATION, INFORMATION AND MISINFORMATION 505

20.7.1 Social media 505

20.7.2 Deliberate misinformation 506

20.7.3 Filter bubbles 507

20.7.4 Poor information 507

20.8 SUMMARY 508

Chapter 21 ■Explainable AI 513

21.1 OVERVIEW 513

21.2 INTRODUCTION 513

21.2.1 Why we need explainable AI 514

21.2.2 Is explainable AI possible? 515

21.3 AN EXAMPLE – QUERY-BY-BROWSING 515

21.3.1 The problem 516

21.3.2 A solution 516

21.3.3 How it works 517

xxii ■ Contents

21.4 HUMAN EXPLANATION – SUFFICIENT REASON 518

21.5 LOCAL AND GLOBAL EXPLANATIONS 519

21.5.1 Decision trees – easier explanations 519

21.5.2 Black-box – sensitivity and perturbations 520

21.6 HEURISTICS FOR EXPLANATION 522

21.6.1 White-box techniques 523

21.6.2 Black-box techniques 524

21.6.3 Grey-box techniques 526

21.7 SUMMARY 529

Chapter 22 ■Models of the mind – Human-Like Computing 533

22.1 OVERVIEW 533

22.2 INTRODUCTION 533

22.3 WHAT IS THE HUMAN MIND? 534

22.4 RATIONALITY 535

22.4.1 ACTR 536

22.4.2 SOAR 537

22.5 SUBCONSCIOUS AND INTUITION 538

22.5.1 Heuristics and imagination 539

22.5.2 Attention, salience and boredom 539

22.5.3 Rapid serial switching 540

22.5.4 Disambiguation 541

22.5.5 Boredom 542

22.5.6 Dreaming 542

22.6 EMOTION 543

22.6.1 Empathy and theory of mind 544

22.6.2 Regret 546

22.6.3 Feeling 548

22.7 SUMMARY 549

Chapter 23 ■Philosophical, ethical and social issues 553

23.1 OVERVIEW 553

23.2 THE LIMITS OF AI 553

23.2.1 Intelligent machines or engineering tools? 554

23.2.2 What is intelligence? 554

23.2.3 Computational argument vs. Searle’s Chinese Room 555

23.3 CREATIVITY 556

Contents ■ xxiii

23.3.1 The creative process 557

23.3.2 Generate and filter 557

23.3.3 The critical edge 558

23.3.4 Impact on creative professionals 558

23.4 CONSCIOUSNESS 559

23.4.1 Defining consciousness 559

23.4.2 Dualism and materialism 560

23.4.3 The hard problem of consciousness 561

23.5 MORALITY OF THE ARTIFICIAL 561

23.5.1 Morally neutral 561

23.5.2 Who is responsible? 563

23.5.3 Life or death decisions 563

23.5.4 The special ethics of AI 565

23.6 SOCIETY AND WORK 565

23.6.1 Humanising AI or dehumanising people 566

23.6.2 Top-down: algorithms grading students 566

23.6.3 Bottom-up: when AI ruled France 568

23.6.4 AI and work 569

23.7 MONEY AND POWER 570

23.7.1 Finance and markets 571

23.7.2 Advertising and runaway AI 572

23.7.3 Big AI: the environment and social impact 573

23.8 SUMMARY 575

Section V Looking Forward

Chapter 24 ■Epilogue: what next? 581

24.1 OVERVIEW 581

24.2 CRYSTAL BALL 581

24.3 WHAT NEXT: AI TECHNOLOGY 582

24.3.1 Bigger and Better 582

24.3.2 Smaller and Smarter 582

24.3.3 Mix and Match 584

24.3.4 Partners with People 584

24.4 WHAT NEXT: AI IN THE WORLD 585

24.4.1 Friend or Foe? 585

24.4.2 Boom then Bust 586

xxiv ■ Contents

24.4.3 Everywhere and nowhere 586

24.5 SUMMARY – FROM HYPE TO HOPE 586

Bibliography 589

Index