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Mind Design III
Philosophy, Psychology, and Artificial Intelligence
| ISBN: 9780262376563 | Copyright 2023
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The essential reader on the philosophical foundations and implications of artificial intelligence, now comprehensively updated for the twenty-first century.
In the quarter century since the publication of John Haugeland's Mind Design II, computer scientists have hit many of their objectives for successful artificial intelligence. Computers beat chess grandmasters, driverless cars navigate streets, autonomous robots vacuum our homes, and ChatGPT answers existential queries in iambic pentameter on command. Engineering has made incredible strides. But have we made progress in understanding and building minds? Comprehensively updated by Carl Craver and Colin Klein to reflect the astonishing ubiquity of machine learning in modern life, Mind Design III offers an essential collection of classic and contemporary essays on the philosophical foundations and implications of artificial intelligence. Contributions from a diverse range of philosophers and computer scientists address the nature of computation, the nature of thought, and the question of whether computers can be made to think. With extensive new material reflecting the explosive growth and diversification of AI approaches, this classic reader equips students to assess the possibility of, and progress toward, building minds out of computers.
New edition highlights:
•New chapters on advances in deep neural networks, reinforcement learning, and causal learning
•New material on the complementary intersection of neuroscience and AI
•Organized thematically rather than chronologically
•Brand new introductions to each section that include suggestions for coursework and further reading
| Expand/Collapse All | |
|---|---|
| Contents (pg. vii) | |
| 1. Introduction to Mind Design III (pg. 1) | |
| Part I: Computers, Computing, and Computation (pg. 5) | |
| 2. What Is Mind Design? (pg. 11) | |
| 2.1 Perspectives and things (pg. 12) | |
| 2.2 Computers (pg. 17) | |
| 2.3 GOFAI (pg. 23) | |
| 2.4 Newfangled Al (pg. 28) | |
| 2.5 What’s missing from mind design? (pg. 33) | |
| Notes for Chapter 2 (pg. 34) | |
| 3. Computer Science as Empirical Inquiry: Symbols and Search (pg. 35) | |
| 3.1 Symbols and physical symbol systems (pg. 36) | |
| 3.2 Heuristic search (pg. 46) | |
| Notes for Chapter 3 (pg. 58) | |
| 4. Vision (pg. 59) | |
| Understanding Complex Information Processing Systems (Vision, Section 1.2) (pg. 59) | |
| Synopsis (Vision, Chapter 6) (pg. 67) | |
| 5. The Analog Alternative (pg. 71) | |
| 5.1 A Brief History of Computational Machines (pg. 72) | |
| 5.2 Representational Types (pg. 74) | |
| 5.3 Computational Types (pg. 82) | |
| 5.4 The Analog Appeal to Anti- Computationalism (pg. 88) | |
| Notes for Chapter 5 (pg. 91) | |
| Part II: What is Intelligence? (pg. 93) | |
| 6. Computing Machinery and Intelligence (pg. 101) | |
| 6.1 The imitation game (pg. 101) | |
| 6.2 Critique of the new problem (pg. 102) | |
| 6.3 The machines concerned in the game (pg. 103) | |
| 6.4 Digital computers (pg. 104) | |
| 6.5 Universality of digital computers (pg. 106) | |
| 6.6 Contrary views on the main question (pg. 108) | |
| 6.7 Learning machines (pg. 118) | |
| Notes for Chapter 6 (pg. 123) | |
| 7. On Our Best Behaviour (pg. 125) | |
| 7.1 Intelligent behaviour (pg. 125) | |
| 7.2 Behavioural tests (pg. 127) | |
| 7.3 Winograd schema questions (pg. 129) | |
| 7.4 Passing the test (pg. 132) | |
| 7.5 Two scientific hurdles (pg. 135) | |
| 7.5.2 The prospects (pg. 137) | |
| Notes for Chapter 7 (pg. 137) | |
| 8. Rationality and Intelligence (pg. 139) | |
| 8.1 Artificial intelligence (pg. 139) | |
| 8.2 Agents (pg. 140) | |
| 8.3 Perfect rationality (pg. 141) | |
| 8.4 Calculative rationality (pg. 143) | |
| 8.5 Metalevel rationality (pg. 145) | |
| 8.6 Bounded optimality (pg. 148) | |
| 8.7 What is to be done? (pg. 151) | |
| 8.8 Summary (pg. 157) | |
| Notes for Chapter 8 (pg. 158) | |
| 9. Central Systems (pg. 159) | |
| Notes for Chapter 9 (pg. 172) | |
| 10. Why AI Is Harder than We Think (pg. 175) | |
| Introduction (pg. 175) | |
| Springs and winters (pg. 176) | |
| Fallacy 1: Narrow intelligence is on a continuum with general intelligence (pg. 179) | |
| Fallacy 2: Easy things are hard and hard things are easy (pg. 180) | |
| Fallacy 3: The lure of wishful mnemonics (pg. 181) | |
| Fallacy 4: Intelligence is all in the brain (pg. 182) | |
| Conclusions (pg. 185) | |
| Notes for Chapter 10 (pg. 187) | |
| Part III: Intentionality and Understanding (pg. 189) | |
| 11. True Believers: The Intentional Strategy and Why It Works (pg. 197) | |
| 11.1 The intentional strategy and how it works (pg. 199) | |
| 11.2 True believers as intentional systems (pg. 204) | |
| 11.3 Why does the intentional strategy work? (pg. 212) | |
| Notes for Chapter 11 (pg. 213) | |
| 12. Minds, Brains, and Programs (pg. 217) | |
| I THE SYSTEMS REPLY (pg. 221) | |
| II THE ROBOT REPLY (pg. 224) | |
| III THE BRAIN-SIMULATOR REPLY (pg. 225) | |
| IV THE COMBINATION REPLY (pg. 226) | |
| V THE OTHER-MINDS REPLY (pg. 228) | |
| VI THE MANY-MANSIONS REPLY (pg. 228) | |
| Notes for Chapter 12 (pg. 234) | |
| 13. Escaping from the Chinese Room (pg. 235) | |
| 14. Computation and Content (pg. 249) | |
| 14.1 Introduction (pg. 249) | |
| 14.2 Why Computational Theories Are Not Intentional (pg. 250) | |
| 14.3 The Explanatory Role of Content (pg. 253) | |
| 14.4 The Ascription of Content (pg. 257) | |
| 14.5 Computational Psychology and Naturalistic Psychology (pg. 259) | |
| 14.6 Scope and Limits of the Account (pg. 261) | |
| Notes for Chapter 14 (pg. 262) | |
| Part IV: Modeling the World (pg. 267) | |
| 15. Transformational Abstraction in Deep Neural Networks (pg. 275) | |
| 15.1 Introduction: Abstraction in Debates over Artificial Intelligence (pg. 275) | |
| 15.2 Abstraction in Empiricist Philosophy of Mind (pg. 278) | |
| 15.3 Deep Convolutional Neural Networks: Basic Architectural Features (pg. 283) | |
| 15.4 Transformational Abstraction in Deep Convolutional Neural Networks (pg. 285) | |
| 15.5 Next Steps for Future Progress (pg. 290) | |
| Notes for Chapter 15 (pg. 293) | |
| 16. The Evaluative Mind (pg. 295) | |
| 16.1 Introduction (pg. 295) | |
| 16.2 Program, concepts, and findings (pg. 297) | |
| 16.3 Valuation (pg. 302) | |
| 16.4 The weaker thesis (pg. 306) | |
| 16.5 The stronger thesis (pg. 308) | |
| 16.6 Conclusion (pg. 311) | |
| Notes for Chapter 16 (pg. 313) | |
| 17. Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science (pg. 315) | |
| 17.1 Introduction: Prediction machines (pg. 315) | |
| 17.2 Representation, inference, and the continuity of perception, cognition, and action (pg. 321) | |
| 17.3 From actionorientedpredictive processing to an architecture of mind (pg. 327) | |
| 17.4 Content and consciousness (pg. 330) | |
| 17.5 Taking stock (pg. 333) | |
| Notes for Chapter 17 (pg. 337) | |
| 18. Theoretical Impediments to Machine Learning with Seven Sparks from the Causal Revolution (pg. 343) | |
| Scientific Backgrou (pg. 343) | |
| The Three- Layer Causal Hierarchy (pg. 344) | |
| The Seven Pillars of the Causal Revolution (or What You Can Do with a Causal Model That You Could Not Do Without?) (pg. 346) | |
| Pillar 1: Encoding Causal Assumptions—Transparency and Testability (pg. 349) | |
| Pillar 2: Do- calculus and the control of confounding (pg. 349) | |
| Pillar 3: The Algorithmization of Counterfactuals (pg. 350) | |
| Pillar 4: Mediation Analysis and the Assessment of Direct and Indirect Effects (pg. 350) | |
| Pillar 5: External Validity and Sample Selection Bias (pg. 350) | |
| Pillar 6: Missing Data (pg. 351) | |
| Pillar 7: Causal Discovery (pg. 351) | |
| Conclusions (pg. 351) | |
| Notes for Chapter 18 (pg. 352) | |
| Part V: Contributions from Cognitive Neuroscience (pg. 353) | |
| 19. The Architecture of Mind: A Connectionist Approach (pg. 361) | |
| 19.1 Why brain- style computation? (pg. 362) | |
| 19.2 The state of the art (pg. 380) | |
| 20. The Computational Brain (pg. 385) | |
| From Chapter 1 (pg. 385) | |
| From Chapter 2 (pg. 390) | |
| From Chapter 3 (pg. 398) | |
| Notes for Chapter 20 (pg. 407) | |
| 21. The Mind Is Not (Just) a System of Modules Shaped (Just) by Natural Selection (pg. 409) | |
| 21.1 Did the Mind Evolve by Natural Selection? (pg. 409) | |
| 21.2 Reverse Engineering: A Backward Step for Psychology (pg. 411) | |
| 21.3 The Mind as A System of Modules (pg. 418) | |
| 21.4 In Search of Mental Modules (pg. 421) | |
| 21.5 Conclusion (pg. 429) | |
| Notes for Chapter 21 (pg. 430) | |
| Part VI: Body and World (pg. 431) | |
| 22. Mind Embodied and Embedded (pg. 439) | |
| 22.1 Intimacy (pg. 439) | |
| 22.2 Simon’s ant (pg. 440) | |
| 22.3 Components, systems, and interfaces (pg. 442) | |
| 22.4 Incorporeal interfaces (pg. 444) | |
| 22.5 Intelligibility as the principle of decomposition (pg. 446) | |
| 22.6 Brooks’s subsumption architecture (pg. 447) | |
| 22.7 Perceiving instead of representing (pg. 448) | |
| 22.8 Affordance and ecological optics (pg. 450) | |
| 22.9 Transduction versus skillful interaction (pg. 452) | |
| 22.10 Output patterns that aren’t instructions (pg. 453) | |
| 22.11 Specific complexity (pg. 455) | |
| 22.12 Getting the whole rug smooth (pg. 456) | |
| 22.13 Vicarious coping (pg. 457) | |
| 22.14 The world itself as meaningful (pg. 458) | |
| 22.15 Knowing the way (pg. 460) | |
| 22.16 Abiding in the meaningful (pg. 461) | |
| 22.17 Conclusion (pg. 462) | |
| Notes for Chapter 22 (pg. 463) | |
| 23. Intelligence without Representation (pg. 465) | |
| 23.1 Introduction (pg. 465) | |
| 23.2 The evolution of intelligence (pg. 466) | |
| 23.3 Abstraction as a dangerous weapon (pg. 467) | |
| 23.4 Incremental intelligence (pg. 470) | |
| 23.5 Who has the representations? (pg. 473) | |
| 23.6 The methodology in practice (pg. 475) | |
| 23.7 What this is not (pg. 481) | |
| 23.8 Key ideas (pg. 482) | |
| 23.9 Limits to growth (pg. 486) | |
| Notes for Chapter 2 (pg. 486) | |
| 24. What Does Biorobotics Offer Philosophy? A Tale of Two Navigation Systems (pg. 487) | |
| 24.1 Introduction (pg. 487) | |
| 24.2 Representation—what is at issue? (pg. 488) | |
| 24.3 Dead reckoning and the traverse board (pg. 490) | |
| 24.4 Path integration and the central complex (pg. 492) | |
| 24.5 Representation and the insect brain (pg. 495) | |
| 24.6 Victory or lessons learned? (pg. 497) | |
| Acknowledgments (pg. 501) | |
| Bibliography (pg. 507) | |
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