From deep learning to rational machines: what the history of philosophy can teach us about the future of artificial intelligence
"This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Oxford University Press
[2024]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems." |
| Beschreibung: | xxi, 415 Seiten Illustrationen, Diagramme |
| ISBN: | 9780197653302 |
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Contents Preface Acknowledgments Note on Abbreviated Citations to Historical Works xi xvii xix 1. Moderate Empiricism and Machine Learning 1 1.1. Playing with fire? Nature versus nurture for computer science 1 1.2. How to simmer things down: From forms and slates to styles of learning 11 1.3. From dichotomy to continuum 24 1.4. Of faculties and fairness: Introducing the new empiricist dogma 27 1.5. Of models and minds 34 1.6. Other dimensions of the rationalist-empiricist debate 42 1.7. The DoGMA in relation to other recent revivals of empiricism 43 1.8. Basic strategy of the book: Understanding deep learning through empiricist faculty psychology 44 1.9. Organization of the remaining chapters: Faculties, philosophers, and modules 45 2. What Is Deep Learning, and How Should We Evaluate Its Potential? 48 2.1. Intuitive inference as deep learning’s distinctive strength 2.2. Deep learning: Other marquee achievements 2.3. Deep learning: Questions and concerns 2.4. Can we (fairly) measure success? Artificial intelligence versus artificial rationality 74 2.5. Avoiding comparative biases: Lessons from comparative psychology for the science of machine behavior 85 2.6. Summary 48 54 60 92
viii CONTENTS 3. Perception 3.1. The importance of perceptual abstraction in empiricist accounts of reasoning 94 3.2. Four approaches to abstraction from the historical empiricists 99 3.3. Transformational abstraction: Conceptual foundations 3.4. Deep convolutional neural networks: Basic features 3.5. Transformational abstraction in DCNNs 3.6. Challenges for DCNNs as models of transformational abstraction 131 3.7. Summary 4. Memory 4.1. The trouble with quantifying human perceptual experience 4.2. Generalization and catastrophic interference 4.3. Empiricists on the role ofmemory in abstraction 4.4. Artificial neural network models of memory consolidation 4.5. Deep reinforcement learning 4.6. Deep-Q learning and episodic control 4.7. Remaining questions about modeling memory 4.8. Summary 94 111 118 127 137 142 142 151 153 160 5. Imagination 5.1. Imagination: The mind’s laboratory 5.2. Fodor’s challenges and Hume’s imaginative answers 5.3. Imagination’s role in synthesizing ideas: Autoencoders and Generative Adversarial Networks 204 5.4. Imagination’s role in synthesizing novel composite ideas: Vector interpolation, variational autoencoders, and transformers 211 5.5. Imagination’s role in creativity: Creative Adversarial Networks 225 5.6. Imagination’s role in simulating experience: Imagination-Augmented Agents 229 5.7. Biological plausibility and the road ahead 5.8. Summary 169 171 181 188 190 190 193 235 237
CONTENTS IX 6. Attention 239 6.1. Introduction: Bootstrapping control 239 6.2. Contemporary theories of attention in philosophy and psychology 243 6.3. James on attention as ideational preparation 248 6.4. Attention-like mechanisms in DNN architectures 261 6.5. Language models, self-attention, and transformers 268 6.6. Interest and innateness 285 6.7. Attention, inner speech, consciousness, and control 295 6.8. Summary 303 7. Social Cognition 305 7.1. From individual to social cognition 305 7.2. Social cognition as Machiavellian struggle 312 7.3. Smith and De Grouchy’s sentimentalist approach to social cognition 321 7.4. A Grouchean developmentalist framework for modeling social cognition in artificial agents 332 7.5. Summary 343 Epilogue References Index 345 349 403 |
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| spelling | Buckner, Cameron J. Verfasser (DE-588)1330957962 aut From deep learning to rational machines what the history of philosophy can teach us about the future of artificial intelligence Cameron J. Buckner New York, NY Oxford University Press [2024] © 2024 xxi, 415 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier "This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems." Machine learning Philosophy Philosophie (DE-588)4045791-6 gnd rswk-swf Deep learning (DE-588)1135597375 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Deep learning (DE-588)1135597375 s Maschinelles Lernen (DE-588)4193754-5 s Philosophie (DE-588)4045791-6 s DE-604 Erscheint auch als Online-Ausgabe, EPUB 978-0-19-765332-6 Erscheint auch als Online-Ausgabe 978-0-19-765331-9 Erscheint auch als Online-Ausgabe 978-0-19-765333-3 Digitalisierung BSB München - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034951579&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Buckner, Cameron J. From deep learning to rational machines what the history of philosophy can teach us about the future of artificial intelligence Machine learning Philosophy Philosophie (DE-588)4045791-6 gnd Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
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| title | From deep learning to rational machines what the history of philosophy can teach us about the future of artificial intelligence |
| title_auth | From deep learning to rational machines what the history of philosophy can teach us about the future of artificial intelligence |
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| title_full | From deep learning to rational machines what the history of philosophy can teach us about the future of artificial intelligence Cameron J. Buckner |
| title_fullStr | From deep learning to rational machines what the history of philosophy can teach us about the future of artificial intelligence Cameron J. Buckner |
| title_full_unstemmed | From deep learning to rational machines what the history of philosophy can teach us about the future of artificial intelligence Cameron J. Buckner |
| title_short | From deep learning to rational machines |
| title_sort | from deep learning to rational machines what the history of philosophy can teach us about the future of artificial intelligence |
| title_sub | what the history of philosophy can teach us about the future of artificial intelligence |
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