26,95 €*
Versandkostenfrei per Post / DHL
auf Lager, Lieferzeit 1-2 Werktage
- Acknowledgments
- Preface
- Note on Abbreviated Citations to Historical Works
- 1 Moderate Empiricism and Machine Learning
- 1.1 Playing with fire? Nature vs. nurture for computer science
- 1.2 How to simmer things down: From Forms and slates to styles of learning
- 1.3 From dichotomy to continuum
- 1.4 Of faculties and fairness: Introducing the new empiricist DoGMA
- 1.5 Of models and minds
- 1.6 Other dimensions of the rationalist-empiricist debate
- 1.7 The DoGMA in relation to other recent revivals of empiricism
- 1.8 Basic strategy of the book: Understanding deep learning through empiricist faculty psychology
- 2 What is Deep Learning, and How Should We Evaluate Its Potential?
- 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 vs. artificial rationality
- 2.5 Avoiding comparative biases: Lessons from comparative psychology for the science of machine behavior
- 2.6 Summary
- 3 Perception
- 3.1 The importance of perceptual abstraction in empiricist accounts of reasoning
- 3.2 Four approaches to abstraction from the historical empiricists
- 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
- 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 of memory 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
- 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
- 5.4 Imagination's role in synthesizing novel composite ideas: vector interpolation, variational autoencoders, and transformers
- 5.5 Imagination's role in creativity: Creative Adversarial Networks
- 5.6 Imagination's role in simulating experience: Imagination-Augmented Agents
- 5.7 Biological plausibility and the road ahead
- 5.8 Summary
- 6 Attention
- 6.1 Introduction: Bootstrapping control
- 6.2 Contemporary theories of attention in philosophy and psychology
- 6.3 James on attention as ideational preparation
- 6.4 Attention-like mechanisms in DNN architectures
- 6.5 Language models, self-attention, and transformers
- 6.6 Interest and innateness
- 6.7 Attention, inner speech, consciousness, and control
- 6.8 Summary
- 7 Social and Moral Cognition
- 7.1 From individual to social cognition
- 7.2 Social cognition as Machiavellian struggle
- 7.3 Smith and De Grouchy's sentimentalist approach to social cognition
- 7.4 A Grouchean developmentalist framework for modeling social cognition in artificial agents
- 7.5 Summary
- Epilogue
- References
- Index
| Erscheinungsjahr: | 2024 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Philosophie |
| Jahrhundert: | Antike |
| Rubrik: | Geisteswissenschaften |
| Thema: | Lexika |
| Medium: | Buch |
| Seiten: | 415 |
| ISBN-13: | 9780197653302 |
| ISBN-10: | 0197653308 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Buckner, Cameron J. |
| Hersteller: | Oxford University Press |
| Maße: | 219 x 152 x 40 mm |
| Von/Mit: | Cameron J. Buckner |
| Erscheinungsdatum: | 15.02.2024 |
| Gewicht: | 0,58 kg |
- Acknowledgments
- Preface
- Note on Abbreviated Citations to Historical Works
- 1 Moderate Empiricism and Machine Learning
- 1.1 Playing with fire? Nature vs. nurture for computer science
- 1.2 How to simmer things down: From Forms and slates to styles of learning
- 1.3 From dichotomy to continuum
- 1.4 Of faculties and fairness: Introducing the new empiricist DoGMA
- 1.5 Of models and minds
- 1.6 Other dimensions of the rationalist-empiricist debate
- 1.7 The DoGMA in relation to other recent revivals of empiricism
- 1.8 Basic strategy of the book: Understanding deep learning through empiricist faculty psychology
- 2 What is Deep Learning, and How Should We Evaluate Its Potential?
- 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 vs. artificial rationality
- 2.5 Avoiding comparative biases: Lessons from comparative psychology for the science of machine behavior
- 2.6 Summary
- 3 Perception
- 3.1 The importance of perceptual abstraction in empiricist accounts of reasoning
- 3.2 Four approaches to abstraction from the historical empiricists
- 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
- 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 of memory 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
- 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
- 5.4 Imagination's role in synthesizing novel composite ideas: vector interpolation, variational autoencoders, and transformers
- 5.5 Imagination's role in creativity: Creative Adversarial Networks
- 5.6 Imagination's role in simulating experience: Imagination-Augmented Agents
- 5.7 Biological plausibility and the road ahead
- 5.8 Summary
- 6 Attention
- 6.1 Introduction: Bootstrapping control
- 6.2 Contemporary theories of attention in philosophy and psychology
- 6.3 James on attention as ideational preparation
- 6.4 Attention-like mechanisms in DNN architectures
- 6.5 Language models, self-attention, and transformers
- 6.6 Interest and innateness
- 6.7 Attention, inner speech, consciousness, and control
- 6.8 Summary
- 7 Social and Moral Cognition
- 7.1 From individual to social cognition
- 7.2 Social cognition as Machiavellian struggle
- 7.3 Smith and De Grouchy's sentimentalist approach to social cognition
- 7.4 A Grouchean developmentalist framework for modeling social cognition in artificial agents
- 7.5 Summary
- Epilogue
- References
- Index
| Erscheinungsjahr: | 2024 |
|---|---|
| Fachbereich: | Allgemeines |
| Genre: | Philosophie |
| Jahrhundert: | Antike |
| Rubrik: | Geisteswissenschaften |
| Thema: | Lexika |
| Medium: | Buch |
| Seiten: | 415 |
| ISBN-13: | 9780197653302 |
| ISBN-10: | 0197653308 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Buckner, Cameron J. |
| Hersteller: | Oxford University Press |
| Maße: | 219 x 152 x 40 mm |
| Von/Mit: | Cameron J. Buckner |
| Erscheinungsdatum: | 15.02.2024 |
| Gewicht: | 0,58 kg |
Ähnliche Produkte
Lieferzeit 1-2 Werktage
Lieferzeit 1-2 Werktage
Lieferzeit 1-2 Werktage
Lieferzeit 1-2 Werktage
Lieferzeit 1-2 Werktage
Lieferzeit 4-7 Werktage
Lieferzeit 1-2 Werktage
Lieferzeit 2-3 Wochen
