Cameron J. Buckner (Associate Professor, Associate Professor, Unive
From Deep Learning to Rational Machines
What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence
Cameron J. Buckner (Associate Professor, Associate Professor, Unive
From Deep Learning to Rational Machines
What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence
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This book explains how recent deep learning breakthroughs realized some of the most ambitious ideas of empiricist 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.
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This book explains how recent deep learning breakthroughs realized some of the most ambitious ideas of empiricist 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.
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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Oxford University Press Inc
- Seitenzahl: 440
- Erscheinungstermin: 15. Februar 2024
- Englisch
- Abmessung: 219mm x 152mm x 40mm
- Gewicht: 592g
- ISBN-13: 9780197653302
- ISBN-10: 0197653308
- Artikelnr.: 68058468
- Verlag: Oxford University Press Inc
- Seitenzahl: 440
- Erscheinungstermin: 15. Februar 2024
- Englisch
- Abmessung: 219mm x 152mm x 40mm
- Gewicht: 592g
- ISBN-13: 9780197653302
- ISBN-10: 0197653308
- Artikelnr.: 68058468
Cameron J. Buckner is an Associate Professor in the Department of Philosophy at the University of Houston. He received an Alexander von Humboldt Postdoctoral Fellowship at Ruhr-University Bochum from 2011 to 2013 and has been a visiting fellow at the University of Cambridge.
* 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
* 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
* 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
* 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