图书简介

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

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