Description
This book provides a framework for thinking about foundational philosophical questions surrounding the use of deep artificial neural networks ("deep learning") to achieve artificial intelligence. Specifically, it links recent breakthroughs to classic works in empiricist philosophy of mind. In recent assessments of deep learning's potential, scientists have cited historical figures from the philosophical debate between nativism and empiricism, which concerns the origins of abstract knowledge. These empiricists were faculty psychologists; that is, they argued that the extraction of abstract knowledge from experience involves the active engagement of psychological faculties such as perception, memory, imagination, attention, and empathy. This book explains how recent deep learning breakthroughs realized some of the most ambitious ideas about these faculties from 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, and philosophers can see how some of the historical empiricists' most ambitious speculations can now be realized in specific computational systems.
Table of Contents
AcknowledgmentsPrefaceNote on Abbreviated Citations to Historical Works1 Moderate Empiricism and Machine Learning1.1 Playing with fire? Nature vs. nurture for computer science1.2 How to simmer things down: From Forms and slates to styles of learning1.3 From dichotomy to continuum1.4 Of faculties and fairness: Introducing the new empiricist DoGMA1.5 Of models and minds1.6 Other dimensions of the rationalist-empiricist debate1.7 The DoGMA in relation to other recent revivals of empiricism1.8 Basic strategy of the book: Understanding deep learning through empiricist faculty psychology2 What is Deep Learning, and How Should We Evaluate Its Potential?2.1 Intuitive inference as deep learning's distinctive strength2.2 Deep learning: Other marquee achievements2.3 Deep learning: Questions and concerns2.4 Can we (fairly) measure success? Artificial intelligence vs. artificial rationality2.5 Avoiding comparative biases: Lessons from comparative psychology for the science of machine behavior2.6 Summary3 Perception3.1 The importance of perceptual abstraction in empiricist accounts of reasoning3.2 Four approaches to abstraction from the historical empiricists3.3 Transformational abstraction: Conceptual foundations3.4 Deep convolutional neural networks: Basic features3.5 Transformational abstraction in DCNNs3.6 Challenges for DCNNs as models of transformational abstraction3.7 Summary4 Memory4.1 The trouble with quantifying human perceptual experience4.2 Generalization and catastrophic interference4.3 Empiricists on the role of memory in abstraction4.4 Artificial neural network models of memory consolidation4.5 Deep reinforcement learning4.6 Deep-Q Learning and Episodic Control4.7 Remaining questions about modeling memory4.8 Summary5 Imagination5.1 Imagination: The mind's laboratory5.2 Fodor's challenges, and Hume's imaginative answers5.3 Imagination's role in synthesizing ideas: Autoencoders and Generative Adversarial Networks5.4 Imagination's role in synthesizing novel composite ideas: vector interpolation, variational autoencoders, and transformers5.5 Imagination's role in creativity: Creative Adversarial Networks5.6 Imagination's role in simulating experience: Imagination-Augmented Agents5.7 Biological plausibility and the road ahead5.8 Summary6 Attention6.1 Introduction: Bootstrapping control6.2 Contemporary theories of attention in philosophy and psychology6.3 James on attention as ideational preparation6.4 Attention-like mechanisms in DNN architectures6.5 Language models, self-attention, and transformers6.6 Interest and innateness6.7 Attention, inner speech, consciousness, and control6.8 Summary7 Social and Moral Cognition7.1 From individual to social cognition7.2 Social cognition as Machiavellian struggle7.3 Smith and De Grouchy's sentimentalist approach to social cognition7.4 A Grouchean developmentalist framework for modeling social cognition in artificial agents7.5 SummaryEpilogueReferences Index
