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From Deep Learning to Rational Machines : What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence by Cameron J. Buckner (2023, Hardcover)

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About this product

Product Identifiers

PublisherOxford University Press, Incorporated

ISBN-100197653308

ISBN-139780197653302

eBay Product ID (ePID)12061603539

Product Key Features

Book TitleFrom Deep Learning to Rational Machines : What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence

TopicMind & Body, General

Publication Year2023

Number of Pages488 Pages

LanguageEnglish

GenrePhilosophy, Science

AuthorCameron J. Buckner

FormatHardcover

Additional Product Features

Intended AudienceTrade

LCCN2023-022566

Dewey Edition23/eng/20230927

Reviews"AI, in the form of in deep learning, is emerging as one of the most transformative technologies of our time. Cameron Buckner provides an extremely useful framework for assessing its contributions and pitfalls. He frames debates over deep learning in terms of the history of philosophical debates between empiricism and rationalism and develops and defends a moderate empiricism that offers an illuminating perspective from which to understand and evaluate the claims and counterclaims about AI's prospects. Non-AI researchers will acquire valuable tools for engaging AI while AI researchers will find insightful suggestions for advancing their endeavor."--William Bechtel, University of California, San Diego"This terrific book is packed full of insights. Based on a deep understanding of deep neural networks, it showcases a variety of ways in which these computational models illuminate aspects of the human mind. Buckner's conclusions will be of interest to researchers from across the cognitive sciences. His accessible treatment will also be useful to philosophers more broadlyDLin any fieldDLwho want to understand what's important about this revolutionary new technology."--Nicholas Shea, Institute of Philosophy, University of London"It is both exciting and alarming that areas once considered the exclusive zone of human rationality are rapidly being conquered by new forms of artificial intelligence. The astonishing success of deep neural networks, in everything from strategic gaming to natural language, raises questions about whether these new machines are rational, and whether we are at some level similar machines ourselves. For anyone searching for answers to such questions, Cameron Buckner is your best possible guide: he delivers a clear explanation of the crucial technical features of contemporary AI, together with a profound philosophical analysis of the relationship between innate structure and experience. This book marks a new stage in the human understanding of rationality." --Jennifer Nagel, University of Toronto, "AI, in the form of in deep learning, is emerging as one of the most transformative technologies of our time. Cameron Buckner provides an extremely useful framework for assessing its contributions and pitfalls. He frames debates over deep learning in terms of the history of philosophical debates between empiricism and rationalism and develops and defends a moderate empiricism that offers an illuminating perspective from which to understand and evaluate the claims and counterclaims about AI's prospects. Non-AI researchers will acquire valuable tools for engaging AI while AI researchers will find insightful suggestions for advancing their endeavor."--William Bechtel, University of California, San Diego"This terrific book is packed full of insights. Based on a deep understanding of deep neural networks, it showcases a variety of ways in which these computational models illuminate aspects of the human mind. Buckner's conclusions will be of interest to researchers from across the cognitive sciences. His accessible treatment will also be useful to philosophers more broadly--in any field--who want to understand what's important about this revolutionary new technology."--Nicholas Shea, Institute of Philosophy, University of London"It is both exciting and alarming that areas once considered the exclusive zone of human rationality are rapidly being conquered by new forms of artificial intelligence. The astonishing success of deep neural networks, in everything from strategic gaming to natural language, raises questions about whether these new machines are rational, and whether we are at some level similar machines ourselves. For anyone searching for answers to such questions, Cameron Buckner is your best possible guide: he delivers a clear explanation of the crucial technical features of contemporary AI, together with a profound philosophical analysis of the relationship between innate structure and experience. This book marks a new stage in the human understanding of rationality." --Jennifer Nagel, University of Toronto, "AI, in the form of in deep learning, is emerging as one of the most transformative technologies of our time. Cameron Buckner provides an extremely useful framework for assessing its contributions and pitfalls. He frames debates over deep learning in terms of the history of philosophical debates between empiricism and rationalism and develops and defends a moderate empiricism that offers an illuminating perspective from which to understand and evaluate the claims and counterclaims about AI's prospects. Non-AI researchers will acquire valuable tools for engaging AI while AI researchers will find insightful suggestions for advancing their endeavor."--William Bechtel, University of California, San Diego "This terrific book is packed full of insights. Based on a deep understanding of deep neural networks, it showcases a variety of ways in which these computational models illuminate aspects of the human mind. Buckner's conclusions will be of interest to researchers from across the cognitive sciences. His accessible treatment will also be useful to philosophers more broadly--in any field--who want to understand what's important about this revolutionary new technology."--Nicholas Shea, Institute of Philosophy, University of London "It is both exciting and alarming that areas once considered the exclusive zone of human rationality are rapidly being conquered by new forms of artificial intelligence. The astonishing success of deep neural networks, in everything from strategic gaming to natural language, raises questions about whether these new machines are rational, and whether we are at some level similar machines ourselves. For anyone searching for answers to such questions, Cameron Buckner is your best possible guide: he delivers a clear explanation of the crucial technical features of contemporary AI, together with a profound philosophical analysis of the relationship between innate structure and experience. This book marks a new stage in the human understanding of rationality." --Jennifer Nagel, University of Toronto, "AI, in the form of in deep learning, is emerging as one of the most transformative technologies of our time. Cameron Buckner provides an extremely useful framework for assessing its contributions and pitfalls. He frames debates over deep learning in terms of the history of philosophical debates between empiricism and rationalism and develops and defends a moderate empiricism that offers an illuminating perspective from which to understand and evaluate the claims and counterclaims about AI's prospects. Non-AI researchers will acquire valuable tools for engaging AI while AI researchers will find insightful suggestions for advancing their endeavor."--William Bechtel, University of California, San Diego"This terrific book is packed full of insights. Based on a deep understanding of deep neural networks, it showcases a variety of ways in which these computational models illuminate aspects of the human mind. Buckner's conclusions will be of interest to researchers from across the cognitive sciences. His accessible treatment will also be useful to philosophers more broadly'e"in any field'e"who want to understand what's important about this revolutionary new technology."--Nicholas Shea, Institute of Philosophy, University of London"It is both exciting and alarming that areas once considered the exclusive zone of human rationality are rapidly being conquered by new forms of artificial intelligence. The astonishing success of deep neural networks, in everything from strategic gaming to natural language, raises questions about whether these new machines are rational, and whether we are at some level similar machines ourselves. For anyone searching for answers to such questions, Cameron Buckner is your best possible guide: he delivers a clear explanation of the crucial technical features of contemporary AI, together with a profound philosophical analysis of the relationship between innate structure and experience. This book marks a new stage in the human understanding of rationality." --Jennifer Nagel, University of Toronto, "AI, in the form of in deep learning, is emerging as one of the most transformative technologies of our time. Cameron Buckner provides an extremely useful framework for assessing its contributions and pitfalls. He frames debates over deep learning in terms of the history of philosophical debates between empiricism and rationalism and develops and defends a moderate empiricism that offers an illuminating perspective from which to understand and evaluate theclaims and counterclaims about AI's prospects. Non-AI researchers will acquire valuable tools for engaging AI while AI researchers will find insightful suggestions for advancing their endeavor."--WilliamBechtel, University of California, San Diego"This terrific book is packed full of insights. Based on a deep understanding of deep neural networks, it showcases a variety of ways in which these computational models illuminate aspects of the human mind. Buckner's conclusions will be of interest to researchers from across the cognitive sciences. His accessible treatment will also be useful to philosophers more broadlyDLin any fieldDLwho want to understand what's important about this revolutionary newtechnology."--Nicholas Shea, Institute of Philosophy, University of London"It is both exciting and alarming that areas once considered the exclusive zone of human rationality are rapidly being conquered by new forms of artificial intelligence. The astonishing success of deep neural networks, in everything from strategic gaming to natural language, raises questions about whether these new machines are rational, and whether we are at some level similar machines ourselves. For anyone searching for answers to such questions, CameronBuckner is your best possible guide: he delivers a clear explanation of the crucial technical features of contemporary AI, together with a profound philosophical analysis of the relationship between innatestructure and experience. This book marks a new stage in the human understanding of rationality." --Jennifer Nagel, University of Toronto"Philosophers who are interested in deep learning and deep learning researchers who are interested in philosophy are natural audiences for Buckner's book. Both will find much of interest and value in it." -- Glenn Branch, Metascience"In this work, the author demonstrates very well that the development of deeply rational machines requires both a very good understanding of human rationality and an evaluative assessment of technological feasibility. He is so successful in doing this that the book should actually find a broad readership, ranging from AI researchers to cognitive scientists and philosophers who want to understand the effects of the new technologies." -- G. Haring, ComputingReviews, AI, in the form of in deep learning, is emerging as one of the most transformative technologies of our time. Cameron Buckner provides an extremely useful framework for assessing its contributions and pitfalls. He frames debates over deep learning in terms of the history of philosophical debates between empiricism and rationalism and develops and defends a moderate empiricism that offers an illuminating perspective from which to understand and evaluate the claims andcounterclaims about AI's prospects. Non-AI researchers will acquire valuable tools for engaging AI while AI researchers will find insightful suggestions for advancing their endeavor., "AI, in the form of in deep learning, is emerging as one of the most transformative technologies of our time. Cameron Buckner provides an extremely useful framework for assessing its contributions and pitfalls. He frames debates over deep learning in terms of the history of philosophical debates between empiricism and rationalism and develops and defends a moderate empiricism that offers an illuminating perspective from which to understand and evaluate the claims and counterclaims about AI's prospects. Non-AI researchers will acquire valuable tools for engaging AI while AI researchers will find insightful suggestions for advancing their endeavor."--William Bechtel, University of California, San Diego"This terrific book is packed full of insights. Based on a deep understanding of deep neural networks, it showcases a variety of ways in which these computational models illuminate aspects of the human mind. Buckner's conclusions will be of interest to researchers from across the cognitive sciences. His accessible treatment will also be useful to philosophers more broadly--in any field--who want to understand what's important about this revolutionary new technology."--Nicholas Shea, Institute of Philosophy, University of London"It is both exciting and alarming that areas once considered the exclusive zone of human rationality are rapidly being conquered by new forms of artificial intelligence. The astonishing success of deep neural networks, in everything from strategic gaming to natural language, raises questions about whether these new machines are rational, and whether we are at some level similar machines ourselves. For anyone searching for answers to such questions, Cameron Buckner is your best possible guide: he delivers a clear explanation of the crucial technical features of contemporary AI, together with a profound philosophical analysis of the relationship between innate structure and experience. This book marks a new stage in the human understanding of rationality." --Jennifer Nagel, University of Toronto"Philosophers who are interested in deep learning and deep learning researchers who are interested in philosophy are natural audiences for Buckner's book. Both will find much of interest and value in it." -- Glenn Branch, Metascience, "AI, in the form of in deep learning, is emerging as one of the most transformative technologies of our time. Cameron Buckner provides an extremely useful framework for assessing its contributions and pitfalls. He frames debates over deep learning in terms of the history of philosophical debates between empiricism and rationalism and develops and defends a moderate empiricism that offers an illuminating perspective from which to understand and evaluate the claims and counterclaims about AI's prospects. Non-AI researchers will acquire valuable tools for engaging AI while AI researchers will find insightful suggestions for advancing their endeavor."--William Bechtel, University of California, San Diego"This terrific book is packed full of insights. Based on a deep understanding of deep neural networks, it showcases a variety of ways in which these computational models illuminate aspects of the human mind. Buckner's conclusions will be of interest to researchers from across the cognitive sciences. His accessible treatment will also be useful to philosophers more broadly--in any field--who want to understand what's important about this revolutionary new technology."--Nicholas Shea, Institute of Philosophy, University of London"It is both exciting and alarming that areas once considered the exclusive zone of human rationality are rapidly being conquered by new forms of artificial intelligence. The astonishing success of deep neural networks, in everything from strategic gaming to natural language, raises questions about whether these new machines are rational, and whether we are at some level similar machines ourselves. For anyone searching for answers to such questions, Cameron Buckner is your best possible guide: he delivers a clear explanation of the crucial technical features of contemporary AI, together with a profound philosophical analysis of the relationship between innate structure and experience. This book marks a new stage in the human understanding of rationality." --Jennifer Nagel, University of Toronto"Philosophers who are interested in deep learning and deep learning researchers who are interested in philosophy are natural audiences for Buckner's book. Both will find much of interest and value in it." -- Glenn Branch, Metascience"In this work, the author demonstrates very well that the development of deeply rational machines requires both a very good understanding of human rationality and an evaluative assessment of technological feasibility. He is so successful in doing this that the book should actually find a broad readership, ranging from AI researchers to cognitive scientists and philosophers who want to understand the effects of the new technologies." -- G. Haring, Computing Reviews

Dewey Decimal006.3/1

Table Of ContentAcknowledgmentsPrefaceNote 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

SynopsisThis 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., 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.