From Deep Learning to Rational Machines: What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence - Tapa dura
Sinopsis
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.
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Acerca del autor
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.
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From Deep Learning to Rational
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Oxford University Press, 2023
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From Deep Learning to Rational Machines : What the History of Philosophy Can Teach Us About the Future of Artificial Intelligence
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Hardcover. Condición: new. Hardcover. 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 theorigins 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 psychologicalfaculties 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 tomine 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 specificcomputational systems. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9780197653302
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Hardback. Condición: New. 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. Nº de ref. del artículo: LU-9780197653302
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From Deep Learning to Rational Machines: What the History of Philosophy Can Teach Us About the Future of Artificial Intelligence
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From Deep Learning to Rational Machines
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ISBN 10: 0197653308
ISBN 13: 9780197653302
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Hardback. Condición: New. 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. Nº de ref. del artículo: LU-9780197653302
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