From deep learning to rational machines : what the history of philosophy can teach us about the future of artificial intelligence
Buckner, Cameron J.
From deep learning to rational machines : what the history of philosophy can teach us about the future of artificial intelligence by Cameron J. Buckner - New York : Oxford University Press, ©2024 - xxi, 415 p. : ill. ; 22 cm.
Includes bibliographical references (pages 349-401) and index.
1. Moderate empiricism and machine learning 2. What is deep learning, and how should we evaluate its potential? 3. Perception 4. Memory 5. Imagination 6. Attention 7. Social and moral cognition.
"This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of 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 on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems"--
9780197653302
Machine learning--Philosophy.
006.3 / BUC-F
From deep learning to rational machines : what the history of philosophy can teach us about the future of artificial intelligence by Cameron J. Buckner - New York : Oxford University Press, ©2024 - xxi, 415 p. : ill. ; 22 cm.
Includes bibliographical references (pages 349-401) and index.
1. Moderate empiricism and machine learning 2. What is deep learning, and how should we evaluate its potential? 3. Perception 4. Memory 5. Imagination 6. Attention 7. Social and moral cognition.
"This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of 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 on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems"--
9780197653302
Machine learning--Philosophy.
006.3 / BUC-F