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.

About the Author
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.

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 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 *



Book Information
ISBN 9780197653302
Author Cameron J. Buckner
Format Hardback
Page Count 440
Imprint Oxford University Press Inc
Publisher Oxford University Press Inc
Weight(grams) 1g
Dimensions(mm) 152mm * 198mm * 56mm