BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Loop Social//loso//EN CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VEVENT UID:576d1ccb-ade2-4f8d-9eaf-c513aedcc8a0@locally-social.com DTSTAMP:20261105T210000Z DTSTART:20261105T210000Z DTEND:20261105T230000Z SUMMARY:Ohio University Philosophy Forum Public Lecture: Dr. Cameron Buckne r LOCATION:West 82 Food Court\, 1 Park Pl\, Athens\, OH 45701\, USA DESCRIPTION:Thirty-First Annual Ohio University Philosophy Forum\n\nGenerat ive Deep Neural Networks as models of imagination\, creativity\, and plann ing”\n\nA Public Lecture by Dr. Cameron Buckner\, University of Florida\ n\nThursday\, November 5th\, 2026\, 4:00PM - 6:00PM\, Scripps Hall 111\n\n LECTURE ABSTRACT: In current debates in Artificial Intelligence\, deep lea rning researchers have noted a historical kinship to the view in philosoph y called "empiricism"\, which holds that abstract knowledge is produced by learning from experience. Critics of deep learning have in turn taken up mantle of philosophical "rationalism"\, which denies that the most sophist icated forms of human cognition can be derived from learning. The stakes o f this philosophical debate are vividly illustrated in a disagreement betw een the empiricist David Hume and the rationalist Jerry Fodor over the rol e that the imagination plays in rational cognition\, which centers on the question as to whether statistical learning procedures could be bootstrapp ed to perform distinctively human forms of creativity. Fodor alleged that empiricists are unable to explain how minds synthesize examples for use in reasoning\, compose novel concepts from simpler concepts\, engage in cons equence-sensitive planning\, or distinguish between causal and intentional relations amongst thoughts. Fodor applauds Hume for observing that ration al cognition involves all these creative operations\, but criticizes Hume ’s delegation of these operations to the faculty of the imagination\, be cause Hume never explains how learning could enable the imagination could perform all these feats. In this talk\, I show how a variety of different "generative" deep learning architectures demonstrate how an empiricist ima gination could perform such operations. I conclude with more general moral s about the prospects for abstraction and reasoning in empiricist theorizi ng\, about a mutually beneficial interaction between philosophy and comput er science in this context\, and about how URL:https://locally-social.com/share/event/576d1ccb-ade2-4f8d-9eaf-c513aedc c8a0 END:VEVENT END:VCALENDAR