Cameron Buckner is an Associate Professor in the Department of Philosophy at the University of Houston. He began his academic career in logic-based artificial intelligence, worked for a time on animal cognition, and is now focusing on deep-neural-network-based approaches to artificial intelligence. This research inspired an interest into the relationship between classical models of reasoning and the (usually very different) ways that humans and animals learn to solve problems, which led him to the discipline of philosophy.
He received a PhD in Philosophy at Indiana University in 2011 and an Alexander von Humboldt Postdoctoral Fellowship at Ruhr-University Bochum from 2011 to 2013. His research interests lie at the intersection of philosophy of mind, philosophy of science, animal cognition, and artificial intelligence, and he teaches classes on all these topics.
Recent representative publications include “Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks” (2018, Synthese), and “Rational Inference: The Lowest Bounds” (2017, Philosophy and Phenomenological Research)—the latter of which won the American Philosophical Association's Article Prize for the period of 2016–2018. From 2020-2022 he was supported by an National Science Foundation grant to write a book on the philosophy of artificial intelligence, tentatively entitled Deeply Rational Machines, which explains recent achievements in deep learning by placing them in the context of empiricist philosophy of mind. The book in particular emphasizes the role of faculties like perception, memory, imagination, attention, and sympathy in the work of historical empiricists and recent attempts to model these faculties in deep learning.
During his time in Cambridge, he will be putting the finishing touches on the book and exploring its implications for ethical, political, aesthetic, and legal issues surrounding the application of deep learning to human society.Back to people