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Cameron Buckner is a philosopher and cognitive scientist at The University of Houston. He is writing a book about the age-old philosophical debate on how much of our knowledge is innate (nature, rationalism) versus how much is learned (nurture, empiricism). In the book and his other works, Cameron argues that modern AI can help settle the debate. In particular, he suggests we focus on what types of psychological "domain-general faculties" underlie our own intelligence, and how different kinds of deep learning models are revealing how those faculties may be implemented in our brains. The hope is that by building systems that possess the right handful of faculties, and putting those systems together in a way they can cooperate in a general and flexible manner, it will result in cognitive architectures we would call intelligent. Thus, what Cameron calls The New DoGMA: Domain-General Modular Architecture. We also discuss his work on mental representation and how representations get their content - how our thoughts connect to the natural external world. 



Cameron's Website.Twitter: @cameronjbuckner.Related papersEmpiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.A Forward-Looking Theory of Content.Other sources Cameron mentions:Innateness, AlphaZero, and Artificial Intelligence (Gary Marcus).Radical Empiricism and Machine Learning Research (Judea Pearl).Fodor’s guide to the Humean mind (Tamás Demeter).



0:00 - Intro
4:55 - Interpreting old philosophy
8:26 - AI and philosophy
17:00 - Empiricism vs. rationalism
27:09 - Domain-general faculties
33:10 - Faculty psychology
40:28 - New faculties?
46:11 - Human faculties
51:15 - Cognitive architectures
56:26 - Language
1:01:40 - Beyond dichotomous thinking
1:04:08 - Lower-level faculties
1:10:16 - Animal cognition
1:14:31 - A Forward-Looking Theory of Content