Cameron J. Buckner, "From Deep Learning to Rational Machines" (Oxford UP, 2023)
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What is the historical background of AI and how does it lead to deep learning?
Hi everyone, this is Marshall Poe. I'm the founder of the New Books Network and I'm also a historian. I used to be a history professor and I love maps, so I'm especially happy to tell you that this episode is sponsored by What's Your Map, which was the winner of the Best Education Podcast at the 2025 British Podcast Awards. What's Your Map is hosted by historian and prize-winning author Professor Jerry Broughton. In each episode, he invites a guest to share a map close to their heart and unfurl the ideas, inspirations, and stories behind it. I can tell you I've listened and it's fascinating. The podcast is in its sixth series and is enjoyed by audiences around the globe. You can explore all the maps in the podcast online.
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Hello, and welcome to New Books and Philosophy, a podcast channel with the New Books Network. I'm Carrie Figdor, Professor of Philosophy at the University of Iowa, and I'm co-host of the channel along with Robert Talese, Sarah Tyson, and Malcolm Keating. Together, we bring you conversations with philosophers about their new books in a wide range of areas of contemporary philosophical inquiry. Today's interview is with Cameron Buckner, Associate Professor of Philosophy at the University of Houston. His new book, From Deep Learning to Rational Machines, is just out from Oxford University Press. Artificial intelligence started with program computers where programmers would manually program human expert knowledge into the systems.
In sharp contrast, today's artificial neural networks or deep learning are able to learn from experience and perform with human-like levels of perceptual categorization, language production, and other cognitive abilities. This difference has been portrayed as roughly parallel to the philosophical divide between rationalists or nativists on the one hand and empiricists on the other. In his book, Buckner lays out a program for future AI development based on discussions of the human mind by such figures as David Hume, Avicenna, and Sophie de Grouchy, among others. He offers a conceptual framework that occupies a middle ground between the extremes of blank slate empiricism and innate domain-specific faculty psychology, and he defends the claim that neural network modelers have found, at least in some cases, a sweet spot of abstraction from the messy details of biological cognition
so as to capture the relevant similarities of cognitive abilities in their neural networks. Let's turn to the interview. Hello, Cameron Buckner. Welcome to New Books and Philosophy.
Yeah. Hi, Carrie. Thanks for having me.
So this should be a very interesting conversation about a very hot topic, AI, deep learning. specifically to rational machines. Before I get started with the content of the book, maybe you can say a bit about yourself as a philosopher and how this book came about.
Yeah, thanks. So I actually started in artificial intelligence in a very different paradigm as an undergraduate at Texas Tech 20 or so years ago. And that was the sort of what we now called good old fashioned AI, right, where you had rules and symbols, expert systems type approach, right? Where the idea is you were supposed to figure out what knowledge it is a human expert has learned very high level abstract sort of rule based knowledge and manually program that into the system. to get the artificial intelligence behavior that you desire out of your system.
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Chapters
5 chapters
1
What is the historical background of AI and how does it lead to deep learning?
0:00–16:25
2
How did Cameron Buckner’s early work in rule‑based AI shape his view of modern deep learning?
16:25–30:47
3
Why does the book frame AI development in terms of the rationalist‑empiricist (nativist‑blank‑slate) debate?
30:47–43:15
4
What is the proposed ‘middle‑ground’ conceptual framework and how does it differ from classical AI approaches?
43:15–1:08:43
5
How can deep convolutional neural networks be understood as abstractions of the visual ventral stream?
1:08:43–1:08:49
Speakers
2 identifiedMore from New Books in Philosophy
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