Connecting Language and (Artificial) Intelligence: Princeton’s Tom Griffiths

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Hi, everyone. While we're on winter break, we're dropping a couple of bonus episodes featuring cutting edge academic researchers. On today's episode, Sam is joined by professor and director of Princeton's computational cognitive science lab, Tom Griffiths. Tom is the author of the forthcoming book, The Laws of Thought, and joined Sam today to speak about AI's mathematical and linguistic backgrounds. It was a fascinating conversation and I hope you enjoy it.

**Tom Griffiths** (1:06)
I'm Tom Griffiths from Princeton University, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (1:12)
Welcome to Me, Myself, and AI, a podcast from MIT Sloan Management Review, exploring the future of artificial intelligence. I'm Sam Ransbotham, Professor of Analytics at Boston College. I've been researching data, analytics, and AI at MIT SMR since 2014, with research articles, annual industry reports, case studies, and now 12 seasons of podcast episodes. On each episode, corporate leaders, cutting-edge researchers, and AI policy makers join us to break down what separates AI hype from AI success.
Hi, listeners. Thanks, everyone, for joining us again. Our guest today is Tom Griffiths, Professor of Psychology and Computer Science and the Director of the Princeton Laboratory for Artificial Intelligence. Tom has a new book, The Laws of Thought, which I suspect our listeners will enjoy learning about. Tom, great to have you on the podcast.

**Tom Griffiths** (2:10)
Thanks, Sam. Great to be here.

**Sam Ransbotham** (2:11)
Why don't we start with, I think people know, it's kind of fun to be a professor because people know what professors are. But maybe let's start with a little bit of a bio. Can you give us some background on what your roles are with the lab at Princeton? Yeah.

**Tom Griffiths** (2:25)
So Princeton has a lot of other educational institutions been trying to figure out how to respond to all of the things that are happening with AI in the world at the moment. And so the AI lab is the starting point for doing that in terms of thinking about being able to make some targeted investments in research areas where we see potential for transformative impact for AI in a way that's maybe more nimble than a traditional academic institution might.

**Sam Ransbotham** (2:50)
Yeah. There's a lot going on within universities trying to figure out what exactly all this means and I guess all of society. But let's start with the laws of thought. Can you explain maybe in some simple term what these laws are and how they relate to human cognition and artificial intelligence?

**Tom Griffiths** (3:07)
Yeah. The idea behind the book is that I think all of us in school learn about the laws of nature, these principles of physics or something like that, that tell us about how it is that the world around us works. One interesting thing is that the same scientists who, all hundreds of years ago, were trying to figure out what those laws of nature were, using math to describe the physical world, were just as interested in using math to try and understand the mental world, the world inside us. So the book is really the story of that effort. It turns out understanding our inside world is a bit harder than understanding our outside world. It took us a little bit longer to figure out what the fundamental principles are. But it charts the story from people first introducing this idea of using mathematics to understand the mind, through some of the first discoveries about what kinds of mathematical principles could be used for explaining how minds work, things like mathematical logic, to the discovery that that was not going to get us all the way to understanding things like how people learn complex concepts that have fuzzy boundaries, things like languages, and then ideas like artificial neural networks, which are very popular at the moment in artificial intelligence, and then probability and statistics as another approach that really helps us understand why it is that some of those AI methods actually work.

**Sam Ransbotham** (4:26)
Yeah. I think you approach this from three different frameworks. Rules and symbols is one framework, neural networks is another, and then Bayesian probability is a third. Maybe I'm grossly oversimplifying these three big prongs in the book, but maybe take a minute and explain what each of those pieces are, and then more importantly, how they all weave together.