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Tom Griffiths - Five Way Interview

Tom Griffiths introduces himself: I’m a cognitive scientist — a professor of psychology and computer science at Princeton University — so I think about minds and mathematics every day. But we are in an interesting moment where people who aren’t professional cognitive scientists are grappling with the same questions: Can machines think? Is it possible to describe minds using mathematics? What are the limits of different approaches to building a mind? Will we be able to create super-human artificial intelligence? These are questions that have come into focus in the last few years with the creation of chatbots that can hold conversations and solve challenging problems, but answering the questions we have about modern AI requires going further back into the past. In writing the book, I hoped to give readers the context for this moment and some of the language for talking about it, as well as highlighting the stories of discovery that brought us to this point and that suggest possible paths forward. 

Tom's new book is The Laws of Thought.

Why does cognition interest you?

I got interested in understanding human cognition because it remains one of the genuine mysteries left to science. I was excited to discover as an undergraduate that people had used math to understand the mind, and I’ve been on that path since then. 

Why this book particularly?

I say in the acknowledgments section in the book that it is my love letter to cognitive science, and that’s absolutely true. I have enjoyed learning all the things in the book and wanted to share them with others. The way I learn things is through stories, which give more depth and texture to ideas, and so that’s the way that I put each idea in context. It helps to understand an idea when you know something about the problem that somebody was trying to solve, why solving that problem was difficult, and what other things they tried along the way. 

Back in 2016 you said you were interested in human strategies for solving decision-making problems. Does AI help or hinder this?

AI helps us understand the strategies people use for making decisions, and also suggests some ways to better support human decision-makers. Even though people are often derided as bad decision-makers, in my lab we explore the hypothesis that people actually do pretty well when you take into account the cognitive constraints that they operate under. Expressing and solving that problem is actually much harder to do than using traditional ideas about rationality, and we use sophisticated ideas from the AI literature to do so. 

Thinking about human errors in terms of resource constraints also suggests a way to help human decision-makers by providing them with extra resources, and we have explored ways to use AI to add computation into the environments where people make decisions to improve the outcomes. For readers who want to dive into the technical details, I just had another book come out with my co-authors that explores these ideas called The Rational Use of Cognitive Resources. 

What’s next?

I’m still thinking about possible directions for my next book, but want to dig deeper into the ways in which human minds and AI are different from one another. In the meantime I’m putting out the interviews I conducted for The Laws of Thought — plus lots more — as a podcast on the history of cognitive science called The Cognition Project. 

What’s exciting you at the moment?

The creation of intelligent machines is a pretty exciting moment for cognitive scientists, as for a long time we’ve only had one kind of system that could use language and answer complicated questions. This creates all sorts of opportunities to use the tools we have developed for studying human cognition to study AI systems, as well as a chance to better understand people by comparison. I’m also excited about the ways in which AI can expand the scope of our science — letting us develop theories in domains that previously resisted formalization — and accelerate discovery by helping us automate the creation of experiments and theories.

Photo © Sameer Khan

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