Tom Griffiths is a professor of psychology and cognitive science at UC Berkeley, where he directs the Computational Cognitive Science Lab. He has received widespread recognition for his scientific work, including awards from the American Psychological Association and the Sloan Foundation.
Algorithms to Live By is reviewed here.
Why science?
BC: I think of my own orientation towards science in essentially religious terms. That anything exists at all (let alone life, let alone my own conscious experience) is wonderfully and sublimely mysterious. The most reverential attitude to adopt toward this grand mystery, in my view, is curiosity. One of the most powerful and profound frameworks we have for expressing that curiosity is science.
TG: When I went to university I deliberately chose not to do science, or at least to do a Bachelor of Arts rather than a Bachelor of Science degree. From my time in school I felt like science was about things that we already understand very well, and I wanted to learn about all the things that are still mysterious — minds, cultures, and thoughts. About half way through my degree I read a philosophy book that had a chapter at the very back about using mathematics to model the mind, and that was it! Suddenly I realized that it was possible to explore those mysterious things using rigorous, quantitative methods, and I was hooked.
Why this book?
BC: Since my teenage years if not even earlier, I have been fascinated by the correspondences and parallels, the homologies and isomorphisms, that exist between formal systems and natural ones. Sometimes drawing on real-world intuition enables us to solve a formal problem; sometimes it goes the other way, and a problem teaches us something that’s more broadly applicable. What we can learn about our own lives from the formal systems we’ve discovered in nature and designed in our own image? Algorithms to Live By explores and pursues this question, using computer science as a way of thinking about human decision-making.
TG: My academic research focuses on developing mathematical models of cognition, drawing on ideas from computer science — artificial intelligence and machine learning — to better understand how human minds work. As a result, I spend a lot of time thinking about the computational structure of everyday life, and out of that comes a vocabulary for describing the decision-making problems people face and a set of strategies for solving them. For me, this book is a way of sharing those insights.
What’s next?
BC: As a lover of both computer science and language, I’ve been fascinated for many years by their intersections in computational linguistics, and I’m excited to work more deeply on some projects at that particular conjunction.
TG: I’m currently working with my students and collaborators on the research questions that relate to topics we discuss in the book, specifically how thinking about human rationality in terms of using efficient algorithms (rather than always producing the right answer, regardless of the effort involved) changes the way we understand human cognition.
What’s exciting you at the moment?
BC: Data visualization. We’re living in an open-data boom, and I see this as the other great literacy, as critical in a civic context as in a scientific one.
TG: The last couple of years have seen significant advances in machine learning and artificial intelligence, and I’m excited about exploring what these advances can tell us about human minds.
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