Mind the Gap: Bridging the Computer-Human Divide
by Doug Hulette
Suppose you’re about to go snorkeling on the Great Barrier Reef or some other area of underwater beauty. You should be thinking about the wondrous marine life you’ll certainly see. But instead you’re fixated on the vanishingly small chance of coming eye to eye with a hungry shark.
Still, Griffiths might describe this situation as one “where human behavior could be improved upon,” and he’s using computers and complex algorithms to analyze how and why people often do things that seem to defy logic.
The snorkeling decision is the kind of thing Griffiths spends his time on. “A lot of our recent work explores the idea that it's not that people are irrational, but that our theory of rationality is not appropriate for real intelligent agents,” he says.
“Humans (and computers) have limited computational resources and limited time — something that is not taken into account in classical theories of rationality, which tend to stipulate that you should always take the very best action regardless of how hard it might be to compute what that action is. Real agents have to trade off the computational cost of selecting an action with the benefits of taking that action. As a consequence, taking shortcuts and doing things that are fast but less accurate — two of the things that are often held up as illustrations of human irrationality — make perfect sense.”
Griffiths comes to Princeton from UC Berkeley, where he was a professor in the Department of Psychology and Cognitive Science and director of the Computational Cognitive Science Lab and the Institute of Cognitive and Brain Sciences. He was born in the U.K. but grew up mainly in Australia.
Jennifer Rexford, chair of the CS department and the Gordon Y.S. Wu Professor of Engineering, described her new colleague as “a leader in moving cognitive science from the era of small-scale laboratory studies to a future of automated experiments with a large number of users. The result is no less than a transformation of experimental methods in cognitive science, enabling breakthrough insights into human decision making.”
Griffiths earned accolades for his 2016 book, “Algorithms to Live By / The Computer Science of Human Decisions,” co-written with his friend, nonfiction author Brian Christian. The editors of MIT’s Technology Review chose it as one of the “Best Books of 2016,” and an article in Forbes listed it as a “must-read brain book” for the year. Amazon.com called it a “dazzlingly interdisciplinary work” that “transforms the wisdom of computer science into strategies for human living.”
At the core of his work is how to mine Big Data — the sea of information, much of it unstructured, that spews from the Internet of Things — for insights that lead to better decisions. In his 2014 “Manifesto for a new (computational) cognitive revolution,” published in the journal Cognition, Griffiths wrote that “my vision is of a very different kind of laboratory for studying the mind – one where the rich information about our behavior that is collected ubiquitously by our devices is fodder for evaluating theories of cognition, and where theories of cognition play a central role in how that information is used.”
“Ultimately I want to be able to describe human intelligence in the form of a mathematical theory,” Griffiths says. “Doing so creates two opportunities: reproducing those same abilities in a computer, and identifying the ways in which computation can further support human intelligence.
“The way my students and I do that is to think about the abstract computational problems that human beings face, and then compare their ideal solutions to what people actually do. This usually means drawing on ideas from artificial intelligence, machine learning, and statistics, and then running behavioral experiments in the lab or online to determine how people behave in different situations.
“As a result of doing this, we are able to gain insight into some of the things that people currently do better than computers — learning from small amounts of data, inferring complex causal relationships, reasoning about our physical world, and interpreting the actions of other people — and also identify some of the situations where human behavior could be improved upon.”
By creating algorithms that explain why people often choose to do seemingly bizarre things in specific cases, Griffiths believes computers will to reinforce human rationality, that is, help people better align what they do with what they should be doing, based on their long-term goals rather than peripheral considerations.
Griffiths says Princeton will be an ideal place to continue his intrinsically interdisciplinary research, and he looks forward to working with new colleagues not only from psychology and computer science but also philosophy, sociology, neuroscience, evolutionary biology, economics, operations research, statistics and mathematics.
“In addition, there has recently been a lot of exciting growth in cognitive science, machine learning, and computational social science on campus, and I am also looking forward to playing a role in building up those programs,” he says. “I really see computation as a principle that is increasingly at the heart of many different disciplines, and I think Princeton is doing a great job of pursuing a similar kind of vision.”
Tom Griffiths presented a TED talk at TEDxSydney last year.
A citizen of Australia, the United Kingdom and the U.S., Griffiths earned his bachelor’s degree in psychology at the University of Western Australia before moving to California, where he earned master’s degrees in psychology and statistics as well as his doctorate in psychology from Stanford. He spent the second half of his doctoral work at MIT’s Brain and Cognitive Sciences Department and the Computer Science and Artificial Intelligence Laboratory (CSAIL).
In 2017, Griffiths was awarded a fellowship by the John Simon Guggenheim Memorial Foundation.
The CS department has had professors collaborate with psychology professors on machine learning and cognitive science. CS professor Sebastian Seung, for example, is also the Evnin Professor in Neuroscience.