University of Cambridge > Talks.cam > as2713@cam.ac.uk > The Margaret Boden Lecture. Abstraction and Analogy: The Keys to Robust AI

The Margaret Boden Lecture. Abstraction and Analogy: The Keys to Robust AI

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“What is Abstract thinking, and why can’t AI do it?” While AI has made dramatic progress over the last decade in areas such as vision, language processing, and robotics, current AI systems still lack key aspects of human intelligence. In this lecture, Melanie will argue that the inability to form conceptual abstractions—and to make abstraction-driven analogies—is a primary source of brittleness and unreliability in state-of-the-art AI systems. She will reflect on the role played by abstraction at all levels of intelligence and on the prospects for developing AI systems with humanlike abilities for abstract reasoning and analogy.

Melanie Mitchell is Professor at the Santa Fe Institute. Her current research focuses on conceptual abstraction and analogy-making in artificial intelligence systems. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her 2009 book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award, and her 2019 book Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux) has been shortlisted for the 2023 Cosmos Prize for Scientific Writing. Melanie is the recipient of the Distinguished Cognitive Scientist Award from UC Merced and the Herbert A. Simon Award of the International Conference on Complex Systems

She will be joined by two respondents, who’ll offer thoughts on the talk and then the Q&A will be open to the floor!

Attendance is free, both in person at the Cambridge Union and Online. Doors open at 5pm and the event will start at 5.30pm.

Tickets available at: https://www.eventbrite.co.uk/e/abstraction-and-analogy-the-keys-to-robust-artificial-intelligence-tickets-675075728677?aff=oddtdtcreator

This talk is part of the as2713@cam.ac.uk series.

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