The 2024 Nobel winner explains what AI has learned from biological intelligence, and how it might one day surpass it.
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This Discourse was recorded at the Ri on 30 May 2025. Find out more about our 200 year old Discourse series here:
Our apologies as we aware there are some audio imperfections at the beginning of the talk, but they are resolved from 00:02:00 onwards.
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Tech headlines in the last couple of years have been dominated by Artificial Intelligence. But what do we actually mean by intelligence? What has AI learned from biological intelligence, and how do they still differ?
Acclaimed computer scientist, and winner of the 2024 Nobel Prize in Physics, Geoffrey Hinton will examine the similarities and differences between artificial and biological intelligence, following his decades of ground-breaking work which has enabled the neural networks of today.
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Geoffrey Hinton is Professor Emeritus at the University of Toronto, and a world renowned expert in the field of deep learning. He is often referred to as the “Godfather of AI”, and in 2024 was jointly awarded the Nobel Prize in Physics “for foundational discoveries and inventions that enable machine learning with artificial neural networks”.
He is a fellow of the Royal Society and the Royal Society of Canada, and has been recognised with many awards around the world including the Turing Award, the Royal Society Royal Medal, and Dickson Prize.
Hinton received a BA in Experimental Psychology from the University of Cambridge in 1970 and his PhD in Artificial Intelligence from the University of Edinburgh in 1978. Following postdoctoral work at Sussex University and the University of California San Diego, he joined the Computer Science department at Carnegie Mellon University, before moving to the Department of Computer Science at the University of Toronto in 1987. He set up the Gatsby Computational Neuroscience Unit at University College London before becoming University Professor in Toronto in 2006 and latterly University Professor Emeritus. Since 2017, Hinton has been Chief Scientific Advisor at the Vector Institute in Toronto.
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