Plenary: Two Paths to Intelligence
Geoffrey Hinton / University of Toronto (emeritus)
Keynote: Geoffrey Hinton Cohere
Monday, July 10 - Time: 09:30–10:30 EDT
Abstract: I will briefly describe the forty year history of neural net language models with particular attention to whether they understand what they are saying. I will then discuss some of the main differences between digital and biological intelligences and speculate on how the brain could implement something like transformers. I will conclude by addressing the contentious issue of whether current multimodal LLMs have subjective experience.
Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978. After five years as a faculty member at Carnegie-Mellon he became a fellow of the Canadian Institute for Advanced Research and moved to the University of Toronto where he is now an emeritus professor. He is also the Chief Scientific Adviser at the Vector Institute.
He was one of the researchers who introduced the backpropagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning and deep learning. His research group in Toronto made major breakthroughs in deep learning that revolutionized speech recognition and object classification.
He is a fellow of the UK Royal Society and a foreign member of the US National Academy of Engineering, the US National Academy of Sciences and the American Academy of Arts and Sciences. His awards include the David E. Rumelhart prize, the IJCAI award for research excellence, the Killam prize for Engineering, the Royal Society Royal Medal, the NSERC Herzberg Gold Medal, the IEEE James Clerk Maxwell Gold medal, the NEC C&C award, the BBVA award, the Honda Prize and the Turing Award.