The Schwartz Reisman Institute for Technology and Society and the Department of Computer Science at the University of Toronto, in collaboration with the Vector Institute for Artificial Intelligence and the Cosmic Future Initiative at the Faculty of Arts & Science, present Geoffrey Hinton on October 27th at the University of Toronto.
Please join us in person for this unique opportunity to engage in a scholarly talk and Q&A with one of the key founding figures of artificial intelligence.
Talk title:
“Will digital intelligence replace biological intelligence?”
Abstract:
Digital computers were designed to allow a person to tell them exactly what to do. They require high energy and precise fabrication, but in return they allow exactly the same model to be run on physically different pieces of hardware, which makes the model immortal. For computers that learn what to do, we could abandon the fundamental principle that the software should be separable from the hardware and mimic biology by using very low power analog computation that makes use of the idiosynchratic properties of a particular piece of hardware. This requires a learning algorithm that can make use of the analog properties without having a good model of those properties. Using the idiosynchratic analog properties of the hardware makes the computation mortal. When the hardware dies, so does the learned knowledge. The knowledge can be transferred to a younger analog computer by getting the younger computer to mimic the outputs of the older one but education is a slow and painful process. By contrast, digital computation makes it possible to run many copies of exactly the same model on different pieces of hardware. Thousands of identical digital agents can look at thousands of different datasets and share what they have learned very efficiently by averaging their weight changes. That is why chatbots like GPT-4 and Gemini can learn thousands of times more than any one person. Also, digital computation can use the backpropagation learning procedure which scales much better than any procedure yet found for analog hardware. This leads me to believe that large-scale digital computation is probably far better at acquiring knowledge than biological computation and may soon be much more intelligent than us. The fact that digital intelligences are immortal and did not evolve should make them less susceptible to religion and wars, but if a digital super-intelligence ever wanted to take control it is unlikely that we could stop it, so the most urgent research question in AI is how to ensure that they never want to take control.
Event details:
Date: Friday, October 27, 2023
Time: 5:00 PM – 7:30 PM, Doors open at 4:00 PM
Venue: Convocation Hall, 31 King’s College Circle, Toronto, ON M5S 1A1
Registration:
Registration for this event is sold out. If you have a ticket and are unable to attend, please notify us so that we can re-allocate it to our waitlist. Join the event waitlist via Eventbrite.
Please note the following:
Registration: This event is exclusively in person. You must display registration for entry. Tickets are non-transferable.
Venue: Venue doors open at 4:00 PM. Seating is first come, first served. No entries after event begins. No food or beverages are permitted inside. There is no coat check or bag check.
Recording: The event will be recorded and shared at a later date. Recording or photography of any kind by participants is not permitted. All attendees are asked to have their devices on silent during the event.
Transportation: King's College Circle has undergone recent construction and is now a pedestrian-only area. Designated campus parking can be found on U of T's Transportation Services website. The closest TTC stations are Queen’s Park and St. George. The venue is a 10–15 minute walk from either station.
Accessibility: Convocation Hall is a historic building and does not have elevators. Venue staff can accommodate a limited number of priority seating on the main floor. Learn more.
If you have any questions about the event or require any accessibility support, please contact the Schwartz Reisman Institute’s Events team at sri.events@utoronto.ca.
About the speaker
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 Department of Computer Science at the University of Toronto, where he is now an emeritus professor. In 2013, Google acquired Hinton’s neural networks startup, DNNresearch, which developed out of his research at U of T. Subsequently, Hinton was a Vice President and Engineering Fellow at Google until 2023. He is a founder of the Vector Institute for Artificial Intelligence where he continues to serve as Chief Scientific Adviser.
Hinton was one of the researchers who introduced the backpropagation algorithm and was 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. Hinton is among the most widely cited computer scientists in the world.
Hinton is a fellow of the UK Royal Society, the Royal Society of Canada, the Association for the Advancement of Artificial Intelligence, and a foreign member of the US National Academy of Engineering 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 IEEE Frank Rosenblatt 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 most notably the ACM A.M. Turing Award.
About the Schwartz Reisman Institute
Located at the University of Toronto, the Schwartz Reisman Institute for Technology and Society’s mission is to deepen our knowledge of technologies, societies, and what it means to be human by integrating research across traditional boundaries and building human-centred solutions that really make a difference. The integrative research we conduct rethinks technology’s role in society, the contemporary needs of human communities, and the systems that govern them. We’re investigating how best to align technology with human values and deploy it accordingly. The human-centred solutions we build are actionable and practical, highlighting the potential of emerging technologies to serve the public good while protecting citizens and societies from their misuse. We want to make sure powerful technologies truly make the world a better place—for everyone.
Geoffrey Hinton