In his words: Geoffrey Hinton reflects on his Nobel Prize win
Empower curiosity-driven research. Follow your convictions. Think not just about how to advance technology, but how to direct its use for good.
These were among the key messages delivered by , Emeritus of computer science at the University of Toronto and winner of the 2024 Nobel Prize in Physics, during an Oct. 8 press conference held by the university to mark his historic award.
Widely regarded as 鈥渢he godfather of AI,鈥 Hinton was named a co-winner of the prize 鈥 alongside John J. Hopfield of Princeton University 鈥 for his work on Boltzmann machines and artificial neural networks, which laid the groundwork for advancements in AI and stimulated new research directions in physics.
麻豆视频 President Meric Gertler hailed Hinton for having 鈥渁 profound impact on multiple fields and disciplines,鈥 crediting 鈥渉is leadership and exemplary mentorship of young scholars鈥 with helping 麻豆视频 become a global leader in AI and machine learning.
鈥淚 think one cannot overstate the impact of a win like this on the ability of Canada, Toronto and the University of Toronto to be able to welcome talented newcomers, great students and wonderful faculty from across the country and around the world because of the recognition that arises with Geoff鈥檚 win,鈥 President Gertler said.
For his part, Hinton echoed his remarks from earlier in the day that he was 鈥渇labbergasted鈥 to receive the prize and pleased that the Nobel committee recognized the advancements in artificial neural networks.
He also answered questions about his influences, legacy and how it feels to go from being an obscure researcher who toiled in a largely forsaken field to a Nobel Laureate 鈥 and his advice for researchers who hope to one day follow in his footsteps.
Here are five key themes that emerged from Hinton's news conference:
His legacy
鈥淚鈥檓 hoping AI will lead to tremendous benefits, to tremendous increases in productivity and to a better life for everybody. I鈥檓 convinced that it will do that in health care.
鈥淢y worry is that it may also lead to bad things, and in particular, when we get things more intelligent than ourselves, no one really knows whether we鈥檙e going to be able to control them.
鈥淲e don鈥檛 know how to avoid [catastrophic AI scenarios] at present. That鈥檚 why we urgently need more research. So I鈥檓 advocating that our best young researchers, or many of them, should work on AI safety and governments should force large companies to provide the computational facilities they need to do that.鈥
A collaborative effort
鈥淚 think of the prize as a recognition of a large community of people who worked on artificial neural networks for many years.
鈥淚鈥檇 particularly like to acknowledge my two main mentors: David Rumelhart, with whom I worked on the backpropagation algorithm 鈥 and my colleague Terry Sejnowsky, who I worked with a lot in the 1980s on Boltzmann machines and who taught me a lot about the brain.
鈥淚鈥檇 also like to acknowledge my students. I was particularly fortunate to have many clever students, much cleverer than me, who actually made things work. They鈥檝e gone on to do great things.
鈥淚 should also acknowledge Yoshua Bengio and Yann LeCun who were close colleagues and very instrumental in developing this whole field.鈥
Canada鈥檚 research strengths
鈥淚 think the main thing about Canada as a place to do research is there isn鈥檛 as much money as there is in the U.S., but it uses its money quite wisely.
鈥淚n particular, the main funding council for this type of research, called NSERC, uses money for basic curiosity-driven research, and all of these advances in neural networks came out of basic curiosity-driven research 鈥 not out of throwing money at applied problems, but out of letting scientists follow their curiosity to try and understand things. And Canada鈥檚 quite good at that.鈥
Many thought he was wasting his time
鈥淚t was a lot of fun doing the research, but it was slightly annoying that many people 鈥 in fact, most people in the field of AI 鈥 said that neural networks would never work.
"They were very confident these things were a waste of time and we would never be able to learn complicated things 鈥 for example, understanding natural language 鈥 using neural networks. And they were wrong."
Believe in your ideas
"My message is this: if you believe in something, don鈥檛 give up on it until you understand why that belief is wrong.
"Often, you believe in things and you eventually figure out why that鈥檚 a wrong thing to believe in. But so long as you believe in something and you can鈥檛 see why that鈥檚 wrong 鈥 like, 鈥榯he brain has to work somehow so we have to figure out how it learns the connection strengths to make it work鈥 鈥 keep working on it and don鈥檛 let people tell you it鈥檚 nonsense if you can鈥檛 see why it鈥檚 nonsense."