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“Godfather of AI” Geoffrey Hinton was already on his way to becoming a household name when he won the 2024 Nobel Prize in Physics for foundational work leading to today’s artificial intelligence boom.  

Two months later, his celebrity has hit a whole new level. 

The emeritus at the University of Toronto says he now gets recognized on the street and that strangers regularly ask him for selfies. On a recent flight to Toronto, one flight attendant even grabbed the intercom to announce his presence on the plane. That’s in addition to a torrent of requests to speak to media, appear on podcasts and read academic papers. 

As he prepares to travel to Stockholm on Dec. 10 to officially accept the honour alongside co-winner John J. Hopfield of Princeton University – which will be celebrated via watch parties and other “Nobel Week” events at U of T – Hinton says he plans to put his growing fame to good use. 

“It will be useful when I talk about AI risks,” he says, referring to about the potential existential threat posed by rapid and unchecked AI development.  

“It will make people take me more seriously.” 

For example, he was recently invited to be part of a webinar with Nobel Peace Prize laureates on whether AI should be used to decide if and when to launch nuclear weapons – a foreboding new twist on a Cold War-era threat that has preoccupied fellow ��������οNobel laureate John Polanyi, a University Professor emeritus of chemistry.  

“I think it’s a bad idea,” Hinton says for the record.   

Yet, as scary as such a scenario is to contemplate, Hinton has focused on what he considers an equally grave threat to humanity: the moment when machine intelligence surpasses that of our own.  

That’s why he’s called on governments to develop regulations to guide AI development and deployment. It’s also why he’s urging companies to devote more funding to AI safety research as they rush to explore the myriad ways the technology can be used to make our lives better – from finding cures for deadly diseases to discovering new materials to help combat climate change.  

At U of T, Hinton has also taken on an advisory role at the , where researchers are at the forefront of exploring AI safety and other issues around the adoption of new technologies. In particular, he highlights the work of Roger Grosse and David Duvenaud – both AI safety experts who are associate professors in the department of computer science in the Faculty of Arts & Science and Schwartz Reisman Chairs in Technology and Society (he says Grosse convinced him to ). 

The institute and ��������οmore broadly have an opportunity to become a world leader in figuring out how to guard against AI threats, he says. “I think that can be a world-class centre for figuring out whether there’s a way to make a superintelligence – which we all think is coming – not want to take control.”   

Hinton’s moment in the spotlight has been a long time coming – and is testament to his curious mind, persistent nature and willingness to go against the grain. 

As a child attending a “mildly Christian school” in England, he says he often felt like an outsider because he refused to accept the idea of a god without evidence (he remains an atheist). Decades later, Hinton again found himself on the fringe as he and a handful of researchers, including the Salk Institute for Biological Science’s Terry Sejnowsky, who did his PhD research under Hopfield, explored the idea that the human brain was essentially a bunch of connection strengths between neurons – not a series of logical expressions, as many in the field then believed. 

That key insight now forms the basis of today neural nets. 

“There were many times when I could easily have given up and sort of joined the mainstream,” Hinton says. “But I think my experience as a child made me far more resistant to that.” 

A self-described cognitive scientist who works in the field of computer science, Hinton says he was surprised to win a Nobel Prize in Physics. But he used ideas from physics to create the Boltzmann machine, which can be used to recognize elements in data. The Boltzmann machine, in turn, was based on the Hopfield network, which was invented by his co-laureate.  

Hinton is donating an early Boltzmann chip, about the size of a postage stamp, to the Nobel Prize Museum – and has decided to use the money from his win to set up a prize for young researchers at the annual Conference on Neural Information Processing Systems. He has also donated to a Canadian charity that supports works with Indigenous communities to address water challenges, and has plans to give to another that supports neurodiverse young adults. 

For young researchers hoping to follow in his footsteps? Hinton advises: focus on a problem that really excites you, don’t become swayed by conventional thinking, persevere until you know you’re wrong – and feel free to wander between different research disciplines.  

“If you're really interested in chasing a criminal, you don't stop at a state border,” he says. “That's a stupid thing to do – so, the boundaries of fields, you just ignore them.”