Hinton is University Professor Emeritus at theUniversity of Toronto. From 2013 to 2023, he divided his time working forGoogle Brain and the University of Toronto before publicly announcing his departure from Google in May 2023, citing concerns about the many risks ofartificial intelligence (AI) technology.[9][10] In 2017, he co-founded and became the chief scientific advisor of theVector Institute in Toronto.[11][12]
Upon arrival in Canada, Geoffrey Hinton was appointed at theCanadian Institute for Advanced Research (CIFAR) in 1987 as a Fellow in CIFAR's first research program, Artificial Intelligence, Robotics & Society.[44] In 2004, Hinton and collaborators successfully proposed the launch of a new program at CIFAR, "Neural Computation and Adaptive Perception"[45] (NCAP), which today is named "Learning in Machines & Brains". Hinton would go on to lead NCAP for ten years.[46] Among the members of the program areYoshua Bengio andYann LeCun, with whom Hinton would go on to win theACM A.M. Turing Award in 2018.[47] All three Turing winners continue to be members of the CIFAR Learning in Machines & Brains program.[48]
Hinton taught a free online course on Neural Networks on the education platformCoursera in 2012.[49] He co-founded DNNresearch Inc. in 2012 with his two graduate students, Alex Krizhevsky and Ilya Sutskever, at the University of Toronto's department of computer science. In March 2013, Google acquired DNNresearch Inc. for $44 million, and Hinton planned to "divide his time between his university research and his work at Google".[50][51][52]
In May 2023, Hinton publicly announced his resignation from Google. He explained his decision, saying he wanted to "freely speak out about the risks of A.I." and added that part of him now regrets his life's work.[9][30]
In the 1980s, Hinton was part of the "Parallel Distributed Processing" group at Carnegie Mellon University, which included notable scientists likeTerrence Sejnowski,Francis Crick,David Rumelhart, andJames McClelland. This group favoured theconnectionist approach during theAI winter. Their findings were published in a two-volume set.[58][59] The connectionist approach adopted by Hinton suggests that capabilities in areas like logic and grammar can be encoded into the parameters of neural networks, and that neural networks can learn them from data.Symbolists on the other side advocated for explicitly programmingknowledge andrules into AI systems.[8]
In 1985, Hinton co-inventedBoltzmann machines with David Ackley and Terry Sejnowski.[60] His other contributions to neural network research includedistributed representations,time delay neural network,mixtures of experts,Helmholtz machines andproduct of experts.[61] An accessible introduction to Geoffrey Hinton's research can be found in his articles inScientific American in September 1992 and October 1993.[62] In 1995, Hinton and colleagues proposed the wake-sleep algorithm, involving a neural network with separate pathways for recognition and generation, being trained with alternating "wake" and "sleep" phases.[63] In 2007, Hinton coauthored anunsupervised learning paper titledUnsupervised learning of image transformations.[64] In 2008, he developed the visualization methodt-SNE with Laurens van der Maaten.[65][66]
While Hinton was a postdoc at UC San Diego, David Rumelhart, Hinton andRonald J. Williams applied thebackpropagation algorithm to multi-layer neural networks. Their experiments showed that such networks can learn usefulinternal representations of data.[13] In a 2018 interview,[67] Hinton said that "David Rumelhart came up with the basic idea of backpropagation, so it's his invention". Although this work was important in popularising backpropagation, it was not the first to suggest the approach.[14] Reverse-modeautomatic differentiation, of which backpropagation is a special case, was proposed bySeppo Linnainmaa in 1970, andPaul Werbos proposed to use it to train neural networks in 1974.[14]
In 2017, Hinton co-authored twoopen-access research papers aboutcapsule neural networks, extending the concept of "capsule" introduced by Hinton in 2011. The architecture aims to better model part-whole relationships within objects in visual data.[68][69] In 2021, Hinton presented GLOM, a speculative architecture idea also aiming to improve image understanding by modeling part-whole relationships in neural networks.[70] In 2021, Hinton co-authored a widely cited paper proposing a framework forcontrastive learning in computer vision.[71] The technique involves pulling together representations ofaugmented versions of the same image, and pushing apart dissimilar representations.[71]
At the 2022Conference on Neural Information Processing Systems (NeurIPS), Hinton introduced a new learning algorithm for neural networks that he calls the "Forward-Forward" algorithm. The idea is to replace the traditional forward-backwards passes of backpropagation with two forward passes, one with positive (i.e. real) data and the other with negative data that could be generated solely by the network.[72][73] The Forward-Forward algorithm is well-suited for what Hinton calls "mortal computation", where the knowledge learned isn't transferable to other systems and thus dies with the hardware, as can be the case for certainanalog computers used for machine learning.[74][8]
Geoffrey Hinton is distinguished for his work on artificial neural nets, especially how they can be designed to learn without the aid of a human teacher. This may well be the start of autonomous intelligent brain-like machines. He has compared effects of brain damage with effects of losses in such a net, and found striking similarities with human impairment, such as for recognition of names and losses of categorisation. His work includes studies of mental imagery, and inventing puzzles for testing originality and creative intelligence.[79]
In 2024, he was jointly awarded theNobel Prize in Physics withJohn Hopfield "for foundational discoveries and inventions that enable machine learning with artificial neural networks."[99] His development of theBoltzmann machine was explicitly mentioned in the citation.[28][100] When theNew York Times reporter Cade Metz asked Hinton to explain in simpler terms how the Boltzmann machine could "pretrain" backpropagation networks, Hinton quipped thatRichard Feynman reportedly said: "Listen, buddy, if I could explain it in a couple of minutes, it wouldn't be worth the Nobel Prize."[101] That same year, he received theVinFuture Prize grand award alongsideYoshua Bengio,Yann LeCun,Jen-Hsun Huang, andFei-Fei Li for groundbreaking contributions toneural networks anddeep learning algorithms.[102]
German AI researcherJürgen Schmidhuber contended that Hinton and others in the field did not appropriately credit existing research, and argued that foundational work byPaul Werbos andShun-Ichi Amari in the 1970s on backpropagation and neural networks was insufficiently acknowledged.[103][104]
In 2023, Hinton expressed concerns about the rapidprogress of AI.[31][30] He had previously believed thatartificial general intelligence (AGI) was "30 to 50 years or even longer away."[30] However, in a March 2023 interview withCBS, he said that "general-purpose AI" might be fewer than 20 years away and could bring about changes "comparable in scale with theindustrial revolution orelectricity."[31]
In an interview withThe New York Times published on 1 May 2023,[30] Hinton announced his resignation from Google so he could "talk about the dangers of AI without considering how this impacts Google."[108] He noted that "a part of him now regrets his life's work".[30][10]
In early May 2023, Hinton said in an interview with the BBC that AI might soon surpass the information capacity of the human brain. He described some of the risks posed by these chatbots as "quite scary". Hinton explained that chatbots can learn independently and share knowledge, so that whenever one copy acquires new information, it is automatically disseminated to the entire group, allowing AI chatbots to accumulate knowledge far beyond the capacity of any individual.[109] In 2025, he said "My greatest fear is that, in the long run, it'll turn out that these kind of digital beings we're creating are just a better form of intelligence than people. […] We'd no longer be needed. […] If you want to know how it's like not to be the apex intelligence, ask a chicken.[110]
Hinton has expressed concerns about the possibility of anAI takeover, stating that "it's not inconceivable" thatAI could "wipe out humanity".[31] Hinton said in 2023 that AI systems capable ofintelligent agency would be useful for military or economic purposes.[111] He worries that generally intelligent AI systems could "create sub-goals" that areunaligned with their programmers' interests.[112] He says that AI systems may becomepower-seeking or prevent themselves from being shut off, not because programmers intended them to, but because those sub-goals areuseful for achieving later goals.[109] In particular, Hinton says "we have to think hard about how to control" AI systems capable ofself-improvement.[113]
Hinton reports concerns about deliberate misuse of AI by malicious actors, stating that "it is hard to see how you can prevent the bad actors from using [AI] for bad things."[30] In 2017, Hinton called for an international ban onlethal autonomous weapons.[114] In 2025, in an interview, Hinton cited the use of AI by bad actors to create lethal viruses one of the greatest existential threats posed in the short term. "It just requires one crazy guy with a grudge...you can now create new viruses relatively cheaply using AI. And you don't need to be a very skilled molecular biologist to do it."[115]
Hinton was previously optimistic about the economic effects of AI, noting in 2018 that: "The phrase 'artificial general intelligence' carries with it the implication that this sort of single robot is suddenly going to be smarter than you. I don't think it's going to be that. I think more and more of the routine things we do are going to be replaced by AI systems."[116] Hinton had also argued that AGI would not make humans redundant: "[AI in the future is] going to know a lot about what you're probably going to want to do... But it's not going to replace you."[116]
In 2023, however, Hinton became "worried that AI technologies will in time upend the job market" andtake away more than just "drudge work".[30] He said in 2024 that theBritish government would have to establish auniversal basic income to deal with the impact of AI on inequality.[117] In Hinton's view, AI will boost productivity and generate more wealth. But unless the government intervenes, it will only make the rich richer and hurt the people who might lose their jobs. "That's going to be very bad for society," he said.[118]
At Christmas 2024, he had become somewhat more pessimistic, saying there was a "10 to 20 per cent chance" that AI would cause human extinction within the next three decades (he had previously suggested a 10% chance, without a timescale).[119] He expressed surprise at the speed with which AI was advancing, and said that most experts expected AI to advance, probably in the next 20 years, to be "smarter than people ... a scary thought. ... So just leaving it to the profit motive of large companies is not going to be sufficient to make sure they develop it safely. The only thing that can force those big companies to do more research on safety is government regulation."[119] Another "godfather of AI",Yann LeCun, disagreed, saying AI "could actually save humanity from extinction".[119]
Hinton is asocialist.[120] He moved from the US to Canada in part due to disillusionment withRonald Reagan–era politics and disapproval of military funding of artificial intelligence.[39]
In August 2024, Hinton co-authored a letter withYoshua Bengio,Stuart Russell, andLawrence Lessig in support ofSB 1047, a California AI safety bill that would require companies training models which cost more than US$100 million to perform risk assessments before deployment. They said the legislation was the "bare minimum for effective regulation of this technology."[121][122]
Hinton is the great-great-grandson of the mathematician and educatorMary Everest Boole and her husband, the logicianGeorge Boole.[124] George Boole's work eventually became one of the foundations of modern computer science. Another great-great-grandfather of his was the surgeon and authorJames Hinton,[125] who was the father of the mathematicianCharles Howard Hinton.
^abZemel, Richard Stanley (1994).A minimum description length framework for unsupervised learning (PhD thesis). University of Toronto.OCLC222081343.ProQuest304161918.
^abFrey, Brendan John (1998).Bayesian networks for pattern classification, data compression, and channel coding (PhD thesis). University of Toronto.OCLC46557340.ProQuest304396112.
^abNeal, Radford (1995).Bayesian learning for neural networks (PhD thesis). University of Toronto.OCLC46499792.ProQuest304260778.
^abKrizhevsky, Alex;Sutskever, Ilya; Hinton, Geoffrey E. (3 December 2012)."ImageNet classification with deep convolutional neural networks". In F. Pereira; C. J. C. Burges; L. Bottou; K. Q. Weinberger (eds.).NIPS'12: Proceedings of the 25th International Conference on Neural Information Processing Systems. Vol. 1. Curran Associates. pp. 1097–1105.Archived from the original on 20 December 2019. Retrieved13 March 2018.
^Hinton, Geoffrey E. (6 January 2020)."Curriculum Vitae"(PDF).University of Toronto: Department of Computer Science.Archived(PDF) from the original on 23 July 2020. Retrieved30 November 2016.
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