Geoffrey Everest Hinton (born 6 December 1947 in London, England, UK) is a British-Canadian computer scientist, cognitive scientist, cognitive psychologist, and Nobel Prize laureate known for his work on artificial neural networks, which earned him the title "the Godfather of AI". He is University Professor Emeritus at the University of Toronto, and in 2017 he co-founded and became the chief scientific advisor of the Vector Institute in Toronto.^[7] His career includes affiliations with the University of Toronto, Google, Carnegie Mellon University, University College London, and the University of California, San Diego, across the disciplines of machine learning, psychology, artificial intelligence, cognitive science, and computer science. Hinton studied at King's College, Cambridge (MA) and completed his PhD in 1977 at the University of Edinburgh under Christopher Longuet-Higgins with the thesis "Relaxation and Its Role in Vision".

Viewed as a leading figure in the deep learning community, Hinton has contributed to research areas and methods including Boltzmann machines, restricted Boltzmann machines, deep belief networks, knowledge distillation ("Dark knowledge"), capsule neural networks, mixture of experts, product of experts, time delay neural networks, t-SNE, and dropout. With David Rumelhart and Ronald J. Williams, he co-authored a highly cited 1986 paper that popularised the backpropagation algorithm for training multi-layer neural networks, although they were not the first to propose the approach.^[50,8,9] The image-recognition neural network AlexNet, designed in collaboration with his students Alex Krizhevsky and Ilya Sutskever, won the ImageNet challenge in 2012 and proved to be a breakthrough in computer vision.^[52,48] His students and academic advisees include Richard Zemel, Brendan Frey, Radford M. Neal, Yee Whye Teh, Ruslan Salakhutdinov, Ilya Sutskever, Alex Krizhevsky, and Peter Brown, while his postdoctoral associates include Yann LeCun, Peter Dayan, Max Welling, Zoubin Ghahramani, and Alex Graves.^{[158,6,7,8,9,10,12]}

Hinton received the 2018 Turing Award together with Yoshua Bengio and Yann LeCun for their work on deep learning, with the three sometimes referred to as the "Godfathers of Deep Learning".^{[53,20,24,51]} Along with John Hopfield, he was awarded the 2024 Nobel Prize in Physics for "foundational discoveries and inventions that enable machine learning with artificial neural networks".^{[54,197,22,190]} His other awards and honors include the Rumelhart Prize (2001), the Dickson Prize (2021), the Princess of Asturias Award (2022), the VinFuture Prize (2024), the Queen Elizabeth Prize for Engineering (2025), and the Sandford Fleming Medal (2025).

From 2013 to 2023, Hinton divided his time working for Google Brain and the University of Toronto before publicly announcing his departure and resignation from Google in May 2023 to be able to freely speak out about the risks of artificial intelligence technology.^[11] He has voiced concerns regarding deliberate misuse by malicious actors, technological unemployment, and existential risk from artificial general intelligence, noting that establishing safety guidelines will require cooperation among those competing in the use of AI to avoid the worst outcomes.^[161,33] After receiving the Nobel Prize, he called for urgent research into AI safety to determine how to control AI systems that become smarter than humans.^[162,33,63]

Hinton's relatives include his father H. E. Hinton, his uncle Colin Clark, his cousin Joan Hinton, his great-great-grandfather George Boole, his great-great-grandmother Mary Everest Boole, and his great-great-granduncle George Everest. His personal life includes relationships with Joanne, Rosalind Zalin, and Jacqueline Ford, to whom he was married from 1997 to 2018.

Hinton was born on 6 December 1947 in Wimbledon in the United Kingdom and was educated at Clifton College in Bristol.^[164,33] In 1967, he matriculated as an undergraduate student at King's College, Cambridge and, after switching between different fields such as natural sciences, history of art, and philosophy, eventually graduated with a Bachelor of Arts in experimental psychology in 1970. He spent a year apprenticing carpentry before returning to academic studies. From 1972 to 1975, he continued his study at the University of Edinburgh, where he was awarded a PhD in artificial intelligence in 1978 for research supervised by Christopher Longuet-Higgins, who favored the symbolic AI approach over the neural network approach.^[157,36,166]

After his PhD, Hinton initially worked at the University of Sussex and at the MRC Applied Psychology Unit. After having difficulty getting funding in Britain, he worked in the US at the University of California, San Diego, and Carnegie Mellon University.^[165,151] He was the founding director of the Gatsby Charitable Foundation Computational Neuroscience Unit at University College London.^[165] He is currently University Professor Emeritus in the Department of Computer Science at the University of Toronto, where he has been affiliated since 1987, except for his time at University College London from 1998 to 2001.^[74] Upon arrival in Canada, Geoffrey Hinton was appointed at the Canadian Institute for Advanced Research (CIFAR) in 1987 as a Fellow in CIFAR's first research program, Artificial Intelligence, Robotics & Society.^[169,163] In 2004, Hinton and collaborators successfully proposed the launch of a new program at CIFAR, "Neural Computation and Adaptive Perception" (NCAP), which today is named "Learning in Machines & Brains".^[172] Hinton would go on to lead NCAP for ten years.^[170] Among the members of the program are Yoshua Bengio and Yann LeCun, with whom Hinton would go on to win the ACM A.M. Turing Award in 2018. All three Turing winners continue to be members of the CIFAR Learning in Machines & Brains program.

Hinton taught a free online course on Neural Networks on the education platform Coursera in 2012.^[37,35] 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".^[39] 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 AI and added that part of him now regrets his life's work.^{[14,160,15,198]}

Hinton's research concerns the use of neural networks for machine learning, memory, perception, and symbol processing, and he has written or co-written more than 200 peer-reviewed publications.^[177] An accessible introduction to his research can be found in his articles in Scientific American published in September 1992 and October 1993.^[33,173] In the 1980s, Hinton was part of the "Parallel Distributed Processing" group at Carnegie Mellon University, which included notable scientists like Terrence Sejnowski, Francis Crick, David Rumelhart, and James McClelland. This group favoured the connectionist approach during the AI winter and published their findings in a two-volume set.^[179] 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 learned from data, whereas symbolists advocated for explicitly programming knowledge and rules into AI systems.

While Hinton was a postdoc at UC San Diego, David Rumelhart, Hinton, and Ronald J. Williams applied the backpropagation algorithm to multi-layer neural networks, with their experiments showing that such networks can learn useful internal representations of data.^[49,178] In a 2018 interview, Hinton stated 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: reverse-mode automatic differentiation, of which backpropagation is a special case, was proposed by Seppo Linnainmaa in 1970, and Paul Werbos proposed using it to train neural networks in 1974.^[50]

In 1985, Hinton co-invented Boltzmann machines with David Ackley and Terry Sejnowski.^[179] His other contributions to neural network research include distributed representations, time delay neural networks, mixtures of experts, Helmholtz machines, and product of experts.^[33,38] In 1995, Hinton and colleagues proposed the wake-sleep algorithm, involving a neural network with separate pathways for recognition and generation trained with alternating "wake" and "sleep" phases.^[181,174] Hinton also coauthored an unsupervised learning paper titled Unsupervised learning of image transformations in 2007, and developed the visualization method t-SNE with Laurens van der Maaten in 2008.^{[123,175,182,183]}

In 2017, Hinton co-authored two open-access research papers about capsule neural networks, extending the concept of "capsule" he introduced in 2011 to better model part-whole relationships within objects in visual data.^[81] In 2021, Hinton presented GLOM, a speculative architecture idea also aiming to improve image understanding by modeling part-whole relationships in neural networks.^[82] That same year, Hinton co-authored a widely cited paper proposing a framework for contrastive learning in computer vision, a technique that involves pulling together representations of augmented versions of the same image and pushing apart dissimilar representations.^[185] At the 2022 Conference on Neural Information Processing Systems (NeurIPS), Hinton introduced the "Forward-Forward" learning algorithm for neural networks, which replaces the traditional forward-backward passes of backpropagation with two forward passes: one with positive (real) data and the other with negative data generated solely by the network.^[186] The Forward-Forward algorithm is well-suited for what Hinton calls "mortal computation", where the knowledge learned is not transferable to other systems and thus dies with the hardware, as can be the case for certain analog computers used for machine learning.^[105,13]

Hinton has been a Fellow of the US Association for the Advancement of Artificial Intelligence (FAAAI) since 1990.^[127] He was elected a Fellow of the Royal Society of Canada (FRSC) in 1996, and then a Fellow of the Royal Society of London (FRS) in 1998.^[187,184] In 2001, he was the first winner of the Rumelhart Prize and was awarded an honorary Doctor of Science (DSc) degree from the University of Edinburgh.^[85] He was recognized as an International Honorary Member of the American Academy of Arts and Sciences and elected a Fellow of the US Cognitive Science Society in 2003.^[87,67] Hinton was the 2005 recipient of the IJCAI Award for Research Excellence lifetime-achievement award, and in 2011 he received the Herzberg Canada Gold Medal for Science and Engineering as well as an honorary DSc degree from the University of Sussex.^[16,125,17] He received the Canada Council Killam Prize in Engineering in 2012, an honorary doctorate from the Université de Sherbrooke in 2013, and was elected an Honorary Foreign Member of the Spanish Royal Academy of Engineering in 2015.^[84]

In 2016, Hinton was elected an International Member of the US National Academy of Engineering "for contributions to the theory and practice of artificial neural networks and their application to speech recognition and computer vision", received the IEEE/RSE Wolfson James Clerk Maxwell Award, and won the BBVA Foundation Frontiers of Knowledge Award in the Information and Communication Technologies category "for his pioneering and highly influential work" to endow machines with the ability to learn.^{[109,111,188,107]} Together with Yann LeCun and Yoshua Bengio, he won the 2018 Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing, and the trio have continued to give public talks together.^{[189,86,191,192]} He also became a Companion of the Order of Canada (CC) in 2018.^[142,114] Hinton received the Dickson Prize in Science from Carnegie Mellon University in 2021, followed in 2022 by an Honorary DSc degree from the University of Toronto and the Princess of Asturias Award in the Scientific Research category, shared with Yann LeCun, Yoshua Bengio, and Demis Hassabis.^[193,128]

In 2023, he was named an ACM Fellow, elected an International Member of the US National Academy of Sciences, and received the Lifeboat Foundation's 2023 Guardian Award along with Ilya Sutskever.^[196] Hinton was jointly awarded the 2024 Nobel Prize in Physics with John Hopfield "for foundational discoveries and inventions that enable machine learning with artificial neural networks", with his development of the Boltzmann machine explicitly cited.^[54,2] When New York Times reporter Cade Metz asked him to explain in simpler terms how the Boltzmann machine could "pretrain" backpropagation networks, Hinton quipped that Richard Feynman reportedly said: "Listen, buddy, if I could explain it in a couple of minutes, it wouldn't be worth the Nobel Prize." German AI researcher Jürgen Schmidhuber contended that Hinton and others in the field did not appropriately credit existing research, arguing that foundational work by Paul Werbos and Shun-Ichi Amari in the 1970s on backpropagation and neural networks was insufficiently acknowledged.^[42,116] Also in 2024, Hinton received the VinFuture Prize grand award alongside Yoshua Bengio, Yann LeCun, Jen-Hsun Huang, and Fei-Fei Li for groundbreaking contributions to neural networks and deep learning algorithms.^[119,43] In 2025, he was awarded the Queen Elizabeth Prize for Engineering jointly with Yoshua Bengio, Bill Dally, John Hopfield, Yann LeCun, Jen-Hsun Huang, and Fei-Fei Li, and was named the recipient of the Sandford Fleming Medal by the Royal Canadian Institute for Science for excellence in science communication.^[44] Hinton was also awarded the King Charles III Coronation Medal, and received an honorary Doctor of Science degree from Harvard University in 2026.^{[200,195,129]}

In 2023, Hinton expressed concerns regarding the rapid progress of AI.^[18] He had previously believed that artificial general intelligence (AGI) was "30 to 50 years or even longer away"; however, in a March 2023 interview with CBS, he stated that "general-purpose AI" might be fewer than 20 years away and could bring about changes "comparable in scale with the industrial revolution or electricity". In an interview with The New York Times published on 1 May 2023, Hinton announced his resignation from Google so he could "talk about the dangers of AI without considering how this impacts Google", noting that a part of him now regrets his life's work.^[14,160,15]

In early May 2023, Hinton stated in an interview with the BBC that AI might soon surpass the information capacity of the human brain, describing some of the risks posed by these chatbots as "quite scary". He 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. In 2025, he remarked: "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."^[45]

Hinton has expressed concerns about the possibility of an AI takeover, stating that "it's not inconceivable" that AI could "wipe out humanity". In 2023, he said that AI systems capable of intelligent agency would be useful for military or economic purposes.^[203] He worries that generally intelligent AI systems could "create sub-goals" that are unaligned with their programmers' interests. He says that AI systems may become power-seeking or prevent themselves from being shut off, not because programmers intended them to, but because those sub-goals are useful for achieving later goals.^[202] In particular, Hinton says "we have to think hard about how to control" AI systems capable of self-improvement.

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." In 2017, Hinton called for an international ban on lethal autonomous weapons.^[66] 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."

Hinton was previously optimistic about the economic effects of AI, noting in 2018: "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."^[199] He had also argued that AGI would not make humans redundant, remarking that while 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." In 2023, however, Hinton became "worried that AI technologies will in time upend the job market" and take away more than just "drudge work". He said in 2024 that the British government would have to establish a universal basic income to deal with the impact of AI on inequality.^[205] 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, warning that "That's going to be very bad for society".^[206]

In December 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, after having previously suggested a 10% chance without a timescale.^[46] He expressed surprise at the speed with which AI was advancing and noted that most experts expected AI to advance, probably in the next 20 years, to be "smarter than people ... a scary thought", adding that "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."^[201] Another "godfather of AI", Yann LeCun, disagreed, saying AI "could actually save humanity from extinction".^[207]

Hinton is a socialist. He moved from the US to Canada in part due to disillusionment with Ronald Reagan–era politics and disapproval of military funding of artificial intelligence. In August 2024, Hinton co-authored a letter with Yoshua Bengio, Stuart Russell, and Lawrence Lessig in support of SB 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".^[47]

Hinton's first wife, Rosalind Zalin, died of ovarian cancer in 1994, and his second wife, Jacqueline "Jackie" Ford, died of pancreatic cancer in 2018. Hinton injured his back at age 19, which makes sitting painful for him. He has also dealt with depression throughout his life.^[40]

Hinton's father was the entomologist Howard Hinton.^[165] Hinton is the great-great-grandson of mathematician and educator Mary Everest Boole and her husband, logician George Boole, whose work eventually became one of the foundations of modern computer science.^[65] Another great-great-grandfather was the surgeon and author James Hinton, who was the father of mathematician Charles Howard Hinton.^[204] His middle name comes from another relative, George Everest, the Surveyor General of India after whom the mountain is named. He is the nephew of the economist Colin Clark, and nuclear physicist Joan Hinton, one of the two female physicists at the Manhattan Project, was his first cousin once removed.