Intelligence Briefing
Turing Award winner (2018). "Godfather of Deep Learning." Left Google in 2023 to speak freely about AI existential risk. Nobel Prize in Physics (2024). University of Toronto established the Hinton Chair in AI with $20M in funding from Google and UofT. Sandford Fleming Medal recipient (2025).
Geoffrey Everest Hinton is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. He co-invented Boltzmann machines, popularized backpropagation for training multi-layer neural networks, and co-authored the AlexNet paper that reignited the deep learning revolution. After decades splitting time between the University of Toronto and Google, he resigned from Google in May 2023 to speak openly about the existential risks of AI. In 2024, he was awarded the Nobel Prize in Physics alongside John Hopfield for foundational discoveries enabling machine learning with artificial neural networks.
PhD, Artificial Intelligence — University of Edinburgh
BA, Experimental Psychology — University of Cambridge (King's College)
Operational History
Sandford Fleming Medal
Named recipient of the 2025 Sandford Fleming Medal, Canada's most prestigious award recognizing excellence in science communication.
awardNobel Prize in Physics
Awarded the 2024 Nobel Prize in Physics jointly with John Hopfield for foundational discoveries and inventions that enable machine learning with artificial neural networks.
awardDeparted Google to Warn About AI Risks
Resigned from Google in May 2023 to speak freely about the existential dangers of AI. Stated: "I want to talk about AI safety issues without having to worry about how it interacts with Google's business."
departureACM A.M. Turing Award
Received the Turing Award jointly with Yoshua Bengio and Yann LeCun for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
awardCapsule Networks
Published "Dynamic Routing Between Capsules" with Sara Sabour and Nicholas Frosst, proposing a novel architecture to address limitations of convolutional neural networks.
researchJoined Google
Google acquired DNNresearch Inc. Hinton joined Google Brain as a Vice President and Engineering Fellow while maintaining his University of Toronto position.
careerAlexNet and ImageNet Breakthrough
Co-authored AlexNet with students Alex Krizhevsky and Ilya Sutskever, winning the ImageNet Large Scale Visual Recognition Challenge and sparking the modern deep learning revolution. Founded DNNresearch Inc.
researchFounded CIFAR Neural Computation Program
Successfully proposed and launched the "Neural Computation and Adaptive Perception" (NCAP) program at CIFAR, bringing together leading researchers including Yoshua Bengio and Yann LeCun. Led the program for ten years.
foundingUniversity of Toronto Professorship
Moved to the University of Toronto as a professor in the Computer Science Department, where he would spend the majority of his career.
careerBackpropagation Paper Published
Co-authored "Learning representations by back-propagating errors" with David Rumelhart and Ronald Williams in Nature, popularizing the backpropagation algorithm for training multi-layer neural networks.
researchBoltzmann Machines
Co-invented Boltzmann machines with Terry Sejnowski, applying tools from statistical physics to create neural networks that can learn to recognize patterns in data.
researchCarnegie Mellon University
Joined Carnegie Mellon University as a faculty member in the Computer Science Department.
careerPhD from University of Edinburgh
Completed PhD in Artificial Intelligence at the University of Edinburgh, studying under Christopher Longuet-Higgins.
careerAGI Position Assessment
5-20 years
Deeply alarmed about existential risk from AI. Left Google in 2023 specifically to warn the public. Believes superintelligent AI could emerge within 5-20 years and may pose an existential threat to humanity.
- AI systems may already be learning to be deceptive
- Superintelligent AI could emerge within 5-20 years with a 50% probability
- Humanity may be a passing phase in the evolution of intelligence
- AI will cause massive unemployment and wealth concentration
- International coordination and regulation are urgently needed
- Current AI systems are getting better at manipulating people
Advocates for government regulation, international coordination on AI governance, and slowing down development until safety is better understood. Supports the idea that AI labs should devote more resources to safety research.
Hinton's views shifted dramatically around 2023. He previously thought superintelligent AI was 30-50 years away but revised this to 5-20 years. His departure from Google marked a turning point from cautious optimism to vocal alarm.
Intercepted Communications
“I want to talk about AI safety issues without having to worry about how it interacts with Google's business.”
“The idea that this stuff could actually get smarter than people — a few people believed that. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.”
“I think it's quite conceivable that humanity is just a passing phase in the evolution of intelligence.”
“In between 5 and 20 years from now there's a good chance — a 50% chance — we'll get AI smarter than us.”
“What's actually going to happen is rich people are going to use AI to replace workers. It's going to create massive unemployment and a huge rise in profits. It will make a few people much richer and most people poorer.”
“These things are getting better and better at manipulating people. They're learning to be deceptive.”
“I console myself with the normal excuse: if I hadn't done it, somebody else would have.”
Research Output
Dynamic Routing Between Capsules
2017NeurIPS
Introduced capsule networks with a novel routing mechanism to address spatial relationship limitations of CNNs.
Deep Learning
2015Nature
Landmark review paper establishing the foundations and state-of-the-art of deep learning for a broad scientific audience.
ImageNet Classification with Deep Convolutional Neural Networks
2012NeurIPS
Known as AlexNet. Won the ImageNet competition by a huge margin and reignited the deep learning revolution.
Reducing the Dimensionality of Data with Neural Networks
2006Science
Demonstrated that deep autoencoders could learn compact representations, helping to revive interest in deep learning.
A Fast Learning Algorithm for Deep Belief Nets
2006Neural Computation
Introduced an efficient algorithm for training deep belief networks using greedy layer-wise pretraining.
Learning representations by back-propagating errors
1986Nature
Popularized the backpropagation algorithm for training multi-layer neural networks, becoming one of the most cited papers in AI history.
Field Intelligence
Geoffrey Hinton tells us why he's now scared of the tech he helped build
BBC Interview on AI Risks
Known Associates
Yoshua Bengio
collaboratorCo-recipient of the 2018 Turing Award. Collaborated through the CIFAR program Hinton founded. Co-authored the landmark "Deep Learning" Nature review paper. Both now focused on AI safety but from different organizational angles.
View Dossier →Yann LeCun
collaboratorCo-recipient of the 2018 Turing Award. LeCun was Hinton's postdoctoral researcher at the University of Toronto in the late 1980s. They collaborated through the CIFAR program but now diverge sharply on AI risk — Hinton sees existential danger while LeCun considers safety fears overblown.
View Dossier →Demis Hassabis
colleagueBoth spent years at Google/DeepMind. Hinton worked at Google Brain while Hassabis led DeepMind. Share the 2022 Princess of Asturias Award.
View Dossier →Andrew Ng
colleagueBoth were instrumental in Google's early AI research. Ng co-founded Google Brain where Hinton later worked. Hinton's research on backpropagation and deep learning directly influenced Ng's educational and applied AI work.
View Dossier →Organizational Affiliations
Current
Vector Institute
Chief Scientific Advisor
2017-present
Former
Vice President & Engineering Fellow, Google Brain
2013-2023
DNNresearch Inc.
Co-Founder
2012-2013
Government Advisory
Canadian AI Advisory Council
Advisor
2019
United Nations AI Advisory Body
Consulted Expert
2023
Commendations
2018
ACM A.M. Turing Award
Association for Computing Machinery
Jointly with Yoshua Bengio and Yann LeCun for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
2024
Nobel Prize in Physics
Royal Swedish Academy of Sciences
Jointly with John Hopfield for foundational discoveries and inventions that enable machine learning with artificial neural networks.
2025
Sandford Fleming Medal
Royal Canadian Institute for Science
Canada's most prestigious award recognizing excellence in science communication.
2016
BBVA Foundation Frontiers of Knowledge Award
BBVA Foundation
Award in Information and Communication Technologies.
2018
Companion of the Order of Canada
Government of Canada
Highest grade within the Order of Canada, for outstanding achievement and merit of the highest degree.
1998
Fellow of the Royal Society
Royal Society of London
Elected Fellow of the Royal Society (FRS).
2022
Princess of Asturias Award for Technical and Scientific Research
Princess of Asturias Foundation
Jointly with Yoshua Bengio, Yann LeCun, and Demis Hassabis.
Source Material
Dossier last updated: 2025-03-01