Person

Geoffrey Hinton

Geoffrey Hinton is a computer scientist born in London whose research on neural networks, including back-propagation and deep convolutional networks, is central to deep learning. He shared the 2018 ACM A.M. Turing Award and the 2024 Nobel Prize in Physics.

Born 1947

AffiliationsUniversity of TorontoCarnegie Mellon UniversityUniversity College LondonVector InstituteAll 6

Events per year

10Events 6Organisations 11Works

Overview

Geoffrey E. Hinton was born on 6 December 1947 in London, United Kingdom. At the time of his Nobel Prize he was affiliated with the University of Toronto in Canada.^[1] His work centres on neural networks that learn their own internal representations of data, a theme that runs through much of what follows.

Education and early research

He earned a BA in experimental psychology from King's College, Cambridge in 1970, and a PhD in artificial intelligence from the University of Edinburgh in 1978, supervised by Christopher Longuet-Higgins.^[6] The mix of psychology and artificial intelligence in his training appears throughout his later research, which studies learning in networks loosely modelled on the brain.

Main contributions

With Terrence Sejnowski, Hinton invented the Boltzmann machine between 1983 and 1985. It is a neural network built with tools from statistical physics, and it can learn the characteristic features of a set of data. The Nobel committee cites this work.^[2] The units of a Boltzmann machine switch on and off with probabilities that depend on the units they connect to, and training adjusts those connections so that the network's activity comes to match patterns in the data.

In 1986 Hinton co-authored, with David Rumelhart and Ronald Williams, the paper on back-propagation published in Nature. It showed that neural networks can learn their own internal representations of data.^[3]

Career

Hinton worked at the University of Sussex, UC San Diego and Carnegie Mellon University before joining the University of Toronto as a professor of computer science in 1987.^[6] From 1998 to 2001 he was founding director of the Gatsby Computational Neuroscience Unit at University College London. In 2006 he was named a University Professor at Toronto, and he is now University Professor Emeritus.^[6]

In 2012 Hinton, with his students Alex Krizhevsky and Ilya Sutskever, built AlexNet, a convolutional network that used rectified linear units and dropout. It almost halved the error rate in the ImageNet competition, a large contest in which programs recognise objects in photographs.^[3]

Hinton co-founded the company DNNresearch with Krizhevsky and Sutskever in 2012. Google acquired it in March 2013, and Hinton worked at Google, later as a vice president and engineering fellow, until he publicly announced his resignation in May 2023. The University of Toronto says he left Google to focus on warning about the risks of rapid, unregulated AI development.^[4]

Influence and recognition

Hinton was founding director of CIFAR's Neural Computation and Adaptive Perception program, later Learning in Machines and Brains. He co-founded the Vector Institute in Toronto in 2017 and is its chief scientific adviser.^[6]

In 2018 Hinton shared the ACM A.M. Turing Award with Yoshua Bengio and Yann LeCun. The award recognised conceptual and engineering breakthroughs that made deep neural networks a critical component of computing, and it was announced on 27 March 2019.^[3] The Turing Award is often described as the top prize in computing.

In 2024 he shared the Nobel Prize in Physics with John J. Hopfield "for foundational discoveries and inventions that enable machine learning with artificial neural networks". The prize was announced on 8 October 2024.^[1]^[2] Other honours include Fellow of the Royal Society (1998), Companion of the Order of Canada (2018), the 2022 Princess of Asturias Award and the 2025 Queen Elizabeth Prize for Engineering.^[5]^[6]

After leaving Google, Hinton has spoken publicly about the risks of AI, which the University of Toronto cites as the reason for his departure.^[4] His career thus includes both the research that made neural networks practical and the public debate about where they lead.

CapabilitiesComputer vision

TechniquesDeep learning

Governance & standardsSafety & governance

HistoryResearch history

Relationships

Contributed to

Backpropagation and gradient descent

Career timeline

1960
- University of California, San Diego Worked at
  A public research university in La Jolla, San Diego, founded in 1960, where connectionist neural-network research flourished in the 1980s and where data science and computing were later organised into dedicated institutes and a school.
1982
- Carnegie Mellon University Worked at 1982–1987
  Carnegie Mellon University in Pittsburgh has one of the earliest US computer science departments. Its researchers built the Logic Theorist, Sphinx speech recognition and the Deep Blue lineage, and won the 2007 DARPA Urban Challenge.
1985
- A Learning Algorithm for Boltzmann Machines Contributed to
  A 1985 Cognitive Science paper by Ackley, Hinton and Sejnowski that presents a general learning rule for connectionist networks and reports simple examples of efficient internal representations.
1986
- Backpropagation and gradient descent Contributed to
  Backpropagation is the procedure that lets a multilayer neural network learn by sending output errors backward to adjust its weights, using gradient descent. A 1986 Nature paper made it widely known, and it is standard in most neural networks today.
- Learning representations by back-propagating errors Contributed to
  In a 1986 Nature paper, Rumelhart, Hinton and Williams showed that backpropagation lets hidden units in a multilayer network learn useful internal features of a task. The method is standard in most neural networks today.
1987
- University of Toronto Worked at 1987–
  A public research university in Toronto where Geoffrey Hinton has been affiliated since 1987 and where AlexNet, the 2012 deep convolutional network, was developed.
1998
- University College London Worked at 1998–2001
  University College London (UCL) is a public research university in central London. Geoffrey Hinton led its Gatsby Computational Neuroscience Unit, and Demis Hassabis, a Gatsby postdoctoral researcher, later co-founded DeepMind.
2006
- A fast learning algorithm for deep belief nets Contributed to
  A 2006 paper by Hinton, Osindero and Teh that uses complementary priors and a greedy layer-by-layer algorithm to train deep belief nets, then fine-tunes them; the resulting three-hidden-layer model both modelled and classified handwritten digits well.
2009
- Learning Multiple Layers of Features from Tiny Images (CIFAR-10/100) Contributed to
  CIFAR-10 and CIFAR-100 are labeled image datasets drawn from the 80 million tiny images collection, documented in a 2009 technical report by Alex Krizhevsky, Vinod Nair and Geoffrey Hinton. CIFAR-10 holds 60,000 32×32 colour images in 10 classes.
2010
- Phone recognition with the mean-covariance restricted Boltzmann Machine Contributed to
  A 2010 NIPS paper by Dahl, Ranzato, Mohamed and Hinton applies Deep Belief Nets and a mean-covariance RBM to acoustic modeling, reaching 20.5% phone error on speaker-independent TIMIT phone recognition.
2012
- AlexNet Contributed to
  AlexNet is the eight-layer convolutional network that won the ILSVRC2012 image classification task by a wide margin, showing that a large network trained on a large labelled dataset with GPUs could beat hand-designed features.
- ImageNet Classification with Deep Convolutional Neural Networks Contributed to
  The 2012 NeurIPS paper that introduced AlexNet, a large convolutional network trained on GPUs. It won the ILSVRC-2012 image classification competition by a wide margin and is widely seen as a turning point for deep learning in computer vision.
- DNNresearch Inc. Founded
  DNNresearch Inc. was a University of Toronto start-up founded in 2012 by Geoffrey Hinton and two of his graduate students; Google bought it for its deep neural network research.
- Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups Contributed to
  A 2012 article in IEEE Signal Processing Magazine in which four research groups argued that deep neural networks, replacing Gaussian mixture models, gave better acoustic models for speech recognition.
2013
- Speech Recognition with Deep Recurrent Neural Networks Contributed to
  A 2013 paper by Graves, Mohamed and Hinton that examines deep recurrent neural networks for speech recognition, arguing for end-to-end sequence training with methods such as connectionist temporal classification.
2014
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting Contributed to
  A 2014 paper by Srivastava, Hinton, Krizhevsky, Sutskever and Salakhutdinov introducing dropout, a training trick that randomly removes units so large neural networks overfit less.
2015
- Deep learning Contributed to
  A 2015 Nature review by Yann LeCun, Yoshua Bengio and Geoffrey Hinton explaining deep learning: models of multiple processing layers that learn layered representations of data, and the results this produced in speech, vision and other fields.
2017
- Vector Institute Worked、Founded 2017–
  An independent, non-profit AI research institute in Toronto, launched in 2017 with provincial and corporate funding, with Geoffrey Hinton among its founding scientists.

Worked at5

University of Toronto1987–Carnegie Mellon University1982–1987 University College London1998–2001 Vector Institute2017–University of California, San Diego1960

Founded2

DNNresearch Inc.2012 Vector Institute2017–

20242

Oct 8, 2024
MilestoneHopfield and Hinton win the Nobel Prize in Physics for neural-network foundations
On 8 October 2024 the Nobel Prize in Physics went to John J. Hopfield and Geoffrey Hinton for foundational work on artificial neural networks, the systems behind much of modern machine learning.
Sep 29, 2024
PolicyCalifornia Governor vetoes SB 1047
California Governor Gavin Newsom vetoed Senate Bill 1047 on 29 September 2024. The bill would have required safety safeguards for large AI models and created a Board of Frontier Models.

20231

May 30, 2023
PolicyAI scientists sign a one-sentence statement on extinction risk
On 30 May 2023 the Center for AI Safety published a one-sentence statement arguing that mitigating the risk of extinction from AI should be a global priority, and leading researchers and lab executives signed it.

20221

Jun 15, 2022
MilestoneHinton, LeCun, Bengio and Hassabis win the 2022 Princess of Asturias Award
In June 2022, the Princess of Asturias Award for Technical and Scientific Research was awarded jointly to Geoffrey Hinton, Yann LeCun, Yoshua Bengio and Demis Hassabis for advancing artificial intelligence and deep learning.

20191

Mar 27, 2019
MilestoneBengio, Hinton and LeCun receive the 2018 ACM Turing Award
In March 2019 ACM named Yoshua Bengio, Geoffrey Hinton and Yann LeCun the recipients of the 2018 A.M. Turing Award, citing breakthroughs that made deep neural networks a critical component of computing.

20131

Mar 12, 2013
AcquisitionGoogle acquires DNNresearch
In March 2013 the University of Toronto announced that Google had acquired DNNresearch, a start-up incorporated in 2012 by Geoffrey Hinton and two of his graduate students for work on deep neural networks.

20122

Oct 13, 2012
BenchmarkAlexNet wins the ImageNet ILSVRC 2012 challenge
In October 2012 AlexNet, a deep neural network built by a University of Toronto team, won the ImageNet image-classification challenge by a wide margin. Its result showed that large neural networks trained on GPUs could outperform hand-designed vision methods.
Aug 2012
BenchmarkMerck Molecular Activity Challenge on Kaggle
A Kaggle competition run in August–October 2012 in which competitors predicted molecules' biological activities from numerical descriptors of their chemical structures; a deep-learning team led by Geoffrey Hinton took first place.

20061

2006
MilestoneThe term 'deep learning' gains currency after 2006
Geoffrey Hinton dated the current popularity of "deep learning" to a 2006 paper on deep belief nets, but the same words had earlier uses, including Rina Dechter's work in 1986.

20041

2004
MilestoneCIFAR launches the Neural Computation & Adaptive Perception program
A 2004 CIFAR research program proposed by Geoffrey Hinton with Yoshua Bengio and David Fleet, which CIFAR describes as the starting point of the deep learning era and the predecessor of today's Learning in Machines & Brains program.

Contributed to11

Notes and sources

Geoffrey Hinton – Facts – 2024https://www.nobelprize.org/prizes/physics/2024/hinton/facts/
Press release: The Nobel Prize in Physics 2024https://www.nobelprize.org/prizes/physics/2024/press-release/
Fathers of the Deep Learning Revolution Receive ACM A.M. Turing Awardhttps://web.archive.org/web/2024/https://awards.acm.org/about/2018-turing
Geoffrey Hinton wins Nobel Prize in Physicshttps://www.utoronto.ca/news/geoffrey-hinton-wins-nobel-prize
2025 Queen Elizabeth Prize for Engineering: Modern Machine Learninghttps://qeprize.org/winners/modern-machine-learning
Geoffrey Hintonhttps://en.wikipedia.org/wiki/Geoffrey_Hinton

Cite this page: Geoffrey Hinton. TellWorld, https://www.tellworld.net/people/geoffrey-hinton. Updated 2026-11-07