Geoffrey Everest Hinton (born 6 December 1947) is a British-Canadian computer scientist, cognitive scientist, and cognitive psychologist. He is widely recognized as a leading figure in the field of artificial intelligence, particularly for his foundational work on artificial neural networks, which earned him the title "the Godfather of AI". He is University Professor Emeritus at the University of Toronto. From 2013 to 2023, he divided his time between working at Google Brain and the university before publicly announcing his departure from Google in May 2023, citing concerns about the risks of AI technology. In 2017, he co-founded and became the chief scientific advisor of the Vector Institute in Toronto.
Hinton's contributions to AI are extensive and influential. With David Rumelhart and Ronald J. Williams, he co-authored a highly cited paper published in 1986 that popularised the Backpropagation algorithm for training multi-layer neural networks, although they were not the first to propose the approach. The image-recognition neural network AlexNet, designed in collaboration with his students Alex Krizhevsky and Ilya Sutskever, won the ImageNet challenge in 2012 and was a breakthrough in computer vision. He received the 2018 Turing Award, together with Yoshua Bengio and Yann LeCun, for their work on deep learning, and was awarded, along with John Hopfield, the 2024 Nobel Prize in Physics for "foundational discoveries and inventions that enable machine learning with artificial neural networks".
Education
Hinton was born on 6 December 1947 in Wimbledon in the United Kingdom and was educated at Clifton College in Bristol. In 1967, he matriculated as an undergraduate student at King's College, Cambridge. After switching between different fields such as natural sciences, history of art, and philosophy, he 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.
Career
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. He was the founding director of the Gatsby Charitable Foundation Computational Neuroscience Unit at University College London. 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.
Upon arrival in Canada, 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. 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". Hinton would go on to lead NCAP for ten years. 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. 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".
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.
Notable former PhD students and postdoctoral researchers from his group include Peter Dayan, Sam Roweis, Max Welling, Richard Zemel, Brendan Frey, Radford M. Neal, Yee Whye Teh, Ruslan Salakhutdinov, Ilya Sutskever, Yann LeCun, Alex Graves, Zoubin Ghahramani, and Peter Fitzhugh Brown.
Research
Hinton's research concerns the use of neural networks for machine learning, memory, perception, and symbol processing. He has written or co-written more than 200 peer-reviewed publications.
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. Their findings were published in a two-volume set. 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 programming knowledge and rules into AI systems.
In 1985, Hinton co-invented Boltzmann machines with David Ackley and Terry Sejnowski. His other contributions to neural network research include distributed representations, time delay neural network, mixtures of experts, Helmholtz machines and product of experts. An accessible introduction to Geoffrey Hinton's research can be found in his articles in Scientific American in September 1992 and October 1993. 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. In 2007, Hinton coauthored an unsupervised learning paper titled "Unsupervised learning of image transformations". In 2008, he developed the visualization method t-SNE with Laurens van der Maaten.
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. Their experiments showed that such networks can learn useful internal representations. This work, published in 1986, was instrumental in reviving interest in neural networks and laid the foundation for modern deep learning.
AlexNet and the Deep Learning Breakthrough
In 2012, Hinton and his students Alex Krizhevsky and Ilya Sutskever designed AlexNet, a deep convolutional neural network that achieved a dramatic improvement in image classification accuracy in the ImageNet Large Scale Visual Recognition Challenge. AlexNet's success demonstrated the power of deep learning on large-scale data and GPU-accelerated training, sparking the rapid adoption of deep learning across computer vision and other fields. This breakthrough is often considered a turning point in the history of AI, leading to the widespread use of deep neural networks in industry and academia.
Awards and Honors
Hinton has received numerous awards and honors throughout his career. In 2018, he was awarded the ACM A.M. Turing Award, often referred to as the "Nobel Prize of Computing", jointly with Yoshua Bengio and Yann LeCun for their contributions to deep learning. In 2024, he was awarded the Nobel Prize in Physics along with John Hopfield for foundational discoveries and inventions that enable machine learning with artificial neural networks. He is also a Fellow of the Royal Society and a Companion of the Order of Canada, among other distinctions.
Views on AI Risks
In May 2023, Hinton announced his resignation from Google to be able to "freely speak out about the risks of AI". He has voiced concerns about deliberate misuse by malicious actors, technological unemployment, and existential risk from artificial general intelligence. He noted that establishing safety guidelines will require cooperation among those competing in use of AI in order to avoid the worst outcomes. After receiving the Nobel Prize, he called for urgent research into AI safety to figure out how to control AI systems smarter than humans.
Legacy
Geoffrey Hinton's work has had a profound impact on the field of artificial intelligence. His pioneering research on neural networks and deep learning has enabled breakthroughs in image recognition, speech recognition, natural language processing, and many other applications. He is widely regarded as one of the most influential computer scientists of his generation, and his students and collaborators have gone on to lead major AI research efforts around the world. His legacy is not only in the algorithms and models he developed but also in the generation of researchers he trained and inspired.