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Jimmy BaAssociate Professor · University of TorontoResearcher↗
Researcher · Academic
Geoffrey Hinton
University of TorontoUniversity Professor Emeritus
🇬🇧🇨🇦 British-CanadianBorn 1947
Hinton is a pioneer of deep learning. His 1986 work popularized backpropagation—training neural networks by correcting errors. With Alex Krizhevsky and Ilya Sutskever, he developed AlexNet, the 2012 image-recognition breakthrough. He shared the 2018 Turing Award and 2024 Nobel Prize in Physics. In 2023 he left Google to speak freely about AI risks.[1]
Career & education
Current roleUniversity of Toronto2014-presentUniversity Professor Emeritus
University of Toronto2014-presentPreviously
Google2013-2023Distinguished Researcher; later VP and Engineering Fellow (half-time)University of Toronto2001-2013Professor
UCL1998-2001Founding Director, Gatsby Computational Neuroscience UnitUniversity of Toronto1987-1998Professor
Carnegie Mellon University1982-1987Assistant Professor then Associate Professor
University of Sussex1976-1978Research Fellow
Google2013-2023DNNResearch2012-2013
Co-founderUniversity of Toronto2001-2013UCL1998-2001University of Toronto1987-1998Carnegie Mellon University1982-1987University of Sussex1976-1978Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.
Citation Trend
Total citations to scholarly work, including research papers, technical reports, books and theses, as recorded by Google Scholar. Each point is a dated snapshot.
citations1,092,086As of Oct 1, '26
citations+11,192Since last snapshot, Sep 1, '26
Updates 1
Selected contributions
Learning representations by back-propagating errors
With David Rumelhart and Ronald J. WilliamsShowed how neural networks could learn useful internal patterns by passing prediction errors backward through their layers, adjusting connections to improve their answers.
A fast learning algorithm for deep belief nets
With Simon Osindero and Yee Whye TehMade training deep neural networks more practical by teaching them one layer at a time, then refining the whole network to recognize handwritten digits.
ImageNet Classification with Deep Convolutional Neural Networks
With Alex Krizhevsky and Ilya SutskeverIntroduced AlexNet, a deep neural network trained on graphics processors that sharply improved image recognition, demonstrating how learning from large datasets could outperform earlier approaches.
References
- University of Toronto: Hinton’s Google departureutoronto.ca
Leaving Google in 2023 to speak freely about AI risks.
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Reviewed October 14, 2026 · Checked for accuracy and new information.
