# Geoffrey Hinton

Geoffrey Hinton is a British-Canadian computer scientist known as one of the godfathers of deep learning for his work on backpropagation, neural networks and AlexNet, and for his 2023 warnings about AI risk.

Geoffrey Hinton is a British-Canadian cognitive psychologist and computer scientist widely credited as one of the "godfathers of deep learning," alongside [yann-lecun](https://www.wikiprompt.org/wiki/yann-lecun) and [yoshua-bengio](https://www.wikiprompt.org/wiki/yoshua-bengio). His decades of work on [neural-network](https://www.wikiprompt.org/wiki/neural-network) learning algorithms, much of it done while others considered the approach a dead end, laid the groundwork for the [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) revolution of the 2010s.

## Career

Born in 1947 in London, Hinton earned a PhD in artificial intelligence from the University of Edinburgh in 1978. After research positions in the United States, he settled at the University of Toronto, where he spent most of his academic career. In 1986, working with [david-rumelhart](https://www.wikiprompt.org/wiki/david-rumelhart) and Ronald Williams, he co-authored the paper that popularized [backpropagation](https://www.wikiprompt.org/wiki/backpropagation) as a practical way to train multi-layer networks, and he continued to develop core techniques in the field, including Boltzmann machines, dropout regularization and improvements to how deep networks are initialized and trained.

The turning point for the field came in 2012, when Hinton's graduate students [alex-krizhevsky](https://www.wikiprompt.org/wiki/alex-krizhevsky) and [ilya-sutskever](https://www.wikiprompt.org/wiki/ilya-sutskever) built [alexnet](https://www.wikiprompt.org/wiki/alexnet), a deep convolutional network that won the [imagenet](https://www.wikiprompt.org/wiki/imagenet) recognition competition by a wide margin. The result convinced much of the computer vision community that deep, GPU-trained networks outperformed the hand-engineered methods that had dominated until then, and is often cited as the moment deep learning went mainstream. Google acquired Hinton's small company, DNNresearch, shortly afterward, and he split his time between the University of Toronto and Google Brain, later folded into [google-deepmind](https://www.wikiprompt.org/wiki/google-deepmind).

## Departure from Google and Nobel Prize

In May 2023, Hinton resigned from Google, saying he wanted to speak freely about the risks of the technology he had helped create, including job displacement, disinformation and longer-term concerns about loss of human control over increasingly capable systems. His public shift toward [ai-safety](https://www.wikiprompt.org/wiki/ai-safety) advocacy made him one of the most prominent voices warning about [existential-risk-from-ai](https://www.wikiprompt.org/wiki/existential-risk-from-ai), a notable turn for a researcher who had spent most of his career simply trying to make neural networks work.

Hinton shared the 2018 Turing Award with LeCun and Bengio for their work on deep learning. In 2024, he was awarded the Nobel Prize in Physics jointly with [john-hopfield](https://www.wikiprompt.org/wiki/john-hopfield) "for foundational discoveries and inventions that enable machine learning with artificial neural networks," part of a broader set of AI-related Nobel recognitions that year covered in the article on the [nobel-prizes-2024-ai](https://www.wikiprompt.org/wiki/nobel-prizes-2024-ai).

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Source: https://www.wikiprompt.org/wiki/geoffrey-hinton
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-02T20:31:27.155592+00:00