Geoffrey HintonFRS, FRSC
Deep Learning · Neural Networks · Cognitive Science · b. 1947 · British-Canadian
The 'Godfather of Deep Learning' whose backpropagation work made modern neural networks possible - and who left Google in 2023 to warn about AI risks.
Biography
Geoffrey Hinton spent four decades championing neural networks when the field had abandoned them, eventually proving that deep architectures trained with backpropagation could outperform every other approach to perception. His 2012 AlexNet result with Krizhevsky and Sutskever triggered the deep-learning revolution. In 2023 he resigned from Google to speak freely about existential risks from AI systems he helped invent. He shared the 2018 Turing Award with Bengio and LeCun and the 2024 Nobel Prize in Physics with Hopfield.
Affiliations
University of Toronto
Professor Emeritus · 1987–present
Google Brain
Engineering Fellow · 2013–2023
Vector Institute
Chief Scientific Advisor · 2017–present
Major contributions
Backpropagation (1986)
Popularized the algorithm that lets deep networks learn from error gradients.
Boltzmann Machines
Co-invented stochastic neural networks for unsupervised representation learning.
AlexNet (2012)
With students Krizhevsky and Sutskever, won ImageNet by a record margin and launched the deep-learning era.
Capsule Networks
Proposed an alternative to CNNs encoding hierarchical pose information.
Forward-Forward Algorithm (2022)
A biologically plausible alternative to backpropagation.
Major works
- Learning representations by back-propagating errors
Nature · 1986
A fast learning algorithm for deep belief nets
Neural Computation · 2006
ImageNet Classification with Deep Convolutional Neural Networks
NeurIPS · 2012
- Distilling the Knowledge in a Neural Network
arXiv · 2015
- The Forward-Forward Algorithm
arXiv · 2022
Awards & honors
- Turing Award · 2018
- Nobel Prize in Physics · 2024
- Order of Canada (Companion) · 2022
Intellectual lineage
Influences
- Donald Hebb
- David Rumelhart
- Frank Rosenblatt
Influenced
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Radford Neal
Timeline
1978
PhD in AI from University of Edinburgh.
1986
Co-authored the seminal backpropagation paper in Nature.
2006
Deep belief networks paper revives deep learning.
2012
AlexNet wins ImageNet, triggering the deep-learning boom.
2013
Joined Google after DNNresearch acquisition.
2018
Awarded the Turing Award with Bengio and LeCun.
2023
Resigned from Google to warn publicly about AI existential risk.
2024
Awarded the Nobel Prize in Physics for foundational neural-network work.
Notable positions
- Believes superintelligent AI is plausible within 5–20 years.
- Advocates urgent international coordination on AI safety.
- Skeptical that current LLMs 'understand' but believes they could.
Related entities
Other scientists
Yann LeCun
Architect of convolutional networks and Meta's chief AI scientist - the field's loudest skeptic of LLM-only paths to general intelligence.
Open profile
Yoshua Bengio
Deep-learning pioneer who pivoted his career toward AI safety governance after the rise of GPT-class models.
Open profile
Demis Hassabis
Neuroscientist turned chess prodigy turned CEO of Google DeepMind - driving AI from games (AlphaGo) to protein folding (AlphaFold) toward AGI.
Open profile
Ilya Sutskever
Co-inventor of AlexNet and Seq2Seq, co-founder of OpenAI, and now founder of a single-product superintelligence-safety lab.
Open profile
Fei-Fei Li
Creator of ImageNet - the dataset that catalyzed the deep-learning revolution - and a leading voice for human-centered AI.
Open profile
Dario Amodei
Physicist turned AI-safety entrepreneur leading Anthropic's mission to build steerable, interpretable frontier models.
Open profile