Geoffrey E. Hinton
Computer scientist
University of Toronto
Canada
Geoffrey E. Hinton is a researcher affiliated with University of Toronto. This profile connects verified scholarly identity information and publication records from OpenAlex and DOI sources. Research areas include Neural Networks and Applications, Generative Adversarial Networks and Image Synthesis, Music and Audio Processing, Domain Adaptation and Few-Shot Learning, Topic Modeling.
Read biography ↓Biography, research & contributions
Research profile
Geoffrey Hinton is a computer scientist associated with the development of neural-network learning. His work addresses how systems can learn useful internal representations from data. He shared the 2018 ACM A. M. Turing Award with Yoshua Bengio and Yann LeCun for advances that established deep neural networks as an important part of computing.
Source: ACM: the 2018 Turing Award ↗
Boltzmann machines and recognition
Hinton used ideas from statistical physics to develop the Boltzmann machine, which learns patterns in data. The 2024 Nobel account highlights this work as part of the foundations of machine learning with artificial neural networks. The connection to physics concerns the mathematical ideas used to describe and train these systems; the network is a computational model, not a literal reconstruction of a brain.
Source: Nobel Prize: Hinton’s research ↗
A landmark paper: learning representations
With David Rumelhart and Ronald Williams, Hinton published Learning Representations by Back-Propagating Errors in 1986. The paper explains how adjustments to connection weights reduce output error and allow hidden units to learn useful features. It gives readers a concrete example of representation learning, a theme running through much of his research.
Source: Rumelhart, Hinton & Williams, Nature 323, 533–536 (1986) ↗
Recognition and further reading
Hinton shared the 2024 Nobel Prize in Physics with John Hopfield. Read the Nobel background for the statistical-physics connection, then the 1986 paper for a direct account of a learning algorithm. These are complementary contributions; the awards should not be read as a claim that one person invented the entire field of artificial intelligence.
At a glance
- Full name
- Geoffrey E. Hinton
- Fields
- Artificial Intelligence, Computer Science
- OpenAlex ID
- A5108093963
Research interests
- Representation learning
- Artificial neural networks
- Boltzmann machines
- Deep learning
Research topics
Education
Not yet documented in this profile.
Selected research & further reading
A curated reading list, not a ranking by citation count. References use DOI metadata, matching public scholarly records or authoritative lecture sources.
- Deep learning ↗2015 · NatureDOI: 10.1038/nature14539
- Learning representations by back-propagating errors ↗1986 · NatureDOI: 10.1038/323533a0
Citation & publication trends
Annual counts are not yet available from a verified author record. Explore the research guide above for the work itself; publication and citation totals depend on database coverage.
Most-cited linked publications
- Managing extreme AI risks amid rapid progress2024-05-20 · Science302record citations
- International AI Safety Report2025-01-29 · arXiv (Cornell University)15record citations
- International AI Safety Report 2025: First Key Update: Capabilities and Risk Implications2025-10-15 · arXiv (Cornell University)3record citations
- International Al Safety Report: First Key Update Capabilities and Risk Implications2025-10-22 · SuperIntelligence - Robotics - Safety & Alignment1record citations
- International AI Safety Report 2025: Second Key Update: Technical Safeguards and Risk Management2025-12-07 · SuperIntelligence - Robotics - Safety & Alignment1record citations
Recent linked publications
- International AI Safety Report 2025: Second Key Update: Technical Safeguards and Risk Management2025-12-07 · SuperIntelligence - Robotics - Safety & Alignment
- International AI Safety Report 2025: Second Key Update: Technical Safeguards and Risk Management2025-11-25 · arXiv (Cornell University)
- International Al Safety Report: First Key Update Capabilities and Risk Implications2025-10-22 · SuperIntelligence - Robotics - Safety & Alignment
- International AI Safety Report 2025: First Key Update: Capabilities and Risk Implications2025-10-15 · arXiv (Cornell University)
- International AI Safety Report2025-01-29 · arXiv (Cornell University)
Journals published in
Related publishers
Institutions
Current verified institution
No verified record links added yet.
Previous institutions
No verified record links added yet.
Awards & honors
- 2018 — ACM A. M. Turing Award, shared with Yoshua Bengio and Yann LeCun
- 2024 — Nobel Prize in Physics, shared with John Hopfield
Career timeline
- 2018 — ACM A. M. Turing Award, shared with Yoshua Bengio and Yann LeCun
- 2024 — Nobel Prize in Physics, shared with John Hopfield
Co-authors
Education & career institution links
No verified record links added yet.
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Sign in to claim this profileSources & data information
Editorial review: 2026-10-03 · Last data update: 2026-10-03
- https://openalex.org/A5108093963
- https://awards.acm.org/binaries/content/assets/press-releases/2019/march/turing-award-2018.pdf
- https://www.nobelprize.org/prizes/physics/2024/hinton/facts/
- https://www.nature.com/articles/323533a0
- https://www.nobelprize.org/prizes/physics/2024/popular-information/2/
Unknown values are left blank. Linked publications may be a subset of total works. Identity verification, data retrieval and profile ownership are separate checks.
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