University Professor Emeritus at the University of Toronto
Geoffrey Hinton
Geoffrey Hinton is a University of Toronto professor emeritus whose research on neural networks helped establish modern deep learning methods.
About Geoffrey Hinton
Geoffrey Hinton is a University Professor Emeritus of computer science at the University of Toronto. His research concerns how neural networks learn from data, with contributions to backpropagation, Boltzmann machines, distributed representations, and deep learning.
He shared the 2018 ACM A.M. Turing Award with Yoshua Bengio and Yann LeCun and the 2024 Nobel Prize in Physics with John Hopfield. After working at Google from 2013 to 2023, he has also spoken publicly about AI risks and the need for safe use of the technology.
Mentioned in our coverage
14 articles name Geoffrey Hinton, excluding author credits.
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- EU Says the AI Act Can Stop Rogue AI Agents: What Virkkunen Told Reuters →
After a string of OpenAI and Anthropic agent incidents, the EU says its AI Act is already equipped to handle rogue systems. Here is what Henna Virkkunen actually said, what the Commission has done so far, and what remains unproven.
- Epoch InnovationEval: Can AI Rediscover a Real ML Breakthrough? →
Epoch AI published InnovationEval on October 7, 2026, a test of whether an AI agent can invent a post-training method as good as a recent human one it has never seen. The best uncontaminated model recovered roughly 15 to 35 percent of the gains. Here is how the test works and what it means for the AI R&D automation debate.
- Bengio to AI Researchers: “If You Prioritize Safety, Leave Frontier AI Companies” →
In an exclusive essay for Transformer on October 8, 2026, Yoshua Bengio asks frontier AI employees to reflect on whether to stay, and says that if they truly prioritize safety it is time to leave. He points to LawZero, which Canada and Germany committed more than $200 million to this month.
- Hinton Points to CASP Intelligence Explosion Paper — What It Says →
On October 3, 2026 (early morning IST), Geoffrey Hinton amplified a Cambridge CASP working paper he co-authored arguing that recursive self-improvement could trigger an intelligence explosion quite soon. explainx.ai reads the paper itself — authors, stated timelines, frictions, and what builders should do differently — without inventing a year the authors did not give.
- Nathan Lambert Launches Trillium Labs for Open Frontier AI →