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
21 articles name Geoffrey Hinton, excluding author credits.
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- LeCun Says His 2023 'LLMs Won't Lead to Human-Level AI' Claim Still Holds. Schmidhuber Disagrees About JEPA. →
Three years after leaving Meta's chief AI scientist role partly over this disagreement, Yann LeCun reposted his own 2023 claim that autoregressive LLMs will not lead to human-level AI, and defended it point by point, citing JEPA, missing Level-4/5 self-driving cars, and Piaget. Jürgen Schmidhuber immediately disputed the JEPA originality claim underneath it, reviving a priority dispute he has pressed since 2025.
- Bengio Backs the Sanders-Casar Superintelligence Ban — Alongside Bannon →
Yoshua Bengio told AFP on September 16, 2026 that humanity is "losing control" of AI and needs guardrails "similar to nuclear arms controls." He's one name in an unusually cross-ideological coalition — Geoffrey Hinton, Steve Wozniak, and Richard Branson alongside Steve Bannon and Glenn Beck — publicly backing the Sanders-Casar bill to permanently ban superintelligent AI. Here's what Bengio actually said, versus the vaguer "recursive self-improvement should be illegal" framing circulating online.
- What Is an Intelligence Explosion? AI Term Explained →
"Intelligence explosion" describes a specific mechanism — not just fast AI progress, but a self-reinforcing loop where a system's own intelligence gains let it improve itself again, faster each round. explainx.ai traces the term from I.J. Good's 1965 essay to Paul Christiano's September 2026 warning at OpenAI, and explains what would actually have to be true for one to happen.
- What Is Recursive Self-Improvement (RSI) in AI? →
Recursive self-improvement is what happens when an AI system helps build a better version of itself, which then helps build an even better one. explainx.ai breaks down the mechanism, the 4-level ladder researchers use to measure it, and real systems — AIDE², NeoHorse-1 — already climbing it.