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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- Sören Mindermann Moves to Tsinghua: Why a Leading AI Safety Researcher Is Going to Beijing →
Sören Mindermann, the scientific lead of the first International AI Safety Report, says he is moving to Beijing in November to join Tsinghua University's College of AI and co-direct the new Beijing AI Safety Hub, while keeping his Cambridge role. Here is his stated reasoning, who he will work with, and what remains unproven.
- Hassabis Says AGI in Two to Three Years: What the WSJ Reports and What His Timeline History Shows →
On October 21, 2026 the Wall Street Journal reported that Google DeepMind CEO Demis Hassabis expects AGI within two to three years, shortened from his earlier three-to-five-year view. We could not read the interview itself, so this post sets out what is confirmed, how his public timeline has moved, and what the number does and does not mean.
- Metis Reasoning: AlphaGo Co-Creator Thore Graepel Launches a London Lab, and What Is Actually Verified →
AlphaGo co-creator Thore Graepel has launched Metis Reasoning, a 12-person London lab pitching AI reasoning that can be checked and needs far less compute than frontier models. Headlines say it will end hallucinations. Here is what the founders actually said, and what nobody has shown yet.
- Google DeepMind Says Self-Improving AI Is Not Close: What Is Claimed vs Verified →
On October 14, 2026 The Decoder reported that Google DeepMind researcher Haydn Belfield sees no measurable acceleration of AI research driven by AI, despite AI writing over 75% of production code. We separate what is claimed, what is confirmed, and what we could not open.