Geoffrey Hinton

Geoffrey Hinton (born in Wimbledon, England, in 1947) is a British-Canadian computer scientist and cognitive psychologist. Often called the "Godfather of AI", he built much of his career on artificial neural networks, the approach that now underpins many of the machine learning systems used for speech and image recognition.

He studied at King's College, Cambridge, where he moved between natural sciences, the history of art and philosophy before graduating in experimental psychology in 1970. He then spent a year working as a carpenter before returning to academic life. His doctorate in artificial intelligence, completed at the University of Edinburgh under Christopher Longuet-Higgins, came at a time when the symbolic approach dominated the field, and Hinton would later take the rival path of neural networks.

After his doctorate he worked at the University of Sussex. Facing difficulties in securing funding in Britain, he moved to the United States, first to the University of California, San Diego, and then to Carnegie Mellon University, where in the 1980s he belonged to a group working on parallel distributed processing. In 1986, together with David Rumelhart and Ronald Williams, he published a widely cited paper that popularised backpropagation for training multi-layer networks, though the method had been proposed before. In 1985 he co-invented Boltzmann machines with David Ackley and Terry Sejnowski, and his work on distributed representations, mixtures of experts and Helmholtz machines added further strands to his research.

From 1987 he has been linked to the University of Toronto, with a spell at University College London between 1998 and 2001, where he founded the Gatsby Computational Neuroscience Unit. In 2012 he co-founded DNNresearch with two of his students, Alex Krizhevsky and Ilya Sutskever. Google acquired the company in 2013, and Hinton joined the firm. That same year, a network designed with his students won the ImageNet challenge, a turning point for computer vision.

Hinton shared the 2018 Turing Award with Yoshua Bengio and Yann LeCun, and in 2024 he received the Nobel Prize in Physics, together with John Hopfield, for foundational work that enables machine learning with neural networks. In May 2023 he left Google so that he could speak freely about the dangers of AI, and he has said that he partly regrets the outcome of his life's work. He warns about deliberate misuse, technological unemployment and the existential risk posed by artificial general intelligence, and he has called for urgent research into how to control systems smarter than humans.

His influence is twofold. His ideas made deep networks a central part of modern computing, and his voice in public debate about AI safety now carries nearly as much weight as his technical legacy.