Geoffrey Hinton

Geoffrey Hinton is a British-Canadian computer scientist and psychologist known for his contributions to the field of artificial intelligence (AI). Hinton's best-known research involves designing machine learning (ML) algorithms to discover a procedure that is efficient at finding complex structures in large, high-dimensional datasets. Hinton was part of the team that introduced the back-propagation algorithm and was the first to use back-propagation for learning word embeddings. Other notable contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts, and deep belief nets. His research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification.

Hinton was born on December 6, 1947. Raised in England, Hinton is related to mathematician Mary Everest Boole and logician George Boole, surgeon James Hinton, and surveyor George Everest. Hinton attended Cambridge University between 1967 and 1970. He tried a range of subjects, including physiology, physics, and philosophy before graduating with a degree in experimental psychology. He briefly worked as a carpenter before starting a PhD in AI at the University of Edinburgh in 1972. At the time, this was the UK's only postgraduate program on the subject. He finished studying at Edinburgh in 1975 and was awarded his PhD in 1978. Hinton's work focused on neural networks, structures that mimicked the human brain. Neural networks were an unpopular field in AI during the 1970s, and Hinton's thesis adviser, Christopher Longuet-Higgins, regularly urged him to change his approach.

After completing his PhD, Hinton held research positions at Sussex University, the University of California, San Diego, the Medical Research Council (MRC), and Carnegie-Mellon University before taking a professorship at University of Toronto in July 1987. Apart from three years at University College London (1998–2001), Hinton worked at the University of Toronto, becoming Emeritus Professor in January 2014. In 2012, Hinton and two of his graduate students (Alex Krizhevsky and Ilya Sutskever) won ImageNet, an annual competition to build the most accurate image-recognition AI systems. They set up a shell company called DNNresearch to auction their expertise with four tech firms (Google, Microsoft, Baidu, and DeepMind) bidding for the company. Hinton chose Google and joined Google Brain, where he would work for half his time (alongside the University of Toronto) from March 2013 until May 2023. Hinton left Google Brain, citing concerns over the impact of AI.

Hinton was awarded the 2018 Turing Award by the Association of Computing Machinery (ACM) alongside Yoshua Bengio and Yann LeCun for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. He is an honorary foreign member of the American Academy of Arts and Sciences and the National Academy of Engineering and a former president of the Cognitive Science Society. He has received honorary doctorates from the University of Edinburgh, the University of Sussex, and the University of Sherbrooke. He was awarded the first David E. Rumelhart Prize (2001), the IJCAI Award for Research Excellence (2005), the Killam Prize for Engineering (2012), the IEEE James Clerk Maxwell Gold Medal (2016), and the NSERC Herzberg Gold Medal (2010), which is Canada's top award in science and engineering.