Geoffrey Everest Hinton, (English: Geoffrey Everest Hinton, December 6, 1947 – ), is a British-born Canadian computer scientist and psychologist, and a professor at the University of Toronto. He is known for his contributions to neural networks. Hinton is one of the inventors of the backpropagation algorithm and the contrastive divergence algorithm, and an active promoter of deep learning, and is known as the "Father of Deep Learning." For his contributions to deep learning, Hinton, together with Yoshua Bengioand Yann LeCunwere jointly awarded the 2018 Turing Award.
Education
Hinton received a bachelor's degree in experimental psychology from the University of Cambridge in the United Kingdom in 1970. He then received a doctorate in artificial intelligence from the University of Edinburgh in 1978. He subsequently worked at the University of Sussex, the University of California, San Diego, the University of Cambridge, Carnegie Mellon University, and University College London. He is the founder of the Gatsby Computational Neuroscience Unit and currently serves as a professor in the Department of Computer Science at the University of Toronto. Hinton is a Canada Research Chair in machine learning and the leader of the "Neural Computation and Adaptive Perception" program sponsored by the Canadian Institute for Advanced Research. Hinton joined Google in March 2013, and at the same time Google acquired DNNresearch, a company he founded. You can learn more about him on Google Research,https://research.google/people/GeoffreyHinton/
Research Achievements
Hinton's research investigated methods of using neural networks for machine learning, memory, perception, and symbol processing, and he authored or co-authored more than 200 peer-reviewed publications. At the 2022 Conference on Neural Information Processing Systems (NeurIPS), Hinton introduced a new neural network learning algorithm, which he called the "Forward-Forward" algorithm. The idea of the new algorithm is to replace the traditional forward-backward pathways of backpropagation with two forward pathways, one with positive (i.e., real) data and the other with negative data, which can be generated by the network itself.
During Hinton's tenure as a professor at Carnegie Mellon University (1982–1987), David E. Rumelhart, Hinton, and Ronald J. Williams applied the backpropagation algorithm to multilayer neural networks. Their experiments showed that such networks could learn useful internal representations of data. In a 2018 interview, Hinton said, "David E. Rumelhart came up with the basic idea of backpropagation, so it is his invention." Although this work was important for popularizing backpropagation, he was not the first to propose the method. Seppo Linnainmaa proposed reverse-mode automatic differentiation in 1970, of which backpropagation is a special case, and Paul Werbos proposed using it to train neural networks in 1974.
During the same period, Hinton, together with David Ackley and Terry Sejnowski, co-invented the Boltzmann machine. His other contributions to neural network research include distributed representations, time-delay neural networks, mixture of experts systems, and Helmholtz machines. In 2007, Hinton co-authored an unsupervised learning paper titled Unsupervised Learning of Image Transformations. An overview of Hinton's research can be found in his articles published in Scientific American in September 1992 and October 1993.
Awards and Honors
- Fellow of the American Association for Artificial Intelligence (1990)
- Fellow of the Royal Society (1998)
- Rumelhart Prize (2001)
- IJCAI Award for Research Excellence (2005)
- IEEE Frank Rosenblatt Award (2014)
- James Clerk Maxwell Medal (2016)
- BBVA Foundation Frontiers of Knowledge Award (2016)
- Turing Award (2018)
- Princess of Asturias Award (2022)