Geoffrey Everest Hinton|
6 December 1947
University of Toronto|
Carnegie Mellon University
University College London
|Thesis||Relaxation and its role in vision (1977)|
|Doctoral advisor||Christopher Longuet-Higgins|
|Other notable students|
Geoffrey Everest Hinton FRS FRSC (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.
Hinton was one of the first researchers who demonstrated the use of generalized backpropagation algorithm for training multi-layer neural nets. He is a leading figure in the deep learning community and is referred to by some as the "Godfather of Deep Learning". His dramatic image-recognition milestone in Imagenet challenge 2012 revolutionized the field of computer vision.
Hinton was educated at King's College, Cambridge graduating in 1970, with a Bachelor of Arts in experimental psychology. He continued his study at the University of Edinburgh where he was awarded a PhD in artificial intelligence in 1977 for research supervised by Christopher Longuet-Higgins.
After his PhD he worked at the University of Sussex, and (after difficulty finding funding in Britain) the University of California, San Diego, and Carnegie Mellon University. He was the founding director of the Gatsby Charitable Foundation Computational Neuroscience Unit at University College London, and is currently a professor in the computer science department at the University of Toronto. He holds a Canada Research Chair in Machine Learning, and is currently an advisor for the Learning in Machines & Brains program at the Canadian Institute for Advanced Research. Hinton taught a free online course on Neural Networks on the education platform Coursera in 2012. Hinton joined Google in March 2013 when his company, DNNresearch Inc., was acquired. He is planning to "divide his time between his university research and his work at Google".
Hinton's research investigates ways of using neural networks for machine learning, memory, perception and symbol processing. He has authored or co-authored over 200 peer reviewed publications in these areas. While a professor at Carnegie Mellon University (1982-1987), Hinton was one of the first researchers who demonstrated the use of generalized back-propagation algorithm for training multi-layer neural networks that has been widely used for practical applications. During the same period, Hinton co-invented Boltzmann machines with David Ackley and Terry Sejnowski. His other contributions to neural network research include distributed representations, time delay neural network, mixtures of experts, Helmholtz machines and Product of Experts. In 2007 Hinton coauthored an unsupervised learning paper titled "Unsupervised learning of image transformations". An accessible introduction to Geoffrey Hinton's research can be found in his articles in Scientific American in September 1992 and October 1993.
In October and November 2017 respectively, Hinton published two open access research papers on the theme of capsule neural networks, which according to Hinton are "finally something that works well."
Notable former PhD students and postdoctoral researchers from his group include Richard Zemel,Brendan Frey,Radford M. Neal,Ruslan Salakhutdinov,Ilya Sutskever,Yann LeCun and Zoubin Ghahramani.
|"||Geoffrey E. Hinton is internationally distinguished for his work on artificial neural nets, especially how they can be designed to learn without the aid of a human teacher. This may well be the start of autonomous intelligent brain-like machines. He has compared effects of brain damage with effects of losses in such a net, and found striking similarities with human impairment, such as for recognition of names and losses of categorization. His work includes studies of mental imagery, and inventing puzzles for testing originality and creative intelligence. It is conceptual, mathematically sophisticated and experimental. He brings these skills together with striking effect to produce important work of great interest.||"|
In 2001, Hinton was awarded an Honorary Doctorate from the University of Edinburgh. He was the 2005 recipient of the IJCAI Award for Research Excellence lifetime-achievement award. He has also been awarded the 2011 Herzberg Canada Gold Medal for Science and Engineering. In 2013, Hinton was awarded an Honorary Doctorate from the Université de Sherbrooke.
In 2016, he was elected a foreign member of National Academy of Engineering "For contributions to the theory and practice of artificial neural networks and their application to speech recognition and computer vision". He also received the 2016 IEEE/RSE Wolfson James Clerk Maxwell Award.
He has won the BBVA Foundation Frontiers of Knowledge Award (2016) in the Information and Communication Technologies category "for his pioneering and highly influential work" to endow machines with the ability to learn.
Hinton is the great-great-grandson both of logician George Boole whose work eventually became one of the foundations of modern computer science, and of surgeon and author James Hinton. His father is Howard Hinton. His middle name is from another relative, George Everest. He is the nephew of the economist Colin Clark. He lost his first wife to ovarian cancer in 1994.
Hinton moved from the U.S. to Canada in part due to disillusionment with Reagan-era politics and disapproval of military funding of artificial intelligence. He believes political systems will use AI to "terrorize people". Hinton has petitioned against lethal autonomous weapons. Regarding existential risk from artificial intelligence, Hinton has stated that superintelligence seems more than 50 years away, but warns that "there is not a good track record of less intelligent things controlling things of greater intelligence". Asked in 2015 why he continues research despite his grave concerns, Hinton stated "I could give you the usual arguments. But the truth is that the prospect of discovery is too sweet." Hinton has also stated that "It is very hard to predict beyond five years" what advances AI will bring.
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