Geoffrey Hinton
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Geoffrey Hinton
Geoffrey Hinton
FRS FRSC
Geoffrey Hinton at UBC.jpg
Born Geoffrey Everest Hinton
(1947-12-06) 6 December 1947 (age 70)[1]
Wimbledon, London
Residence Canada
Alma mater
Known for
Awards
Website www.cs.toronto.edu/~hinton/
Scientific career
Fields
Institutions University of Toronto
Google
Carnegie Mellon University
University College London
Thesis Relaxation and its role in vision (1977)
Doctoral advisor Christopher Longuet-Higgins[3][4][5]
Doctoral students
Other notable students

Geoffrey Everest Hinton FRS FRSC[11] (born 6 December 1947) is a British cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. As of 2015 he divides his time working for Google and University of Toronto.[12] He was one of the first researchers who demonstrated the use of generalized backpropagation algorithm for training multi-layer neural nets and is an important figure in the deep learning community.[13][14][15]

Education

Hinton was educated at King's College, Cambridge graduating in 1970, with a Bachelor of Arts in experimental psychology.[1] 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.[3][16]

Career and research

After his PhD he worked at the University of Sussex, the University of California, San Diego, and Carnegie Mellon University.[1] He was the founding director of the Gatsby Charitable Foundation Computational Neuroscience Unit at University College London,[1] and is currently[17] a professor in the computer science department at the University of Toronto. He holds a Canada Research Chair in Machine Learning. He is the director of the program on "Neural Computation and Adaptive Perception" which is funded by the Canadian Institute for Advanced Research. Hinton taught a free online course on Neural Networks on the education platform Coursera in 2012.[18] 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".[19]

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[2][20] 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[21]. During the same period, Hinton co-invented Boltzmann machines with David Ackley and Terry Sejnowski.[22] 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".[23] 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[24][25] on the theme of capsule networks, which according to Hinton are "finally something that works well."[26]

Notable former PhD students and postdoctoral researchers from his group include Richard Zemel,[3][6]Brendan Frey,[7]Radford M. Neal,[8]Ruslan Salakhutdinov,[9]Ilya Sutskever,[10]Yann LeCun[] and Zoubin Ghahramani.

Honours and awards

From left to right Russ Salakhutdinov, Richard S. Sutton, Geoffrey Hinton, Yoshua Bengio and Steve Jurvetson in 2016

Hinton was elected a Fellow of the Royal Society (FRS) in 1998.[11] He was the first winner of the David E. Rumelhart Prize in 2001.[27] His certificate of election for the Royal Society reads:

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.[29] 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".[30] He also received the 2016 IEEE/RSE Wolfson James Clerk Maxwell Award.[31]

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.

Personal life

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.[32] His father is Howard Hinton.[33] His middle name is from another relative, George Everest.[34]

References

  1. ^ a b c d Anon (2015) Hinton, Prof. Geoffrey Everest. ukwhoswho.com. Who's Who (online Oxford University Press ed.). A & C Black, an imprint of Bloomsbury Publishing plc.  closed access publication - behind paywall doi:10.1093/ww/9780199540884.013.20261 (subscription required)
  2. ^ a b Geoffrey Hinton publications indexed by Google Scholar Edit this at Wikidata
  3. ^ a b c Geoffrey Hinton at the Mathematics Genealogy Project
  4. ^ Geoffrey E. Hinton's Academic Genealogy
  5. ^ Gregory, R. L.; Murrell, J. N. (2006). "Hugh Christopher Longuet-Higgins". Biographical Memoirs of Fellows of the Royal Society. 52: 149. doi:10.1098/rsbm.2006.0012. 
  6. ^ a b Zemel, Richard Stanley (1994). A minimum description length framework for unsupervised learning. proquest.com (PhD thesis). University of Toronto. OCLC 222081343. 
  7. ^ a b Frey, Brendan John (1998). Bayesian networks for pattern classification, data compression, and channel coding. proquest.com (PhD thesis). University of Toronto. OCLC 46557340. 
  8. ^ a b Neal, Radford (1995). Bayesian learning for neural networks. proquest.com (PhD thesis). University of Toronto. OCLC 46499792. 
  9. ^ a b Salakhutdinov, Ruslan (2009). Learning deep generative models. proquest.com (PhD thesis). University of Toronto. ISBN 9780494610800. OCLC 785764071. 
  10. ^ a b Sutskever, Ilya (2013). Training Recurrent Neural Networks. proquest.com (PhD thesis). University of Toronto. OCLC 889910425. 
  11. ^ a b Anon (1998). "Professor Geoffrey Hinton FRS". London: Royal Society. Archived from the original on 2015-11-03.  One or more of the preceding sentences incorporates text from the royalsociety.org website where:

    "All text published under the heading 'Biography' on Fellow profile pages is available under Creative Commons Attribution 4.0 International License." --"Royal Society Terms, conditions and policies". Archived from the original on 11 November 2016. Retrieved . 

  12. ^ Daniela Hernandez (7 May 2013). "The Man Behind the Google Brain: Andrew Ng and the Quest for the New AI". Wired. Retrieved 2013. 
  13. ^ "How a Toronto professor's research revolutionized artificial intelligence". Toronto Star, Kate Allen, Apr 17 2015
  14. ^ "The Next Generation of Neural Networks" on YouTube
  15. ^ AMA Geoffrey Hinton (self.MachineLearning) www.reddit.com Ask Me Anything : Geoffrey Hinton
  16. ^ Hinton, Geoffrey Everest (1977). Relaxation and its role in vision. lib.ed.ac.uk (PhD thesis). University of Edinburgh. hdl:1842/8121. OCLC 18656113. EThOS uk.bl.ethos.482889.  Free to read
  17. ^ https://www.cs.toronto.edu/~hinton/fullcv.pdf
  18. ^ https://www.coursera.org/learn/neural-networks
  19. ^ "U of T neural networks start-up acquired by Google" (Press release). Toronto, ON. 12 March 2013. Retrieved 2013. 
  20. ^ Geoffrey Hinton's publications indexed by the Scopus bibliographic database, a service provided by Elsevier. (subscription required)
  21. ^ Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (1986-10-09). "Learning representations by back-propagating errors". Nature. 323 (6088): 533-536. doi:10.1038/323533a0. ISSN 1476-4687. 
  22. ^ Ackley, David H; Hinton Geoffrey E; Sejnowski, Terrence J (1985), "A learning algorithm for Boltzmann machines", Cognitive science, Elsevier, 9 (1): 147-169
  23. ^ Hinton, Geoffrey E. "Geoffrey E. Hinton's Publications in Reverse Chronological Order". 
  24. ^ Sabour, Sara; Frosst, Nicholas; Hinton, Geoffrey. October 2017. "Dynamic Routing Between Capsules"
  25. ^ "Matrix capsules with EM routing" November 3, 2017. OpenReview.net
  26. ^ Geib, Claudia. November 2nd 2017. "We've Finally Created an AI Network That's Been Decades in the Making" Futurism.com
  27. ^ "Current and Previous Recipients". David E. Rumelhart Prize. 
  28. ^ Anon (1998). "Certificate of election EC/1998/21: Geoffrey Everest Hinton". London: Royal Society. Archived from the original on 5 November 2015. 
  29. ^ "Artificial intelligence scientist gets M prize". CBC News. 14 February 2011. 
  30. ^ "National Academy of Engineering Elects 80 Members and 22 Foreign Members". NAE. 8 February 2016. 
  31. ^ "2016 IEEE Medals and Recognitions Recipients and Citations" (PDF). IEEE. Retrieved 2016. 
  32. ^ The Isaac Newton of logic
  33. ^ Salt, George (1978). "Howard Everest Hinton". Biographical Memoirs of Fellows of the Royal Society. 24 (0): 150-182. doi:10.1098/rsbm.1978.0006. ISSN 0080-4606. 
  34. ^ Smith, Craig S. (23 June 2017). "The Man Who Helped Turn Toronto Into a High-Tech Hotbed". The New York Times. Retrieved 2017. 

  This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.


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