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September 20, 2019
The Honda Prize 2019Awarded to Dr. Geoffrey Hinton, Professor Emeritus, the University of Toronto andChief Scientific Adviser, Vector Institute—Pioneering research that paved the way to the application of artificial intelligence (AI) in abroad range of areas and contributed to its practical application
Honda Foundation, the public interest incorporated foundation established by Soichiro Honda and hisyounger brother Benjiro and currently led by President Hiroto Ishida, is pleased to announce that theHonda Prize 2019 will be awarded to Dr. Geoffrey Hinton, Professor Emeritus of the University ofToronto and Chief Scientific Adviser of the Vector Institute for his pioneering research in the field of
deep learning*1 in artificial intelligence (AI) and his contribution to practical application of thetechnology.
The Honda Prize, established in 1980 and awarded once each year, is an international award thatrecognizes the work of individuals or groups generating new knowledge to drive the next generation,from the standpoint of eco-technology*2. Dr. Hinton has created a number of technologies that haveenabled the broader application of AI, including the backpropagation algorithm*3 that forms the basisof the deep learning approach to AI. AI is expected to play an important role not only in theadvancement of science and technology but also in resolving many different global issues thathumankind must address in the areas of energy and climate change. The Prize will be awarded to Dr.
Hinton for his outstanding achievements worthy of the highest recognition.
This year marks the 40th award of the prize. The presentation ceremony will be held on November 18,
2019 at the Imperial Hotel Tokyo, at which Dr. Hinton will be presented the medal, the certificate andan honorarium of 10 million yen.
<Research by Dr. Geoffrey Hinton>
When AI was in its infancy in the 1960s, the dominant paradigm involved using symbolic,hand-coded representations of knowledge that could be processed by a computer using rules ofinference. The biologically inspired alternative to symbolic AI was artificial neural networks thatlearned to use the activity patterns of large sets of neurons as distributed representations of data. The
neural network paradigm was largely unsuccessful until 1986 when Dr. Hinton and his collaborators
introduced the backpropagation algorithm and demonstrated that neural networks could learn
distributed representations of concepts from symbolic data. Currently, this technology is the standard
method for learning neural networks, with the number of references in academic papers to dateexceeding 60,000. However, the practical applications were limited because the datasets were too
small and computers were too slow. Interest then faded in artificial neural networks and theyexperienced a "winter" in the 1990s.
-1-Amid the fluctuations in popular interest, Dr. Hinton continued to pursue research on neural networks
with great diligence. In 1993, he introduced variational inference (a form of approximate Bayesianinference), for neural networks. In 2002, he introduced a fast learning algorithm for restricted
Boltzmann machines (RBM) that allowed them to learn a single layer of distributed representation
without requiring any labeled data. These methods allowed deep learning to work better and they led
to the current deep learning revolution. In 2009, Dr. Hinton and two of his students used multilayer
neural nets to make a major breakthrough in speech recognition that led directly to greatly improved
speech recognition. In 2012, Dr. Hinton and two more students revolutionized computer vision by
showing that deep learning worked far better than the existing state-of-the-art for recognizing objects
in images.
To achieve their dramatic results, Dr. Hinton also invented a widely used new method called “dropout”
which reduces overfitting in neural networks by preventing complex co-adaptations of featuredetectors. He also invented “t-SNE” for visualizing high-dimensional data in a two-dimensional map.
Of the countless AI-based technological services across the world, it is no exaggeration to say that few
would have been possible without the results Dr. Hinton created.
AI has become widely used in various situations in our everyday lives, including image recognition bycomputers, audio responses by smart phones, experiments in self-driving vehicles and automateddiagnosis of medical images. Since its birth 70 years ago, AI technology has finally gained a statusthat enables it to make major contributions to humankind. The achievements of Dr. Hinton make AI
the means for creating a new society, and AI is expected to play an important role not only in creating
safety and security in society, represented by the development of advanced transit systems, but also in
resolving many global issues in the fields of energy and climate change that humankind must address.
For these reasons, the Prize will be awarded to Dr. Geoffrey Hinton for his outstanding achievements.
*1 Deep learning: A machine learning method that employs neural networks, inspired by the neuronsin the human brain, to allow a computer system to find structure in data automatically without
human intervention.
*2 Ecotechnology: a neologism combining imaging of the natural world (ecology), including
civilization as a whole, and technology. Advocated by the Honda Foundation in 1979, it seeks newtechnological concepts required by human society in the sense of coexistence of people andtechnology.
*3 Backpropagation algorithm: An efficient procedure for computing how to change the connection
strengths in a neural network so as to reduce the error in the network’s output.
For more information, contact the Honda Foundation via:phone at +81-3-3274-5125 or fax at +81-3-3274-5103
Honda Yaesu Building, 2-6-20 Yaesu, Chuo-ku, Tokyo 104-0028, Japan
https://www.hondafoundation.jp/en/You may also contact Honda Motor’s Corporate PR Department via phone at +81-3-5412-1512.

-2-Dr. Geoffrey E. Hinton
Professor Emeritus, the University of Toronto
Born
December 1947, UK (British & Canadian citizenship)
Education and Training
1970: Cambridge University, BA in Experimental Psychology
1978: University of Edinburgh, Ph.D. in Artificial Intelligence

Employment History
1978–80: University of California, San Diego, Postdoctoral Fellow
1980–82: MRC Applied Psychology Unit, Cambridge, Research Scientist
1982–87: Carnegie Mellon University, Assistant & Associate Professor
1987–current: University of Toronto, Professor, Computer Science Department
1998–2001: University College London, Director, Gatsby Computational Neuroscience Unit
2013–current: Google Brain Team, Engineering Fellow, (half-time)
2016–current: Vector Institute, Chief Scientific Adviser (pro bono)
Biographical Sketch
Dr. Geoffrey Hinton has been researching artificial neural networks for 47 years. Working with David
Rumelhart and Ronald Williams, he showed that deep neural networks can learn useful distributed
representations of complex data. Working with Terry Sejnowski, he invented Boltzmann machines.
His other contributions include mixtures of experts, variational inference and learning for neural networks,
Deep Belief Nets and Capsule Nets. His research group at the University of Toronto used deep learning torevolutionize speech recognition in 2009 and object classification in 2012.
Honors and Awards
1998: Fellow of the Royal Society
2001: David E. Rumelhart Prize in Cognitive Science
2005: IJCAI Award for Research Excellence in Artificial Intelligence
2011: Herzberg Canada Gold Medal for Science and Engineering
2016: Foreign Member of American National Academy of Engineering
2016: IEEE/RSE James Clerk Maxwell Gold Medal
2016: BBVA Foundation of Knowledge Award, Information and Communication Technologies
2016: NEC C&C Award
2018: ACM Turing Award (with Yann LeCun and Yoshua Bengio)
Major Publications
A Learning Algorithm for Boltzmann Machines: (with Ackley, D. H. & Sejnowski, T. J.),
Cognitive Science, Elsevier, 9 (1): 147–169, 1985
Learning Representations by Back-Propagating Errors: (with Rumelhart, D. E. & Williams,R.J.) Nature 323 (6088): 533–536, 9 October 1986
A Fast Learning Algorithm for Deep Belief Nets: (with Osindero, S. & Teh, Y.) NeuralComputation, 18 (7): 1527-1554, July 2006
Reducing the Dimensionality of Data with Neural Networks: (with Salakhutdinov, R.R.)
Science, 313 (5786): 504–507, 28 July 2006
ImageNet Classification with Deep Convolutional Neural Networks: (with Krizhevsky, A. &Sutskever, I.), NIPS 2012. Curran Associates Inc.: 1097–1105, 3 December 2012