Geoffrey Hinton

Geoffrey Everest Hinton FRS (born 6 December 1947) is a British-born 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. 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 movement.

Education

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 H. Christopher Longuet-Higgins.

Career

He has worked at Sussex, University of California San Diego, Cambridge, Carnegie Mellon University and University College London. He was the founding director of the Gatsby 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. 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. 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

Hinton may refer to:

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Deep learning

Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

Neural network (disambiguation)

Neural network(s) may refer to:

University of Toronto

The University of Toronto (U of T, UToronto, or Toronto) is a public research university in Toronto, Ontario, Canada, situated on the grounds that surround Queen's Park. It was founded by royal charter in 1827 as King's College, the first institution of higher learning in the colony of Upper Canada. Originally controlled by the Church of England, the university assumed the present name in 1850 upon becoming a secular institution. As a collegiate university, it comprises twelve colleges, which differ in character and history, each retaining substantial autonomy on financial and institutional affairs. It has two satellite campuses located in Scarborough and Mississauga.

Academically, the University of Toronto is noted for influential movements and curricula in literary criticism and communication theory, known collectively as the Toronto School. The university was the birthplace of insulin and stem cell research, and was the site of the first practical electron microscope, the development of multi-touch technology, the identification of Cygnus X-1 as a black hole, and the theory of NP-completeness. By a significant margin, it receives the most annual scientific research funding of any Canadian university. It is one of two members of the Association of American Universities located outside the United States.