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Geoffrey Everest Hinton (born 6 December 1947) is a British-Canadian computer scientist, cognitive scientist, cognitive psychologist, and Nobel Prize winner in Physics, known for his work on artificial neural networks which earned him the title as the "Godfather of AI". Hinton is University Professor Emeritus at the University of Toronto.
Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units) A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
AlexNet is a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto in 2012. It had 60 million parameters and 650,000 neurons. [1]
Geoffrey Hinton invented a method [the Boltzmann machine] that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures.” ...
It's the first time that a Nobel Prize has been awarded to the field of AI.
The original contribution in applying such energy-based models in cognitive science appeared in papers by Geoffrey Hinton and Terry Sejnowski. [ 17 ] [ 18 ] [ 19 ] In a 1995 interview, Hinton stated that in 1983 February or March, he was going to give a talk on simulated annealing in Hopfield networks, so he had to design a learning algorithm ...
The newly minted Nobel Prize winner Geoffrey Hinton says he's proud that one of his former students had a part to play in Sam Altman's brief ouster from OpenAI in November.
It is based on Stochastic Neighbor Embedding originally developed by Geoffrey Hinton and Sam Roweis, [1] where Laurens van der Maaten and Hinton proposed the t-distributed variant. [2] It is a nonlinear dimensionality reduction technique for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions ...