Geoffrey Hinton

Related

Sources

1. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature 323: 533–536.
2. Hinton, G. E., Osindero, S. & Teh, Y.-W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation 18(7): 1527–1554.
3. Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25.

Mentioned in

Yann LeCunYann LeCun, prop. n.: A French-American computer scientist who pioneered the neural networks that recognize images, led Meta's AI research for over a decade, and argues that chatbots alone will not reach human-level AI., AlexNetAlexNet, prop. n.: The neural network that won a major image-recognition contest by a huge margin in 2012, convincing researchers and companies that deep learning trained on graphics chips was the way forward., Boltzmann machineBoltzmann machine, n.: A network of randomly flipping on–off units, including hidden ones, that learns the statistical patterns in its training data and can then produce new examples like them., David RumelhartDavid Rumelhart, prop. n.: An American psychologist who, with Geoffrey Hinton and Ronald Williams, showed in 1986 how to train multilayer neural networks, launching the modern revival of the field., deep learning revolutiondeep learning revolution, n.: The rapid takeover of AI by many-layered neural networks from about 2012, when they suddenly began beating older methods at recognizing images and speech, leading to today's AI systems., godfathers of AIgodfathers of AI, phr.: A media nickname for three scientists — Geoffrey Hinton, Yoshua Bengio and Yann LeCun — whose decades of work on neural networks underpins modern AI. They shared computing's top prize in 2019., Google BrainGoogle Brain, prop. n.: Google's deep-learning research team from 2011 to 2023, where the transformer — the design behind today's chatbots — and the TensorFlow software were created., Ilya SutskeverIlya Sutskever, prop. n.: A Russian-born Israeli-Canadian computer scientist who co-built the AlexNet image recognizer, helped found OpenAI and lead its research until 2024, and now heads the startup Safe Superintelligence Inc., Jeff DeanJeff Dean, prop. n.: An American computer scientist who built much of Google's computing infrastructure, co-founded the Google Brain deep-learning team and was Google's chief scientist before leaving in 2026 to co-found a startup., John HopfieldJohn Hopfield, prop. n.: An American physicist who showed in 1982 how a network of simple units can store and recall memories, and who shared the 2024 Nobel Prize in Physics for work that enabled machine learning., Nobel Prizes for AINobel Prizes for AI, phr.: The two 2024 Nobel Prizes, in physics and chemistry, that honored artificial intelligence research for the first time, recognizing neural-network pioneers and the protein-structure system AlphaFold., Statement on AI RiskStatement on AI Risk, prop. n.: A one-sentence public statement from May 2023, signed by hundreds of leading AI scientists and company heads, declaring that guarding against the chance of AI wiping out humanity should rank as a priority for the whole world., Turing AwardTuring Award, prop. n.: Nicknamed the 'Nobel Prize of computing', the top honor in computer science, awarded each year since 1966 by the Association for Computing Machinery. Several AI pioneers have received it., Yoshua BengioYoshua Bengio, prop. n.: A Canadian computer scientist who helped found deep learning, including ideas behind today's language models, and who has become a leading voice on the risks of advanced AI., Alex KrizhevskyAlex Krizhevsky, prop. n.: A Canadian computer scientist who built AlexNet, the image-recognition network that was trained on gaming graphics chips and whose 2012 contest win launched the deep learning boom., deep belief networkdeep belief network, n.: A many-layered neural network trained one layer at a time, introduced in 2006. It showed that deep networks could be trained after all and helped launch the deep learning boom, before simpler methods replaced it., Michael I. JordanMichael I. Jordan, prop. n.: An American computer scientist and statistician who did much to bring probability and statistics into machine learning, and who trained many of the field's leaders., Oriol VinyalsOriol Vinyals, prop. n.: A Spanish computer scientist who co-created key neural methods for translation and image captioning, led DeepMind's StarCraft-playing AlphaStar, co-led Google's Gemini models, and in 2026 co-founded the startup Discovery Loop., Parallel Distributed ProcessingParallel Distributed Processing, prop. n.: A two-volume 1986 book by a group of psychologists and computer scientists that revived interest in brain-like neural networks and made the training method behind modern AI widely known., Paul WerbosPaul Werbos, prop. n.: An American scientist whose 1974 doctoral thesis set out backpropagation, the method of passing errors backward that now trains nearly all neural networks., Seppo LinnainmaaSeppo Linnainmaa, prop. n.: A Finnish computer scientist who in 1970 first published the method for computing all of a program's derivatives in one backward pass, the mathematical core of backpropagation., Terry SejnowskiTerry Sejnowski, prop. n.: An American neuroscientist who co-invented the Boltzmann machine with Geoffrey Hinton, built an early network that learned to read aloud, and helped found the NeurIPS conference community., Vector InstituteVector Institute, prop. n.: Toronto's publicly and privately funded AI institute, set up in 2017 with Geoffrey Hinton as an adviser, which supports university researchers and helps companies, hospitals and government put their work to use., Volodymyr MnihVolodymyr Mnih, prop. n.: A DeepMind researcher who led the 2013–2015 work in which one neural network learned to play dozens of Atari video games from screen pixels, launching deep reinforcement learning.