Geoffrey Hinton

Geoffrey Hinton (born December 6, 1947, London, England) is a British-Canadian cognitive psychologist and computer scientist known as the “godfather of AI.” He revolutionized the field of artificial intelligence with his work on neural network models. He contributed significantly to AI research with novel insights and key discoveries in the areas of backpropagation, Boltzmann machines, distributed representations, and time-delay neural networks. Although he spent the majority of his career advancing AI, Hinton became an outspoken critic of the technology in 2023 and highlighted its potential harms. He was awarded the 2024 Nobel Prize in Physics and shared the prize with American physicist John J. Hopfield.

Hinton was born into a family with a rich intellectual history. His father, Howard Everest Hinton, was a distinguished entomologist, and all three of his siblings conducted scholarly work. His family includes multiple mathematicians, among them Mary Everest Boole and her husband, George Boole, whose algebra of logic (known as Boolean logic) became the basis for modern computing. Other notable relatives include Joan Hinton, one of the few women to work on the Manhattan Project; Charles Howard Hinton, the mathematician famous for visualizing higher dimensions; and George Everest, the surveyor Mount Everest is named for.

Hinton attended the University of Cambridge, where he switched his studies between physiology, philosophy, and physics before earning a degree in experimental psychology in 1970. He then attended the University of Edinburgh, where he received a Ph.D. in AI in 1978. Although discouraged by his professors, Hinton embraced unconventional computer networks modeled after neural nodes and the structure of the human brain. He began researching systems known as neural networks and completed postdoctoral research at the University of California, San Diego.

In 1982 Hinton joined the faculty of Carnegie Mellon University, where he worked with psychologist David Rumelhart and computer scientist Ronald J. Williams to develop an algorithm to work backward from output to input when measuring error. The process, called “backpropagation,” was discussed by the trio in 1986 in an influential paper that laid the groundwork for neural network development.

Hinton left the United States for Canada in 1987, a decision fueled by disdain for the U.S. military and the Reagan administration. The majority of American AI research at the time was funded by the U.S. Department of Defense, and Hinton opposed using AI for combat. He continued his research, this time as a professor at the University of Toronto, for the next 11 years. In 1998 Hinton left Toronto to found and direct the Gatsby Computational Neuroscience Unit at University College London. While a researcher there, he studied neural networks and their applications.

Hinton returned to the University of Toronto in 2001 and continued to make advances in neural network models. His research group developed and began to apply practical means for deep-learning technology in the 2000s. In 2012 Hinton and two of his graduate students, Alex Krizhevsky and Ilya Sutskever, developed an eight-layer neural network program, which they named AlexNet, to identify images on ImageNet, a massive online dataset of images. AlexNet outperformed the next most accurate program by more than 40 percent. The trio created a company, DDNresearch, for AlexNet. In 2013 Google acquired the company for $44 million. That same year Hinton joined Google Brain, the company’s AI research team, and he was eventually named a vice president and engineering fellow.

In May 2023 Hinton quit his job at Google, because he wanted to be able to speak freely about the risks of commercial AI use. He expressed concerns particularly about its power to create fake content and its potential to upend the job market. Hinton has stated that he does not fully regret his life’s work but fears that AI will become uncontrollable in the long run.

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Hinton has received extensive recognition for his role in revolutionizing AI. Among his numerous awards are the Cognitive Science Society’s first-ever David E. Rumelhart Prize (2001) and the Gerhard Herzberg Canada Gold Medal (2010), the country’s highest award for science and engineering. In 2018 Hinton was named a joint recipient of the Turing Award, often described as the “Nobel Prize of Computing,” for his breakthrough research on neural networks, and four years later he received the Royal Society’s Royal Medal for his pioneering work on deep learning.