Geoffrey Hinton

Background

Geoffrey Hinton was born into a family with a strong background in mathematics and science. His great-great-grandparents were George Boole, whose work in algebra was among the foundations of modern computing, and mathematician Mary Everest Boole. His great-grandfather, mathematician and science fiction writer Charles Hinton, coined the phrase “tesseract” to describe a four-dimensional cube. Geoffrey Hinton’s father, Howard, was a noted British entomologist (see Entomology). He is also related to surveyor George Everest, who Mount Everest was named after. In a 2018 interview, when asked what it was like to grow up with such a background, Hinton noted that “it felt like pressure.”

Education and Career

Geoffrey Hinton explored academic programs ranging from physics to physiology before earning a BA in experimental psychology at the University of Cambridge in 1970. After working for a year as a carpenter, he pursued a PhD in AI at the University of Edinburgh, which he received in 1978. “I had a stormy graduate career,” he told the Globe and Mail in 2017, “where every week we would have a shouting match.” Despite constant discouragement from his professors who believed he was wasting his time, Hinton pursued research into computer networks that were patterned on the human brain, which became known as neural networks.

Hinton’s early academic postings included work as a research fellow at Sussex University (1976-78), visiting scholar at University of California San Diego (1978-80), and assistant professor and associate professor in the computer science department at Carnegie Mellon University (1982-87)

Research Highlights

While at Carnegie Mellon, Geoffrey Hinton worked with psychologist David Rumelhart and computer scientist Ronald J. Williams on backpropagation, an algorithm which worked backwards when measuring errors.

Between 1983 and 1985, Hinton created the Boltzmann machine, which was named in honour of 19^th century Austrian physicist/philosopher Ludwig Boltzmann, who devised the equation it was built upon. The program aimed to avoid absolute yes/no answers typical of computer analysis at that time by analyzing data and choosing the most appropriate answer. The foundation of this work was the “Hopfield network” created by American physicist John Hopfield, with whom Hinton would share the Nobel Prize four decades later. (See also Nobel Prizes and Canada.)

Hinton moved to Canada in 1987 after receiving funding for AI research from the Canadian Institute for Advanced Research, which he conducted while working in the University of Toronto’s computer science department. His decision to move to Canada was also motivated by his belief that AI should not be used for military combat--AI research was being funded by the U.S. Department of Defense.

After working as the founding director of the Gatsby Computational Neuroscience Unit at University College London between 1998 and 2001, Hinton returned to the University of Toronto, where he was appointed University Professor in 2006. His research group explored areas of deep learning which led to breakthroughs in areas such as object classification and speech recognition. Many of his graduate students found work at large technology firms to continue their work on neural networks.

In 2012, Hinton and two graduate students, Alex Krizhevsky and Ilya Sutskever, created AlexNet, a multi-layered neural network used to identify images from a large online database. They created a company, DNNresearch, which was bought by Google in 2013.

Hinton joined Google on a part-time basis and continued his AI research. He was affiliated with a Toronto branch of Google Brain, an AI research team, established by Google in 2017. Hinton left Google in 2023 over concerns regarding the risks of rapid, unrestrained AI development.

In 2017 Hinton became the co-founder and chief scientific advisor at the Toronto-based Vector Institute, an independent, non-profit corporation which dedicated itself to AI research related to its application, adoption, and commercialization in Canada.

Nobel Prize

Geoffrey Hinton shared the 2024 Nobel Prize in Physics with John Hopfield for “foundational discoveries and inventions that enable machine learning with artificial neural networks.” It marked the fourth time in a decade a Canadian had received a Nobel in this category, following Arthur B. McDonald (2015), Donna Strickland (2018), and James Peebles (2019). (See also Nobel Prizes and Canada.) Hinton was shocked that he was honoured, having, in his words, “dropped out of physics after my first year in university because I couldn’t do the complicated math.”

Hinton donated half of the money ($350,000) he earned from the prize to Water First, a Canadian charity which works with Indigenous communities on issues surrounding drinking water.

Criticism of AI

Geoffrey Hinton has described himself as a “worried pessimist.” While he has promoted the benefits of AI technology in terms of increasing productivity and assisting fields such as health, he has grown increasingly critical of its potential for abuse and the possibility it could overwhelm humanity if it ever learns to write its own code. Hinton is quoted in the New York Times stating that “We have no experience of what it’s like to have things smarter than us.”

In his 2024 Nobel Prize banquet speech, Hinton warned about the risks that had emerged with the rapid progress of AI, including echo chambers, its use by authoritarian government for mass surveillance and by cyber criminals, and its potential for creating lethal viruses and weaponry. He also warned of longer-term existential threats from creating AI that is smarter than humans, especially If it is created by companies seeking short-term profit. “We urgently need research on how to prevent these new beings from wanting to take control. They are no longer science fiction.” (See also Computers and Canadian Society.)

Legacy

When asked to describe himself in a 2024 interview with Adam Smith for Nobel Prize Outreach, Geoffrey Hinton noted that he was “someone who doesn’t really know what field he’s in but would like to understand how the brain works. And in my attempts to understand how the brain works, I’ve helped to create a technology that works surprisingly well.”

Commenting on Hinton’s Nobel win, Melanie Woodin, dean of the Faculty of Arts & Science at the University of Toronto, observed that he “is an historic visionary whose groundbreaking work in deep learning and neural networks” made Toronto a leader in AI, and that “it speaks volumes about his integrity that while he helped lay the foundation for the artificial intelligence revolution, he is also one of the leading voices urging that we develop this technology responsibly and ethically.”

Select Awards and Honours

Geoffrey Hinton has appeared on numerous lists of influential people, including Wired magazine’s 100 most influential (2016), the Globe and Mail Report on Business’s 50 most powerful people in Canadian business (2017), and five appearances on Toronto Life magazine’s 50 most influential people in Toronto list between 2017 and 2024 (which included being named #1 in 2023). In addition, Hinton is the recipient of several honours and awards:

• Fellow, Royal Society of Canada (1996)
• Fellow, Royal Society (1998)
• David E. Rumelhart Prize, Cognitive Science Society (2001)
• Gerhard Herzberg Canada Gold Medal for Science and Engineering, Natural Sciences and Engineering Research Council of Canada (2010)
• Killam Prize in Engineering, National Research Council Canada (2012)
• Companion, Order of Canada (2018)
• Toronto Region Builder Award, Toronto Region Board of Trade (2019)
• M. Turing Award, Association for Computing Machinery (shared with Yann LeCun and Yoshua Bengio) (2018) 
• Royal Medal, Royal Society (2022)
• Nobel Prize in Physics, Royal Swedish Academy of Sciences (2024)