Geoffrey Everest Hinton (born 6 December 1947) is a British-Canadian computer scientist and cognitive psychologist, most noted for his work on artificial neural networks.
He received the 2024 Nobel prize in Physics together with John J. Hopfield “for foundational discoveries and inventions that enable machine learning with artificial neural networks”. Both Hinton and Hopfield had signed the open letter calling for a pause on the training of frontier AI systems.
He received the 2018 Turing Award, often referred to as the “Nobel Prize of Computing”, together with Yoshua Bengio and Yann LeCun, for their work on deep learning.
Hinton is viewed as a leading figure in the deep learning community. His contributions have been instrumental in advancing the field of deep learning, particularly through his groundbreaking work on Boltzmann machines and their application in unsupervised learning.
In May 2023, Hinton announced his resignation from Google to be able to “freely speak out about the risks of A.I.”
He recently stated that his true assessment about probability AI will soon wipe out humanity is 50%, but he officially submits 20% to account for a chance he might be missing something.
🚨 Geoffrey Hinton's independent assessment of P(doom) is MORE THAN 50%
— Liron Shapira (@liron) July 7, 2024
After humbly adjusting for others' assessments (as if they would know better???) he arrives at 10-20%.
It's cool that the godfather of AI keeps getting more explicit that *he* sees how fucked we are. pic.twitter.com/T7vfIaRyV8
From 2013 to 2023, he divided his time working for Google (Google Brain) and the University of Toronto, before publicly announcing his departure from Google in May 2023, citing concerns about the risks of artificial intelligence (AI) technology. In 2017, he co-founded and became the chief scientific advisor of the Vector Institute in Toronto.
With David Rumelhart and Ronald J. Williams, Hinton was co-author of a highly cited paper published in 1986 that popularized the backpropagation algorithm for training multi-layer neural networks.
The dramatic image-recognition milestone of the AlexNet designed in collaboration with his students Alex Krizhevsky and Ilya Sutskever for the ImageNet challenge 2012 was a breakthrough in the field of computer vision.
He has voiced concerns about deliberate misuse by malicious actors, technological unemployment, and existential risk from artificial general intelligence.
He has repeatedly expressed concerns about the possibility of an AI takeover, stating that “it’s not inconceivable” that AI could “wipe out humanity.”
Hinton states that AI systems capable of intelligent agency will be useful for military or economic purposes. He worries that generally intelligent AI systems could “create sub-goals” that are unaligned with their programmers’ interests.
He states that AI systems may become power-seeking or prevent themselves from being shut off, not because programmers intended them to, but because those sub-goals are useful for achieving later goals. In particular, Hinton says “we have to think hard about how to control” AI systems capable of self-improvement.
