Geoffrey Hinton
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Geoffrey Hinton is a British-Canadian cognitive psychologist and computer scientist, widely recognized as one of the pioneers of artificial neural networks and deep learning. Born on December 6, 1947, Hinton is a descendant of George Boole, the inventor of Boolean algebra. He obtained his BA in Experimental Psychology from the University of Cambridge and his Ph.D. in Artificial Intelligence from Edinburgh University. His career has spanned multiple prestigious institutions including Carnegie Mellon University, Sussex University, and the Canadian Institute for Advanced Research.
From 2013 until May 2023, Hinton divided his time between Google's AI research division, Google Brain, and the University of Toronto. During his tenure, he contributed significantly to machine learning with innovations like backpropagation, Boltzmann machines, contrastive divergence, dropout, capsules, and transformers. His contributions have been recognized with numerous awards, including the Turing Award in 2018, the IEEE/RSE James Clerk Maxwell Medal in 2016, the Order of Canada in 2018, and he was elected a Fellow of the Royal Society in 2001.
In May 2023, Hinton left Google to speak more freely about the risks associated with AI development. His concerns include the potential for AI to be maliciously used by bad actors, the massive job displacement that AI could cause, and the existential risks posed by the creation of artificial general intelligence (AGI). He has warned that AI could surpass human intelligence, potentially leading to scenarios where humans could lose control over such systems.
In 2024, Hinton's influence was further acknowledged when he, alongside John Hopfield, was awarded the Nobel Prize in Physics for their foundational work in neural networks that enable machine learning. His warnings about AI have since become more pronounced, with Hinton estimating a 10% to 20% chance that AI could lead to human extinction within the next 30 years due to its rapid advancement and potential autonomy. He has also expressed skepticism about humanity's ability to control superintelligent AI, likening it to the relationship between a chicken and a human in terms of intelligence disparity.
Hinton has advocated for stringent regulations on AI development, emphasizing the need for global cooperation to manage its risks, and has supported efforts to ensure that AI benefits are broadly distributed across society rather than concentrated among the wealthy, warning of social instability otherwise. His recent public statements and interviews continue to highlight these concerns, positioning him as a critical voice in the discourse on AI's future trajectory.
Geoffrey Hinton was instrumental in the development of AlexNet, a groundbreaking convolutional neural network that significantly advanced the field of computer vision. Alongside his students Alex Krizhevsky and Ilya Sutskever, Hinton co-authored the paper that introduced AlexNet, which won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012 by a significant margin. This victory demonstrated the superior performance of deep learning methods, particularly deep convolutional neural networks, over traditional machine learning approaches in image recognition tasks. AlexNet's architecture, which included techniques like ReLU activation functions, dropout for reducing overfitting, and data augmentation, became a blueprint for subsequent neural network designs, setting a new standard in the field and catalyzing the deep learning revolution.
Research & papers
Sources
Forum of Artificial Intelligence in Medicine https://faimglobal.org/geoffrey-hinton/
Home Page of Geoffrey Hinton https://www.cs.toronto.edu/~hinton/
Alex Krizhevsky • Ilya Sutskever • Geoffrey E. Hinton
https://dl.acm.org/doi/abs/10.1145/3065386