2 HN users recommended this interview2 months2024score 2
Years with an HN recommendation, 2007–2026
HN recommends it for Hinton's research history and the two pioneers reflecting on a decade of progress.
A 2023 University of Toronto conversation between Geoffrey Hinton and Fei-Fei Li on responsible AI development. They look back at the history of deep learning and ImageNet and discuss the ethics and risks of AI.
“Goes over Hinton's history and why he went the direction he did with his research” (HN, 2024)
This 'conversation' with Geoffrey Hinton and Fei-Fei Li goes over a lot of the history of things (1h50m): * [this video] * [link] ('mirror') Goes over Hinton's history and why he went the direction he did with his research, as well as Li's efforts with ImageNet.
Covers Hinton's research history and Li's ImageNet work.
See "Geoffrey Hinton in conversation with Fei-Fei Li" where the two got together and looked back at the developments over the last ~10 years, including why and how they did their part: * [this video]
Hinton and Li look back at a decade of developments.
François Chollet says LLMs do not learn in-context. But Geoff Hinton says LLMs' few-shot learning compares quite favorably with people! [this video] The truth is in the middle, I think. They learn in-context, but not as well as humans. The approach in the article hides the unreliability of current LLMs by generating thousands of programs, and still the results aren't human-level. (This is impressive work though -- I'm not criticizing it.)
Hinton says LLM few-shot learning compares well with people.
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