Geoffrey Hinton in conversation with Fei-Fei Li — Responsible AI development
Why Hacker News recommends it
HN recommends it for Hinton's research history and the two pioneers reflecting on a decade of progress.
What it is
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.
Recommended by 2 HN commenters across 2 different months, 2024 to 2024.
What Hacker News said
Recommends throw0101d · 2024-03 · on “The Google employees who created transformers”
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.
Recommends throw0101b · 2024-11 · on “A stubborn computer scientist accidentally launched the deep learning boom”
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]
Cites trott · 2024-06 · on “Getting 50% (SoTA) on Arc-AGI with GPT-4o”
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.)