226K views · Feb 23, 2024 · 1:48:12 · 2 HN recommenders · 3 mentions · discussed 2024
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.
Why Hacker News points people here: HN recommends it for Hinton's research history and the two pioneers reflecting on a decade of progress.
“Goes over Hinton's history and why he went the direction he did with his research”HN comment, 2024
“Goes over Hinton's history and why he went the direction he did with his research”
Covers Hinton's research history and Li's ImageNet work.
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.
“looked back at the developments over the last ~10 years”
Hinton and Li look back at a decade of developments.
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
“But Geoff Hinton says LLMs' few-shot learning compares quite favorably with people!”
Hinton says LLM few-shot learning compares well with people.
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.)
