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Geoffrey Hinton in conversation with Fei-Fei Li — Responsible AI development

Arts & Science - University of Toronto

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  • Recommended by 2 people: 2 on Hacker News.
  • Presented by Geoffrey Hinton, Professor Emeritus at U of T, and Fei-Fei Li, Stanford University Professor
  • An opinion. It argues one side of a question engineers disagree on.

Beginner. No background in the topic needed.

225K views2 years ago2 recommendations

AI and machine learningHistory and cultureNeural networks and deep learningTransformers and LLMsAI safety and impact

Geoffrey Hinton and Fei-Fei Li discuss the history of deep learning, ImageNet, and the ethical and existential risks of artificial intelligence.

Recorded October 4, 2023. U of T Professor Emeritus Geoffrey Hinton is joined by Stanford University Professor Fei-Fei Li for a special talk on ethical AI, presented by Radical Ventures and hosted by the University of Toronto. They address the ethical considerations of AI development and its advancement — as well as its potential to solve some of the world’s most pressing problems. They also discuss their collaboration on one of the most important milestones in the history of modern AI. Since leaving Google in May 2023, Geoffrey Hinton, known as the “godfather of AI,” has become vocal about the existential risks posed by rapid advancements in artificial intelligence. Li is co-director of Stanford’s Human-Centered AI Institute. She agrees with Hinton on the threat and emphasizes the need to invest in public institutions to help guide the technology’s future.

Why it's here

2 people recommend this

What people say. Recommended for its historical overview of deep learning and the personal motivations of two AI pioneers.

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This 'conversation' with Geoffrey Hinton and Fei-Fei Li goes over a lot of the history of things (1h50m): * link to 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.

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trotton Hacker News • Mentions

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! link to 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.)