Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton
The Royal Institution3 HN recommenders · recommended in 3 months, 2025-2025
878K views · 1 year ago · lecture · dated
Geoffrey Hinton's 2025 Royal Institution Discourse on what AI has learned from biological intelligence and how it might surpass it. He explains neural networks and argues that human language use resembles what backpropagation-trained networks do.
HN readers recommend it as an intuitive explanation of neural networks that shifts how people think about AI and language.
42:30Full interview: "Godfather of artificial intelligence" talks impact and potential of AI
CBS Mornings
2M views • 3 years ago
1:48:12Geoffrey Hinton in conversation with Fei-Fei Li — Responsible AI development
Arts & Science - University of Toronto
226K views • 2 years ago
1:25Geoff Hinton: On Radiology
Creative Destruction Lab
45K views • 9 years ago
1:00:19Brains, Sex, and Machine Learning
Google TechTalks
152K views • 14 years ago
1:05:18Recent Developments in Deep Learning
Google TechTalks
85K views • 16 years ago
38:59StrictlyVC in conversation with Sam Altman, part two (OpenAI)
Connie Loizos
254K views • 3 years ago
1:11:36Geoffrey Hinton talk "What is wrong with convolutional neural nets ?"
trwappers
200K views • 9 years ago
36:02ChatGPT with Rob Miles - Computerphile
Computerphile
517K views • 3 years ago
1:57:02How We Prevent the AI’s from Killing us with Paul Christiano
Bankless
63K views • 3 years ago
20:39AI Language Models & Transformers - Computerphile
Computerphile
354K views • 7 years ago
1:03:32John Schulman - Reinforcement Learning from Human Feedback: Progress and Challenges
UC Berkeley EECS
88K views • 3 years ago
15:27Why Not Just: Think of AGI Like a Corporation?
Robert Miles AI Safety
163K views • 7 years ago
48:32Sparks of AGI: early experiments with GPT-4
Sebastien Bubeck
1.8M views • 3 years ago
47:41Ilya Sutskever (OpenAI Chief Scientist) — Why next-token prediction could surpass human intelligence
Dwarkesh Patel
789K views • 3 years ago
1:07:31The A.I. Dilemma - March 9, 2023
Center for Humane Technology
3.7M views • 3 years ago
30:10The Inside Story of ChatGPT’s Astonishing Potential | Greg Brockman | TED
TED
1.9M views • 3 years ago
17:52Training AI Without Writing A Reward Function, with Reward Modelling
Robert Miles AI Safety
259K views • 6 years ago
1:27:44Andrew Ng: Artificial Intelligence is the New Electricity
Stanford Graduate School of Business
524K views • 9 years ago
51:33Human Computation
GoogleTalksArchive
31K views • 14 years ago
18:05Intro to AI Safety, Remastered
Robert Miles AI Safety
210K views • 5 years ago
3 comments from Hacker News
Geoffrey Hinton's recent lecture at the Royal Institute[1] is a fascinating watch. His assertion that human use of language being exactly analogous to neural networks with back-propagation really made me think about what LLMs might be able to do, and indeed, what happens in me when I "think". A common objection to LLM "intelligence" is that "they don't know anything". But in turn... what do biological intelligences "know"? For example, I "know" how to do things like write constructs that make complex collections of programmable switches behave in certain ways, but what do I really "understand"? I've been "taught" things about quantum mechanics, electrons, semiconductors, transistors, integrated circuits, instruction sets, symbolic logic, state machines, assembly, compilers, high-level-languages, code modules, editors and formatting. I've "learned" more along the way by trial and error. But have I in effect ended up with anything other than an internalised store of concepts and interconnections? (c.f. features and weights). Richard Sutton takes a different view in an interview with Dwarkesh Patel[2] and asserts that "learning" must include goals and reward functions but his argument seemed less concrete and possibly just a semantic re-labelling. [1] ▶ this video [2] link
I muck about on HN and get ~80% of my video content on youtube. I think it's great overall. The rest ~5% twitter, 0% TV although I have one sitting switched off with a £174 bbc fee. 5% pirated maybe. If you go with the official HN guide of "anything that gratifies one's intellectual curiosity" and avoid "stories about politics, or crime, or sports, or celebrities" then youtube is much better - watching Hinton explain AI and neural networks at the mo. link and ▶ this video which is kind of interesting.
It was a Royal Institution public lecture, "Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton", ▶ this video Ultimately I somehwat disagreed with some of Hintons points in this talk, and after some thought I came up with specific reasons/doubts, and yet at the same time, his intuitive explanations helped shift my views somewhat as well.