Skip to content
0:00 / 0:00

#geoffreyhinton#neuralnetworks#backpropagation

Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton

The Royal Institution

1.8M subscribers • 3 videos on Hallway

YouTube
  • Recommended by 3 people: 3 on Hacker News.
  • Presented by Geoffrey Hinton, computer scientist
  • An opinion. It argues one side of a question engineers disagree on.

Intermediate. Assumes working knowledge.

881K views1 year ago3 recommendations

AI and machine learningNeural networks and deep learningAI safety and impactTransformers and LLMs

Geoffrey Hinton explains what artificial neural networks have learned from biological brains and why they may eventually surpass human intelligence.

The 2024 Nobel winner explains what AI has learned from biological intelligence, and how it might one day surpass it. There's a Q&A session for this talk available here, exclusively for our Science Supporters: youtu.be/-hZlv90HNm4 --- This Discourse was recorded at the Ri on 30 May 2025. Find out more about our 200 year old Discourse series here: rigb.org/explore-science/explore/blog/history-friday-evening… Our apologies as we aware there are some audio imperfections at the beginning of the talk, but they are resolved from onwards. -- Tech headlines in the last couple of years have been dominated by Artificial Intelligence. But what do we actually mean by intelligence? What has AI learned from biological intelligence, and how do they still differ? Acclaimed computer scientist, and winner of the 2024 Nobel Prize in Physics, Geoffrey Hinton will examine the similarities and differences between artificial and biological intelligence, following his decades of ground-breaking work which has enabled the neural networks of today. --- Geoffrey Hinton is Professor Emeritus at the University of Toronto, and a world renowned expert in the field of deep learning. He is often referred to as the "Godfather of AI", and in 2024 was jointly awarded the Nobel Prize in Physics "for foundational discoveries and inventions that enable machine learning with artificial neural networks". He is a fellow of the Royal Society and the Royal Society of Canada, and has been recognised with many awards around the world including the Turing Award, the Royal Society Royal Medal, and Dickson Prize. Hinton received a BA in Experimental Psychology from the University of Cambridge in 1970 and his PhD in Artificial Intelligence from the University of Edinburgh in 1978. Following postdoctoral work at Sussex University and the University of California San Diego, he joined the Computer Science department at Carnegie Mellon University, before moving to the Department of Computer Science at the University of Toronto in 1987. He set up the Gatsby Computational Neuroscience Unit at University College London before becoming University Professor in Toronto in 2006 and latterly University Professor Emeritus. Since 2017, Hinton has been Chief Scientific Advisor at the Vector Institute in Toronto. -- #ai #godfatherofai #geoffreyhinton #artificialintelligence #understandai #whatisai #futureofai #science #technology #chatgpt #llms #whatisai #whatisthefutureofai #learning #educational #technologynews #lecture #nobelprize #nobelprize2024 #nobelprizewinner ___ The Ri is on Facebook: facebook.com/royalinstitution and TikTok: tiktok.com/@ri_science Listen to the Ri podcast: podcasters.spotify.com/pod/show/ri-science-podcast Donate to the RI and help us bring you more lectures: rigb.org/support-us/donate-ri Our editorial policy: rigb.org/editing-ri-talks-and-moderating-comments Subscribe for the lates

Why it's here

3 people recommend this

What people say. Recommended for Geoffrey Hinton's intuitive explanations of neural networks and his perspective on the future of AI intelligence.

j1mr10rd4non Hacker News • Recommends

On “The Case That A.I. Is Thinking”

…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 Patel2 and asserts that "learning" must include goals and reward functions but his argument seemed less concrete and possibly just a semantic re-labelling. 1 link to this video 2 link…

calfon Hacker News • Recommends

On “Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens”

It was a Royal Institution public lecture, "Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton", link to 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.

tim333on Hacker News • Recommends

On “YouTube Just Ate TV. It's Only Getting Started”

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 link to this video which is kind of interesting.