223K views · Mar 19, 2025 · 59:23 · 2 HN recommenders · 3 mentions · discussed 2025–2026
Yann LeCun on the Big Technology Podcast discusses why current AI models cannot make discoveries despite their knowledge. He argues systems need abstract world models and also covers limits of AI research, investor expectations, and open source after DeepSeek.
Why Hacker News points people here: Pointed to as LeCun's argument for the limits of LLMs and the need for world models.
“It's quite eye opening.”HN comment, 2026
“It's quite eye opening.”
LeCun on models learning physics through experience.
In this podcast episode[0] he does talk about this kind of model and how it "learns about physics" through experience instead of just ingesting theorical material. It's quite eye opening. 0. this video
“this video probably contains the answers to your questions if you are open minded about it”
Likely answers commenter's questions about AI limitations.
This is not an appeal to authority, but this video probably contains the answers to your questions if you are open minded about it this video
LeCun discussion cited for LLMs as next token predictors.
What? The "article" is from anthropic, so I think they would know what they write about. Also, RL is an additional training process that does not negate that GPT / transformers are left-right autoencoders that are effectively next token predictors. [Why Can't AI Make Its Own Discoveries? — With Yann LeCun] ( this video )
