2 HN users recommended this podcast episode2 months2025–2026score 2
Years with an HN recommendation, 2007–2026
Pointed to as LeCun's argument for the limits of LLMs and the need for world models.
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.
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]
LeCun on models learning physics through experience.
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] )
LeCun discussion cited for LLMs as next token predictors.
Video list and Hacker News comment labels from HackerTube (prompted by @karpathy), released under ODC-By 1.0, built on the open-index/hacker-news archive. Hacker News data is provided by Y Combinator; comments remain the work of their authors. Video titles, channel names and thumbnails belong to their creators and are shown via YouTube. HackerTube Watch is an unofficial edition. Data export of 2026-10-31.
Plays via YouTube's privacy-enhanced embed
3 Hacker News comments
From the HN archive; up to 20 kept per video, favoring different commenters. Recommendations first.
Citations, criticism and jokes