alt Hacker NewsLeCun also said back in 2022 that "if you train a machine, as powerful as it could be, your 'GPT-5000', on text", it will never be able to learn basic common-sense physics like that objects placed on tables will move along with them.
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It would be good if one's reputation tracked one's track record of predictive accuracy. But many people will take what LeCun says as gospel regardless of how badly wrong he has been and continues to be.
yeah but taking what lecun says then training an AI on that special skill set to prove him wrong is not exactly proving him wrong because you are just missing the bigger picture, just like LLMs are
You're missing the point here. He's not talking about whether or not they can learn facts or inferences derived from the text itself, but the more holistic intuition that results from learning from something like an embodied experience in the physical world. GPT-6 Astras web demo homepage thing is an example. It chose euclidean rather than quaternion for letting a user rotate the galaxy thing, and anyone who has ever used hands to rotate something would immediately recognize on trying it that something is fucked and you shouldnt do that. Thats the kind of common sense physics that is inherently beyond these llms and I run into it ALL the time in vr programming.
Every AI expert any either side of this debate has made very wrong predictions.
LeCunn actually wanted to pivot Meta's entire AI strategy away from LLMs just before he was ousted. He was sure they had nowhere further to go and wanted to pivot to world model generation. The LLM models have since progressed massively.
An analogy on LLMs is that you have a pretty clear straight highway ahead of you for some distance right now. Maybe that doesn't lead to AGI but it's clear there's progress to be made. For a big tech company it makes sense to push as hard and fast down that clear straight highway of LLMs asap.
Meanwhile LeCunn wanted to turn off the road and go down an unproven track. I say this as someone working on world model generation right now (creating the ability to learn game world model and have it play the game https://tfmbot.com for an example of my system pointed at a very complex board game). LeCunn wanted to pivot all of Meta into world model generation. It's good as a side track research project but the entire pivot he wanted to do was madness.
People are literally talking about an AI researcher who was fired for terrible direction here.
> ... it will never be able to learn basic common-sense physics like that objects placed on tables will move along with them.
I use LLMs daily to help me code etc. but... It wasn't long ago that frontier models were confidently recommending to walk, without the car, to the car wash to wash the car no?
As a daily user of LLMs I do certainly see my fair share of WTF "solutions" to coding problems. I'm not saying it's not super useful: it is super useful. But I don't exactly feel like I'm talking to something that understands that the car needs to be present to be washed.
Yeah, and he's probably right.
LLMs do not learn at all!
This was facetious of course, but humans generally don't learn this through analysis the way you'd have to train an LLM to answer questions about expectations about the world. In this sense he is accurate.
I keep wanting to use LLMs for creative writing that heavily involves physics like this, and it's been a definite struggle to say the least. I recently discovered that Gemini 3.1 Pro is the first model I've found to clearly beat the original November 2022 ChatGPT release in terms of implied physics. Man did the world really take its sweet time to get back here. I think it will continue to be a struggle until another genuine architectural shift happens -- it's still not anywhere close to perfect, just better.
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LeCun took credit for the work of https://en.wikipedia.org/wiki/Kunihiko_Fukushima
> never be able to learn basic common-sense physics
And has it at this stage, within in-depth take of said "learning", foundationally?
I have not been able to properly check the studies for a long time now, but I remain unaware of achieved solutions on the problem of reliably referencing a world model out of a language model - that "counting the 'r's in 'raspberry'" be not guessing, not memory, but actually counting.