There's many ways to skin a cat so you can probably do something with a JEPA approach as well, but I doubt he actually catches up to having agents on the level of where Anthropic/OpenAI will be at any point.
What exactly was he dead wrong about that is proven by any of this?
GPT getting better has absolutely nothing to do with completely disproving anything LeCun has been saying.
He never said LLMs couldn't get better. He never said they couldn't score 7.6% on Arc AGI 3.
He's merely said they don't think, and you probably want something that actually thinks if you want a model that can be trained cheaply on a small amount of data and provide a ton of value.
Spending $5B to train a model that scores better than an older model does not disprove any of that in any way.
He said years ago even 'GPT 5000' couldnt do things that they ended up doing fine a month later, let alone by 5000. His later predictions are just moving that goal post including towards them not being able to do more general, harder problems of which Arc AGI is a counter-example.
What things specifically and when?
You probably wont like the edit but I dont have the timestamp of the original on hand, you can find it.
LeCun's ideas cannot be reduced to a 6 second clip...
You're missing the forrest for the trees, taking a singular example of a problem and thinking that if an LLM can solve the singular example it completely disproves LeCun is comical...
Ive read and watched more of his interviews and lectures it seems, it feels like you just have a rosier idea of his views than the views he repeatedly presents.
That LLMs don't have common sense and don't have good physical reasoning abilities; that you can't scale LLMs all the way to AGI; or that they can't predict the consequences of their actions which is the foundation of agentic behavior all seem like still (mostly) accurate predictions to me.
While LeCun has his share of problems, I think largely his criticisms on LLMs are more right than wrong. What remains to see is how good JEPA can be at filling in the gaps left behind from the brittleness of LLMS.
The human brain manages to self-organize with only a fraction of the information that LLMs get trained on. To train an LLM you need a lot of high-quality data.
There are two threads in history: firstly the compute thread leading to GPUs and AlexNet in 2012. Secondly the model architecture thread that started long before we had the compute and lead to transformers in 2017.
If the compute thread had been 30 years behind then we might be spending this century coming up with better architectures to make do with the more limited compute. However since the compute came first, we settled on the first thing that worked (transformers) and all effort went into polishing that.
There's something wrong with transformers though. No matter how many trillions of dollars get thrown at the problem, they still don't learn like humans do.
So? The question isnt can we get to ASI as efficiently as a brain, the question is can we get there, which we likely can. The inefficiencies can also be fixed after that.
>No matter how many trillions of dollars get thrown at the problem, they still don't learn like humans do.
Again, so? Humans are efficient but also bad at many things that transformers are already better at because of it. You are looking at the wrong thing if you think it needs to be like humans.
To me LLMs have gotten better since 2024, but their fundamental flaws still seem there.
They hallucinate when it comes to really challenging tasks such as math proofs. They still do not reuse code well and will rewrite functions instead of perusing the standard library.
But this is good news. LLMs are awesome and they are only the first step towards AI being applied everywhere. They are a Model T
He said as you need more and more tokens models will fall apart because each additional token is a chance for a mistake and they will just exponentially fall apart. But in practice models have learned to identify and self-correct mistakes and if you look at the graphs more inference reasoning tokens almost always give far better accuracy.
His other criticism of LLMs that I like better is that they try to predict tokens instead of learned embeddings. Tokens are arbitrary and in order to decode LLMs you need technical analysis (see mechanistic interpretability).
With JEPA models so far, it seems that PCA on latent vectors suffices.
tldr: embeddings have a lot more room for improvement
The difficulty in predicting a latent is so called "collapse"; the embedding neutral network can always output the zero vector and this would predict the output correctly.
There are different ways to solve this, DINO uses two different models - a teacher and a student and LeCunn uses an explicit term against collapsing to a single output.
Yann mentions DINO in his talks
"we use a rough estimate of a total 9M GPU hours"
From CoreWeave, at current prices (~$2.46/hr spot to ~$6.16/hr on demand) would correspond to $22M–$55M.
The dataset is really where the cost is though - they used LVD-1689M - 1.6B images of curated web data from roughly 17B instagram images. This probably cost a huge amount of hours in human annotation, compute for algorithmic filtering, etc and not to mention probably a 20-50 person team working on this model.
You might want to change assumptions about how expensive these models are.
The 9M GPU hours includes the DINO v2 inference used in order to curate the data set.
The final training run used like 300000 dollars of compute.
Unfortunately we don't know how much RLVR + Agent training costs these companies. I'm just gonna say it's in the hundreds of millions, because they are supposedly making billions of profit on inference yet making billion dollar losses