301–310 of 529 posts
Yann LeCun seeks $5B+ valuation for world model startup AMI (Amilabs). He has hired LeBrun to the helm as CEO. AMI has also hired LeFunde as CFO and LeTune as head of post-training. They’re also considering hiring LeMune as Head of Growth and LePrune to lead inference efficiency. https://techcrunch.com/2025/12/19/yann-lecun-confirms-his-ne...
Justifiable. There are a lot more degrees of freedom in world models. LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (ground…
The term LLM is confusing your point because VLMs belong to the same bin according to Yann. Using the term autoregressive models instead might help.
Earlier quoted context omitted.
> virtual simulation of 3d world Virtual simulations are not substitutable for the physical world. They are fundamentally different theory problems that have almost no overlap in applicability. You could in principle create a simulation with the same mathematical properties as the physical world but no one has ever done that. I'm not sure if we even know how. Physical world dynamics are metastable and non-linear at e…
> You could in principle create a simulation with the same mathematical properties as the physical world but no one has ever done that. I'm not sure if we even know how. What do you mean by that? Simulating physics is a rich field, which incidentally was one of the main drivers of parallel/super computing before AI came along.
Reconstructing ground truth from these measurements, which is what you really want to train on, is a difficult open inference problem. The idiosyncratic effects induce large changes in the relationships learnable from the data model. Many measurements map to things that aren't real. How badly that non-reality can break your inference is context dependent. Because the samples are sparse and irregular, you have to constantly model the noise floor to make sure there is actually some signal in the synthesized "ground truth".
In simulated physics, there are no idiosyncratic measurement issues. Every data point is deterministic, repeatable, and well-behaved. There is also much less algorithmic information, so learning is simpler. It is a trivial problem by comparison. Using simulations to train physical world models is skipping over all the hard parts.
I've worked in HPC, including physics models. Taking a standard physics simulation and introducing representative idiosyncratic measurement seems difficult. I don't think we've ever built a physics simulation with remotely the quantity and complexity of fine structure this would require.
Justifiable. There are a lot more degrees of freedom in world models. LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (ground…
> LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. No hate, but this is just your opinion. The definition of "text" here is extremely broad – an SVG is text, but it's also an image format. It'…
But yeah, I can't imagine that LLMs don't already have a world model in there. They have to. The internet's corpus of text may not contain enough detail to allow a LLM to differentiate between similar-looking celebrities, but it's plenty of information to allow it to create a world model of how we perceive the world. And it's a vastly more information-dense means of doing so.
Justifiable. There are a lot more degrees of freedom in world models. LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (ground…
Earlier quoted context omitted.
That's such a terrible take. For a hot minute Meta had a top 3 LLM and open sourced the whole thing, even with LeCunn's reservations around the technology. At the same time Meta spat out huge breakthroughs in: - 3d model generation - Self-supervised label-free training (DINO). Remember Alexandr Wang built a multibillion dollar company just around having people in third world countries label data, so this is a huge br…
> - Self-supervised label-free training (DINO). Remember Alexandr Wang built a multibillion dollar company just around having people in third world countries label data, so this is a huge breakthrough. If it was a breakthrough, why did Meta acquire Wang and his company? I'm genuinely curious.
Unfotunately the dude knows very little about ai or ml research. He's just another wealthy grifter.
At this point decision making at Meta is based on Zuckerberg's vibes, and i suspect the emperor has no clothes.
Earlier quoted context omitted.
I think LeCun has been so consistently wrong and boneheaded for basically all of the AI boom, that this is much, much more likely to be bad than good for Europe. Probably one of the worst people to give that much money to that can even raise it in the field.
Could you please elaborate on what he was wrong about?
I believe he didn't think that reasoning/CoT would work well or scale like it has
Justifiable. There are a lot more degrees of freedom in world models. LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (ground…
I don't understand this view. How I see it the fundamental bottleneck to AGI is continual learning and backpropagation. Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation. World models don't solve any of these problems; they are fundamentally the same kind of deep learning architectures we are used to work with. Heck, if you think learning from the world i…
Earlier quoted context omitted.
I really hate the world model terminology, but the actual low level gripe between LeCunn and autoregressive LLMs as they stand now is the fact that the loss function needs to reconstruct the entirety of the input. Anything less than pixel perfect reconstruction on images is penalized. Token by token reconstruction also is biased towards that same level of granularity. The density of information in the spatiotemporal…
Isn't the Sora video model a ViT with spatiotemporal inputs (so they've found a way to compress that down), but at the same time LeCunn wouldn't consider that a world model?
I assume that when you get out of bed in the morning, the first thing you dont do is paint 1000 1080p pictures of what your breakfast looks like.
LeCunns models predict purely in representation space and output no pixel scale detailed frames. Instead you train a model to generate a dower dimension representation of the same thing from different views, penalizing if the representation is different ehen looking at the same thing
Earlier quoted context omitted.
The premise is wrong, we are not seeing diminishing returns. By basically any metric that has a ratio scale, AI progress is accelerating, not slowing down.
For example?
The METR time-horizon benchmark shows steady exponential growth. The frontier lab revenue has been growing exponentially from basically the moment they had any revenues. (The latter has confounding factors. For example it doesn't just depend on the quality of the model but on the quality of the apps and products using the model. But the model quality is still the main component, the products seem to pop into existence the moment the necessary model capabilities exist.)