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Just because there are errors in this doesn't mean it isn't significant. If a machine learning model understands how physical objects interact with each other that is very useful.
A proper world model like Jepa should be predicting in latent space where the representation of what is going on is highly abstract.
Video generation models by definition are either predicting in noise or pixel space (latent noise if the diffuser is diffusing in a variational encoders latent space)
It seems like what this lab is doing is quite vanilla, and I'm wondering if they are doing any sort of research in less demo sexy joint embedding predictive spaces.
There was a recent paper, LeJepa from LeCunn and a postdoc that actually fixes many of the mode distribution collapse issues with the Jepa embedding models I just mentioned.
I'm waiting on the startup or research group that gives us an unsexy world model. Instead of giving us 1080p video of supermodels camping, gives us a slideshow of something a 6 year old child would draw. That would be a more convincing demonstrator of an effective world model.
As a machine learning researcher, I don't get why these are called world models. Visually, they are stunning. But it's nowhere near physical. I mean look at that video with the girl and lion. The tail teleports between legs and then becomes attached to the girl instead of the tiger. Just because the visuals are high quality doesn't mean it's a world model or has learned physics. I feel like we're conflating these thi…
>As a machine learning researcher, I don't get why these are called world models. It's called "world models" because it's a grift. An out-in-the-open, shameless grift. Investors, pile on.
Edit: I said a bit more in the reply to the sibling comment. But we're probably on a similar page.
The reason they are called "world models" is because the internal representation of what they display represents a "world" instead of a video frame or image. The model needs to "understand" geometry and physics to output a video. Just because there are errors in this doesn't mean it isn't significant. If a machine learning model understands how physical objects interact with each other that is very useful.
As a machine learning researcher, I don't get why these are called world models. Visually, they are stunning. But it's nowhere near physical. I mean look at that video with the girl and lion. The tail teleports between legs and then becomes attached to the girl instead of the tiger. Just because the visuals are high quality doesn't mean it's a world model or has learned physics. I feel like we're conflating these thi…
> Visually, they are stunning. The input images are stunning, model's result is another disappointing trip to uncanny valley. But we feel Ok as long as the sequence doesn't horribly contradict the original image or sound. That is the world model.
> But we feel Ok as long as the sequence doesn't horribly contradict the original image or sound.
> That is the world model.
I've yet to see a claimed world model that actually captures this behavior. Yet it's something every game engine[1] gets very well. We'd call it a bad physics engine if they made the same mistakes we see even the most advanced "world models" do.
This is part of why I'm trying to explain that visual quality is actually orthogonal. Even old Atari games have consistent world models despite being pixelated. Or think about Mario on the original NES. Even the physics breaking in that game are more edge cases and not the norm. But here, things like the lion's tail is not consistent even to a 2D world. I've never bought the explanation that teleporting in front of and behind the leg is an artifact of embedding 3D into 2D[2] because the issue is actually the model not understanding collision and occlusion. It does not understand how the sections relate to one another in the image.
The major problem with these systems is that they just hope that the physics is recovered through enough examples of videos. Yet if one studied physics (beyond your basic college courses) you'd understand the naïveté of that. It took a long time to develop physics due to these specific limitations. These models don't even have the advantage of being able to interact with the environment. They have no mechanisms to form beliefs and certainly no means to test them. It's essentially impossible to develop physics through observation alone
[0] with respect to the physics of the world being simulated. I want you distinguish real world physics from /a physics/
[1] a game physics engine is a world model. Which, as in stressing in [0], does not necessarily need follow real world physics. Mistakes happen of course but things are generally consistent.
[2] no video and almost no game is purely 2D. They tend to have backgrounds which places some layering but we'll say 2D for convenience and since we have a shared understanding
The reason they are called "world models" is because the internal representation of what they display represents a "world" instead of a video frame or image. The model needs to "understand" geometry and physics to output a video. Just because there are errors in this doesn't mean it isn't significant. If a machine learning model understands how physical objects interact with each other that is very useful.
> what they display represents a "world" instead of a video frame or image.
I'm unconvinced. The tiger and girl video is the clearest example. Nothing about that seems world representing
The reason they are called "world models" is because the internal representation of what they display represents a "world" instead of a video frame or image. The model needs to "understand" geometry and physics to output a video. Just because there are errors in this doesn't mean it isn't significant. If a machine learning model understands how physical objects interact with each other that is very useful.
No it doesn't. It merely needs to mimic.
I feel like there's a bit if a disconnect with the cool video demos demonstrated here and say, the type of world models someone like Yann Lecunn is talking about. A proper world model like Jepa should be predicting in latent space where the representation of what is going on is highly abstract. Video generation models by definition are either predicting in noise or pixel space (latent noise if the diffuser is diffusi…