That is not the goal.
The purpose of world models like Genie is to be the "imagination" of next-generation AI and robotics systems: a way for them to simulate the outcomes of potential actions in order to inform decisions.
The whole reason for LLMs inferencing human-processable text, and "world models" inferencing human-interactive video, is precisely so that humans can connect in and debug the thing.
I think the purpose of Genie is to be a video game, but it's a video game for AI researchers developing AIs.
I do agree that the entertainment implications are kind of the research exhaust of the end goal.
When you simulate a stream of those latents, you can decode them into video.
If you were trying to make an impressive demo for the public, you probably would decode them into video, even if the real applications don't require it.
Converting the latents to pixel space also makes them compatible with existing image/video models and multimodal LLMs, which (without specialized training) can't interpret the latents directly.
I think robots imagining the next step (in latent space) will be useful. It’s useful for people. A great way to validate that a robot is properly imagining the future is to make that latent space renderable in pixels.
[1] “By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment.”
Yeah, I think this is what the person above was saying as well. This is what people at google have said already (a few podcasts on gdm's channel, hosted by Hannah Fry). They have their "agents" play in genie-powered environments. So one system "creates" the environment for the task. Say "place the ball in the basket". Genie creates an env with a ball and a basket, and the other agent learns to wasd its way around, pick up the ball and wasd to the basket, and so on. Pretty powerful combo if you have enough compute to throw at it.
I do wonder if I can frankenstein together a passable VLA using pretrained LTX-2 as a base.
Even if you just wire this output (or probably multiples running different counterfactuals) into a multimodal LLM that interprets the video and uses it to make decisions, you have something new.
Soft disagree. What is the purpose of that imagination if not to map it to actual real world outfcomes. For this to compare them to the real world and possibly backpropagate through them you'll need video frames.
If you don't decode, how do you judge quality in a world where generative metrics are famously very hard and imprecise? How do you go about integrating RLHF/RLAF in your pipeline if you don't decode, which is not something you can skip anymore to get SotA?
Just look at the companies that are explicitly aiming for robotics/simulation, they *are* doing video models.
First of all, there are a variety of different types of world models. Simulation, video, static asset, etc. It's a loaded term, just as the use cases are widespread.
There are world models you can play in your browser inferred entirely by your CPU:
https://madebyoll.in/posts/game_emulation_via_dnn/ (my favorite, from 2022!)
https://madebyoll.in/posts/world_emulation_via_dnn/ (updated, in 3D)
There are static asset generating world models, like WorldLabs' Marble. These are useful for video games, previz, and filmmaking.
I wrote open source software to leverage marble for filmmaking (I'm a filmmaker, and this tech is extremely useful for scene consistency):
https://www.youtube.com/watch?v=wJCJYdGdpHg
https://github.com/storytold/artcraft
There are playable video-oriented models, many of which are open source and will run on your 3080 and above:
https://github.com/Robbyant/lingbot-world
There are things termed "world models" that really shouldn't be:
https://github.com/Tencent-Hunyuan/HunyuanWorld-1.0
There are robotics training oriented world models:
https://github.com/leggedrobotics/robotic_world_model
Genie is not strictly robotics-oriented.
The other examples you've given are neat, but for players like Google they are mostly an afterthought.
Gaming: $350B TAM
All media and entertainment: $3T TAM
Manufacturing: $5T TAM
Roughly the same story.
This tech is going to revolutionize "films" and gaming. The entire entertainment industry is going to transform around it.
When people aren't buying physical things, they're distracting themselves with media. Humans spend more time and money on that than anything else. Machines or otherwise.
AI impact on manufacturing will be huge. AI impact on media and entertainment will be huge. And these world models can be developed in a way that you develop exposure and competency for both domains.
edit: You can argue that manufacturing will boom when we have robotics that generalize. But you can also argue that entertainment will boom when we have holodecks people can step into.
Robots is also just one example. A hypothetically powerful AI agent (which might also use a world model) that controls a mouse and keyboard could replace a big chunk of white-collar work too.
Those are worth 10's of trillions of dollars. You can argue about whether they are actually possible, but the people backing this tech think they are.
They would try it once, think its cool and stop there. You would probably have a niche group of "world surfers" that would keep playing with it.
Most people do not have an idea on what they would want to play and how it would look like - they want a curated experience. As games adapted to the mass market, they became more and more curated experiences with lots of hand-holding the player.
Yeah, a holodeck would be popular, but that's a whole different technology ballpark and akin to talking about flying cars in this context.
This will have a giant impact on robotics and general models tho, as now they can simulate action/reaction inside a world in parallel, choosing the best course, by just having a picture of the world and probably a generated image of the end result or "validators" to check if task is accomplished.
And while robotics is $88B TAM nowadays, expect it to hit $888B in the next 5-10 years, with world simulators like this being one of the reasons.
From the team side, gotta be cool to build this, feels like one of those things all devs dream about.
I think you are anthropomorphising the AI too much. Imagination is inspired by reality, which AI does not have. Introducing a reality which the AI fully controls (looking beyond issues of vision and physics simulation) would only induce psychosis in the AI itself since false assumptions would only be amplified.
I think you're anthropomorphising the AI too much: what does it mean for an LLM to have psychosis? This implies that LLMs have a soul, or a consciousness, or a psyche. But... do they?
Speaking of reality, one can easily become philosophical and say that we humans don't exactly "have" a reality either. All we have are sensor readings. LLMs' sensors are texts and images they get as input. They don't have the "real" world, but they do have access to tons of _representations_ of this world.
I don’t get it. Is that supped to be a gotchya? Have you tried maliciously messing with an LLM? You can get it into a state that resembles psychosis. I mean you give it a context that is removed from reality, yet close enough to reality to act on and it willl give you crazy output.
As soon as this thing is hooked up to VR and reaches a tipping point with the general public we all know exactly what is going to happen. The creation of the most profitable, addictive and ultimately dystopian technology Big Tech has ever come up with.
Surely a small percentage, at least, would go on to colonize.
So, like, it's very important to understand the lineage of training and not just the "this is it"
It's not really as much of a boon as you'd think though, since throwing together a 3D model is not the bottleneck to making a sellable video game. You've had model marketplaces for a long time now.
It is for filmmaking! They're perfect for constructing consistent sets and blocking out how your actors and props are positioned. You can freely position the camera, control the depth of field, and then storyboard your entire scene I2V.
Example of doing this with Marble: https://www.youtube.com/watch?v=wJCJYdGdpHg
Marble definitely changes the game if the game is "move the camera", just most people would not consider that a game (but hey there's probably a good game idea in there!)
I prefer real danger as living in the simulation is derivative.
Read "Stars don't dream" by Chi Hui (vol1 of "Think weirder") :)
You cannot invent data.
For example, image generators like stable diffusion carry strong representations of depth and geometry, such that performant depth estimation models can be built out of them with minimal retraining. This continues to be true for video generation models.
Early work on the subject: https://arxiv.org/pdf/2409.09144
If instead of a photo you have a video feed, this is one step closer to implementing subjective experience.
This is a paper that recently got popular ish and discusses the counter to your viewpoint.
> Paradox 1: Information cannot be increased by deterministic processes. For both Shannon entropy and Kolmogorov complexity, deterministic transformations cannot meaningfully increase the information content of an object. And yet, we use pseudorandom number generators to produce randomness, synthetic data improves model capabilities, mathematicians can derive new knowledge by reasoning from axioms without external information, dynamical systems produce emergent phenomena, and self-play loops like AlphaZero learn sophisticated strategies from games
In theory yes, something like the rules of chess should be enough for these mythical perfect reasoners that show up in math riddles to deduce everything that *can* be known about the game. And similarly a math textbook is no more interesting than a book with the words true and false and a bunch of true => true statements in it.
But I don't think this is the case in practice. There is something about rolling things out and leveraging the results you see that seems to have useful information in it even if the roll out is fully characterizable.
What I object to are the "scaling maximalists" who believe that if enough training data were available, that complicated concepts like a world model will just spontaneously emerge during training. To then pile on synthetic data from a general-purpose generative model as a solution to the lack of training data becomes even more untenable.
Besides, we already know that agents can be trained with these world models successfully. See[1]:
> By learning behaviors in imagination, Dreamer 4 is the first agent to obtain diamonds in Minecraft purely from offline data, without environment interaction. Our work provides a scalable recipe for imagination training, marking a step towards intelligent agents