In this view, we are essentially living inside a high-fidelity generative model. Our brains are constantly 'hallucinating' a predicted reality based on past experience and current goals. The data from our senses isn't the source of the image; it's the error signal used to calibrate that internal model. Much like Genie 3 uses latent actions and frames to predict the next state of a world, our brains use 'Active Inference' to minimize the gap between what we expect and what we experience.
It suggests that our sense of 'reality' isn't a direct recording of the world, but a highly optimized, interactive simulation that is continuously 'regularized' by the photons hitting our retinas.
It’s also easy to find this treated in various philosophy/religion through time and space. And anyway as consciousness is eager to project whatever looks like a possible fit, elements of suggesting prior arts can be inferred back as far as traces can be found.
At what point does the processing become so strong that it's less a photograph and more a work of computational impressionism?
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.
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.
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.”
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.
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.
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.
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.
I prefer real danger as living in the simulation is derivative.
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.
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!)
So, like, it's very important to understand the lineage of training and not just the "this is it"
You cannot invent data.
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.
If instead of a photo you have a video feed, this is one step closer to implementing subjective experience.
- https://youtu.be/15KtGNgpVnE?si=rgQ0PSRniRGcvN31&t=197 walking through various cities
- https://x.com/fofrAI/status/2016936855607136506 helicopter / flight sim
- https://x.com/venturetwins/status/2016919922727850333 space station, https://x.com/venturetwins/status/2016920340602278368 Dunkin' Donuts
- https://youtu.be/lALGud1Ynhc?si=10ERYyMFHiwL8rQ7&t=207 simulating a laptop computer, moving the mouse
- https://x.com/emollick/status/2016919989865840906 otter airline pilot with a duck on its head walking through a Rothko inspired airport
https://www.youtube.com/watch?v=FyTHcmWPuJE
It's an experimental research prototype, but it also feels like a hint of the future. Feel free to ask any questions.
It's neat I guess that I can use a few words and generate the equivalent of an Unreal 5 asset flip and play around in it. Also I will never do that, much less pay some ongoing compute cost for each second I'm doing it.
Ironically, he covered PixVerse's world model last week and it came close to your ask: https://youtu.be/SAjKSRRJstQ?si=dqybCnaPvMmhpOnV&t=371
(Earlier in the video it shows him live prompting.)
World models are popping up everywhere, from almost every frontier lab.
From a product perspective, I still don't have a good sense of what the market for WMs will look like. There's a tension between serious commercial applications (robotics, VFX, gamedev, etc. where you want way, way higher fidelity and very precise controllability), vs current short-form-demos-for-consumer-entertainment application (where you want the inference to be cheap-enough-to-be-ad-supported and simple/intuitive to use). Framing Genie as a "prototype" inside their most expensive AI plan makes a lot of sense while GDM figures out how to target the product commercially.
On a personal level, since I'm also working on world models (albeit very small local ones https://news.ycombinator.com/item?id=43798757), my main thought is "oh boy, lots of work to do". If everyone starts expecting Genie 3 quality, local WMs need to become a lot better :)
Although that probably precludes her from having animations in those worlds...
>Genie 3’s consistency is an emergent capability. Other methods such as NeRFs and Gaussian Splatting also allow consistent navigable 3D environments, but depend on the provision of an explicit 3D representation. By contrast, worlds generated by Genie 3 are far more dynamic and rich because they’re created frame by frame based on the world description and actions by the user.
> Diego Rivas, Shlomi Fruchter, and Jack Parker-Holder from the Project Genie team join host Logan Kilpatrick for an in-depth discussion on Google DeepMind’s latest breakthrough in world models. Project Genie is an experimental research prototype that allows users to generate, explore, and interact with infinitely diverse, photorealistic worlds in real-time. Learn more about the shift from passive video generation to interactive media, the technical challenges of maintaining world consistency and memory, and how these models serve as an essential training ground for AI agents.
Quite how they stopped a line forming three decks long outside every holodeck on the Enterprise is a mystery to me.
Your neighbors in the street protesting for comprehensive single payer healthcare? Yeah they're perfectly fine leaving your existence up to "market forces".
Copy-paste office workers everywhere reciting memorized platitudes and compliance demands.
You're telling me I could interact even less with such selfish (and often useless given their limited real skillset) people? Deal.
America needs to rethink the compensation package if it wants to survive as a socio-political meme. Happy to call myself Canadian or Chinese if their offer is better. No bullets needed.
You have a dangerously low opinion of your fellow man, and while I sympathize with your frustration, I would humbly suggest you direct that anger at owners of companies/politicians, rather than aim it at your everyday citizen.
Those owners and politicians are the result of exposure to American communities, schools, other institutions; they do not spontaneously exist.
Americans prop up the system as such Americans will defer or their faith was misplaced to begin with. And that ain't right; they're America! So the awfulness will continue until moral improves!
Atheist semantics while living theist like devotion to civil religion memes.
Perhaps better to roam a virtual reality than be starved in the real world.
Although, I am feeling a bit lazy so let me see if I can simulate a walk.
Maybe they can unplug from 500+ AQI pollution and spend time with their loved ones and friends in a simulated clean world?
Imagine working for 10-12 hours a day, and you come home to a pod (and a building could house thousands of pods, paid for by the company) where you plug in and relax for a few hours. Maybe a few more decades of breakthroughs can lead to simulated sleep as well so they get a full rest.
Wake up, head to the factory to make whatever the developed world needs.
(holy fuck that is a horrible existence but you know some people would LOVE for that to be real)
Except you'll never have to leave your pod. Extract the $$ from their attention all day, then sell them manufactured virtual happiness all night. It's just a more streamlined version of how many people live right now.
I'll be running away from that hellscape, thanks.
They'd have their own economy and "life" and leave the rest of us alone. It would be completely transactional, so I'd have zero reason to feel bad if they do it voluntarily.
If they can be happy in a simulated world, and others can be happy in the real world, then everyone wins!
I'm developing filmmaking tools with World Labs' Marble world model:
https://www.youtube.com/watch?v=wJCJYdGdpHg
https://github.com/storytold/artcraft
I think we'll eventually get to the point where these are real time and have consistent representations. I've been excited about world models since I saw the in-the-browser Pokemon demo:
https://madebyoll.in/posts/game_emulation_via_dnn/demo/
At some point, we'll have the creative Holodeck. If you've seen what single improv performers can do with AI, it's ridiculously cool. I can imagine watching entertainers in the future that summon and create entire worlds before us:
https://www.youtube.com/watch?v=MYH3FIFH55s
(If you haven't seen CodeMiko, she's an incredibly talented engineer and streamer. She develops mocap + AI streams.)
Just like how people in the 50s thought we would have flying cars and nuclear fusion by 2000.
It's reality privilege. Most of humanity will yearn for the worlds that AI will cook up for them, customized to their whims.
What data/metric are you drawing from to arrive at this conclusion? How could you even realistically make such a statement?
I think he's lucky he got out with his reputation relatively intact.
When the right move (strategically, economically) is to not compete, the head of the AI division acknowledging the above and deciding to focus on the next breakthrough seems absolutely reasonable.
Non-developers I know use them to organise meetings, write emails, research companies, write down and summarise counselling sessions (not the clients, the counselor), write press reports, help with advertising campaigns management, review complex commercial insurance policies, fix translations... The list of uses is endless, really. And I'm only talking of work-related usage, personal usage goes of course well beyond this.
Genie looks at the video, "when this group of pixels looks like this and the user presses 'jump', I will render the group different in this way in the next frame."
Genie is an artist drawing a flipbook. To tell you what happens next, it must draw the page. If it doesn't draw it, the story doesn't exist.
JEPA is a novelist writing a summary. To tell you what happens next, it just writes "The car crashes." It doesn't need to describe what the twisted metal looks like to know the crash happened.
I don't have access to the DeepMind demo, but from the video it looks like it takes the idea up a notch.
(I don't know the exact lineage of these ideas, but a general observation is that it's a shame that it's the norm for blog posts / indie demos to not get cited.)
[1] https://news.ycombinator.com/item?id=43798757
[2] https://madebyoll.in/posts/world_emulation_via_dnn/demo/
- That forest trail world is ~5 million parameters, trained on 15 minutes of video, scoped to run on a five-year-old iPhone through a twenty-year old API (WebGL GPGPU, i.e OpenGL fragment shaders). It's the smallest '3D' world model I'm aware of.
- Genie 3 is (most likely) ~100 billion parameters trained on millions of hours of video and running across multiple TPUs. I would be shocked if it's not the largest-scale world model available to the public.
There are lots of neat intermediate-scale world models being developed as well (e.g. LingBot-World https://github.com/robbyant/lingbot-world, Waypoint 1 https://huggingface.co/blog/waypoint-1) so I expect we'll be able to play something of Genie quality locally on gaming GPUs within a year or two.
Look at how much prompting it takes to vibe code a prototype. And they want us to think we'll be able to prompt a whole world?
Problem is, that's not what we've observed to happen as these models get better. In reality there is some metaphysical coarse-grained substrate of physics/semantics/whatever[1] which these models can apparently construct for themselves in pursuit of ~whatever~ goal they're after.
The initially stated position, and your position: "trying to hallucinate an entire world is a dead-end", is a sort of maximally-pessimistic 'the universe is maximally-irreducible' claim.
The truth is much much more complicated.
Eh? Context rot is extremely well known. The longer you let the context grow, the worse LLMs perform. Many coding agents will pre-emptively compact the context or force you to start a new session altogether because of this. For Genie to create a consistent world, it needs to maintain context of everything, forever. No matter how good it gets, there will always be a limit. This is not a problem if you use a game engine and code it up instead.
Once you hit a billion or so parameters, rocks suddenly start to think.
LLMs can barely remember the coding style I keep asking it to stick to despite numerous prompts, stuffing that guideline into my (whatever is the newest flavour of product-specific markdown file). They keep expanding the context window to work around that problem.
If they have something for long-term learning and growth that can help AI agents, they should be leveraging it for competitive advantage.
This is only a useful premise if it can do any of those things accurately, as opposed to dreaming up something kinda plausible based on an amalgamation of every vaguely related YouTube video.
What's the use? Current scientific models clearly showing natural disasters and how to prevent them are being ignored. Hell, ignoring scientific consensus is a fantastic political platform.
Let's say, you simulate a long museum hallway with some vases in it. Who holds what? The basic game engine has the geometry, but once the player pushes it and moves it, it needs to inform the engine it did, and then to draw the next frame, read from the engine first, update the position in the video feed, then again feed it back to the engine.
What happens if the state diverges. Who wins? If the AI wins then...why have the engine at all?
It is possible but then who controls physics. The engine? or the AI? The AI could have a different understanding of the details of the base. What happens if the vase has water inside? who simulates that? what happens if the AI decides to break the vase? who simulates the AI.
I don't doubt that some sort of scratchpad to keep track of stuff in game would be useful, but I suspect the researchers are expecting the AI to keep track of everything in its own "head" cause that's the most flexible solution.
> Why are they not training models to help write games instead?
| It simulates physics and interactions for dynamic worlds, while its breakthrough consistency enables the simulation of any real-world scenario — from robotics and modelling animation and fiction, to exploring locations and historical settings.
Same with Nvidia's Earth 2 and Cosmos (and a bit like Isaac). Games or VR environments are not the primary drive, the primary drive is training robots (including non-humanoids, such as Waymo) and just getting the data. It's exactly because of this that perfect physics (or let's be honest, realistic physics[0,1]). Getting 50% of the way there in simulation really does cut down the costs of development, even if we recognize that cost steepens as we approach "there". I really wish they didn't call them "world models" or more specifically didn't shove the word "physics" in there, but hey, is it really marketing if they don't claim a golden goose can not only lay actual gold eggs but also diamonds and that its honks cure cancer?
[0] Looking right does not mean it is right. Maybe it'll match your intuition or undergrad general physics classes with calculus but talk to a real physicist if you doubt me here. Even one with just an undergrad will tell you this physics is unrealistic and any one worth their salt will tell you how unintuitive physics ends up being as you get realistic, even well before approaching quantum. Go talk to the HPC folks and ask them why they need superocmputers... Sorry, physics can't be done from observation alone.
[1] Seriously, I mean look at their demo page. It really is impressive, don't get me wrong, but I can't find a single video that doesn't have major physics problems. That "A high-altitude open world featuring deformable snow terrain." looks like it is simulating Legolas[2], not a real person. The work is impressive, but it isn't anywhere near realistic https://deepmind.google/models/genie/
I think it really comes down to dev time and adaptability. But honestly I'm fairly with you. I don't think this is a great route. I have a lot of experience in synthetic data generation and nothing beats high quality data. I do think we should develop world models but I wouldn't all something a world model unless it actually models a physics. And I mean "a physics" not "what people think of as 'physics'" (i.e. the real world). I mean having a counterfactual representation of an environment. Our physics equations are an extremely compressed representation of our reality. You can't generate these representations through observation alone, and that is the naive part of the usual way to develop world models. But we'd need to go into metaphysics and that's a long conversation not well suited for HN.
These simulations are helping but they have a clear limit to their utility. I think too many people believe that if you just feed the models enough data it'll learn. Hyperscaleing is a misunderstanding of the Bitter Lesson that slows development despite showing some progress.
If making games out of these simulations work, it't be the end for a lot of big studios, and might be the renaissance for small to one person game studios.
There's obviously something insanely impressive about these google experiments, and it certainly feels like there's some kind of use case for them somewhere, but I'm not sure exactly where they fit in.
If I am wrong, then the huge supply of fun games will completely saturate demand and be no easier for indie game devs to stand out.
You COULD create a sailing sim but after ten minutes you might be walking on water, or in the bath, and it would use more power than a small ferry.
There's no way this tech can run on a PS5 or anything close to it.
Indie games are already bigger than ever as far as I know.
I mean, if making a game eventually boils down to cooking a sufficient prompt (which to be clear, I'm not talking about text, these prompts are probably going to be more like video databases) then I'm not sure if it will be a renaissance for "one person game studios" any more than AI image generation has been a renaissance for "one person artists".
I want to be optimistic but it's hard to deny the massive distribution stranglehold that media publishing landscape has, and that has nothing to do with technology.
We saw a very diverse group of users, the common uses was paragliders, gliders, and pilots who wanted to view their or other peoples flights. Ultramarathons, mountain bike and some road-races where it provided an interactive way to visualize the course from any angle and distance. Transportation infrastructure to display train routes to be built. The list goes on.
"Sure it can write a single function but the code is terrible when it tries to write a whole class..."
The deadness you're talking about is there in procedural worlds too, and it stems from the fact that there's not actually much "there." Think of it as a kind of illusion or a magic trick with math. It replicates some of the macro structure of the world but the true information content is low.
Search YouTube for procedural landscape examples. Some of them are actually a lot more visually impressive than this, but without the interactivity. It's a popular topic in the demo scene too where people have made tiny demos (e.g. under 1k in size) that generate impressive scenes.
I expect to see generative AI techniques like this show up in games, though it might take a bit due to their high computational cost compared to traditional procedural generation.
Try it in Google Labs: https://labs.google/projectgenie
(Project Genie is available to Google AI Ultra subscribers in the US 18+.)
This guy a month ago for example: https://youtu.be/SGJC4Hnz3m0
Or in gaming terms do these models think FPS or RTS?
Text models and pixel grid vision models is easy but struggling to wrap my head around what world model "sees" so to speak.
The game is called "Explorers' Guild", or "xg" for short. It's easier for Claude to act as a player than a director (xg's version of a dungeon master or game master), again mainly because of permance and learning issues, but to the extent that I can help it past those issues it's also fairly good at acting as a director. It does require some pretty specific stuff in the system prompt to, for example, avoid confabulating stuff that doesn't fit the world or the scenario.
But to really build a version of xg on Claude it needs better ways to remember and improve what it has learned about playing the game, and what it has learned about a specific group of players in a specific scenario as it develops over time.
I can't even fathom what it would be like for the future of simulation and physical world when it gets far more accurate and realistic.
This is most evident in the way things collide.
If there is a possibility where it continue to improve at a similar rate with llms. A way to simulate fluid dynamics or structural dynamics with reasonable accuracy and speed can unlock much faster pace of innovation in the physical world. (And validated with rigorous scientific methods)
I'm not certain but I think the LLM is also generating the physics itself. It's generating rules based on its training data, e.g. watch a cat walk enough and you can simulate how the cat moves in the generated "world".
The goal of world models like Genie is to be a way for AI and robots to "imagine" things. Then, they could practice tasks inside of the simulated world or reason about actions by simulating their outcome.
How are you justifying the enormous energy cost this toy is using, exactly?
I don't find anything "responsible" about this. And it doesn't even seem like something that has any actual use - it's literally just a toy.
Of course, maybe its a bridge to something else, but all I see is an image generator that's working really fast, so nothing novel.
Humanity goes into the box and it never comes back out. It's better in there than it is out there for 99% of the population.
I mean, yes, the probability of having that level of tech in decades is quite high.
But the technology is moving very fast right now. It sounds crazy, but I think that there is a 50% chance of having ready player one level technology before 2030.
It's absolutely possible it will take more time to become economical.
RIP Stadia.
While "journalists" were busy bootlicking a laggy 720p Android only xCloud beta, Stadia was already delivering flawless 4K@60FPS in a web browser
They killed the only platform that actually worked just to protect Microsoft
This will be a textbook case study in how a legacy monopoly kills innovation to protect its own mediocrity
Microsoft won't survive the century, they are a dinosaur on borrowed time that has already lost the war in mobile, AI, and robotics
They don't create,, they just buy marrket share to suffocate the competition and ruin every product they touch
Even their cloud dominance is about to end, as they are already losing their grip on the European market to antitrust and sovereign alternatives
But I do think it's a partial existence proof.