The model, called GameNGen, was made by Dani Valevski at Google Research and his colleagues, who declined to speak to New Scientist. According to their paper on the research, the AI can be played for up to 20 seconds while retaining all the features of the original, such as scores, ammunition levels and map layouts. Players can attack enemies, open doors and interact with the environment as usual.
After this period, the model begins to run out of memory and the illusion falls apart.
I really hope this doesn’t catch on, Games are already horifically inefficient, imagine if we started making them like this and a 4090 becomes the minnimum system requirement for goddamn DOOM.
Games are already horifically inefficient
That’s so far from the truth, it hurts me to read it. Games are one of the most optimised programs you can run on your computer. Just think about it, it’s a application rendering an entire imaginary world every dozen milliseconds. Compare it to anything else you run, like say slack or teams, which makes your CPU sweat just to notify you about a new message.
Many games, especially AAA games or ones relying on common game engines, are actually horribly inefficient. It’s hard to run any Unity/Unreal game in 4k on my 1070. Even if it has shit graphics like Lethal Company. What does run well? Smaller, custom engines, even Metro Exodus runs with 60+ FPS in 4k on my 1070, and still looks very good. Why? Because 4A Game is/was actually interested in creating a good engine and games. That’s the whole reason they split from the S.T.A.L.K.E.R team: Because, in their opinion, the engine was too inefficient.
Most games are just a quick cash grab tho, especially ones by large companies like EA. Other large companies with a significantly lower output of games, eg. Valve, do produce programmatically higher quality games tho.
It’s hard to run any Unity/Unreal game in 4k on my 1070
Both of these engines are capable of making very optimized games, it’s just that most of the developers using them either don’t have the expertise or don’t care to put in the effort.
Note that the image here isn’t from the AI project, it’s from actual Doom. Their own screenshots have weird glitches including a hit splat that looks like a butt in the image I’ve seen closest to this one.
And when they say they’ve “run the game” they do not mean that there was a playable version that was publicly compared to the original. Rather they released short video clips of alleged gameplay and had their evaluators try to identify if they were from the AI recreation or from actual Doom.
Even by the abysmal standards of generative AI projects this is a hell of a grift.
I’m pretty sure that screenshot is from the video. The zombieman has no feet
Possibly fair. I’m pretty sure I’ve seen that exact screenshot used in other articles about Doom, but I’m not enough of a Doom nerd to be sure.
There’s a decent writeup over at Pivot-to-AI that looks at the paper as a whole in more detail.
Thinking quickly, Generative AI constructs a playable version of Doom, using only some string, a squirrel, and a playable version of Doom.
It’s cool but it’s more or less just a party trick.
Is it though? We can show an AI thousands of hours of something and it can simulate it almost perfectly. All the game mechanics work! It even makes you collect keys and stock up on ammo. For a stable diffusion model that’s pretty profound emergent behavior.
I feel like you’re kidding yourself if you don’t think this has real world applications. This is the kind breakthrough we need for self-driving: the ability to simulate what would happen in real life given a precise current state and a set of fictional inputs.
Doom is a low-graphics game, so it’s definitely easier to simulate, but this method could make the next generation of niche “VidGen” models extremely accurate.
Honestly I thinkyour self driving example is something this could be really cool for. If the generation can exceed real time (I.e. 20 secs of future image prediction can happen in under 20 secs) then you can preemptively react with the self driving model and cache the results.
If the compute costs can be managed maybe even run multiple models against each other to develop an array likely branch predictions (you know what I turned left)
Its even cooler that player input helps predict the next image.
I’m not convinced. The ammo seems to go up and down on a whim, as does the health
Because “AI” isn’t actually “artificial intelligence.” It’s the marketing term that seems to have been adapted by every corporation to describe “LLMs…” which are more like extra fancy power guzzling parrots.
Its why the best cases for them are mimicking things brainlessly, like voice cloning for celebrity impressions… but that doesn’t mean it can act or comprehend emotion, or know how many fingers a hand should have and why they constantly hallucinate contextless bullshit… because just like a parrot doesn’t actually know any meaning of what it is saying when it goes “POLLY WANT A CRACKER…” it just knows the tall thing will give it a treat if it makes this specific squawk with its beak.
It’s a proof of concept demonstration not a final product. You might as well say the Wright brothers didn’t have anything other than their party trick.
So many practical applications for being able to do this beyond just video games in fact video games are probably the least useful application for this technology.
Regardless of the technology, isn’t this essentially creating a facsimile of a game that already exists? So the tech isn’t really about creating a new game, it’s about replicating something that already exists in a fairly inefficient manner. That doesn’t really help you to create something new, like I’m not going to be able to come up with an idea for a new game, throw it at this AI, and get something playable out of it.
That and the fact it “can be played for up to 20 seconds” before “the model begins to run out of memory” seems like, I don’t know, a fairly major roadblock?
It’s just a research paper, not a product. It’s about discovering and learning new possible methods and applications.
So you think a project should be killed immediately upon inception because it’s not immediately perfect? That is a really really weird attitude.
I’m more taking issue with this quote from the article:
“Researchers behind the project say similar AI models could be used to create games from scratch in the future, just as they create text and images today.”
This doesn’t strike me as something that can create a game from scratch, it’s something that can take an existing game and replicate it without having access to the underlying source code, and use an immense amount of processing power to do it.
Since it seems they’re using generative AI based technology underneath it, they’re effectively building a Doom model. You might be able to spin a Doom clone off from that but I don’t see it as something you could practically throw another game type at.
That being said as I said in a different reply, I was viewing it through the lens of something more product based rather than that of a research project. As a field of research, it’s an interesting topic. But I’m not sure how you connect it to “create games from scratch” if you don’t already have an existing game available to train the model on.
Why do you think it needs an existing game to train the model on? They used Doom precisely because it already exists.
The entire point to the research paper was to see if humans could tell the difference between the generated content and the real game, that way they have a measurable metric of how viable this technology is even if only in theory, that means that they have to make something that’s based off a real game.
Obviously the technology isn’t commercially viable yet. But the fact that it looks even remotely like Doom shows that there is promise to the technology.
Perhaps you could be missing the trajectory of continuous improvement. How long until The Matrix?
It’s an exponential increase as well and humans are very bad at judging exponential increases they look at something like this and they see no promise in it because they can’t see that four or five iterations down the line (and in the world of AI that could very easily be 3 months) it will be hundreds of times better.
