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.
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.
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.
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.
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.
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?
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.
It’s just a research paper, not a product. It’s about discovering and learning new possible methods and applications.
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.
This is just a pile of garbage. Jim Sterling’s break down is the most complete argument. But this is just a plain ol bag of shit.
Link to the video. I agree, it was a really good video on this topic and how wrong it is philosophically.
“Playable” nah. “Interactive” yes.
