I like the DualSense controller. Yes, it’s “for playstation” but all controllers work on PC nowadays. Especially on Linux, the driver for PS controllers is in the kernel, and they can work both wired and via Bluetooth.
It even supports using the special features of the DualSense in some games, like the adaptive triggers when playing Rift Apart or Forbidden West.
And the touchpad works as a mouse, which is handy.
My argument was and is that neural models don’t produce anything truly new. That they can’t handle things outside what is outlined by the data they were trained on.
Are you not claiming otherwise?
You say it’s possible to guide models into doing new things, and I can see how that’s the case, especially if the model is a very big one, meaning it is more likely that it has relevant structures to apply to the task.
But I’m also pretty damn sure they have insurmountable limits. You can’t “guide” and LLM into doing image generation, except by having it interact with an image generation model.
Bloated, as in large and heavy. More expensive, more power hungry, less efficient.
I already brought it up. They can’t deal with something completely new.
When you discuss what you want with a human artist or programmer or whatever, there is a back and forth process where both parties explain and ask until comprehension is achieved, and this improves the result. The creativity on display is the kind that can unfold and realize a complex idea based on simple explanations even when it is completely novel.
It doesn’t matter if the programmer has played games with regenerating health before, one can comprehend and implement the concept based on just a couple sentences.
Now how would you do the same with a “general” model that didn’t have any games that work like that in the training data?
My point is that “general” models aren’t a thing. Not really. We can make models that are really, really big, but they remain very bad at filling in gaps in reality that weren’t in the training data. They don’t start magically putting two and two together and comprehending all the rest.
I know the input doesn’t alter the model, that’s not what I mean.
And “general” models are only “general” in the sense that they are massively bloated and still crap at dealing with shit that they weren’t trained on.
And no, “comprehending” new concepts by palette swapping something and smashing two existing things together isn’t the kind of creativity I’m saying these systems are incapable of.
Try to get an image generator to create an image of a tennis racket, with all racket-like objects or relevant sport data removed from the training data.
Explain the concept to it with words alone, accurately enough to get something that looks exactly like the real thing. Maybe you can give it pictures, but one won’t really be enough, you’ll basically have to give it that chunk of training data you removed.
That’s the problem you’ll run into the second you want to realize a new game genre.
“The potential here is absurd,” wrote app developer Nick Dobos in reaction to the news. “Why write complex rules for software by hand when the AI can just think every pixel for you?”
“Can it run Doom?”
“Sure, do you have a spare datacenter or two full of GPUs, and perhaps a nuclear powerplant for a PSU?”
What the fuck are these people smoking. Apparently it can manage 20 fps on one “TPU” but to get there it was trained on shitload of footage of Doom. So just play Doom?!
The researchers speculate that with the technique, new video games might be created “via textual descriptions or examples images” rather than programming, and people may be able to convert a set of still images into a new playable level or character for an existing game based solely on examples rather than relying on coding skill.
It keeps coming back to this, the assumption that these models, if you just feed them enough stuff will somehow become able to “create” something completely new, as if they don’t fall apart the second you ask for something that wasn’t somewhere in the training data. Not to mention that this type of “gaming engine” will never be as efficient as an actual one.
Even if it is, it’s a derivation I’ve been sorely missing. Ever since Battleborn got shut down, there’s been a Battleborn shaped hole in my heart. Deadlock fits in that hole really well.
It’s possible that the whole impetus for creating Deadlock came from something like that. Someone at valve, like me, enjoyed the hell out this particular mix of mechanics.
There’s nothing like it. Dota doesn’t do the trick, neither does Overwatch. Of all things, the closest thing might be Titanfall 2’s titan combat.
I’d say yes. But you do have to figure out how to apply the MOBA way of thinking. How to stack the stats of items, abilities and leveling up, into doing a shitload of damage without dying.
That applied to Battelborn, and it does in Deadlock, too.