2048 is so good. I love the teams each having three craft types. The tracks are really fun, with both wide sections where you can go flat out without a care, and narrow parts where you have to really thread the needle and use the airbrakes with total precision.
As long as there are killer 1st party titles exclusive to a console platform, there’s a reason to buy one.
Counterargument: some of us consider this kind of arbitrary BS a reason NOT to buy one.
And that’s before you even consider the additional crap consoles pull, like Nintendo making the only way to back up your saves a fucking subscription service.
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.