I can't speak for GPT-4, but in my experience with ChatGPT, I would definitely not say it is better with code. It's just absurdly, terribly, unbelievably bad at maths. It's a bit better at dealing with code, but it doesn't mean it's good, you're just comparing it with its weakest area. It's not really capable of generating code that does anything even a little complex without heavy guidance directing it towards mistakes and getting it to make revision after revision (and even that is non-trivial to get it to do, it tends to just start generating completely different programs with completely different problems instead)

That being said, I can definitely believe it could do okay at decompilation. It's an easy enough task in general, comparatively, and the "trickiest" bit (interpreting what the program is supposed to be doing, to have the context to name variables etc) feels like the kind of thing it'd perform surprisingly well at. Getting a general "vibe" and sticking with it, and translating A to B, it tends to do okay. It's when it needs to generate entirely novel outputs that need to fulfill multiple requirements at once that it starts failing miserably.

You could certainly do that to some extent, but I suspect that wouldn't generalize very well to programs that do things significantly different from anything in the training set. Transforming the syntax alone would probably be straightforward enough, but the parts that need more "interpretation" of what's going on (such as assigning plausible variable/function/class names, nevermind something like writing comments) I just can't see a standard supervised model handling particularly gracefully. Whereas that's one of the areas LLM excel at.

We already know of complex organisms that essentially don't age, and also others that are cancer-free or close to it. In any case, "prevent any and all aging and cancer before it happens" is a stupid goalpost. "Be able to quickly and affordably detect, identify and treat arbitrary strains of cancer and/or symptoms of aging" is essentially "just as good", and frankly seems like it could well already be within the reach of current models if they had the adequate "bioengineering I/O" infrastructure, and fast & accurate bioengineering simulations to train on.

ML could plausibly help in getting those online sooner, but unless you take the philosophical stance that "if we just made AGI they'd be able to solve every problem we have, so everything is effectively an ML problem", it doesn't seem like it'd be fair to say the bottlenecks to solving either of those are even related to ML in the first place. It's essentially all a matter of bioengineering coming up with the tools required.

"Smart character" would seem to be an awfully generous description for what you could realistically do with this, especially when mentioned alongside games like GTA, which very much do not revolve around text-based interactions. You can't really do a cutscene with an LLM today (you could have it generate a script, but how are you going to translate that to the screen automatically? that's highly non-trivial), nevermind leverage it to have individual characters actually behaving smartly within the game world.

If you're a game developer, do you want to dedicate the bulk of the user's VRAM/GPU time to text inference to... add some mildly dynamic textual descriptions to NPCs you encounter? Or would you rather use those resources to, y'know, actually render the game world?

It's probably not the most efficient method. However, in general, methods that converge faster tend to lead to slightly worse minima (think momentum-based methods vs "plain" SGD), which "intuitively" makes some degree of sense (the additional time spent training isn't completely wasted, with some of it effectively helping explore the possibility space, optimizing the model in ways that simple gradient-following might miss entirely)

I would be shocked if there doesn't exist a method that does even better than SGD while also being significantly more efficient. But it's probably not going to be easy to find, and I expect most simple heuristics ("this seems to be helping, do it more" or "this doesn't seem to be helping, do it less") will lead to training time vs accuracy tradeoffs, rather than universal improvements.

As a mere hobbyist, why is that? Leaving aside the merits of this particular study, MOND seems like a mildly-promising (if arguably inelegant) approach that still leaves a few unanswered questions (as does standard GR, I should add), and which has had various potential modalities already invalidated by empirical evidence (which may not sound promising, but it means 1) at least it's a falsifiable theory that makes testable predictions, and 2) current evidence isn't able to entirely rule it out, and hey, there's only a single reality that needs to be explained)

From my perspective, I see it in a similar position to supersymmetry in QFT: an a priori reasonable attempt at addressing the issues in the standard model they are motivated by, with a not-so-great track record that makes them ever superseding the standard model look fairly unlikely right now, but still not within "statistical impossibility" territory. Except supersymmetry doesn't seem to get anywhere near as much hate.