> But it's objectively not capable without a human specifying things through prompts and training. Same as an oven can't cook a three course meal without a chef.

Not in the same way. The only thing an oven can do by itself is thermostatic.

All machine learning (LLMs included, but way broader than that) is bad at learning compared to any organic brain, to the extent that any organism this bad would starve before learning to eat. However, even a human can't become a chef unless trained, we learn a lot more than we innovate, and what we happen to want without prompting is not generally well aligned with what is desired by people who pay us, which is why we need all those boring workplace things like "a boss".

But even then, this diversion is like saying "an oven can't cook a meal" in response to someone saying they built an oven and "it cooked a meal". Like, it's obvious they didn't mean it did every step by itself without anyone ever even bothering to ask it to: if they meant that version, they'd be a lot louder about it.

> We get around that with training data but there will always be things with no/less data or outdated knowledge.

And? The linked git page (implicitly) claims that there is sufficient training data to do this task.

It may, of course, be wrong. I won't be surprised if it turns out this simulation is too far from reality. But the claim is "it does ${thing} now", not "it's generally intelligent and can do everything now".

> Like, it's obvious they didn't mean it did every step by itself without anyone ever even bothering to ask it to: if they meant that version, they'd be a lot louder about it.

"AI is now capable of developing its own inference hardware"