That feels like a _huge_ assumption. It would arguably be quite surprising just based on the prior history of ML-y stuff; there's a tendency for things to get 95% good enough quickly, and then never quite make it to 100% (because 100% is really a completely different sort of problem, arguably requiring AGI).

My favourite example of this: speech recognition. In the 90s this went quite quickly from a joke to a tech demo that required enormously expensive hardware to a tech demo that you could run on your laptop to actually marginally useful (you may remember Apple and particularly MS claiming that it was the future, and would soon be the primary way that people interacted with computers).

And then, well... like, voice recognition today is certainly better than Dragon Dictate was 25 years ago, but you probably still wouldn't want to trust it with anything important, and it's certainly far worse at it than a human is.

I'm struggling to think of _any_ ML-ish thing in the human-imitating space which has gotten all the way to virtually perfect/as good as human. Maybe OCR? Though even then, you still sometimes get odd results.

>I'm struggling to think of _any_ ML-ish thing in the human-imitating space which has gotten all the way to virtually perfect/as good as human.

Go?

Oh, yeah, actually, fair, a few games. Though, can an expert human player reliably determine that they're playing against a machine? I suspect they can, but I don't know.

In general the goal of these progrms is to win, not to pretend to be human.

In chess, there is a project called Maia which aims at predicting the human move rather than the best move. Even then it blunders less than humans of a similar rating, so it can still be detected.

https://maiachess.com/

> That feels like a _huge_ assumption.

Huh, seems like a very reasonable inference to me.

Will it?

I think so or I wouldn't have said so. You obviously disagree which is why you're obtusely indicating that.

Guess we'll see.