Stephen Wolfram

The one counterexample to this is if you think you're modeling the whole universe all the way down, then there is a notion of a correct model.

But even that is more complicated because it depends on kind of how observers sample things and so on.

That's a separate story.

But at least at the first level,

to say this thing about, oh, it's an approximation, you're capturing one aspect, you're not capturing other aspects.

When you really think you have a complete model for the whole universe, you better be capturing ultimately everything, even though to actually run that model is impossible because of computational irreducibility.

The only thing that successfully runs that model is the actual running of the universe.

It's the universe itself.

But okay, so what you care about...

Yes.

So, I mean, for example, you know, we could, we'll have a thing about, you know, data about movies, let's say.

We could be describing every individual pixel in every movie and so on, but that's not the level that people care about.

And it's, yes, this is a, I mean, and that level that people care about

is somewhat related to what's described in natural language.

But what we're trying to do is to find a way to sort of represent precisely so you can compute things.

See, one thing when you say, you give a piece of natural language, question is, you feed it to a computer.

You say, does the computer understand this natural language?

well, the computer processes it in some way, it does this, maybe it can make a continuation of the natural language, maybe it can go on from the prompt and say what it's gonna say.

You say, does it really understand it?

Hard to know.