Marcus Hutter

And after a while it predict, oh, the next coin flip will be head with probability 60%.

So it's the stochastic version of that.

Yes, yeah.

Well, in Solomon of Induction, precisely what you do is, so you combine, so looking for the shortest program is like applying Opaque's razor, like looking for the simplest theory.

There's also Epicurus' principle, which says, if you have multiple hypotheses, which equally well describe your data, don't discard any of them.

Keep all of them around, you never know.

And you can put that together and say, okay, I have a bias towards simplicity, but I don't rule out the larger models.

And technically what we do is we weigh the shorter models higher and the longer models lower.

And you use a Bayesian technique.

You have a prior, which is precisely two to the minus the complexity of the program.

And you weigh all this hypothesis and take this mixture, and then you get also this stochasticity in.

I essentially have already explained it.

So compression means, for me, finding short programs for the data or the phenomenon at hand.

You could interpret it more widely as finding simple theories, which can be mathematical theories, or maybe even informal, like just in words.

Compression means finding short descriptions, explanations, programs for the data.

Well, at least all of science I see as an endeavor of compression, not all of humanity, maybe.

And well, there are also some other aspects of science like experimental design, right?

I mean, we create experiments specifically to get extra knowledge, and that isn't part of the decision-making process.

But once we have the data, to understand the data is essentially compression.

So I don't see any difference between compression