Marcus Hutter

So how should I evaluate my system if I can't do cross-validation?

How should I do my learning if my standard regularization doesn't work well?

So the answer is always this.

We have a system which does everything.

That's ICSI.

It's just, you know...

completely in the ivory tower, completely useless from a practical point of view.

But you can look at it and see, ah, yeah, maybe, you know, I can take some aspects and, you know, instead of Kolmogorov complexity, let's just take some compressors, which has been developed so far.

And for the planning, well, we have UCT, which has also, you know, been used in Go.

And at least it's inspired me a lot to have this formal

um definition and if you look at other fields you know like i always come back to physics because i have a physics background think about the phenomenon of energy that was long time a mysterious concept and at some point it was completely formalized and that really helped a lot and you can point out a lot of these things which were first mysterious and wake and then they have been rigorously formalized

speed and acceleration has been confused until it was formally defined.

There was a time like this.

And people often who don't have any background still confuse it.

And this ICSI model or the intelligence definitions, which is sort of the dual to it, we come back to that later, formalizes the notion of intelligence uniquely and rigorously.

And so the major difference is that essentially all other approaches, they make stronger assumptions.

So in reinforcement learning, the Markov assumption is that the next state or next observation only depends on the previous observation and not the whole history.

which makes, of course, the mathematics much easier rather than dealing with histories.

Of course, they profit from it also because then you have algorithms that run on current computers and do something practically useful.

But for general AI, all the assumptions which are made by other approaches, we know already now they are limiting.