Marcus Hutter

I mean, think about the induction problem is more in the philosophy department.

There's virtually no paper who cares about how long it takes to compute the answer.

That is completely secondary.

Of course, once we have figured out the first problem, so intelligence without computational resources,

Then the next and very good question is, could we improve it by including computational resources?

But nobody was able to do that so far in an even halfway satisfactory manner.

Yeah, we have developed a couple of approximations.

And what we do there is that the Solomoff induction part,

which was find the shortest program describing your data, which just replaces by standard data compressors.

And the better compressors get, the better this part will become.

We focused on a particular compressor called context-free weighting, which is pretty amazing, not so well known.

It has beautiful theoretical properties, also works reasonably well in practice.

So we used that for the approximation of the induction and the learning and the prediction part.

And for the planning part, we essentially just took the ideas from a computer girl from 2006.

It was Chaba Sepespari, also now at DeepMind, who developed the so-called UCT algorithm, upper confidence bound for trees algorithm, on top of the Monte Carlo tree search.

So we approximate this planning part by sampling.

It's successful on some small toy problems.

We don't want to lose the generality, right?

And that's sort of the handicap, right?

If you want to be general, you have to give up something.