Marcus Hutter

understanding and prediction.

Kolmogorov complexity is a notion of simplicity or complexity, and it takes the compression view to the extreme.

So I explained before that if you have some data sequence, just think about a file on a computer and best sort of just a string of bits.

And we have data compressors, like we compress big files into zip files with certain compressors.

And you can also produce self-extracting archives.

That means as an executable, if you run it, it reproduces your original file without needing an extra decompressor.

It's just the decompressor plus the archive together in one.

and now there are better and worse compressors and you can ask what is the ultimate compressor so what is the shortest possible self-extracting archive you could produce for a certain data set yeah which reproduces the data set and the length of this is called the Kolmogorov complexity

And arguably, that is the information content in the data set.

I mean, if the data set is very redundant or very boring, you can compress it very well, so the information content should be low, and it is low according to this definition.

That's a tricky and difficult question.

As I said before, I believe that the whole universe, based on the evidence we have, is very simple, so has a very short description.

So you need a very short program.

and you run it... To get the thing going.

To get the thing going, and then it will reproduce our universe.

There's a problem with noise.

We can come back to that later, possibly.

Is noise a problem, or is it a bug or a feature?

I would say it makes our life as a scientist really, really much harder.

I mean, think about without noise, we wouldn't need all of the statistics.