Thanks for the attempt at a position summary!
General purpose systems have their attractions. The human brain has done well out of the generality that it has.
However, I do see many virtues in narrower systems. Indeed, if you want to perform some specific task, a narrow expert system focussed on the problem domain will probably do a somewhat better job than a general purpose system. So, I would not say:
It is not interesting to build specialized compressors.
Rather, each specialized compressor encodes a little bit of a more general intelligence.
This is also a bit of a misrepresentation:
but the only empirical fact we require is that the world is computable
Occam's razor is the critical thing, really. That is an "empirical fact" - and without it we are pretty lost.
We do want general-purpose systems. If we have those, they can build whatever narrow systems we might need.
There are two visions of the path towards machine intelligence - one is of broadening narrow systems, and the other is of general forecasting systems increasing in power: the "forecasting first" scenario. Both seem likely to be important. I tend to promote the second approach partly for technical reasons, but partly because it currently gets so little air time and attention.
I searched the posts but didn't find a great deal of relevant information. Has anyone taken a serious crack at it, preferably someone who would like to share their thoughts? Is the material worthwhile? Are there any dubious portions or any sections one might want to avoid reading (either due to bad ideas or for time saving reasons)? I'm considering investing a chunk of time into investigating Legg's work so any feedback would be much appreciated, and it seems likely that there might be others who would like some perspective on it as well.