You might be correct on that. As of now I suppose I should focus on mastering the basics. I've nearly finished Legg's write up of Solomonoff Induction, but since it seems like there is a good bit of controversy over the AIXI approach I suppose I'll go ahead and get a few more of the details of algorithmic probability theory under my belt and move on to something more obviously useful for a bit; like the details of machine learning and vision and maybe the ideas for category theoretic ontologies.
Again, I would point at Solomonoff Induction as being the really key idea, with AIXI being icing and complication to some extent.
The whole area seems under-explored, with many potential low-hanging fruit to me. Which is pretty strange, considering how important the whole area is. Maybe people are put off by all the dense maths.
On the other hand, general systems tend to suffer from jack-of-all-trades syndrome. So: you may have to explore a little and then decide where your talents are best used.
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.