I was hoping nobody would outthink me and ask that. I am going equivocate a bit. Now that we have kernel methods (for interactions) and graphical models (for

I was hoping nobody would outthink me and ask that. I am going equivocate a bit. Now that we have kernel methods (for interactions) and graphical models (for dependencies) you could claim prediction is "solved" in that if you guess the right feature design (no small task) current optimization methods are up to the task of prediction. Of course some better method could come along and we would be forced to say the current level of prediction turned out to be inadequate. Clustering and characterization are indeed where a lot of the current applications lie and can range from fascinating to unpleasant (depending on details of the problem domain).