Unknowns, we've been over this issue before. You don't need to engage in perfect prediction in order to be able to usefully predict. Moreover, even if you can't predict everything you can still examine and improve specific modules. For example, if an AI has a module for factoring integers using a naive, brute-force factoring algorithm, it could examine that and decide to replace it with a quicker, more efficient module for factoring (that maybe used the number field sieve for example). It can do that even though it can't predict the precise behavior of the module without running it.