Recent theoretical and practical advances are currently driving a renaissance in the fields of universal learners and optimal search . A new kind of AI is emerging. Does it really deserve the attribute ``new,'' given that its roots date back to the 1960s, just two decades after Zuse built the first general purpose computer in 1941? An affirmative answer seems justified, since it is the recent results on practically feasible computable variants of the old incomputable methods that are currently reinvigorating the long dormant field. The ``new'' AI is new in the sense that it abandons the mostly heuristic or non-general approaches of the past decades, offering methods that are both general and theoretically sound, and provably optimal in a sense that does make sense in the real world.
We are led to claim that the future will belong to universal or near-universal learners that are more general than traditional reinforcement learners / decision makers depending on strong Markovian assumptions, or than learners based on traditional statistical learning theory, which often require unrealistic i.i.d. or Gaussian assumptions. Due to ongoing hardware advances the time has come for optimal search in algorithm space, as opposed to the limited space of reactive mappings embodied by traditional methods such as artificial feedforward neural networks.
It seems safe to bet that not only computer scientists but also physicists and other inductive scientists will start to pay more attention to the fields of universal induction and optimal search, since their basic concepts are irresistibly powerful and general and simple. How long will it take for these ideas to unfold their full impact? A very naive and speculative guess driven by wishful thinking might be based on identifying the ``greatest moments in computing history'' and extrapolating from there. Which are those ``greatest moments''? Obvious candidates are: