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Most traditional artificial intelligence (AI) systems of the past 50 years are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based on Occam's razor, problem solving, decision making, and reinforcement learning in environments of a very general type. Since inductive inference is at the heart of all inductive sciences, some of the results are relevant not only for AI and computer science but also for physics, provoking nontraditional predictions based on Zuse's thesis of the computer-generated universe.
Optimal Ordered Problem Solver
Universal Learning Algorithms
Optimal universal search
Super Omegas and Generalizations of Algorithmic Information and Probability
In the Beginning was the Code!
Comments on Wolfram's 2002 book
All computable universes
Algorithmic Theories of Everything