Human learning outstrips modern machine learning and AI in at least three abilities: rapid robust learning, in effectively open worlds, in near-real time with very little energy. Mathematical formalization of signature human abilities has the possibility to advance machine learning theory and practice. In this talk, I will demonstrate the possibility of rigorous mathematical formalizations of rapid and robust learning via cooperative communication, and discuss generalizations and directions.