We discuss the problem of flexibility in intelligence, a relatively little-studied topic in machine learning and AI. Flexibility can be understood as out-of-distribution generalization, and it can be achieved by converting novel distribution into known distributions. Such conversions may play the role of knowledge and is accumulated in the intelligent system, leading to human-like learning and generalizations.
