I will talk about the active, unsupervised, curious, creative systems we have developed since 1990. They not only learn to solve externally posed tasks, but also their own self-generated tasks, to improve their understanding of the world according to our Formal Theory of Fun and Creativity, which requires two interacting modules: (1) an adaptive predictor or compressor or model of the growing data history as the agent is interacting with its environment, and (2) a reinforcement learner. The learning progress of (1) is the FUN or intrinsic reward of (2). That is, (2) is motivated to invent skills leading to interesting or surprising novel patterns that (1) does not yet know but can easily learn (until they become boring). I will discuss how this simple principle explains science & art & music & humour.