I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or
compress the data in a better way, thus making it subjectively simpler and more
beautiful. Curiosity is the desire to create or discover more non-random, nonarbitrary, regular data that is novel and surprising not in the traditional sense of
Boltzmann and Shannon but in the sense that it allows for compression progress
because its regularity was not yet known. This drive maximizes interestingness, the
first derivative of subjective beauty or compressibility, that is, the steepness of the
learning curve. It motivates exploring infants, pure mathematicians, composers,
artists, dancers, comedians, yourself, and (since 1990) artificial systems.