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.
For example, Newton’s law of gravity can be formulated as a short piece of code which allows for substantially compressing many observation sequences involving falling apples and other objects.
2.6 True Novelty & Surprise vs Traditional Information Theory
Consider two extreme examples of uninteresting, unsurprising, boring data: A visionbased agent that always stays in the dark will experience an extremely compressible, soon totally predictable history of unchanging visual inputs. In front of a screen full of white noise conveying a lot of information and “novelty” and “surprise” in the traditional sense of Boltzmann and Shannon , however, it will experience highly unpredictable and fundamentally incompressible data. In both cases the data is boring [72, 88] as it does not allow for further compression progress. Therefore we reject the traditional notion of surprise. Neither the arbitrary nor the fully predictable is truly novel or surprising—only data with still unknown algorithmic regularities are [57, 58, 61, 59, 60, 108, 68, 72, 76, 81, 88, 87, 89]!