So, here's the postulate:
Among several patterns classified as "comparable" by some subjective observer, the subjectively most beautiful is the one with the simplest (shortest) description, given the observer's particular method for encoding and memorizing it.
Taken from: http://www.idsia.ch/~juergen/beauty.html Which also contains the link to this PDF summary of Schmidhuber's ideas: http://www.idsia.ch/~juergen/sice2009.pdf
In this summary of previous work, 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 more “beautiful.” Curiosity is the desire to create or discover more non-random, non-arbitrary, “truly novel,” regular data that 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 recent artificial systems.
Here's some excerpts from a lecture by Schmidthuber on his ideas on this and other things from the Singularity Summit last year:
[youtube]http://www.youtube.com/watch?v=Ipomu0MLFaI[/youtube]
And a wiki entry:
Jürgen Schmidhuber described an algorithmic theory of beauty which takes the subjectivity of the observer into account and postulates: among several observations classified as comparable by a given subjective observer, the aesthetically most pleasing one is the one with the shortest description, given the observer’s previous knowledge and his particular method for encoding the data[39][40]. This is closely related to the principles of algorithmic information theory and minimum description length. One of his examples: mathematicians enjoy simple proofs with a short description in their formal language. Another very concrete example describes an aesthetically pleasing human face whose proportions can be described by very few bits of information[41][42], drawing inspiration from less detailed 15th century proportion studies by Leonardo da Vinci and Albrecht Dürer. Schmidhuber's theory explicitly distinguishes between what's beautiful and what's interesting, stating that interestingness corresponds to the first derivative of subjectively perceived beauty. Here the premise is that any observer continually tries to improve the predictability and compressibility of the observations by discovering regularities such as repetitions and symmetries and fractal self-similarity. Whenever the observer's learning process (which may be a predictive neural network - see also Neuroesthetics) leads to improved data compression such that the observation sequence can be described by fewer bits than before, the temporary interestingness of the data corresponds to the number of saved bits. This compression progress is proportional to the observer's internal reward , also called curiosity reward. A reinforcement learning algorithm is used to maximize future expected reward by learning to execute action sequences that cause additional interesting input data with yet unknown but learnable predictability or regularity. The principles can be implemented on artificial agents which then exhibit a form of artificial curiosity.
http://en.wikipedia.org/wiki/Aesthetics ... aesthetics
Let's see where this goes.
