Simple test: imagine making a hill-climbing algorithm that maximized the increase in proportional compressibility of a piece of music as you listened to it. So you’d feed in some random seed, and out would come the local maximum of “interestingness.” What would this local maximum look like? It would actually be quite unlike current music. A real piece of music might start by introducing simple themes and then elaborating on them. But this gives away all the compressibility at the start! Elaborating on a known theme is “boring.” Instead you should start with noisy data that reveals itself to be random deviations around a simple pattern!
Other problems include the unsubstantiated downplaying of enjoying something more than once and ignoring the relationship with emotions.
So although this sort of algorithm might enjoy something vaguely like music, it would prefer to listen to many variations of simple statistical patterns at the highest speed possible—I’m looking for something a bit more human.
Simple test: imagine making a hill-climbing algorithm that maximized the increase in proportional compressibility of a piece of music as you listened to it. So you’d feed in some random seed, and out would come the local maximum of “interestingness.” What would this local maximum look like? It would actually be quite unlike current music. A real piece of music might start by introducing simple themes and then elaborating on them. But this gives away all the compressibility at the start! Elaborating on a known theme is “boring.” Instead you should start with noisy data that reveals itself to be random deviations around a simple pattern!
Other problems include the unsubstantiated downplaying of enjoying something more than once and ignoring the relationship with emotions.
So although this sort of algorithm might enjoy something vaguely like music, it would prefer to listen to many variations of simple statistical patterns at the highest speed possible—I’m looking for something a bit more human.