It is difficult to judge at first, but it appears that some music genres (classical and jazz especially) have seen a radically slowed or non-existent output of significant works in recent decades. By significant, I mean works that would be seen in league with the most acclaimed artists in the genre (eg Beethoven, John Coltrane). In a similar way, there is a building popular consensus that Hollywood is not pursuing original ideas as much anymore and is relying on rebooting old stories and franchises. But is this because of a fault of the Hollywood system, or is it because there are few significant movie story ideas left that have not been done?
It seems to me that there are limited possibilities in art (or any field) of what we can discover or create. It appears that the matter and rules of the universe (in concert with human nature) manifests itself into these limited possibilities. When we develop a field or genre, we learn and create at an increasing rate but then eventually "complete" the field (in a boom and bust pattern).
I wrote a brief article called The Big Niche to try and explain this concept.
The relevant questions seem to be:
Do you agree that from experience and observation, we can tell that certain genres or fields appear to become completed?
Does this concept or another concept best explain what is happening?
I'm interested if any of you agree with this line of thinking or have other possible explanations of this phenomena.
I think you misunderstood me, or maybe I wasn't clear. I meant "of the strategies which we used to search for musical ideas, none of them involved solving NP-complete problems, and some of them have dried up." I think what neural nets do to learn about music are pretty close to what humans do -- once a learning tool finds a local minimum, it keeps attacking that local minimum until it refines it into something neat. I think a lot of strategies to produce music work like that.
I definitely don't think most humans intentionally sit down and try to solve NP-complete problems when they write music, and I don't think humans should do that either.
Actually, what this network does is a lot closer to pure improvisation than the process of successive refinement you're describing here. Optimization i.e. (search for a local minimum) is used in the training stage, where the network uses a trial-and-error strategy to fit a model of "how music goes". Once the model is fitted however, generating new pieces is a process of linear prediction, based on what the model has 'written' so far - this is actually among the most significant limitations of these models; they're great for pure inspiration, but ... (read more)