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AI today is to human intelligence what the phonograph was to human musicians. Superficially, it may seem like it has a comparable output & is ready to replace musicians (which was a real fear back then). But it has radically different capabilities: it's merely a recording device.
Don't confuse music creation with sound recording. Musicians are the origin of music. Unlike a phonograph, they can play a never-played before score, or write never-heard before music. Or take up a new instrument. Or invent a new musical genre.
Historically, we went technology to record sound to technology capable of helping with music production and creation. New, more powerful tools. Eventually, we will see open-ended autonomous music creation. This will be the trajectory of AI as well.
From recorded abstraction, to cognitive augmentation tools, to autonomous abstraction.
I wonder if inventing/creating something new anything more than just a result of iterative search of latent space or combining two priors which weren't combined before and/or just randomness? 
This is simply PSR.
https://www.youtube.com/watch?v=f_ZSR9E_8x0 …
https://opera.media.mit.edu/publications/machover_hyperinstruments_progress_report.pdf …
Have fun reinventing the past with a neural twist! #USA1 42 #NWO 99>1pic.twitter.com/Q8Ffrms4hb
Well, GAN can also play a never-played before music. You may say that GAN is learned using human-recorded music, but we, humans, also learn from other humans. Everything that we create is a result of something we have seen before. So can we name GAN an abstract creator?
Could I ask a series of genuine questions? I don't know much about this area but found your messages interesting. I'll start with, what makes human musicians unique, as in, do they make generally interesting new patterns not influenced by past music?
Is a new genre anything more than a new set of patterns that are liked by humans? Humans that are open to new patterns and are based on prior learning? It tends to be that new genres don't just spring up, they are derivative of something else. Could that be similar to machines?
Spot on about the superficiality of current ML, and lack of abstractions.
@fchollet I wonder - is past music from previous generations, very different from "labelled data" we use in ML?
What are human musicians are able to learn from past data, that ML models are not not?
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