It may be instructive to look at David Cope's [1] work (what he calls "recombinant music" [2]). Cope's been writing algorithms to compose in the styles of the masters (Mozart/Chopin/et al) for about 3 decades now, well before the recent surge in "AI". His techniques are much less sexy for the "deep learning" enthusiasts, and yet he managed to outrage an audience of connoisseurs who assembled to listen to a "lost Chopin piece" only to be told, after they shared their applause, that it was composed by a computer taught to mimic Chopin's style (the composition was performed by a musician). The response, in my opinion, also points to music as a social constructed experience and not purely attributable to the sound signal itself. i.e. if I give you a romantic background story for a lost composition of a master, you may be inclined to experience the piece in a more favorable light than if I told you it was generated by an algorithm (or the converse).
You're absolutely right that the musical output of the current crop of "AI" projects (especially the ones using deep learning / neural networks) are crappy to even a modestly trained listener .. or even a lay untrained listener for that matter. However, more involved modeling (such as Cope's) has produced some very compelling results decades ago, so it would be a mistake to assume that the current crop won't get close enough [3]. The fact that DL systems don't need to be instructed in the way Cope has had to encode his musical understanding is also something to be considered in the evaluation as well as in scoping their capabilities going forward.
[1]: https://en.wikipedia.org/wiki/David_Cope [2]: https://www.recombinantinc.com [3]: https://deepmind.com/blog/article/wavenet-generative-model-r... (see "Making Music" section and examples there)
However, we have to make a clear distinction between creative and recreative methods. David Cope's work is impressive, but it focusses on the recreation of existing musical styles. This is interesting from a musicologist perspective, but not very interesting artistically.
I would certainly say that deep learning generates lots of interesting “material“ (like many other methods of algorithmic composition), but we still need a human being to curate, edit and assemble the material into a meaningful piece of art.
Finally, I think the current AI debate can be very fruitful for the arts. In a way, it raises similar questions as the concept of the “readymade“ and the pop art movement did in the 20th century.
Btw, I'm currently working on an opera which uses AI generated lyrics :-)
BTW - I'm curious, what do you think about birds songs? Are their songs interesting artistically? How do you think they were composed?
On the other hand, you have composers like John Cage (or more recently: Peter Ablinger) who claim that the act of listening itself can be/create art, blurring the borders between nature and art. There are conceptual pieces which only consist of listening instructions.
Finally, bird "songs" have been used as the source material for musical composition for centuries. You can find it in Beethoven, Mahler, Debussy, Stravinsky, etc. Olivier Messiaen even was a hobby ornithologist; he faithfully transcribed hundreds of bird songs and used them in his music (see for example his piano cycle "Catalogue d’oiseaux").
As for the question of who composed the actual bird songs, the answer probably depends on the theological background of the person you ask ;-)
I think this topic will keep reverting to the point you raise - "meaningful art". As long as the "meaning" is a construct in a human brain that we're looking for, we have little to say about AI and it's capabilities (like Joshua Bell's hardly-noticed playing of Bach classics at New York's subway station as opposed to when he's performing at a concert hall).
.. (edit) and I do think that active listening is itself a creative act.
I think you're right, in that AI won't be able to create deeper themes and patterns, but I disagree with the above point: AI will take over the music industry because the vast, vast majority of people aren't educated listeners. The popularity of 6six9ine is a fantastic example.
To put it another way, I don't need another Terry Riley, Clint Mansell, or Meredith Monk, I just need something good enough to occupy some brainspace while I drive home after work; a move soundtrack just needs something sad, or exciting, or tension building. The AI can and will get there soon enough.
Lack of "larger structures" is the key here. That's where GPT-1 was. Each sentence, in isolation, seemed to make sense, but after a few lines, it was clear the text wasn't going anywhere. By GPT-2, paragraphs seemed semi-reasonable, but multiple paragraphs didn't hold together. GPT-3 is able to keep it together for a few paragraphs, but probably not for a book chapter.
Music synthesis has the same scaling issue. Generators which imitate known patterns work for a few bars, but after a while you realize the music is going nowhere. The GPT results on text indicate that a scaleup may fix that problem.
Lossy is bad. Humans will never stand for it.
Perfections will not stand for it. Pragmatists won’t notice.
This isn’t a bad thing. We need perfectionists to drag us across the “good enough” line. Despite our childish kicking&screaming.
Is originality the key point? Because AI-generated music has high probability containing piece of rhythm from their training dataset.
> Unfortunately it turns out that classical music and waxing poetic are easily generative in an enjoyable way
On the contrary, I would say that generating convincing and original classical is an incredibly hard (if not impossible) task. All the current music AI projects give results which may sound “good“ to a casual listener, but they sound horribly wrong to any educated listener. The reason is that AI can only imitate the surface, but completely misses to recognize/synthesize larger structures. This might be ok for some background noodling in a TV drama, but not for the concert stage.
Finally, we rarely perceive art works in isolation. We know and appreciate the fact that a certain work has been created by a certain person in a certain time.