Have a read, and let’s discuss.
As no-one seems to have taken up the challenge I thought I would risk some random thoughts. I guess all cultural forms search for the new, the different, tho often alongside the traditional. For me it is the incorporation of something new within the otherwise conventional that has the greatest impact rather than say some of the more consistently experimental -classical- music say of the 1960s.
I remember too well sitting through performances of 60-70s algorithmic music in the 80s where every next note was a surprise and a 4 minute piece felt more like 4 hours, or the overwhelmingly tedious - on their own admission- months it had taken the composer to write the pieces. I guess the algorithms discussed here are much more sophisticated and applied judiciously could spice up otherwise pedestrian pieces. But I fear that the trend, particularly in pop music , would be a mccdonaldsisation of music a high fat high salt high sugar mix that would become the new baseline.
I guess I have myself become addicted to shuffle-playing playlists where the random juxtapositions of pieces within the overall framework of the playlist can heighten the experience tho working against the intentions of the composer/performer. Or in pre-streaming days a john cage cd which was cut into many short -gapless- tracks so that it could be shuffle played. tho in that case I am sure it was with the composer s approval it just added another level to his iconoclasm.
Hopefully others will add their better thought through contributions
“Lets discuss…”
Probably not the best line to start a discussion
Ouch! 
I thought it would be a good discussion topic as computational-everything is coming. If we look at cameras (mostly smartphones for now), they’re integrating more tech and ML/AI systems to enhance the photographers’ toolkit. Are there worse photos out there as a result? Sure, probably millions. But there are still some absolute gems.
One question for me is, would this play out in the same way for music. I’m thinking yes. Already, for me, much popular music is drivel, with a few gems. Note, I used “popular music” to avoid pointing the finger at a particular genre.
Yes. Apologies.
There has been for a little while some computational learning program creating images of Art. Going through many compositional and colour images of old Masters to “learn” what’s behind a great painting.
I very much like this attempt.
Hey no worries
That’s exactly what I’m talking about. I think we’re often quick to dismiss this new ideas and write them off as disastrous for our predilections, but I think its much more nuanced than that.
Computational- has the potential to provide some very interesting creations, despite some risk of chaff.
Here is a new Nirvana song written by Artificial Intelligence that analyzed their albums:
To create the songs, O’Connor and his staff enlisted Google’s AI program Magenta, which learns how to compose in the style of given artists by analyzing their works. Previously, Sony has used the software to make a “new” Beatles song, and the electropop group Yacht used it to write their 2019 album Chain Tripping .
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