So many of the selections that great DJs make are curveballs and surprising choices. This is less true in more samey genres, but true pioneering selectors will tend to surprise you with their taste and juxtapositions in a way that's hard or maybe even impossible to really capture in an algorithm.
Put another way, even in something as simple a song selection, the weirdness and contingencies in human creative decision making is a feature not a bug.
The efficiency and cleverness of algorithms can create create great powerful recommendation engines but there's nothing like the idiosyncrasies of a person as a curator.
The combination it came up with that they then tried selling on a food stall was chocolate on pizza. It wasn't to everybody's taste but some liked it, with one describing it as "weirdly delicious".
It doesn't seem completely ridiculous that if an AI could be trained to recognise patterns of music that worked well together that it could analyse a large corpus of recorded music and come up with surprising mixes that you wouldn't think would work, but do.
Many restaurant chains sell chocolate on their pizza base as a dessert; chocolate is also known to complement many savoury dishes as a 'secret ingredient'.
Of course, there's always the possibility algorithmic composers could do even better than humans. Clarke told a story about that in 1957: https://en.wikipedia.org/wiki/The_Ultimate_Melody
Text here: https://archive.org/details/1957-02_IF/page/n71/mode/2up?vie...
That would be enough to construct a robot DJ that could track the variations it was making, and rate them for 'better/worse'. Then you just make a cool-looking puppet to store the camera in, that can move around and possibly wave an actuator like a proper DJ, and the rest is machine learning.
There was an interesting youtube video I watched, "All my homies hate Skrillex" that said that the evolution of agressive Skrillex-style "dubstep" over the older, more introspective dubstep was a result of smoking being banned indoors in UK clubs.
This made the smokers have to leave every now and then, which forced the DJs to have to play more agressive, instantly impactful music to draw their attention when they returned.
https://www.youtube.com/watch?v=-hLlVVKRwk0
I guess the video maker knows what he's talking about, he's the one who was at the clubs and clearly has great knowledge about the genre.
He said that he didn't care either way about the smoke, but personally I'd be much more likely to stay in a club with clear air than one filled with horrible smoke.
Another theory about the changing for more aggressive beats: maybe the dj's themself were affected and got angry having to wait for their next cigarette?
There are lots of games that do this kind of thing at a rhythm level, like Necrodancer and more recently BPM [1], but I think there'd be the potential to do a lot more with it than just rewarding beat-alignment with a pre-cooked solo that blazes over top of the base track.
Maybe put a row of buttons at the top: "I am [Loving it] [Grooving] [Fine] [A Bit Bored] [About to Quit]". Then use that (plus browser data and window-close data) to train ML models to maximize engagement. Or perhaps to hook it up to facial expression recognition.
When I watch friends DJ, there's this great feedback loop between how the crowd's behaving and what they're putting on. They're clearly using the music to achieve certain mental states among listeners. I'd love to see how well that can be done algorithmically.