My friend John R. sent me this excellent Buzzfeed feature on music playlists.
Here are some choice quotes to whet your appetite:
In 2014, when Tim Cook explained Apple’s stunning $3 billion purchase of Beats by repeatedly invoking its “very rare and hard to find” team of music experts, he was talking about these guys. And their efforts since, which have pointed toward curated playlists (specifically, an industrial-scale trove of 14,000 and counting) as the format of the future, have helped turn what was once a humble labor of love for music fans into an increasingly high-stakes contest between some of the richest companies in the world.
The algorithm that can judge the merits of new Gucci Mane, or intuit that you want to sing “A Thousand Miles” by Vanessa Carlton in the shower, has yet to be written... the job has fallen to an elite class of veteran music nerds — fewer than 100 working full-time at either Apple, Google, or Spotify — who are responsible for assembling, naming, and updating nearly every commute, dinner party, or TGIF playlist on your phone.
Spotify says 50% of its more than 100 million users globally are listening to its human-curated playlists (not counting those in the popular, algorithmically personalized “Discover Weekly”), which cumulatively generate more than a billion plays per week.
Machines have always been great at repetitive tasks that follow set rules. But many problems do not fall into that category.
We’ve come to expect that virtually all of our problems can be solved with code, so much so that we summon it unthinkingly before doing almost anything...But what if music is somehow different? What if there’s something immeasurable but essential in the space between what is now called “discovery” and, you know, that old stupidly human ritual of finding and falling in love with a song?
It's the revenge of the humans. Recommendation engines are not good enough. This doesn't mean "science" is not important. The article later explains:
Hypotheses, of course, are meant to be tested, and Spotify curators regularly make adjustments to playlists based on data that shows how people are actually interacting with them.
One frequently used application is a performance tracker called “PUMA,” or Playlist Usage Monitoring and Analysis, which breaks down each song on a playlist by things like number of plays, number of skips, and number of saves.
This is really the way forward for "machines". Machines and humans are both needed: the sum ought to be greater than its parts. Forget the idea that one replaces the other.