On the music snob

Digging through the dusty record bins of the internet

Much has been written about AI and its flaws. We all know by now that ML models are susceptible to a feedback loop in which biased data feed into their biased predictions, which feed back into the biases of the systems in which we reside, resulting in even more biased data, and so on. For example, Drew Austin writes about the decline of the music snob in the age of ML recommendations, the loss of their arcane wisdom and traditional canons, replaced by the decisions of the model which instead prioritize features like recency.

That’s all true, but does it have to be that way? We could view the problem as actually being an optimization problem or a tuning problem, or better yet, an engineering problem. We could just as well build a model that doesn’t prioritize recency or clickbait as a part of its optimization criteria. We could engineer a feed that shows you content you’re likely to “like”, but that doesn’t amplify a rich-get-richer, race-to-the-bottom content economy — a straightforward way to do this would be to simply not show already-popular content, but only surface less-popular while still-relevant content.

I believe most Twitter power users are familiar with this dynamic: The “Top Tweets” timeline shows a furious stream of mind-numbingly stupid viral content, not much different from the average TikTok timeline. On the other hand, the “Latest Tweets” chronological timeline is a peaceful oasis in comparison, at least when paired with a good mechanism for filtering out the garbage, like a brutally, meticulously curated list of followed accounts. I for one cherish the lowbies, the three-digit-follower-count accounts; and it goes without saying that there’s rarely a good reason to follow a bluecheck, who’ll never tell you anything you don’t already know.

A similar situation occurs with the music snob, who has always thrived on obscurity. Their value was as much in the vast cultural knowledge that they kept as in the artificial scarcity they created by holding their cards close to their chests. And when a band “sold out”, it wasn’t just that their music would begin to decline, but that their appeal would inevitably be dulled by the mainstream gaze.

I’d agree with Austin that an equivalent to the music snob seems to be missing from Spotify’s recommendation engine. But YouTube recommendations seem to be tuned just right for me, and it’s introduced me to a large swath of interesting, wonderful, obscure music I’d never have found otherwise. The most famous recent example is Mariya Takeuchi's “Plastic Love”, which was first released in 1985 but only broke the Japanese top ten charts 36 years later, having been rediscovered by millions of new listeners due to YouTube recommendations.

Further examples abound. Another one of my favorite YouTube discoveries is the jazz pianist Ryo Fukui. Under any of his videos on YouTube, you’re sure to find dozens of appreciative commenters praising the magic of the algorithm for bringing them such sublime art.

It’s always been hard to find diamonds in the rough, those special people or songs or works of art with which you feel truly connected. You can find them through hard work, like the music snobs who endlessly rifled through hundreds of dusty record bins. Otherwise, you need a smarter strategy. Google harnessed the hyperlinks of the internet as a coarse proxy for alignment or endorsement, and Twitter similarly uses the retweet function together with the directed social graph of followers.

In an increasingly hyperlink-less internet, we can still make use of ML models to serve us in similar ways, to help us find the good stuff, as we choose, if we choose.