A corporation such as Spotify was founded a few years ago by a 23-year-old man, and it now plays a key role in the music industry. YouTube is now the big-boss of music delivery. Had I predicted these things ten years ago, you would have thought me odd.
Kurzweil, the inventor, popularized the notion of a technological singularity. It is a moment in our history where things move so fast, we can no longer comprehend the world nor make even short-term predictions. Are you game to predict the next ten years of the music industry?
Let us think about the present.
There is an explosion of diversity because it becomes affordable. Spotify can offer millions of songs. This was unthinkable in the past. The net result is that more musicians than ever can be found by more people than ever.
Chris Anderson in 2004 promised us a world where more of us could make a living on the “long tail” (the long stream of less popular products and services) without needing a big break. Is it what happened? I think we still debate it. Recently, a study on the Google Play store found that it is a super-star market where the bulk of the money ends up in the pocket of the few, with most people hardly making a penny. Should we blame the bulk of the players for their bad luck?
Having your data stored in the index is not enough to be found. Being visible is not the same thing as being found. When you use Google to search for something, it can return 20,000 relevant pages, but you are unlikely to look at more than 3 or 4. Tools like Spotify are not different. They use recommender systems or, in other words, AI, to help us find what we are looking for. They think for us.
People don’t always realize that tools such as Google know about you and provide personalized results. These tools are mirrors, necessarily imperfect ones. They are not neutral.
What do I mean by neutral? When using polls to predict election results, we measure what people think, but we also change what people think by publishing the results. A top-10 list in music is not just a mirror, it is also an active agent that changes what people listen to.
The rules of engagement also matter. Our democratic systems are often setup to favor the emergence of two dominant parties. It is the intended effect. It couldn’t be more obvious in the USA. Yet who is setting up the rules in the digital world? Do you know?
The big players like Google or Spotify have access to a remarkable wealth of data. They know who you are, where you are and what you do. They know your age, your race, your sexual orientation. No business could have dreamed of having so much data 30 years ago.
As such, it is not harmful.
But any complex and powerful system can have unexpected and undesirable effects. For example, “there’s a general tendency for automated decisions to favor those who belong to the statistically dominant groups.”
At a minimum, I think we should study what systems like Google and Spotify do so we can discuss it openly.
Problems don’t necessarily arise because the big players are malicious. Datta et al. (2015) found that online ads for highly paid jobs tended to be shown more often to men than women. It seems that the reason is that younger women are a prized demographic so they get ads from advertisers with deeper pockets instead.
What could we do in concrete terms? We could use volunteers who agree to have some of their Internet interactions monitored. We could also proceed with automated sousveillance, where we create fake accounts to keep an eye on the big players.
We already keep track of journalists, to check whether they are being fair, I think we should spend a bit more time tracking Google, Apple, Amazon, Spotify… This would seem only prudent in such a fast changing world.
Can “deep learning” (whatever that means) understand music?
Music is for me a particularly interesting – and hard – problem.
My tastes in music have never fit within a main group. I find the streaming services worthless, as they play a lot of crap I do not want to hear. My tastes do not fall in a large group.
Are we in the middle of a transient?
In the last decade, the web helped me find my neighborhood in the “long tail” of music. I found musicians I liked, bought music, and even hosted house concerts. The web made this all possible.
In the present, “big” services are discovering “big” markets in music, in a somewhat automated fashion. But so far they are hopeless in the “long tail” market.
Eventually, someone is going to find the right clever notion, and do much better with automated discovery for the “long tail” market.
Will there someday be a streaming music service smart enough to meet my tastes? Will smart services eventually serve the long tail?
Your comments treat Spotify as an “other” to be studied, so I thought I’d say hi. I work on recommendations at Spotify. I’ve been reading and enjoying your blog for a long enough time that I no longer remember how I originally discovered it. Maybe something to do with language design or the semantic web?
Anyway, studying and monitoring are eminently justified precautions to be collectively taken against any large systems, especially ones that by corporate nature are never wholly transparent about how they operate.
But if it’s any oblique consolation, I can report that Spotify, at least, is fairly self-aware. Daniel Ek isn’t 23 any more, and to get Spotify to this point over the last decade he’s hired a couple thousand other people both older and younger. We are vividly aware of the tendency for automated systems to reinforce ambient biases, because we measure the performance of our features against pretty much every other variable we can quantify. So when something works better for men than women, or for Brazilians than Germans, we know and care.
Preston, I’d be curious to hear if Spotify is better at meeting your tastes now than it was whenever you last tried it. “My tastes do not fall in a large group”, you say. But Discover Weekly, Release Radar and Daily Mix, all of which are relatively new features in Spotify, definitely do not rely on your tastes falling into any mainstream groove. I have 1496 genres on everynoise.com, and that’s hardly a complete set yet, so maybe the things you like aren’t anywhere on that map. But maybe there are. Maybe the future you’re waiting for has already started arriving.
Thanks Glenn for the great comment.
Daniel Ek isn’t 23 any more, (…)
Just so we are clear, I was not being dismissive of Daniel Ek or of his achievements.
This was part of the setup of my post where I argued that things move fast, faster than we often realize. So fast that I don’t think most of us can comprehend what is going on.
Ten years ago, Daniel Ek was a kid that was certainly being dismissed by the music industry. Today, he probably can get meetings with just about everyone in the music industry. Do you think that the music industry saw it coming? I think not.
Do you think that the music industry sees what is coming in the next ten years? Or do we assume that things are going to look the same, except maybe that Spotify will have a few more songs and a better UI?
Anyway, studying and monitoring are eminently justified precautions to be collectively taken against any large systems, especially ones that by corporate nature are never wholly transparent about how they operate.
Right. I think most of us who have dabbled long enough in non-trivial software take this for granted, but it is a lot less obvious to many others.
We are vividly aware of the tendency for automated systems to reinforce ambient biases, because we measure the performance of our features against pretty much every other variable we can quantify. So when something works better for men than women, or for Brazilians than Germans, we know and care.
This makes me very happy. I am sure that the YouTube folks would say something similar.
As you no doubt realize, this is good but probably not sufficient. For one thing, external monitoring tends to keep people honest. For another, without hard data, the rest of the world is working from a hopelessly incomplete picture… and that’s not great in a fast changing world.