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> The problem with habit reinforcement is that you've simply created a feedback loop. > "You like [music type]? Here are more bands/artists of [music type]."

> The problem with habit reinforcement is that you've simply created a feedback loop. > "You like [music type]? Here are more bands/artists of [music type]." So far I've found the opposite. Pandora has exposed me to music that I never would have sought out on my own. In fact I can think of several artists that I would have judged by their cover, so to speak, and never given a chance even if I had stumbled on them on my own, instead of coming to awareness mid-song that "I don't know what this is, but I kind of like it." My wife teases me about this a little bit when she finds me listening to music that she says isn't 'me' or doesn't seem like something I'd like. She's right, but I guess Pandora knows my tastes better than she does (and better than I do, to be fair.)

jschwartzi 10y ago
Contrast that with Spotify, which when given a song or artist that I like goes out of its way to recommend music that is superficially similar but that I hate. Or how it can recommend 80 songs to me a week based on things I have listened to and end up recommending me nothing that I like.
- lswainemoore 10y ago
  Huh. I've actually found the "Discover Weekly" feature to be remarkably good at picking music that I like. I'd say I affirmatively like about half to three-quarters of the songs per week, with only a couple that I find myself skipping. I wonder if it's better at certain genres than others.
  - cdcarter 10y ago
    > I wonder if it's better at certain genres than others. The Discover Weekly algorithm is, as I understand it, based on what songs have been added to other user playlists. So if you're primarily listening to a genre that has a lot of intense people making carefully curated playlists, you're gonna get a better Discovery Weekly.
tempestn 10y ago
And more generally, I would say it just depends on the quality of the AI. There is no fundamental reason why such a system would have to recommend only similar music. It could try contrasting music, and build a picture of what aspects of the music you like. It could draw conclusions to recommend seemingly different genres which nonetheless share some commonality that might make them appeal to you. It could also occasionally try out completely different things, to broaden your horizons (and give you more of that if you react favourably). And that's just scratching the surface. You could do all that without AI. An AI could potentially do all that, plus other interesting things that we wouldn't even think to try.
- DonaldFisk 10y ago
  The algorithms used to make musical recommendations do statistical analysis on numerical ratings given by different people to different songs. They typically know nothing about music, as musical knowledge is understood not to improve their results. I wouldn't call them AI - they're neither knowledge-based systems nor neural networks.
  - grahamburger 10y ago
    I don't think that that's true for Pandora: https://en.m.wikipedia.org/wiki/Music_Genome_Project https://en.m.wikipedia.org/wiki/Music_Genome_Project
  - mattkrause 10y ago
    > I wouldn't call them AI - they're neither knowledge-based systems nor neural networks. Out of curiosity, why are you drawing the AI line at neural networks? Neural networks are just function approximators; there's nothing magical about them except they work really well on problems we're interested in. Would you really not consider an SVM or a Naive Bayes classifier "AI" since they don't use a very rough, very loose analogy to the brain?

jschwartzi 10y ago

Contrast that with Spotify, which when given a song or artist that I like goes out of its way to recommend music that is superficially similar but that I hate. Or how it can recommend 80 songs to me a week based on things I have listened to and end up recommending me nothing that I like.

lswainemoore 10y ago
Huh. I've actually found the "Discover Weekly" feature to be remarkably good at picking music that I like. I'd say I affirmatively like about half to three-quarters of the songs per week, with only a couple that I find myself skipping. I wonder if it's better at certain genres than others.
- cdcarter 10y ago
  > I wonder if it's better at certain genres than others. The Discover Weekly algorithm is, as I understand it, based on what songs have been added to other user playlists. So if you're primarily listening to a genre that has a lot of intense people making carefully curated playlists, you're gonna get a better Discovery Weekly.

tempestn 10y ago

And more generally, I would say it just depends on the quality of the AI. There is no fundamental reason why such a system would have to recommend only similar music. It could try contrasting music, and build a picture of what aspects of the music you like. It could draw conclusions to recommend seemingly different genres which nonetheless share some commonality that might make them appeal to you. It could also occasionally try out completely different things, to broaden your horizons (and give you more of that if you react favourably). And that's just scratching the surface. You could do all that without AI. An AI could potentially do all that, plus other interesting things that we wouldn't even think to try.

DonaldFisk 10y ago
The algorithms used to make musical recommendations do statistical analysis on numerical ratings given by different people to different songs. They typically know nothing about music, as musical knowledge is understood not to improve their results. I wouldn't call them AI - they're neither knowledge-based systems nor neural networks.
- grahamburger 10y ago
  I don't think that that's true for Pandora: https://en.m.wikipedia.org/wiki/Music_Genome_Project https://en.m.wikipedia.org/wiki/Music_Genome_Project
- mattkrause 10y ago
  > I wouldn't call them AI - they're neither knowledge-based systems nor neural networks. Out of curiosity, why are you drawing the AI line at neural networks? Neural networks are just function approximators; there's nothing magical about them except they work really well on problems we're interested in. Would you really not consider an SVM or a Naive Bayes classifier "AI" since they don't use a very rough, very loose analogy to the brain?