Earlier quoted context omitted.
My thoughts exactly. r/iamverysmart vibes… someone who has watched one Lex whatever his name was video on the maths of neural networks…
Probably just BS by GPT3.
Could you not shitpost like the parent, please?
Show HN: I trained an AI model on 120M+ songs from iTunes
381–390 of 444 posts
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#382Tried few recommendations and can definitely use an alternative for Spotify radio.
Seems like you are dodging the question of data access and I am not sure why. Even running an intelligent scraper to download 120M song previews sounds too complex and might take days to months as you have to rotate IPs and not bombard the server all the time. If you manage to do that, kudos. That itself is a great achievement. If not, can you let us know how did you get the data access? You might help other devs who want to try something similar
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#383It produces similar sounding music. but that's not really what one is looking for when discovering music. Example is when I put a Bollywood song, it produces for me similar sounding Latin music which isn't going to fall into a good queue.
how Spotify does this is matching songs that have been human added to the same playlists - hence matching tastes.
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#384Earlier quoted context omitted.
They were acquired by Spotify, and there's been some work done by/for Spotify since then. I'm not at liberty to say what, sadly, as I work for Spotify. I think I can say that one of the main challenges is running this analysis for users. It's prohibitively expensive (or was prohibitively expensive) to use this to keep track of and run recommendations for what users are listening for each user. It can be used on small…
Can you say why Spotify's recommendations are so bad? Something like what OP has made should have been relatively simple to make for Spotify for many, many years already, yet that hasn't happen. Is the whole system just rigged to only recommended a few "sponspored" artists?
I doubt that "why does your product suck" is one of the things a Spotify employee is allowed to talk freely about in public!
But I've been watching them, I will speculate. A few years ago, Spotify had two young interns, Sander Dieleman and Aäron van den Oord. We know a bit of what they worked on, because Dieleman blogged on it, and indeed it was something a lot like what OP has made here - only better, I would say. I asked him, and Dieleman was allowed to say that the thing they built was one of the inputs into the then-new Discover Weekly, which made headlines for how outrageously good it was.
But Dieleman and v.d.Oord did not stay at Spotify. They were headhunted by DeepMind, and have had a VERY impressive track record there over the years.
And I wonder why. Was there a conflict between the old school ML of the Echo Nest people and the new fancy neural net kids? Or was it just, as GP alludes to, that the NN methods were just too computationally expensive and they failed to justify their costs to leadership?
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#385Finding sources for input data is something I struggle with when building deep learning models. Out of curiosity, how did you go about programmatically accessing the music files for all 120M+ songs, in order to create your embedding vector? I can't imagine iTunes has an API which would let a person do that.
They do, it’s just rate limited. See https://news.ycombinator.com/item?id=34641623
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#386Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#387This is very interesting, but unfortunately I haven't had the greatest luck in finding new songs I would enjoy listening to. It absolutely finds similar sounding tracks, but it doesn't distinguish which part of the song made it enjoyable. There's no tempo consistency or genre consistency or even main instrument/vocal timbre consistency between recommendations. I think locking one or more of those dimensions would all…
It doesn't seem to find similar-sounding tracks at all for me.
Examples:
The Oblio Joes - "Captain of the Moon"
The Bondage Fairies - "Levenus Supremus"
... both chosen so "Just shove a bunch of recent pop-rock at the user" won't work.
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#388This works great! I like the way it finds resonances between genres and am one of the folks that see that as a feature :-) Very well done!
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#389It works surprisingly well. Good job. It confuses bluegrass and latino music but they share tempo and instruments so I get it. Impressive.
Re: Show HN: I trained an AI model on 120M+ songs from iTunes
#390This is a project I've always dreamed of building! Already pretty excited about some of the recommendations I'm getting in some niche genres.
Feature request: allow me to auth Spotify and click a button next to each track to add a track to a "Maroofy Recs" playlist