artbench VS RAVE

This example is a data set that's been created for training AI models on differentiating between different styles of art. But at the end of that data set an AI is no more capable of defining what impressionism is than it was before but it can produce new art that appears to be impressionist, surrealist, cubist, whatever based on the examples it's been given and the only reason they don't seem to be copies of any handful of paintings is because the AI has scanned and indexed 1,000 distinct paintings in that style. But a human brain can look at a handful of impressionist painting and intuit the commonalities that make "impressionism" a unique style.

Also, theres new tools like https://github.com/acids-ircam/RAVE which can be used to create new sounds, i feel like the more granular you work with AI on art, the better can help you out. Magentajs is pretty cool! Im still just playing with it but is easy to use so far.

https://github.com/acids-ircam/RAVE hope this wasn't too much off topic, i'm just super enthusiastic about RAVE and have been trying to squeeze a sonification in with it with no good applications so far. this feels kinda awesome tho. maybe it fits?

I'm in the deep learning music scene, which is due for its stable diffusion moment in the next year or two. The (primarily) timbre transfer system called RAVE is where I'm starting, and my contribution is to optimize the system to improve training time.

[] https://github.com/acids-ircam/RAVE/tree/master/rave

I'm obsessed with generative audio models, particularly RAVE[0].

Music is set to have its GPT-3 / Stable Diffusion moment within a couple years.

I believe in 10 years the venn diagram of music made with computers and music made with neural nets will be a circle, and that now is a great time to jump in.

Would LOVE to swap notes with anyone else here into this. Email in bio.

[0] https://github.com/acids-ircam/RAVE

Source code is here: https://github.com/caillonantoine/RAVE