As someone who works a lot with both music and ML, I'm really excited to see these multi-modal approaches. The image description -> music generation was really cool to see. But it would be incredible to see a (good/large) multi-modal model that can go from audio -> image. Free album artwork and visualizations for all my songs.

oh wow this looks super interesting. I had no idea what Tsetlin machines were until today. It's actually something I've basically tried emulating with standard ML approaches.

I have an unsolved mathematics problem that I've been working on for almost a decade since my professor showed me in undergrad. It's a very specific problem that maybe 10-20 combinatorists are working on or aware of and it's still unsolved to this day. One of the biggest parts of the problem is finding a bijection between these two infinite classes of integer partitions. Being able to find a rules-based bijection would prove a large part of the overall problem.

My idea was to try and model these bijections as a supervised learning problem and feed them into various ML models. I've tried standard feed-forward networks, auto encoders, CNNs, and many more. But it's never worked because of the rules-based nature of the problem. I suspect the rules that govern the bijecection are a bit too complicated to be modeled by the approximation methods found in standard models. But this looks very promising or at least something to play around with. I'm going to try it out this weekend. Thanks!