I mean it is a (higher order) Markov chain.

If you search it with hashtags you'll get a lot more relevant/topical posts (e.g. https://sigmoid.social/tags/nlproc). The local timeline may be full of ML people, but not everything ML people post is about ML. If you want a curated feed you'll have to create that yourself just like on any other SNS.

Ah, I didn't intend to say that it's old or useless, just that I think it receives disproportionate research focus/effort.

The issue is that requiring a model to be differentiable puts far too many limitations on the types of models you can formulate. Much of the research in the last few decades has focused on how to deal with issues caused purely because of the artificial constraint of differentiability. It's purely "local optimization" in the space of potential models, when what we really should be doing is "basin-hopping".

Backprop has really overstayed its welcome. It's great to see people doing something about it.