Sounds like a rebranding of Inductive Logic Programming. It does not scale, while all recent advances are about scaling simple systems. Think that for a vanilla transformer, the bottleneck is often the size of the attention because it is N^2, and people are switching to linear attention.

So the main problem with MIL or ILP is that it would not be able to handle the scale of the quantity of raw input data that the system would have to process?

Usually, the problem is the combinatorial nature of the possible number of rules that could apply. Here they seem to be able to find a subset of possible rules with a polynomial complexity, but as table 7 of the second paper contains tiny 'wrt ML/RL data) instances of problems, I would answer yes to your questions. ILP is something coming with strong guarantees, while ML comes with a statistical risk. Theses guarantees aren't free.

Alright, thank you. Then I guess last question, if you happen to know; what is the current state of ILP in the ML/AI industry? Is it pretty much dead? Is it merely an interesting theory but hasn't found much application in the market? Does anyone see a bright future for it?

Even for protein folding it has been overridden by deep models. It might be useful for critical tasks where error is not allowed and everything is deterministic, but I'm not expert of the field.

Got it. Appreciate the insight.