I have been recently reading the article The Case for Learned Index Structures which is about applying ML methods to a deeply investigated problem of search indexes. From the article: “Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets”.

This was an eye-opener to me as I always thought about B-Trees indexes as a very well investigated area which is essentially “solved” and we already know a lot about theoretical bounds, etc. You see, it is one thing to use ML to solve a previously unsolved or poorly done problem (e.g. object detection in CV), it is an entirely different thing to revisit a problem that we already claim we know the best general solution to.

So I was wondering if there are other examples that you know of that would describe an ML based method which beats well investigated and established methods in real-world situations?