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[D] Examples of machine learning applied to “solved” problems

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?

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.