The human limit of machine learning.

The other day a discussion at work resurfaced the old topic of the merits and problems of advanced recommendation algorithms. The problem has many subtle angles, but in its simplest form it boils down to our better relevance algorithms being completely obscure to us about the “why”. The issue is not new and has been topic of conversation among us for a while. But this time I saw a new facet to it: there’s a wall on how far we can take our relevance algorithms, before we start seeing negative returns. It has nothing to do with limitation of the methods, or with how smart we can be on analyzing the data. It is not about the limited amount of training data we can gather, nor it is about having the…

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