> Currently, we are still far from a point where machines are able to abstract high-level concepts from data or engage in reasoning and reflection Of course when an AI does that, we then say its just doing statistics, not reasoning. Until you have built a recommendation engine from scratch, it is hard to appreciate the complexity. I don't mean the complexity of the code or algorithm (ALS and Spark are straightforward…
no, AI simply doesn't do that. Even Demis Hassabis of Deepmind fame in a recent interview pointed this out. Machine learning is great on averaging out a large amount of data, which is often useful, but it doesn't generate true novelty in any human sense. AI can play Go, it can't invent Go.
In the same way today's recommender systems are great at averaging out my last 50 shopping items or spotify playlist but they can't take a real guess at what truly new thing I'd like based on a genuine understanding of say, my personality. Which is reflected in the quality of recommendations which is mostly "the thing you just bought/watched", which is ironically often incredibly uninteresting.