[Nydhal]

I thought it was a stretch when reading that medium post. Now reading this and thinking about it after finishing two undergraduate classes one on operating systems and another on compilers, the machine learning for systems part makes a lot of sense, apart from the heuristics the learned index structures idea is just fascinating.

Another illuminating sentence from the paper was this:

>This leads to an interesting observation: a model which predicts the position given a key inside a sorted array effectively approximates the cumulative distribution function (CDF). We can model the CDF of the data to predict the position as: p = F(Key) ∗ N

Maybe it's just my very limited knowledge as an undergrad but I'm feeling that this can be the start of something big. Another idea that just came to me after is how much of this ML is applicable to the domain of cryptography. In my security class it seemed like much of the famous hash functions for example were somehow "found" in vast space of potential schemes.