ML is (smooth) surface fitting. That's mostly it. But that is not undermining it in any way. Approximation theory and statistical learning have long history, f

ML is (smooth) surface fitting. That's mostly it. But that is not undermining it in any way. Approximation theory and statistical learning have long history, full of beautiful results. The kitchen sinks that we can throw at ML are incredibly powerful these days. So, we can quickly iterate through different neural net architecture configurations, check accuracy on a few standard datasets and report whatever configuration that does better on Archiv. Some might call it 'p-hacking'.