I think ML is a bit too complex (different approaches trees, DNNs,...) to oversimplify it like that. One could say, it's just math - but even experts can't real

I think ML is a bit too complex (different approaches trees, DNNs,...) to oversimplify it like that. One could say, it's just math - but even experts can't really tell some of the why it works behind back propagation in DNNs. A lot of research is empirical, like switching to ReLU... ML is a commodity now, even popular with artists to create art. Being a commodity means you dont have to understand the inner workings to benefit from it. I don't see anything wrong with that, but a lack of proof for biases/backdoors/bugs in models is an issue that I'm worried about when its applied too enthusiastically in critical areas. Article is weak though, and hard to follow conclusion.