About 13 years ago I made https://jeena.net/catdog using machine learning algorithms to classify pictures of faces of dogs and cats and decide if we're looking at a cat or a dog. I was in university and this was what I came up with doing for one of the assignments in the ML course.

Another guy came up with a evolutionary way to find math formulas which would over time be able to get closer and closer to a specific shape of a graph by doing copies and introducing random mutations to them, it was also very fascinating.

Anyway, now that Jev came out the decision models are all the rage right now but I'm struggling to quite understand how different they are from what I did as a student in 2013. Is the big difference that I don't need to manually choose the features to extract and do the preperation of the raw images? Or is there really something fundamentally new to their approach?