In my opinion, current SOTA machine learning / deep learning is actually quite simple on a conceptual level. January last year, I decided to give it one year to

In my opinion, current SOTA machine learning / deep learning is actually quite simple on a conceptual level. January last year, I decided to give it one year to understand transformers on a "I could totally code that" level. There were so many good video tutorials and texts that after 2 months there wasn't so much left to learn. In particular, the math did not feel much more difficult than what I learned in high school (and much easier than what we did in university). I think this is a particularity of those DL approaches that don't need much more than some (higher dimensional) linear algebra and calculus. There are other AI or ML approaches that I cannot even grasp on a superficial level, but fortunately (from a learner's perspective) DL has completely eaten machine learning, for the time being. Nevertheless, that entire field depends so much on complex heuristics and subtle optimizations that I completely understand that learning and getting an intuition for those details takes a long time (and is more akin to black magic). The development experience is absolutely horrible. Debugging a model takes such a long time, is rather expensive, and observability is absolutely dismal (at least it was a year ago). It really felt like debugging a program into existence by staring at graphs and retrying infinitely many times.