@BowsacNoodle @tyler @unabomber
if you are like myself and have enough math understanding that you can decipher more difficult proofs, youll find that machine learning is basically a bunch of probability theory, statistics, and matrix operations

youre basically trying to find some predictor that will give a desired output given some input. the math has been more or less set in stone since the 80's, just most of the issues about it was 1: comparability problems and 2: data storage and the viability of a market for that to propagate in (and as seen it finally happened because drooling retards got access to computers)

on your note about ai as a crux, ive gotten extremely jaded on it. It super tiring to se basically everyone I know relying on it to the point where they are unable to approach or work problems without one. I could argue thats been the trend of computer science in recent years, but even then, discarding one task means getting good at another task that circumvents that (usually harder) task. Unlike that, AI is more of a "success by brute force" method in the way that to get better results, you have to tweak the initial conditions (read prompt) or expand/tweak/refit/retrain the model with more data, whether more specific data, or a wider range of data. i.e. its the O(n^2) solution because the investment of "how to i join videos in ffmpeg" is *insane* compared to just referencing documentation.