It's refreshing to read something about ML and inference and have it not be anything related to a transformer architecture sending up fruit growing from a huge heap of rotten, unknown, mostly irrelevant data. With traditional ML, it's useful to talk about the sources of bias and error, and even measure some of them. You can do things that improve them without starting over on everything else.

With LLMs, it's more like you buy a large pancake machine that you dump all of your compost into (and you suspect the installers might have hooked up to your sewage line as input too). It triples your electricity bill, it makes bizarre screeching noises as it runs, you haven't seen your cat in a week, but at the end out come some damn fine pancakes.

I apologize. I'm talking about the thing that I was saying was a relief to be not talking about.