LLMs are trained to predict a bunch of tokens (GenAI produces text) from all the previous seen tokens, based on the data it was trained on. It does not understand anything about the spatial relationships like lines, objects etc in an image. "Not even the people who built them" - We have no real understanding of how LLMs work, yet. Traditional ML theory (classification/regression/clustering) largely does not apply to LLM's emergent capabilities like coding, arithmetic and reasoning. No such theory exists today. People are trying.

Imagine an equation several miles long, comprised of billions of variables, billions of constants, and billions of exponents.

You know none of the variables. None of the constants. None of the exponents. No one does, really, but even if you did it wouldn’t help, because no one bothered to write down the operators and the parentheses are randomly shifted around every time the equation is resolved.

All you know is that if you ask it for tea, it will always, invariably, and forever, give you back something that is almost, but not quite entirely, unlike tea. Sometimes it might be more unlike coffee, some times more unlike vodka and cow urine.

What you’ll never, ever, ever reliably know is what’s in the cup.

That’s about the best way I know to explain black box abstractions. In a few decades we might have a workable theory as to why these things function, to the degree that they do, though I’ll bet a rather large amount of money that we won’t.

It is Claude. Via Perplexity.

You can select models on Perplexity. He used Sonnet 4.5 Thinking