zlacker

Think of the LLM as a slightly lossy compression algorithm fed by various pattern classifiers that weight and bin inputs and outputs.

The user of the LLM provides a new input, which might or might not closely match the existing smudged together inputs to produce an output that's in the same general pattern as the outputs which would be expected among the training dataset.

We aren't anywhere near general intelligence yet.

>>mjevan+(OP)
Ignoring your last line, which is poorly defined, this view contradicts observable reality. It can’t explain an LLM’s ability to diagnose bugs in code it hasn’t seen before, exhibit a functional understanding of code it hasn’t seen before, explain what it’s seeing and doing to a human user, etc.

Functionally, on many suitably scoped tasks in areas like coding and mathematics, LLMs are already superintelligent relative to most humans - which may be part of why you’re having difficulty recognizing that.