Comment by PaulHoule

One way to think about prompting is as a conditional probability distribution. There is a particular song by Taylor Swift or the set of all songs by Taylor Swift but ChatGPT is particularly talented at sampling the "set of all songs in the style of Taylor Swift".

One of the worst problems in the "Expert Systems" age of A.I. was reasoning over uncertainty, for instance this system

https://en.wikipedia.org/wiki/Mycin

had a half-baked approach that worked well enough for a particular range of medical diagnosis. In general it is an awful problem because it involves sampling over a joint probability distribution. If you have 1000 variables you have to sample a 1000-dimensional space, to do it the brute force way you'd have sample the data in an outrageous number of hypercubes.

Insofar as machine learning is successful it is that we have algorithms that take a comparatively sparse sample and make a good guess of what the joint p.d. is. The success of deep learning is particularly miraculous in that respect.