To be fair, a lot of creative work requires plenty of trial and error. And since no problems are solved from scratch, all things considered, the most immediate contributors to your result and you might have iterated through tens of dozens of possibilities.
My advantage as a human is I can often tell you why I am eliminating this branch of the search space. The catch is my reasoning can be flawed. But we do ok.
> just copying previous solutions with slight adjustments.
It's not just doing that, Copilot can do a workable job providing suggestions for an invented DSL. A better analogy than autocomplete is inpainting missing or corrupted details based on a surrounding context. Except instead of a painting we are probabilistically filling in patterns common in solutions to leetcode style problems. Novelty beyond slight adjustments comes in when constraints are insufficient to pin down a problem to a known combination of concepts. The intelligence of the model is then how appropriate its best guesses are.
The limitations to GPT3 codex and AlphaCode seems to be they're relatively weak at selection and that they require problem spaces with enough data to distill a sketch of and how to inpaint well in them. Leetcode style puzzles are constructed to be soluble in a reasonable number of lines, are not open ended and have a trick to them. One can complain that while we're closer to real world utility, we're still restricted to the closed worlds of verbose apis, games and puzzles.
While lots of commenters seem concerned about jobs, I look forward to having the dataset oliphaunt and ship computer from Fire Upon Deep someday soon.
