This is all awesome, but a bit off topic for the thread which focuses on AI for science

The disconnect here is that the cost of iteration is low and it’s relatively easy to verify the quality of a generated C program (does the compiler issue warnings or errors? Does it pass a test suite?) or a recipe (basic experience is probably enough to tell if an ingredient sends out of place or proportions are wildly off)

In science, verifying a prediction is often super difficult and/or expensive because at prediction time we’re trying to shortcut around an expensive or intractable measurement or simulation. Unreliable models can really change the tradeoff point of whether AI accelerates science or just massively inflated the burn rate