With synthetic data for large languages models it’s more about QA pairs and reasoning trails for solving complicated problems
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[1] : I told AI to make me a protein. Here's what it came up with : https://www.nature.com/articles/d41586-025-01586-y
[2] : AI Models for Protein Structure Prediction : https://frontlinegenomics.com/ai-models-for-protein-structur...
[3] : AI model deciphers the code in proteins that tells them where to go : https://news.mit.edu/2025/ai-model-deciphers-code-proteins-t...
One can escape the original NIL, NULL, None issue by using boolean logic, but that implies rules.
The 'strange thing' about shrodinger's cat, one can never be certain that one didn't pick the context before the cat existed and/or the references after the deceased remains were no longer visible. So, exercise is arguably statistically skewed toward 3/4 deceased, 1/4 alive. Add statistical sampling, and one can get an approximation of where things might be relative to cat life span. Only works one finds at least one instance of an 'alive cat' first. Much easier to just start with Boole's cat to avoid shrodinger's cat issues (aka lambda term). LLM's will happily supply relevant Boole's cat expression with/or without shrodinger's cat input.
Pauli might consider that a half baked cracker.