You really want to be careful with that, Monty Python made an excellent documentary about the weaponization of such high grades of humor and the results, to put it mildly, weren't funny.
Just the right amount: https://www.youtube.com/watch?v=_yo9WHrTvks
It was a skit, and it was funny. Not their best work.
Diet response is so genetically confounded that I think it's going to be a while before you can make any sort of confident prediction, and just plain hard to predict even when you're using identical twins. Probably more leverage in figuring out how to make continuous glucose monitors more feasible to measure individual response directly.
But a GAN where the discriminator is determining if a joke is made by an AI or a human might be pretty cool :)
Even if the data exists, doesn't mean the AI folks would use it. In my research (a particular subfield of fluid dynamics), the machine learning/AI papers/talks always seem to have incomplete or even bad data, as if how advanced their algorithm is makes up for that. (I don't think they actually believe that. I think they just have bad habits.) I've published pretty good linear regressions of a much larger data compilation and received much less attention, despite the fact that my linear regressions are probably more accurate than the ML models...
The whole thing is about how to build these fancy networks, and it created a fair bit of buzz. Table S1, in the supplement, however shows that it's rather pointless.
The deep model has an AUC (95% CI) of [0.94, 0.96] for in-patient mortality. The "full feature-enhanced" logistic regression baseline has an AUC of [0.92, 0.95]. Same pattern for 30 remission. Length of stay is the only one that's not overlapping, and it just squeaks that out: [0.86, 0.87] for the deep model vs. [0.84, 0.85] for the baseline.
https://efficiencyiseverything.com/food-nutrition-per-dollar...
EDIT: Direct link to the data https://efficiencyiseverything.com/data/Nutrition%20Per%20Do...