So, two questions: 1. What parts of AI/ML have to be nondeterministic? Once the model is trained then it should be completely deterministic. There are some tr

So, two questions: 1. What parts of AI/ML have to be nondeterministic? Once the model is trained then it should be completely deterministic. There are some training techniques that randomize the order or grouping of data, but they are primarily used because random is almost as good as finding the optimal ordering/grouping and finding the optimal ordering/grouping is hard. Sometimes randomness is used to augment the data to give you more examples to for against (because the model is too complex to train/fit with the data you have it you want to be robust to certain variations in input. If you're talking outputs then you should get the same outputs from a model given the same inputs. There are some really bad models out there that are way over fitted (relevant xkcd - https://m.xkcd.com/2048/ https://m.xkcd.com/2048/ see "house of cards") and some people working on ML have a terrible grasp of statical techniques (just try models until they find one that fits), but even these models are deterministic. What is so bad about nondeterminism anyway? Ethernet used nondeterminism to handle collision resolution. There best prime finding algorithms are nondeterministic. There is nothing wrong with appropriate nondeterminism.