> For example, cryptography falls into precision computing. There is no room for being incorrect even by a single bit. Where as machine learning is about getting a range of answers, with tolerance for error.

Doesn't both of them rely on randomness in real use cases/usage? And it's only once you have fixed seeds that cryptography becomes deterministic, and then you can make the same claim for most of ML, when the seeds are fixed you get fixed replies.

It happens to be that most people seem to use LLM clients that aren't deterministic, as they're using temperature + random seeds for each inference, but that doesn't mean someone couldn't do it in a different way.