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> 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 gettin
by CaptainOfCoit 1y ago
> 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.
- happa 1y agoFixing the seed wouldn't necessarily make LLMs deterministic. LLMs do lots of computation in parallel and the order in which these computations are performed is often indeterministic and can lead to different final results.
- razodactyl 1y agoYep. And to answer the question about randomness - it's absolutely vital to have a good source of noise to obscure the underlying pattern to prevent the secret information leaking - but the mathematical part that manipulates that noise into the encrypted output has to be precise. That's the distinction made here relating to probability. Disclaimer: Not a crypto expert. Just like reading about it. Check actual sources for a better insight. Very interesting technology and much smarter people working in this field who deserve a lot of praise.