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[D] "AI systems are always deterministic," AI teacher says. How can I reply (with examples and papers)? (alien.top)

submitted 1 year ago by shiftingsmith@alien.top to c/machinelearning@academy.garden

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I had a discussion in class with one of my teachers. He says that AI is and can only be always deterministic because "even a deep learning neural network is a set of equations running on a computer, and the stochastic factor is added at the beginning. But the output of a model is always deterministic, even if it's not interpretable by humans."

How would you reply? (Possibly with examples and papers)

Tysm!

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[–] Great_Airport_4495@alien.top 1 points 1 year ago

Well, that's a correct statement. The only nondeterministic things that today's computers do, strictly speaking, are from one of two places:

Hardware failures
True random entropy hardware inputs (e.g. a hardware sensor recording noise in mouse or keyboard actions, hardware background noise, EM field strength, etc, or even better detecting things like random cosmic ray muon decays)

If we narrow our definition of nondeterministic to the PoV of a single process running on a multi-tenant machine, then there is more, coming from things like OS-controlled thread processing timings that the application has no visibility into.

But today's ML algorithm outputs are generally deterministic, outside hardware failure, and after you decide on random initialization seeds. They aren't drawing from random entropy during computation invisibly.

The model weights, input, system memory and storage state and random seeds completely determine the output, generally.