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

/!\ Unpopular opinion /!\
It seems that everyone here seems to "like" determinism, like everything in a computer should be deterministic, and the only things we could be talking about are floating point additions and random numbeer generators...
Indeed a model IS a determination. Most modern AI, like most classical math functions, are models.
When you have an algorithm that compute the result of a model, of course the computation should be deterministic !
Unless your model is stochastic : what you are modelling can be a form of randomness. In that case, the result of the model can be the density function itself, or a generator, that draws item from this distribution.
But if you are doing a probability exercise in a math class, the simulation you are coding is made "deterministically" : you determine what the density is.
Statistical learning is about letting the data determine most of the model (its parameters). And assuming the data you have sampled is "random", then "randomness" has determined the precise model you have trained. (And in theory, for that to be possible, a lot of assumptions must hold. Assuming the object you want to model contain some randomness, statistical learning can be the best way to make a good probabilistic model.)
In that sense, AI is "non-deterministic" : the developper don't determine what it do exactly, and it is often a model from a random reality.
To train a statistical model IS to draw a set of parameters from a given distribution. The answers are in the questions of statistical learning : you're model is assumed to be "random".

"Four" is a random number, no a deterministic one... in this sentence at least.