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If for a given set of inputs there is a deterministic output, then the overall behaviour to a series of inputs is just as deterministic and predictable.

I'm not sure what you mean with "is not predictable" when you also admit that it's repeatable.

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Predictable as in, can say in advance what it will do.

Repeatable means you get the same output a second time, given the same input. Predictable means you can say in advance what it will do, and can then check the output against your prediction.

If you can't predict the outcome, you can't validate the process, and can't guarantee its performance.

That much is clear but any remotely continuous AI model is very predictable.

I think the point is that reality is essentially a chaotic system. That is, yes, you can repeat a failure from an input once you've seen it, but the search space is too big to enumerate beforehand.

That entirely depends on what exactly you implement here, it's entirely possible to implement an AI with continuous & linear properties, meaning that you can extrapolate it's behaviour between a set of inputs with decent accuracy and it won't suddenly change it's behaviour between continuous & linear inputs.

But AI isn't different than existing software systems either. Both will take an input from reality and take actions upon it.

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