> You have not shown how a large scale collection of neural networks irrespective of their architecture is more deterministic
Its software. Without an external randomness source, its 100% deterministic excluding impacts of hardware glitches. This...isn’t debatable. You can make it seem non-deterministic by concealing inputs (e.g., when batching multiple requests, any given request is “nondeterministic” when viewed in isolation in many frameworks because batches use shared state and aren’t isolated), but even then it is still deterministic you are just choosing to look at an incomplete set of the inputs that determine the output.
> Its software. Without an external randomness source, its 100% deterministic excluding impacts of hardware glitches. This...isn’t debatable.
I don't think anyone would even go as far as to include all deep neural networks which are indeed a large scale collection of neural networks as being "100% deterministic"; regardless of their architecture. Not even you yourself and I can explain transparently why it sometimes works or doesn't work the way it does especially with any inputs. (Which adverse inputs can really mess up the model).
But first of all, the entire sentence that you should be quoting for complete context is:
>> You have not shown how a large scale collection of neural networks irrespective of their architecture is more deterministic when compared to a 'compiler' and only repeating a known misconception of tweaking the temperature to 0 which does not bring the determinism you claim it brings with LLMs [0] [1] [2], otherwise you would not have this problem in the first place.
So given this "100% determinism" you just said, surely that means that LLMs can replace a traditional compiler which needs this said determinism, since that LLMs are so useful for such a use case in production?
Then, as we practically test this, all of this quickly falls into my secondary point:
>> By even doing that, the result of the outputs are useless anyway. So this really does not help your point at all.
Again, there is just no point with repeating such myths from AI boosters that deep neural networks like LLMs are '100% deterministic'; even with temp=0 tweaks and in the practical sense.
> I don't think anyone would even go as far as to include all deep neural networks which are indeed a large scale collection of neural networks as being "100% deterministic"
Any other position is pure magical thinking. Adding more linear algebra never gets you out of 100% determinism. It gets you more complexity, and potentially makes systems chaotic, but chaotic systems are still deterministic, just highly sensitive to small input variations.