“There are business problems” and “most business problems” are not the same thing.

> you have serious regulatory issues

... until people decide they are OK with things being less than 100% and relax the regulations. Helped along by the purveyors of the AI tools no doubt

This has been going down over time.

A lot of the software industry has been moving away from assigning humans individual responsibility for failure (e.g. blameless post mortems).

Some business problems need that. That’s not the same as asserting most do and it’s certainly not the same all business problems.

Some things need to be deterministic. Many don’t.

Even your business will have many such problems that don’t need 100% all those properties - every task performed by a human for example. You as a developer are not all of these things 100%!

And your help query may need to be deterministic but does it need to be explainable? Many ml solutions aren’t really explainable, certainly not to 100% whatever that may mean, but can easily be deterministic.

If you were on a real flight and asked a human for help, they wouldn't give a deterministic answer. This doesn't seem like an actual requirement that is needed, but rather something that is post hoc rationalized because it was cheaper to make that way. While terms like consistency may come up when referring to having deterministic output as a requirement, the true reason could actually just be cost.

I thought this too, but it seems that is not the case. I could not remember the reason I saw why so I googled it (AI excerpt).

Large Language Models (LLMs) are not perfectly deterministic even with temperature set to zero , due to factors like dynamic batching, floating-point variations, and internal model implementation details. While temperature zero makes the model choose the most probable token at each step, which is a greedy, "deterministic" strategy, these other technical factors introduce subtle, non-deterministic variations in the output

Not that it's incorrect but there is some data showing variability even with the very same input and all parameters. Especially if we have no control over the model behind the API with engineering optimizations etc. See Berk Atil et al.: Non-Determinism of "Deterministic" LLM Settings, https://arxiv.org/abs/2408.04667v5

Ignoring that you are making an assumption about how the randomness is handled, this is a very vacuous definition of "deterministic" in the context of the discussion here, which is AI controlling large and complex systems. The fact that each inference can be repeated if and only if you know and control the seed and it is implemented with a simple PRNG is much less important to the conversation than its high level behavior, which is nondeterministic in this application.

If your system is only deterministic if it processes its huge web of interconnected agentic prompts in exactly the same order, then its behavior is not deterministic in any sense that could ever be important in the context of predictable and repeatable system behavior. If I ask you whether it will handle the same task the same exact way, and its handling of it involves lots of concurrent calls that are never guaranteed to be ordered the same way, then you can't answer "yes".

> LLMs are deterministic if you control all parameters, including the “temperature” and random “seed”.

This is not true. Even my LLM told me this isn't true: https://www.perplexity.ai/search/are-llms-deterministic-if-y...

The LLMs most of us are using have some element of randomness to every token selected, which is non-deterministic. You can try to attempt to corral that, but statistically, with enough iteration, it may provide nonsense, unintentional, dangerous, opposite solutions/answers/action, even if you have system instructions defining otherwise and a series of LLMs checking themselves. Be sure that you fully understand this. Even if you could make it fully deterministic, it would be deterministic based on the model and state, and you’ll surely be updating those. It amazes me how little people know about what they’re using.

In the hard, logically rigorous sense of the word, yes they are deterministic. Computers are deterministic machines. Everything that runs on a computer is deterministic. If that wasn’t the case, computers wouldn’t work. Of course I am considering the idealized version of a computer that is immune to environmental disturbances (a stray cosmic ray striking just the right spot and flipping a bit, somebody yanking out a RAM card, etc etc).

LLMs are computation, they are very complex, but they are deterministic. If you run one on the same device, in the same state, with exactly the same input parameters multiple times, you will always get the same result. This is the case for every possible program. Most of the time, we don’t run them with exactly the same input parameters, or we run them on different devices, or some part of the state of the system has changed between runs, which could all potentially result in a different outcome (which, incidentally, is also the case for every possible program).

> Are they? I mean I wouldn't say they are strictly deterministic, but with a temperature and topk of 0 and topp of 1 you can at least get them to be deterministic if I'm correct.

the mathematics might be

but not on a GPU, because floating point numbers are an approximation, and their operations are not commutative

if the GPUs internal scheduler reorders the operations you will get a different outcome

remember GPUs were designed to render quake, where drawing pixels slightly off is imperceptible

I'm not sure it eliminates but reducing the temperature and top-k/p?

Sure, but the discussion here is around "in production"? I'm trying to imagine a scenario and I'm coming up short.