> I think we agree then? the tech is useful; you need systems around them (like sandboxes and commit hooks that prevent leaking secrets) to use them effectively (along with learned skills)

No.

> very little software (or hardware) used in production is formally verified. tons of non-deterministic software (including neural networks) are operating in production just fine, including in heavily regulated sectors (banking, health care)

It's what happens when it all goes wrong.

You have to explain exactly why, a system failed in heavily regulated sectors.

Saying 'everything is probabilistic' as the reason for the cause of an issue, is a non answer if you are a chip designer, air traffic controller, investment banker or medical doctor.

So your point does not follow.

▲

dkdcio 3 hours ago | parent [-]

that’s not what I said. you honestly seem like you just want to argue about stuff (e.g. not elaborating on the “no” when I basically repeated and agreed with what you said). and you seem to consistently miss my point (in the second part of your response; I’m saying these non-deterministic neural networks are already widespread in industry with these regulations, and it’s fine. they can be explained despite your repeated assertions they cannot be. also the entire point on CPUs which you may have noticed I dropped from my responses because you seemed distracted arguing about it). this is not productive and we’re both clearly stubborn, glhf

	▲	rvz 2 hours ago \| parent [-]
		> that’s not what I said. you honestly seem like you just want to argue about stuff (e.g. not elaborating on the “no” when I basically repeated and agreed with what you said). and you seem to consistently miss my point I have repeated myself many times and you decide to continue to ignore the reliability points that inherently impede LLMs in many use-cases which exclude them in areas where predictability in critical systems is required in production. Vibe coders can use them, but the gulf between useful for prototyping and useful for production is riddled with hard obstacles as such a software like LLMs are fundamentally unpredictable hence the risks are far greater. > I’m saying these non-deterministic neural networks are already widespread in industry with these regulations, and it’s fine. So when a neural network scales beyond hundreds of layers and billions of parameters, equivalent to a production-grade LLM, explain exactly how is such a black-box on that scale explainable when it messes up and goes wrong? > they can be explained despite your repeated assertions they cannot be. With what methods exactly? Early on, I said formal verification and testing on CPUs for explaining when they go wrong at scale. It is you that provided absolutely nothing of your own assertions with the equivalent for LLMs other than "they can be explained" without providing any evidence. > also the entire point on CPUs which you may have noticed I dropped from my responses because you seemed distracted arguing about it). this is not productive and we’re both clearly stubborn, glhf You did not make any point with that as it was a false equivalence, and I explained why the reliability of a CPU isn't the same as the reliability of a LLM.