llms are not strictly deterministic in the sense that even if you had the RNG state, context, and prompt you would likely not get an identical output even if there was no other randomness involved, because the concurrent scheduling of the massive amounts of floating point calculations can produce different results, since floating point arithmetic is not truly associative [(a+b)+c can differ from a+(b+c)] and the order in which these operations happen can result in subtly different final tensors. To reproduce it deterministically you'd have to also reproduce the exact scheduling of all matrix calculations among all the GPU cores (across different physical gpus!) that it took place on, which afaik is currently impossible.

We also have engineer blindness, so having human in the loop confirming thousands of requests would quickly start to confirm everything without looking.

It would become just another system to hack through, and slow the development process as well. The OpenAI video in the article recommends an autonomous defense mechanism. For rapid reaction, but I don’t know how sustainable or effective that would be, or if as humans we will be able to keep up.

> step by step

That’s basically what “pari passu” means.

I'm not sure I understand. Are you going to debug the neurons?

They are trying to do that, but there are too many of them, so they're building new AIs to help them do that...