For my day job developing software, and for the RPG campaigns and books I run and publish today, …

I think we need to disqualify humans as well. Their brains have been shown to operate on probabilistic chemical interactions and even quantum effects.

> I am failing to see how the inherent probabilistic nature of the technology can be fully overcome. This is common in image generation pipelines because if you find an image you really like, you can store the seed and then reproduce it with small tweaks, otherwise - to quote Borges - “Look at it well. You will never see it again" User-facing deterministic pipelines do exist for generative AI. I know you make this argument in your post, but that's really the answer if you want repeatable results. For a classifier or a detector, determinism is a requirement, but for an LLM non-determinism desirable property because it feels like a more natural conversation. The downside is it's extremely difficult to replicate a response without pointing the model to an earlier conversation. And specifically for the RPG case, don't you want non-determinism? You don't want the model spinning up the same id…

Setting aside that we're living in a universe that's full of (practically) deterministic processes built over probabilistic components (and which behave sufficiently reliably without any human in the loop), I think the specific failure mode you're citing is that there aren't enough gates and constraints applied to the processes you've seen. LLMs can contribute quite reliably given very narrow prompts and short horizons (keeping turns low and context brief). If you chain a bunch of these narrow contributions together and define guardrails (structured outputs, online evals, other-llm-as-judge/jury, etc...) you can produce a very repeatable workflow that reliably delivers to defined service levels. The obvious issue being - you've got to define the workflow and implement all the guardrails, not hope that the LLM will infer them during a session or a one-shot prompt.

The trouble is: there is no deterministic algorithm that can do the things neural networks can do. For many of these problems, I think it is likely that no deterministic algorithm can exist because the problems are fundamentally underspecified. E.g. a common task in computer vision is generating a 3D depth map from a 2D image. This is inverting a lossy projection, so any solution must be a least partially a hallucination. I think we just have to accept this. It's a different type of algorithm, built out of statistics instead of logic, with different strengths and weaknesses compared to traditional software.

> So far, I am failing to see how the inherent probabilistic nature of the technology can be fully overcome I deeply hope we never reach the point where that’s overcome. What we’ve seen over the past few years is how AI will destroy humanness from pretty much the entire digital realm. It’s by far the most evil, anti-human technology ever created, corrupting everything it touches. The last thing we need is for it to become reliable