[zephen]

> I would say [whether nondeterminism is bad] is unambiguously defined by the problem you're solving.

Sure. You gave an example where it can work. Another example is something like a PCB or chip layout engine.

That particular domain (layout) is NP-complete. You'll never have an exhaustive brute force search for the optimal layout, but...

You can subsequently easily check whether the produced layout meets all your acceptance criteria or not.

Another example that successfully utilizes non-determinism for good outcomes is the application of genetic algorithms to things like antenna design.

This works because (a) you have a relatively cheap fitness test; and (b) as with real evolution, the mix of combining working designs and randomly introducing mutations often eventually produces outstanding results.

Presumably, if you applied genetic algorithm techniques to, e.g., creating your LLM prompts, you could also get good results, but that would probably quickly get expensive in terms of tokens.

So we're left with people just semi-randomly modifying prompts in order to try to tweak results.

When it works, it can be amazing. When it doesn't work, it's like a brick wall.

I like your "embarrassingly nondeterministic" term, but I somewhat disagree with:

> A "bug" then would be considered "weak accuracy" not "crash" or "incorrect behavior",

When a lawyer asks an LLM for citations of cases that support his position, he is arguably doing something stupid, because embedding an assertion such as "Show me cases which support X" is just asking for hallucinatory trouble with many current LLMs.

Nonetheless, I submit that hallucinations are, by definition, "incorrect behavior" and not merely "weak accuracy."

Now, nondeterminism could certainly be useful to the lawyer, in that it could help an LLM make connections that LexisNexis might not have in their database. So asking an LLM for help with legal issues is theoretically not an insane thing to do, but the results need to be checked very carefully.

[wseqyrku]

I think I made an implicit distinction there that almost always get overlooked in these discussions, between runtime inference (when you run a transformer in your product) and offline inference (where you use AI in any ways anytime before that).

Chip design, etc would go to the latter category which is actually a huge space for innovation regardless of how you do it, while I was focusing more on the former which I think a lot of discussion seems to be concerned about.

Now if you are "running" your "generated code" that's where people will have strong opinions because it conflates the two completely different processes in the worst way possible.

[zephen]

It's kind of a useful distinction, but the categories are really blurry.

For example, the layout software vendors provide a solution that both uses non-determinism in their final product, and also checks the results against whatever criteria you give it.

If it tells you it will work, it's probably not lying, and if it tells you it won't work, it's almost certainly not lying.

So in your own creative process of building a board, you can rely on that.

Many LLMs don't have this property at all, and honestly, it really doesn't matter whether you are using the LLM to craft a legal document or to craft code; if you don't check the output yourself, you're possibly in for a world of hurt.

> Now if you are "running" your "generated code" that's where people will have strong opinions because it conflates the two completely different processes in the worst way possible.

I think a primary driver of the attractive nuisance here is that, because the execution of the resultant code is itself a deterministic process (even if the process that produced that code it isn't) people think that they can tame the process, by some combination of automated testing and looped automated reprompting.

At some level, they may not be wrong -- computer chips themselves are built on top of stochastic atomic processes, and google famously proved in its early days that it could have reliable processes on top of shitty commodity hardware.

But one key difference is that the nondeterminism in the atomic processes or in the non-ECC memory of the white box computers is uncorrelated.

And another key difference is that, unlike the layout software vendors, so far the LLM vendors seem unable or unwilling to properly self-check their own outputs.

To me, this is kind of the canary in the coal mine. You would think that the LLM vendors have every incentive to weed out bad results, that they, more than anybody else, have the understanding of root causes and probabilities that a particular output might be bad, and have more than sufficient resources to fix this, if it's not an intractable problem.

So Occam's Razor says that (currently) this is still an intractable problem.