[js8]

> Does JIT compiles some other program code instead of the one being run? Does it produce bytecodes for a different VM? Does it tries to compile parts of the program that have not been executed or aren’t going to be?

Sure, but the same is true for LLMs - the lead models no longer make trivial mistakes like answering "What is the capital of France?" wrong.

> JITs and GC are deterministic algorithms, you can predict its behavior by just reading their code.

On large enough systems, you can't, just like it's difficult to predict weather. Determinism has little to do with it. At work, I have just witnessed a bug in JIT (it seems to have been fixed in OpenJDK 25). It inlined a wrong method. We weren't able to reproduce the error conditions without a private customer dataset.

And the fact is, historically, there have been many bugs in compilers, or they have been bad at their job, writing performant programs. The output (resulting program) of a good compiler is difficult to understand (because it is written to be efficient). LLMs (for the programming use case) are different quantitatively, not qualitatively.

[skydhash]

It’s really weird how you shift the goalposts and your own definitions.

No one is saying that a compiler can’t have bugs. What we have been saying is that if we take the compiler has a blackbox, we’re reasonably certain given we know the input, what the outputs will be. And the output will stay the same if you keep the input the same.

But you can send the LLM the same prompt, and it will gives you a different answer each time. And it’s not even about the verbiage used.

[js8]

LLM doesn't have to be non-deterministic, it can work just like any other deterministic algorithm.

But I am not sure why the insistence on the relevance of (non)determinism, rather than on the chaotic relation of the output to the input (which is true for both compilers and LLMs). In practice, inputs to the LLM, as well as to the compiler, change. And the fact is, the output can change radically due to that.

I think nobody really sends the same prompt twice to the LLM, so nobody cares about it being deterministic. I think what you're looking for is something different, some form of stability (as opposed to chaotic behavior). Although it's hard to define exactly, because in case of LLMs theory lacks behind praxis. (And as I said - we already gave up on stability with respect to performance by using compilers. We resolve that issue by doing performance testing.)

(I asked AI what's the opposite of "chaotic", I use "stable", but it seems that people use "deterministic" or "predictable" also in that meaning. So if you're using "deterministic" in that meaning, then you don't really care about sampling and temperature, i.e. determinism in the philosophical sense, but rather whether the output is consistent, albeit expressed differently.)

[skydhash]

The whole point of technology is about control and consistency. Even with random parameters, we want their value to an item in a specific sets. When I use a tool, I want it to produce the outcome I want, not any other outcome it wants to produce. If it fails at that, it’s a defective tool.

[js8]

I disagree, technologies have tradeoffs. What's your take on Monte Carlo algorithms, or any other randomized algorithm? Do you reject that too?