I can’t believe programming is now at the stage where advice like "first have the computer give you totally wrong answers, then just find a function that maps the wrong answers to the correct ones!" is a thing.
He clearly didn't know enough about vector embeddings.
Except not as much as I'd like... they often also don't know what the hell I'm talking about, and it still takes them twenty minutes of Googling to find the right page!
It nearly always works.
Because LLMs are artificial neural networks[0] (ANN), which are statistical in nature, and thus intrinsically non-deterministic. Pretty much every AI algorithm has randomness involved in its definition and many (most?) incorporate probabilities.
0 - https://en.wikipedia.org/wiki/Neural_network_(machine_learni...
This assertion is "oddly" similar to the GPT answer "neural network inference determinism" produced:
Neural network inference is often non-deterministic due to
factors like floating-point arithmetic and concurrent
execution, which can lead to variations in output even with
the same input.
Regarding your previous statement:
> However, on a technical level, neural network inference truly is inherently deterministic.
This holds for a vanishingly small set of conditions, none of which include randomness, nor when context and transformers are involved, let alone underlying model evolution (thus making model use over time non-deterministic).
My statement and GPT's statement are both correct answers to the same question, so I think it makes sense that they would be similar. Are you accusing me of having paraphrased an LLM in writing my answer? I did not, I just remembered having read Thinky's post on the subject [0], which GPT has probably read also.
> This holds for a vanishingly small set of conditions, none of which include randomness, nor when context and transformers are involved, let alone underlying model evolution (thus making model use over time non-deterministic).
There are plenty of ways to introduce nondeterminism into any system. By your standards, I doubt you could point to a single deterministic system in the world. print("hello, world") is only deterministic if your CPU is properly shielded from cosmic rays and your OS isn't out of memory etc. There are some inherently nondeterministic processes, like the stochastic methods used to train models or the random sampling used at inference time if you have temperature!=0, but inference under greedy decoding is conceptually deterministic.
[0]: https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
Fixing the seed is still intentional bias. Or you could force it to always take the one token with the highest probability, but that is still biased sampling. Deterministic, sure, but intentionally wrong just to avoid a technically
What I meant is something more like explicitly programmed vs. learned. Intelligence can result from learned behavior, but not from explicit programming of rules by humans.
An aspect of this is that “learning” is unpredictable - we can’t predict in advance exactly how the resulting model will behave, except broadly. It seems non-deterministic if only by virtue of its complexity, which is beyond anything we’re able to predictively model.
This is incorrect.
Simulated intelligence can and has been encoded explicitly by humans defining rules programmatically in the form of expert systems[0].
I also have worked on/with expert systems.
> I don't agree that they achieve "simulated intelligence". They're preprogrammed with a set of domain-specific rules that are trivially simple by comparison to even relatively simple and small neural networks.
This position does not account for fuzzy logic[0] nor an expert system's ability to produce an answer of "I do not know and here is why", which neural networks are incapable of doing.
I am not saying expert systems are "better" than ANNs as both are algorithms having significant value for what they provide. What I am saying is neural networks are pattern-matching algorithms, quite useful in their own right, and do not possess the ability to identify the lack of existence.
Comparing the two in 2026 seems like a bit of a joke to me. I'm not saying there's no role in future for traditional expert systems or fuzzy logic (or hand-written code, for that matter), but to claim they're "intelligence" or even "simulated intelligence" implies such a trivial definition of "intelligence" as to make it a useless term.
> an expert system's ability to produce an answer of "I do not know and here is why", which neural networks are incapable of doing.
Why do you believe that? Here's an excerpt from a response I received from Claude tonight:
> "I want to be honest about a limitation: I can't reliably confirm fine construction details — like exactly which sub-assembly is bolted to the spoke flange versus the fixed axle — from a marketing cutaway graphic at typical web resolution. Those images tend to be stylized/exploded-view illustrations meant to show 'there's a battery and a motor in here,' not engineering-accurate cross-sections with clear rotating/stationary boundaries marked."
This is after it examined two images I provided it with, and related it to the discussion we'd been having.
This demonstrates that it can indeed answer "I do not know and here is why", so your idea about what neural networks "are incapable of doing" is clearly incorrect.
And even if I grant your trivial threshold for intelligence, an interaction like that one clearly demonstrates a far superior degree of multi-modal intelligence, reasoning, and understanding that no expert system or fuzzy logic has ever even come close to achieving.
Often it's a difference between repeatable versus predictable, or whether a system has chaotic aspects like the configurations of a double-pendulum or weather-forecasting.
Sometimes it's the difference between determinism in-theory versus in-practice, especially when various optimizations are being applied to save money.
The LLM inference process can be 100% deterministic but the weights can still make the end result quite chaotic. Just because temperature>0 improves results doesn't mean its an innate part of the mechanism. Just because scale-out architectures introduce jitter in communication doesn't mean that's an innate part of the mechanism.
It's the same for so many things:
- reading documentation (what do I expect this function to be called?)
- finding clothes in a shop (something long-sleeved and light)
- picking the fridge for dinner
- finding a book in the library...
so many analogues where I'm not coming cold to a choice.
Worse is when you don't know whether the answers you have are totally wrong.
The entire problem of search is that the user has the wrong data and wants to use it to receive the correct data. That was the start, not the state we’ve ended up at - it is unironically how we got to LLMs.
“Hallucinate” is misleading here. In the given example, a classification is being done very successfully - it’s just that it requires an extra step to map it to an arbitrary predefined list of classifications.
If you can articulate why you think this isn’t a good approach, I’d be interested to hear it.