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Don't classify, hallucinate

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Re: Don't classify, hallucinate

#111

Post by antonvs »

AdieuToLogic wrote: post #96
antonvs wrote: post #88

Earlier quoted context omitted.

Valid point, it’s why I included the word “traditional”, to try to qualify that. 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-determini…

> Intelligence can result from learned behavior, but not from explicit programming of rules by humans. This is incorrect. Simulated intelligence can and has been encoded explicitly by humans defining rules programmatically in the form of expert systems[0]. 0 - https://en.wikipedia.org/wiki/Expert_system

I've worked on 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.

Re: Don't classify, hallucinate

#112

Post by kanjinoguchi »

ashu1461 wrote: post #20
braiamp wrote: post #13

Earlier quoted context omitted.

Because that's structured data and structured data is usually hidden away from users _and_ machines. Product rarely want to be honest, unless it's B2B in a very competitive market (and even then!). So, yeah, it's not that they are bad, it's that there are few good sources of information. (Lets ignore for now that no one seems to agree to what should be the spec sheets)

With agentic commerce protocol / unified commerce protocol open ai and gemini are trying to solve this problem. The idea is to make structured queries using these protocols which can be used to fetch top products matching the user needs instead of just relying on semantic search. https://developers.openai.com/commerce/specs/file-upload/pro...

[flagged]

Re: Don't classify, hallucinate

#113

Post by AdieuToLogic »

ChadNauseam wrote: post #103
AdieuToLogic wrote: post #95

Earlier quoted context omitted.

> I feel like I'm fighting a losing battle but I don't see why so many people consider LLMs innately non-deterministic ... 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_(m…

Now that you mention it, I think "statistical" might be a good word choice as it probably closely matches what people mean when they say an LLM is nondeterministic. However, on a technical level, neural network inference truly is inherently deterministic. The only nondeterminism in LLMs is parallelism-dependency in the way floating point operations are carried out and in the decoding strategy

> The only nondeterminism in LLMs is parallelism-dependency in the way floating point operations are carried out and in the decoding strategy

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.
Surely this is but a coincidence.

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).

Re: Don't classify, hallucinate

#114

Post by AdieuToLogic »

antonvs wrote: post #111
AdieuToLogic wrote: post #96

Earlier quoted context omitted.

> Intelligence can result from learned behavior, but not from explicit programming of rules by humans. This is incorrect. Simulated intelligence can and has been encoded explicitly by humans defining rules programmatically in the form of expert systems[0]. 0 - https://en.wikipedia.org/wiki/Expert_system

I've worked on 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.

> I've worked on expert systems.

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.

0 - https://en.wikipedia.org/wiki/Fuzzy_logic

Re: Don't classify, hallucinate

#115

Post by antonvs »

AdieuToLogic wrote: post #114
antonvs wrote: post #111

Earlier quoted context omitted.

I've worked on 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.

> I've worked on expert systems. 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 w…

No, fuzzy logic doesn't change what I said at all. In terms of intelligence, both of those technologies were at best limited and simplistic attempts at achieving what LLMs have actually achieved.

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.

Re: Don't classify, hallucinate

#116

Post by yencabulator »

Terr_ wrote: post #97
ChadNauseam wrote: post #86

Earlier quoted context omitted.

I feel like I'm fighting a losing battle but I don't see why so many people consider LLMs innately non-deterministic, an LLM running on a CPU with greedy decoding would be 100% deterministic and still have the intelligence we associate with LLMs

I've seen this debate several times and often there's a terminology issue, where the same word isn't being interpreted the same way by different sides. 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…

Also, people seem to not realize that chaos can be 100% deterministic. Consider Mandelbrot or Conway's Game of Life.

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.

Re: Don't classify, hallucinate

#117

Post by ChadNauseam »

AdieuToLogic wrote: post #113
ChadNauseam wrote: post #103

Earlier quoted context omitted.

Now that you mention it, I think "statistical" might be a good word choice as it probably closely matches what people mean when they say an LLM is nondeterministic. However, on a technical level, neural network inference truly is inherently deterministic. The only nondeterminism in LLMs is parallelism-dependency in the way floating point operations are carried out and in the decoding strategy

> The only nondeterminism in LLMs is parallelism-dependency in the way floating point operations are carried out and in the decoding strategy 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…

> This assertion is "oddly" similar to the GPT answer "neural network inference determinism" produced

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...

Re: Don't classify, hallucinate

#118

Post by wseqyrku »

devmor wrote: post #74
Sharlin wrote: post #50

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.

This been the case for a long time! 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.

I think you mean "incomplete", It's not "wrong" to look for 'schism tool lyrics'
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