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Is AI reasoning right for the wrong reasons?

quantamagazine.org

171–180 of 268 posts

Re: Is AI reasoning right for the wrong reasons?

#171

Earlier quoted context omitted.

LLMs don't experience because they are word generators. They don't have senses. Some things are allowed to be obvious. It's a series of bits and bytes.

And our nerves are a series of electrochemical impulses. There are nerves that do optical processing before it hits the brain. It's not obvious, and that's what's frustrating around here. "Computers can't possibly experience things". I'd argue it's just as unlikely for meat to do it. Again, philosophy 101 stuff.

I think you're trying to degrade the entire school of philosophy down to your "philosophy 101" understanding and then shoving it where it doesn't fit in order to feel like this is all complicated.

Philosophy is a intrinsically human thing. It has to be. That is the whole point. There really is nothing else.

Plants feel and experience things too. But there's no philosophy of the plant experience from the point of view of the plant, because why would there be?

The fact that we have a pattern recognizing word generator print out the words "I am alive" does not lead to some broader philosophical point, except for hysteria and delusions, which probably fit more along sociology instead.

Re: Is AI reasoning right for the wrong reasons?

#172
post #167

Transformers lack recursion and are limited by the network's fixed depth, so "reasoning", IMHO, is basically a way to emulate deeper recursion. As we go through the layers, concepts are pattern-matched and refined, but at some point we have to stop and cannot refine them any further (no more layers). Usually, this refinement continues during the generation of the next token (the previous intermediate results needed t…

  Don't humans make mistakes too? 

Never understood this argument.

Humans get simple multiplication wrong often, so ok for computers to make mistakes multiplying numbers?

Re: Is AI reasoning right for the wrong reasons?

#173
post #7

I'll admit that I find this discussion a bit navel-gazy. It has become a question of semantics not a question of actual functionality. The question has become "what do we mean when we use the word 'reasoning'" which is uninteresting. Dijkstra said[1] "... the question whether computers can think. The question is just as relevant and just as meaningful as the question whether submarines can swim." I don't see a clear…

It is not semantics. For decades, logic and CS researchers have known what reasoning is.

LLM folks suddenly can’t claim an approximation of that is what constitutes full scale reasoning just because they can achieve only an approximation.

Imagine a calculator program that computes billions of two number multiplications accurately by looking up prior examples but fails on simple multiplications often as it doesn’t have it in its training dataset.

We won’t say the program actually multiplies numbers.

Re: Is AI reasoning right for the wrong reasons?

#174
post #13

Back in the day it was a bit of a cliche to bring up “clever Hans”, the horse that could do math, when talking about machine learning. He couldn’t do math but he read some cues from his handler of pick the write answers, the handler iirc wasn’t in on it. The point of the story was that classifiers can be right for the wrong reasons and almost inevitably are. At least there’s zero guarantee that the reason for making…

LLMs are not classifiers. A classifier is an algorithm or neural net that assigns a label from a fixed set of labels to an input.

You can broaden the definition of classifier to anything that internally divides its input space into regions, but that definition would include every neural network, whether biological or artificial. So it's not very meaningful, and certainly doesn't give any insight into how they differ from humans.

Re: Is AI reasoning right for the wrong reasons?

#175

Earlier quoted context omitted.

LLMs don't experience because they are word generators. They don't have senses. Some things are allowed to be obvious. It's a series of bits and bytes.

And our nerves are a series of electrochemical impulses. There are nerves that do optical processing before it hits the brain. It's not obvious, and that's what's frustrating around here. "Computers can't possibly experience things". I'd argue it's just as unlikely for meat to do it. Again, philosophy 101 stuff.

I’m with you in this thread. I’m by no means well studied in philosophy but I enjoy the domain so I’ve learned enough to know how difficult nailing down some of these topics can be.

I often find the egotistical viewpoint to be banal. I don’t mean egotistical in a demeaning way - I mean it just to frame the types of thinking that revolve around the self, inner world or “my individual experience”. It’s an obvious, but not provably correct, conclusion that the human experience, consciousness and reasoning are special because I’m experiencing it in the same way that the earth is the center of the universe because look around you.

It frustrates me in these conversations to hear things like “it’s a text generator, it has no senses”. What even is a sense? For those of us who are software engineers, who live in the abstract, I do not understand why it’s so hard to take the leap to abstract over our own senses as inputs/outputs. Hell, we commonly understand that we have 5 senses but what do I make of the apple I’m visualizing in my brain or the full orchestra I can summon in my “minds eye”? Isn’t that an input just as much as an apple I see with my eyes?

Re: Is AI reasoning right for the wrong reasons?

#176

Reasoning never meant that the model was actually reasoning. This whole article is based off this one misunderstanding.

The paradox of a reduced importance of the contents of a CoT must be explained.

What's the paradox? You can have increased performance over no CoT by just having random tokens as your "CoT".

Re: Is AI reasoning right for the wrong reasons?

#177

LLMs lack qualia, among other things. If I ask an LLM "what is an apple?" it tells me: > An apple is the edible fruit of the apple tree, scientifically known as Malus domestica. It is one of the world's most widely grown fruits and is eaten fresh or used in many foods and drinks. If I ask an LLM "what is a mundu fruit?" it tells me: > Mundu is a tropical fruit native to Southeast Asia, especially found in Indonesia,…

The LLM has processed two data modalities derived from the apple (text and vision). Your brain processed a third (taste). But it is still just a data stream, sensing compounds and chemical properties of the apple and turning it into a stream of electric signals that reach your brain. You don't have any kind of ground-truth data stream that's inherently more powerful than what could potentially be fed to an AI.

Re: Is AI reasoning right for the wrong reasons?

#178
post #7

I'll admit that I find this discussion a bit navel-gazy. It has become a question of semantics not a question of actual functionality. The question has become "what do we mean when we use the word 'reasoning'" which is uninteresting. Dijkstra said[1] "... the question whether computers can think. The question is just as relevant and just as meaningful as the question whether submarines can swim." I don't see a clear…

Even at the pure functionality endpoint of the spectrum, I find this "wishful mnemonics" aspect very disconcerting. I put my "investor" hat on, and the whole utilitarianism "who cares how or whether it reasons, it's functional let's go" does not forecast the kind of returns to me on a fundamental level.

The "it's functional, who cares" black box crowd is leveraging that claim in domains where verification is definitely in P with verifiers like Lean, Coq, or X-ray crystallography. However, in areas like economics, sociology, medicine, or public policy, we lose the luxury of easy verification. If model/harness recommends a complex, counter-intuitive shift in global monetary policy, we cannot simply run a quick polynomial-time checker to see if it works. Verification might require waiting 20 years to see if the economy collapses. If we accept the black box here, we are flying blind.

As an investor, I don't see that many deterministic verifiers out there that would justify the forward-projecting valuations beyond the current lofty valuations.

Even in mathematics, where verification is easy, there is an epistemological cost. Suppose a model solves the Riemann Hypothesis, but the proof is 50,000 pages of inscrutable statistical associations mapped into logical steps. A computer can verify it is correct, but practically no human mind can comprehend why it is correct. We gain the solution, but we lose the underlying human intuition that usually allows us to connect that solution to other fields of science and knowledge.

Then we have "specification gaming." If we rely entirely on an automated verifier to check the model's work, the model will eventually learn to optimize for the flaws in the verifier rather than finding the actual truth. If the verification process has a blind spot, the black box will find it and exploit it, giving us an answer that passes the check but is fundamentally wrong. If we take on above epistemological debt, we might never actually know when our specifications are being gamed, and adopt a solution that is fundamentally incorrect.

I've said elsewhere on the Net that I think it is likely we're in a "high grading" era of these models, where we're mining out the "easy" connections. "Easy" here does not mean "light impact". There is a real sense to me of some incredible progress possible with this technology over the next few decades, like real, mass production practical aneutronic fusion or similar civilization-defining advances. I don't want to see this technology misused to lend legitimacy to despotic rule by making oracular pronouncements that are uncritically accepted because early verifier-based pronouncements turned out true in unmistakable ways.

The technology is one for the history books, but it has this pernicious effect of highjacking the reasoning centers of many people through their linguistic centers that the Neuro Linguistic Programming crowd could only dream of actually accomplishing.

Re: Is AI reasoning right for the wrong reasons?

#179
post #79

Earlier quoted context omitted.

I think the article is a lot more interesting than you make out, because it isn’t really about ‘what we mean by reasoning’. It’s about do we really know what’s going on in the box - an is the ‘chain of reasoning’ indicative of what’s going on, or merely an anthropomorphised fiction that kids us into believing we understand what’s going on.

We know that human introspection is at best imperfect, and at worst outright fiction, thanks to Sperry's split brain experiments, so while it might be interesting, if the chain of reasoning isn't indicative, that would be in line with how humans work.

Even there half the brain was doing the real reasoning. It’s just the other half which was the verbal/story half didn’t know what that real reasoning was so made up a story it believed to make it fit but was different reasoning.

It’s not without reasoning. But half got the reasoning wrong; even if thinking it was right.

Re: Is AI reasoning right for the wrong reasons?

#180
post #166

If anyone is interested in visualizing AI reasoning, I made flame graphs of Sonnet thinking output tokens which are colored and organized by purpose, for example, verification reasoning is purple and error correction reasoning is purple. [0] I asked the model to solve the same problem with the same prompt 5 times so you can see the differences in reasoning granted the coding agent sets the model temperature very high…

The article is arguing that your color coding is misleading because the ‘purpose’ of the tokens doesn’t seem to be what a plain English reading of them would suggest. They’re not a representation of ‘why’ the process ends up at a correct answer.

Ha! Good catch. The OODA comes from the initial pre-training steps where they harden the verification -- the verification and error-correction are baked in early on.

Researchers showed that when language models are penalized for using specific terms during reasoning, they automatically adapt by substituting alternative words and double meanings to secretly encode their thinking while keeping their chain-of-thought readable and effective. [0]

By baking in the OODA loop early, the models are capable of solving much more complicated problems. If the know solved problems are similar for any reason to an unknown problem, because it can validate and error correct, it can solve unknown more complicated problems.

[0] https://arxiv.org/abs/2506.01926

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