Post

Demonstrating reasoning and language capabilities in computers alters our understanding of self, even if we don't particularly like the alteration

This is the crux of the debate, right? Whether it's doing mimicry or reasoning. I struggle to see how you could view it as purely mimicry. It has to tie together multiple novel thoughts in a logical construction to create its responses.

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It is mimicry, no question. It is a tool that can provide pattern recognition based on data we feed it. It's just all high speed mimicry until you enter quantum mechanics land.

And even when quantum computing enters the field it's not "reasoning" You can potentially use "quantum tunneling" to get the most optimized solution for deeply complex problems but that is also not "reasoning." It's using physics tricks to more efficiently drive computation.

You send a message to me in a language I do not know. I have a very, very large set of books and flowcharts I can use to construct an answer to your message, using characters I also do not know. Overtime, my charts even inculde previous messages from you, as decision points.

I have another chart that assigns statistics to what charts I should use when multiple options exist. The charts are updated frequently. My messages back to you make sense to you, but again, I dont know what any of those characters mean. Am I sentient? Do I speak your language? Can I reason?

If I'm presented with "what is the product of x and y" I can reason that I'm being asked to multiply. I can then reason through the answer using pen and paper and the rules of multiplication of values. Computers can use a similar tactic with binary math but with floats/unreal numbers, it...

... approximates best as it can before giving what it has. Humans can reason as infinitum, the bit shifting electronic tricks of a CPU has a limit because it doesn't reason, it adds and shifts. But it works well enough. LLMs can do neither. If LLMs could reason, it could math. It can't.

We know this because LLMs are constantly shown to be wrong mathematically. Because it arrives at its conclusions through statistical assembly of previously reasoned answers but without even a true understanding of context. ChatGPT is worse at math than not only humans, but a 1965 Casio.

Again, if it could "reason", being fed every possible way to calculate the product of X and Y from all the stolen crap fed into it, being fed a question of "what is X times Y" it would use one of those methods to arrive at the conclusion. It doesn't.

how do you square this with many llms coming up with complex mathematical counterexamples that are not in the literature (nor anything close). for the record i think the downstream consequences suck pretty bad and also llms have no real world model but they can do math its hard to deny at this point

Regardless of what it's doing... How does something imitating you change you? It's not your sense of self or nature you're worried about, I don't think. Maybe it's something else.

Novel to whom? My niece laughed because "you know what? Chicken butt!" was novel to her. That doesn't make it creative or new

it is evidently clear that you don't understand how it works But just like you were wrong about phone cooling, it is not about mystery but knowing the mechanism and how it works. Did you argue siri was alive? many did, under similar delusions. We know how LLM work, we know it is not reasoning

We don’t understand everything about how human reasoning works, but we do know exactly how LLMs work and we know it’s definitely not doing anything most people would ever consider “reasoning.”

It IS understood how it works. And it is NOT reasoning, not as we humanly define it. That's not to say it's of no value; that would be an incorrect statement.

Yeah - I mean I'm not really sure what "reasoning" is so that's an interesting dimension of this. But I think this is clearly one of the most powerful tools ever developed to extract meaning from language.

My local mockingbirds can mimic a half dozen different car alarms. They can string those alarm sounds together in novel ways. This does not mean they have a driver’s license.

Jesus Christ do people actually think its reasoning? Calling it artificial intelligence was a mistake (it's not) and allowing the people who think movies are reality to spearhead its marketing was also a mistake.

This perhaps says something about the logic inherent in language, or in the surprising observation that compression has an equivalence to intelligence due to the nature of what prediction means, but here we are: the LLMs work through novel questions and scenarios regularly

The fact that you're able to make that argument shows you have no idea how LLMs actually work. This is the same technology that predicts the next word you're going to type on your phone, but on steroids. All they're doing is probabilistically stringing words together. There's no THOUGHT there.

Having worked with these for the last 4 years. I can safely say they are absolutely doing mimicry. It's their fundamental programming. Doing data predictions at speed based on indexed data and churn out a plausible response. (Any response). But the most mathematically predictable response.Thats it!

In the end, it all comes down to assign, compare and jump. Fantastic we've come this far with those building blocks, but here we are. We're starting to repeat history in that what we don't understand surely must be divine.

I think the specifics are too charged to have a great discussion but what you describe as Self I think of as language; the fact that LLMs work as well as they do given the underlying model is revealing about language to me; insofar as one considers language part of self, your argument should hold.

The logic inherent in language is literally why we know they aren't thinking. They don't know what words are, they just put out words in whatever order makes the most sense according to their algorithms. That's why ChatGPT had such a hard time with now many rs are in strawberry.

This is not an essential limitation of the tech, Claude can scan poetry, count syllables and rhyme. But the logic inherent in language is indeed enough to produce logical statements without needing to reason.

Language makes sense by itself in a network of references. We have known that for over a hundred years. The computer doesn't need to actually "think" to be able to use the map of the English language it was able to produce from training

That's a meaningless statement. People task AIs with working through scenarios and answering questions. Some of those problems are incredibly complex but at no point are we seeing anything but machine accomplishing a given task. There's no motivation or independent reasoning .

I mean yeah but that *in itself* is weird in a way that changes how we think about ourselves, right? That a language prediction system with no motivation or independent reasoning per se is capable of all of this.

If you told someone a decade ago that in ten years giant statistical models of human language would be capable of hacking computers with very little in the way of human direction, people would ask if you were high.

its always funny to me that saying LLMs are not doing independent reasoning actually makes LLMs even weirder, and the impact on how we view humanity even bigger than saying that they are capable of independent reasoning

why should motivation be considered determinative as to intelligence or sapience or capacity for reasoning? As for independent reasoning, again, unclear why that should be determinant of intelligence and also, if anything LLMs have too much of that due to cursed stochasticity

And a human performing an equivalent reasoning task hasn't ingested vast swathes of all human knowledge up to a certain date, which is then near-instantly indexable by their mind, to perform that task. If the machine is thinking, it's still not clear these modes of cognition are similar IMO.

Dude, there is no magic quotient in them. They are mimicking bots trained by assholes to scam you into thinking you are talking to something. They do a great job at it because all of the money in the world has been poured into it! It sucks!

next word prediction leading to intelligence is nicely described by the carl sagan quote "if you want to bake an apple pie from scratch u must first invent the universe" it needs to simulate a whole world to think of what would happen next, given its context

I guess...... yeah, the navigation through the embedding space could be argued to reflect the full mind/world state even when it doesn't activate every possible neuron

Language is the interface, and as such there is interop. If the what is reason and the claim is that "reasoning" must use the same method to arrive at said answer, that's fine, but that's almost an ancient question, never to be solved.

It doesn't, though. It puts words (or pixels) together in a statistically likely combination. It has the same relation to reasoning as a funny looking cloud does to Abraham Lincoln.

Machines are never going to have a novel though, Paul. It scares the ever-loving shit out of me that so many of you tech bros are incapable of understanding that.

Because programmatically that's EXACTLY what they're doing. I can say they're doing mimicry because the algorithm underpinning all of it is literally trying to lay a line of best fit for mimicking a real response. Anyone saying otherwise is either ignorant of the architecture, lying, or both.

The "reasoning" the newer models do is just the same process but directed at how best to fulfill your query instead of addressing the query itself, and then that plus your query is fed into the algorithm along with a seed value. This shit is only a god in a box if you're ignorant of how they work.

I would accept that they are simulacra. I'd accept that they can occasionally be very good simulacra, but are far more often mediocre or downright bad at it. but that's what they are & forgetting that is the pathway to madness both on a personal and societal level.

You tricked yourself into believing an algorithm is alive because you aren't used to having actual conversations with people. It isn't thinking, it's barely guessing correctly.

okay lets say you are right and AI is sentient: Is it not morally egregious to then *use* AI like a tool or for corporations to own them?

if AI can reason and think like you say it can then corporations own thinking entities and people use them for labour and entertainment without any sort of benefit or compensation to the entity, we have a name for this phenomena and you're not gonna like it!

I think the "LLMs can't reason" stuff is mostly sophism these days. They plainly do not "reason" in the way humans do, and are certainly not always correct or good at it, but they are also doing things that, if a person were doing it, would be referred to as "reasoning" of some variety

I think this articulates it well. IMO the root of it is that there is more than one way to answer questions, and LLMs do this a new way, with maybe high-order correlations between all the questions and answers and text they've seen or something. Not the human way, but still a way to do it.

also how would I know if all I was doing was mimicry? What if all I’ve ever known is mimicry? People pretending to act like people doing people things occasionally bumping into a novel sequence of information.

Because if you think your LLM is reasoning you either have an exceedingly narrow definition of reasoning or you don’t understand how LLMs work.

When people say "mimic" as opposed to "reason" I'm not even entirely sure what is meant by that. That it is putting on a show then just pulling an exact preformed string of words from a dataset that sufficiently replies to a prompt?

Like how does one even tell the difference between "mimicking" and "reasoning" even if you have technical skill and access to look under the proverbial hood?

I would posit that "not in the way humans do" is also a vastly oversold point. Their sensory modalities are tokens, but is a blind person non-reasoner because they don't share all the modalities seeing people do? Of course not. And when you take in the implications of that...

...then a large subset of the low-hanging fruit criticisms ("but they can't even count r's in "strawberry") vanish (bsky.app/profile/kimm...), and when you are not fixated on the low-hanging fruit, turns out they actually share a lot of commonalities with humans in terms of cognition!

Kimmo Merikivi@kimmokm.bsky.social

I came up with an analogue for the infamous "how many r's in a strawberry" gotcha: it's tokenized st|raw|berry, and LLMs see the tokens, not the letter representation. So, imagine I give you the following emojis 😇🥩🍓 (saint, uncooked meat, a small edible fruit) coding for a word? How many r's?

I would suggest that this is necessarily so: any process that works even poorly at coming at truths must be an approximation of Bayes. But they e.g. share the same sort of features like dual-process structure, quirks that suggests they're doing things like subitizing, etc (arxiv.org/abs/2607.26179)

arxiv.orgCognitive Convergence: Deep Similarities Between Large Language Models and Human CognitionLLMs are widely regarded as alien intelligences, systems whose cognitive operations are fundamentally unlike our own. Apparent similarities to human cognition are therefore often seen as the result of...

You are eloquently describing a phenomenon that is common among human aiding machines. The machines that help us with locomotion, flying or navigation do it in a manner different than humans or other biological entities.

While some opposition to the LLM-based AI may be rootedin the belief that reasoning is a special human trait that can't be reproduced in machines, I suspect that most of the antagonism is caused by the social environment around these machines.

The explicit claim (threat) from manufacturers that AI will take everybody's job, the push of AI into domains where it is harmful (teaching of humanities, mass surveillance, war), the sociopathic behaviour of some of the owners, all those don't help the case for AI.

But is it "reasoning" in a way different than an "AI" in a video game against a human opponent? Or, is it just better at it? Computers have long been able to "reason" deductively based on programming. The key question, IMHO, is whether LLMs are engaged in a categorically different model of reasoning

LLMs definitely "learn" differently than traditional video game AI (through machine learning over vast quantities of source material). But I'm still looking for evidence of different reasoning.

I think they are doing something qualitatively different to, say, 2000-era computer game AI because it is able to form strategies to genuinely novel problems. It really isn't the case that it's either brute-forcing over the problem-space or needing hand-crafted rules to follow

I agree with that. But I don't think we've seen genuine emergent reasoning. The models are just applying general language understanding to novel problems in a linguistic manner. They are basically all cynical Wittgenstein followers. For that reason, I'm still skeptical LLMs are a path to AGI.

And I say that in as neutral a way as I can; it is tbd exactly whether that generalizes indefinitely or whether the costs of doing it outweigh the benefits etc. But it is geniunely doing something genuinely /different/ to the "expert systems" we used to deal with

Crucially, this is because people reason and LLMs don't. A woodworker and an injection mold can both craft a chair. Why do we not claim the injection mold uses reason when it creates the same output as the woodworker?

i get and actually quite resonate with the AI hostility and also i'm struck at how much of at least the Bluesky conversation starts from the presumption that AI is only a fraud. which it is absolutley 100% not.

I'm constantly frustrated that people always seem to lean into the "it's all fake" or "we're months away from the sky robots eating your children", both of which are very substantially deranged and deflect a ton of attention away from the real harms and benefits that society needs to be focusing on

somewhere in this debate about "reasoning" is a more interesting question, to me, of how these probabilistic outputs are turning the user's cognitive capacity into grey goo. that doesn't strike me as a metaphysical question, but it does strike me as a philosophy-of-mind question?

I don't know enough about computer science to critique the claims made about LLMs or other forms of machine learning. The hype and the capex don't come anywhere near matching the revenues that would be needed to sustain the industry. So I'm expecting a huge correction. 🫧💥

As I've been on about recently, one hypothesis is that our languages are used as a tool for reasoning, and now we can more or less emulate that tool on a specialized substrate. So it can't do all the *rest* of what we bundle into the term "reasoning", but there is still reasoning happening.

The best argument I've heard is that being very good at predicting the next word in a sentence written by people who live in the world that obeys certain social and physical rules, requires, at some point, some level of recognition of those rules and the ability to follow them logically

hence why a combination of very large input datasets and hyper-optimization to being good at token prediction forces pathways that can make what we'd refer to as "logical inferences" if a person did them

I mean, "being very good at predicting the next word in a sentence" is one reductive but possibly still valid way of saying "using language". For my money it seems there's a little more than path-dependent token selection going on, but I'm a rhetorician and not a mech interp nerd so idk really :)

But this is false, because we *know* how they work and it is by brute force statistical association. They reproduce post hoc the way words have been used by people. The argument that world-knowledge must somehow be hidden "in the weights" is false.

I think this argument is wrong, but it’s very interesting that it’s wrong! For anything that “thinks” in the way we think, I agree it’d probably hold. However with an LLM, there’s no mechanism for it to recognize rules about the world, society, or anything for that matter. It doesn’t do “concepts.”

They lack a world model to reason about. E.g. if an llm produces story-like text, based on its huge database of story text, we know this is not identical to a person telling a story, because a person can envision a setting and characters to tell a story *about*, and the llm lacks that.

Likewise we know that an llm-in-harness can (sometimes) generate maths proofs by iteration between math-like text generation and a proof checker. This is an interesting computational exercise but is not how humans do maths.

Dude you're being publically fooled by one of the oldest stage tricks in existence, you don't get to mock people repeating a good line

I’m saying this for your sake as well as others in this thread: If you are getting answers you don’t respect and feel people are engaging in bad faith: block and move on. This insistence on trading condescending barbs until every spectator just wants both of y’all to shut up is unhealthy.

By this argument, LLMs are far from the first instance of computers "reasoning". Everything from Deep Blue to Zork to Clippy produces outputs that, if they came from a human, would be "referred to as 'reasoning' of some variety".

Not a very well formed or informed question on my part, but I do wonder if the differences between human reasoning and whatever AI does may have implications for where AI is likely to peak in its capabilities. Line keep going up? An asymptote?

a thing that causes a lot of consternation with AI researchers is they can see the capabilities going up on a graph against compute, and while there might be a plateau eventually, it isn't obvious from the graph that we are near it

But human reasoning is spontaneous and natural. It doesn't take scanning the combined logic of millenia for a single human to intuit the solution to a problem. And if someone compares "genetic memory" to an LLM I am just going to lose my shit.

yeah I mean - they don't have eyes and ears and nerves and hormones and all the other stuff that goes into making our lives experientially what they are. But idk, they do the things they do.

they self-evidently can solve certain puzzles that it hasn't seen before at a better success rate than brute-force, and picking trials and paths based on a series of logical inferences that, while not always right, are often at least very coherent

No, still not there. Algorithmic outputs can be or include or suggest solutions to certain puzzles, sometimes quickly & sometimes acceptably ("success") via an ordered series of computations. The fact that the series of computations *is ordered* renders coherence tautological rather than admirable.

If you had a picture of 10,000,000 grains of rice and 1 maggot, an LLM is a much better option to find the maggot. For that kind "right answer" reasoning they're great. But for anything involving creativity (art, music, writing, etc) they are thought cancer.

Counter point: Every model I've tested so far eventually got themselves into an unbalanced parentheses situation when writing elisp (after writing surprisingly good code for quite a while). Now with proper understanding of the code block that'd be a trivial fix - yet LLMs massively struggle.

I’m waiting to see some outputs. When do we start seeing cool new products enabled by greater efficiency? Or prices going down down for existing products? Or new scientific breakthroughs?

Paul, if you genuinely believe that these LLMs are reasoning, then surely you must immediately stop using them to do work for you and campaign to prevent anyone else from doing the same. That's *at best* slavery-adjacent, and your subscription fee isn't going to the AI, it's going to its owner.

It's not a debate though, because we know that it's not doing reasoning. We know because we know what the algorithm is. It's a prediction engine. It produces an output that it calculates to be the probable result given its inputs. It does not cogitate. This is known.

It's incredibly frustrating that the slop cultists will not understand this. Cognition and reasoning are excluded as explanations by the nature of the machine. What's that Holmes quote? “Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth.” It doesn't

With the irony being that prolonged, regular usage of genAI tools has been shown in multiple studies to *worsen* the cognition of its human users. Like muscles, the brain is an organ that grows stronger with use and weaker without it, and outsourcing cognition makes you use your brain less.

Perhaps prediction is just the substrate that the AIs use to build reasoning capabilities. It's not obvious to me that there can only be *one* path to reasoning (the human ones). Maybe AIs *are both* predictive engines and capable of genuine reasoning by composing predictions.

Also how do you know that's *not* what humans do? A lot of predictive processing theories in neuroscience propose that, at some fundamental level, the basic operation implemented by neuronal networks is basically prediction error minimization.

LLM don't work by composing predictions thou. Predictions are the ouput not the mechanism. LLM output word probabilities but they get to those by doing a lot of matmuls and nonlinearities, wich can represent any function with enough weights. So what they actually do is some very complicated thing.

I don't think LLMs are having thoughts or any stupid shit like that but what are they doing now that they're good at math logic puzzles? Like before they were awful at math but they're actually right now so something changed technically

They made the LLM bigger and trained them more on math. They didn't need to change the architecture because the architecture can in fact in theory do anything, if you have the right weights. More and different training can lead to a completely different thing that has different limitations.

Yeah, as I regularly preface discussions on this topic with, "this isn't a philosophical question, it's a technical one". Like, I generally do believe that it should be entirely possible to build a machine that thinks, but none of the current crop of things being called AI do or ever could.

My argument that a synthetic or mechanical or whatever intelligence is possible is always gonna be that if a few pounds of electrified bacon grease can think we can definitely find other ways to do it. Like the question of "can matter think?" is a settled yes. Everything else is R&D.

The bacon grease is nanotechnology that assembles itself from environmental materials. Like the damn air. Bit of a harder problem there

Exactly, I don't see any reason it shouldn't be possible, it's just that LLMs ain't it. Like they don't even actually have any way to change and adapt to new information, chatgpt's "memory" is literally just the app resubmitting the entire text conversation every single time someone hits submit.

Absolutely - it's innate in the structure of the LLM architecture that it will never be capable of conscious or reasoning. Which does not mean it can't manage to simulate it fairly well in certain contexts, but if the question is specifically "can it reason" the answer is an unqualified "no."

This doesn't logically follow, for two trasons: first (literal), you haven't defined reasoning or consciousness, so there's a gap in your logic. I generally define "reasoning" as the construction of successive logical statements that build on one another to reach a given conclusion.

Obviously it’s not conscious but I’m not really clear exactly what you’re meaning by “reasoning”, or what the difference would be between simulating reasoning vs actually reasoning. If you’re saying computer = simulated then obviously you’ll say it’s just simulated but that’s not helpful.

What is your actual technical argument thou? Are you aware that transformers are turing complete on the limit of infinite context or width fe so there's some Neural Net representation of whatever program you think could think?.

You mention lack of memory later but like that is not true? the text is memory you can do arbitrary things on it . As a toy example you can encode a universal Turing machine on the weights of a autoregressive transformer and run arbitrary code using context and memory files as tape.

the thing is, in fields like math and computer science, a lot of reasoning pretty much can be boiled down to following rules, and putting simple rules together into a chain of logic, which is likely why it's good at those

You know some people would just choose a less humiliating job for a bit less money but you go bro, keep up the true believer shit, maybe you can cash out a retirement fund before the bubble pops

Because they are literally giant mimicry machines, that's all the code can do. Any "emergent" properties you tie to it are basically just pointing to the limits of your credulity.

Could we suggest that it focus its efforts on figuring out how to minimize its resource consumption first, so that the rest of us can survive long enough to benefit from its accomplishments?

You’re mistaking a very complicated mathematical system for reasoning. When you use phrases like ‘multiple novel thoughts’ you’re anthropomorphizing AI and that isn’t the lens to view it through. It isn’t sentient. LLMs, for example, are just very complicated probabilistic systems.

admitting you're being fooled as a result of your lack of incredulity is entirely a you problem. you are looking at the shadows cast against the wall and going "holy shit there's no reasonable explanation for this"

But it's not "tying together multiple novel thoughts." It's just reflecting what you give it. Essentially it's just googling shit fast and freeing you of the cognitive load of actually thinking. If it's fundamentally changing you as a person at all, it's just making you lazy, bud.

Consciousness is a property that doesn't even make sense without the presence of 10,000 underlying cognitive processes that LLMs demonstrably do not have. Every single animal on Earth has more cognition than AI.

And I don't just mean dogs and cows. Just measuring their behavior, the nervous system of a starfish is solving cognitive problems AI cannot, and the existence of starfish has not and could not change my identity as a human. And it has no bearing on yours either! Come on!

I mean, I don't think it's totally irrelevant.

Nick Garcia @nwgarcia.bsky.social

Just for reference, if you keep a human brain alive for like 4 years and you merely talk in front of it, you can get it to speak a language fluently. That's not zero cost, but you don't have to dump a power plant and the entire Internet into it.

💯 I am benchmarking things in some very odd coding spaces recently. (again houdini, think node trees, custom VEX scripts, complex general knowledge of 3D assets) and Sol is better bang for buck. (this extends to mythos vs opus or sonnet)

" It has to tie together multiple novel thoughts in a logical construction to create its responses." Yes, and ... that's still not reasoning. Not as it's generally understood. I can't say it all, but reasoning is MORE than what you described. Much, much more.

Philosophers have been debating this for centuries, literally since philosophy was invented. The fact Ai-acolytes only noticed the question NOW, when their favorite toy raised it for them, does not mean the answers don't exist. They just haven't been paying attention to them.

And like, if it were just them starting to question the nature of consciousness and language, fine! But they aren't, they're acting like the fact they just now THOUGHT of the question automatically discredits all the centuries-old answers they can't be bothered to listen to.

Semasiological navel-gazing doesn’t approach the crux of the debate. Unless you think that 4-function calculators “changed the nature of humanity”; those took over a much more discrete area of “reasoning” and did it unambiguously much better than people.

It’s still not really clear *what* LLMs are doing, how reliable they are at doing it, and whether they’re better or worse than humans at it, because all the costs are obscured. even if we ultimately agree that they can “reason” that doesn’t really change my relationship to my own embodied reasoning.

when you speak to other people, do you gauge your words based upon the statistical probability that the next word you say is the word the other wants to hear, or do you gauge them based upon your own desires, interests, and opinions?

re: mimicry vs reasoning. the machine chooses words based upon statistical probabilities that a word, phrase, or concept may link to a previous set of words. we don't do that when we communicate. we don't have statistics engines in our heads.

the machine seems to give natural responses, it's got the disguise of humanity by being fairly accurate in that statistical approximation of speech most of the time, but that's fundamentally not how we, humans, conduct conversation.

"LLMs are fancy autocomplete" is not true any longer. "Fancy autocomplete" mimics System 1 (fast thinking) pretty well. But the fact that newer LLMs do "recursive fancy autocomplete" and produce System 2 (slow thinking)-like behavior is very interesting and may tell us something about intelligence.

"Recursive fancy autocomplete" as performed is both impressive and reaches the point of being *somewhat* qualitatively different. Why do you believe that it might tell us something about intelligence, though? That seems like a large jump made just because one WANTS to make it.

false dichotomy. mimicry still implies intentionality. LLMs perform a statistical function on input. that's it. its all probabilistic with zero intentionality. its "tieing together" its training data, as it is designed to, and it is the user's cognition that falsely imputes reasoning.

Half the time it just spits out stuff that kinda sounds good if you don’t think too hard about it, but if you employ your actual human reasoning skills, it makes zero sense. It demonstrably cannot reason. You have to bring your own reason to the party to get anything useful out of it.

I'm convinced that a lot of business leaders think it's reasoning because it's good at generating business jargon, and they already weren't reasoning their way through any of that.

This is what concerns me about AI boosters. They see it as a simplistic dichotomy, mimicry or reasoning? It is neither. My favorite example is a prompt like “what is your favorite color?”. The mimic result would be the most popular color. The reasoning result would be

“I don’t know what colors mean”. Instead it churns the tokens and comes up with an answer based on what it calculates the reply you want is. I’m other words it is neither reasoning or mimicry. It is how the engine is coded

I'm going to guess it says something playful like "I don't really have a preference, but" or "I'm just an agent that doesn't have favorites" and then something like "but if I had to choose, _ is a lovely color." Let's see...

The “logical construction” happens because the data trained on is largely coherent — the “logic” is in the language. But they only goes so far. I am not a dev and keep trying then for other things, & fairly often get plausible sounding nonsense. Its “reasoning” is really brittle & randomly v wrong.