Even with continuous backpropagation and "learning", enriching the training data, so called online-learning, the limitations will not disappear. The LLMs will not be able to conclude things about the world based on fact and deduction. They only consider what is likely from their training data. They will not foresee/anticipate events, that are unlikely or non-existent in their training data, but are bound to happen due to real world circumstances. They are not intelligent in that way.
Whether humans always apply that much effort to conclude these things is another question. The point is, that humans fundamentally are capable of doing that, while LLMs are structurally not.
The problems are structural/architectural. I think it will take another 2-3 major leaps in architectures, before these AI models reach human level general intelligence, if they ever reach it. So far they can "merely" often "fake it" when things are statistically common in their training data.
Kahneman’s whole framework points the same direction. Most of what people call “reasoning” is fast, associative, pattern-based. The slow, deliberate, step-by-step stuff is effortful and error-prone, and people avoid it when they can. And even when they do engage it, they’re often confabulating a logical-sounding justification for a conclusion they already reached by other means.
So maybe the honest answer is: the gap between what LLMs do and what most humans do most of the time might be smaller than people assume. The story that humans have access to some pure deductive engine and LLMs are just faking it with statistics might be flattering to humans more than it’s accurate.
Where I’d still flag a possible difference is something like adaptability. A person can learn a totally new formal system and start applying its rules, even if clumsily. Whether LLMs can genuinely do that outside their training distribution or just interpolate convincingly is still an open question. But then again, how often do humans actually reason outside their own “training distribution”? Most human insight happens within well-practiced domains.
I've never heard about the Wason selection task, looked it up, and could tell the right answer right away. But I can also tell you why: because I have some familiarity with formal logic and can, in your words, pattern-match the gotcha that "if x then y" is distinct from "if not x then not y".
In contrast to you, this doesn't make me believe that people are bad at logic or don't really think. It tells me that people are unfamiliar with "gotcha" formalities introduced by logicians that don't match the everyday use of language. If you added a simple additional to the problem, such as "Note that in this context, 'if' only means that...", most people would almost certainly answer it correctly.
Mind you, I'm not arguing that human thinking is necessarily more profound from what what LLMs could ever do. However, judging from the output, LLMs have a tenuous grasp on reality, so I don't think that reductionist arguments along the lines of "humans are just as dumb" are fair. There's a difference that we don't really know how to overcome.
> You are shown a set of four cards placed on a table, each of which has a number on one side and a color on the other. The visible faces of the cards show 3, 8, blue and red. Which card(s) must you turn over in order to test that if a card shows an even number on one face, then its opposite face is blue?
Confusion over the meaning of 'if' can only explain why people select the Blue card; it can't explain why people fail to select the Red card. If 'if' meant 'if and only if', then it would still be necessary to check that the Red card didn't have an even number. But according to Wason[0], "only a minority" of participants select (the study's equivalent of) the Red card.
[0] https://web.mit.edu/curhan/www/docs/Articles/biases/20_Quart...
Though note that as GP said, on the Wason selection task, people famously do much better when it's framed in a social context. That at least partially undermines your theory that its lack of familiarity with the terminology of formal logic.
You're right that the Wason task is partly about a mismatch between how "if" works in formal logic and how it works in everyday language. That's a fair point. But I think it actually supports what I'm saying rather than undermining it. If people default to interpreting "if x then y" as "if and only if" based on how language normally works in conversation, that is pattern-matching from familiar context. It's a totally understandable thing to do, and I'm not calling it a cognitive defect. I'm saying it's evidence that our default mode is contextual pattern-matching, not rule application. We agree on the mechanism, we're just drawing different conclusions from it.
Your own experience is interesting too. You got the right answer because you have some background in formal logic. That's exactly what I'd expect. Someone who's practiced in a domain recognizes the pattern quickly. But that's the claim: most reasoning happens within well-practiced domains. Your success on the task doesn't counter the pattern-matching thesis, it's a clean example of it working well.
On the broader point about LLMs having a "tenuous grasp on reality," I hear that, and I don't want to flatten the differences. There probably is something meaningfully different going on with how humans stay grounded. I just think the "humans reason, LLMs pattern-match" framing undersells how much human cognition is also pattern-matching, and that being honest about that is more productive than treating it as a reductionist insult.
Agreed. More broadly, classical logic isn't the only logic out there. Many logics will differ on the meaning of implication if x then y. There's multiple ways for x to imply y, and those additional meanings do show up in natural language all the time, and we actually do have logical systems to describe them, they are just lesser known.
Mapping natural language into logic often requires a context that lies outside the words that were written or spoken. We need to represent into formulas what people actually meant, rather than just what they wrote. Indeed the same sentence can be sometimes ambiguous, and a logical formula never is.
As an aside, I wanna say that material implication (that is, the "if x then y" of classical logic) deeply sucks, or rather, an implication in natural language very rarely maps cleanly into material implication. Having an implication if x then y being vacuously true when x is false is something usually associated with people that smirk on clever wordplays, rather than something people actually mean when they say "if x then y"
We keep benchmarking models against the best humans and the best human institutions - then when someone points out that swarms, branching, or scale could close the gap, we dismiss it as "cheating". But that framing smuggles in an assumption that intelligence only counts if it works the way ours does. Nobody calls a calculator a cheat for not understanding multiplication - it just multiplies better than you, and that's what matters.
LLMs are a different shape of intelligence. Superhuman on some axes, subpar on others. The interesting question isn't "can they replicate every aspect of human cognition" - it's whether the axes they're strong on are sufficient to produce better than human outcomes in domains that matter. Calculators settled that question for arithmetic. LLMs are settling it for an increasingly wide range of cognitive work. The fact that neither can flip a burger is irrelevant.
Humans don't have a monopoly on intelligence. We just had a monopoly on generality and that moat is shrinking fast.
Some references on that
https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow
https://thedecisionlab.com/reference-guide/philosophy/system...
System 1 really looks like a LLM (indeed completing a phrase is an example of what it can do, like, "you either die a hero, or you live enough to become the _"). It's largely unconscious and runs all the time, pattern matching on random stuff
System 2 is something else and looks like a supervisor system, a higher level stuff that can be consciously directed through your own will
But the two systems run at the same time and reinforce each other
S1 is “bare” language production, picking words or concepts to say or think by a fancy pattern prediction. There’s no reasoning at this level, just blabbering. However, language by itself weeds out too obvious nonsense purely statistically (some concepts are rarely in the same room), but we may call that “mindlessly” - that’s why even early LLMs produced semi-meaningful texts.
S2 is a set of patterns inside the language (“logic”), that biases S1 to produce reasoning-like phrases. Doesn’t require any consciousness or will, just concepts pushing S1 towards a special structure, simply backing one keeps them “in mind” and throws in the mix.
I suspect S2 has a spectrum of rigorousness, because one can just throw in some rules (like “if X then Y, not Y therefore not X”) or may do fancier stuff (imposing a larger structure to it all, like formulating and testing a null hypothesis). Either way it all falls down onto S1 for a ultimate decision-making, a sense of what sounds right (allowing us our favorite logical flaws), thus the fancier the rules (patterns of “thought”) the more likely reasoning will be sounder.
S2 doesn’t just rely but is a part of S1-as-language, though, because it’s a phenomena born out (and inside) the language.
Whether it’s willfully “consciously” engaged or if it works just because S1 predicts logical thinking concept as appropriate for certain lines of thinking and starts to involve probably doesn’t even matter - it mainly depends on whatever definition of “will” we would like to pick (there are many).
LLMs and humans can hypothetically do both just fine, but when it comes to checking, humans currently excel because (I suspect) they have a “wider” language in S1, that doesn’t only include word-concepts but also sensory concepts (like visuospatial thinking). Thus, as I get it, the world models idea.
Humans can produce new concepts and then symbolize them for communication purposes. The meaning of concepts is grounded in operational definitions - in a manner that anyone can understand because they are operational, and can be reproduced in theory by anyone.
For example, euclid invented the concepts of a point, angle and line to operationally represent geometry in the real world. These concepts were never "there" to begin with. They were created from scratch to "build" a world-model that helps humans navigate the real world.
Euclid went outside his "training distribution" to invent point, angle, and line. Humans have this ability to construct new concepts by interaction with the real world - bringing the "unknown" into the "known" so-to-speak. Animals have this too via evolution, but it is unclear if animals can symbolize their concepts and skills to the extent that humans can.
Sure, but the question is how often this actually happens versus how often people are doing something closer to recombination and pattern-matching within familiar territory. The point was about the base rate of genuine novel reasoning in everyday human cognition, and I don't think this addresses that.
> Euclid invented the concepts of a point, angle and line to operationally represent geometry in the real world. These concepts were never "there" to begin with.
This isn't really true though. Egyptian and Babylonian surveyors were working with geometric concepts long before Euclid. What Euclid did was axiomatize and systematize knowledge that was already in wide practical use. That's a real achievement, but it's closer to "sophisticated refinement within a well-practiced domain" than to reasoning from scratch outside a training distribution. If anything the example supports the parent comment.
There's also something off about saying points and lines were "never there." Humans have spatial perception. Geometric intuitions come from embodied experience of edges, boundaries, trajectories. Formalizing those intuitions is real work, but it's not the same as generating something with no prior basis.
The deeper issue is you're pointing to one of the most extraordinary intellectual achievements in human history and treating it as representative of human cognition generally. The whole point, drawing on Kahneman, is that most of what we call reasoning is fast associative pattern-matching, and that the slow deliberate stuff is rarer and more error-prone than people assume. The fact that Euclid existed doesn't tell us much about what the other billions of humans are doing cognitively on a Tuesday afternoon.
Your point rings true with most human reasoning most of the time. Still, at least some humans do have the capability to run that deductive engine, and it seems to be a key part (though not the only part) of scientific and mathematical reasoning. Even informal experimentation and iteration rest on deductive feedback loops.
I can perform symbolic calculations too. But most people have limited versions of this skill, and many people who don’t learn to think symbolically have full lives.
I think it is fair to say humans don’t naturally think in formal or symbolic reasoning terms.
People pattern match,
Another clue is humans have to practice things, become familiar with them to reason even somewhat reliable about them. Even if they already learned some formal reasoning.
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Higher level reasoning is always implemented as specific forms of lower order reasoning.
There is confusion about substrate processing vs. what higher order processes can be created with that substrate.
We can “just” be doing pattern matching from an implementation view, and yet go far “beyond” pattern matching with specific compositions of pattern matching, from a capability view.
How else could neurons think? We are “only” neurons. Yet we far surpass the kinds of capabilities neurons have.
This is because, the 'reasoning' part of our brain came from evolution when we started to communicate with others, we needed to explain our behaviour.
Which is fascinating if you think of the implications of that. In the most part we think we are being logical, but in reality we are pattern matching/impulsive and using our reasoning/logic to come up for excuses for why we have chosen what we had already decided.
It explains a lot about the world and why it's so hard to reason with someone, we are assuming the decision came from reason in the first place, which when you look at such peoples choices, makes sense as it's clear it didn't.
While humans did seemingly evolve socially very fast, with the tools we seem to have had for a few hundred thousand years it could have been far faster if there were not some other limitations that are being applied.
That's what I said. Backpropagation cannot be enough; that's not how neurons work in the slightest. When you put biological neurons in a Pong environment they learn to play not through some kind of loss or reward function; they self-organize to avoid unpredictable stimulation. As far as I know, no architecture learns in such an unsupervised way.
https://www.sciencedirect.com/science/article/pii/S089662732...
This sounds very similar to me as to what neurons do (avoid unpredictable stimulation)
f(x)=y' => loss(y',y) => how good was my prediction? Train f through backprop with that error.
While a model trained with reinforcement learning is more similar to this. Where m(y) is the resulting world state of taking an action y the model predicted.
f(x)=y' => m(y')=z => reward(z) => how good was the state I was in based on my actions? Train f with an algorithm like REINFORCE with the reward, as the world m is a non-differentiable black-box.
While a group of neurons is more like predicting what is the resulting word state of taking my action, g(x,y), and trying to learn by both tuning g and the action taken f(x).
f(x)=y' => m(y')=z => g(x,y)=z' => loss(z,z') => how predictable was the results of my actions? Train g normally with backprop, and train f with an algorithm like REINFORCE with negative surprise as a reward.
After talking with GPT5.2 for a little while, it seems like Curiosity-driven Exploration by Self-supervised Prediction[1] might be an architecture similar to the one I described for neurons? But with the twist that f is rewarded by making the prediction error bigger (not smaller!) as a proxy of "curiosity".
Our training data is a lot more diverse than an LLMs. We also leverage our senses as a carrier for communicating abstract ideas using audio and visual channels that may or may not be grounded in reality. We have TV shows, video games, programming languages and all sorts of rich and interesting things we can engage with that do not reflect our fundamental reality.
Like LLMs, we can hallucinate while we sleep or we can delude ourselves with untethered ideas, but UNLIKE LLMs, we can steer our own learning corpus. We can train ourselves with our own untethered “hallucinations” or we can render them in art and share them with others so they can include it in their training corpus.
Our hallucinations are often just erroneous models of the world. When we render it into something that has aesthetic appeal, we might call it art.
If the hallucination helps us understand some aspect of something, we call it a conjecture or hypothesis.
We live in a rich world filled with rich training data. We don’t magically anticipate events not in our training data, but we’re also not void of creativity (“hallucinations”) either.
Most of us are stochastic parrots most of the time. We’ve only gotten this far because there are so many of us and we’ve been on this earth for many generations.
Most of us are dazzled and instinctively driven to mimic the ideas that a small minority of people “hallucinate”.
There is no shame in mimicking or being a stochastic parrot. These are critical features that helped our ancestors survive.
This is critical. We have some degree of attentional autonomy. And we have a complex tapestry of algorithms running in thalamocortical circuits that generate “Nows”. Truncation commands produce sequences of acts (token-like products).
I guess I just always thought it was obvious that you can't do better than nature. You can do different things, sure, but if a society of unique individuals wasn't the most effective way of making progress, nature itself would not have chosen it.
So in a way I think Yan is smart because he got money, but in a way I think he's a fucking idiot if he can't see just how very, very very far we are from competing with organic intelligence.
You're onto something there.
If everyone knew they were to die tomorrow, all of a sudden they'd choose to act differently. There is no logical thought process that determines that - it's something else. Something we can't concretely point toward as an object.
Can you be a bit more specific at all bounds? Maybe via an example?
So my question is: when is there enough training data that you can handle 99.99% of the world ?
