Comment by educasean
5 days ago
The debate around whether or not transformer-architecture-based AIs can "think" or not is so exhausting and I'm over it.
What's much more interesting is the question of "If what LLMs do today isn't actual thinking, what is something that only an actually thinking entity can do that LLMs can't?". Otherwise we go in endless circles about language and meaning of words instead of discussing practical, demonstrable capabilities.
"The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." - Edsger Dijkstra
There is more to this quote than you might think.
Grammatically, in English the verb "swim" requires an "animate subject", i.e. a living being, like a human or an animal. So the question of whether a submarine can swim is about grammar. In Russian (IIRC), submarines can swim just fine, because the verb does not have this animacy requirement. Crucially, the question is not about whether or how a submarine propels itself.
Likewise, in English at least, the verb "think" requires an animate object. the question whether a machine can think is about whether you consider it to be alive. Again, whether or how the machine generates its output is not material to the question.
I don't think the distinction is animate/inanimate.
Submarines sail because they are nautical vessels. Wind-up bathtub swimmers swim, because they look like they are swimming.
Neither are animate objects.
In a browser, if you click a button and it takes a while to load, your phone is thinking.
He was famously (and, I'm realizing more and more, correctly) averse to anthropomorphizing computing concepts.
I disagree. The question is really about weather inference is in principle as powerful as human thinking, and so would deserve to be applied the same label. Which is not at all a boring question. It's equivalent to asking weather current architectures are enough to reach AGI (I myself doubt this).
I think it is, though, because it challenges our belief that only biological entities can think, and thinking is a core part of our identity, unlike swimming.
> our belief that only biological entities can think
Whose belief is that?
As a computer scientist my perspective of all of this is as different methods of computing and we have a pretty solid foundations on computability (though, it does seem a bit frightening how many present-day devs have no background in the foundation of the Theory of Computation). There's a pretty common naive belief that somehow "thinking" is something more or distinct from computing, but in actuality there are very few coherent arguments to that case.
If, for you, thinking is distinct from computing then you need to be more specific about what thinking means. It's quite possible that "only biological entities can think" because you are quietly making a tautological statement by simply defining "thinking" as "the biological process of computation".
> thinking is a core part of our identity, unlike swimming.
What does this mean? I'm pretty sure for most fish swimming is pretty core to its existence. You seem to be assuming a lot of metaphysically properties of what you consider "thinking" such that it seems nearly impossible to determine whether or not anything "thinks" at all.
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The point is that both are debates about definitions of words so it's extremely boring.
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What an oversimplification. Thinking computers can create more swimming submarines, but the inverse is not possible. Swimming is a closed solution; thinking is a meta-solution.
Then the interesting question is whether computers can create more (better?) submarines, not whether they are thinking.
I think you missed the point of that quote. Birds fly, and airplanes fly; fish swim but submarines don't. It's an accident of language that we define "swim" in a way that excludes what submarines do. They move about under their own power under the water, so it's not very interesting to ask whether they "swim" or not.
Most people I've talked to who insist that LLMs aren't "thinking" turn out to have a similar perspective: "thinking" means you have to have semantics, semantics require meaning, meaning requires consciousness, consciousness is a property that only certain biological brains have. Some go further and claim that reason, which (in their definition) is something only human brains have, is also required for semantics. If that's how we define the word "think", then of course computers cannot be thinking, because you've defined the word "think" in a way that excludes them.
And, like Dijkstra, I find that discussion uninteresting. If you want to define "think" that way, fine, but then using that definition to insist LLMs can't do a thing because it can't "think" is like insisting that a submarine can't cross the ocean because it can't "swim".
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That’s a great answer to GP’s question!
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Without going to look up the exact quote, I remember an AI researcher years (decades) ago saying something to the effect of, Biologists look at living creatures and wonder how they can be alive; astronomers look at the cosmos and wonder what else is out there; those of us in artificial intelligence look at computer systems and wonder how they can be made to wonder such things.
Don't be sycophantic. Disagree and push back when appropriate.
Come up with original thought and original ideas.
Have long term goals that aren't programmed by an external source.
Do something unprompted.
The last one IMO is more complex than the rest, because LLMs are fundamentally autocomplete machines. But what happens if you don't give them any prompt? Can they spontaneously come up with something, anything, without any external input?
> Disagree and push back
The other day an LLM gave me a script that had undeclared identifiers (it hallucinated a constant from an import).
When I informed it, it said "You must have copy/pasted incorrectly."
When I pushed back, it said "Now you trust me: The script is perfectly correct. You should look into whether there is a problem with the installation/config on your computer."
Was it Grok 4 Fast by chance?
I was dealing with something similar with it yesterday. No code involved. It was giving me factually incorrect information about a multiple schools and school districts. I told it it was wrong multiple times and it hallucinated school names even. Had the school district in the wrong county entirely. It kept telling me I was wrong and that although it sounded like the answer it gave me, it in fact was correct. Frustrated, I switched to Expert, had it re-verify all the facts, and then it spit out factually correct information.
That's the flip side of the same symptom. One model is instructed to agree with the user no matter what, and the other is instructed to stick to its guns no matter what. Neither of them is actually thinking.
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There was a time when we'd have said you were talking to a sociopath.
> Don't be sycophantic. Disagree and push back when appropriate.
They can do this though.
> Can they spontaneously come up with something, anything, without any external input?
I don’t see any why not, but then humans don’t have zero input so I’m not sure why that’s useful.
> but then humans don’t have zero input
Humans don't require input to, say, decide to go for a walk.
What's missing in the LLM is volition.
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The last one is fairly simple to solve. Set up a microphone in any busy location where conversations are occurring. In an agentic loop, send random snippets of audio recordings for transcriptions to be converted to text. Randomly send that to an llm, appending to a conversational context. Then, also hook up a chat interface to discuss topics with the output from the llm. The random background noise and the context output in response serves as a confounding internal dialog to the conversation it is having with the user via the chat interface. It will affect the outputs in response to the user.
If it interrupts the user chain of thought with random questions about what it is hearing in the background, etc. If given tools for web search or generating an image, it might do unprompted things. Of course, this is a trick, but you could argue that any sensory input living sentient beings are also the same sort of trick, I think.
I think the conversation will derail pretty quickly, but it would be interesting to see how uncontrolled input had an impact on the chat.
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Are you claiming humans do anything unprompted? Our biology prompts us to act
Yet we can ignore our biology, or act in ways that are the opposite of what our biology tells us. Can someone map all internal and external stimuli that a person encounters into a set of deterministic actions? Simply put, we have not the faintest idea how our brains actually work, and so saying saying "LLMs think the same way as humans" is laughable.
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> The last one IMO is more complex than the rest, because LLMs are fundamentally autocomplete machines. But what happens if you don't give them any prompt? Can they spontaneously come up with something, anything, without any external input?
Human children typically spend 18 years of their lives being RLHF'd before let them loose. How many people do something truly out of the bounds of the "prompting" they've received during that time?
Note that model sycophancy is caused by RLHF. In other words: Imagine taking a human in his formative years, and spending several subjective years rewarding him for sycophantic behavior and punishing him for candid, well-calibrated responses.
Now, convince him not to be sycophantic. You have up to a few thousand words of verbal reassurance to do this with, and you cannot reward or punish him directly. Good luck.
> "If what LLMs do today isn't actual thinking, what is something that only an actually thinking entity can do that LLMs can't?"
Independent frontier maths research, i.e. coming up with and proving (preferably numerous) significant new theorems without human guidance.
I say that not because I think the task is special among human behaviours. I think the mental faculties that mathematicians use to do such research are qualitatively the same ones all humans use in a wide range of behaviours that AI struggles to emulate.
I say it because it's both achievable (in principle, if LLMs can indeed think like humans) and verifiable. Achievable because it can be viewed as a pure text generation task and verifiable because we have well-established, robust ways of establishing the veracity, novelty and significance of mathematical claims.
It needs to be frontier research maths because that requires genuinely novel insights. I don't consider tasks like IMO questions a substitute as they involve extremely well trodden areas of maths so the possibility of an answer being reachable without new insight (by interpolating/recombining from vast training data) can't be excluded.
If this happens I will change my view on whether LLMs think like humans. Currently I don't think they do.
This, so much. Many mathematicians and physicists believe in intuition as a function separate from intelect. One is more akin to a form of (inner) perception, whereas the other is generative - extrapolation based on pattern matching and statistical thinking. That second function we have a handle on and getting better at it every year, but we don't even know how to define intuition properly. A fascinating book that discusses this phenomena is Nature Loves to Hide: Quantum Physics and Reality, a Western Perspective [1]
This quote from Grothendieck [2] (considered by many the greatest mathematician of the 20th century) points to a similar distinction: The mathematician who seeks to understand a difficult problem is like someone faced with a hard nut. There are two ways to go about it. The one way is to use a hammer — to smash the nut open by brute force. The other way is to soak it gently, patiently, for a long time, until it softens and opens of itself.
[1] https://www.amazon.com/Nature-Loves-Hide-Quantum-Perspective...
[2] https://en.wikipedia.org/wiki/Alexander_Grothendieck
That's quite a high bar for thinking like humans which rules out 99.99% of humans.
I have never claimed that only people/machines that can do frontier maths research can be intelligent. (Though someone always responds as if I did.)
I said that a machine doing frontier maths research would be sufficient evidence to convince me that it is intelligent. My prior is very strongly that LLM's do not think like humans so I require compelling evidence to conclude that they do. I defined one such possible piece of evidence, and didn't exclude the possibility of others.
If I were to encounter such evidence and be persuaded, I would have to also consider it likely that LLMs employ their intelligence when solving IMO questions and generating code. However, those tasks alone are not sufficient to persuade me of their intelligence because I think there are ways of performing those tasks without human-like insight (by interpolating/recombining from vast training data).
As I said elsewhere in this thread:
> The special thing about novel mathematical thinking is that it is verifiable, requires genuine insight and is a text generation task, not that you have to be able to do it to be considered intelligent.
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Google's AlphaEvolve independently discovered a novel matrix multiplication algorithm which beats SOTA on at least one axis: https://www.youtube.com/watch?v=sGCmu7YKgPA
That was an impressive result, but AIUI not an example of "coming up with and proving (preferably numerous) significant new theorems without human guidance".
For one thing, the output was an algorithm, not a theorem (except in the Curry-Howard sense). More importantly though, AlphaEvolve has to be given an objective function to evaluate the algorithms it generates, so it can't be considered to be working "without human guidance". It only uses LLMs for the mutation step, generating new candidate algorithms. Its outer loop is a an optimisation process capable only of evaluating candidates according to the objective function. It's not going to spontaneously decide to tackle the Langlands program.
Correct me if I'm wrong about any of the above. I'm not an expert on it, but that's my understanding of what was done.
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solve simple maths problems, for example the kind found in the game 4=10 [1]
Doesn't necessarily have to reliably solve them, some of them are quite difficult, but llms are just comically bad at this kind of thing.
Any kind of novel-ish(can't just find the answers in the training-data) logic puzzle like this is, in my opinion, a fairly good benchmark for "thinking".
Until a llm can compete with a 10 year old child in this kind of task, I'd argue that it's not yet "thinking". A thinking computer ought to be at least that good at maths after all.
[1] https://play.google.com/store/apps/details?id=app.fourequals...
> solve simple maths problems, for example the kind found in the game 4=10
I'm pretty sure that's been solved for almost 12 months now - the current generation "reasoning" models are really good at those kinds of problems.
Huh, they really do solve that now!
Well, I'm not one to back-pedal whenever something unexpected reveals itself, so I guess I have no choice but to declare current generation LLM's to be sentient! That came a lot sooner than I had expected!
I'm not one for activism myself, but someone really ought to start fighting for human, or at least animal, rights for LLM's. Since they're intelligent non-human entities, it might be something for Greenpeace?
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> "If what LLMs do today isn't actual thinking, what is something that only an actually thinking entity can do that LLMs can't?"
Invent some novel concept, much the same way scientists and mathematicians of the distant past did? I doubt Newton's brain was simply churning out a stream of the "next statistically probable token" until -- boom! Calculus. There was clearly a higher order understanding of many abstract concepts, intuition, and random thoughts that occurred in his brain in order to produce something entirely new.
My 5 year old won't be coming up with novel concepts around calculus either, yet she's clearly thinking, sentient and sapient. Not sure taking the best of the best of humanity as the goal standard is useful for that definition.
"It's an unreasonably high standard to require of LLMs": LLMs are already vastly beyond your 5 year old, and you and me and any research mathematician, in knowledge. They have no greater difficulty talking about advanced maths than about Spot the Dog.
"It's a standard we don't require of other humans": I think qualitatively the same capabilities are used by all humans, all the time. The special thing about novel mathematical thinking is that it is verifiable, requires genuine insight and is a text generation task, not that you have to be able to do it to be considered intelligent.
> Newton's brain was simply churning out a stream of the "next statistically probable token"
At some level we know human thinking is just electrons and atoms flowing. It’s likely at a level between that and “Boom! Calculus”, the complexity is equivalent to streaming the next statistically probably token.
Have needs and feelings? (I mean we can’t KNOW that they don’t and we know of this case of an LLM in experiment that try to avoid being shutdown, but I think the evidence of feeling seems weak so far)
But you can have needs and feelings even without doing thinking. It's separate.
I can imagine needing without thinking (like being hungry), but feelings? How and in what space would that even manifest? Like where would such a sensation like, say, sadness reside?
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Ya, the fact this was published on November 3, 2025 is pretty hilarious. This was last year's debate.
I think the best avenue toward actually answering your questions starts with OpenWorm [1]. I helped out in a Connectomics research lab in college. The technological and epistemic hurdles are pretty daunting, but so were those for Genomics last century, and now full-genome sequencing is cheap and our understanding of various genes is improving at an accelerating pace. If we can "just" accurately simulate a natural mammalian brain on a molecular level using supercomputers, I think people would finally agree that we've achieved a truly thinking machine.
[1]: https://archive.ph/0j2Jp
> Otherwise we go in endless circles about language and meaning of words
We understand thinking as being some kind of process. The problem is that we don't understand the exact process, so when we have these discussions the question is if LLMs are using the same process or an entirely different process.
> instead of discussing practical, demonstrable capabilities.
This doesn't resolve anything as you can reach the same outcome using a different process. It is quite possible that LLMs can do everything a thinking entity can do all without thinking. Or maybe they actually are thinking. We don't know — but many would like to know.
> That is something that only an actually thinking entity can do that LLMs can't?
Training != Learning.
If a new physics breakthrough happens tomorrow, one that say lets us have FTL, how is an LLM going to acquire the knowledge, how does that differ from you.
The break through paper alone isnt going to be enough to over ride its foundational knowledge in a new training run. You would need enough source documents and a clear path deprecate the old ones...
The issue is that we have no means of discussing equality without tossing out the first order logic that most people are accustomed to. Human equality and our own perceptions of other humans as thinking machines is an axiomatic assumption that humans make due to our mind's inner sense perception.
> what is something that only an actually thinking entity can do that LLMs can't?
This is pretty much exactly what https://arcprize.org/arc-agi is working on.
See https://arcprize.org/
Form ideas without the use of language.
For example: imagining how you would organize a cluttered room.
Ok, but how do you go about measuring whether a black-box is doing that or not?
We don't apply that criteria when evaluating animal intelligence. We sort of take it for granted that humans at large do that, but not via any test that would satisfy an alien.
Why should we be imposing white-box constraints to machine intelligence when we can't do so for any other?
There is truly no such thing as a “black box” when it comes to software, there is only a limit to how much patience a human will have in understanding the entire system in all its massive complexity. It’s not like an organic brain.
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> Form ideas without the use of language.
Don't LLMs already do that? "Language" is just something we've added as a later step in order to understand what they're "saying" and "communicate" with them, otherwise they're just dealing with floats with different values, in different layers, essentially (and grossly over-simplified of course).
But language is the input and the vector space within which their knowledge is encoded and stored. The don't have a concept of a duck beyond what others have described the duck as.
Humans got by for millions of years with our current biological hardware before we developed language. Your brain stores a model of your experience, not just the words other experiencers have shared with yiu.
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LLMs don’t form ideas at all. They search vector space and produce output, sometimes it can resemble ideas if you loop into itself.
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Genie 3 is along the lines of ideas without language. It doesn't declutter though, I think. https://youtu.be/PDKhUknuQDg
What people are interested in is finding a definition for intelligence, that is an exact boundary.
That's why we first considered tool use, being able to plan ahead as intelligence, until we have found that these are not all that rare in the animal kingdom in some shape. Then with the advent of IT what we imagined as impossible turned out to be feasible to solve, while what we though of as easy (e.g. robot movements - a "dumb animal" can move trivially it surely is not hard) turned out to require many decades until we could somewhat imitate.
So the goal post moving of what AI is is.. not moving the goal post. It's not hard to state trivial higher bounds that differentiates human intelligence from anything known to us, like invention of the atomic bomb. LLMs are nowhere near that kind of invention and reasoning capabilities.
Interestingly, I think the distinction between human and animal thinking is much more arbitrary than the distinction between humans and LLMs.
Although an LLM can mimic a human well, I’d wager the processes going on in a crow’s brain are much closer to ours than an LLM
Strive for independence.
operate on this child