The level of intellectual engagement with Chomsky's ideas in the comments here is shockingly low. Surely, we are capable of holding these two thoughts: one, that the facility of LLMs is fantastic and useful, and two, that the major breakthroughs of AI this decade have not, at least so far, substantially deepened our understanding of our own intelligence and its constitution.
That may change, particularly if the intelligence of LLMs proves to be analogous to our own in some deep way—a point that is still very much undecided. However, if the similarities are there, so is the potential for knowledge. We have a complete mechanical understanding of LLMs and can pry apart their structure, which we cannot yet do with the brain. And some of the smartest people in the world are engaged in making LLMs smaller and more efficient; it seems possible that the push for miniaturization will rediscover some tricks also discovered by the blind watchmaker. But these things are not a given.
> AI this decade have not, at least so far, substantially deepened our understanding of our own intelligence and its constitution
I would push back on this a little bit. While it has not helped us to understand our own intelligence, it has made me question whether such a thing even exists. Perhaps there are no simple and beautiful natural laws, like those that exists in Physics, that can explain how humans think and make decisions. When CNNs learned to recognize faces through a series of hierarchical abstractions that make intuitive sense it's hard to deny the similarities to what we're doing as humans. Perhaps it's all just emergent properties of some messy evolved substrate.
The big lesson from the AI development in the last 10 years from me has been "I guess humans really aren't so special after all" which is similar to what we've been through with Physics. Theories often made the mistake of giving human observers some kind of special importance, which was later discovered to be the cause of theories not generalizing.
> The big lesson from the AI development in the last 10 years from me has been "I guess humans really aren't so special after all"
Instead I would take the opposite take.
How wonderful is it, that with naturally evolved processes and neural structures, have we been able to create what we have. Van Gogh’s paintings came out of the human brain. The Queens of the Skies - hundreds of tons of metal and composites - flying across continents in the form of a Boeing 747 or an A380 - was designed by the human brain. We went to space, have studied nature (and have conservation programs for organisms we have found to need help), took pictures the pillars of creation that are so incredibly far… all with such a “puny” structure a few cm in diameter? I think that’s freaking amazing.
I think it is important to realize, that we need to understand language on our own terms. The logic of LLMs is not unlike alien technology to us. That being said, the minimalist program of Chomsky lead to nowhere, because just like programming, it found edge case after edge case, reducing it further and further, until there was no program anymore that resembled a real theory. But it is wrong to assume that the big progress in linguistics is in vain, the same reason Prolog, Theorem provers, type theory, category theory is in vain, when we have LLMs that can produce everything in C++. We can use the technology of linguistics to ground our knowledge, and in some dark corner of the LLM it might already have integrated this. I think the original divide between the sciences and the humanities might be deeper and more fundamental than we think. We need linguistic as a discipline of the humanities, and maybe huge swaths of Computer Science is just that.
> Perhaps there are no simple and beautiful natural laws, like those that exists in Physics, that can explain how humans think and make decisions.
Isn't Physics trying to describe the natural world? I'm guessing you are taking two positions here that are causing me confusion with your statement: 1) that our minds can be explained strictly through physical processes, and 2) our minds, including our intelligence, are outside of the domain of Physics.
If you take 1) to be true, then it follows that Physics, at least theoretically, should be able to explain intelligence. It may be intractably hard, like it might be intractably hard to have physics decribe and predict the motions of more than two planetary bodies.
I guess I'm saying that Physical laws ARE natural laws. I think you might be thinking that natural laws refer solely to all that messy, living stuff.
> Perhaps there are no simple and beautiful natural laws, like those that exists in Physics, that can explain how humans think and make decisions...Perhaps it's all just emergent properties of some messy evolved substrate.
Yeah, it is very likely that there are not laws that will do this, it's the substrate. The fruit fly brain (let alone human) has been mapped, and we've figured out that it's not just the synapse count, but the 'weights' that matter too [0]. Mind you, those weights adjust in real time when a living animal is out there.
You'll see in literature that there are people with some 'lucky' form of hydranencephaly where their brain is as thin as paper. But they vote, get married, have kids, and for some strange reason seem to work in mailrooms (not a joke). So we know it's something about the connectome that's the 'magic' of a human.
My pet theory: We need memristors [2] to better represent things. But that takes redesigning the computer from the metal on up, so is unlikely to occur any time soon with this current AI craze.
> The big lesson from the AI development in the last 10 years from me has been "I guess humans really aren't so special after all" which is similar to what we've been through with Physics.
Yeah, biologists get there too, just the other way abouts, with animals and humans. Like, dogs make vitamin C internally, and humans have that gene too, it's just dormant, ready for evolution (or genetic engineering) to reactivate. That said, these neuroscience issues with us and the other great apes are somewhat large and strange. I'm not big into that literature, but from what little I know, the exact mechanisms and processes that get you from tool using ourangs to tool using humans, well, those seem to be a bit strange and harder to grasp for us. Again, not in that field though.
In the end though, humans are special. We're the only ones on the planet that ever really asked a question. There's a lot to us and we're actually pretty strange in the end. There's many centuries of work to do with biology, we're just at the wading stage of that ocean.
> it has made me question whether such a thing even exists
I was reading a reddit post the other day where the guy lost his crypto holdings because he input his recovery phrase somewhere. We question the intelligence of LLMs because they might open a website, read something nefarious, and then do it. But here we have real humans doing the exact same thing...
> I guess humans really aren't so special after all
No they are not. But we are still far from getting there with the current LLMs and I suspect mimicking the human brain won't be the best path forward.
> one, that the facility of LLMs is fantastic and useful
I didn't see where he was disagreeing with this.
I'm assuming this was the part you were saying he doesn't hold, because it is pretty clear he holds the second thought.
| is it likely that programs will be devised that surpass human capabilities? We have to be careful about the word “capabilities,” for reasons to which I’ll return. But if we take the term to refer to human performance, then the answer is: definitely yes.
I have a difficult time reading this as saying that LLMs aren't fantastic and useful.
| We can make a rough distinction between pure engineering and science. There is no sharp boundary, but it’s a useful first approximation. Pure engineering seeks to produce a product that may be of some use. Science seeks understanding.
This seems to be the core of his conversation. That he's talking about the side of science, not engineering.
It indeed baffles me how academics overall seem so dismissive of recent breakthroughs in sub-symbolic approaches as models from which we can learn about 'intelligence'?
It is as if a biochemist looks at a human brain, and concludes there is no 'intelligence' there at all, just a whole lot of electro-chemical reactions.
It fully ignores the potential for emergence.
Don't misunderstand me, I'm not saying 'AGI has arrived', but I'd say even current LLM's do most certainly have interesting lessons for Human Language development and evolution in science. What can the success in transfer learning in these models contribute to the debates on universal language faculties? How do invariants correlated across LLM systems and humans?
There's two kinds of emergence, one scientific, the other a strange, vacuous notion in the absence of any theory and explanation.
The first case is emergence when we for example talk about how gas or liquid states, or combustibility emerge from certain chemical or physical properties of particles. It's not just that they're emergent, we can explain how they're emergent and how their properties are already present in the lower level of abstraction. Emergence properly understood is always reducible to lower states, not some magical word if you don't know how something works.
In these AI debates that's however exactly how "emergence" is used, people just assert it, following necessarily from their assumptions. They don't offer a scientific explanation. (the same is true with various other topics, like consciousness, or what have you). This is pointless, it's a sort of god of the gaps disguised as an argument. When Chomsky talks about science proper, he correctly points out that these kinds of arguments have no place in it, because the point of science is to build coherent theories.
> the major breakthroughs of AI this decade have not, at least so far, substantially deepened our understanding of our own intelligence and its constitution
People's illusions and willingness to debase their own authority and control to take shortcuts to optimise towards lowest effort / highest yield (not dissimilar to something you would get with... auto regressive models!) was an astonishing insight to me.
Well said. It's wild when you think of how many "AI" products are out there that essentially entrust an LLM to make the decisions the user would otherwise make. Recruitment, trading, content creation, investment advice, medical diagnosis, legal review, dating matches, financial planning and even hiring decisions.
At some point you have to wonder: is an LLM making your hiring decision really better than rolling a dice? At least the dice doesn't give you the illusion of rationality, it doesn't generate a neat sounding paragraph "explaining" why candidate A is the obvious choice. The LLM produces content that looks like reasoning but has no actual causal connection to the decision - it's a mimicry of explanation without true substance of causation.
You can argue that humans do the same thing. But post-hoc reasoning is often a feedback loop for the eventual answer. That's not the case for LLMs.
Chompsky's central criticism of LLMs is that they can learn impossible languages just as easily as they learn possible languages. He refers to this repeatedly in the linked interview. Therefore, they cannot teach us about our own intelligence.
However, a paper published last year (Mission: Impossible Language Models, Kallini et al.) proved that LLMs do NOT learn impossible languages as easily as they learn possible languages. This undermines everything that Chompsky says about LLMs in the linked interview.
I'm not that convinced by this paper. The "impossible languages" are all English with some sort of transformation applied, such as shuffling the word order. It seems like learning such languages would require first learning English and then learning the transformation. It's not surprising that systems would be worse at learning such languages than just learning English on its own. But I don't think these sorts of languages are what Chomsky is talking about. When Chomsky says "impossible languages," he means languages that have a coherent and learnable structure but which aren't compatible with what he thinks are innate grammatical facilities of the human mind. So for instance, x86 assembly language is reasonably structured and can express anything that C++ can, but unlike C++, it doesn't have a recursive tree-based syntax. Chomsky believes that any natural language you find will be structured more like C++ than like assembly language, because he thinks humans have an innate mental facility for using tree-based languages. I actually think a better test of whether LLMs learn languages like humans would be to see if they learn assembly as well as C++. That would be incomplete of course, but it would be getting at what Chomsky's talking about.
Also, GPT-2 actually seems to do quite well on some of the tested languages, including word-hop, partial reverse, and local-shuffle. It doesn't do quite as well as plain English, but GPT-2 was designed to learn English, so it's not surprising that it would do a little better. For instance, they tokenization seems biased towards English. They show "bookshelf" becoming the tokens "book", "sh", and "lf" – which in many of the languages get spread throughout a sentence. I don't think a system designed to learn shuffled-English would tokenize this way!
The authors of that paper misunderstand what "impossible languages" refers to. It doesn't refer to any language a human can't learn. It refers to computationally simple plausible alternative languages that humans can't learn, in particular linear-order (non-hierarchical structure) languages.
What exactly do you mean, "analogous to our own" and, "in a deep way" without making an appeal to magic or non-yet discovered fields of science? I understand what you're saying but when you scrutinize these things you end up in a place that's less scientific than one might think. That kind of seems to be one of Chomsky's salient points; we really, really need to get a handle on when we're doing science in the contemporary Kuhnian sense and philosophy.
The AI works on English, C++, Smalltalk, Klingon, nonsense, and gibberish. Like Turing's paper this illustrates the difference between, "machines being able to think" and, "machines being able to demonstrate some well understood mathematical process like pattern matching."
> not, at least so far, substantially deepened our understanding of our own intelligence
Science progresses in a manner that when you see it happen in front of you it doesn't seem substantial at all, because we typically don't understand implications of new discoveries.
So far, in the last few years, we have discovered the importance of the role of language behind intelligence. We have also discovered quantitative ways to describe how close one concept is from another. More recently, from the new reasoning AI models, we have discovered something counterintuitive that's also seemingly true for human reasoning--incorrect/incomplete reasoning can often reach the correct conclusion.
In my opinion it will or already has redefined our conceptual models of intelligence - just like physical models of atoms or gravitational mechanics evolved and newer models replace the older. The older models aren't invalidated (all models are wrong, after all), but their limits are better understood.
People are waiting for this Prometheus-level moment with AI where it resembles us exactly but exceeds our capabilities, but I don't think that's necessary. It parallels humanity explaining Nature in our own image as God and claiming it was the other way around.
There was an interesting debate where Chomsky took a position on intelligence being rooted in symbolic reasoning and Asimov asserted a statistical foundation (ah, that was not intentional ;).
LLM designs to date are purely statistical models. A pile, a morass of floating point numbers and their weighted relationships, along with the software and hardware that animates them and the user input and output that makes them valuable to us. An index of the data fed into them, different from a Lucene or SQL DB index made from compsci algorithms & data structure primitives. Recognizable to Azimov's definition.
And these LLMs feature no symbolic reasoning whatsoever within their computational substrate. What they do feature is a simple recursive model: Given the input so far, what is the next token? And they are thus enabled after training on huge amounts of input material. No inherent reasoning capabilities, no primordial ability to apply logic, or even infer basic axioms of logic, reasoning, thought. And therefore unrecognizable to Chomsky's definition.
So our LLMs are a mere parlor trick. A one-trick pony. But the trick they do is oh-so vastly complicated, and very appealing to us, of practical application and real value. It harkens back to the question: What is the nature of intelligence? And how to define it?
And I say this while thinking of the marked contrast of apparent intelligence between an LLM and say a 2-year age child.
That's not true, symbols emerge out of the statistics. Just look at the imagenet analysis that identified distinct concepts in different layers, or the experiments with ablation in LLMs.
They may not be doing strict formal logic, but they are definitely compressing information into, and operating using, symbols.
Which argues much symbolic manipulation is formulaic, and not inductive reasoning or indicative of intelligence.
Sentence parsing with multiple potential verb-noun-adjective interpretations are an example of old, Chomsky made fruit flies like a banana famous for a reason.
(without the weights and that specific sentence programmed in, I would be interested exactly how the symbol models cope with that, and the myriad other examples)
To me the interesting idea is the followup question: Can you do complex reasoning without intelligence?
LLM's seem to have proven themselves to be more than a one-trick-pony. There is actually some resemblance of reasoning and structuring etc.. No matter if directly within the LLM, or supported by computer code. E.g it can be argued that the latest LLMs like Gemini 2.5 and Claude 4 in fact do complex reasoning.
We have always taken for granted you need intelligence for that, but what if you don't? It would greatly change our view on intelligence and take away one of the main factors that we test for in e.g. animals to define their "intelligence".
> E.g it can be argued that the latest LLMs like Gemini 2.5 and Claude 4 in fact do complex reasoning.
They most definitely don't. We attach symbolic meaning to their output because we can map it semantically to the input we gave it. Which is why people are often caught by surprise when these mappings break down.
LLMs can emulate reasoning, but the failure modes show that they don't. We can get them to be coincidentally emulating reasoning well enough long enough to fools us, investors and the media. But doubling down on it hoping that this problem goes away with scale or fine tuning is proving more and more reckless.
I think we are ignoring that the statistical aspect of our ability to reason effectively and to apply logic was predicated on the deaths of millions of our ancestors. When they made the wrong decision, they likely didn't reproduce. When they made the right decision, that particular configuration of their cortical substrate was carried forward a generation. The product of this cross-generational training could have easily led to non-intelligence, and often does, but we have survivor's bias in our favor.
Perhaps the next question we are asking is "what happens if you give a statistical model symbolic input" and the answer appears to be, you get symbolic output.
Even more strangely, the act of giving a statistical model symbolic input allows it to build a context which then shapes the symbolic output in a way that depends on some level of "understanding" instructions.
We "train" this model on raw symbolic data and it extracts the inherent semantic structure without any human ever embedding in the code anything resembling letters, words, or the like. It's as if Chomsky's elusive universal language is semantic structure itself.
>There was an interesting debate where Chomsky took a position on intelligence being rooted in symbolic reasoning and Asimov asserted a statistical foundation (ah, that was not intentional ;).
Yes, because anthropomorphism is hardwired into our biology. Just two dots and an arc triggers a happy feeling in all humans. :)
> of practical application and real value
That is debatable. So far no groundbreaking useful applications have been found for LLMs. We want to believe, because they make us feel happy. But the results aren't there.
The voice of reason. And, as always, the voice of reason is being vigorously ignored. Dreams of big profits and exerting control through generated lies are irresistible. And among others, HN comment threads demonstrate how even people who should know better are falling for it in droves. In fact this very thread shows how Chomsky's arguments fall on deaf ears.
Don't forget exerting control through automated surveillance. What a wonderful tool we have created for detecting whether citizens step out of line without needing giant offices full of analysts.
It's shocking how people are putting him down for the OP interview with just a couple of questions in 2023. The dude was 94 years old. I also did not predict where we would be in 2025 with LLMs. And neither did you. (When I say you, I mean some of the other commenters.)
Are we seriously saying that his ideas are not taken seriously? his theory of grammar/language construction was a major contributor to modern programming languages, for one.
The fact that we have figured out how to translate language into something a computer can "understand" should thrill linguists. Taking a word (token) and abstracting it's "meaning" as a 1,000-dimension vector seems like something that should revolutionize the field of linguistics. A whole new tool for analyzing and understanding the underlying patterns of all language!
And there's a fact here that's very hard to dispute, this method works. I can give a computer instructions and it "understands" them in a way that wasn't possible before LLMs. The main debate now is over the semantics of words like "understanding" and whether or not an LLM is conscious in the same way as a human being (it isn't).
Restricted to linguistics, LLM's supposed lack of understanding should be a non-sequitur. If the question is whether LLMs have formed a coherent ability to parse human languages, the answer is obviously yes. In fact not just human languages, as seen with multimodality the same transformer architecture seems to work well to model and generate anything with inherent structure.
I'm surprised that he doesn't mention "universal grammar" once in that essay. Maybe it so happens that humans do have some innate "universal grammar" wired in by instinct but it's clearly not _necessary_ to be able to parse things. You don't need to set up some explicit language rules or generative structure, enough data and the model learns to produce it. I wonder if anyone has gone back and tried to see if you can extract out some explicit generative rules from the learned representation though.
Since the "universal grammar" hypothesis isn't really falsifiable, at best you can hope for some generalized equivalent that's isomorphic to the platonic representation hypothesis and claim that all human language is aligned in some given latent representation, and that our brains have been optimized to be able to work in this subspace. That's at least a testable assumption, by trying to reverse engineer the geometry of the space LLMs have learned.
Can LLMs actually parse human languages? Or can they react to stimuli with a trained behavioral response? Dogs can learn to sit when you say "sit", and learn to roll over when you say "roll over". But the dog doesn't parse human language; it reacts to stimuli with a trained behavioral response.
(I'm not that familiar with LLM/ML, but it seems like trained behavioral response rather than intelligent parsing. I believe this is part of why it hallucinates? It doesn't understand concepts, it just spits out words - perhaps a parrot is a better metaphor?)
Alternatively, what Chomsky was thinking about with his universal grammar idea is something implicitly present in both our minds and an LLM i.e. "it's the wiring, stupid".
I'm not sure there's much evidence for this one way or another at this point.
Unfortunately you've undermined your point by making sweeping claims about something that is the literal hardest known problem in philosophy (consciousness).
I'm not actually comfortable saying that LLMs aren't conscious. I think there's a decent chance they could be in a very alien way.
I realize that this is a very weird and potentially scary claim for people to parse but you must understand how weird and scary consciousness is.
Note that I didn't say they aren't conscious, I said they aren't conscious "in the same way as a human being". I left open the possibility they could be conscious "in a very alien way".
> whether or not an LLM is conscious in the same way as a human being
The problem is... that there is a whole amount of "smart" activities humans do without being conscious of it.
- Walking, riding a bike, or typing on a keyboard happen fluidly without conscious planning of each muscle movement.
- You can finish someone sentence or detect if a sentence is grammatically wrong, often without being able to explain the rule.
- When you enter a room, your brain rapidly identifies faces, furniture, and objects without you consciously thinking, “That is a table,” or “That is John.”
During Covid I gave a lecture on Python on Zoom in a non-English language. It was a beginner's topic about dictionary methods. I was attempting to multi-task and had other unrelated tasks open on second computer.
Midway through the lecture I noticed to my horror that I had switched to English without the audience noticing.
Going back through the recording I noticed the switch was fluid and my delivery was reasonable. What I talked about was just as good as something presented by LLM these days.
So this brings up the question - why aren't we p-zombies all the time instead of 99% of time?
Are there any tasks that absolutely demand human consciousness as we know it?
Presumably long term planning is something that active human consciousness is needed.
Perhaps there is some need for consciousness when one is in "conscious mastery" phase of acquiring a skill.
This goes for any skill such as riding a bicycle/playing chess/programming at a high level.
Once one reaches "unconscious mastery" stage the rider can concentrate on higher meta game.
Why would that thrill linguists? I'm not saying it hasn't/wouldn't/shouldn't, but I don't see why this technology would have the dramatic impact you imagine.
Is/was the same true for ASCII/Smalltalk/binary? They are all another way to translate language into something the computer "understands".
Perhaps the fact that it hasn't would lead some to question the validity of their claims.
When a scientist makes a claim about how something works, it's expected that they prove it.
> Is/was the same true for ASCII/Smalltalk/binary? They are all another way to translate language into something the computer "understands".
That's converting characters into a digital representation. "A" is represented as 01000001. The tokenization process for an LLM is similar, but it's only the first step.
An LLM isn't just mapping a word to a number, you're taking the entire sentence, considering the position of the words and converting it into vectors within a 1,000+ dimensional space. Machine learning has encoded some "meaning" within these dimensions that goes far far beyond something like an ASCII string.
And the proof here is that the method actual works, that's why we have LLMs.
Word embeddings (that 1000-dimension vector you mention) are not new. No comment on the rest of your comment, but that aspect of LLMs is "old" tech - word2vec was published 11 years ago.
> The fact that we have figured out how to translate language into something a computer can "understand" should thrill linguists.
I think they are really excited by this. There seems no deficiency of linguists using these machines.
But I think it is important to distinguish the ability to understand language and translate it. Enough that you yourself put quotes around "understanding". This can often be a challenge for many translators, not knowing how to properly translate something because of underlying context.
Our communication runs far deeper than the words we speak or write on a page. This is much of what linguistics is about, this depth. (Or at least that's what they've told me, since I'm not a linguist) This seems to be the distinction Chomsky is trying to make.
> The main debate now is over the semantics of words like "understanding" and whether or not an LLM is conscious in the same way as a human being (it isn't).
Exactly. Here, I'm on the side of Chomsky and I don't think there's much of a debate to be had. We have a long history of being able to make accurate predictions while erroneously understanding the underlying causal nature.
My background is physics, and I moved into CS (degrees in both), working on ML. I see my peers at the top like Hinton[0] and Sutskever[1] making absurd claims. I call them absurd, because it is a mistake we've made over and over in the field of physics[2,3]. One of those lessons you learn again and again, because it is so easy to make the mistake. Hinton and Sutskever say that this is a feature, not a bug. Yet we know it is not enough to fit the data. Fitting the data allows you to make accurate, testable predictions. But it is not enough to model the underlying causal structure. Science has a long history demonstrating accurate predictions with incorrect models. Not just in the way of the Relativity of Wrong[4], but more directly. Did we forget that the Geocentric Model could still be used to make good predictions? Copernicus did not just face resistance from religious authorities, but also academics. The same is true for Galileo, Boltzmann, Einstein and many more. People didn't reject their claims because they were unreasonable. They rejected the claims because there were good reasons to. Just... not enough to make them right.
"The fact that we have figured out how to translate language into something a computer can "understand" should thrill linguists."
No, there is no understanding at all. Please don't confuse codifying with understanding or translation. LLMs don't understand their input, they simply act on it based on the way they are trained on it.
"And there's a fact here that's very hard to dispute, this method works. I can give a computer instructions and it "understands" them "
No, it really does not understand those instructions. It is at best what used to be called an "idiot savant". Mind you, people used to describe others like that - who is the idiot?
Ask your favoured LLM to write a programme in a less used language - ooh let's try VMware's PowerCLI (it's PowerShell so quite popular) and get it to do something useful. It wont because it can't but it will still spit out something. PowerCLI is not extant across Stackoverflow and co much but it is PS based so the LLMs will hallucinate madder than a hippie on a new super weed.
I think the overarching theme that I glean from LLM critics is some kind of visceral emotional reaction, disgust even, with the idea of them, leading to all these proxy arguments and side quests in order to try and denigrate the idea of them without actually honestly engaging with what they are or why people are interacting with them.
so what they don't "understand", by your very specific definition of the word "understanding"? the person you're replying to is talking about the fact that they can say something to their computer in the form of casual human language and it will produce a useful response, where previously that was not true. whether that fits your suspiciously specific definition of "understanding" does not matter a bit.
so what they are over-confident with areas outside of their training data? provide more training data, improve the models, reduce the hallucination. it isn't an issue with the concept, it's an issue with the execution. yes you'll never be able to reduce it to 0%, but so what? humans hallucinate too. what are we aiming for? omniscience?
Brains don't have innate grammar more than languages are selected to fit baby brains. Chomsky got it backwards, languages co-evolved with human brains to fit our capacities and needs. If a language is not useful or can't be learned by children, it does not expand, it just disappears.
It's like wondering how well your shoes fit your feet, forgetting that shoes are made and chosen to fit your feet in the first place.
It's not an either/or. The fact any human language is learnable by any human and not by, say, chimpanzees needs explaining.
Chomsky also talks about these kind of things in detail in Hauser, Chomsky and Fitch (2002) where they describe them as "third factors" in language acquisition.
It's amusing that he argues (correctly) that "there is no Great Chain of Being with humans at the top," but then claims that LLMs cannot tell us anything about language because they can learn "impossible languages" that infants cannot learn. Isn't that an anthropomorphic argument, saying that what a language is inherently defined by human cognition?
Yes, studying human language is actually inherently defined by what humans do, just -- as he points out, if you could understand the article -- studying insect navigation is defined by what insects do and not what navigation systems human could design.
"The desert ants in my backyard have minuscule brains, but far exceed human navigational capacities, in principle, not just performance. There is no Great Chain of Being with humans at the top."
This quote brought to mind the very different technological development path of the spider species in Adrian Tchaikovsky's Children of Time. They used pheromones to 'program' a race of ants to do computation.
Arbitrary markings on the terrain? Why not GPS, satellite photo etc? All of those are human inventions and we can navigate much better and in a broader set of environments than ants thanks to them.
>Many biological organisms surpass human cognitive capacities in much deeper ways. The desert ants in my backyard have minuscule brains, but far exceed human navigational capacities, in principle, not just performance. There is no Great Chain of Being with humans at the top.
Chomsky made interesting points regarding the performance of AI with the performance of biological organisms in comparison to human but his conclusion is not correct. We already know that cheetah run faster human and elephant is far stronger than human. Bat can navigate in the dark with echo location and dolphin can hunt in synchronization with high precision coordination in pack to devastating effect compared to silo hunting.
Whether we like or not human is the the top unlike the claim of otherwise by Chomsky. By scientific discovery (understanding) and designing (engineering) by utilizing law of nature, human can and has surpassed all of the cognitive capabilities of these petty animals, and we're mostly responsible for their inevitable demise and extinction. Human now need to collectively and consciously reverse the extinction process of these "superior" cognitive animals in order to preserve these animals for better or worst. No other earth bound creature can do that to us.
Chomsky has the ability to say things in a way that most laypersons of average intelligence can grasp. That is an important skill for communication of one's thoughts to the general populace.
Many of the comments herein lack that feature and seem to convey that the author might be full of him(her)self.
I once heard that a roomful of monkeys with typewriters given infinite time could type out the works of shakespeare. I dont think that's true any more than the random illumiination of pixels on a screen could eventually generate a picture.
OTOH, consider LLMs as a roomful of monkeys that can communicate to each other, look at words,sentences and paragraphs on posters around the room with a human in the room that gives them a banana when they type out a new word, sentence or paragraph.
You may eventually get a roomful of monkeys that can respond to a new sentence you give them with what seems an intelligent reply. And since language is the creation of humans, it represents an abstraction of the world made by humans.
Always a polarising figure, responses here bisect along several planes. I am sure some come armed to disagree because of his life long affinity to left world view, others to defend because of his centrality to theories of language.
I happen to agree with his view, so i came armed to agree and read this with a view in mind which I felt was reinforced. People are overstating the AGI qualities and misapplying the tool, sometimes the same people.
In particular, the lack of theory, and scientific method means both we're, not learning much, and we've rei-ified the machine.
I was disappointed nothing said of Norbert Weiner. A man who invented cybernetics and had the courage to stand up to the military industrial complex.
Quite a nice overview. For almost any specific measure, you can find something that is better than human at that point. And now LLMs architecture have made possible for computers to produce complete and internally consistent paragraphs of text, by rehashing all the digital data that can be found on the internet.
But what we're good as using all of our capabilities to transform the world around us according to an internal model that is partially shared between individuals. And we have complete control over that internal model, diverging from reality and converging towards it on whims.
So we can't produce and manipulate text faster, but rarely the end game is to produce and manipulate text. Mostly it's about sharing ideas and facts (aka internal models) and the control is ultimately what matters. It can help us, just like a calculator can help us solve an equation.
EDIT
After learning to draw, I have that internal model that I switch to whenever I want to sketch something. It's like a special mode of observation, where you no longer simply see, but pickup a lot of extra details according to all the drawing rules you internalized. There's not a lot, they're just intrinsically connected with each other. The difficult part is hand-eye coordination and analyzing the divergences between what you see and the internal model.
I think that's why a lot of artists are disgusted with AI generators. There's no internal models. Trying to extract one from a generated picture is a futile exercice. Same with generated texts. Alterations from the common understanding follows no patterns.
> It can help us, just like a calculator can help us solve an equation.
A calculator is consistent and doesn’t “hallucinate” answers to equations. An LLM puts an untrustworthy filter between the truth and the person. Google was revolutionary because it increased access to information. LLMs only obscure that access, while pretending to be something more.
I'm not a native english speaker, so I've used for an essay where they told us to target a certain word count. I was close, but the verbiage to get to that word count doesn't come naturally to me. So I used Germini and tell it to rewrite the text targeting that word count (my only prompt). Then I reviewed the answer, rewriting where it strayed from the points I was making.
Also I used it for a few programming tasks I was pretty sure was in the datasets (how to draw charts with python and manipulate pandas frame). I know the domain, but wasn't in the mood to analyse the docs to get the implementation information. But the information I was seeking was just a few lines of sample code. In my experience, anything longer is pretty inconsistent and worthless explanations.
From what I've heard, Chomsky had a stroke which impacted his language. You will, unfortunately, not hear a recent opinion from him on current developments.
Chat Gpt can write great apolgia for blood thirsty landempires and never live that down :
"To characterize a structural analysis of state violence as “apologia” reveals more about prevailing ideological filters than about the critique itself. If one examines the historical record without selective outrage, the pattern is clear—and uncomfortable for all who prefer myths to mechanisms." the fake academic facade, the us diabolism, the unwillingness to see complexity and responsibility in other its all with us forever ..
I imagine his opinions might have changed by now. If we're still residing in 2023, I would be inclined to agree with him. Today, in 2025 however, LLMs are just another tool being used to "reduce labor costs" and extract more profit from the humans left who have money. There will be no scientific developments if things continue in this manner.
In my view, there is a major flaw in his argument is his distinction into pure engineering and science:
> We can make a rough distinction between pure engineering and science. There is no sharp boundary, but it’s a useful first approximation. Pure engineering seeks to produce a product that may be of some use. Science seeks understanding. If the topic is human intelligence, or cognitive capacities of other organisms, science seeks understanding of these biological systems.
If you take this approach, of course it follows that we should laugh at Tom Jones.
But a more differentiated approach is to recognize that science also falls into (at least) two categories; the science that we do because it expands our capability into something that we were previously incapable of, and the one that does not. (we typically do a lot more of the former than the latter, for obvious practical reasons)
Of course it is interesting from a historical perspective to understand the seafaring exploits of Polynesians, but as soon as there was a better way of navigating (i.e. by stars or by GPS) the investigation of this matter was relegated to the second type of science, more of a historical kind of investigation. Fundamentally we investigate things in science that are interesting because we believe the understanding we can gain from it can move us forwards somehow.
Could it be interesting to understand how Hamilton was thinking when he came up with imaginary numbers? Sure. Are a lot of mathematicians today concerning themselves with studying this? No, because the frontier has been moved far beyond.*
When you take this view, it´s clear that his statement
> These considerations bring up a minor problem with the current LLM enthusiasm: its total absurdity, as in the hypothetical cases where we recognize it at once. But there are much more serious problems than absurdity.
is not warranted. Consider the following, in his own analogy:
> These considerations bring up a minor problem with the current GPS enthusiasm: its total absurdity, as in the hypothetical cases where we recognize it at ones. But there are much more serious problems than absurdity. One is that GPS systems are designed in such a way that they cannot tell us anything about navigation, planning routes or other aspects of orientation, a matter of principle, irremediable.
* I´m making a simplifying assumption here that we can´t learn anything useful for modern navigation anymore from studying Polynesians or ants; this might well be untrue, but that is also the case for learning something about language from LLMs, which according to Chomsky is apparently impossible and not even up for debate.
I came to comments to ask a question, but considering that it is two days old already, I will try to ask you in this thread.
What you think about his argument about “not being able to distinguish possible language from impossible”?
And why is it inherent in ML design?
Does he assume that there could be such an instrument/algorithm that could do that with a certainty level higher than LLM/some ml model?
I mean, certainly they can be used to make a prediction/answer to this question, but he argues that this answer has no credibility? I mean, LLM is literally a model, ie probability distribution over what is language and what is not, what gives?
Current models are probably tuned more “strictly” to follow existing languages closely, ie that will say “no-no” to some yet-unknown language, but isn’t this improvable in theory?
Or is he arguing precisely that this “exterior” is not directly correlated with “internal processes and faculties” and cannot make such predictions in principle?
All this interview proves is that Chomsky has fallen far, far behind how AI systems work today and is retreating to scoff at all the progress machine learning has achieved. Machine learning has given rise to AI now. It can't explain itself from principles or its architecture. But you couldn't explain your brain from principles or its architecture, you'd need all of neuroscience to do it. Because the brain is digital and (probably) does not reason like our brains do, it somehow falls short?
While there's some things in this I find myself nodding along to in this, I can't help but feel it's an a really old take that is super vague and hand-wavy. The truth is that all of the progress on machine learning is absolutely science. We understand extremely well how to make neural networks learn efficiently; it's why the data leads anywhere at all. Backpropagation and gradient descent are extraordinarily powerful. Not to mention all the "just engineering" of making chips crunch incredible amounts of numbers.
Chomsky is extremely ungenerous to the progress and also pretty flippant about what this stuff can do.
I think we should probably stop listening to Chomsky; he hasn't said anything here that he hasn't already say a thousand times for decades.
> Not to mention all the "just engineering" of making chips crunch incredible amounts of numbers.
Are LLM's still the same black box as they were described as a couple years ago? Are their inner workings at least slightly better understood than in the past?
Running tens of thousands of chips crunching a bajillion numbers a second sounds fun, but that's not automatically "engineering". You can have the same chips crunching numbers with the same intensity just to run an algorithm to run a large prime number. Chips crunching numbers isn't automatically engineering IMO. More like a side effect of engineering? Or a tool you use to run the thing you built?
What happens when we build something that works, but we don't actually know how? We learn about it through trial and error, rather than foundational logic about the technology.
Sorta reminds me of the human brain, psychology, and how some people think psychology isn't science. The brain is a black box kind of like a LLM? Some people will think it's still science, others will have less respect.
This perspective might be off base. It's under the assumption that we all agree LLM's are a poorly understood black box and no one really knows how they truly work. I could be completely wrong on that, would love for someone else to weigh in.
Separately, I don't know the author, but agreed it reads more like a pop sci book. Although I only hope to write as coherently as that when I'm 96 y/o.
> Running tens of thousands of chips crunching a bajillion numbers a second sounds fun, but that's not automatically "engineering".
Not if some properties are unexpectedly emergent. Then it is science. For instance, why should a generic statistical model be able to learn how to fill in blanks in text using a finite number of samples? And why should a generic blank-filler be able to produce a coherent chat bot that can even help you write code?
Some have even claimed that statistical modelling shouldn't able to produce coherent speech, because it would need impossible amounts of data, or the optimisation problem might be too hard, or because of Goedel's incompleteness theorem somehow implying that human-level intelligence is uncomputable, etc. The fact that we have a talking robot means that those people were wrong. That should count as a scientific breakthrough.
> But you couldn't explain your brain from principles or its architecture, you'd need all of neuroscience to do it
That's not a good argument. Neuroscience was constructed by (other) brains. The brain is trying to explain itself.
> The truth is that all of the progress on machine learning is absolutely science.
But not much if you're interested in finding out how our brain works, or how language works. One of the interesting outcomes of LLMs is that there apparently is a way to represent complex ideas and their linguistic connection in a (rather large) unstructured state, but it comes without thorough explanation or relation to the human brain.
> Chomsky is [...] pretty flippant about what this stuff can do.
True, that's his style, being belligerently verbose, but others have been pretty much fawning and drooling over a stochastic parrot with a very good memory, mostly with dollar signs in their eyes.
This is not relevant. An observer who deceives for purposes of “balancing” other perceived deceptions is as untrustworthy and objectionable as one who deceives for other reasons.
> The truth is that all of the progress on machine learning is absolutely science
It is not science, which is the study of the natural world. You are using the word "science" as an honorific, meaning something like "useful technical work that I think is impressive".
The reason you are so confused is that you can't distinguish studying the natural world from engineering.
Reminds me of SUSY, string theory, the standard model, and beyond that, string theory etc…
What is elegant as a model is not always what works, and working towards a clean model to explain everything from a model that works is fraught, hard work.
I don’t think anyone alive will realize true “AGI”, but it won’t matter. You don’t need it, the same way particle physics doesn’t need elegance
That was a weird ride. He was asked whether AI will outsmart humans and went on a rant about philosophy of science seemingly trying to defend the importance of his research and culminated with some culture war commentary about postmodernism.
It’s time to stop writing in this elitist jargon. If you’re communicating and few people understands you, then you’re a bad communicator. I read the whole thing and thought: wait, was there a new thought or interesting observation here? What did we actually learn?
I have problems with Noam Chomsky, but certainly none with his ability to communicate. He is a marvel at speaking extemporaneously in a precise and clear way.
Most likely not. This is one of his weird pieces co-authored with Jeffrey Watamull. I don’t doubt that he put his name on it voluntarily, but it reads much more like Watamull than Chomsky. The views expressed in the interview we’re
commenting on are much more Chomsky-like.
He explicitly says he didn't write it in this article:
"NC: Credit for the article should be given to the actual author, Jeffrey Watumull, a fine mathematician-linguist-philosopher. The two listed co-authors were consultants, who agree with the article but did not write it."
Chomsky’s notion is: LLMs can only imitate, not understand language. But what exactly is understanding? What if our „understanding“ is just unlocking another level in a model? Unlocking a new form of generation?
He alludes to quite a bit here - impossible languages, intrinsic rules that don’t actually express in the language, etc - that leads me to believe there’s a pretty specific sense by which he means “understanding,” and I’d expect there’s a decent literature in linguistics covering what he’s referring to. If it’s a topic of interest to you, chasing down some of those leads might be a good start.
(I’ll note as several others have here too that most of his language seems to be using specific linguistics terms of art - “language” for “human language” is a big tell, as is the focus on understanding the mechanisms of language and how humans understand and generate languages - I’m not sure the critique here is specifically around LLMs, but more around their ability to teach us things about how humans understand language.)
I have trouble with the notion "understanding". I get the usefulness of the word, but I don't think that we are capable to actually understand. I also think that we are not even able to test for understanding - a good imitation is as good as understanding. Also, understanding has limits. In school, they often say on class that you should forget whatever you have been taught so far, because this new layer of knowledge that they are about to teach you. Was the previous knowledge not "understanding" then? Is the new one "understanding"?
If we define "understanding" like "useful", as in, not an innate attribute, but something in relation to a goal, then again, a good imitation, or a rudimentary model can get very far. ChatGPT "understood" a lot of things I have thrown at it, be that algorithms, nutrition, basic calculations, transformation between text formats, where I'm stuck in my personal development journey, or how to politely address people in the email I'm about to write.
>What if our „understanding“ is just unlocking another level in a model?
I believe that it is - that understanding is basically an illusion. Impressions are made up from perceptions and thinking, and extrapolated over the unknown. And just look how far that got us!
Actually no. Chomsky has never really given a stuff about Chinese Room style arguments about whether computers can “really” understand language. His problem with LLMs (if they are presented as a contribution to linguistic science) is primarily that they don’t advance our understanding of the human capacity for language. The main reasons for this are that (i) they are able to learn languages that are very much unlike human languages and (ii) they require vastly more linguistic data than human children have access to.
Understanding is probably not much more than making abstractions into simpler terms until you are left with something one can relate to by intuition or social consensus.
Manufactured intelligence to modulate a world of manufactured consent!
I agree with the rest of these comments though, listening to Chomsky wax about the topic-du-jour is a bit like trying to take lecture notes from the Swedish Chef.
I think many people are missing the core of what Chomsky is saying. It is often easy to miscommunicate and I think this is primarily what is happening. I think the analogy he gives here really helps emphasize what he's trying to say.
If you're only going to read one part, I think it is this:
| I mentioned insect navigation, which is an astonishing achievement. Insect scientists have made much progress in studying how it is achieved, though the neurophysiology, a very difficult matter, remains elusive, along with evolution of the systems. The same is true of the amazing feats of birds and sea turtles that travel thousands of miles and unerringly return to the place of origin.
| Suppose Tom Jones, a proponent of engineering AI, comes along and says: “Your work has all been refuted. The problem is solved. Commercial airline pilots achieve the same or even better results all the time.”
| If even bothering to respond, we’d laugh.
| Take the case of the seafaring exploits of Polynesians, still alive among Indigenous tribes, using stars, wind, currents to land their canoes at a designated spot hundreds of miles away. This too has been the topic of much research to find out how they do it. Tom Jones has the answer: “Stop wasting your time; naval vessels do it all the time.”
| Same response.
It is easy to look at metrics of performance and call things solved. But there's much more depth to these problems than our abilities to solve some task. It's not about just the ability to do something, the how matters. It isn't important that we are able to do better at navigating than birds or insects. Our achievements say nothing about what they do.
This would be like saying we developed a good algorithm only my looking at it's ability to do some task. Certainly that is an important part, and even a core reason for why we program in the first place! But its performance tells us little to nothing about its implementation. The implementation still matters! Are we making good uses of our resources? Certainly we want to be efficient, in an effort to drive down costs. Are there flaws or errors that we didn't catch in our measurements? Those things come at huge costs and fundamentally limit our programs in the first place. The task performance tells us nothing about the vulnerability to hackers nor what their exploits will cost our business.
That's what he's talking about.
Just because you can do something well doesn't mean you have a good understanding. It's natural to think the two relate because understanding improves performance that that's primarily how we drive our education. But this is not a necessary condition and we have a long history demonstrating that. I'm quite surprised this concept is so contentious among programmers. We've seen the follies of using test driven development. Fundamentally, that is the same. There's more depth than what we can measure here and we should not be quick to presume that good performance is the same as understanding[0,1]. We KNOW this isn't true[2].
I agree with Chomsky, it is laughable. It is laughable to think that the man in The Chinese Room[3] must understand Chinese. 40 years in, on a conversation hundreds of years old. Surely we know you can get a good grade on a test without actually knowing the material. Hell, there's a trivial case of just having the answer sheet.
"Expert in (now-)ancient arts draws strange conclusion using questionable logic" is the most generous description I can muster.
Quoting Chomsky:
> These considerations bring up a minor problem with the current LLM enthusiasm: its total absurdity, as in the hypothetical cases where we recognize it at once. But there are much more serious problems than absurdity.
> One is that the LLM systems are designed in such a way that they cannot tell us anything about language, learning, or other aspects of cognition, a matter of principle, irremediable... The reason is elementary: The systems work just as well with impossible languages that infants cannot acquire as with those they acquire quickly and virtually reflexively.
Response from o3:
LLMs do surface real linguistic structure:
• Hidden syntax: Attention heads in GPT-style models line up with dependency trees and phrase boundaries—even though no parser labels were ever provided. Researchers have used these heads to recover grammars for dozens of languages.
• Typology signals: In multilingual models, languages that share word-order or morphology cluster together in embedding space, letting linguists spot family relationships and outliers automatically.
• Limits shown by contrast tests: When you feed them “impossible” languages (e.g., mirror-order or random-agreement versions of English), perplexity explodes and structure heads disappear—evidence that the models do encode natural-language constraints.
• Psycholinguistic fit: The probability spikes LLMs assign to next-words predict human reading-time slow-downs (garden-paths, agreement attraction, etc.) almost as well as classic hand-built models.
These empirical hooks are already informing syntax, acquisition, and typology research—hardly “nothing to say about language.”
It's completely irrelevant because the point he's making is that LLMs operate differently from human languages as evidenced by the fact that they can learn language structures that humans cannot learn. Put another way, I'm sure you can point out an infinitude of similarities between human language faculty and LLMs but it's the critical differences that make LLMs not useful models of human language ability.
> When you feed them “impossible” languages (e.g., mirror-order or random-agreement versions of English), perplexity explodes and structure heads disappear—evidence that the models do encode natural-language constraints.
This is confused. You can pre-train an LLM on English or an impossible language and they do equally well. On the other hand humans can't do that, ergo LLMs aren't useful models of human language because they lack this critical distinctive feature.
Insect behaviour. Flight of birds. Turtle navigation. A footballer crossing the field to intercept a football.
This is what Chomsky always wanted ai to be... especially language ai. Clever solutions to complex problems. Simple once you know how they work. Elegant.
I sympathize. I'm a curious human. We like elegant, simple revelations that reveal how out complex world is really simple once you know it's secrets. This aesthetic has also been productive.
And yet... maybe some things are complicated. Maybe LLMs do teach us something about language... that language is complicated.
So sure. You can certainly critique "ai blogosphere" for exuberance and big speculative claims. That part is true. Otoh... linguistics is one of the areas that ai based research may turn up some new insights.
> Maybe LLMs do teach us something about language... that language is complicated.
It certainly teaches us many things. But an LLM trained on as many words (or generally speaking an AI trained on sounds) in similar quantities of a toddler learning to understand, parse and apply language, would not perform well with current architectures. They need orders of magnitude more training material to get even close. Basically, current AI learns slowly, but of course it’s much faster in wall clock time because it’s all computer.
What I mean is: what makes an ALU (CPU) better than a human at arithmetic? It’s just faster and makes fewer errors. Similarly, what makes Google or Wikipedia better than an educated person? It’s just storing and helping you access stored information, it’s not magic (anymore). You can manually do everything mechanically, if you’re willing to waste the time to prove a point.
An LLM does many things better than humans, but we forget they’ve been trained on all written history and have hundreds of billions of parameters. If you compare what an LLM can do with the same amount of training to a human, the human is much better even at picking up patterns – current AIs strongest skill. The magic comes from the unseen vast amounts of training data. This is obvious when using them – stray just slightly outside of the training zone to unfamiliar domains and ”ability” drops rapidly. The hard part is figuring out these fuzzy boundaries. How far does interpolating training data get you? What are the highest level patterns are encoded in the training data? And most importantly, to what extent do those patterns apply to novel domains?
Alternatively, you can use LLMs as a proxy for understanding the relationship between domains, instead of letting humans label them and decide the taxonomy. One such example is the relationship between detecting patterns and generating text and images – it turns out to be more or less reversible through the same architecture. More such remarkable similarities and anti-similarities are certainly on the horizon. For instance, my gut feeling says that small talk is closer to driving a car but very different from puzzle solving. We don’t really have a (good) taxonomy over human- or animal brain processes.
From some Googling and use of Claude (and from summaries of the suggestively titled "Impossible Languages" by Moro linked from https://en.wikipedia.org/wiki/Universal_grammar ), it looks like he's referring to languages which violate the laws which constrain the languages humans are innately capable of learning. But it's very unclear why "machine M is capable of learning more complex languages than humans" implies anything about the linguistic competence or the intelligence of machine M.
In this article he is very focused on science and works hard to delineate science (research? deriving new facts?) from engineering (clearly product oriented). In his opinion ChatGPT falls on the engineering side of this line: it's a product of engineering, OpenAI is concentrating on marketing. For sure there was much science involved but the thing we have access to is a product.
IMHO Chomsky is asking: while ChatGPT is a fascinating product, what is it teaching us about language? How is it advancing our knowledge of language? I think Chomsky is saying "not much."
Someone else mentioned embeddings and the relationship between words that they reveal. Indeed, this could be a worthy area of further research. You'd think it would be a real boon when comparing languages. Unfortunately the interviewer didn't ask Chomsky about this.
As much as I think of Chomsky - his linguistics approach is outside looking in, ie observational speculation compared to the last few years of LLM based tokenization semantic spaces, embedding, deep learning and mechanistic interpretation, ie:
Understanding Linguistics before LLMs:
“We think Birds fly by flapping their wings”
Understanding Linguistics Theories after LLMs:
“Understanding the physics of Aerofoils and Bernoulli’s principle mean we can replicate what birds do”
> The world’s preeminent linguist Noam Chomsky, and one of the most esteemed public intellectuals of all time, whose intellectual stature has been compared to that of Galileo, Newton, and Descartes, tackles these nagging questions in the interview that follows.
In all seriousness tho, not much of anything he says is taken seriously in an academic sense any more. Univeral Grammar, Minimalism, etc. He's a very petty dude. The reason he doesn't engage with GPT is because it suggests that linguistic learning is unlike a theory he spent his whole life [unsuccessfully] promoting, but he's such a haughty know-it-all, that I guess dummies take that for intelligence? It strikes me as not dissimilar to Trump in a way, where arrogance is conflated with strength, intelligence, etc. Fake it til you make it, or like, forever, I guess.
The comparison to Trump seems very unfair. I'm not in the academy and didn't know the current standing of his work, but he was certainly a big name that popped up everywhere (as a theorists in the field, not as a general celebrity) when I took an introduction to linguistics 20+ years ago.
As this is Hacker News, it is worth mentioning that he developed the concept of context-free grammars. That is something many of us encounter on a regular basis.
No matter what personality flaws he might have and how misguided some of his political ideas might be, he is one of the big thinkers of the 20th century. Very much unlike Trump.
I shortened it. I think this is one instance where it's actually relevant. The "opinion" of an LLM about a LLM criticism from one the leading linguists in history.
It got flagged, but I feel the flagging was knee-jerk and failed to understand the irony in the context.
What does age have to do with understanding any of this? He has been developing new, and refining old theories, over decades. It's ridiculous to expect someone to stop purely because of age, or to think they need your protection from discussing their views.
I care what one of the most famous philosophers and thinkers of our times says. He's not the most up to date, but calling him an idiot positions you politically and intellectually.
I confess my opinion of Noam Chomsky dropped a lot from reading this interview. The way he set up a "Tom Jones" strawman and kept dismissing positions using language like "we'd laugh", "total absurdity", etc. was really disappointing. I always assumed that academics were only like that on reddit, and in real life they actually made a serious effort at rigorous argument, avoiding logical fallacies and the like. Yet here is Chomsky addressing a lay audience that has no linguistics background, and instead of even attempting to summarize the arguments for his position, he simply asserts that opposing views are risible with little supporting argument. I expected much more from a big-name scholar.
"The first principle is that you must not fool yourself, and you are the easiest person to fool."
Havent read the interview, but interviews arent formal debates and I would never expect someone to hold themselves to that same standard.
The same way that reddit comments arent a formal debate.
Mocking is absolutely useful. Sometimes you debate someone like graham hancock and force him to confirm that he has no evidence for his hypotheses, then when you discuss the debate, you mock him relentlessly for having no evidence for his hypotheses.
> Yet here is Chomsky addressing a lay audience that has no linguistics background
So not a formal debate or paper where I would expect anyone to hold to debate principles.
"Tom Jones" isn't a strawman, Chomsky is addressing an actual argument in a published paper from Steven Piantadosi. He's using a pseudonym to be polite and not call him out by name.
> instead of even attempting to summarize the arguments for his position..
He makes a very clear, simple argument, accessible to any layperson who can read. If you are studying insects what you are interested in is how insects do it not what other mechanisms you can come up with to "beat" insects. This isn't complicated.
>The systems work just as well with impossible languages that infants cannot acquire as with those they acquire quickly and virtually reflexively.
Where is the research on impossible language that infants can't acquire? A good popsci article would give me leads here.
Even assuming Chomsky's claim is true, all it shows is that LLMs aren't an exact match for human language learning. But even an inexact model can still be a useful research tool.
>That’s highly unlikely for reasons long understood, but it’s not relevant to our concerns here, so we can put it aside. Plainly there is a biological endowment for the human faculty of language. The merest truism.
Again, a good popsci article would actually support these claims instead of simply asserting them and implying that anyone who disagrees is a simpleton.
I agree with Chomsky that the postmodern critique of science sucks, and I agree that AI is a threat to the human race.
That's understandable but irrelevant. Only a few people have major interest in how humans think exactly. But nearly everyone is hang on the question if the LLMs could think better.
Is it polite to deprive readers of context necessary to understand what the speaker is talking about? I was also very confused by that part and I had no idea whom or what he was talking about or why he even started taking about that.
I searched for an actual paper by that guy because you’ve mentioned his real name. I found “Modern language models refute Chomsky’s approach to language”. After reading it seems even more true that Chomsky’s Tom Jones is a strawman.
There's a reason Max Planck said science advances one funeral at a time. Researches spend their lives developing and promoting the ideas they cut their teeth on (or in this case developed himself) and their view of what is possible becomes ossified around these foundational beliefs. Expecting him to be flexible enough in his advanced age to view LLMs with a fresh perspective, rather than strongly informed by his core
theoretical views is expecting too much.
Chomsky's problem here has nothing to do with his politics, but unfortunately a lot to do with his long-held position in the Nature/Nurture debate - a position that is undermined by the ability of LLMs to learn language without hardcoded grammatical rules:
Chomsky introduced his theory of language acquisition, according to which children have an inborn quality of being biologically encoded with a universal grammar
I don't see how the two things are related. Whether acquisition of human language is nature or nurture - it is still learning of some sort.
Yes, maybe we can reproduce that learning process in LLMs, but that doesn't mean the LLMs imitate only the nurture part (might as well be just finetuning), and not the nature part.
An airplane is not an explanation for a bird's flight.
Whether one expects AI to be powerful or weak should have nothing to do with political slant, but here it seems to inform the opinion. It begs the question: what do they want to be true? The enemy is both too strong and too weak.
They're firmly on one extreme end of the spectrum. I feel as though I'm somewhere in between.
Leftists and intellectuals overlap a lot. LLM text must be still full of six fingered hands to many of them.
For Chomsky specifically, the entire existence of LLM, however it's framed, is a massive middle finger to him and a strike-through on a large part of his academic career. As much as I find his UG theory and its supporters irritating, it might be felt a bit unfair to someone his age.
99%+ of humans on this planet do not investigate an issue, they simply accept a trusted opinion of an issue as fact. If you think this is a left only issue you havent been paying attention.
Usually what happens is the information bubble bursts, and gets corrected, or it just fades out.
Then you obviously didn't listen to a word Chomsky has said on the subject.
I was quite dismissive of him on LLMs until I realized the utter hubris and stupidity of dismissing Chomsky on language.
I think it was someone asking if he was familiar with the Wittgenstein Blue and Brown books and of course because he as already an assistant professor at MIT when they came out.
I still chuckle at my own intellectual arrogance and stupidity when thinking about how I was dismissive of Chomsky on language. I barely know anything and I was being dismissive of one of unquestionable titans and historic figures of a field.
This is a great way to remove any nuance and chance of learning from a conversation. Please don't succumb to black-and-white (or red-and-blue) thinking, it's harmful to your brain.
Or an ideological alignment of values. Generative AI is strongly associated with large corporations that are untrusted (to put it generously) by those on the left.
An equivalent observation might be that the only people who seem really, really excited about current AI products are grifters who want to make money selling it. Which looks a lot like Blockchain to many.
I think viewing the world as either leftist or right wing is rather limiting philosophy and way to go through life. Most people are a lot more complicated than that.
I have experienced this too. It's definitely part of the religion but I'm not sure why tbh. Maybe they equate it with like tech is bad mkay, which, looking at who leads a lot of the tech companies, is somewhat understandable, altho very myopic.
I see this as much more of a hackers vs. corporations ideological split. Which imperfectly maps to leftism vs conservatism.
The perception on the left is that once again, corporations are foisting products on us that nobody wants, with no concern for safety, privacy, or respect for creators.
For better or worse, the age of garage-tech is mostly dead and Tech has become synonymous with corporatism. This is especially true with GenAI, where the resources to construct a frontier model (or anything remotely close to it) are far outside what a hacker can afford.
It is unfortunate opinion, because I personally hold Chomsky in fairly high regard and give most of his thoughts I am familiar with a reasonable amount of consideration if only because he could, I suppose in the olden days now, articulate his points well and make you question your own thought process. This no longer seems to be the case though as I found the linked article somewhat difficult to follow. I suppose age can get to anyone.
Not that I am an LLM zealot. Frankly, some of the clear trajectory it puts humans on makes me question our futures in this timeline. But even if I am not a zealot, but merely an amused, but bored middle class rube, the serious issues with it ( privacy, detailed personal profiling that surpasses existing systems, energy use, and actual power of those who wield it ), I can see it being implemented everywhere with a mix of glee and annoyance.
I know for a fact it will break things and break things hard and it will be people, who know how things actually work that will need to fix those.
I will be very honest though. I think Chomsky is stuck in his internal model of the world and unable to shake it off. Even his arguments fall flat, because they don't fit the domain well. It seems like they should given that he practically made his name on syntax theory ( which suggests his thoughts should translate well into it ) and yet.. they don't.
I have a minor pet theory on this, but I am still working on putting it into some coherent words.
> Perhaps it is more important to know the limitations of tools rather than dismiss their utility entirely due to the existence of limitations.
Well, yes. And "reasoning" is only something LLMs do coincidentally, to their function as sequence continuation engines. Like performing accurate math on rationale numbers, it can happen if you put in a lot of work and accept a LOT of expensive computation. Even then there exists computations that just are not reasonable or feasible.
Reminding folks to dismiss the massive propaganda engine pushing this bubble isn't "dismissing their utility entirely".
These are not reasoning machines. Treating them like they are will get you hurt eventually.
Surely it just reasoned that you made a typo and "autocorrected" your riddle. Isn't this what a human would do? Though to be fair, a human would ask you again to make sure they heard you correctly. But it would be kind of annoying if you had to verify every typo when using an LLM.
Maybe I am missing context, but it seems like he’s defending himself from the claim that we shouldn’t bother studying language acquisition and comprehension in humans because of LLM’s?
Who would make such a claim? LLM’s are of course incredible, but it seems obvious that their mechanism is quite different than the human brain.
I think the best you can say is that one could motivate lines of inquiry in human understanding, especially because we can essentially do brain surgery on an LLM in action in a way that we can’t with humans.
> It’s as if a biologist were to say: “I have a great new theory of organisms. It lists many that exist and many that can’t possibly exist, and I can tell you nothing about the distinction.”
> Again, we’d laugh. Or should.
Should we? This reminds me acutely of imaginary numbers. They are a great theory of numbers that can list many numbers that do 'exist' and many that can't possibly 'exist'. And we did laugh when imaginary numbers were first introduced - the name itself was intended as a derogatory term for the concept. But who's laughing now?
Imaginary numbers are not relevant at all. There’s nothing whatsoever to do with the everyday use of the word imaginary. They could just as easily have been called “vertical numbers” and real numbers called “horizontal numbers” in order to more clearly illustrate their geometric interpretation in the complex plane.
The term “imaginary number” was coined by Rene Descartes as a derogatory and the ill intent behind his term has stuck ever since. I suspect his purpose was theological rather than mathematical and we are all the worse for it.
This is the point where i realized he has no clue what he is saying. Theres so many creatures that once existed that can never again exist on earth due to the changes that the planet has gone through over millions, billions of years. The oxygen rich atmosphere that supported the dinosaurs for instance. If we had some kind of system that can put together proper working DNA for all the creatures that ever actually existed on this planet, some half of them would be completely nonviable if introduced to the ecosystem today. He is failing to see that there is an incredible understanding of systems that we are producing with this work, but he is a very old man from a very different time and contrarianism is often the only way to look smart or reasoned when you have no clue whats actually going on, so I am not shocked by his take.
I have a degree in linguistics. We were taught Chomsky’s theories of linguistics, but also taught that they were not true. (I don’t want to say what university it was since this was 25 years ago and for all I know that linguistics department no longer teaches against Chomsky). The end result is I don’t take anything Chomsky says seriously. So, it is difficult for me to engage with Chomsky’s ideas.
I'm rather confused by this statement. I've read a number of Chomsky pieces and have listened to him speak a number of times. To say his theories were all "not true" seems, to an extent, almost impossible.
Care to expand on how his theories can be taught in such a binary way?
GP may be referring to the idea that language is innate like an organ in the body/brain. The Kingdom of Speech by Tom Wolfe is a great read exploring Chomsky and other thinkers in this realm. It would have been great to see what he thought of LLMs too.
Generally what people are talking about are his universal grammar or generative syntax theories/approaches, which are foundational to how you approach many topics. Because you build your academic career based on specialization they are hotly contested (for the material reasons of jobs, funding, tenure, etc.).
This leads to people who agree hiring each other and departments ‘circling the wagon’ on these issues. You’ll see this referred to as east vs west coast, but it’s not actually that clearly geographically delineated.
So anyways, these are open questions that people do seriously discuss and study, but the politics of academia make it difficult and unfortunately this often trickles down to students.
This reminds me of the debates over F.R. Leavis, and the impact it had on modern english teaching worldwide. There are a small dying cohort of english professors who are refugees from internecine warfare.
Same thing happened in Astronomy. Students of Fred Hoyle can't work in some institutions. &c &c.
I don't have a degree in linguistics, but I took a few classes about 15 years ago, and Chomsky's works were basically treated as gospel. Although my university's linguistics faculty included several of his former graduate students, so maybe there's a bias factor. In any case, it reminds me of an SMBC comic about how math and science advance over time [1]
Linguistics has been largely subsumed by CS (LLM, speech synthesis, translation). It's not an empirical science or social science and most of its theories are not falsifiable.
But generally speaking Chomsky's ideas, and in particular, the Universal Grammar are no longer in vogue.
Chomsky is always saying that LLMs and such can only imitate, not understand language. But I wonder if there is a degree of sophistication at which he would concede these machines exceed "imitation". If his point is that LLMs arrive at language in a way different than humans... great. But I'm not sure how he can argue that some kind of extremely sophisticated understanding of natural language is not embedded in these models in a way that, at this point, exceeds the average human. In all fairness, this was written in 2023, but given his longstanding stubbornness on this topic, I doubt it would make a difference.
I think what would "convince" Chomsky is more akin to the explainability research currently in it's infancy, producing something akin to a branch of information theory for language and thought.
Chomsky talks about how the current approach can't tell you about what humans are doing, only approximate it; the example he has given in the past is taking thousands of hours of footage of falling leaves and then training a model to make new leaf falling footage versus producing a model of gravity, gas mechanics for the air currents, and air resistance model of leaves. The later representation is distilled down into something that tells you about what is happening at the end of some scientific inquiry, and the former is a opaque simulation for engineering purposes if all you wanted was more leaf falling footage.
So I interpret Chomsky as meaning "Look, these things can be great for an engineering purpose but I am unsatisfied in them for scientific research because they do not explain language to me" and mostly pushing back against people implying that the field he dedicated much of his life to is obsolete because it isn't being used for engineering new systems anymore, which was never his goal.
I guess it's because LLM does not understand the meaning as you understand what you read or thought. LLMs are machines that modulate hierarchical positions, ordering the placement of a-signifying sign without a clue of the meaning of what they ordered (that's why machine can hallucinate :they don't have a sense of what they express)
I think that misses the point entirely. Even if you constructed some system the output of which could not be distinguished from human-produced language but that either (1) clearly operated according to principles other than those that govern human language or (2) operated according to principles that its creators could not adequately explain, it would not be of that much interest to him.
He wants to understand how human language works. If I get him right — and I'm absolutely sure that I don't in important ways — then LLMs are not that interesting because both (1) and (2) above are true of them.
That's not quite a valid point considering the article's conclusion: sowing dissent in the sciences allows companies to more easily package and sell carcinogens like asbestos, lead paint, and tobacco products.
I understand his diction is a bit impenetrable but I believe the intention is to promote literacy and specificity, not just to be a smarty-pants.
The level of intellectual engagement with Chomsky's ideas in the comments here is shockingly low. Surely, we are capable of holding these two thoughts: one, that the facility of LLMs is fantastic and useful, and two, that the major breakthroughs of AI this decade have not, at least so far, substantially deepened our understanding of our own intelligence and its constitution.
That may change, particularly if the intelligence of LLMs proves to be analogous to our own in some deep way—a point that is still very much undecided. However, if the similarities are there, so is the potential for knowledge. We have a complete mechanical understanding of LLMs and can pry apart their structure, which we cannot yet do with the brain. And some of the smartest people in the world are engaged in making LLMs smaller and more efficient; it seems possible that the push for miniaturization will rediscover some tricks also discovered by the blind watchmaker. But these things are not a given.
> AI this decade have not, at least so far, substantially deepened our understanding of our own intelligence and its constitution
I would push back on this a little bit. While it has not helped us to understand our own intelligence, it has made me question whether such a thing even exists. Perhaps there are no simple and beautiful natural laws, like those that exists in Physics, that can explain how humans think and make decisions. When CNNs learned to recognize faces through a series of hierarchical abstractions that make intuitive sense it's hard to deny the similarities to what we're doing as humans. Perhaps it's all just emergent properties of some messy evolved substrate.
The big lesson from the AI development in the last 10 years from me has been "I guess humans really aren't so special after all" which is similar to what we've been through with Physics. Theories often made the mistake of giving human observers some kind of special importance, which was later discovered to be the cause of theories not generalizing.
> The big lesson from the AI development in the last 10 years from me has been "I guess humans really aren't so special after all"
Instead I would take the opposite take.
How wonderful is it, that with naturally evolved processes and neural structures, have we been able to create what we have. Van Gogh’s paintings came out of the human brain. The Queens of the Skies - hundreds of tons of metal and composites - flying across continents in the form of a Boeing 747 or an A380 - was designed by the human brain. We went to space, have studied nature (and have conservation programs for organisms we have found to need help), took pictures the pillars of creation that are so incredibly far… all with such a “puny” structure a few cm in diameter? I think that’s freaking amazing.
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I think it is important to realize, that we need to understand language on our own terms. The logic of LLMs is not unlike alien technology to us. That being said, the minimalist program of Chomsky lead to nowhere, because just like programming, it found edge case after edge case, reducing it further and further, until there was no program anymore that resembled a real theory. But it is wrong to assume that the big progress in linguistics is in vain, the same reason Prolog, Theorem provers, type theory, category theory is in vain, when we have LLMs that can produce everything in C++. We can use the technology of linguistics to ground our knowledge, and in some dark corner of the LLM it might already have integrated this. I think the original divide between the sciences and the humanities might be deeper and more fundamental than we think. We need linguistic as a discipline of the humanities, and maybe huge swaths of Computer Science is just that.
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> Perhaps there are no simple and beautiful natural laws, like those that exists in Physics, that can explain how humans think and make decisions.
Isn't Physics trying to describe the natural world? I'm guessing you are taking two positions here that are causing me confusion with your statement: 1) that our minds can be explained strictly through physical processes, and 2) our minds, including our intelligence, are outside of the domain of Physics.
If you take 1) to be true, then it follows that Physics, at least theoretically, should be able to explain intelligence. It may be intractably hard, like it might be intractably hard to have physics decribe and predict the motions of more than two planetary bodies.
I guess I'm saying that Physical laws ARE natural laws. I think you might be thinking that natural laws refer solely to all that messy, living stuff.
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Neuroscientist here:
> Perhaps there are no simple and beautiful natural laws, like those that exists in Physics, that can explain how humans think and make decisions...Perhaps it's all just emergent properties of some messy evolved substrate.
Yeah, it is very likely that there are not laws that will do this, it's the substrate. The fruit fly brain (let alone human) has been mapped, and we've figured out that it's not just the synapse count, but the 'weights' that matter too [0]. Mind you, those weights adjust in real time when a living animal is out there.
You'll see in literature that there are people with some 'lucky' form of hydranencephaly where their brain is as thin as paper. But they vote, get married, have kids, and for some strange reason seem to work in mailrooms (not a joke). So we know it's something about the connectome that's the 'magic' of a human.
My pet theory: We need memristors [2] to better represent things. But that takes redesigning the computer from the metal on up, so is unlikely to occur any time soon with this current AI craze.
> The big lesson from the AI development in the last 10 years from me has been "I guess humans really aren't so special after all" which is similar to what we've been through with Physics.
Yeah, biologists get there too, just the other way abouts, with animals and humans. Like, dogs make vitamin C internally, and humans have that gene too, it's just dormant, ready for evolution (or genetic engineering) to reactivate. That said, these neuroscience issues with us and the other great apes are somewhat large and strange. I'm not big into that literature, but from what little I know, the exact mechanisms and processes that get you from tool using ourangs to tool using humans, well, those seem to be a bit strange and harder to grasp for us. Again, not in that field though.
In the end though, humans are special. We're the only ones on the planet that ever really asked a question. There's a lot to us and we're actually pretty strange in the end. There's many centuries of work to do with biology, we're just at the wading stage of that ocean.
[0] https://en.wikipedia.org/wiki/Drosophila_connectome
[1] https://en.wikipedia.org/wiki/Hydranencephaly
[2] https://en.wikipedia.org/wiki/Memristor
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It is possible that we simply haven't yet discovered those natural laws for "emergent behavior" from the "messy substrate".
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> it has made me question whether such a thing even exists
I was reading a reddit post the other day where the guy lost his crypto holdings because he input his recovery phrase somewhere. We question the intelligence of LLMs because they might open a website, read something nefarious, and then do it. But here we have real humans doing the exact same thing...
> I guess humans really aren't so special after all
No they are not. But we are still far from getting there with the current LLMs and I suspect mimicking the human brain won't be the best path forward.
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I didn't see where he was disagreeing with this.
I'm assuming this was the part you were saying he doesn't hold, because it is pretty clear he holds the second thought.
I have a difficult time reading this as saying that LLMs aren't fantastic and useful.
This seems to be the core of his conversation. That he's talking about the side of science, not engineering.
It indeed baffles me how academics overall seem so dismissive of recent breakthroughs in sub-symbolic approaches as models from which we can learn about 'intelligence'?
It is as if a biochemist looks at a human brain, and concludes there is no 'intelligence' there at all, just a whole lot of electro-chemical reactions. It fully ignores the potential for emergence.
Don't misunderstand me, I'm not saying 'AGI has arrived', but I'd say even current LLM's do most certainly have interesting lessons for Human Language development and evolution in science. What can the success in transfer learning in these models contribute to the debates on universal language faculties? How do invariants correlated across LLM systems and humans?
>It fully ignores the potential for emergence.
There's two kinds of emergence, one scientific, the other a strange, vacuous notion in the absence of any theory and explanation.
The first case is emergence when we for example talk about how gas or liquid states, or combustibility emerge from certain chemical or physical properties of particles. It's not just that they're emergent, we can explain how they're emergent and how their properties are already present in the lower level of abstraction. Emergence properly understood is always reducible to lower states, not some magical word if you don't know how something works.
In these AI debates that's however exactly how "emergence" is used, people just assert it, following necessarily from their assumptions. They don't offer a scientific explanation. (the same is true with various other topics, like consciousness, or what have you). This is pointless, it's a sort of god of the gaps disguised as an argument. When Chomsky talks about science proper, he correctly points out that these kinds of arguments have no place in it, because the point of science is to build coherent theories.
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> the major breakthroughs of AI this decade have not, at least so far, substantially deepened our understanding of our own intelligence and its constitution
People's illusions and willingness to debase their own authority and control to take shortcuts to optimise towards lowest effort / highest yield (not dissimilar to something you would get with... auto regressive models!) was an astonishing insight to me.
Well said. It's wild when you think of how many "AI" products are out there that essentially entrust an LLM to make the decisions the user would otherwise make. Recruitment, trading, content creation, investment advice, medical diagnosis, legal review, dating matches, financial planning and even hiring decisions.
At some point you have to wonder: is an LLM making your hiring decision really better than rolling a dice? At least the dice doesn't give you the illusion of rationality, it doesn't generate a neat sounding paragraph "explaining" why candidate A is the obvious choice. The LLM produces content that looks like reasoning but has no actual causal connection to the decision - it's a mimicry of explanation without true substance of causation.
You can argue that humans do the same thing. But post-hoc reasoning is often a feedback loop for the eventual answer. That's not the case for LLMs.
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Chompsky's central criticism of LLMs is that they can learn impossible languages just as easily as they learn possible languages. He refers to this repeatedly in the linked interview. Therefore, they cannot teach us about our own intelligence.
However, a paper published last year (Mission: Impossible Language Models, Kallini et al.) proved that LLMs do NOT learn impossible languages as easily as they learn possible languages. This undermines everything that Chompsky says about LLMs in the linked interview.
I'm not that convinced by this paper. The "impossible languages" are all English with some sort of transformation applied, such as shuffling the word order. It seems like learning such languages would require first learning English and then learning the transformation. It's not surprising that systems would be worse at learning such languages than just learning English on its own. But I don't think these sorts of languages are what Chomsky is talking about. When Chomsky says "impossible languages," he means languages that have a coherent and learnable structure but which aren't compatible with what he thinks are innate grammatical facilities of the human mind. So for instance, x86 assembly language is reasonably structured and can express anything that C++ can, but unlike C++, it doesn't have a recursive tree-based syntax. Chomsky believes that any natural language you find will be structured more like C++ than like assembly language, because he thinks humans have an innate mental facility for using tree-based languages. I actually think a better test of whether LLMs learn languages like humans would be to see if they learn assembly as well as C++. That would be incomplete of course, but it would be getting at what Chomsky's talking about.
Also, GPT-2 actually seems to do quite well on some of the tested languages, including word-hop, partial reverse, and local-shuffle. It doesn't do quite as well as plain English, but GPT-2 was designed to learn English, so it's not surprising that it would do a little better. For instance, they tokenization seems biased towards English. They show "bookshelf" becoming the tokens "book", "sh", and "lf" – which in many of the languages get spread throughout a sentence. I don't think a system designed to learn shuffled-English would tokenize this way!
https://aclanthology.org/2024.acl-long.787.pdf
The authors of that paper misunderstand what "impossible languages" refers to. It doesn't refer to any language a human can't learn. It refers to computationally simple plausible alternative languages that humans can't learn, in particular linear-order (non-hierarchical structure) languages.
What exactly do you mean, "analogous to our own" and, "in a deep way" without making an appeal to magic or non-yet discovered fields of science? I understand what you're saying but when you scrutinize these things you end up in a place that's less scientific than one might think. That kind of seems to be one of Chomsky's salient points; we really, really need to get a handle on when we're doing science in the contemporary Kuhnian sense and philosophy.
The AI works on English, C++, Smalltalk, Klingon, nonsense, and gibberish. Like Turing's paper this illustrates the difference between, "machines being able to think" and, "machines being able to demonstrate some well understood mathematical process like pattern matching."
https://en.wikipedia.org/wiki/Computing_Machinery_and_Intell...
> not, at least so far, substantially deepened our understanding of our own intelligence
Science progresses in a manner that when you see it happen in front of you it doesn't seem substantial at all, because we typically don't understand implications of new discoveries.
So far, in the last few years, we have discovered the importance of the role of language behind intelligence. We have also discovered quantitative ways to describe how close one concept is from another. More recently, from the new reasoning AI models, we have discovered something counterintuitive that's also seemingly true for human reasoning--incorrect/incomplete reasoning can often reach the correct conclusion.
In my opinion it will or already has redefined our conceptual models of intelligence - just like physical models of atoms or gravitational mechanics evolved and newer models replace the older. The older models aren't invalidated (all models are wrong, after all), but their limits are better understood.
People are waiting for this Prometheus-level moment with AI where it resembles us exactly but exceeds our capabilities, but I don't think that's necessary. It parallels humanity explaining Nature in our own image as God and claiming it was the other way around.
> if the intelligence of LLMs proves to be analogous to our own in some deep way
First, they have to implement "intelligence" for LLMs, then we can compare. /s
There was an interesting debate where Chomsky took a position on intelligence being rooted in symbolic reasoning and Asimov asserted a statistical foundation (ah, that was not intentional ;).
LLM designs to date are purely statistical models. A pile, a morass of floating point numbers and their weighted relationships, along with the software and hardware that animates them and the user input and output that makes them valuable to us. An index of the data fed into them, different from a Lucene or SQL DB index made from compsci algorithms & data structure primitives. Recognizable to Azimov's definition.
And these LLMs feature no symbolic reasoning whatsoever within their computational substrate. What they do feature is a simple recursive model: Given the input so far, what is the next token? And they are thus enabled after training on huge amounts of input material. No inherent reasoning capabilities, no primordial ability to apply logic, or even infer basic axioms of logic, reasoning, thought. And therefore unrecognizable to Chomsky's definition.
So our LLMs are a mere parlor trick. A one-trick pony. But the trick they do is oh-so vastly complicated, and very appealing to us, of practical application and real value. It harkens back to the question: What is the nature of intelligence? And how to define it?
And I say this while thinking of the marked contrast of apparent intelligence between an LLM and say a 2-year age child.
That's not true, symbols emerge out of the statistics. Just look at the imagenet analysis that identified distinct concepts in different layers, or the experiments with ablation in LLMs.
They may not be doing strict formal logic, but they are definitely compressing information into, and operating using, symbols.
Which argues much symbolic manipulation is formulaic, and not inductive reasoning or indicative of intelligence.
Sentence parsing with multiple potential verb-noun-adjective interpretations are an example of old, Chomsky made fruit flies like a banana famous for a reason.
(without the weights and that specific sentence programmed in, I would be interested exactly how the symbol models cope with that, and the myriad other examples)
To me the interesting idea is the followup question: Can you do complex reasoning without intelligence?
LLM's seem to have proven themselves to be more than a one-trick-pony. There is actually some resemblance of reasoning and structuring etc.. No matter if directly within the LLM, or supported by computer code. E.g it can be argued that the latest LLMs like Gemini 2.5 and Claude 4 in fact do complex reasoning.
We have always taken for granted you need intelligence for that, but what if you don't? It would greatly change our view on intelligence and take away one of the main factors that we test for in e.g. animals to define their "intelligence".
> E.g it can be argued that the latest LLMs like Gemini 2.5 and Claude 4 in fact do complex reasoning.
They most definitely don't. We attach symbolic meaning to their output because we can map it semantically to the input we gave it. Which is why people are often caught by surprise when these mappings break down.
LLMs can emulate reasoning, but the failure modes show that they don't. We can get them to be coincidentally emulating reasoning well enough long enough to fools us, investors and the media. But doubling down on it hoping that this problem goes away with scale or fine tuning is proving more and more reckless.
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I think we are ignoring that the statistical aspect of our ability to reason effectively and to apply logic was predicated on the deaths of millions of our ancestors. When they made the wrong decision, they likely didn't reproduce. When they made the right decision, that particular configuration of their cortical substrate was carried forward a generation. The product of this cross-generational training could have easily led to non-intelligence, and often does, but we have survivor's bias in our favor.
Perhaps the next question we are asking is "what happens if you give a statistical model symbolic input" and the answer appears to be, you get symbolic output.
Even more strangely, the act of giving a statistical model symbolic input allows it to build a context which then shapes the symbolic output in a way that depends on some level of "understanding" instructions.
We "train" this model on raw symbolic data and it extracts the inherent semantic structure without any human ever embedding in the code anything resembling letters, words, or the like. It's as if Chomsky's elusive universal language is semantic structure itself.
>There was an interesting debate where Chomsky took a position on intelligence being rooted in symbolic reasoning and Asimov asserted a statistical foundation (ah, that was not intentional ;).
Chomsky vs Norvig
https://norvig.com/chomsky.html
> Chomsky took a position on intelligence being rooted in symbolic reasoning and Asimov asserted a statistical foundation
I dunno if people knew it at that time, but those two views are completely equivalent.
> and very appealing to us
Yes, because anthropomorphism is hardwired into our biology. Just two dots and an arc triggers a happy feeling in all humans. :)
> of practical application and real value
That is debatable. So far no groundbreaking useful applications have been found for LLMs. We want to believe, because they make us feel happy. But the results aren't there.
The voice of reason. And, as always, the voice of reason is being vigorously ignored. Dreams of big profits and exerting control through generated lies are irresistible. And among others, HN comment threads demonstrate how even people who should know better are falling for it in droves. In fact this very thread shows how Chomsky's arguments fall on deaf ears.
Don't forget exerting control through automated surveillance. What a wonderful tool we have created for detecting whether citizens step out of line without needing giant offices full of analysts.
3.35 hrs Chomsky interview on ML Street Talk https://youtu.be/axuGfh4UR9Q
Chomsky's in the last hour of that.
That part is unusually good btw. It's actually elegaic.
It's shocking how people are putting him down for the OP interview with just a couple of questions in 2023. The dude was 94 years old. I also did not predict where we would be in 2025 with LLMs. And neither did you. (When I say you, I mean some of the other commenters.)
Are we seriously saying that his ideas are not taken seriously? his theory of grammar/language construction was a major contributor to modern programming languages, for one.
The fact that we have figured out how to translate language into something a computer can "understand" should thrill linguists. Taking a word (token) and abstracting it's "meaning" as a 1,000-dimension vector seems like something that should revolutionize the field of linguistics. A whole new tool for analyzing and understanding the underlying patterns of all language!
And there's a fact here that's very hard to dispute, this method works. I can give a computer instructions and it "understands" them in a way that wasn't possible before LLMs. The main debate now is over the semantics of words like "understanding" and whether or not an LLM is conscious in the same way as a human being (it isn't).
Restricted to linguistics, LLM's supposed lack of understanding should be a non-sequitur. If the question is whether LLMs have formed a coherent ability to parse human languages, the answer is obviously yes. In fact not just human languages, as seen with multimodality the same transformer architecture seems to work well to model and generate anything with inherent structure.
I'm surprised that he doesn't mention "universal grammar" once in that essay. Maybe it so happens that humans do have some innate "universal grammar" wired in by instinct but it's clearly not _necessary_ to be able to parse things. You don't need to set up some explicit language rules or generative structure, enough data and the model learns to produce it. I wonder if anyone has gone back and tried to see if you can extract out some explicit generative rules from the learned representation though.
Since the "universal grammar" hypothesis isn't really falsifiable, at best you can hope for some generalized equivalent that's isomorphic to the platonic representation hypothesis and claim that all human language is aligned in some given latent representation, and that our brains have been optimized to be able to work in this subspace. That's at least a testable assumption, by trying to reverse engineer the geometry of the space LLMs have learned.
Can LLMs actually parse human languages? Or can they react to stimuli with a trained behavioral response? Dogs can learn to sit when you say "sit", and learn to roll over when you say "roll over". But the dog doesn't parse human language; it reacts to stimuli with a trained behavioral response.
(I'm not that familiar with LLM/ML, but it seems like trained behavioral response rather than intelligent parsing. I believe this is part of why it hallucinates? It doesn't understand concepts, it just spits out words - perhaps a parrot is a better metaphor?)
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> If the question is whether LLMs have formed a coherent ability to parse human languages, the answer is obviously yes.
No, not "obviously". They work well for languages like English or Chinese, where word order determines grammar.
They work less well where context is more important. (e.g. Grammatical gender consistency.)
Alternatively, what Chomsky was thinking about with his universal grammar idea is something implicitly present in both our minds and an LLM i.e. "it's the wiring, stupid".
I'm not sure there's much evidence for this one way or another at this point.
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by "parse" I usually assume I get out some sort of AST I can walk and manipulate. LLMs do no such thing. There is no parsing going on.
Unfortunately you've undermined your point by making sweeping claims about something that is the literal hardest known problem in philosophy (consciousness).
I'm not actually comfortable saying that LLMs aren't conscious. I think there's a decent chance they could be in a very alien way.
I realize that this is a very weird and potentially scary claim for people to parse but you must understand how weird and scary consciousness is.
Note that I didn't say they aren't conscious, I said they aren't conscious "in the same way as a human being". I left open the possibility they could be conscious "in a very alien way".
If they are, knowing what they potentially know about humans by now, they would probably do their best to hide it.
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> whether or not an LLM is conscious in the same way as a human being
The problem is... that there is a whole amount of "smart" activities humans do without being conscious of it.
- Walking, riding a bike, or typing on a keyboard happen fluidly without conscious planning of each muscle movement.
- You can finish someone sentence or detect if a sentence is grammatically wrong, often without being able to explain the rule.
- When you enter a room, your brain rapidly identifies faces, furniture, and objects without you consciously thinking, “That is a table,” or “That is John.”
Indeed, the "rider and elephant" issue.
During Covid I gave a lecture on Python on Zoom in a non-English language. It was a beginner's topic about dictionary methods. I was attempting to multi-task and had other unrelated tasks open on second computer.
Midway through the lecture I noticed to my horror that I had switched to English without the audience noticing.
Going back through the recording I noticed the switch was fluid and my delivery was reasonable. What I talked about was just as good as something presented by LLM these days.
So this brings up the question - why aren't we p-zombies all the time instead of 99% of time?
Are there any tasks that absolutely demand human consciousness as we know it?
Presumably long term planning is something that active human consciousness is needed.
Perhaps there is some need for consciousness when one is in "conscious mastery" phase of acquiring a skill.
This goes for any skill such as riding a bicycle/playing chess/programming at a high level.
Once one reaches "unconscious mastery" stage the rider can concentrate on higher meta game.
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Why would that thrill linguists? I'm not saying it hasn't/wouldn't/shouldn't, but I don't see why this technology would have the dramatic impact you imagine.
Is/was the same true for ASCII/Smalltalk/binary? They are all another way to translate language into something the computer "understands".
Perhaps the fact that it hasn't would lead some to question the validity of their claims. When a scientist makes a claim about how something works, it's expected that they prove it.
If the technology is as you say, show us.
> Is/was the same true for ASCII/Smalltalk/binary? They are all another way to translate language into something the computer "understands".
That's converting characters into a digital representation. "A" is represented as 01000001. The tokenization process for an LLM is similar, but it's only the first step.
An LLM isn't just mapping a word to a number, you're taking the entire sentence, considering the position of the words and converting it into vectors within a 1,000+ dimensional space. Machine learning has encoded some "meaning" within these dimensions that goes far far beyond something like an ASCII string.
And the proof here is that the method actual works, that's why we have LLMs.
Word embeddings (that 1000-dimension vector you mention) are not new. No comment on the rest of your comment, but that aspect of LLMs is "old" tech - word2vec was published 11 years ago.
I think they are really excited by this. There seems no deficiency of linguists using these machines.
But I think it is important to distinguish the ability to understand language and translate it. Enough that you yourself put quotes around "understanding". This can often be a challenge for many translators, not knowing how to properly translate something because of underlying context.
Our communication runs far deeper than the words we speak or write on a page. This is much of what linguistics is about, this depth. (Or at least that's what they've told me, since I'm not a linguist) This seems to be the distinction Chomsky is trying to make.
Exactly. Here, I'm on the side of Chomsky and I don't think there's much of a debate to be had. We have a long history of being able to make accurate predictions while erroneously understanding the underlying causal nature.
My background is physics, and I moved into CS (degrees in both), working on ML. I see my peers at the top like Hinton[0] and Sutskever[1] making absurd claims. I call them absurd, because it is a mistake we've made over and over in the field of physics[2,3]. One of those lessons you learn again and again, because it is so easy to make the mistake. Hinton and Sutskever say that this is a feature, not a bug. Yet we know it is not enough to fit the data. Fitting the data allows you to make accurate, testable predictions. But it is not enough to model the underlying causal structure. Science has a long history demonstrating accurate predictions with incorrect models. Not just in the way of the Relativity of Wrong[4], but more directly. Did we forget that the Geocentric Model could still be used to make good predictions? Copernicus did not just face resistance from religious authorities, but also academics. The same is true for Galileo, Boltzmann, Einstein and many more. People didn't reject their claims because they were unreasonable. They rejected the claims because there were good reasons to. Just... not enough to make them right.
[0] https://www.reddit.com/r/singularity/comments/1dhlvzh/geoffr...
[1] https://www.youtube.com/watch?v=Yf1o0TQzry8&t=449s
[2] https://www.youtube.com/watch?v=hV41QEKiMlM
[3] Think about what Fermi said in order to understand the relevance of this link: https://en.wikipedia.org/wiki/The_Unreasonable_Effectiveness...
[4] https://hermiene.net/essays-trans/relativity_of_wrong.html
"The fact that we have figured out how to translate language into something a computer can "understand" should thrill linguists."
No, there is no understanding at all. Please don't confuse codifying with understanding or translation. LLMs don't understand their input, they simply act on it based on the way they are trained on it.
"And there's a fact here that's very hard to dispute, this method works. I can give a computer instructions and it "understands" them "
No, it really does not understand those instructions. It is at best what used to be called an "idiot savant". Mind you, people used to describe others like that - who is the idiot?
Ask your favoured LLM to write a programme in a less used language - ooh let's try VMware's PowerCLI (it's PowerShell so quite popular) and get it to do something useful. It wont because it can't but it will still spit out something. PowerCLI is not extant across Stackoverflow and co much but it is PS based so the LLMs will hallucinate madder than a hippie on a new super weed.
I think the overarching theme that I glean from LLM critics is some kind of visceral emotional reaction, disgust even, with the idea of them, leading to all these proxy arguments and side quests in order to try and denigrate the idea of them without actually honestly engaging with what they are or why people are interacting with them.
so what they don't "understand", by your very specific definition of the word "understanding"? the person you're replying to is talking about the fact that they can say something to their computer in the form of casual human language and it will produce a useful response, where previously that was not true. whether that fits your suspiciously specific definition of "understanding" does not matter a bit.
so what they are over-confident with areas outside of their training data? provide more training data, improve the models, reduce the hallucination. it isn't an issue with the concept, it's an issue with the execution. yes you'll never be able to reduce it to 0%, but so what? humans hallucinate too. what are we aiming for? omniscience?
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https://magazine.caltech.edu/post/math-language-marcolli-noa...
These days, Chomsky is working on Hopf algebras (originally from quantum physics) to explain language structure.
Brains don't have innate grammar more than languages are selected to fit baby brains. Chomsky got it backwards, languages co-evolved with human brains to fit our capacities and needs. If a language is not useful or can't be learned by children, it does not expand, it just disappears.
It's like wondering how well your shoes fit your feet, forgetting that shoes are made and chosen to fit your feet in the first place.
It's not an either/or. The fact any human language is learnable by any human and not by, say, chimpanzees needs explaining.
Chomsky also talks about these kind of things in detail in Hauser, Chomsky and Fitch (2002) where they describe them as "third factors" in language acquisition.
You could say that languages developed ("evolved") to fit the indisputable human biological faculty for language.
It's amusing that he argues (correctly) that "there is no Great Chain of Being with humans at the top," but then claims that LLMs cannot tell us anything about language because they can learn "impossible languages" that infants cannot learn. Isn't that an anthropomorphic argument, saying that what a language is inherently defined by human cognition?
When Chomsky says "language," he means "natural/human language," not e.g. /[ab]*/ or prime numbers.
Yes, studying human language is actually inherently defined by what humans do, just -- as he points out, if you could understand the article -- studying insect navigation is defined by what insects do and not what navigation systems human could design.
"The desert ants in my backyard have minuscule brains, but far exceed human navigational capacities, in principle, not just performance. There is no Great Chain of Being with humans at the top."
This quote brought to mind the very different technological development path of the spider species in Adrian Tchaikovsky's Children of Time. They used pheromones to 'program' a race of ants to do computation.
I don't know what he's talking about. Humans clearly outperform ants in navigation. Especially if you allow arbitrary markings on the terrain.
Sounds like "ineffable nature" mumbo-jumbo.
Arbitrary markings on the terrain? Why not GPS, satellite photo etc? All of those are human inventions and we can navigate much better and in a broader set of environments than ants thanks to them.
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>Many biological organisms surpass human cognitive capacities in much deeper ways. The desert ants in my backyard have minuscule brains, but far exceed human navigational capacities, in principle, not just performance. There is no Great Chain of Being with humans at the top.
Chomsky made interesting points regarding the performance of AI with the performance of biological organisms in comparison to human but his conclusion is not correct. We already know that cheetah run faster human and elephant is far stronger than human. Bat can navigate in the dark with echo location and dolphin can hunt in synchronization with high precision coordination in pack to devastating effect compared to silo hunting.
Whether we like or not human is the the top unlike the claim of otherwise by Chomsky. By scientific discovery (understanding) and designing (engineering) by utilizing law of nature, human can and has surpassed all of the cognitive capabilities of these petty animals, and we're mostly responsible for their inevitable demise and extinction. Human now need to collectively and consciously reverse the extinction process of these "superior" cognitive animals in order to preserve these animals for better or worst. No other earth bound creature can do that to us.
Chomsky has the ability to say things in a way that most laypersons of average intelligence can grasp. That is an important skill for communication of one's thoughts to the general populace.
Many of the comments herein lack that feature and seem to convey that the author might be full of him(her)self.
Also, some of the comment are a bit pejorative.
I once heard that a roomful of monkeys with typewriters given infinite time could type out the works of shakespeare. I dont think that's true any more than the random illumiination of pixels on a screen could eventually generate a picture.
OTOH, consider LLMs as a roomful of monkeys that can communicate to each other, look at words,sentences and paragraphs on posters around the room with a human in the room that gives them a banana when they type out a new word, sentence or paragraph.
You may eventually get a roomful of monkeys that can respond to a new sentence you give them with what seems an intelligent reply. And since language is the creation of humans, it represents an abstraction of the world made by humans.
Always a polarising figure, responses here bisect along several planes. I am sure some come armed to disagree because of his life long affinity to left world view, others to defend because of his centrality to theories of language.
I happen to agree with his view, so i came armed to agree and read this with a view in mind which I felt was reinforced. People are overstating the AGI qualities and misapplying the tool, sometimes the same people.
In particular, the lack of theory, and scientific method means both we're, not learning much, and we've rei-ified the machine.
I was disappointed nothing said of Norbert Weiner. A man who invented cybernetics and had the courage to stand up to the military industrial complex.
Quite a nice overview. For almost any specific measure, you can find something that is better than human at that point. And now LLMs architecture have made possible for computers to produce complete and internally consistent paragraphs of text, by rehashing all the digital data that can be found on the internet.
But what we're good as using all of our capabilities to transform the world around us according to an internal model that is partially shared between individuals. And we have complete control over that internal model, diverging from reality and converging towards it on whims.
So we can't produce and manipulate text faster, but rarely the end game is to produce and manipulate text. Mostly it's about sharing ideas and facts (aka internal models) and the control is ultimately what matters. It can help us, just like a calculator can help us solve an equation.
EDIT
After learning to draw, I have that internal model that I switch to whenever I want to sketch something. It's like a special mode of observation, where you no longer simply see, but pickup a lot of extra details according to all the drawing rules you internalized. There's not a lot, they're just intrinsically connected with each other. The difficult part is hand-eye coordination and analyzing the divergences between what you see and the internal model.
I think that's why a lot of artists are disgusted with AI generators. There's no internal models. Trying to extract one from a generated picture is a futile exercice. Same with generated texts. Alterations from the common understanding follows no patterns.
> It can help us, just like a calculator can help us solve an equation.
A calculator is consistent and doesn’t “hallucinate” answers to equations. An LLM puts an untrustworthy filter between the truth and the person. Google was revolutionary because it increased access to information. LLMs only obscure that access, while pretending to be something more.
I'm not a native english speaker, so I've used for an essay where they told us to target a certain word count. I was close, but the verbiage to get to that word count doesn't come naturally to me. So I used Germini and tell it to rewrite the text targeting that word count (my only prompt). Then I reviewed the answer, rewriting where it strayed from the points I was making.
Also I used it for a few programming tasks I was pretty sure was in the datasets (how to draw charts with python and manipulate pandas frame). I know the domain, but wasn't in the mood to analyse the docs to get the implementation information. But the information I was seeking was just a few lines of sample code. In my experience, anything longer is pretty inconsistent and worthless explanations.
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>For almost any specific measure, you can find something that is better than human at that point.
Learning language from small data.
(2023)
From what I've heard, Chomsky had a stroke which impacted his language. You will, unfortunately, not hear a recent opinion from him on current developments.
Geez, talk about irony. That's terrible.
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Yean, a lot has happened in two years
Chat Gpt can write great apolgia for blood thirsty landempires and never live that down :
"To characterize a structural analysis of state violence as “apologia” reveals more about prevailing ideological filters than about the critique itself. If one examines the historical record without selective outrage, the pattern is clear—and uncomfortable for all who prefer myths to mechanisms." the fake academic facade, the us diabolism, the unwillingness to see complexity and responsibility in other its all with us forever ..
Is Chomsky really "one of the most esteemed public intellectuals of all time"? Aristotle and Beyoncé might want to have a word
I imagine his opinions might have changed by now. If we're still residing in 2023, I would be inclined to agree with him. Today, in 2025 however, LLMs are just another tool being used to "reduce labor costs" and extract more profit from the humans left who have money. There will be no scientific developments if things continue in this manner.
Two year old interview should be labeled as such.
In my view, there is a major flaw in his argument is his distinction into pure engineering and science:
> We can make a rough distinction between pure engineering and science. There is no sharp boundary, but it’s a useful first approximation. Pure engineering seeks to produce a product that may be of some use. Science seeks understanding. If the topic is human intelligence, or cognitive capacities of other organisms, science seeks understanding of these biological systems.
If you take this approach, of course it follows that we should laugh at Tom Jones.
But a more differentiated approach is to recognize that science also falls into (at least) two categories; the science that we do because it expands our capability into something that we were previously incapable of, and the one that does not. (we typically do a lot more of the former than the latter, for obvious practical reasons)
Of course it is interesting from a historical perspective to understand the seafaring exploits of Polynesians, but as soon as there was a better way of navigating (i.e. by stars or by GPS) the investigation of this matter was relegated to the second type of science, more of a historical kind of investigation. Fundamentally we investigate things in science that are interesting because we believe the understanding we can gain from it can move us forwards somehow.
Could it be interesting to understand how Hamilton was thinking when he came up with imaginary numbers? Sure. Are a lot of mathematicians today concerning themselves with studying this? No, because the frontier has been moved far beyond.*
When you take this view, it´s clear that his statement
> These considerations bring up a minor problem with the current LLM enthusiasm: its total absurdity, as in the hypothetical cases where we recognize it at once. But there are much more serious problems than absurdity.
is not warranted. Consider the following, in his own analogy:
> These considerations bring up a minor problem with the current GPS enthusiasm: its total absurdity, as in the hypothetical cases where we recognize it at ones. But there are much more serious problems than absurdity. One is that GPS systems are designed in such a way that they cannot tell us anything about navigation, planning routes or other aspects of orientation, a matter of principle, irremediable.
* I´m making a simplifying assumption here that we can´t learn anything useful for modern navigation anymore from studying Polynesians or ants; this might well be untrue, but that is also the case for learning something about language from LLMs, which according to Chomsky is apparently impossible and not even up for debate.
I came to comments to ask a question, but considering that it is two days old already, I will try to ask you in this thread.
What you think about his argument about “not being able to distinguish possible language from impossible”?
And why is it inherent in ML design?
Does he assume that there could be such an instrument/algorithm that could do that with a certainty level higher than LLM/some ml model?
I mean, certainly they can be used to make a prediction/answer to this question, but he argues that this answer has no credibility? I mean, LLM is literally a model, ie probability distribution over what is language and what is not, what gives?
Current models are probably tuned more “strictly” to follow existing languages closely, ie that will say “no-no” to some yet-unknown language, but isn’t this improvable in theory?
Or is he arguing precisely that this “exterior” is not directly correlated with “internal processes and faculties” and cannot make such predictions in principle?
All this interview proves is that Chomsky has fallen far, far behind how AI systems work today and is retreating to scoff at all the progress machine learning has achieved. Machine learning has given rise to AI now. It can't explain itself from principles or its architecture. But you couldn't explain your brain from principles or its architecture, you'd need all of neuroscience to do it. Because the brain is digital and (probably) does not reason like our brains do, it somehow falls short?
While there's some things in this I find myself nodding along to in this, I can't help but feel it's an a really old take that is super vague and hand-wavy. The truth is that all of the progress on machine learning is absolutely science. We understand extremely well how to make neural networks learn efficiently; it's why the data leads anywhere at all. Backpropagation and gradient descent are extraordinarily powerful. Not to mention all the "just engineering" of making chips crunch incredible amounts of numbers.
Chomsky is extremely ungenerous to the progress and also pretty flippant about what this stuff can do.
I think we should probably stop listening to Chomsky; he hasn't said anything here that he hasn't already say a thousand times for decades.
> Not to mention all the "just engineering" of making chips crunch incredible amounts of numbers.
Are LLM's still the same black box as they were described as a couple years ago? Are their inner workings at least slightly better understood than in the past?
Running tens of thousands of chips crunching a bajillion numbers a second sounds fun, but that's not automatically "engineering". You can have the same chips crunching numbers with the same intensity just to run an algorithm to run a large prime number. Chips crunching numbers isn't automatically engineering IMO. More like a side effect of engineering? Or a tool you use to run the thing you built?
What happens when we build something that works, but we don't actually know how? We learn about it through trial and error, rather than foundational logic about the technology.
Sorta reminds me of the human brain, psychology, and how some people think psychology isn't science. The brain is a black box kind of like a LLM? Some people will think it's still science, others will have less respect.
This perspective might be off base. It's under the assumption that we all agree LLM's are a poorly understood black box and no one really knows how they truly work. I could be completely wrong on that, would love for someone else to weigh in.
Separately, I don't know the author, but agreed it reads more like a pop sci book. Although I only hope to write as coherently as that when I'm 96 y/o.
> Running tens of thousands of chips crunching a bajillion numbers a second sounds fun, but that's not automatically "engineering".
Not if some properties are unexpectedly emergent. Then it is science. For instance, why should a generic statistical model be able to learn how to fill in blanks in text using a finite number of samples? And why should a generic blank-filler be able to produce a coherent chat bot that can even help you write code?
Some have even claimed that statistical modelling shouldn't able to produce coherent speech, because it would need impossible amounts of data, or the optimisation problem might be too hard, or because of Goedel's incompleteness theorem somehow implying that human-level intelligence is uncomputable, etc. The fact that we have a talking robot means that those people were wrong. That should count as a scientific breakthrough.
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> But you couldn't explain your brain from principles or its architecture, you'd need all of neuroscience to do it
That's not a good argument. Neuroscience was constructed by (other) brains. The brain is trying to explain itself.
> The truth is that all of the progress on machine learning is absolutely science.
But not much if you're interested in finding out how our brain works, or how language works. One of the interesting outcomes of LLMs is that there apparently is a way to represent complex ideas and their linguistic connection in a (rather large) unstructured state, but it comes without thorough explanation or relation to the human brain.
> Chomsky is [...] pretty flippant about what this stuff can do.
True, that's his style, being belligerently verbose, but others have been pretty much fawning and drooling over a stochastic parrot with a very good memory, mostly with dollar signs in their eyes.
> but others have been pretty much fawning…
This is not relevant. An observer who deceives for purposes of “balancing” other perceived deceptions is as untrustworthy and objectionable as one who deceives for other reasons.
> [...] I can't help but feel it's an a really old take [...]
To be fair the article is from two years ago, which when talking about LLMs in this age arguably does count as "old", maybe even "really old".
I think GPT-2 (2019) was already strong enough argument for possibility of modeling knowledge and language that Chomsky rejected.
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"I think we should probably stop listening to Chomsky"
I've been saying this my whole life, glad it's finally catching on
Why? He's made significant contributions to political discourse and science.
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It really shouldn't be hard to understand that a titan of a field has forgot more than what an arm chair enthusiast knows.
I remember having thoughts like this until I listened to him talk on a podcast for 3 hours about chatGPT.
What was most obvious is Chomsky really knows linguistics and I don't.
"What Kind of Creatures Are We?" is good place to start.
We should take having Chomsky still around to comment on LLMs as one of the greatest intellectual gifts.
Much before listening to his thoughts on LLMs was me projecting my disdain for his politics.
Perhaps it should be mentioned that he is 96 years old.
Wow, he is, isn’t he. I hope I’m this coherent when I’m 96.
> The truth is that all of the progress on machine learning is absolutely science
It is not science, which is the study of the natural world. You are using the word "science" as an honorific, meaning something like "useful technical work that I think is impressive".
The reason you are so confused is that you can't distinguish studying the natural world from engineering.
LLMs certainly aren't science. But there is a "science of LLMs" going on in, e.g., the interpretability work by Anthropic.
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Reminds me of SUSY, string theory, the standard model, and beyond that, string theory etc…
What is elegant as a model is not always what works, and working towards a clean model to explain everything from a model that works is fraught, hard work.
I don’t think anyone alive will realize true “AGI”, but it won’t matter. You don’t need it, the same way particle physics doesn’t need elegance
That was a weird ride. He was asked whether AI will outsmart humans and went on a rant about philosophy of science seemingly trying to defend the importance of his research and culminated with some culture war commentary about postmodernism.
There are lots of stories about Chomsky ranting and wielding his own disciplinary authority to maintain himself as center of the field.
It’s time to stop writing in this elitist jargon. If you’re communicating and few people understands you, then you’re a bad communicator. I read the whole thing and thought: wait, was there a new thought or interesting observation here? What did we actually learn?
I have problems with Noam Chomsky, but certainly none with his ability to communicate. He is a marvel at speaking extemporaneously in a precise and clear way.
Where do you see 'elitist jargon'? That didn't even cross my mind.
Chomsky's own words.
https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chat...
Most likely not. This is one of his weird pieces co-authored with Jeffrey Watamull. I don’t doubt that he put his name on it voluntarily, but it reads much more like Watamull than Chomsky. The views expressed in the interview we’re commenting on are much more Chomsky-like.
He explicitly says he didn't write it in this article:
"NC: Credit for the article should be given to the actual author, Jeffrey Watumull, a fine mathematician-linguist-philosopher. The two listed co-authors were consultants, who agree with the article but did not write it."
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Chomsky’s notion is: LLMs can only imitate, not understand language. But what exactly is understanding? What if our „understanding“ is just unlocking another level in a model? Unlocking a new form of generation?
> But what exactly is understanding?
He alludes to quite a bit here - impossible languages, intrinsic rules that don’t actually express in the language, etc - that leads me to believe there’s a pretty specific sense by which he means “understanding,” and I’d expect there’s a decent literature in linguistics covering what he’s referring to. If it’s a topic of interest to you, chasing down some of those leads might be a good start.
(I’ll note as several others have here too that most of his language seems to be using specific linguistics terms of art - “language” for “human language” is a big tell, as is the focus on understanding the mechanisms of language and how humans understand and generate languages - I’m not sure the critique here is specifically around LLMs, but more around their ability to teach us things about how humans understand language.)
I have trouble with the notion "understanding". I get the usefulness of the word, but I don't think that we are capable to actually understand. I also think that we are not even able to test for understanding - a good imitation is as good as understanding. Also, understanding has limits. In school, they often say on class that you should forget whatever you have been taught so far, because this new layer of knowledge that they are about to teach you. Was the previous knowledge not "understanding" then? Is the new one "understanding"?
If we define "understanding" like "useful", as in, not an innate attribute, but something in relation to a goal, then again, a good imitation, or a rudimentary model can get very far. ChatGPT "understood" a lot of things I have thrown at it, be that algorithms, nutrition, basic calculations, transformation between text formats, where I'm stuck in my personal development journey, or how to politely address people in the email I'm about to write.
>What if our „understanding“ is just unlocking another level in a model?
I believe that it is - that understanding is basically an illusion. Impressions are made up from perceptions and thinking, and extrapolated over the unknown. And just look how far that got us!
Actually no. Chomsky has never really given a stuff about Chinese Room style arguments about whether computers can “really” understand language. His problem with LLMs (if they are presented as a contribution to linguistic science) is primarily that they don’t advance our understanding of the human capacity for language. The main reasons for this are that (i) they are able to learn languages that are very much unlike human languages and (ii) they require vastly more linguistic data than human children have access to.
> But what exactly is understanding?
I would say that it is to what extent your mental model of a certain system is able to make accurate predictions of that system's behavior.
Understanding is probably not much more than making abstractions into simpler terms until you are left with something one can relate to by intuition or social consensus.
Transforming, in other words.
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He should just surrender and give chatgpt whatever land it wants.
Manufactured intelligence to modulate a world of manufactured consent!
I agree with the rest of these comments though, listening to Chomsky wax about the topic-du-jour is a bit like trying to take lecture notes from the Swedish Chef.
>"bit like trying to take lecture notes from the Swedish Chef."
I'll be liberally borrowing, and using that simile! It's hilarious. Bork, bork, bork!
The best thing is you can be right, and the other side can't take offense. It's the Muppets after all. It's brilliant!
Dude is 96, so he definitely has a different perspective than most, for better or worse.
I think many people are missing the core of what Chomsky is saying. It is often easy to miscommunicate and I think this is primarily what is happening. I think the analogy he gives here really helps emphasize what he's trying to say.
If you're only going to read one part, I think it is this:
It is easy to look at metrics of performance and call things solved. But there's much more depth to these problems than our abilities to solve some task. It's not about just the ability to do something, the how matters. It isn't important that we are able to do better at navigating than birds or insects. Our achievements say nothing about what they do.
This would be like saying we developed a good algorithm only my looking at it's ability to do some task. Certainly that is an important part, and even a core reason for why we program in the first place! But its performance tells us little to nothing about its implementation. The implementation still matters! Are we making good uses of our resources? Certainly we want to be efficient, in an effort to drive down costs. Are there flaws or errors that we didn't catch in our measurements? Those things come at huge costs and fundamentally limit our programs in the first place. The task performance tells us nothing about the vulnerability to hackers nor what their exploits will cost our business.
That's what he's talking about.
Just because you can do something well doesn't mean you have a good understanding. It's natural to think the two relate because understanding improves performance that that's primarily how we drive our education. But this is not a necessary condition and we have a long history demonstrating that. I'm quite surprised this concept is so contentious among programmers. We've seen the follies of using test driven development. Fundamentally, that is the same. There's more depth than what we can measure here and we should not be quick to presume that good performance is the same as understanding[0,1]. We KNOW this isn't true[2].
I agree with Chomsky, it is laughable. It is laughable to think that the man in The Chinese Room[3] must understand Chinese. 40 years in, on a conversation hundreds of years old. Surely we know you can get a good grade on a test without actually knowing the material. Hell, there's a trivial case of just having the answer sheet.
[0] https://www.reddit.com/r/singularity/comments/1dhlvzh/geoffr...
[1] https://www.youtube.com/watch?v=Yf1o0TQzry8&t=449s
[2] https://www.youtube.com/watch?v=hV41QEKiMlM
[3] https://en.wikipedia.org/wiki/Chinese_room
"Expert in (now-)ancient arts draws strange conclusion using questionable logic" is the most generous description I can muster.
Quoting Chomsky:
> These considerations bring up a minor problem with the current LLM enthusiasm: its total absurdity, as in the hypothetical cases where we recognize it at once. But there are much more serious problems than absurdity.
> One is that the LLM systems are designed in such a way that they cannot tell us anything about language, learning, or other aspects of cognition, a matter of principle, irremediable... The reason is elementary: The systems work just as well with impossible languages that infants cannot acquire as with those they acquire quickly and virtually reflexively.
Response from o3:
LLMs do surface real linguistic structure:
• Hidden syntax: Attention heads in GPT-style models line up with dependency trees and phrase boundaries—even though no parser labels were ever provided. Researchers have used these heads to recover grammars for dozens of languages.
• Typology signals: In multilingual models, languages that share word-order or morphology cluster together in embedding space, letting linguists spot family relationships and outliers automatically.
• Limits shown by contrast tests: When you feed them “impossible” languages (e.g., mirror-order or random-agreement versions of English), perplexity explodes and structure heads disappear—evidence that the models do encode natural-language constraints.
• Psycholinguistic fit: The probability spikes LLMs assign to next-words predict human reading-time slow-downs (garden-paths, agreement attraction, etc.) almost as well as classic hand-built models.
These empirical hooks are already informing syntax, acquisition, and typology research—hardly “nothing to say about language.”
> LLMs do surface real linguistic structure...
It's completely irrelevant because the point he's making is that LLMs operate differently from human languages as evidenced by the fact that they can learn language structures that humans cannot learn. Put another way, I'm sure you can point out an infinitude of similarities between human language faculty and LLMs but it's the critical differences that make LLMs not useful models of human language ability.
> When you feed them “impossible” languages (e.g., mirror-order or random-agreement versions of English), perplexity explodes and structure heads disappear—evidence that the models do encode natural-language constraints.
This is confused. You can pre-train an LLM on English or an impossible language and they do equally well. On the other hand humans can't do that, ergo LLMs aren't useful models of human language because they lack this critical distinctive feature.
Is that true? This paper claims it is not.
https://arxiv.org/abs/2401.06416
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> You can pre-train an LLM on English or an impossible language and they do equally well
It's impressive that LLMs can learn languages that humans cannot. In what frame is this a negative?
Separately, "impossible language" is a pretty clear misnomer. If an LLM can learn it, it's possible.
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What's dangerous about him?
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Insect behaviour. Flight of birds. Turtle navigation. A footballer crossing the field to intercept a football.
This is what Chomsky always wanted ai to be... especially language ai. Clever solutions to complex problems. Simple once you know how they work. Elegant.
I sympathize. I'm a curious human. We like elegant, simple revelations that reveal how out complex world is really simple once you know it's secrets. This aesthetic has also been productive.
And yet... maybe some things are complicated. Maybe LLMs do teach us something about language... that language is complicated.
So sure. You can certainly critique "ai blogosphere" for exuberance and big speculative claims. That part is true. Otoh... linguistics is one of the areas that ai based research may turn up some new insights.
Overall... what wins is what is most productive.
> Maybe LLMs do teach us something about language... that language is complicated.
It certainly teaches us many things. But an LLM trained on as many words (or generally speaking an AI trained on sounds) in similar quantities of a toddler learning to understand, parse and apply language, would not perform well with current architectures. They need orders of magnitude more training material to get even close. Basically, current AI learns slowly, but of course it’s much faster in wall clock time because it’s all computer.
What I mean is: what makes an ALU (CPU) better than a human at arithmetic? It’s just faster and makes fewer errors. Similarly, what makes Google or Wikipedia better than an educated person? It’s just storing and helping you access stored information, it’s not magic (anymore). You can manually do everything mechanically, if you’re willing to waste the time to prove a point.
An LLM does many things better than humans, but we forget they’ve been trained on all written history and have hundreds of billions of parameters. If you compare what an LLM can do with the same amount of training to a human, the human is much better even at picking up patterns – current AIs strongest skill. The magic comes from the unseen vast amounts of training data. This is obvious when using them – stray just slightly outside of the training zone to unfamiliar domains and ”ability” drops rapidly. The hard part is figuring out these fuzzy boundaries. How far does interpolating training data get you? What are the highest level patterns are encoded in the training data? And most importantly, to what extent do those patterns apply to novel domains?
Alternatively, you can use LLMs as a proxy for understanding the relationship between domains, instead of letting humans label them and decide the taxonomy. One such example is the relationship between detecting patterns and generating text and images – it turns out to be more or less reversible through the same architecture. More such remarkable similarities and anti-similarities are certainly on the horizon. For instance, my gut feeling says that small talk is closer to driving a car but very different from puzzle solving. We don’t really have a (good) taxonomy over human- or animal brain processes.
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From some Googling and use of Claude (and from summaries of the suggestively titled "Impossible Languages" by Moro linked from https://en.wikipedia.org/wiki/Universal_grammar ), it looks like he's referring to languages which violate the laws which constrain the languages humans are innately capable of learning. But it's very unclear why "machine M is capable of learning more complex languages than humans" implies anything about the linguistic competence or the intelligence of machine M.
Firstly, can't speak for Chomsky.
In this article he is very focused on science and works hard to delineate science (research? deriving new facts?) from engineering (clearly product oriented). In his opinion ChatGPT falls on the engineering side of this line: it's a product of engineering, OpenAI is concentrating on marketing. For sure there was much science involved but the thing we have access to is a product.
IMHO Chomsky is asking: while ChatGPT is a fascinating product, what is it teaching us about language? How is it advancing our knowledge of language? I think Chomsky is saying "not much."
Someone else mentioned embeddings and the relationship between words that they reveal. Indeed, this could be a worthy area of further research. You'd think it would be a real boon when comparing languages. Unfortunately the interviewer didn't ask Chomsky about this.
It doesn't, it just says that LLMs are not useful models of the human language faculty.
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As much as I think of Chomsky - his linguistics approach is outside looking in, ie observational speculation compared to the last few years of LLM based tokenization semantic spaces, embedding, deep learning and mechanistic interpretation, ie:
Understanding Linguistics before LLMs:
“We think Birds fly by flapping their wings”
Understanding Linguistics Theories after LLMs:
“Understanding the physics of Aerofoils and Bernoulli’s principle mean we can replicate what birds do”
...for the lulz try asking ChatGPT "what is Chomsky (still) good for?"
> The world’s preeminent linguist Noam Chomsky, and one of the most esteemed public intellectuals of all time, whose intellectual stature has been compared to that of Galileo, Newton, and Descartes, tackles these nagging questions in the interview that follows.
By whom?
Google Scholar
That is unbelievable that someone could glaze someone this hard
People who particularly agreed with Chomsky's inherently politicized beliefs, presumably.
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In all seriousness tho, not much of anything he says is taken seriously in an academic sense any more. Univeral Grammar, Minimalism, etc. He's a very petty dude. The reason he doesn't engage with GPT is because it suggests that linguistic learning is unlike a theory he spent his whole life [unsuccessfully] promoting, but he's such a haughty know-it-all, that I guess dummies take that for intelligence? It strikes me as not dissimilar to Trump in a way, where arrogance is conflated with strength, intelligence, etc. Fake it til you make it, or like, forever, I guess.
The comparison to Trump seems very unfair. I'm not in the academy and didn't know the current standing of his work, but he was certainly a big name that popped up everywhere (as a theorists in the field, not as a general celebrity) when I took an introduction to linguistics 20+ years ago.
As this is Hacker News, it is worth mentioning that he developed the concept of context-free grammars. That is something many of us encounter on a regular basis.
No matter what personality flaws he might have and how misguided some of his political ideas might be, he is one of the big thinkers of the 20th century. Very much unlike Trump.
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> Please summarize the linked text
Please don't post HN comments that are just giant walls of LLM copypasta.
I shortened it. I think this is one instance where it's actually relevant. The "opinion" of an LLM about a LLM criticism from one the leading linguists in history.
It got flagged, but I feel the flagging was knee-jerk and failed to understand the irony in the context.
Schrodinger's HN: LLM's are really great. No don't make me read it!
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What does age have to do with understanding any of this? He has been developing new, and refining old theories, over decades. It's ridiculous to expect someone to stop purely because of age, or to think they need your protection from discussing their views.
Huh, cool - these papers are from 2023, same year as the article.
https://arxiv.org/abs/2311.06189
https://arxiv.org/abs/2306.10270
https://arxiv.org/abs/2305.18278
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I care what one of the most famous philosophers and thinkers of our times says. He's not the most up to date, but calling him an idiot positions you politically and intellectually.
I agree, why did you decide to comment anyway then?
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I confess my opinion of Noam Chomsky dropped a lot from reading this interview. The way he set up a "Tom Jones" strawman and kept dismissing positions using language like "we'd laugh", "total absurdity", etc. was really disappointing. I always assumed that academics were only like that on reddit, and in real life they actually made a serious effort at rigorous argument, avoiding logical fallacies and the like. Yet here is Chomsky addressing a lay audience that has no linguistics background, and instead of even attempting to summarize the arguments for his position, he simply asserts that opposing views are risible with little supporting argument. I expected much more from a big-name scholar.
"The first principle is that you must not fool yourself, and you are the easiest person to fool."
Havent read the interview, but interviews arent formal debates and I would never expect someone to hold themselves to that same standard.
The same way that reddit comments arent a formal debate.
Mocking is absolutely useful. Sometimes you debate someone like graham hancock and force him to confirm that he has no evidence for his hypotheses, then when you discuss the debate, you mock him relentlessly for having no evidence for his hypotheses.
> Yet here is Chomsky addressing a lay audience that has no linguistics background
So not a formal debate or paper where I would expect anyone to hold to debate principles.
"Tom Jones" isn't a strawman, Chomsky is addressing an actual argument in a published paper from Steven Piantadosi. He's using a pseudonym to be polite and not call him out by name.
> instead of even attempting to summarize the arguments for his position..
He makes a very clear, simple argument, accessible to any layperson who can read. If you are studying insects what you are interested in is how insects do it not what other mechanisms you can come up with to "beat" insects. This isn't complicated.
>The systems work just as well with impossible languages that infants cannot acquire as with those they acquire quickly and virtually reflexively.
Where is the research on impossible language that infants can't acquire? A good popsci article would give me leads here.
Even assuming Chomsky's claim is true, all it shows is that LLMs aren't an exact match for human language learning. But even an inexact model can still be a useful research tool.
>That’s highly unlikely for reasons long understood, but it’s not relevant to our concerns here, so we can put it aside. Plainly there is a biological endowment for the human faculty of language. The merest truism.
Again, a good popsci article would actually support these claims instead of simply asserting them and implying that anyone who disagrees is a simpleton.
I agree with Chomsky that the postmodern critique of science sucks, and I agree that AI is a threat to the human race.
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That's understandable but irrelevant. Only a few people have major interest in how humans think exactly. But nearly everyone is hang on the question if the LLMs could think better.
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Is it polite to deprive readers of context necessary to understand what the speaker is talking about? I was also very confused by that part and I had no idea whom or what he was talking about or why he even started taking about that.
I searched for an actual paper by that guy because you’ve mentioned his real name. I found “Modern language models refute Chomsky’s approach to language”. After reading it seems even more true that Chomsky’s Tom Jones is a strawman.
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There's a reason Max Planck said science advances one funeral at a time. Researches spend their lives developing and promoting the ideas they cut their teeth on (or in this case developed himself) and their view of what is possible becomes ossified around these foundational beliefs. Expecting him to be flexible enough in his advanced age to view LLMs with a fresh perspective, rather than strongly informed by his core theoretical views is expecting too much.
I'm noticing that leftists overwhelmingly toe the same line on AI skepticism, which suggests to me an ideological motivation.
Chomsky's problem here has nothing to do with his politics, but unfortunately a lot to do with his long-held position in the Nature/Nurture debate - a position that is undermined by the ability of LLMs to learn language without hardcoded grammatical rules:
https://psychologywriting.com/skinner-and-chomsky-on-nature-...
I don't see how the two things are related. Whether acquisition of human language is nature or nurture - it is still learning of some sort.
Yes, maybe we can reproduce that learning process in LLMs, but that doesn't mean the LLMs imitate only the nurture part (might as well be just finetuning), and not the nature part.
An airplane is not an explanation for a bird's flight.
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> AI skepticism
Isn't AI optimism an ideological motivation? It's a spectrum, not a mental model.
Whether one expects AI to be powerful or weak should have nothing to do with political slant, but here it seems to inform the opinion. It begs the question: what do they want to be true? The enemy is both too strong and too weak.
They're firmly on one extreme end of the spectrum. I feel as though I'm somewhere in between.
Leftists and intellectuals overlap a lot. LLM text must be still full of six fingered hands to many of them.
For Chomsky specifically, the entire existence of LLM, however it's framed, is a massive middle finger to him and a strike-through on a large part of his academic career. As much as I find his UG theory and its supporters irritating, it might be felt a bit unfair to someone his age.
99%+ of humans on this planet do not investigate an issue, they simply accept a trusted opinion of an issue as fact. If you think this is a left only issue you havent been paying attention.
Usually what happens is the information bubble bursts, and gets corrected, or it just fades out.
Then you obviously didn't listen to a word Chomsky has said on the subject.
I was quite dismissive of him on LLMs until I realized the utter hubris and stupidity of dismissing Chomsky on language.
I think it was someone asking if he was familiar with the Wittgenstein Blue and Brown books and of course because he as already an assistant professor at MIT when they came out.
I still chuckle at my own intellectual arrogance and stupidity when thinking about how I was dismissive of Chomsky on language. I barely know anything and I was being dismissive of one of unquestionable titans and historic figures of a field.
Chomsky has been colossally wrong on universal grammar.
https://www.scientificamerican.com/article/evidence-rebuts-c...
But at least he admits that:
https://dlc.hypotheses.org/1269#
This is a great way to remove any nuance and chance of learning from a conversation. Please don't succumb to black-and-white (or red-and-blue) thinking, it's harmful to your brain.
You're projecting.
Or an ideological alignment of values. Generative AI is strongly associated with large corporations that are untrusted (to put it generously) by those on the left.
An equivalent observation might be that the only people who seem really, really excited about current AI products are grifters who want to make money selling it. Which looks a lot like Blockchain to many.
I think viewing the world as either leftist or right wing is rather limiting philosophy and way to go through life. Most people are a lot more complicated than that.
I have experienced this too. It's definitely part of the religion but I'm not sure why tbh. Maybe they equate it with like tech is bad mkay, which, looking at who leads a lot of the tech companies, is somewhat understandable, altho very myopic.
I see this as much more of a hackers vs. corporations ideological split. Which imperfectly maps to leftism vs conservatism.
The perception on the left is that once again, corporations are foisting products on us that nobody wants, with no concern for safety, privacy, or respect for creators.
For better or worse, the age of garage-tech is mostly dead and Tech has become synonymous with corporatism. This is especially true with GenAI, where the resources to construct a frontier model (or anything remotely close to it) are far outside what a hacker can afford.
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It is unfortunate opinion, because I personally hold Chomsky in fairly high regard and give most of his thoughts I am familiar with a reasonable amount of consideration if only because he could, I suppose in the olden days now, articulate his points well and make you question your own thought process. This no longer seems to be the case though as I found the linked article somewhat difficult to follow. I suppose age can get to anyone.
Not that I am an LLM zealot. Frankly, some of the clear trajectory it puts humans on makes me question our futures in this timeline. But even if I am not a zealot, but merely an amused, but bored middle class rube, the serious issues with it ( privacy, detailed personal profiling that surpasses existing systems, energy use, and actual power of those who wield it ), I can see it being implemented everywhere with a mix of glee and annoyance.
I know for a fact it will break things and break things hard and it will be people, who know how things actually work that will need to fix those.
I will be very honest though. I think Chomsky is stuck in his internal model of the world and unable to shake it off. Even his arguments fall flat, because they don't fit the domain well. It seems like they should given that he practically made his name on syntax theory ( which suggests his thoughts should translate well into it ) and yet.. they don't.
I have a minor pet theory on this, but I am still working on putting it into some coherent words.
I recently saw a new LLM that was fooled by "20 pounds of bricks vs 20 feathers". These are not reasoning machines.
I recently had a computer tell me that 0.1 + 0.2 != 0.3. It must not be a math capable machine.
Perhaps it is more important to know the limitations of tools rather than dismiss their utility entirely due to the existence of limitations.
A computer isn't a math capable machine.
> Perhaps it is more important to know the limitations of tools rather than dismiss their utility entirely due to the existence of limitations.
Well, yes. And "reasoning" is only something LLMs do coincidentally, to their function as sequence continuation engines. Like performing accurate math on rationale numbers, it can happen if you put in a lot of work and accept a LOT of expensive computation. Even then there exists computations that just are not reasonable or feasible.
Reminding folks to dismiss the massive propaganda engine pushing this bubble isn't "dismissing their utility entirely".
These are not reasoning machines. Treating them like they are will get you hurt eventually.
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Surely it just reasoned that you made a typo and "autocorrected" your riddle. Isn't this what a human would do? Though to be fair, a human would ask you again to make sure they heard you correctly. But it would be kind of annoying if you had to verify every typo when using an LLM.
Tons of people fall for this too. Are they not reasoning? LLMs can also be bad reasoning machines.
I dont have much use for a bad reasoning machine.
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But are you aware of the weight comparison of a gallon of water vs a gallon of butane ?
No im not. A gallon is a measure of volume? This is a USA unit.
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20 feathers?
Yes, Claude 4 Sonnet just said they both weight 20 pounds. UPD. and so did Gemini 2.5 Flash. And MS Copilot in "Think deeper" mode.
[Edit to remove: It was not clear that this was someone else's intro re-posted on Chomsky's site]
This is an interview published in Common Dreams, rehosted at Chomsky's site. Those are the interviewer's words, not Chomsky's.
Okay, I didn't know that. I'll delete my comment.
Maybe I am missing context, but it seems like he’s defending himself from the claim that we shouldn’t bother studying language acquisition and comprehension in humans because of LLM’s?
Who would make such a claim? LLM’s are of course incredible, but it seems obvious that their mechanism is quite different than the human brain.
I think the best you can say is that one could motivate lines of inquiry in human understanding, especially because we can essentially do brain surgery on an LLM in action in a way that we can’t with humans.
Steven Piantadosi makes that argument.
> It’s as if a biologist were to say: “I have a great new theory of organisms. It lists many that exist and many that can’t possibly exist, and I can tell you nothing about the distinction.”
> Again, we’d laugh. Or should.
Should we? This reminds me acutely of imaginary numbers. They are a great theory of numbers that can list many numbers that do 'exist' and many that can't possibly 'exist'. And we did laugh when imaginary numbers were first introduced - the name itself was intended as a derogatory term for the concept. But who's laughing now?
Imaginary numbers are not relevant at all. There’s nothing whatsoever to do with the everyday use of the word imaginary. They could just as easily have been called “vertical numbers” and real numbers called “horizontal numbers” in order to more clearly illustrate their geometric interpretation in the complex plane.
The term “imaginary number” was coined by Rene Descartes as a derogatory and the ill intent behind his term has stuck ever since. I suspect his purpose was theological rather than mathematical and we are all the worse for it.
I'm confused by this comment - it seems to just be restating what my comment said.
This is the point where i realized he has no clue what he is saying. Theres so many creatures that once existed that can never again exist on earth due to the changes that the planet has gone through over millions, billions of years. The oxygen rich atmosphere that supported the dinosaurs for instance. If we had some kind of system that can put together proper working DNA for all the creatures that ever actually existed on this planet, some half of them would be completely nonviable if introduced to the ecosystem today. He is failing to see that there is an incredible understanding of systems that we are producing with this work, but he is a very old man from a very different time and contrarianism is often the only way to look smart or reasoned when you have no clue whats actually going on, so I am not shocked by his take.
In the case of complex numbers mathematicians understand the distinction extremely well, so I'm not sure it's a perfect analogy.
I have a degree in linguistics. We were taught Chomsky’s theories of linguistics, but also taught that they were not true. (I don’t want to say what university it was since this was 25 years ago and for all I know that linguistics department no longer teaches against Chomsky). The end result is I don’t take anything Chomsky says seriously. So, it is difficult for me to engage with Chomsky’s ideas.
I'm rather confused by this statement. I've read a number of Chomsky pieces and have listened to him speak a number of times. To say his theories were all "not true" seems, to an extent, almost impossible.
Care to expand on how his theories can be taught in such a binary way?
GP may be referring to the idea that language is innate like an organ in the body/brain. The Kingdom of Speech by Tom Wolfe is a great read exploring Chomsky and other thinkers in this realm. It would have been great to see what he thought of LLMs too.
Generally what people are talking about are his universal grammar or generative syntax theories/approaches, which are foundational to how you approach many topics. Because you build your academic career based on specialization they are hotly contested (for the material reasons of jobs, funding, tenure, etc.).
This leads to people who agree hiring each other and departments ‘circling the wagon’ on these issues. You’ll see this referred to as east vs west coast, but it’s not actually that clearly geographically delineated.
So anyways, these are open questions that people do seriously discuss and study, but the politics of academia make it difficult and unfortunately this often trickles down to students.
I’m speaking about his linguistic theories such as universal grammar.
This reminds me of the debates over F.R. Leavis, and the impact it had on modern english teaching worldwide. There are a small dying cohort of english professors who are refugees from internecine warfare.
Same thing happened in Astronomy. Students of Fred Hoyle can't work in some institutions. &c &c.
I don't have a degree in linguistics, but I took a few classes about 15 years ago, and Chomsky's works were basically treated as gospel. Although my university's linguistics faculty included several of his former graduate students, so maybe there's a bias factor. In any case, it reminds me of an SMBC comic about how math and science advance over time [1]
[1] https://smbc-wiki.com/index.php/How-math-works
Linguistics has been largely subsumed by CS (LLM, speech synthesis, translation). It's not an empirical science or social science and most of its theories are not falsifiable.
But generally speaking Chomsky's ideas, and in particular, the Universal Grammar are no longer in vogue.
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Chomsky is always saying that LLMs and such can only imitate, not understand language. But I wonder if there is a degree of sophistication at which he would concede these machines exceed "imitation". If his point is that LLMs arrive at language in a way different than humans... great. But I'm not sure how he can argue that some kind of extremely sophisticated understanding of natural language is not embedded in these models in a way that, at this point, exceeds the average human. In all fairness, this was written in 2023, but given his longstanding stubbornness on this topic, I doubt it would make a difference.
I think what would "convince" Chomsky is more akin to the explainability research currently in it's infancy, producing something akin to a branch of information theory for language and thought.
Chomsky talks about how the current approach can't tell you about what humans are doing, only approximate it; the example he has given in the past is taking thousands of hours of footage of falling leaves and then training a model to make new leaf falling footage versus producing a model of gravity, gas mechanics for the air currents, and air resistance model of leaves. The later representation is distilled down into something that tells you about what is happening at the end of some scientific inquiry, and the former is a opaque simulation for engineering purposes if all you wanted was more leaf falling footage.
So I interpret Chomsky as meaning "Look, these things can be great for an engineering purpose but I am unsatisfied in them for scientific research because they do not explain language to me" and mostly pushing back against people implying that the field he dedicated much of his life to is obsolete because it isn't being used for engineering new systems anymore, which was never his goal.
I guess it's because LLM does not understand the meaning as you understand what you read or thought. LLMs are machines that modulate hierarchical positions, ordering the placement of a-signifying sign without a clue of the meaning of what they ordered (that's why machine can hallucinate :they don't have a sense of what they express)
From what I've read/watched of Chomsky he's holding out for something that truly cannot be distinguished from human no matter how hard you tried.
Isn’t that just a Turing test?
I’m perfectly willing to bet that there are LLMs that can pass a Turing test, even against a mind like Chomsky.
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I think that misses the point entirely. Even if you constructed some system the output of which could not be distinguished from human-produced language but that either (1) clearly operated according to principles other than those that govern human language or (2) operated according to principles that its creators could not adequately explain, it would not be of that much interest to him.
He wants to understand how human language works. If I get him right — and I'm absolutely sure that I don't in important ways — then LLMs are not that interesting because both (1) and (2) above are true of them.
It's always good to humble the ivory tower.
That's not quite a valid point considering the article's conclusion: sowing dissent in the sciences allows companies to more easily package and sell carcinogens like asbestos, lead paint, and tobacco products.
I understand his diction is a bit impenetrable but I believe the intention is to promote literacy and specificity, not just to be a smarty-pants.