Norvig (and Chomsky) are talking at cross purposes.
Norvig confuses engineering for science. If an engineer wants something that works (and guarantees that it will work) then you use the most effective tool available to achieve that goal. No problem there. But there's absolutely no reason to think that tells you anything scientific about the phenomenon you're modeling. For example, aeronautical engineering says very little about how birds fly. And - currently relevant - LLMs learn to use and understand language, but they use millions of times more training data, and millions of times more computing power, than a child does. So an LLM may be a great technology, but it's probably not a good model for how humans learn and use language.
Chomsky OTOH doesn't see any use for statistics in science, at least not in linguistics. Fortunately for the field of linguistics, Chomsky's influence in linguistics is waning and linguists are beginning to use statistical tools to do science.
I don’t think Norvig is confused. He explicitly addresses this point right at the top of the article.
His argument is that successfully engineering a system using a statistical method provides some bits of evidence for the hypothesis that the system itself is statistical.
Which is obviously true, so long as we take care to weigh evidence properly.
> there's absolutely no reason to think that tells you anything scientific about the phenomenon you're modeling
No, this is not how evidence works. If you can accurately model a system with method X and you cannot come up with a model that works with method Y, you should think it’s more likely (but not certain) that X is the actual physical model.
And indeed modern cognitive science (see Predictive Processing) also has a bunch of evidence for the brain as a hierarchical predictor, rather than a rule engine.
To your points - planes are not model organisms of birds, but they do shed insight on the bounds that birds must obey, eg thrust-to-weight etc. And the sample efficiency point really needs to die. The human brain is born with an extremely complex prior baked in. An LLM begins pretraining with almost nothing (just a very simple architecture and a few simple algorithms like attention), and has to learn the basic architecture of cognition from scratch. A better baseline would be how many samples were processed by every animal in our genetic ancestry that had a brain, since brains existed.
> No, this is not how evidence works. If you can accurately model a system with method X and you cannot come up with a model that works with method Y, you should think it’s more likely (but not certain) that X is the actual physical model.
Try to model radioactive decay via a process other than statistical and you will almost certainly fail, since that is our best understanding of it.
I was also going to suggest coin flips, but it’s not quite the same… I think any chaotic system is generally best modeled that way, and if you cared about the effect the system has on other things, it’s unlikely you can do better since measuring the initial state will not be feasible.
i agree. but it's uncanny that stochastic generation of language yields a form of recognizable intelligence.
in college, i was somewhat involved in cogsci wrt semantic mapping of linguistic concepts based on human surveys (this was well before LLM/AI-word2vec), and it surprises me today how the mathematical properties of relatedness/distance seem now to drive machine intelligence, something which we did not consider at the time at all.
maybe i'm getting in my metaphysical sentimental stage, a turn a lot of adults make after middle age, but i wouldn't fully dismiss the fact that there may be an equivalence.
Indeed. Who'd have thought that mere statistics could do that? Hat's off to them - they made the darn thing work.
There's pretty interesting (I think) research in how effectively it's possible to scale down LLMs. If you're interested, take a look at the BabyLM conferences:
(I have no connection to them.) The work there demonstrates that gap between what we can do now with human-relevant amounts of training text, and what remains to be discovered. Quite a bit, it looks like.
"Mere statistics" is how physical reality works; I don't mean the language, the syntax
I mean shifting influence of gravity relative to an object with enough mass type ratios are everywhere
The language of math started as visual art to depict such ratios; in modern times this ground truth associations is frequently overlooked or never understood to exist in the first place without engagement
Differential diagnosis in medicine is exactly the same process; such processes and their successes are everywhere.
The issue we have in software is the outcome isn't as obvious as jabbing a blood draw needle in a patients eye; we spent a lot of time wasting time on illusory needs
“Mere statistics” is heavily underselling what the transformer architecture does. Unless you think “mere ion channels” is a sensible way to think about the human brain.
> stochastic generation of language yields a form of recognizable intelligence.
Can you use a human language well enough and it not sound intelligent? And no, I don't mean just syntactically correct because semantics is a part of human languages too. I think the astounding thing is that they managed to model a language this well. It's like trying to learn English without a functional understanding of the word "Please".
My take is that a lot of the intelligence we natively ascribe to humans exist in human languages and just like the nutrients in your food, by harnessing a language that possesses latent intelligence, you too can behave like an intelligent person. And how did the nutrients of intellect got in there in the first place? Just like languages evolve nowadays: via contributions from speakers over time. Not unlike soil fertilisation.
And language itself is so very 'stochastic' too, due to complex subjectivity. Every word floats on a variable sea of individual choice.
For example, if I compose your post as :
--
"Good prose embodies intellect gradually evolved by myriad humans over time, so it wows me to see LLMs create it, though I soon realised that the intelligence is not being created by the LLM but rather is already present in the human-evolved input datasets - the prose itself! - which they statistically arrange."
--
..the particulars of the language are entirely different (and omit some of your chosen nuance) but most readers would receive the same "core truth" from it, the same measure of beneficial information.
Which highlights the great fluidity of language; that is another of the key underlying mechanisms allowing LLMs to flourish.
I think there is a distinction to draw there between stochasticity in the use and in the evolution of language (which is not clean as they are not independent). The language evolves stochastically, as it does so by being used in exchanges. But the use could be deterministic with the exchanges being random and the function of update based on exchange. I still think there is stochasticity in the use itself, I choose my words, go back and delete and rewrite, explain again in a different way when the audience looks at me unimpressed etc. I just don’t see fluidity as a proof of stochasticity of evolution or the other way around and in fact think the interaction is a quite interesting domain.
> i agree. but it's uncanny that stochastic generation of language yields a form of recognizable intelligence.
It's funny, I used to think it was complete and utter horseshit when literature professors would say that the meaning of a text is created by the reader, not the author.
Since LLMs have become commonplace, I have completely reversed my opinion on that.
That you see recognizable intelligence in the output of stochastic generation is because you are imposing meaning on it. It is why "claudish" is so hard to read--you are trying to impose meaning on a sequence of words that was stochastically generated, and it takes mental effort to do that.
If Claude can write formal mathematical proofs (that a machine can check), or it can write software that works, where's the head of the reader that imposes the meaning?
> you see recognizable intelligence in the output of stochastic generation is because you are imposing meaning on it
i am not an expert in any of this, but intuitively i largely feel the same... the character we see as "claude" is mostly in our heads; its almost like a "super rubber duck" phenomenon or something
I fail to see what exactly is explained, or clarified by the theory here. Postmodernist philosophers are notorious for writing texts which make readers struggle. Surely, it doesn't mean "Foucault-ese" is somehow more stochastically generated than "Claudese".
The existence of LLMs is telling us something, surely it means something. I'm not sure we've quite figured it out yet but its probably something along the lines of 'intelligence might not be as complicated as we thought' ... a fourth narcissistic blow to humanity after Copernicus, Darwin and Freud.
We train them on the sum of human knowledge available in digital format, so I'm not sure that's an indictment, and the large models are incredibly complicated to the point it's hard to reverse engineer how they work so well and what they're actually doing to build internal models to predict the next token. Several billion to several trillion parameters is anything but simple.
Right, we needed compute on a large and fast scale to make LLMs actually work. But the basic principles are quite simple: train on a huge dataset for long enough, using some fairly simple ML maths but across a very large dimensional space, and then out the other side you get something that can competently use language, and is capable of doing useful work. This is somewhat surprising, no?
The narcissism might not be in thinking 'intelligence might not be as complicated as we thought’ - the narcissism might be in thinking ‘LLM intelligence is the way we thought’.
It's also not the first time a breakthrough technology was compared to the human brain/mind, but we also know biologically animal nervous systems are not digital computers, telephone/telegraph networks or hydraulic systems.
sure, but its facile to dismiss these breakthroughs as naive metaphors. physicans do understand now many bodily systems can be modeled with the same tools an engineer will model his machines. its not because these abstractions are incomplete, they are wrong.
I'm not saying LLMs work in the same way as us. But a few years ago humans used to be the only competent users of language on the planet. Now its humans + LLMs who are competent users of langauge. This is a big change, and I'm saying that it might be pointing to something deeper about complexity and how approaches to deal with complexity arise in the universe.
> it's uncanny that stochastic generation of language yields a form of recognizable intelligence.
It most certainly does not. It's obvious to anyone who critically examines LLMs, without letting the hype override sense, that there's zero intelligence there.
To the cynic, Chomskyism is exactly what Thomas Kuhn warned you about "normal science".
That is, you can apply for grants and write papers in the transformational grammar paradigm endlessly. You can pose and answer questions that make sense within in the system.
Chomskyism was disappointing in two respects: (1) was that it was an inadequate basis for machine understanding of text, (2) was that structuralists were hoping everything in culture could be understood in terms of "... having the structure of language" but syntactic structures weren't all adequate for that task.
(1) and (2) both pointed to Chomskyism being hermetic and not something really useful or inspiring to other fields and maybe just a little less "true".
Actually, there have been a number of rounds of paradigm shift (or attempted paradigm shift) in linguistics over the past 70 years. The unique thing about linguistics is that they've been compressed into the lifetime of the same central figure.
Also, I'll mention that there is a lot of empirical knowledge about natural language syntax in generative grammar, even if the details aren't quite right about their theoretical underpinnings. And a lot of them were "low-hanging fruit" waiting for someone to point out how to find them and characterize them. That Chomsky did that is the best argument for his intellectual value - permitting all that "normal science" to happen.
Science doesn't guarantee technological applications, whether or not that science is correct.
Finally, just saying: use of a word like "Chomskyism" makes you sound like one of those creationists who call evolution "Darwinism".
> You can pose and answer questions that make sense within in the system.
Yes this is one of my critiques of the academic system (I have finished a PhD and will hopefully never work at a university ever again). Many papers in the introduction start with explaining how the study builds on plenty of previous research, but that alone doesn’t prove that something is a good direction to investigate. If academics were completely honest, many papers would write “We researched topic X because we got funding for it. But since we actually don’t care about X, we just wrote a proposal about our favorite niche topic that only one of the co-authors cares about, namely topic Y, and wrote it such that it sounds like X. Next we show that Y is actually very cool in the hope to convince others that it is cool plus to convince others of the conclusion that our most senior professor already found during the PhD which was 30 years ago. Apart from the professor, we don’t believe in the main argument of the paper but since he got the grant we’ll just go with it in the hope to one day be able to push our own ideas forward”.
> Norvig confuses engineering for science. If an engineer wants something that works (and guarantees that it will work) then you use the most effective tool available to achieve that goal. No problem there. But there's absolutely no reason to think that tells you anything scientific about the phenomenon you're modeling.
this distinction is superficial. Scientists use less precise models all the time. Like, you don't solve the schrodinger equation even when you're a scientist doing organic chemistry. You reach for lewis dot models. Science as a discipline is about building better models of the world, not uncovering fundamental truth.
Care to back this up? What evidence do we have to suggest that back propagation, self attention, and byte pair encoding are mechanistically similar to human cognition? Similarity of the outputs says nothing about similarity of the machinery underneath. Turboprop engines tell us nothing about how birds fly - doesn't matter that they successfully transport millions of people around the world.
I would say that when it comes to higher level cognition, logic engines are more human like even if they only have a tiny fraction of the practical utility of LLMs.
> Turboprop engines tell us nothing about how birds fly
That is not true.
Turboprop engines and flying birds are different things, but there is a set of common properties.
A blade of a screw propeller functions exactly in the same way as a flapping wing of a bird, except that the wing cannot rotate without limits around the body of the bird, so it must stop, change angles and revert the motion, while the blade can rotate continuously around the axis, functioning on one side like a falling wing and on the other side like a raising wing. The similarities between propeller blades and insect wings are even greater, because insect wings cannot change their shapes so much as those of birds.
For now, our understanding of the networks composed of the neurons of animals is much less advanced than our understanding of wings and propellers, which is why there is a much greater uncertainty about whether there are any similar mechanisms in biological neural networks and in LLMs.
Also, AFAIK, insects rely on their (flat) flapping wings to create lift by creating vortices.
"Nothing" may be a little too strong -- physics is physics, after all -- but (small) birds and airplanes have Reynolds numbers about 1000x different and different underlying material science.
Airplanes are engineered (with a very small number of exceptions) to be stable during flight, whereas bird flight is inherently unstable, requiring a lot of specialized cognition so birds can fly.
I would think a lot of cognitive ornithologists (who I really hope exist!) would be interested in finding out about this.
At this point, I'm at the complete end of my knowledge of bird flight!
We already know that the most basic mechanisms in ANN and actual neurons are very different. Spiking and neurotransmitters are basically everything in brains and don't exist in ANNs.
ok, compare a wheel to a human leg. If you look long enough you will find similarities, but you honestly cannot say that you "understand how humans walk" when all you can do is put a man in a wheelbarrow, move it, and say "look! my device accomplishes the same task!"
> A blade of a screw propeller functions exactly in the same way as a flapping wing of a bird, except that the wing cannot rotate without limits around the body of the bird
The "except" in your argument is doing an incredible amount of work. The rotation of a turboprops generates forward force, wings (whether a bird's or a plane's) generate lift. And plane's wings are called "wings" because they, and not the turboprop, mimic the structure and function of a bird's wings.
Regardless, this whole line of argument is missing the forest for the trees.
LLMs behave like they're closest, but only during execution. During training, obviously, they don't resemble the human brain at all. No human reads a million books before they begin to speak.
And I'd be hard pressed to identify many cognitive characteristics of LLMs during execution that resemble human thinking, aside from those that are readily parrotable.
What reason to we have to believe that LLMs are the closest model we have for how humans learn language.
We considered the hypothesis that humans are a general intelligence that figures language out by exposure. The evidence has consistently come back that that is not how humans actually work. A model where humans are hardwired with a lot of knowledge about language, then fill in the gaps based on exposure still appears to be far more accurate.
The only thing LLMs show is that it is possible to learn language de-novo.
>closest model we have for how humans learn language
Is there some other competing model which can give results as good? If not, the statement seems true in the trivial sense that it's the _only_ model which we've observed to give good results.
Humans decidedly take much more data than LLMs. Like orders of magnitude. We happen to feed only distilled data to LLMs though. That's why they appear so efficient.
If you are counting by bytes and including all sensory input, sure. In terms of amount of text, it's not close in the other direction. It's very difficult to make an apples to apples comparison here.
The BabyLM challenge has shown that multimodal models don't seem to learn in a more data-efficient way with respect to tokens. Adding an unlimited amount of images doesn't help you learn syntactic islands faster.
Not a specific entry, but in four years, there's never been a clear advantage of multimodal models and the results have been extremely lackluckster, even when you allow unbounded images alongside a fixed quantity of text.
I find it hard to see how Chomsky has a leg to stand on.
> I understand how Chomsky might arrive at the conclusion that probabilistic models are unnecessary, from his study of the generation of language.
I think the point turns here for me. We have probabilistic models now that generate unbelievably convincing language. Norvig's following argument on the value of interpreting language statistically becomes secondary.
In order to stay moored in reality, our models must follow from evidence. I think it would take a peculiar obstinance to deny that the latest models show evidence of generating language.
The models require orders of magnitude more data to learn language than children do, and children's task is harder as they have to segment speech themselves.
The comparison isn't so straightforward, since children's language-learning environment is multimodal and extremely rich. They're exposed not just to sequences of words, but also to behaviors, emotions, social interactions, and the physical world.
I think you just explained the situation, even if you didn't realize it.
LLMs process millions of times more text data and use millions of times more compute power to learn a language, mostly because we know how to do that technologically.
Children use much less text data, but also use a lot of other kinds of data, including interacting with other humans, to learn language. We have no idea how that works. And, on top of that, people can interact with pets of various species in the same way as children, but those pets never learn language.
So, something strange is going on that children can do, but that LLMs and puppies can't. Probably, part of it is that human children are much better at learning by copying what adults are doing, and part of it is that humans evolved to learn and use language. The details of what's going on there is what linguistics is about.
Yes, I believe arguments like GP vastly underestimate the amount of data involved in children's learning. All the data coming into their sensorium, from heard language to physical movement feedback, contributes to training all their skills, from physical movement to language.
A newborn brain is not a blank slate, it comes with a very rich prior (as Chomsky himself would argue), for example the ability to rapidly acquire language, control a body, etc.
An LLM must construct all of its grammatical structure, world models, etc. from scratch during pretraining.
LLM pre-training is more like recapitulating evolution of the mammalian brain than childhood development.
We could innately have built-in statistical models, then everyone can be happy. Given how well statistics seems to work for language acquisition the brain is probably approximating a statistical formula at some level.
The idea that there is some level of statistical inference involved in language acquisition is not contrary to generative thought, look at Charles Yang's work or Lidz or a fair number of other people. Chomsky just thinks this is uninteresting so he is dismissive of it as a central explanation for language acquisition.
The central question remains whether there are categorical rules built into the brain for grammar (plus "some" statistical inference possibly), or whether the entirety of our grammatical processing is statistical. It is entirely possible that evolution encoded statistical priors into our brains with grammar overall being a fully statistical process. Therefore, the fact that children learn more quickly isn't really hard evidence one way or another.
LLMs (at the autoregressive pretraining stage) are trained to predict what an (adult) human who could have said preceding words would have said next. You should add 15-30 years to the human training time.
Peter makes some excellent points in TFA and it’s a fun read.
But the situation has moved on, moving even further beyond the assertions Peter makes about the utility of the black-box generative approach.
In particular, LLMs are telling us a lot about those low-probability, never before seen sentences — it’s called CALU, the creative aspects of language use.
LLMs exhibit CALU; as we know they will happily create such sentences. And inspecting the LLMs does provide insight:
“It does change the epistemic situation substantially. A working system can be inspected, perturbed, retrained, lesioned, and compared with other systems. Building is not understanding, but it gives understanding a strong foothold.
Explaining creative language use in LLMs would not, by itself, explain creative language use in humans. But the artificial case matters even if the mechanisms turn out to be substantially different. At a minimum, LLMs are an existence proof of one pathway by which open-ended, context-sensitive language can arise.”
For much more, see this substack by Chandra Sripada, a U of Michigan linguist:
> But the vast majority of people who study interpretation tasks (such as speech recognition) quickly see that interpretation is an inherently probabilistic problem: given a stream of noisy input to my ears, what did the speaker most likely mean? Einstein said to make everything as simple as possible, but no simpler. Many phenomena in science are stochastic, and the simplest model of them is a probabilistic model
Why the "simplest" model? Is a probabilistic model not the only model able to deal with uncertainty? What other kind of models are possible to represent uncertainty and inherent ambiguity that are not probabilistic?
I don't think it's the simplest, actually. Off the top of my mind:
Ternary logic is a super simple model; where true AND unknown equals unknown, and true OR unknown equals true. This deals with uncertainty and ambiguity, just not the probability; notionally you could say everything is 50-50.
Same thing if you have e.g. text-to-speech which returns "the b??? dog" rather than "the (0.8:black, 0.1:blue, 0.1:boy) dog" - no probabilities. Of course, maybe later in the processing you add probabilities back in.
Then there's fuzzy logic, which uses scores that are typically numbers, but those don't express actual probabilities, but more like vibes. They don't necessarily add up to 1, for instance, and you don't apply Bayes' formula to them, or even multiply them, you can use addition too - yolo.
Edit: and also of course backtracking, beam search, that sort of stuff - needn't use probabilities, just heuristics.
That is true, but to me it seems like ternary logic is like probabilities in disguise (false, true and unknown would be probability of 0, 1, and "between 0 and 1"). For most application, the unknown class is too broad, thus we need actual probabilities. But indeed, ternary logic is simpler than working with probabilities.
Old school search methods allow you to pursue multiple paths, but not necessarily based on probability. At the end (or while pruning) you can use "word knowledge".
Anyway, the probabilistic model doesn't exist. Simple probabilistic models work quite badly on speech recognition (word classification, etc.). A lot of structure needs to be added before they start performing.
My objection is "simpler." What's so "simple" about a probabilistic model? It takes an enormous amount of calculations, so much that you have to predigest incoming information into high-level chunks in order to have any chance of calculating it over small inputs with reasonable effort.
A probabilistic model is the most complicated model. It doesn't even attempt to model the problem, it just looks at the results of an enormous number of tries with different inputs, goes through them all, finds the most similar cases to the current input, and blends them together in a way that carefully avoids any self-contradictory or absurd result.
We see this plainly with LLMs. You train an LLM on a thousand times the (high-level, pre-digested) input that the average human has gotten, and the average human thinks it's a hallucinating moron. It doesn't start to converge on something that passes the smell test to you average human idiot until it has been trained on a million times the input the average human has gotten.
It's not doing what a human does, it's guessing what a human (or any system) might do when confronted with X input. They're not even similar activities, even if they have identical output in the limit. It's like confusing gravity for equations about gravity. Being able to make perfect predictions about the positions of moving, nearby frictionless spherical things in a vacuum at time t doesn't tell us a thing about what gravity is. It tells us how it looks. It should, we distilled it by watching things for a very long time.
> Is a probabilistic model not the only model able to deal with uncertainty?
No, all models deal with uncertainty. The entire point of a model is that it is what you know about a system when you don't know its inputs. The variables in a model are uncertainty. The model is dealing with it.
edit: I think you're interpreting "uncertainty" as a numerical probability. A numerical probability is an artifact of the set of data you're calculating it from, not a thing with independent existence. "Uncertainty" is just a thing that you don't know yet.
If you assume that "uncertainty" is a knowable numeric probability that contributes to some calculation, you're begging the question when you say that the only way you can model it is through probabilistic calculations.
To me uncertainty on a given quantity, is represented by a probability distribution function giving how probable the value of a given quantity is. Loosely, it can also be represented by a confidence interval. It is not intrinsic to the quantity, but how much we know about this quantity.
For the domain I am working in, a model is considered deterministic if for a set of input it always provide the same output and the input are assumed to be perfectly known. A model become probabilistic, once the input or the model itself are considered as uncertain. As an example: previously weather models always provided singe prediction for tomorrow, for example rain or no rain. But now, such models are probabilistic in the sense that an ensemble of model forecast are made (with slightly different input representing our uncertainties) to provide an estimate of the probabilities. Indeed such probabilistic model is not the simplest, but to most complete and so far the only practical way to handle with uncertainty for system like the weather as far as I am aware of.
No empirical fact is restricted to a singular vector through human language.
"..if we think of truth as something of granite-like solidity and of personality as something of rainbow-like intangibility and reflect that the aim of [human writing] is to weld these two into one seamless whole, we shall admit that the problem is a stiff one and that we need not wonder if [human writers], for the most part failed to solve it." - Virginia Woolf
[human writing, human writers] = [biography, biographers] in the original quote. My edit is because I infer that her statements key point is that of the biological subjectivity inherent to all human prose.
For decades, Chomsky was the most-cited author who still had a pulse. And a lot of that was for his political writing - maybe 2/3rds? But he still has a lot of cites for his intellectual work, in several different fields.
Linguistics, obviously.
Philosophers love him, because he always gives them an argument, and that's all they really care about.
There probably wouldn't even be cognitive science without Chomsky. And psychology is still responding to Chomsky's nuking of behaviorism in the 1950's.
So, there's a lot there, but admittedly most of it is for politics.
Just look at his Wikipedia entry. He finished his dissertation in 1955, starting teaching at MIT the same year, and continued to publish seminal linguistic works over the next 12 years. That's all before he gained any notoriety whatsoever as a critic of the U.S.'s role in the Vietnam War.
Manufacturing Consent was published in 1988!
Additionally, I strongly doubt Chomsky had any "name recognition" all the way through to the late 90s. Outside of a single episode of Firing Line, where would the general public have heard his views?
Chomsky is incredibly dogmatic; his views and personality have held the field back.
There's enormous irony in Chomsky's mathematicization of linguistics sweeping away the linguists who came before him compared to his own stubborn refusal to acknowledge the fruits of the statistical approach. As far as I know, he's more or less been swept aside.
As an aside? The "I before E except after C" works well if the sound makes the long-EE sound. Which is believe was implied in it's original formulation. You obviously don't need the rule for "society" and "science".
Of older English words, there are extremely few genuine exceptions. Seize is one.
It's more or less the classic clash of two modernist attitudes, that of the rationalist (Chomsky) and the empiricist (Norvig). The intellectual wound this division introduces is longstanding. However, I would say Norvig has the overall advantage.
1. Norvig is correct that observation and fact/data collection are necessary for science. However, Chomsky is correct that the ultimate goal of science is to determine the principles governing something. Ergo: you need the first to do the second. The intellect abstracts from source data, but nothing is in the intellect that is not first in the senses.
2. Chomsky here is uninterested in things like speech - he only cares about the underlying syntax. But syntax - expressed and instantiated in matter - is known through speech, and probabilities presuppose an underlying determinate syntax. Ergo: a complete science of speech is end-to-end, from the "reading" or interpretation of noisy data through syntactic processing all the way through to expression.
3. Chomsky focuses heavily on internal generation. Norvig points out that interpretation is a separate, highly practical challenge. Figuring out what someone means in a noisy, ambiguous world is an exercise in prudence which involves weighing contingencies and probabilities to arrive at the most likely truth. Of course, this can only be done against a presupposed acceptance of the function of speech which is to communicate meaning and what is true (hence why lies work and why they are an abuse of speech).
Probabilistic reasoning is still reasoning. It's reasoning in the face of incomplete information (against a backdrop of presuppositions) which is a component of prudential reasoning.
It's so clear that Norvig knows so little about linguistics as a subject, I mean the suggestion that English is "kinda" a pro-drop language like Spanish is just unserious. There's this kind of lazy criticism of generative work where people hear the vague idea of a theory and critique without even seriously reading anything about it. These are facts that generativists are perfectly aware of and have explanations for.
It's sort of like criticising Galileo's putative experiment about dropping two balls from the leaning tower of Pisa by pointing out that a feather indeed falls slower than a iron ball.
He never made claim that English is kinda a pro-drop language. His argument that it's easy to make a binary statement that Spanish is a pro-drop language and English is not, but reality is messy. He shows counter example in English, might not be common, nothing is stopping English from evolving like that. No matter the rules, trying to describe natural languages with rules always breaks down, the exception just grows. I think his argument was well presented, we can't brush off probabilistic models because we don't understand it all when they work so well. This essay is 15 years old way before the LLMs of today. It would be interesting to see his additional take on it.
My point is that is not at all a counter example. It is left-edge deletion which is a distinct phenomenon which has been studied since the 70s. It might look like pro-drop to a naive person but it is fundamentally different. For example, you can delete plenty of things on the left edge that _aren't_ pronouns and you can't delete pronouns if they're not on the left-most edge. These are things that don't occur in pro-drop languages where you can always exclude the pronoun.
do you want a cracker -> you want a cracker?
I think he is dumb -> * I think is dumb
It's a lazy dismissal from someone who doesn't know the relevant facts.
I agree with you that the criticism of government & binding theory is lazy. However, it is not too far off from how people actually criticize it, wrt. giving example after example that G&B simply handles inelegantly (particularly wrt. Principle C). It is not a nice theory, and that is why no one believes in it anymore. Despite it being deep in every linguist's bones...
Chomsky was completely incorrect about everything. Says nothing about him personally or his political activities, but academically he was completely, almost incredibly, wrong.
Norvig (and Chomsky) are talking at cross purposes.
Norvig confuses engineering for science. If an engineer wants something that works (and guarantees that it will work) then you use the most effective tool available to achieve that goal. No problem there. But there's absolutely no reason to think that tells you anything scientific about the phenomenon you're modeling. For example, aeronautical engineering says very little about how birds fly. And - currently relevant - LLMs learn to use and understand language, but they use millions of times more training data, and millions of times more computing power, than a child does. So an LLM may be a great technology, but it's probably not a good model for how humans learn and use language.
Chomsky OTOH doesn't see any use for statistics in science, at least not in linguistics. Fortunately for the field of linguistics, Chomsky's influence in linguistics is waning and linguists are beginning to use statistical tools to do science.
> Norvig confuses engineering for science
I don’t think Norvig is confused. He explicitly addresses this point right at the top of the article.
His argument is that successfully engineering a system using a statistical method provides some bits of evidence for the hypothesis that the system itself is statistical.
Which is obviously true, so long as we take care to weigh evidence properly.
> there's absolutely no reason to think that tells you anything scientific about the phenomenon you're modeling
No, this is not how evidence works. If you can accurately model a system with method X and you cannot come up with a model that works with method Y, you should think it’s more likely (but not certain) that X is the actual physical model.
And indeed modern cognitive science (see Predictive Processing) also has a bunch of evidence for the brain as a hierarchical predictor, rather than a rule engine.
To your points - planes are not model organisms of birds, but they do shed insight on the bounds that birds must obey, eg thrust-to-weight etc. And the sample efficiency point really needs to die. The human brain is born with an extremely complex prior baked in. An LLM begins pretraining with almost nothing (just a very simple architecture and a few simple algorithms like attention), and has to learn the basic architecture of cognition from scratch. A better baseline would be how many samples were processed by every animal in our genetic ancestry that had a brain, since brains existed.
> No, this is not how evidence works. If you can accurately model a system with method X and you cannot come up with a model that works with method Y, you should think it’s more likely (but not certain) that X is the actual physical model.
Could you expand on this point?
Try to model radioactive decay via a process other than statistical and you will almost certainly fail, since that is our best understanding of it.
I was also going to suggest coin flips, but it’s not quite the same… I think any chaotic system is generally best modeled that way, and if you cared about the effect the system has on other things, it’s unlikely you can do better since measuring the initial state will not be feasible.
i agree. but it's uncanny that stochastic generation of language yields a form of recognizable intelligence.
in college, i was somewhat involved in cogsci wrt semantic mapping of linguistic concepts based on human surveys (this was well before LLM/AI-word2vec), and it surprises me today how the mathematical properties of relatedness/distance seem now to drive machine intelligence, something which we did not consider at the time at all.
maybe i'm getting in my metaphysical sentimental stage, a turn a lot of adults make after middle age, but i wouldn't fully dismiss the fact that there may be an equivalence.
Indeed. Who'd have thought that mere statistics could do that? Hat's off to them - they made the darn thing work.
There's pretty interesting (I think) research in how effectively it's possible to scale down LLMs. If you're interested, take a look at the BabyLM conferences:
https://babylm.github.io/
(I have no connection to them.) The work there demonstrates that gap between what we can do now with human-relevant amounts of training text, and what remains to be discovered. Quite a bit, it looks like.
"Mere statistics" is how physical reality works; I don't mean the language, the syntax
I mean shifting influence of gravity relative to an object with enough mass type ratios are everywhere
The language of math started as visual art to depict such ratios; in modern times this ground truth associations is frequently overlooked or never understood to exist in the first place without engagement
Differential diagnosis in medicine is exactly the same process; such processes and their successes are everywhere.
The issue we have in software is the outcome isn't as obvious as jabbing a blood draw needle in a patients eye; we spent a lot of time wasting time on illusory needs
“Mere statistics” is heavily underselling what the transformer architecture does. Unless you think “mere ion channels” is a sensible way to think about the human brain.
> stochastic generation of language yields a form of recognizable intelligence.
Can you use a human language well enough and it not sound intelligent? And no, I don't mean just syntactically correct because semantics is a part of human languages too. I think the astounding thing is that they managed to model a language this well. It's like trying to learn English without a functional understanding of the word "Please".
My take is that a lot of the intelligence we natively ascribe to humans exist in human languages and just like the nutrients in your food, by harnessing a language that possesses latent intelligence, you too can behave like an intelligent person. And how did the nutrients of intellect got in there in the first place? Just like languages evolve nowadays: via contributions from speakers over time. Not unlike soil fertilisation.
And language itself is so very 'stochastic' too, due to complex subjectivity. Every word floats on a variable sea of individual choice.
For example, if I compose your post as :
--
"Good prose embodies intellect gradually evolved by myriad humans over time, so it wows me to see LLMs create it, though I soon realised that the intelligence is not being created by the LLM but rather is already present in the human-evolved input datasets - the prose itself! - which they statistically arrange."
--
..the particulars of the language are entirely different (and omit some of your chosen nuance) but most readers would receive the same "core truth" from it, the same measure of beneficial information.
Which highlights the great fluidity of language; that is another of the key underlying mechanisms allowing LLMs to flourish.
I think there is a distinction to draw there between stochasticity in the use and in the evolution of language (which is not clean as they are not independent). The language evolves stochastically, as it does so by being used in exchanges. But the use could be deterministic with the exchanges being random and the function of update based on exchange. I still think there is stochasticity in the use itself, I choose my words, go back and delete and rewrite, explain again in a different way when the audience looks at me unimpressed etc. I just don’t see fluidity as a proof of stochasticity of evolution or the other way around and in fact think the interaction is a quite interesting domain.
> i agree. but it's uncanny that stochastic generation of language yields a form of recognizable intelligence.
It's funny, I used to think it was complete and utter horseshit when literature professors would say that the meaning of a text is created by the reader, not the author.
Since LLMs have become commonplace, I have completely reversed my opinion on that.
That you see recognizable intelligence in the output of stochastic generation is because you are imposing meaning on it. It is why "claudish" is so hard to read--you are trying to impose meaning on a sequence of words that was stochastically generated, and it takes mental effort to do that.
I'm not so sure about that.
If Claude can write formal mathematical proofs (that a machine can check), or it can write software that works, where's the head of the reader that imposes the meaning?
I fail to see what exactly is explained, or clarified by the theory here. Postmodernist philosophers are notorious for writing texts which make readers struggle. Surely, it doesn't mean "Foucault-ese" is somehow more stochastically generated than "Claudese".
The existence of LLMs is telling us something, surely it means something. I'm not sure we've quite figured it out yet but its probably something along the lines of 'intelligence might not be as complicated as we thought' ... a fourth narcissistic blow to humanity after Copernicus, Darwin and Freud.
Freud doesn't belong in that group. Freud was a fraud.
We train them on the sum of human knowledge available in digital format, so I'm not sure that's an indictment, and the large models are incredibly complicated to the point it's hard to reverse engineer how they work so well and what they're actually doing to build internal models to predict the next token. Several billion to several trillion parameters is anything but simple.
Right, we needed compute on a large and fast scale to make LLMs actually work. But the basic principles are quite simple: train on a huge dataset for long enough, using some fairly simple ML maths but across a very large dimensional space, and then out the other side you get something that can competently use language, and is capable of doing useful work. This is somewhat surprising, no?
Yes, it is surprising.
The narcissism might not be in thinking 'intelligence might not be as complicated as we thought’ - the narcissism might be in thinking ‘LLM intelligence is the way we thought’.
I understand the charge, but it's soemwhat unfair. It's more like we start to see a correspondence which even 20 years ago was barely considered
It's also not the first time a breakthrough technology was compared to the human brain/mind, but we also know biologically animal nervous systems are not digital computers, telephone/telegraph networks or hydraulic systems.
sure, but its facile to dismiss these breakthroughs as naive metaphors. physicans do understand now many bodily systems can be modeled with the same tools an engineer will model his machines. its not because these abstractions are incomplete, they are wrong.
I'm not saying LLMs work in the same way as us. But a few years ago humans used to be the only competent users of language on the planet. Now its humans + LLMs who are competent users of langauge. This is a big change, and I'm saying that it might be pointing to something deeper about complexity and how approaches to deal with complexity arise in the universe.
> it's uncanny that stochastic generation of language yields a form of recognizable intelligence.
It most certainly does not. It's obvious to anyone who critically examines LLMs, without letting the hype override sense, that there's zero intelligence there.
To the cynic, Chomskyism is exactly what Thomas Kuhn warned you about "normal science".
That is, you can apply for grants and write papers in the transformational grammar paradigm endlessly. You can pose and answer questions that make sense within in the system.
Chomskyism was disappointing in two respects: (1) was that it was an inadequate basis for machine understanding of text, (2) was that structuralists were hoping everything in culture could be understood in terms of "... having the structure of language" but syntactic structures weren't all adequate for that task.
(1) and (2) both pointed to Chomskyism being hermetic and not something really useful or inspiring to other fields and maybe just a little less "true".
Actually, there have been a number of rounds of paradigm shift (or attempted paradigm shift) in linguistics over the past 70 years. The unique thing about linguistics is that they've been compressed into the lifetime of the same central figure.
Also, I'll mention that there is a lot of empirical knowledge about natural language syntax in generative grammar, even if the details aren't quite right about their theoretical underpinnings. And a lot of them were "low-hanging fruit" waiting for someone to point out how to find them and characterize them. That Chomsky did that is the best argument for his intellectual value - permitting all that "normal science" to happen.
Science doesn't guarantee technological applications, whether or not that science is correct.
Finally, just saying: use of a word like "Chomskyism" makes you sound like one of those creationists who call evolution "Darwinism".
The irony is that while (1) is currently dying a terrible death, (2) is on its peak influence of culture and politics
> You can pose and answer questions that make sense within in the system.
Yes this is one of my critiques of the academic system (I have finished a PhD and will hopefully never work at a university ever again). Many papers in the introduction start with explaining how the study builds on plenty of previous research, but that alone doesn’t prove that something is a good direction to investigate. If academics were completely honest, many papers would write “We researched topic X because we got funding for it. But since we actually don’t care about X, we just wrote a proposal about our favorite niche topic that only one of the co-authors cares about, namely topic Y, and wrote it such that it sounds like X. Next we show that Y is actually very cool in the hope to convince others that it is cool plus to convince others of the conclusion that our most senior professor already found during the PhD which was 30 years ago. Apart from the professor, we don’t believe in the main argument of the paper but since he got the grant we’ll just go with it in the hope to one day be able to push our own ideas forward”.
> Norvig confuses engineering for science. If an engineer wants something that works (and guarantees that it will work) then you use the most effective tool available to achieve that goal. No problem there. But there's absolutely no reason to think that tells you anything scientific about the phenomenon you're modeling.
this distinction is superficial. Scientists use less precise models all the time. Like, you don't solve the schrodinger equation even when you're a scientist doing organic chemistry. You reach for lewis dot models. Science as a discipline is about building better models of the world, not uncovering fundamental truth.
> LLM may be a great technology, but it's probably not a good model for how humans learn and use language.
but from all models, it's the closest, "all models are wrong, but some are useful"
> but from all models, it's the closest
Care to back this up? What evidence do we have to suggest that back propagation, self attention, and byte pair encoding are mechanistically similar to human cognition? Similarity of the outputs says nothing about similarity of the machinery underneath. Turboprop engines tell us nothing about how birds fly - doesn't matter that they successfully transport millions of people around the world.
I would say that when it comes to higher level cognition, logic engines are more human like even if they only have a tiny fraction of the practical utility of LLMs.
> Turboprop engines tell us nothing about how birds fly
That is not true.
Turboprop engines and flying birds are different things, but there is a set of common properties.
A blade of a screw propeller functions exactly in the same way as a flapping wing of a bird, except that the wing cannot rotate without limits around the body of the bird, so it must stop, change angles and revert the motion, while the blade can rotate continuously around the axis, functioning on one side like a falling wing and on the other side like a raising wing. The similarities between propeller blades and insect wings are even greater, because insect wings cannot change their shapes so much as those of birds.
For now, our understanding of the networks composed of the neurons of animals is much less advanced than our understanding of wings and propellers, which is why there is a much greater uncertainty about whether there are any similar mechanisms in biological neural networks and in LLMs.
Also, AFAIK, insects rely on their (flat) flapping wings to create lift by creating vortices.
"Nothing" may be a little too strong -- physics is physics, after all -- but (small) birds and airplanes have Reynolds numbers about 1000x different and different underlying material science.
Airplanes are engineered (with a very small number of exceptions) to be stable during flight, whereas bird flight is inherently unstable, requiring a lot of specialized cognition so birds can fly.
I would think a lot of cognitive ornithologists (who I really hope exist!) would be interested in finding out about this.
At this point, I'm at the complete end of my knowledge of bird flight!
We already know that the most basic mechanisms in ANN and actual neurons are very different. Spiking and neurotransmitters are basically everything in brains and don't exist in ANNs.
ok, compare a wheel to a human leg. If you look long enough you will find similarities, but you honestly cannot say that you "understand how humans walk" when all you can do is put a man in a wheelbarrow, move it, and say "look! my device accomplishes the same task!"
> A blade of a screw propeller functions exactly in the same way as a flapping wing of a bird, except that the wing cannot rotate without limits around the body of the bird
The "except" in your argument is doing an incredible amount of work. The rotation of a turboprops generates forward force, wings (whether a bird's or a plane's) generate lift. And plane's wings are called "wings" because they, and not the turboprop, mimic the structure and function of a bird's wings.
Regardless, this whole line of argument is missing the forest for the trees.
attention + residual seems kind of like Chomsky's merge operation.
LLMs behave like they're closest, but only during execution. During training, obviously, they don't resemble the human brain at all. No human reads a million books before they begin to speak.
And I'd be hard pressed to identify many cognitive characteristics of LLMs during execution that resemble human thinking, aside from those that are readily parrotable.
As a model of human cognition, LLMs deep suck.
Humans have billions of years of evolution going for them, that must count…
What reason to we have to believe that LLMs are the closest model we have for how humans learn language.
We considered the hypothesis that humans are a general intelligence that figures language out by exposure. The evidence has consistently come back that that is not how humans actually work. A model where humans are hardwired with a lot of knowledge about language, then fill in the gaps based on exposure still appears to be far more accurate.
The only thing LLMs show is that it is possible to learn language de-novo.
>closest model we have for how humans learn language
Is there some other competing model which can give results as good? If not, the statement seems true in the trivial sense that it's the _only_ model which we've observed to give good results.
> The only thing LLMs show is that it is possible to learn language de-novo.
I agree, but would add, "But not under conditions that humans learn language."
I think it's far too early to say LLMs are not how humans learn language.
Regarding "hard-wired"; it's not if you consider that "hard-wiring" of an individual is the result of millions of years of evolution.
Humans decidedly take much more data than LLMs. Like orders of magnitude. We happen to feed only distilled data to LLMs though. That's why they appear so efficient.
If you are counting by bytes and including all sensory input, sure. In terms of amount of text, it's not close in the other direction. It's very difficult to make an apples to apples comparison here.
You can obviously count by bytes. They are bytes for the model or for a person. Bytes to bytes is apples to apples.
You can't count by text, because text is not the only thing children learn on. So unlike the bytes comparison the text comparison is totally invalid.
The BabyLM challenge has shown that multimodal models don't seem to learn in a more data-efficient way with respect to tokens. Adding an unlimited amount of images doesn't help you learn syntactic islands faster.
Which entry are you referring to?
Not a specific entry, but in four years, there's never been a clear advantage of multimodal models and the results have been extremely lackluckster, even when you allow unbounded images alongside a fixed quantity of text.
> when you allow unbounded images alongside a fixed quantity of text
Any specific entries?
I find it hard to see how Chomsky has a leg to stand on.
> I understand how Chomsky might arrive at the conclusion that probabilistic models are unnecessary, from his study of the generation of language.
I think the point turns here for me. We have probabilistic models now that generate unbelievably convincing language. Norvig's following argument on the value of interpreting language statistically becomes secondary.
In order to stay moored in reality, our models must follow from evidence. I think it would take a peculiar obstinance to deny that the latest models show evidence of generating language.
The models require orders of magnitude more data to learn language than children do, and children's task is harder as they have to segment speech themselves.
The comparison isn't so straightforward, since children's language-learning environment is multimodal and extremely rich. They're exposed not just to sequences of words, but also to behaviors, emotions, social interactions, and the physical world.
I think you just explained the situation, even if you didn't realize it.
LLMs process millions of times more text data and use millions of times more compute power to learn a language, mostly because we know how to do that technologically.
Children use much less text data, but also use a lot of other kinds of data, including interacting with other humans, to learn language. We have no idea how that works. And, on top of that, people can interact with pets of various species in the same way as children, but those pets never learn language.
So, something strange is going on that children can do, but that LLMs and puppies can't. Probably, part of it is that human children are much better at learning by copying what adults are doing, and part of it is that humans evolved to learn and use language. The details of what's going on there is what linguistics is about.
Yes, I believe arguments like GP vastly underestimate the amount of data involved in children's learning. All the data coming into their sensorium, from heard language to physical movement feedback, contributes to training all their skills, from physical movement to language.
A newborn brain is not a blank slate, it comes with a very rich prior (as Chomsky himself would argue), for example the ability to rapidly acquire language, control a body, etc.
An LLM must construct all of its grammatical structure, world models, etc. from scratch during pretraining.
LLM pre-training is more like recapitulating evolution of the mammalian brain than childhood development.
Of course children are not a blank state. My point is to argue that LLMs are not an argument against UG.
You're forgetting the millions of years of evolution that "pre-trained" child brains for this task.
I agree that language is innate.
Yes but it can be both innate and built on statistical models, contrary to Chomksy.
We could innately have built-in statistical models, then everyone can be happy. Given how well statistics seems to work for language acquisition the brain is probably approximating a statistical formula at some level.
Well, Chomsky wouldn’t be happy, that’s the point of OP; he explicitly argues against this.
But the statistics only work well at vastly unrealistic scales of data, and the learning pattern doesn't match children's behaviour at all.
The idea that there is some level of statistical inference involved in language acquisition is not contrary to generative thought, look at Charles Yang's work or Lidz or a fair number of other people. Chomsky just thinks this is uninteresting so he is dismissive of it as a central explanation for language acquisition.
The central question remains whether there are categorical rules built into the brain for grammar (plus "some" statistical inference possibly), or whether the entirety of our grammatical processing is statistical. It is entirely possible that evolution encoded statistical priors into our brains with grammar overall being a fully statistical process. Therefore, the fact that children learn more quickly isn't really hard evidence one way or another.
LLMs (at the autoregressive pretraining stage) are trained to predict what an (adult) human who could have said preceding words would have said next. You should add 15-30 years to the human training time.
The hardest theory to disprove is one an entire field livelihood rests upon
Peter makes some excellent points in TFA and it’s a fun read.
But the situation has moved on, moving even further beyond the assertions Peter makes about the utility of the black-box generative approach.
In particular, LLMs are telling us a lot about those low-probability, never before seen sentences — it’s called CALU, the creative aspects of language use.
LLMs exhibit CALU; as we know they will happily create such sentences. And inspecting the LLMs does provide insight:
“It does change the epistemic situation substantially. A working system can be inspected, perturbed, retrained, lesioned, and compared with other systems. Building is not understanding, but it gives understanding a strong foothold.
Explaining creative language use in LLMs would not, by itself, explain creative language use in humans. But the artificial case matters even if the mechanisms turn out to be substantially different. At a minimum, LLMs are an existence proof of one pathway by which open-ended, context-sensitive language can arise.”
For much more, see this substack by Chandra Sripada, a U of Michigan linguist:
https://chandrasripada.substack.com/p/ai-and-the-retreat-of-...
He is interviewed by Sean Carroll on his excellent Mindscape podcast:
https://preposterousuniverse.com/podcast/2026/08/10/363-chan...
Related. Others?
Chomsky and the Two Cultures of Statistical Learning (2011) - https://news.ycombinator.com/item?id=46286533 - Dec 2025 (119 comments)
On Chomsky and the Two Cultures of Statistical Learning - https://news.ycombinator.com/item?id=35081102 - March 2023 (135 comments)
On Chomsky and the Two Cultures of Statistical Learning (2011) - https://news.ycombinator.com/item?id=16489828 - March 2018 (12 comments)
On Chomsky and the Two Cultures of Statistical Learning (2011) - https://news.ycombinator.com/item?id=11951444 - June 2016 (102 comments)
On Chomsky and the Two Cultures of Statistical Learning - https://news.ycombinator.com/item?id=2591154 - May 2011 (107 comments)
(Reposts are fine after a year or so; links to past threads are just to satisfy extra-curious readers)
> But the vast majority of people who study interpretation tasks (such as speech recognition) quickly see that interpretation is an inherently probabilistic problem: given a stream of noisy input to my ears, what did the speaker most likely mean? Einstein said to make everything as simple as possible, but no simpler. Many phenomena in science are stochastic, and the simplest model of them is a probabilistic model
Why the "simplest" model? Is a probabilistic model not the only model able to deal with uncertainty? What other kind of models are possible to represent uncertainty and inherent ambiguity that are not probabilistic?
I don't think it's the simplest, actually. Off the top of my mind:
Ternary logic is a super simple model; where true AND unknown equals unknown, and true OR unknown equals true. This deals with uncertainty and ambiguity, just not the probability; notionally you could say everything is 50-50.
Same thing if you have e.g. text-to-speech which returns "the b??? dog" rather than "the (0.8:black, 0.1:blue, 0.1:boy) dog" - no probabilities. Of course, maybe later in the processing you add probabilities back in.
Then there's fuzzy logic, which uses scores that are typically numbers, but those don't express actual probabilities, but more like vibes. They don't necessarily add up to 1, for instance, and you don't apply Bayes' formula to them, or even multiply them, you can use addition too - yolo.
Edit: and also of course backtracking, beam search, that sort of stuff - needn't use probabilities, just heuristics.
That is true, but to me it seems like ternary logic is like probabilities in disguise (false, true and unknown would be probability of 0, 1, and "between 0 and 1"). For most application, the unknown class is too broad, thus we need actual probabilities. But indeed, ternary logic is simpler than working with probabilities.
Old school search methods allow you to pursue multiple paths, but not necessarily based on probability. At the end (or while pruning) you can use "word knowledge".
Anyway, the probabilistic model doesn't exist. Simple probabilistic models work quite badly on speech recognition (word classification, etc.). A lot of structure needs to be added before they start performing.
My objection is "simpler." What's so "simple" about a probabilistic model? It takes an enormous amount of calculations, so much that you have to predigest incoming information into high-level chunks in order to have any chance of calculating it over small inputs with reasonable effort.
A probabilistic model is the most complicated model. It doesn't even attempt to model the problem, it just looks at the results of an enormous number of tries with different inputs, goes through them all, finds the most similar cases to the current input, and blends them together in a way that carefully avoids any self-contradictory or absurd result.
We see this plainly with LLMs. You train an LLM on a thousand times the (high-level, pre-digested) input that the average human has gotten, and the average human thinks it's a hallucinating moron. It doesn't start to converge on something that passes the smell test to you average human idiot until it has been trained on a million times the input the average human has gotten.
It's not doing what a human does, it's guessing what a human (or any system) might do when confronted with X input. They're not even similar activities, even if they have identical output in the limit. It's like confusing gravity for equations about gravity. Being able to make perfect predictions about the positions of moving, nearby frictionless spherical things in a vacuum at time t doesn't tell us a thing about what gravity is. It tells us how it looks. It should, we distilled it by watching things for a very long time.
> Is a probabilistic model not the only model able to deal with uncertainty?
No, all models deal with uncertainty. The entire point of a model is that it is what you know about a system when you don't know its inputs. The variables in a model are uncertainty. The model is dealing with it.
edit: I think you're interpreting "uncertainty" as a numerical probability. A numerical probability is an artifact of the set of data you're calculating it from, not a thing with independent existence. "Uncertainty" is just a thing that you don't know yet.
If you assume that "uncertainty" is a knowable numeric probability that contributes to some calculation, you're begging the question when you say that the only way you can model it is through probabilistic calculations.
To me uncertainty on a given quantity, is represented by a probability distribution function giving how probable the value of a given quantity is. Loosely, it can also be represented by a confidence interval. It is not intrinsic to the quantity, but how much we know about this quantity.
For the domain I am working in, a model is considered deterministic if for a set of input it always provide the same output and the input are assumed to be perfectly known. A model become probabilistic, once the input or the model itself are considered as uncertain. As an example: previously weather models always provided singe prediction for tomorrow, for example rain or no rain. But now, such models are probabilistic in the sense that an ensemble of model forecast are made (with slightly different input representing our uncertainties) to provide an estimate of the probabilities. Indeed such probabilistic model is not the simplest, but to most complete and so far the only practical way to handle with uncertainty for system like the weather as far as I am aware of.
No empirical fact is restricted to a singular vector through human language.
"..if we think of truth as something of granite-like solidity and of personality as something of rainbow-like intangibility and reflect that the aim of [human writing] is to weld these two into one seamless whole, we shall admit that the problem is a stiff one and that we need not wonder if [human writers], for the most part failed to solve it." - Virginia Woolf
[human writing, human writers] = [biography, biographers] in the original quote. My edit is because I infer that her statements key point is that of the biological subjectivity inherent to all human prose.
Really great post, though a bit old, still relevant. Peter Norvig is one of the better thinkers of our generation.
Have to say that I confronted myself with the fact that I can't read longer posts anymore due to overuse of AI. Guess a detox is required...
Would Chomsky have the name recognition and standing he has, or any at all, without his extracurricular political beliefs?
For decades, Chomsky was the most-cited author who still had a pulse. And a lot of that was for his political writing - maybe 2/3rds? But he still has a lot of cites for his intellectual work, in several different fields.
Linguistics, obviously.
Philosophers love him, because he always gives them an argument, and that's all they really care about.
There probably wouldn't even be cognitive science without Chomsky. And psychology is still responding to Chomsky's nuking of behaviorism in the 1950's.
So, there's a lot there, but admittedly most of it is for politics.
Without question.
Just look at his Wikipedia entry. He finished his dissertation in 1955, starting teaching at MIT the same year, and continued to publish seminal linguistic works over the next 12 years. That's all before he gained any notoriety whatsoever as a critic of the U.S.'s role in the Vietnam War.
Manufacturing Consent was published in 1988!
Additionally, I strongly doubt Chomsky had any "name recognition" all the way through to the late 90s. Outside of a single episode of Firing Line, where would the general public have heard his views?
Every introductory book in theoretical computer science will have a discussion of the Chomsky language hierarchy. It is hugely insightful and helpful.
Not with laypeople, but among cognitive scientists, absolutely.
Yup, no regular expressions without his theory.
I mean, his linguistic work was a very, very big deal
Chomsky is incredibly dogmatic; his views and personality have held the field back.
There's enormous irony in Chomsky's mathematicization of linguistics sweeping away the linguists who came before him compared to his own stubborn refusal to acknowledge the fruits of the statistical approach. As far as I know, he's more or less been swept aside.
A field of study being held back by an individual is more of an indictment of the field of study rather than the individual.
Appeal to Authority is over-emphasized in academia in general though.
As an aside? The "I before E except after C" works well if the sound makes the long-EE sound. Which is believe was implied in it's original formulation. You obviously don't need the rule for "society" and "science".
Of older English words, there are extremely few genuine exceptions. Seize is one.
This is an old, old post by Norvig fyi.
It's more or less the classic clash of two modernist attitudes, that of the rationalist (Chomsky) and the empiricist (Norvig). The intellectual wound this division introduces is longstanding. However, I would say Norvig has the overall advantage.
1. Norvig is correct that observation and fact/data collection are necessary for science. However, Chomsky is correct that the ultimate goal of science is to determine the principles governing something. Ergo: you need the first to do the second. The intellect abstracts from source data, but nothing is in the intellect that is not first in the senses.
2. Chomsky here is uninterested in things like speech - he only cares about the underlying syntax. But syntax - expressed and instantiated in matter - is known through speech, and probabilities presuppose an underlying determinate syntax. Ergo: a complete science of speech is end-to-end, from the "reading" or interpretation of noisy data through syntactic processing all the way through to expression.
3. Chomsky focuses heavily on internal generation. Norvig points out that interpretation is a separate, highly practical challenge. Figuring out what someone means in a noisy, ambiguous world is an exercise in prudence which involves weighing contingencies and probabilities to arrive at the most likely truth. Of course, this can only be done against a presupposed acceptance of the function of speech which is to communicate meaning and what is true (hence why lies work and why they are an abuse of speech). Probabilistic reasoning is still reasoning. It's reasoning in the face of incomplete information (against a backdrop of presuppositions) which is a component of prudential reasoning.
It's so clear that Norvig knows so little about linguistics as a subject, I mean the suggestion that English is "kinda" a pro-drop language like Spanish is just unserious. There's this kind of lazy criticism of generative work where people hear the vague idea of a theory and critique without even seriously reading anything about it. These are facts that generativists are perfectly aware of and have explanations for.
It's sort of like criticising Galileo's putative experiment about dropping two balls from the leaning tower of Pisa by pointing out that a feather indeed falls slower than a iron ball.
He never made claim that English is kinda a pro-drop language. His argument that it's easy to make a binary statement that Spanish is a pro-drop language and English is not, but reality is messy. He shows counter example in English, might not be common, nothing is stopping English from evolving like that. No matter the rules, trying to describe natural languages with rules always breaks down, the exception just grows. I think his argument was well presented, we can't brush off probabilistic models because we don't understand it all when they work so well. This essay is 15 years old way before the LLMs of today. It would be interesting to see his additional take on it.
My point is that is not at all a counter example. It is left-edge deletion which is a distinct phenomenon which has been studied since the 70s. It might look like pro-drop to a naive person but it is fundamentally different. For example, you can delete plenty of things on the left edge that _aren't_ pronouns and you can't delete pronouns if they're not on the left-most edge. These are things that don't occur in pro-drop languages where you can always exclude the pronoun.
do you want a cracker -> you want a cracker?
I think he is dumb -> * I think is dumb
It's a lazy dismissal from someone who doesn't know the relevant facts.
I agree with you that the criticism of government & binding theory is lazy. However, it is not too far off from how people actually criticize it, wrt. giving example after example that G&B simply handles inelegantly (particularly wrt. Principle C). It is not a nice theory, and that is why no one believes in it anymore. Despite it being deep in every linguist's bones...
Chomsky was completely incorrect about everything. Says nothing about him personally or his political activities, but academically he was completely, almost incredibly, wrong.
"Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something."
https://news.ycombinator.com/newsguidelines.html