#426 – Edward Gibson: Human Language, Psycholinguistics, Syntax, Grammar & LLMs

The following is a conversation with Edward Gibson or Ted as everybody calls him. He is a Psycholinguistics professor in MIT. He heads the MIT Language Lab that investigates why human languages look the way they do, the relationship between cultural language and how people represent process and learn language. Also, he should have a book titled Syntax, a cognitive approach published by MIT Press coming out this fall. So, look out for that.

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as to what it was I was told to do. I didn't understand what the theory was behind it, but I found it very interesting. So when you look at grammar, you're almost thinking about a puzzle, like almost like a mathematical puzzle? Yeah, I think that's right. I didn't know I was going to work on this at all at that point. I was really just, I was kind of a math geek person, computer scientist. I really liked computer science. Then I found language as a neat puzzle to work on

from an engineering perspective. Actually, as I accidentally, I decided after I finished my undergraduate degree, which was computer science and math and candidate in Queens University, I decided to go to grad school. That's why I always thought I would do. I went to Cambridge, where they had a master's in a master's program in computational linguistics. I hadn't taken a single language class before. All I had taken was CS, computer science, math classes, pretty much

mostly as an undergrad. I just thought this was an interesting thing to do for a year, because it was a single year program. Then I end up spending my whole life doing it. So fundamentally, your journey through life was one of a mathematician and computer scientist. Then you kind of discovered the puzzle, the problem of language and approached it from that angle, to try to understand it from that angle, almost like a mathematician or maybe even an engineer.

As an engineer, I'd say, I mean, to be frank, I had taken an AI class, I guess it was 83 or 85, somewhere 84 in there a long time ago. There was a natural language section in there. It didn't impress me. There must be more interesting things we can do. It didn't seem very, it seemed just a bunch of hacks to me. It didn't seem like a real theory of things in any way. So I just thought this was, this seemed like an interesting area where there wasn't enough good work.

Did you have a come across the philosophy angle of logic? If you think about the 80s with AI, the expert systems where you try to kind of maybe sidestep the poetry of language and some of the syntax and the grammar and all that kind of stuff and go to the underlying meaning, the language is trying to communicate and try to somehow compress that in a computer-representable way. Do you ever come across that in your studies? I mean, I probably did, but I wasn't

as interested in it. I was trying to do the easier problems first than ones I could thought maybe we're hand-alible, which seems like the syntax is easier, which is just the forms as opposed to the meaning. When you're starting to talk about the meaning, that's very hard problem, and it still is a really, really hard problem. But the forms is easier. So I thought at least figuring out the forms of human language, which sounds really hard, but it actually may be more

tractable. So it's interesting. You think there is a big divide. There's a gap. There's a distance between form and meaning. Because that's a question you have discussed a lot with LLM's because they're damn good at form. Yeah, I think it's really good at this form. Yeah, that's why they're good because they can do form. Meaning is hard. Do you think there's,

oh, wow. That means an open question, right? How close form and meaning are? We'll discuss it, but to me, studying form, maybe it's the romantic notion, gives you, form is like the shadow of the bigger meaning thing under line language. As I, it forms, language is how we communicate ideas. We communicate with each other using language. So in understanding the structure of that communication, I think you start to understand the structure of thought and the structure of meaning behind those

thoughts and communication to me. But to you, big gap. Yeah. What do you find most beautiful about human language? Maybe the form of human language, the expression of human language. What I find beautiful about human language is some of the generalizations that happen across the human language is within and across a language. So let me give you an example of something which I find kind of remarkable. That is if like a language, if it has a word order such that

the verbs tend to come before their objects. So that's like English does that. So we have the, the first, the subject comes first in a, in a simple sentence. So I say, you know, the, the dog chased the cat or Mary kicked the ball. So the subject's first, and then after the subject, there's the verb. And then we have objects. All these things come after in English. So it's generally a verb. And most of the stuff that we want to say comes after the subject. It's the, it's the

object. There's a lot of things we want to say they come after. And, and, and there's a lot of languages like that. About 40% of the languages of the world are like that. They're sub, subject verb object languages. And then these languages tend to have prepositions. These little markers on the nouns that, that connect nouns to other nouns are nouns to verb. So I, when I, so a verb like,

I'm sorry, preposition like in or on or off or about, I say, I talk about something. The something is the object of that preposition that we have, these little markers come also just like verbs. They come before their, their nouns. Okay. And then, so now we look at other languages that like Japanese or, or Hindi or some, these are, these are so called verb final languages. Those is about maybe a little more than 40%, maybe 45% of the world's languages are more, I mean, 50%

of the world's languages are verb final. Those tend to be post positions, those markers, the same, we have the states have the same kinds of markers as we do in English, but they put them after. So, sorry, they put them, first, the markers come first. So you say instead of, you know, talk about a book, you say a book about the opposite order there in Japanese or in Hindi. You do the opposite. And the talk comes at the end. So the verb will come at the end as well. So instead of

marry, kick the ball, it's marry ball kicked. And then, if it says, marry, kick the ball to John, it's John to the two, the little marker there, the preposition, it's a post position in these languages. And so the interesting thing, a fascinating thing to me is that within a language, this order aligns, it's harmonic. And so if it's one or the other, if it's either verb initial or verb final, then you'll have prepositions, prepositions or post positions. And that's across

the languages that we can look at. We got around a thousand languages, there's around 7,000 languages around on the earth right now. But we have information about, say, word order on around a thousand of those pretty decent amount of information. And for those thousand, what we know about, about 95% fit that pattern. So they will have either verb, it's about half and half for verb initial, like English and half for verb final, like Japanese. So just to clarify,

verb initial is subject verb object. That's correct. verb final is still subject object verb. That's correct. Yeah, the subject is generally first. That's so fascinating. I ate an apple or I apple ate. Yes. Okay. In this fascinating that there's a pretty even division in the world amongst those 45% yeah, it's pretty it's pretty even. And those two are the most common by far,

those two words, the subject tends to be first. There's so many interesting things, but these things are with thing I find so fascinating is there these generalizations within and across a language. And not only those are the and there's actually a simple explanation, I think, for a lot of that. And that is you're trying to like minimize dependencies between words. That's basically the story. I think behind a lot of why word order looks the way it is is you we're always connecting.

What is it? What is the thing I'm telling you? I'm talking to you in sentences. You're talking to me in sentences. These are sequences of words which are connected. And the connections are dependencies between the words. And it turns out that what we're trying to do in a language is actually minimize those dependency links. It's easier for me to say things if the words that are connecting for their meaning are close together. It's easier for you in understanding if that's also

true. If they're far away, it's hard to produce produce that and it's hard for you to understand. And the language is the world within a language and across languages, you know, fit that generalization, which is, you know, so I, you know, it turns out that having verbs initial and then having prepositions ends up making dependencies shorter and having verbs final and having post positions ends up making dependencies shorter than if you cross them. If you cross them and ends up,

you just end up, it's possible. You can do it. It just within a language. Within a language, you can do it. It just ends up with longer dependencies than if you didn't. And so language is tend to go that way. They tend to, they, they, they, they, they, they, they, they call it harmonic. So it was observed a long time ago by, without the explanation by a guy called Joseph Greenberg, who's a famous typologist from Stanford, he observes a lot of generalizations about how word order

works. And these are some of the harmonic generalizations that he observed. Harmonic generalizations about word, word, word, word, there's so many things I want to ask you. Okay. Let me just, it's sometimes basics. You mentioned dependencies a few times. Yeah. Yeah. What do you mean by dependencies? Well, what I mean is in, in language, there's kind of three structures to, three components to the structure of language. One is the sounds. So cat is cut at and to in English. I'm not talking

about that part. I'm talking, and there's two meaning parts. And those are the words. And, and you're talking about meaning earlier. So words have a form and they have a meaning associated with them. And so cat is a full form in English. And it has a meaning associated with whatever a cat is. And then the combinations of words, that's what I'll call grammar or syntax. And that's like,

when I have a combination like the cat or two cats. Okay. So where I take a two different words there and put them together and I get a compositional meaning from putting those two different words together. And, and so that's the syntax. And in any sentence or utterance, whatever I'm talking to you, you're talking to me, we have a bunch of words and we're putting together in a sequence. They, it turns out they are connected. So that every word is connected to just one other

word in that, in that sentence. And so you end up with what's, what's called a technically a tree. It's a tree structure. So there, where there's a root of that, of that utterance of that sentence. And then there's a bunch of dependence like branches from that root that go down to the words. The words are the leaves in this metaphor for a tree. So trees also sort of a mathematical construct. Yeah, yeah. It's a graph theory. A theoretical thing.

Graph theory. Yeah. So in this fascinating that you can break down a sentence into a tree. And then one, every word is hanging on to another. It's depending on it. And everyone agrees on that. So all linguists will agree with that. No one. It's not a controversial. That is not controversial. There's nobody sitting here listening to mad at you. I don't think so. Okay. The language sitting there matter this. No, I think in every language, I think everyone agrees that

all sentences are trees at some level. Can I pause on that? Sure. Because it's to me, just as a layman, it's surprising. Yeah. That you can break down sentences in many, mostly all languages into a tree. I think so. That's I've never heard of anyone disagreeing with that. That's weird. The details of the trees are what people disagree about. Well, okay. So what's what's the root of a tree? How do you construct? How hard is it?

What is the process of constructing a tree from a sentence? Well, this is where, you know, depending on what your there's different theoretical notions, I'm going to say this simplest thing. Dependency grammar. It's like a bunch of people invented this. Ten year was the first French guy back in. I mean, the paper was published in 1959, but he was working on the 30s and stuff. So

and it goes back to, you know, philologist Pignini was doing this in ancient India. Okay. And so, you know, doing something like this, the simplest thing we can think of is that there's just connections between the words to make the utterance. And so it's just say I have like two dogs entered a room. Okay, here's a sentence. And so we're connecting two and dogs together. That's like there's some dependency between those words to make some bigger meaning. And then we're

connecting dogs now to entered, right? And we connect a room somehow to entered. And so I'm going to connect to room and then room back to enter. These are, that's the tree is I, the root is entered. That's the thing is like an entering event. That's what we're saying here. And the the subject, which is whatever that dog is two dogs, it was. And the connection goes back to dogs, which goes back to them, then that goes back to two. I'm just that, that's my tree. It starts at entered,

it goes to dogs down to two. And then the other side, after the verb, the object, it goes to room. And then that goes back to the the determiner or article, whatever you want to call that word. So there's a bunch of categories of words here. We're noticing. So there are verbs. Those are these things that typically mark, they refer to events and states in the world. And they're nouns, which typically refer to people, places and things is what people say, but they can refer to other

more, they can refer to events themselves as well. They're, they're marked by, you know, how they, how they get you, the category, the part of speech of a word is how it gets used in language. It's like, that's how you decide what the, what the category of a word is. Not, not by the meaning, but how it's, how it gets used. How it's used. What's usually the root? Is it going to be the verb that defies the event usually? Yes, yes. Okay. Yeah. I mean, if I don't say a verb, then there won't

be a verb until it'll be something else. What if you're messing? Are we talking about language that's like correct language? What if you're doing poetry and messing with stuff? Is it then, then rules got the window, right? Then it's no. No, no, no, no, no, you're constrained by whatever language you're dealing with. Probably you have other constraints in poetry such that you're, like usually in poetry, there's multiple constraints that you want to, like you want to,

usually convey multiple meanings is the idea. And maybe you have like a rhythm or a rhyming structure as well. And depending on, so, but you usually are constrained by your, the rules of your language for the most part. And so you don't violate those too much. You can violate them somewhat, but not too much. So it has to be recognizable as your language. Like in English, I can't say dogs to entered room. Ah, I mean, I meant the, you know, two dogs entered a room. And I can't

mess with the order of the, the articles, the articles and the nouns. You just can't do that. In some languages, you can, you can mess around with the order of words much more. I mean, you speak Russian. Russian has a much freer word order than English. And so in fact, you can move around words in, you know, I told you that English has the subject verb object word order. So does Russian, but Russian is much freer than English. And so you can actually mess around with the word

order. So probably Russian poetry is going to be quite different from English poetry because the word order is much less constrained. Yeah, there's a much more extensive culture of poetry throughout the history of the last 100 years in Russia. And I always wondered why that is, but it seems that there's more flexibility in the way the languages use. There's more, you're more female language easier by altering the words, altering the order of the words, messing with it.

Well, you can just mess with different things in each language. And so in Russian, you have case markers on the end, which is these endings on the nouns, which tell you how it connects, each noun connects to the verb, right? We don't have that in English. And so when I say Mary kissed John, I don't know who the agent or the patient is except by the order of the words,

right? In Russian, you actually have a marker on the end if you're using a Russian name and each of those names, you'll also say is that, you know, it'll be the, you know, a nominative, which is marking the subject or an accusative will mark the object. And you could put the them in the reverse order. You could put accusative first as you could put subject, you could put the patient first and then the verb and then the the subject. And that would be a

perfectly good Russian sentence. And it was still mean Mary, I could say John kissed Mary, meaning Mary kissed John, as long as I use the case markers in the right way, you can't do that in English. And so I love the terminology of agent and patient and yeah, and the other ones you use those are sort of linguistic terms, correct? Those are for like kind of meaning, those are meaning and subject and object are generally used for position. So subject is just like the thing

that comes before the verb and the object is when it comes after the verb. The agent is kind of like the thing doing it. That's kind of what that means, right? The subject is often the person doing the action, right? The thing itself. Okay, this is fascinating. So how hard is it to form a tree in general? Is there a procedure to it? Like if you look at different languages, is it supposed to be a very natural, like is it automatable? Or is there some human genius involved in? Because I think

it's pretty automatable at this point. People can figure out the words are they can figure out the more themes, which are the technically more themes are the the minimal meaning units within a language. Okay. And so when you say eats or drinks, it actually has two more themes in an English. There's the there's the route, which is the verb and then there's some ending on it, which tells you, you know, that's this third person, third person singular. So more themes are

more themes are just the minimal meaning units within a language. And the word is just kind of the things we put spaces between English and they're a little bit more. They have the morphology as well. They have the endings, this inflectional morphology on the endings on the roots. They modify something about the word that adds additional meaning. They tell you yeah, yeah. And so we have a little bit of that in English, very little, much more in Russian, for instance. And but we have a

little bit in English. And so we have a little on the on the nouns. You can say it's either singular plural and you can say same thing for verbs, like simple past tense, for example, like, you know, notice in English, we say drinks, you know, he drinks, but everyone else is I drink you drink, we drink it's unmarked in a way. And then but in the past tense, it's just drank for everyone. There's no morphology at all for past tense. It's if there is morphology is marking past tense,

but it's kind of it's an irregular now. So we don't even, you know, it drink to drink, you know, it's not even a regular word. So in most verbs, many verbs, there's an E.D. we kind of add so walk to walked. We add that to say it's the past tense. That I just happened to choose an irregular because the high frequency word and the high frequency words tend to have irregular as an English for what's an irregular. It's just there's there's a rule. So drink to drink is an irregular drink

drink. Okay, go for it. As opposed to walk, walked, talked, talked, and there's a lot of irregular irregular in English. There's a lot of irregular in English. The frequent ones, the common words tend to be irregular. There's many, many more on low frequency words. And those tend to be those irregular ones. The evolution of the irregular is fascinating. It's essentially slang that's sticky because you're breaking the rules and then we use it and doesn't follow the rules. And they

say, screwed to the rules. It's fascinating. So you said it morphemes, lots of questions. So morphology is what the study of morphemes morphology is the connections between the morphemes onto the roots, the roots. So in English, we mostly have suffixes. We have endings on the words, not very much, but a little bit. And as opposed to prefixes, some words, depending on your language,

can have mostly prefixes, mostly suffixes or mostly or both. And then even languages, several languages have things called infixes where you have some kind of a general form for the root. And you put stuff in the middle, you change the vowels. That's fascinating. So in general, there's what two morphemes per word, one or two or three. Well, in English, it's one or two. In English, it tends to be one or two. There can be more.

In other languages, a language like Finnish, which has a very elaborate morphology, there may be 10 morphemes on the end of a root. And so there may be millions of forms of a given word. Okay. I'll ask the same question over and over. But how does it just sometimes to understand things like morphemes? It's nice to just ask the question, how does these kinds of things evolve?

So you have a great book studying sort of the, how the cognitive processing, how language used for communication, so the mathematical notion of how effective language is for communication, what role that plays in the evolution of language. But just high level, like how do we, how does a language evolve with where English is two morphemes or want it to morphemes per word, and then finish as infinity per word? So what, how does that happen? Is it just people?

That's a really good question. Yeah. That's a very good question. It's like, why do languages have more morphology versus less morphology? And I don't think we know the answer to this. I know, I think there's just like a lot of good solutions to the problem of communication. So I believe as you hinted that language is an invented system by humans for communicating their ideas. And I think we, it comes down to we label things we want to talk about. Those are the morphemes and words.

Those are things we want to talk about in the world and we invent those things. And then we put them together in ways that are easy for us to convey to process. But that's like a naive view. And I don't, I mean, I think it's probably right, right? It's naive and probably right. One that's the most, I don't know if it's naive, I think it's simple. Simple. Yeah. I think naive was, naive is an indication that it's an incorrect somehow. It's a trivial, too simple. I think

it could very well be correct. But it's interesting how sticky it feels like two people got together. It just feels like once you figure out certain aspects of a language that just becomes sticky and the tribe forms around that language, maybe the language, maybe the triforms first in the language of alls. And then you just kind of agree and you stick to whatever that is.

I mean, these are very interesting questions. We don't know really about how words, even words get invented very much about, you know, we don't really, I mean, assuming they get invented, they, we don't really know how that process works and how these things evolve. What we have is kind of a current picture, a current picture of a few thousand languages, a few thousand instances. We don't have any pictures of really how these things are evolving really. And then

the evolution is massively confused by contact. So as soon as one language group, one group runs into another, we are smart. Humans are smart. And they take on whatever is useful in the other group. And so any kind of contrast, which you're talking about, which I find useful, I'm going to, I'm going to start using this well. So I worked a little bit in, in specific areas of words, in, in number words and in, in color words. And in color words, so we have, in English,

we have around 11 words that everyone knows for colors. And, and many more, if you happen to be interested in color for some reason or other, if you're a fashion designer or an artist or something, you may have many, many more words. But we can see millions. Like if you have normal color vision, normal trichromatic color vision, you can see millions of distinctions in colors.

So we don't have millions of words. You know, the most efficient, no, so the most, you know, detailed color vocabulary would have over a million terms to distinguish all the different colors that we can see. But of course, we don't have that. So it's somehow, it's been, it's kind of useful for English to have evolved in some way to such as 11 terms that people find useful to talk about, you know, black, white, red, blue, green, yellow, purple, gray,

pink. And I probably missed something there. Anyway, there's, there's 11 that everyone knows. Yeah. And, and depending on your, and, but you could have different cultures, especially the non industrialized cultures. And there'll be many fewer. So some cultures will have only two, believe it or not, that the Dani and, in Papua New Guinea have only two labels that, that the group uses for color. And those are roughly black and white. They are very, very dark and very,

very light, which are roughly black and white. And you might think, oh, they're dividing the whole color space into, you know, light and dark or something. And that's not really true. They mostly just only label the light, the black and the white things. They just don't talk about the colors for the other ones. And so, and, and then there's other groups. I worked with a group called the Chamani down in, in Bolivia, in South America. And they have three words that everyone

knows. But there's a few others that are that that several people like, and that many people know. And so they have me, it's just kind of depending on how you count between three and seven words that the group knows. Okay. And, and again, they're black and white. Everyone knows those. And red, red is, you know, like that tends to be the third word that everyone that cultures bring in. Nice. If there's a word, it's always red, the third one. And then after that, it's kind of all

bets are off about what they bring in. And so after that, they bring in a sort of a big blue green spade grew, grew, they have one for that. And then they have, and then, you know, different people have different words that they'll use for other parts of the space. So, anyway, and it's probably related to what they want to talk. And what they, not what they, not what they see, because they see the same colors as we see. So it's not like they have, they don't, they have a, a, a, a, a, a,

a low color palette and the things they're looking at. They're looking at a lot of beautiful scenery. Okay. A lot of different colored flowers and berries and things. And, you know, and so there's lots of things of very bright colors, but they just don't label the color in those cases. And the reason probably we don't know this, but we think probably what's going on here is that what you do, why you label something is you need to talk to someone else about it. And why do I need to talk

about a color? Well, if I have two things which are identical, and I want you to give me the one that's different. And the only way it varies is color. Then I invent a word, which tells you, you know, this is the one I want. So I want the red sweater off the rack, not the, not the green sweater. Right. There's two. And until those, those things will be identical, because these are things we made and they're dyed. And there's nothing different about them. And so in, in industrialized

society, we have, you know, everything, everything we've got is pretty much arbitrarily colored. But you go to non industrialized group. That's not true. And so they don't, it's not only they're not interested in color. If you bring bright colored things to them, they like them just like we like them. Bright colors are great. They're beautiful. They are, but they just don't need to, nobody to talk about them. They don't have. So probably color words is a good example of how language evolves from

sort of function when you need to communicate the use of something. I think so. Then you kind of invent different variations. And basically, you couldn't imagine that the evolution of a language has to do with what the early tribes doing. Like what, what they want it, what kind of problems are facing them? And they're quickly figuring out how to efficiently communicate the solution to those problems. Well, there's aesthetic or function, all that kind of stuff running away from a

mammoth or whatever. But you know, it's so I think what you're pointing to is that we don't have data on the evolution of language because many languages have formed a long time ago. So you don't get the chatter. We have a little bit of like old English to modern English because there was a writing system. And we can see how old English looked. So the word order changed, for instance, in old English to middle English to modern English. And so it, you know, we can see things like that.

But most languages don't even have a writing system. So of the 7,000, only, you know, a small subset of those have a writing system. And even if they have a writing system, it's not a very modern writing system. So they don't have it. So we just basically have for Mandarin, for Chinese, we have a lot of a lot of evidence from from for long time and for English and not for much else, not for Mandarin a little bit, but not for a whole lot of like long term language evolution.

We don't have a lot. We have snapshots is what we've got of current languages. Yeah, you get an inkling of that from the rapid communication and certain platforms like on Reddit. There's different communities. And they'll come up with different slang, usually from my perspective, during by a little bit of humor. Or maybe mockery or whatever,

it's, you know, just talking shit in different kinds of ways. And you could see the evolution of language there because I think a lot of things on the internet, you don't want to be the boring mainstream. So you like want to deviate from the proper way of talking. And so you get a lot of deviation, like rapid deviation. Then when communities collide, you get like just like you said, humans adapt to it. And you can see it through the lines of humor. I mean, it's very difficult

to study, but you can imagine like a hundred years from now. Well, if there's a new language, born, for example, we'll get really high resolution data. I mean, English changing, English changes all the time. All languages change all the time. So, you know, there's a famous result. But the Queen's English. So the Queen, if you look at the Queen's vowels, the Queen's English is supposed to be, you know, originally the proper way for the talk was sort of defined by however

the Queen talked or the King whoever was in charge. And so if you look at how her vowels changed from when she first became Queen in 1952 or 1953 when she was current, the first, I mean, that's Queen Elizabeth who died recently, of course, until 50 years later, her vowels changed, her vowels shifted a lot. And so that even in the sounds of British English, in her, the way she was talking was changing. The vowels were changing slightly. So that's just in the sounds there's change.

I don't know what's, you know, we're, we're, I'm interested, we're all interested in what's driving any of these changes. The word order of English changed a lot over a thousand years, right? So it used to look like German. You know, it looks, it used to be a verb final language with case marking and it shifted to a verb, medial language. A lot of contact, so a lot of contact with French and it became a verb medial language with no case marking. And so it became this, you

know, verb, verb initially thing. So, and so that's evolving. We had, it totally evolved. And so it may vary, I mean, you know, it doesn't evolve maybe very much in 20 years is maybe what you're talking about. But over 50 and a hundred years things change a lot, I think. Well now have good data. Yeah. Which is great. That's for sure. Can you talk to what is syntax and what is grammar? So you wrote a book on syntax. I did. You were asking me before about what, you know, how do I figure out what a

dependency structure is? I'd say the dependency structures aren't that hard to generally. I think it's a lot of agreement of what they, of what they are for almost any sentence in most languages. I think people will agree on a lot of that. There are other parameters in the mix such that some people think there's a more complicated grammar than just a dependency structure. And so, you know, like, no, I'm trying to, he's the most famous linguist ever. And he, he is famous for proposing

a slightly more complicated syntax. And so he, he invented phrase structure grammar. So he's well known for many, many things. But in the 50s and early 60s, like, but late 50s, he was basically figuring out what's called formal language theory. So, and he figured out sort of a framework for figuring out how complicated language, you know, a certain type of language might be,

so-called phrase structure grammars of language might be. And so he, his idea was that maybe we can, we can think about the complexity of a language by how complicated the rules are, okay? And the rules will look like this. They will have a left-hand side and it'll have a right-hand side. Look, something will, on the left-hand side, we'll expand the thing on the right-hand side.

So we'll say we'll start with an s, which is like the root, which is a sentence, okay? And then we're going to expand to things, like, a noun phrase and a verb phrase is what he would say, for instance, okay? An s goes to an np and a vp is a kind of a phrase structure rule. And then we figure out what an np is, an np is a, a, a determiner and a noun, for instance. And a verb, verb phrase is something else is a verb and another noun phrase and another np, for instance. Those

are the rules of a very simple phrase structure, okay? And so he proposed phrase structure grammar as a way to sort of cover human languages. And then he actually figured out that, well, depending on the formalization of those grammars, you might get more complicated or less complicated languages. And so he could, he could, he could said, well, you, these are things called, you know, context-free languages, that rule, that he got, you know, human languages tend to be what he calls

context-free languages. And they, but there are simpler languages, which are so-called regular languages, and they have a more constrained form to the rules of the, of the phrase structure of, of these particular rules. So he, he basically discovered and kind of invented ways to describe the language. And those are phrase, those are phrase structure, a human language. And he was, mostly interested in English initially in his, his work in the fifties.

So quick questions around all this. So formal language theory is the big field of just studying language formally. Yes. And it doesn't have to be human language there. We can have a computer language, is any kind of system, which is generating a, some set of expressions in a language. And those could be like the, the, you know, the statements in a, in a computer language, for example. So a formal, it could be that work, it could be human language. So technically, you can study

programming languages. Yes. And heaven, I mean, heavily studied using this formalism. There, there's a big field of programming languages within the formal language. Okay. And then phrase structure, grammar is this idea that you can break down language into this S N P V P type of thing. It's a particular formalism for describing language. Okay. So and, and Chomsky was the first one, he's going to figure that stuff out back in the fifties. And, and, but he, and that's

equivalent. Actually, the context for grammar is actually, is kind of equivalent in the sense that it generates the same sentences as a dependency grammar would. You know, as the dependency grammar is a little simpler in some way. You just have a root. And it goes, like, we don't have any of these, the rules are implicit, I guess. And we just have connections between words. The free structure, grammar is kind of a different way to think about the dependency grammar.

So it's slightly more complicated, but it's kind of the same in some ways. So to clarify, dependency grammar is the framework under which you see language in your case that this is a good way to describe language. That's correct. And, uh, no, no, no, Chomsky's watching this is very upset right now. So it's, uh, just kidding, but, uh, what's the difference between, uh, where's the, the place of disagreement between phrase structure, grammar and dependency grammar?

They're very close. So free structure grammar and dependency grammar aren't that aren't that far apart. I like dependency grammar because it's more perspicuous, it's more transparent about representing the connections between the words. It's just a little harder to see in phrase structure grammar. You know, the place where Chomsky sort of devolved or went off from, from, from this is, he also thought there was, um, something called movement. Okay. And so, and that's where we

disagree. Okay. That's the place where I would say we disagree. And, and, and, and I mean, well, maybe we'll get into that later, but the idea is if you want to, do you want me to explain that now? I would love to explain movement movement. Okay. So, so many interesting things. Yeah. Okay. So here's the movement is Chomsky basically sees English. And he says, okay, I said, um, you know, this is, we had that sentence sentence earlier, like it was like two dogs entered the room. It's

changed a little bit. Say two dogs will enter the room. And he notices that, hey, English, if I want to make a question, a yes, no question from that same sentence, I, I say instead of two dogs will enter the room, I say will two dogs enter the room. Okay. There's a different way to, to say the same idea. And it's like, well, the auxiliary verb that will thing, it's at the front

as opposed to in the middle. Okay. And so, and he looked, you know, if you look at English, you see that that's true for all those modal verbs and for other kinds of auxiliary verbs in English, you always do that. You always put an auxiliary verb at the front. And, and what he, when he saw that, so, you know, if I say, um, I can win this bet, can I win this bet? Right. So I move a can to the front. So actually, that's a theory. I just gave you a theory there. He talks about it

as movement. That word in the decalitines, the declarative is the root is, is the sort of default way to think about the sentence. And you move the auxiliary verb to the front. That's a movement theory. Okay. And he's, he just thought that was just so obvious that it must be true. That, that there's nothing more to say about that. That this is how auxiliary verbs work in English. There's a movement rule such that you're move like to get from the declarative to the interrogative,

you're moving the auxiliary to the front. And it's a little more complicated as soon as you go to simple, simple, present and simple past because, you know, if I say, you know, John slept, you have to say, did John sleep, not slept John, right? And so you have to somehow get an auxiliary verb and I guess underlyingly, it's like slept is, it's a little more complicated than that, but his, that's his idea. There's a movement. Okay. And, and so a different way to think about that that

isn't, I mean, the, then he ended up showing later. So he proposed this theory of grammar, which has movement. There's other places where he thought there's movement, not just auxiliary verbs, but things like the passive in English and things like a question, WH questions, a bunch of places where he thought there's also movement going on. And, and, and each, each one of those he thinks there's words, well phrases and words are moving around from one structure to another,

which he called deep structure to surface structure. I mean, there's like two different structures in his, in his theory. Okay. There's a different way to think about this, which is there's no movement at all. There's a lexical copying rule such that the word will or the word can these auxiliary reverbs, they just have two forms. And, and, and one of them is the declarative and one of them is interrogative. And you basically have the declarative one. And oh, I formed the interrogative,

or I can form one from the other, it doesn't matter which direction you go. And, and I just have a new entry, which has the same meaning, which has a slightly different argument structure, argument structure, it's a fancy word for the ordering of the words. And so if I say, you know, it was, um, the, the dogs, two dogs can or will enter the room. The, the, there's two forms of will. One is will declarative. And, and then okay, I've got my subject to the left. It comes before me. And

the verb comes after me in that one. And then the will interrogate. If it's like, oh, I go first, interrogative will is first. And then I have the subject immediately after and then the verb after that. And so you just, you can just generate from one of those words, another word with a slightly different argument structure with different ordering. And these are just lexical copies that don't, they're not necessarily moving from one to another. There's no movement. There's a romantic notion

that you have like one main way to use the word. And then you can move it around. Right, right. Which is essentially what movement is applying. Yeah, but that's, that's the lexical copying is similar. So that you, so that then we, we do lexical copying for that same idea that maybe the declarative is the source. And then we can copy it. And so in advantage, for whether there's

multiple ventures of the lexical copying story, it's not my story. This is like, Ivan Sog, linguist, a bunch of linguists have been proposing these stories as well, you know, in tandem with the movement story. Okay, you know, he's, he's, Ivan Sog died a while ago, but he was a one of the proponents of the non-movement of the lexical copying story. And so that is that a great advantage is, well, Chomsky, really famously in 1971, showed that the movement story

leads to learnability problems. It leads to problems for how language is learned. It's really, really hard to figure out what the underlying structure of a language is. If you have both phrase structure and movement, it's like really hard to figure out what came from what. There's like a lot of possibilities there. If you don't have that problem, learning, the learning problem gets a lot easier. Just say there's lexical copies. Yeah, yeah, yeah. Well, we say the learning

problem. Do you mean like humans learning a new language? Yeah, just learning English. So baby is lying around listening to the crib, listening to me talk. And you know, how are they learning English? Or, or, you know, maybe it's a two-year-old who's learning, you know, interrogatives and stuff or one, you know, they're, how are they doing that? Are they doing it from like, are they figuring out? Or like, no, so Chomsky said it's impossible to figure it out actually. He said it's actually

impossible, not, not hard, but impossible. And therefore, that's where a universal grammar comes from. Is that it has to be built in. And so what they're learning is, there's some built-in movement is built in in his story is absolutely part of your language module. And then you are, you're just setting parameters. You're, you're said, depending on English, it's just sort of a variant of the universal grammar. And you're figuring out, oh, which orders does English do these

things. That's the, the non-movement story doesn't have this. It's like much more bottom-up. You're learning rules. You're learning rules one by one. And oh, there's this, this word is connected to that word. A great advantage, another advantage is learnable. Another advantage of it is that it predicts that not all exhilaries might move. Like it might depend on the word, depending on whether you, and that turns out to be true. So there's words that, that don't really work as a

auxiliary, they work in declarative and not an interrogative. So I can say, I'll give you the opposite first. So I can say, aren't I invited to the party? Okay. And that's an, that's an interrogative form. But it's not from, I aren't invited to the party. There is no I aren't. Right. So that's, that's interrogative only. And, and then we also have forms like, uh, I, I ought to do this. And, and I guess some British old British people can say, I, exactly, it doesn't

sound right. Does it? For me, it sounds ridiculous. I don't even think, oh, it is great. But I mean, I totally recognize I ought to do it. I can say, oh, to do this. That sounds very good. Yeah. If I'm trying to sound sophisticated, maybe I don't know. It just sounds completely up to me. Yeah. I, anyway, it's, it's, so there are variants here. Uh, and a lot of these words just work in one

versus the other. And, and that's like fine under the lexical copying story. It's like, well, you just learn the usage, whatever the usage is is what you, is what you do with this, with, with you with this word. But, um, it doesn't, it's a little bit harder in the movement story. The movement story, like that's an advantage. I think of lexical copying in all these different places. There's, there's all these usage variants, which make the movement story a little bit harder

to work. So one of the main divisions here is the movement story versus the lexical copying story that has to do about the auxiliary words and so on. But you rewind to the phrase structure grammar versus dependency grammar. Those are equivalent in some sense in that for any dependency grammar, I can generate a dependency phrase structure grammar, which generates exactly the same sentences.

I just, I just like the dependency grammar, uh, formalism, because it makes something really salient, which is the dependent, the lengths of dependencies between words, which isn't so obvious in the phrase structure. In the phrase structure, it's just kind of hard to see. It's in there. It's just very, very, it's opaque. Uh, technically, I think phrase structure grammar is mapable to dependency grammar and vice versa. In vice versa. Yeah, yeah. There's like these like little labels S on

PVP. Yeah. For a particular dependency grammar, you can make a phrase structure grammar, which generates exactly those same sentences and vice versa. But there are many phrase structure grammars, which you can't really make a dependency grammar. I mean, they're, you can do a lot more in a phrase structure grammar, but you get many more of these extra nodes basically. You, you can have more structure in there. Uh, and some people like that. And maybe there's value to that. I, I,

I don't like it. Well, for you, so, which clarifies, so, so dependency grammar, it's just, uh, well, one word depends on only one other word and you form these trees. Yes. And that makes, it really puts priority on those dependencies, just like as a, as a tree that you can then measure the distance of the dependency from what to work to the other, they can then map to the cognitive processing of the, of the sentences, how well, how easy is to understand and all that kind of

stuff. So it just puts the focus on just like the mathematical, um, uh, distance of dependence between words. So like, it's just a different focus. Absolutely. Just continue on a thread of JavaScript, because it's really interesting. Because it, as you're discussing this agreement to the degree there's this agreement, you're also telling the history of the study of language, which is really awesome. So you mentioned context free versus regular. Does that distinction

come into play for dependency grammars? No, not at all. I mean, regular, regular languages are too simple for human languages. They are, uh, they, it's a part of the hierarchy, but human languages are in, in the phrase structure world are definitely, they're, at least context free,

maybe a little bit more, a little bit harder than that. But, uh, so there's something called context sensitive as well, where you can have, like this is just the formal language description, in a context free grammar, you have one, this is like a bunch of like formal language theory. We're doing here, but I love it. Okay. So you have, you have a left hand side category, and you're

expanding to anything on the right is, is a, uh, that's a context free. So like the idea is that that category on the left expands in independent of context to those things, whatever they're on the right, it doesn't matter what. And, and a context sensitive says, okay, I actually have more than one thing on the left. I can tell you only in this context, you know, I have maybe you have like a left in a right context or just a left context or a right context. I have two or more stuff on the left,

tells you how to expand that those things in that way. Okay. So it's context sensitive. A regular language is just more constrained. And so it, it doesn't allow anything on the right. It allows very, it allows, basically, it's a one very complicated rule is kind of what a, a, a regular language is. And so it doesn't have any, um, let's just say the long distance dependencies. It doesn't allow

recursion, for instance. There's no recursion. Yeah, recursion is where you, which is, human languages have recursion, they have embedding, and you can't, well, it doesn't allow center embedded recursion, which human languages have, which is what center embedded recursion within a sentence, within a sentence. Yeah, within a sentence. So here we're going to get to that. But I, you know, the formal language stuff is a little aside. It was, Chomsky wasn't proposing

it for human languages, even he was just pointing out that human languages are context free. And then he was most in for for human, because that was kind of stuff we did for formal languages. And what he was most interested in was human language. And that's like the, the movement is where we, we, we, we, where he sort of set off in on the, I would say, I'm very interesting, but wrong foot. It was kind of interesting. It's a very, I agree. It's kind of, it's a very interesting

history. So there's this, so he proposed this multiple theories in 57 and then 65. They're, they all have this framework though. It was phrase structure plus movement, different versions of the, of the phrase structure and the movement in the 57. This is the most famous original bits of Chomsky's work. And then 71 is when he figured out that those lead to learning problems, that, that there's cases where a kid could never figure out which rule, which set of rules was intended.

And, and so, and then he said, well, that means it's innate. It's kind of interesting. He just really thought the movement was just so obviously true. That he couldn't, he didn't even entertain

giving it up. It's just obvious. That's, that's obviously right. And it was later where people figured out that there's all these like subtle ways in which things, which, which look like generalizations aren't generalizations and they, you know, across the category, they're, they're words specific and they have, and they kind of work, but they don't work across various other words in the category. And so it's easier to just think of these things as lexical copies.

And, and I think he was very obsessed. I don't know. I'm just guessing that he, he just, he really wanted this story to be simple in some sense. And language is a little more complicated. In some sense, you know, he didn't like words. He never talks about words. He likes to know about combinations of words. And words are, you know, look up addiction area. There's 50 census for a common word, right? The word take will have 30 or 40 census in it. So, there'll be many different

census for common words. And he just doesn't think about that. It's, it doesn't think that's language. I think he doesn't think that's language. He thinks that words are distinct from combinations of words. I think they're the same. If you look at my brain on in the scanner while I'm listening to a language I understand, and you compare, I can localize my language network in a few minutes,

in like 15 minutes. And what you do is I listen to a language I know, I listen to, you know, maybe some language I don't know or I listen to muffled speech or I read sentences that I read non words. Like I can do anything like this. Anything that sort of really like English and anything that's not very like English. So, I've got something like it and not and I got to control. And the voxels, which is just, you know, the 3D pixels in my in my brain that are responding most

are, is a language area. And that's this left lateralized area in my head. And wherever I look in that network, if you look for the combinations versus the words, it's, it's it's it's it's everywhere. It's the same. That's fascinating. And so it's like hard to find. There are no areas that we know. I mean, that's, it's a little overstayed right now at this, at this point, the

the technology isn't great. It's not bad. But we have the best, the best way to figure out what's going on in my brain when I'm listening or reading language is to use FMRI functional magnetic resonance imaging. And that's a very good localization method. So I can figure out where exactly these signals are coming from pretty, you know, down to, you know, millimeters, you know, cubic millimeters are smaller. Okay, very small. We can figure those out very well. The problem is the when.

Okay. It's it's measuring oxygen. Okay. And oxygen takes a little while to get to those cells. That's what takes on the order of seconds. So I talk fast. I probably listen fast. And I can probably understand things really fast. So a lot of stuff happens in two seconds. And so to say that we know what's going on that the words right now in that network are best guesses that whole network is doing something similar, but maybe different parts of that network are doing different

things. And that's probably the case. We just don't have very good methods to figure that out right at this moment. And so since we're kind of talking about the history of the study of language, what other interesting disagreements and you're both at MIT or were for a long time, what kind of interesting disagreements their attention of ideas are there between you and

no chance game. We should say that known was in the linguistics department. And you're, I guess, for a time we're affiliated there, but primarily brain and cognitive science department. Which is another way of studying language. And you've been talking about FRIRI. So like, what is there something else interesting to bring to the surface about the disagreement between the two of you or other people in the industry? Yeah, I mean, I've been at MIT for 31 years since 1993.

And he, John, he's been there much longer. So I met him. I knew him. I met him. I first got there, I guess. And we would interact every now and then I'd say that. So I'd say our biggest difference is our methods. And so that's the biggest difference between me and known is that I gather data from people. I do experiments with people and I gather corpus data. Whatever, whatever corpus data is available and we do quantitative methods to evaluate any kind of hypothesis

we have, he just doesn't do that. And so, you know, you know, he has never once been associated with any experiment or corpus work ever. And so it's all thought experiments. It's his own intuitions. So I just don't think that's the way to do things. That's a, that's a, you know, across the street, they're across the street from us kind of difference between brain and cogsai and linguistics. I mean, not all linguists, some of the linguists, depending on what you do, more speech-oriented,

they do more quantitative stuff. But in the, in the meaning words and, well, it's combinations of words and texamantics, they tend not to do experiments and corpus analyses. So I know the linguistics size probably, well, but the method is a symptom of a bigger approach, which is sort of a psychology philosophy side unknown for you. It's more sort of data-driven, so they're almost like mathematical approach. Yeah, I mean, I'm psychologist. So I would say we're in psychology. You know, I'm

in brain cognitive sciences is MIT's old psychology department. It was a psychology department up until 1985 and that became the brain cognitive science department. And so I, I mean, my training is in psychology. I mean, my training is math and computer science, but I'm a psychologist. I mean, I mean, I don't know what I am. So data-driven, psychologist. Yeah, yeah, you are. I know what I am, but I'm having to call the linguist. I'm having to be called a computer scientist. I'm having to

be called psychologists. Any of those things? But in the actual, like how that manifests itself outside of the methodology is like these differences, these subtle differences about the movement story versus the lexical copy story. Yeah, those are theories, right? So the theory is like the theories are, but I think the reason we differ in part is because of how we evaluate the theories. And so I evaluate theories quantitatively and no, I'm doesn't. Got it. Okay, well, let's explore

the theories that you explore in your book. Let's return to this dependency grammar framework of looking at language. What's a good justification? Why the dependency grammar framework is a good way to explain language. What's your intuition? So the reason I like dependency grammar, as I've said before, is that it's very transparent about its representation of distance between words.

So it's like, all it is is you've got a bunch of words, you're connecting them together to make a sentence and a really neat insight, which turns out to be true, is that the further apart the pair of words are that you're connecting the harder it is to do the production, the harder it is to do the comprehension. It says harder to produce, hard to understand when the words are far apart, when they're close together, it's easy to produce and it's easy to comprehend. Let me give you an

example. Okay, so we have in any language, we have mostly local connections between words, but they're abstract. The connections are abstracted between categories of words. And so you can always make things further apart if you add modification, for example, after a noun, so a noun in English comes before verb, the subject noun comes before verb, and then there's an object after, for example. So I can say what I said before, you know, the dog entered the room or something like that. So I can

modify dog. If I say something more about dog after it, then what I'm doing is indirectly, I'm lengthening the dependence between dog and entered by adding more stuff to it. So I just make it explicit here if I say the boy who the cat scratched cried. We're going to have a mean cat here. And so what I've got here is I get the boy cried. It would be a very short, simple sentence. And I just told you something about the boy and I told you it was the boy who the cat scratched. Okay.

So the cry is connected to the boy. The cry at the end. Yeah. It's connected to the boy in the beginning. Right. And so I can do that. I can say that. That's a perfectly fine English sentence. And I can say the cat, which the dog chased ran away or something. Okay. I can do that. But it's really, so it's really hard now. I've got, you know, whatever I have here, I have the boy who the cat. Now let's say I try to modify cat. Okay. The boy who the cat, which the dog chased scratched ran away.

Oh my God, that's hard, right? I can, I'm sort of just working that through my head. How to produce and how to and it's really just horrendous to understand. It's not so bad. At least I've got intonation there to sort of mark the boundaries and stuff. But it's, that's really complicated. That's sort of English in a way. I mean, that follows the rules of English. But so what's interesting about that is is that what I'm doing is nesting dependencies there. I'm putting one, I've got a

subject connected to a verb there. And, and then I'm modifying that with a clause, another clause, which happens to have a subject in a verb relation. I'm trying to do that again on the second one. And what that does is it lengthens out the dependence, multiple dependence actually get length and out there. The dependencies get get longer longer on the outside ones get long. And even the ones in between get kind of long. And, and you just, so what's fascinating is that that's bad. That's

really horrendous in English. But that's horrendous in any language. And so in no matter what language you look at, if you do, just figure out some structure where I'm going to have some modification following some head, which is connected some later head. And I do it again, it won't be good. It guaranteed. Like 100% that will be uninterpretable in that language. In the same way that was uninterpretable in English. So clarify the distance of the dependencies is whenever the boy cried,

there's a dependence between two words. And then you counting the number of what morphines between them. That's a good question. I just say words. Your words are morphines between. We don't know that. Actually, that's a very good question. What is the distance metric? But let's just say it's words. Sure. Okay. So that, and you're saying the longer the distance is that dependence, the more, no matter the language, except legalese, even legalese. Okay. We'll talk about it. Okay. Okay.

But that the people will be very upset that speak that language, not upset, but they'll either not understand it. They'll be like, this is the brain will be working in overtime. Yeah. They would have a hard time either producing your comprehending it. They might tell you that's not their language. You know, it's sort of the language. I mean, it's following their, like, they'll agree with each of those pieces as part of the language. But somehow that combination will

be very, very difficult to produce and understand. Is that a chicken or the egg issue here? So like, is, well, I'm giving you an explanation. Right. So the, I mean, I mean, and then there's, I'm giving you two kinds of explanations. I'm telling you that centrum bending, that's nesting. Those are the same, those are synonyms for the same concept here. And I'm the explanation for what those are always hard centrum bending and nesting are always hard.

And I give you an explanation for why they might be hard, which is long distance connections. There's a, when you do centrum bending, you're nesting, you always have long distance connections between the dependence you just, and so that's not necessarily the right explanation. It just happened. I can go through reasons why that's probably a good explanation. And it's not really just about one of them. It, so probably it's a pair of them or something of these dependents

that you get, get long that drives you to like be really confused in that case. And so what the, the behavioral consequence there, if you, I mean, we, this is kind of methods, like how do we get at this? You could try to do experiments to give people to produce these things. They're going to have a hard time producing them. You can try to do experiments to get them to understand them and you get, you see how well they understand them, can they understand them? Another method you can do is

give people partial materials and ask them to complete them. You know, those, those centrum bedded materials and they, they'll fail. So I've done that. I've done all these kinds of things. So, so, so, so, so central bedding meaning like you can take a normal sentence like boy, cried and inject a bunch of crap in the middle. Yes. That separates the boy and the cried. Okay. That central bedding and nesting is on top of that. No, nesting is the same thing.

Central bedding, those are totally equivalent terms. I'm sorry. I sometimes use one or sometimes one. Oh, God, I got it. Totally. Anything different. Got it. And then what you're saying is there's a bunch of different kinds of experiments you can do. I mean, I like to understand anyone is like have more embedding, more central bedding. Is it easier or harder to understand, but then you have to measure the level of understanding, I guess. Yeah. Yeah, you could. I mean, there's multiple

ways to do that. I mean, there's there's the simplest ways just ask people how good is it sound? How natural is it sound? That's a very blunt but very good measure. It's very, very reliable. People will do the same thing. And so it's like, I don't know what it means exactly, but it's doing something such that we're measuring something about the confusion, the difficulty associated with those. And those like those are giving you a signal. That's why you can say them. Okay.

Yeah. Very. What about the completion of this with the central bed? So if you give them a partial sentence, say I say the book, which the author, who, and I ask you to now finish that off. I mean, either say, yeah, yeah, but you can just say it's written in front of you and you can just type and have much time as you want. They will, even though that one's not too hard, right? So if I say it's like the book is like, oh, the book, which the author, who I met,

wrote was good. You know, that's a very simple completion for that. If I give that completion on online somewhere to a, you know, a crowdsourcing platform and ask people to complete that, they will miss off of a verb, very regular, like half a time, maybe two thirds of the time. They'll say, they'll just leave off one of those verb phrases, even with that simple so to say the book, which the author, who, and they'll say, was, you need three verbs, right? I need three verbs,

or who I met, wrote, was good, and they'll give me two. They'll say, who was famous was good, or something like that? They'll just give me two. And that'll happen about 60% of the time. So 40%, maybe 30, they'll do it correctly, correctly, meaning they'll do a three verb phrase. I don't know what's correct or not. You know, it's hard. It's a hard task. Yeah, I get it. I'm struggling

with it in my head. Well, it's easier when you when you look at it, if you look at a little easier, then listening is pretty tough because you have to, because there's no trace of it, you have to remember the words that I'm saying, which is very hard, auditarily. We wouldn't do it this way. We do it written. You can look at it and figure it out. It's easier in many dimensions in some ways, depending on the person's easier to gather written data for, I mean, most sort of cycle,

I work in cycle linguistics, right? Psychology of language and stuff. And so a lot of our work is based on written stuff because it's so easy to gather data from people doing written kinds of tasks. Spoken tasks are just more complicated to administer and analyze because people do weird things when they speak. And it's harder to analyze what they do, but they generally point to the same kinds of things. So, okay. So the universal theory of language, yeah, by Ted Gibson is that you

can form dependency, you can form trees from any senses. That's right. You can measure the distance in some way of those dependencies. And then you can say that most languages have very short dependencies. All languages. All languages. All languages have short dependencies. You can actually measure that. So a next student of mine is guys at University of California Irvine, Richard Futrell did a thing a bunch of years ago now where he looked at all the languages we could look at,

which was about 40 initially. And now I think there's about 60 for which there are dependency structures. So there are meaning that it's got to be like a big text, a bunch of texts, which have been parsed for the dependency structures. And there's about 60 of those which have been parsed that way. And for all of those, you can what he did was take any any sentence in one of those languages and you can do the dependency structure. And then start at the root, we were talking about

dependency structures. That's pretty easy now. And he's trying to figure out what a control way you might say the same sentence is in that language. And so we just just like, all right, there's a root. And it has a say as a sentences, let's go back to two dogs entered the room. So entered is the root and entered has two dependents that's got dogs and it has room. Okay. And what he does is like, let's scramble that order. That's three things, the root and the head and the two dependents

and into some random order, just random. And then just do that for all the dependents down the two. So now look, do it for the and whatever was two in dogs and for room. And that's, you know, that's not a it's a very short sentence. When sentences get longer and you have more dependents, there's more scrambling that's possible. And when he found what so that so that's one, you can figure out one scrambling for that sentence. He did it like a hundred times for every sentence

in every corp in every one of these texts, every corpus. And then he just compared the dependency lengths in those random scramblings to what actually happened with what the English or the French or the German was in the original language or Chinese or what all these like 80 like, you know, 60 languages, okay. And the dependency lengths are always shorter in the real language compared to these, this kind of a control. And there's another that he, it's a little more rigid his control.

So the way I described it, you could have crossed dependencies like that by scrambling that way, you could scrambling anyway at all. Languages don't do that. They tend not to cross dependencies very much. Like so the dependency structure, they just, they tend to keep things non-crossed. And there's a, you know, there's a technical term they call that projective, but it's just non-crossed is all that is projective. And so if you just constrain the scrambling so that it only gives you projective

sort of non-crossed, it's the same thing holds. So it's, so the, you still, still human languages are much shorter than these, this kind of a control. So there's like, it, what it means is that that, that we're in every language, we're trying to put things close relative to this kind of a control. Like there, it doesn't matter about the word order. Some of these are verb final, some of them use a verb, media like English. And some are even verb initial. There are a few languages of the

world, which have VSO, world order, word order, verb, subject, object language. I haven't talked about those. It's like 10% of the, and even even in those languages, it's still short dependencies. Certain dependencies is rules. Okay, so what, what, what are some possible explanations for that? For why, why languages have evolved that way? So that, that's one of the, I suppose, disagreements you might have with Chomsky. So you consider the evolution of language in,

in terms of information theory. And for you, the purpose of languages is of communication, I am processing. That's right. That's right. So I mean, the story here is just about communication. It is just about production, really. It's about ease of production is the story. When we say production, can you, can you, oh, I just mean ease of language production. It's easier for me to say things when the, come, when I'm doing, whenever I'm talking to you,

it's somehow I'm formulating some idea in my head and I'm putting these words together. And it's easier for me to do that, to put, to say something where the words are close, closely connected, and it depends the, as opposed to separated, like by putting something in between and over and over again, it's just hard for me to keep that in my head. Like that's, that's the whole story. Like the story, it's basically, it's like the dependency grammar sort of gives that to you,

like just like long, long as bad, sure, it's good. It's like easier to keep in mind because you have to keep it in mind for, probably for production, probably matters in comprehension as well. Like also matters in comprehension. So I'm both sizing the production in the end. But I would guess it's probably evolved for production. It's about producing. It's about what's easier for me to say that ends up being easier for you also. And that's a very hard to disentangle

this idea of who's it for. Is it for me, the speaker, or is it for you, the listener? I mean, part of my language is for you. Like the way I talk to you is going to be different from how I talk to different people. So I'm, I'm definitely angling what I'm saying to who I'm saying, right? It's not like I'm just talking the same way to every single person. And so I am sensitive to my audience. But how does that, does that work itself out in the dependency length differences? I don't know.

Maybe that's about just the words that part, you know, which words I select. My initial intuition is that you optimize language for the audience. Yeah. But it's just kind of like messing with my head a little bit to say that some of the optimization might be, and maybe the primary objective of the optimization might be the ease of production. Yeah. We have different senses, I guess. I'm like very selfish. And you're like, I don't think it's like it's all about me. I'm like, I'm just doing

this easiest for me. I don't want to, I'm like, I'll, I mean, but I have to of course choose the words that I think you're going to know. I'm not going to choose words you don't know. In fact, I'm going to fix that when I, you know, so there it's about, but, but maybe for, for the syntax, for the combinations, it's just about me. I feel like it's, I don't know though. It's great. Wait, wait, wait, wait, wait, but the purpose of communications to be understood is to convince

others and so on. Yeah. So like the selfish thing is to be understood. It's about the listener. It's a little circular there too, then. Okay. Right. I mean, like the ease of production helps me be understood then. I don't think it's circular. So I think the primary, I think the primary objective is to be understood is about the listener. Because otherwise, if you're optimizing to, for the ease of production, then you're, you're not going to have any

of the interesting complexity of language. Like you're trying to like explain what it is I want to say. Like I, I'm saying, let's control for the thing the, the message control for the message. But I mean, the message needs to be understood. That's the goal. But that's the meaning. So I'm still talking about the form. Just the form of the meaning. How do I frame the form of the meaning is all I'm talking about. You're talking about a harder thing, I think. It's like how am I,

like, like, try to change the, like, let's, let's keep the meaning constant. Like which you got it. We have you keep the meaning constant. How can I phrase whatever it is I need to say? Like I get to pick the right words. And I'm going to pick the order so that it's so it's easy for me. You know, that's, that's, that's what I think is probably. I think I'm still tying meaning and form

together in my head. But you're saying if you keep the meaning of you're saying constant, yeah, what the optimization, yeah, it could be the primary objective of that optimization is the for production. That's interesting. I'm struggling to keep constant the meaning. It's just so, I mean, I'm such a human, right? So for me, the form without having introspected on this,

the form and the meaning are tied together, like, deeply, because I'm a human. Like for me, when I'm speaking, that I haven't thought about language, like in a rigorous way about the form of language. But look, for any event, there's, there's an unbounded, I don't want to see infinite, but sort of unbounded ways of that I might communicate that same event. This two dogs entered a room, I can say in many, many different ways. I can say, hey, there's two dogs. They entered the room.

Hey, the room was entered by something. The thing that was entered was two dogs. I mean, there's, I mean, it's kind of awkward and weird stuff. But those are all similar messages with different forms, different ways that might frame. And of course, I use the same words there all the time. I could have referred to the dogs as, you know, a Dalmatian and a Poodler or something. You know, I could have been more specific or less specific about what they are. And I could have said,

been more abstract about about the number. There's like, so I, like, I'm trying to keep the meaning, which is this event constant. And then how am I going to describe that to get that to you, it kind of depends on what you need to know, right? And what I think you need to know. But I'm like, turn it, let's go control for all that stuff. And not, and, and they're like, I'm just like

choosing, but I'm doing something simpler than you're doing, which is just forms. Yes, just words to use specifying the species of the breed of dog and whether they're cute or not is changing the meaning. That might be, yeah, yeah, that would be changing. Oh, that would be changing the meaning for sure. Right. So you're just, yeah, yeah, yeah, that's changing the meaning. But say, even if we

keep that constant, we can still talk about what's easier hard for me, right? The listener and the which phrase structures I use, which combinations, which, you know, this is so fascinating and just like a, a really powerful window into human language. But I wonder still throughout this, this how vast the gap between meaning and form, I just, I just have this like, maybe romanticize notion that they're close together, that they evolve close to like hand in hand, that you can't

just simply optimize for one without the other being in the room with us. Like it's, well, it's kind of like an iceberg form is the tip of the iceberg and the rest, the, the meaning is the iceberg, but you can't like, except, but I think that's why these large language models are so successful,

because they're good at form and form isn't that hard. In some sense, and meaning is tough still, and that's why they're not, they're, you know, they don't understand what they're, we're going to talk about that later, maybe, but like we can distinguish in our, forget about large language models, like humans, where maybe you'll talk about that later too, is like the difference between language, which is a communication system and thinking, which is meaning. So language is a communication

system for the meaning, it's not the meaning. And so that's why, I mean, that, and there's a lot of interesting evidence we can talk about relevant, relevant to that. Well, I mean, that's a really interesting question. What is the different, what is the difference between language written, communicated versus thought? What to use the difference between them? Well, you or anyone has to think of a task, which they think is, is a good thinking task. And there's lots and lots and

tasks, which should be good thinking tasks. And whatever those tasks are, let's say it's, you know, playing chess or that's a good thinking task, or playing some game, we're doing some complex puzzles, maybe, maybe remembering some digits that's thinking, remembering some, a lot of different tasks we might think, maybe just listening to music is thinking, or there's a lot of different tasks we might think of as thinking. There's a woman in my department at Federico and she's done a lot of

work on this question about what's the connection between language and thought? And so she uses, I was referring earlier to MRI, FMRI, that's her primary method. And so she has been really fascinated by this question about whether, what language is? Okay. And so as I mentioned earlier, you can localize my language area, your language area in a few minutes. Okay. Like 15 minutes,

I can listen to language, listen to non-language or backward speech or something. And we'll find areas left lateralized network in my head, which is very sensitive to language as opposed to whatever that control was. Okay. Can you specify what you mean by language, like communicating the language? Like what is the thing? Just sentences. You know, I'm listening to English of any kind, story, or it can read sentences, anything at all that I understand, if I understand it,

then it'll activate my language network. So right now my language network is going like crazy when I'm talking and when I'm listening to you because we're both communicating. And that's pretty stable. Yeah. It's incredibly stable. So I've, I happen to be married to her this woman at Federico. So I've been scanned by her over and over and over since 2007 or six or something. And so my language network is exactly the same, you know, like a month ago as it was back in 2007.

It's amazingly stable. It's astounding. And with that, it's, it's a really fundamentally cool thing. And so my language network is, it's like my face. Okay. It's not changing much over time inside my head. Can I ask a quick question? Sorry, is it small tangent? At which point in the, as you go up from

baby to adult, does it stabilize? We don't know. Like that's a very hard question. They're working on that right now because of the problem scanning little kids, like doing the, trying to do local, trying to do the, the localization on little children in this scanner where you're lying in the FMRI scan. That's the best way to figure out where something's going on inside our brains. And the scanner is loud and you're in this tiny little, you know, area, your claustrophobic. And

it doesn't bother me at all. I can go sleep in there. But some people are bothered by it. And little kids don't really like it. And they don't like to lie still. And you have to be really still because you move around that, that messes up the coordinates of where, where everything is. And so, you know, try to get, you know, your question is, how and when are language developing? You know, how, when, when, how does this left lateralized system come to play? Where's it?

You know, and it's really hard to get a two year old to do this task. But you can maybe, they're starting to get three and four and five year olds to do this task for short periods. And it looks like it's there pretty early. So clearly when you lead up to like a baby's first words before that, there's a lot of fascinating turmoil going on about like figuring out like, what are these people saying? And you're trying to like make sense. How does that connect to the

world? No, that kind of stuff. Yeah, that might be just fascinating development that's happening there. That's hard to interest. But anyway, you, we're back to the scanner. And I can find my network in 15 minutes. And now we can ask a, we can ask, find my network, find yours, find, you know, 20 other people do this task. And we can do some other tasks. Anything else you think is thinking of some other thing. I can do a spatial memory task. I can do a music perception task.

I can do programming task if I program. Okay. I can do what where I can like understand computer programs. And none of those tasks tap the language network at all. Like at all. There's no overlap. They do, they're, they're highly activated in other parts of the brain. There's a, there's a bilateral network, which I think she tends to call the multiple demands network, which does anything kind of hard and sort of anything that's kind of difficult in some ways will activate that

multiple demands network. I mean, music will be in some music area. You know, there's music specific kinds of areas. And so, but there, but but none of them are activating the language area at all. Unless there's words. Like, so if you have music and there's a song and you can hear the words then, then then you get the language area. We're talking about speaking and listening, but are, or we also talking about reading. This is all comprehension of any kind. And so, that is

fascinating. So what this this this network doesn't make any difference if it's written or spoken. So the, the, the thing that she calls, Federico calls the language network is this high level language. So it's not about the spoken, the spoken language. And it's not about the written

language. It's about either one of them. And so we're, so when you do speech, you're sort of list, you're you're either you're listening to speech and you're you know, subtract away some language you don't understand and so we're just, and so you subtract away back, backward speech, which sounds sounds like speech, but isn't. And, and then so you, you take away the sound part all together.

And so, and then if you do written, you get exactly the same network. So for just reading the language versus reading sort of nonsense words or something like that, you'll find exactly the same network. And so it's about high level, um, the comprehension of language. Yeah. In this case. And the same thing happened, productions a little harder to run the scanner, but the same thing happens in

production. You get the same network. So productions a little harder, right? You have to figure out how do you run a task, you know, in the network, such that you're doing some kind of production. And I can't remember what they've done a bunch of different kinds of tasks there where you get people to produce things. Yeah. Figure out how to produce. And the same network goes on there. It's actually the same place. And so you should wait, wait. So if you read random words, yeah, you need

things like like gibberish. Yeah. Yeah. Lewis Carroll's, it was brilliant. Geberwaki, right? They call that Geberwaki speech. The network doesn't get activated. Not as much. There are words in there. Yeah. Because it's cool. There's function words and stuff. So it's lower activation. Yeah. Yeah. So there's like the more language like it is, the higher it goes in the language network. And that network is there from when you speak from as soon as you learn language. And

and it's it's there. Like you speak multiple languages. The same network is going for your multiple languages. So you speak English, you speak Russian. The, the, the, both of them are hitting that same network. If you, if you're affluent in those languages, programming, not at all. Isn't that amazing? Even if you're a really good programmer, that is not a human language. It's just not conveying the same information. And so it is not in the language network. And so that as mind blowing is

I think that's pretty cool. That's weird. It's amazing. And so that's like one set of days. This hers like shows that what you might think is thinking is is not language language is just the seek just just this conventionalized system that we've worked out in human languages. Oh, another fascinating little bit to bit is that even if they're these constructed languages like Klingon or I don't know the languages from Game of Thrones. I'm sorry. I don't remember those

languages. Maybe a lot of people are finding right now. There's people that speak those languages. They really speak those languages because the people that wrote the languages for the shows, they did an amazing job of constructing something like a human language. And those that lights up the language area. That's like because they can speak, you know, pretty much arbitrary thoughts

in a human language. It's not a, it's a constructed human language. It's probably it's related to human languages because the people that were constructing them, where's it we're making them like human languages in various ways. But it also activates the same network, which is pretty, really cool. Anyway, sorry to go into a place where you may be a little bit philosophical, but is it possible that this area of the brain is doing some kind of translation into a deeper

set of almost like concepts? Mr. It has to be doing. So it's doing in communication, right? It is translating from thought, whatever that is, is more abstract. And it's doing that. That's what it's doing. Like it is. That is kind of what it is doing. It's kind of a meaning network, I guess. You have a translation network. Yeah. But I wonder what is at the core at the bottom of it? Like what are thoughts? Are they? Are thoughts to me like thoughts and words? Are they neighbors or

are, is it one turtle sitting on top of the other? Meaning like, is there a deep set of concepts that we? Well, there's connections right between the, what, what these things mean? And then there's probably other parts of the brain that what these things mean. And so, you know, when I'm talking about whatever it is I want to talk about, if it's some, it'll be represented somewhere else. That knowledge of whatever that is will be represented somewhere else.

Well, I wonder if there's like some stable, nicely compressed encoding of meanings that's separate from language that link, you know, I guess, I guess the implication here is that that we don't think in language. That's correct. Isn't that cool? And that's so interesting. So people, I mean, this is like hard to do experiments on, but there is this idea of inner

voice and a lot of people have an inner voice. And so if you do a poll on the internet and ask, if you, you hear self-talking when you're just thinking or whatever, about 70 or 80% of people will say, yes, most people have an inner voice. I don't. And so I always find this strange when, so when people talk about an inner voice, I always thought this was a metaphor. And they hear, I know most of you, whoever's listening to this thinks I'm crazy now because I don't have an

inner voice and I just don't know what you're listening to. I just, it sounds so kind of annoying to me, but that to have this voice going on while you're thinking. But I guess most people have that. And I don't have that. And we don't really know what that connects to. I wonder if the inner voice activates that same note or I wonder, I don't know. I don't know. I mean, this could be speechy, right? So that's like, do you hear, do you have an

inner voice? I don't think so. A lot of people have this sense that they hear other people, they hear themselves. And then say they read someone's email, I've heard people tell me that they hear that other person's voice when they read other people's emails. And I'm like, wow, that sounds so disruptive. I do think I like vocalize what I'm reading, but I don't think I hear a voice. Well, that's probably not have an inner voice. Yeah, I don't think I have an inner voice. People have

an inner voice. People have this strong percept of hearing sound in their heads when they're just thinking. I refuse to believe that's the majority of people. Majority, absolutely. What? It's like two thirds or three quarters. It's what I would never ask class. And I went internet. They always say that. So you're in a minority. It could be a self report flaw. It could be, you know, when I'm reading, yeah. Inside my head, I'm kind of like saying the words,

we're probably the wrong way to read. But I don't hear a voice. There's no percept of voice. I refuse to believe the majority people have. Anyway, it's a fascinating human brain. It's fascinating. But it still blew my mind that the that language does appear comprehension does appear to be separate from thinking. So that's one set. One set of data from Feder Enkho's group is that the matter what tasks you do, if it doesn't have words and combinations of words in it, then it won't

light up the language network. You know, you could it'll be active somewhere else, but not there. So that's one. And then this other piece of evidence relevant to that question is it turns out there are these group of people who've had a massive stroke on the left side and wiped out their language network. And as long as they didn't wipe out everything on the right as well, in that case, they wouldn't be cognitively functional. But if they just wiped out language, which is

pretty tough to do because it's it's very expansive on the left. But if they have, then there are these there's patients like this called so called global aphasics who can do any task just fine, but not language. They can't you can't talk to them. I mean, they don't understand you. They can't speak. They can't write. They can't read, but they can do well. They can play chess. They can drive their cars. They can do all kinds of other stuff. You know, do math. They can do all like so math

is not in the language area, for instance. You do arithmetic and stuff. That's not language area. It's got symbols. So people sort of confuse some kind of symbolic processing with language. And symbolic processing is not the same. So there are symbols and they have meaning, but it's not language. It's not a, you know, conventionalized language system. And so language, so math isn't there. And so they can do math. They do just as well as their control, age match controls and all

these tasks. This is Rosemary Varley over in University College London, who has a bunch of patients who she's shown this that they're just so that that sort of combination suggests that language isn't necessary for thinking. It doesn't mean you can't think in language. You could think in language because language allows a lot of expression, but it's just you don't need it for thinking. It's it's just that language is separate is a separate system. It's kind of blowing my mind

right now. It's cool. I'm trying to load that in because it has implications for large language models. It sure does. And they've been working on that. Well, let's take a stroll there. You wrote that the best current theories of human language are arguably large language models. So this has to do with form. It's a kind of a big theory. And but the reason it's arguably the best is that it does the best at predicting what's English, for instance. It's it's like incredibly good. You know, it better

than any other theory. It's so you know, but you know, we don't you know, there's it's not sort of there's not enough detail. It's opaque. Like there's not you know, no, what's going on? It's another black box. But I think it's you know, it is a theory. It's a definition of a theory because it's a gigantic gigantic black box with a, you know, a very large number of parameters controlling it. To me, theory usually requires a simplicity, right? Well, I don't know. Maybe I'm

just being loose there. I think it's a, it's not a great theory, but it's a theory. It's a good theory in one sense and then it covers all the data. Like anything you want to say in English, it does. And so that's why it's that's how it's arguably the best is that no other theory is as good as a large language model and it's in predicting exactly what's good and what's bad in English. You know, you know, now you're saying is it a good theory? Well, probably not, you know, because I

want a smaller theory than that. It's too big. I agree. You could probably construct mechanism by which it can generate a simple explanation of a particular language, like I said, of rules. Something like a, you could generate a dependency grammar for a language, right? Yeah. You could probably, you could probably just ask it about itself. Well, you know, that's, I mean, that presumes and there's some evidence for this that that that some large language models are

implementing something like dependency grammar inside them. And so there's work from a guy called Chris Manning and colleagues over at Stanford in natural language. And they looked at, I don't know how many large language model types, but certainly Bert and some others were and we're, where you do some kind of fancy math to figure out exactly what the sort of what kind of abstractions of representations are going on. And they, and they were saying does look like dependency structure

is what they're constructing. It doesn't, like, so it's actually a very, very good map. So kind of a, they are constructing something like that. Does it mean that, you know, that they're using that for meaning? I mean, probably, but we don't know. You write that the kinds of theories of language that LLM's are closest to are called construction based theories. Can you explain what construction based theories are? It's just a general theory of language such that there's a form and a meaning

pair for, for lots of pieces of the language. And so it's, it's, it's primarily usage based, is a construction grammar. It's just, it's trying to deal with the things that people actually say, actually say and actually write. And so that's, it's a usage based idea. And what's a construction, a construction is either a simple word, so of, like, a more feme plus its meaning or a combination of words, it's basically, combinations of words, like the rules. So, but it's, it's a, it's a,

un-specified as to what the form of the grammar is under, under-lyingly. And so I would, I would argue that the dependency grammar is maybe the, the right form to use for the types of construction grammar. Construction grammar typically isn't kind of formalized quite. And so maybe the formalization, a formalization of that, it might be a dependency grammar. I mean, I, I would think so. But I mean, it's up to people, other researchers in that area if they agree or not.

So, do you think that large language models understand language? Are they mimicking language? I guess the deeper question there is, are they just understanding the surface form? Or do they understand something deeper about the meaning that then generates the form? I mean, I would argue they're doing the form. They're doing the form, they're doing it really, really well. And are they doing the meaning? No, probably not. I mean, there's lots of these examples from various groups

showing that they can be tricked in all kinds of ways. They really don't understand the, the meaning of what's going on. And so there's a lot of examples that he and other groups have given, which just, which show they don't really understand what's going on. So, you know, the Monty Hall problem is this silly problem, right? Where, you know, if you have three door, it's less make a deal as this old game show. And there's three doors. And there's a prize behind one. And

there's some junk prizes behind the other two. And you're trying to select one. And if you, you know, he knows Monty, he knows where the target item is. The good thing, you know, everything is back there. And you're supposed to, he gives you a choice. You choose one of the three. And then he opens one of the doors and it's some junk prize. And then the question is, should you trade to get the other one? And the answer is yes, you should trade because he knew

which ones you could turn around. And so now the odds are two thirds. Okay. And then you just change that a little bit to the large language. The larger thing is, well, just seeing that, that, that explanation so many times, it just, if you change the story, it's a little bit, but it makes it sound like it's the Monte Hall problem, but it's not. You just say, oh, there's three doors. And one behind them is a good prize. There's two bad doors. I happen to know it's behind door number

one. The good prize, the car is behind door number one. So I'm going to choose door number one. Monte Hall opens door number three and shows me nothing there. Should I trade for door number two? Even though I know the good prize in door number one. And then the large language, I'm also yes, you should trade because it's, it just goes through the, the, the, the, the, the, the forms that it's seen before so many times on these cases where it, yes, you should trade because you know,

your odds have shifted from one and three now to two out of three to being that thing. It doesn't have any way to remember that actually you have a hundred percent probability behind that door number one. You know that that's not part of the, of the scheme that it's seen hundreds and hundreds of times before. And so you can't, you can't, even you try to explain to it that it's wrong that

they can't do that. It'll just keep giving you back the, the problem. But it's also possible the larger language model would be aware of the fact that there's sometimes over representation of a, of a particular kind of formulation. And it's easy to get tricked by that. And so you could see if they get larger and larger models be a little bit more skeptical.

So you see over representation. So like you, it just feels like form can be training on form can go really far in terms of being able to generate things that look like the thing understands deeply the underlying world world model of the kind of mathematical world, physical world, psychological world that would generate these kinds of sentences. It just feels like you're creeping close to the meaning part easily fooled all this kind of stuff. But that's humans too.

So it just seems really impressive how often it seems like it ununderstands concepts. I mean, you don't have to convince me that I'm, I am very, very impressed. But does it does do, I mean, you're, you're giving a possible world where maybe someone's going to train some other versions such that it'll be somehow abstracting away from types of forms. I mean, I don't think that's happened. And so, well, no, no, no, I'm not saying that. I think when you just look at anecdotal

examples and just showing a large number of them where it doesn't seem to understand. Yeah. It's easily fooled. Yes. That does not seem like a scientific, um, data driven like analysis of like how many places is a damn impressive? Oh, no, in terms of meaning and understanding and how many places is easily fooled. And like, that's not the inference. Yeah. So I don't want to make that. The inference I don't, I wouldn't

want to make was that infertile. The inference I'm trying to push is just that is it, is it like humans here? It's probably not like humans here. It's different. So humans don't make that error. If you explain that to them, they're not going to make that error. You know, they don't make that error. And so that's something is doing something different from humans that they're doing in

that case. Well, what's the mechanism by which humans figure out that it's an error? I'm just saying the error there is like, if I explain to you, there's a hundred percent chance that the cars behind this case, that this door, well, you do want to trade. If you'll say no, but this thing will say, yes, because it's so true. It's that that trick. It's so wound up on the form that it's that's an error that a human doesn't make, which is kind of interesting. Less likely to make, I should say.

Yeah, less likely because humans are very, oh, yeah. I mean, you're asking, you know, you're asking humans to, you're asking a system to understand a hundred percent, like asking some mathematical concepts. And so like, like the places where large language models are, the form is amazing. So let's go back to nested structure, centrum-bedded structures. Okay, if you ask a human to complete those, they can't do it. Neither can a large language model. They're just like humans in that.

If you ask, if I ask a large language model, that's fascinating. By the way, that's the central embedding. Yeah, central embedding is struggles with just like human, exactly like humans. Exactly the same way as humans. And that's not trained. So they do exactly, so that is the similarity. So, but then it's, that's not meaning, right? This is form. But when we get into meaning, this is where they get kind of messed up when you start to saying, oh, what's behind this

door? Oh, it's, you know, it's the thing I want. Humans don't mess that up as much. You know, here, the form is, it's just like the form of the match is amazing, similar. Without being trained to do that, I mean, it's trained in the sense that it's getting lots of data, which is just like human data, but it's not being trained on, you know, bad sentences and being told what's bad, it just can't do those. It'll actually say things like those are too hard for me to complete

or something, which is kind of interesting. Actually, kind of, how does it know that? I don't know. But it really often doesn't just complete, essentially, often, very often says stuff that's true. And sometimes says stuff that's not true. And almost always the form is great. But it's still very surprising that with really great form, it's able to generate a lot of things that are true. Based on what is trained on and so on. So it's not just form that is generating.

It's mimicking true statements from the internet. I guess, I guess the underlying idea there is that on the internet, truth is overrepresented versus falsehoods. I think that's probably right. So, but the fundamental thing is trained on, you're saying is just form. I think so. Yeah. Yeah. I think so. Well, that's a sad, if that's true, me that's still a little bit of open question. I probably lean agreeing with you, especially now you just blown my mind that there's a separate module in the

brain for language versus thinking. Maybe there's a fundamental part missing from the large language model approach that lacks the thinking, the reason and capability. Yeah, that's what this group argues. So the same group, Federanko's group has a recent paper arguing exactly that. There's a guy called Kyle Mahuel who's here in Austin, Texas actually. He's an old student of mine, but he's a faculty and linguistics at Texas. And he was the first author on that.

Yeah. That's fascinating. Still to me an open question. Yeah. What do you have the interesting limits of LLMs? You know, I, I don't see any limits to their form. Their form is perfect. Yeah. Yeah. Yeah. It's pretty, I mean, it's close to what you said ability to complete central embeddings. Yeah. It's just the same as humans. It seems the same. But that's not perfect. Right? It should be good. No, but I want to be like humans. I'm trying to, I want a model of

humans. But, but all the way, so perfect is as close to humans as possible. I got it. Yeah. But you should be able to, if you're not human, you're like, you're superhuman. You should be able to complete central embedded senses, right? I mean, that's the mechanism is, if it's modeling,

something, I think it's kind of really interesting that it's really interesting. It's more like, like I think it's potentially underlyingly modeling something like what the way the form is processed, the form of human language, the way that how and how humans process the language. Yeah. Yeah. I think that's plausible and how they generate language, process language, in general, language as fast as anything. Yeah. So in that sense, they're perfect.

If we can just linger on the center embedding thing, that's hard for our allows produce and that seems really impressive because that's hard for humans to produce. And how does that connect to the thing we've been talking about before, which is the dependency grammar framework and which you view language and the finding that short dependencies seem to be a universal part of language. So why is it hard to complete center embeddings? So what I like about dependency grammar is it makes

the cognitive cost associated with longer distance connections very transparent. Basically, there's some, there turns out there is a cost associated with producing and comprehending connections between words, which are just not beside each other. The further apart they are, the worse it is, according to, well, we can measure that. And there is a cost associated with that. Can you just linger on what do you mean by cognitive cost? Sure. Oh, you can measure it in a lot of

ways. The simplest is just asking people to say whether, you know, how good a sentence is. Which is asking that's one way to measure. And you try to like triangulate then across sentences and across structures to try to figure out what the source of that is. You can look at reading times in controlled materials. You know, and so in certain kinds of materials, when the, and then we can like measure the dependency distances there, we can, there's a recent

study which looked at we're talking about the brain here. We could look at the language network. Okay. We could look at the language network and we could look at the activation in the language network. And how big the activation is depending on the length of the dependencies and turns out in just random sentences that you're listening to. If you're listening to, so it turns out there are people listening to stories here. And the bigger, the longer the dependency is the, the stronger the

activation and the language in the language network. And so there's some measure, there's a different, there's a bunch of different measures we could do. That's kind of a neat measure, actually, of actual activations activation in the brain. So that you can somehow, in different ways, convert it to a number. I wonder if there's a beautiful equation connecting cognitive cost and length of dependency equals empty squared kind of thing. Yeah. It's complicated, but probably it's

doable. I would, I would guess it's doable. I, you know, I tried to do that a while ago and I was reasonably successful. But some, for some reason, I stopped working on that. I do, I agree with you that it would be nice to figure out. So there's like some way to figure out the, the, the cost. I mean, it's complicated. Another issue you raised before was like, how do you measure distance? Is it words? Is it, it probably isn't? Is the part of the problem is that some words matter than

more than others. And probably, you know, meaning like nouns might matter depending, and then maybe depends on which kind of noun is it a noun we've already introduced or a noun that's already been mentioned? Is it a pronoun versus a name like, like all these things probably matter? So probably the simplest thing to do is just like, let's forget about all that and just think about words or morphine. For sure, but there might be a, like, there might be some insight in the

kind of function that fits the data, meaning like quadratic, like what? I think it's an exponential. So we think it's probably an exponential such that the longer the distance, the less it matters. And so then, then it's the sum of those is my, that was our best guess a while ago. So that you've got a bunch of dependencies. If you've got a bunch of them that are being connected at some point, that's at the ends of those, the cost is the, is some exponential function of those

is my guess. But because the reason it's probably an exponential is like, it's not just the distance between two words, because I can make a very, very long subject verb dependency by adding lots and lots of noun phrases and prepositional phrases. And it doesn't matter too much. It's when you do nested, when I have multiple of these, then things get go really bad. Go south. Probably somehow connected to working memory. Yeah, that's probably the function of the memory here

is, is the access is trying to find those earlier things. It's kind of hard to figure out what was referred to earlier. Those are those connections. That's, that's the sort of notion of working as opposed to a storage thing, but trying to connect, retrieve, retrieve those earlier words, depending on what was in between. And then, then we're talking about interference of similar things in between. That's the right theory probably has that kind of notion and it is an

interference of similar. And so I'm dealing with an abstraction over the right theory, which is just, you know, it's count words. It's not right, but it's close. And then maybe you're right, though, there's some sort of an exponential or something on the, on the, to figure out the total. So we can figure out a function for any given, for any given sentence in any given language. But, you know, it's funny. You know, people haven't done that too much, which I do think is, I'm interested

that you find that interesting. I really find that interesting. And I'm, a lot of people haven't found it interesting. And I don't know why I haven't got people to want to work on that. I really like that too. No, it's a, that's a beautify in the underlying idea is beautiful that there's a cognitive cost that correlates with the length of the dependency. And it just, it feels like it's a deep, I mean, language is so fundamental to the human experience. And this is a nice, clean

theory of language where it's like, wow, okay. So like we like our words close together. Yeah, depending on where it's close together. That's why I like it too. It's so simple. Yeah, the simplicity of the theory. And yet it explains some very complicated phenomena. If you, if I write these very complicated sentences, it's kind of hard to know why they're so hard. And you can like, oh, nail it down. I can do, like, I give you a math formula for why each one of them is bad and

where. And that's kind of cool. I think that's very neat. Have you gone through the process? Is there like a, if you take a piece of text and then simplify sort of like there's an average length of dependency and then you like, you know, reduce it and see comprehension on the entire, not just single sounds, but like, you know, you go from James Joyce to Hemingway or something. No, no, simple answers. No, that does, there's probably things you can do in that, in that kind of

direction. That's fun. We might, you know, we're going to talk about legalese at some point. And so we may we will talk about that kind of thinking with applied to legalese, but let's talk about legalese because you mentioned that as an exception. We should take your attention upon tangent. That's an interesting one. You give it as an exception. It's an exception. That you say that most natural languages, as we've been talking about, have local dependencies with one exception, legalese.

That's right. So what is legalese? First of all, oh, well, legalese is what you think it is. It's just any legal language. I mean, like I actually know, no very little about the kind of language the lawyers use. So I'm just thinking about language in laws and language in contracts. Got it. So the stuff that you have to run into, we have to run into every other day or every day. And you skip over because it reads poorly. And or, you know, partly it's just long, right?

There's a lot of texts there that we don't really want to know about. And so, but the thing I'm interested in, so I've been working with this guy called Eric Martinez, who is a he was a lawyer who was taking my class. I was teaching a psycho linguistics lab class at I have been teaching you for a long time at MIT. And he's a he was a law student at Harvard. And he took the class because

he had done some linguistics as an undergrad. And he was interested in the problem of why legalese sounds hard to understand, you know, why and so why is it hard to understand and why do they write that way if it is so hard to understand? It seems apparent that it's hard to understand. The question

is why is it and so we didn't know. And we did an evaluation of much contracts. Actually, we just took a bunch of random contracts because I don't know, you know, there's contracts and laws might not be exactly the same, but contracts are kind of the things that most people have to deal with the most of the time. And so that's kind of the most common thing that humans have like humans that adults in our industrialized society have to deal with a lot. And so that's what we pulled.

And we didn't know what was hard about them, but it turns out that the way they're written is very centrum-bedded has nested structures of them. So it has low frequency words as well. That's not surprising. Lots of texts have low, it does have surprising slightly lower frequency words than other kinds of control texts, even sort of academic texts. Legal leads is even worse. It is the worst that we were feeling disappointed. You just revealed again that lawyers are playing.

They're optimizing it different. Well, you know, it's interesting. That's like, now you're getting at why. And so, and I don't think, so now you're saying it's, they're doing intentionally. I don't think they're doing intentionally. But let's, let's, let's, let's, let's get to that. We'll get to that. And so, but we wanted to see why. So, so we see what first as opposed, so like as, because it turns out that we're not the first to observe that legal leads is weird. Like back to Nixon had a

plain language act in, in 1970 and Obama had one. And, uh, boy, a lot of these, you know, a lot of presidents have said, oh, we've got to simplify legal language, most simplified. But you don't know how it's complicated. It's not easy to simplify it. You need to know what it is you're supposed to do before you can fix it. Right. And so you need to like, you need a cycle linguist to analyze the text and see what's wrong with it before you can like fix it. You don't know how to fix it. How

am I supposed to fix something? I don't know what's wrong with it. And so what we did was just, that's what we did. We figured out, let's look. Okay. We just a bunch of contracts had people and we encoded them for the, the, the, a bunch of features. And so another feature of the people, one of them was the centrum betting. And so, uh, that is like basically how often a, um, a clause would, would, would intervene between a subject and a verb, for example, that's one kind

of a cent, centrum betting of a clause. Okay. And, um, turns out they're massively centrum betting. Like so I think in random contracts and in random laws, I think you get about 70% or 80, something 70% of sentences have a centrum betting clause in them, which is insanely high. If you go to any other text, it's down to 20% or something. It's, it's, it's so much higher than the, any control you can think of, including, you think, oh, people think, oh, technical, um, academic text, no,

people don't write centrum betting sentences in, in technical academic text. I mean, they do a little bit, but much, it's, it's on the 20% 30% realm as opposed to 70. And so, and so there's that. And there's low frequency words. And then people, oh, maybe it's passive. People don't like the passive, passive for some reason. The passive voice in English has a bad rap. And

I'm not really sure where that comes from. Um, and, and there is a lot of passive in, uh, the, there's much more passive voice in the, in the, uh, in legalese than there is in other texts. And passive voice that calls for some of the low frequency words. No, no, no, no, no, no, those are separate. Those are separate. Oh, so passive voice sucks. These are free. These are free. These are free. These are free. These are free. These are different. So these are different

different. Yeah, yeah, yeah. Pass the drop the judgment. It's just like these are frequent. These are things which happen in legalese texts. Then we can ask the dependent measure is like, how well you understand those things with those features. Okay. And so then, and it turns out the passive makes no difference. So it has a zero effect on your comprehension ability, on your recall ability. No, it does nothing at all. That has no effect. You're, the, the words matter a

little bit. They do a low frequency words are going to hurt you in recall and understanding. But what really, what really hurts the centrum bed, that kills you. That is like that slows people down. That makes them that makes them very, very poor at understanding that makes them, uh, they, they, they can't recall what was said as well, nearly as well. And we did this not only on lay people, we did not have a lot of lay people. We ran out of 100 lawyers. We recruited lawyers

from a, from a wide range of, of, um, sort of different levels of law firms and stuff. And they have the same pattern. So they also, like, why, when, when they did this, I did not know it happened. I thought maybe they could process, they're used to legalese. They think process just as well as if it was normal. No, no, they, they, they're much better than lay people. So they're much, like, they can much better recall, much better understanding, but they have the same

main effects as, as lay people, as lay people, exactly the same. So they also much prefer the non-centrum. So we, we, we, we constructed non-centrum bedded versions of each of these. We constructed versions which have, um, higher frequency words in those places. And we, we did, we, un, un, un passivized, we turned them into active versions. The passive active made no difference. The words made a little difference. And the un, uncentrum bedding makes, makes big differences in

all the populations. Uncentrum bedding. How hard is that process, by the way? So, so I don't question, but how hard is it to detect center embedding? Oh, easy, easy to detect. You just look at long dependencies or you serve, you can just, you can, so there's automatic parsers for English, which are pretty good. And they can detect center point. Oh, yeah. Very, very, very, very, very, very, very, very, perfectly. Yeah, you've learned, you know, pretty much.

So you, you're not just looking for long dependencies. You're just literally looking for center embedding. Yeah, we are in this case, in these case, but long dependencies are, they're highly correlated. So like a center embedding is a, is a big bomb you throw inside of a sentence that just blows up the, that makes sure. Can I read a sentence for you from these things? I, I see, I mean, this is just like one of the things that, this is just my eyes, my glaze over in middle, mid-settings.

No, I understand that. I mean, legal easy. So here we go. This is a go. It goes in the event that any payment or benefit by the company, all such payments and benefits, including the payments and benefits under section 3a here of being here at here and after referred to as a total payments, would be subject to the X-I's tax, then the cash severance payments shall be reduced. So that's something we

pulled from a regular text from a, from a contract. Wow. And, and the center embedded bit there's just for some reason, there's a definition. They throw the definition of what payments and benefits are in between the subject and the verb. Let's, how about don't do that? How about put the definition somewhere else as opposed to in the middle of the sentence? And so that's, that's very, very common. By the way, that's, that's what happens. You just throw your definitions, you use a word,

a couple words, and then you define it, and then you continue the sentence. Like, just don't write like that. And, and you ask, so then we ask lawyers, we said, oh, maybe lawyers like this. Lawyers don't like this. They don't like this. They don't want, they don't want to write like this. They, they, we asked them to rate materials, which are with the same meaning with, with unscentribed and centred, and they much preferred the unscentribed versions on the comprehension,

on the reading side. Yeah, well, and we asked them, we asked them, would you hire someone who writes like this or this? We asked them all kinds of questions. And they always preferred the less complicated version, all of them. So I don't even think they want it this way. Yeah, but how did it happen? How did I have it? That's a very good question. And, and the answer is, they still don't know.

Oh, but I have some theories. Well, our, our best theory at the moment is that there's, there's actually some kind of a performative meaning in the center embedding in the style, which tells you it's legalese. We think that that's the kind of a style which tells you it's legalese. Like, that's a, it's a reasonable guess. And maybe it's just, so for instance, if you're, like, it's like a magic spell. So we can't call this the magic spell hypothesis. So when you give

them, when you kill someone to put a magic spell on someone, what do you do? They, you know, people know what a magic spell is and they, they do a lot of rhyming. You know, that's what, that's kind of what people will tend to do. They'll do rhyming and they'll do sort of like some kind of poetry kind of thing. Abracadabra, a type of thing. Yeah. And maybe that's, there's a syntactic

sort of reflex here of a, of a magic spell, which is center embedding. And so that's like, oh, it's trying to like tell you this is like this is something which is true, which is what the goal of law law is, right, is telling you something that we want you to believe as certainly true, right? That's what legal contracts are trying to enforce on you, right? And so maybe that's like a, a form, which has, this is like an abstract, very abstract form, centered from betting, which

has a has a has a meaning associated with it. Well, don't you think there's an incentive for lawyers to generate things that are hard to understand? That was one of our working hypotheses. We just couldn't find any evidence of that. No, lawyers also don't understand it. But you're creating space. Why you yourself, but I mean, you ask in a communist Soviet union, the individual members, their self report is not going to correctly reflect what is broken about the gigantic

bureaucracy, then leads to Chernobyl or something like this. I think the incentives under which you operate are not always transparent to the members within that system. So like it just feels like a strange coincidence that like there is benefit if you just zoom out, look at the system, it's supposed to ask individual words that making something hard to understand is going to make a

lot of people money. Yeah. Like there's going to, you're going to need a lawyer to figure that out, I guess, from the perspective of the individual, but then that could be the performative as it could it could be as opposed to the incentive driven to be complicated, it could be performative to where we lawyers speak in this sophisticated way and you regular humans don't understand it so you need to hire a lawyer. Yeah, I don't know which one it is, but it's suspicious. Suspicious that

it's hard to understand and everybody's eyes glaze over and they don't read. I'm suspicious as well. I'm still suspicious and I hear what you're saying, it could be kind of no individual and even average of individuals, it could just be a few bad apples in a way which are driving the effect in some way. Influential bad apples at the sort of, then everybody looks up to whatever they're like in central figures and how it turns, but it is kind of interesting that among our 100 lawyers,

they did not. They didn't want to. They really didn't like it. And they weren't better at than regular people had comprehending it or they were an average better, but they had the same difference. The same, the same difference. Exactly the same difference. But they wanted it fixed.

Hope that because it actually isn't very hard to construct a material which is unsentering bad and has the same meaning, it's not very hard to do, just basically in that situation, just putting definitions outside of the subject verb relation in that particular example. That's kind of, that's pretty general what they're doing is just throwing stuff in there which you don't have to put in there. There's extra words involved. Typically, you may need a few extra

words to refer to the things that you're defining outside in some way. Because if you only use it in that one sentence, then there's no reason to introduce extra terms. So we might have a few more words, but it'll be easier to understand. So I mean, I have hope that now that maybe we can make legal easily less, less convoluted in this. So maybe the next president in the United States can set a saying generic things say, exactly. I ban center embeddings and make Ted the

language are. But center embeddings are the bad thing to have. That's right. So if you get rid of that, they'll do a lot of it. That'll fix a lot. That is so fascinating. And it's really fascinating. I'm many fronts that humans are just not able to deal with this kind of thing. And that language because of that involved in the way you did, it's fascinating. So one of the mathematical formulations you have when talking about languages communication is, let's say, do you have noisy

channels? What's the noisy channel? So that's about communication. And so this is going back to Shannon. So Shannon, Claude Shannon was a student at MIT in the 40s. And so he wrote this very influential piece of work about communication theory or information theory. And he was interested in human language. Actually, he was trying to, he was interested in this problem of communication, of getting a message from my head to your head. And so, and he was concerned or interested in

what was a robust way to do that. And so that assuming we both speak the same language, we both already speak English, whatever, you know, whatever language is, we speak that. What is a way that I can say the language so that it's most likely to get the signal that I want to you. And so, and then the problem there in the communication is the noisy channel is that there's, I make, there's a lot of noise in the system. I don't speak perfectly. I make errors. That's noise.

There's background noise. You know, you know that as well. Like a literal background noise. There is like white noise in the background or some other kind of noise. There's some speaking going on that you're just, you're at a party. That's background noise. You're trying to hear someone. It's hard to understand them because there's almost other stuff going on in the background. And, and then there's noise on the communication on the, on the

receiver side. So that you have some problem maybe understanding me for stuff that's this internal to you in some way. So you've got some other problems, whatever, with understanding for whatever reasons. Maybe you're, if you've had too much to drink, you know, who knows why you're not able to pay attention to the signal. So that's the noisy channel. And so, so that language, if it's a communication system, we are trying to optimize in some sense the the passing of the

message from one side to the other. And so it turn, I mean, one idea is that maybe, you know, aspects of like word order, for example, might have optimized in some way to make language a little more easy to be passed from speaker to listener. And so let's Shannon's the guy that did the stuff way back in the forties. You know, it's very interesting, you know, historically, he was interested

in working in linguistics. He was in MIT. And he did, this is his master's thesis of all things, you know, it's crazy how much, how much he did for his master's thesis in 1948, I think, or 49 or something. And he wanted to keep working in language. And it just wasn't a popular communication as a, as a reason source for what language was wasn't popular at the time. So Trump's he was becoming, it was moving in there. He was, and he just wasn't able to get a handle there,

I think. And so, and so he moved to Bell Haps and worked on communication from a mathematical point of view and was, you know, did all kinds of amazing work. And so he's just more on the signal side versus like the language side. Yeah. Hi, I would have been interesting to see if you pursue the language side. Yeah, that's really interesting. Yeah, he was interested in that. His

examples in the 40s are are kind of like they're very language like, like things. Yeah. We can kind of show that there's a noisy channel process going on in when you're listening to me, you know, you're, you can often sort of guess what I meant by what I, you know, what you think I meant, given what I said. And I mean, with respect to sort of why language looks the way it does, we might, there might be sort of, I said alluded to, there might be ways in which word orders is somewhat

optimized for for because of the noisy channel in some way. I mean, that's really cool to sort of model. If you don't hear certain parts of a sentence or have some probability of missing that part, like how do you construct the language that's resilient to that? That's somewhat robust to that. Yeah, that's the idea. And then you're you're kind of saying like the word order and the syntax of language, the dependency length are all helpful. Yeah, well, the fancy length is

really about memory. I think that's like about sort of what's easier or harder to produce in some way. And these other ideas are about sort of robustness to communication. So the problem of potential loss of it, loss of signal due to noise. And so that, there's, there may be aspects of word order, which is somewhat optimized for that. And you know, we have this one guess in that direct. And these are kind of just so stories. I have to be, you know, pretty frank. They're not,

like I can't show this is true. All we can do is like look at the current languages of the world. This is a like we can't sort of see how languages change or anything because we've got these snapshots of a few, you know, 100 or a few thousand languages. We don't have we don't really we can't do the right kinds of modifications to test these things experimentally. And so, you know, so just take that this with the grain of salt. Okay, from here, this, this stuff,

the dependency stuff I can I'm much more solid on. And like here's what the lengths are and here's what's hard. Here's what's easy. And this is a reasonable structure. I think I'm pretty reasonable. Here's like why, you know, why does the word order look the way it does is we're now into shaky territory, but it's kind of cool. We're talking about just to be clear. We're talking about maybe just actually the sounds of community like you and I are sitting in the bars very loud.

And you yeah, you model with a noisy channel. The loudness, the noise and we have the signal that's coming across that and you're saying word order might have something to do with optimizing that. Yeah, presence of noise. Yeah. Yeah. I mean, it's really interesting. I mean, to me, it's interesting. How much you can load into the noisy channel? Like how much can you bake in? You said like, you know, cognitive load on the receiver end. We think that those are there's three at least

three different kinds of things going on there. And we probably don't want to treat them all as the same. Sure. And so I think that you know, the right model, a better model of a noisy channel would treat would have three different sources of noise, which which are background noise. You know, speaker speaker, inherent noise and listener inherent noise. And those are not this. Those are all different things. Sure. But then underneath it, there's a million other

subsets. Oh, yeah. That's true. And they're receiving. I mean, I just mentioned cognitive load on both sides. Then there's like speaking speech impediments or just everything. World view. I mean, the meaning was such a creep into the meeting realm of like we have different world views. Well, how about just form still though? Like just just what language you know? Like so how well you know the language? And so if it's second language for you versus first language and how maybe

what other languages you know, these are still just form stuff. And that's like potentially very informative. And you know, how old you are? These things probably matter, right? So like child learning a language is is a you know, as a noisy representation of English grammar, you know, depending on a old they are. So maybe when they're six, they're perfectly formed. But you mentioned one of the things is like a way to measure the language is learning problems.

So like what's the correlation between everything we've been talking about and how easy it is to learn a language? So it's like short dependencies correlated to ability to learn a language. Is there some kind of or like the dependency grammar? Is there some kind of connection there? How easy is to learn? Yeah, well, all the languages in the world's language, none is right now. We know is any better than any other with respect to sort of optimizing dependency lengths. For example,

they're all kind of do it. Do it well. They all keep low. It's so that I think of every human language is some kind of an optosid sort of an optimization problem, a complex optimization problem to this communication problem. And so they've like they've solved it. They know they're just sort of noisy solutions to this problem of communication. There's just so many ways you can do this. So they're not optimized for learning. They're probably less. And learning. So yes, one of the factors

which is yeah, so learning is messing this up a bit. And so so for example, if it were just about minimizing dependency lengths and that was all that matters, you know, then we you know, so then then we might find grammars which didn't have regularity in their rules, but languages always have regularity in their rules. So what I mean by that is that if I wanted to say something to you in the optimal way to say it was really mattered to me. All that mattered was keeping the dependencies

as close together as possible. Then I then I would have a very lack set of free structure or dependency rule. It wouldn't have very many of those. I would have very little of that. And I would just put the words as close the things that refer to the things that are connected right beside each other. But we don't do that. Like there are like there are word order rules, right? So they're very and depending on the language, they're more and less strict, right? So you speak Russian,

they're less strict than English. English is very rigid word order rules. We order things in a very particular way. And so why do we do that? Like that's probably not about communication. That's probably about learning. I mean, then we're talking about learning. So I probably easier to learn regular things, which are very predictable and easy to. So that's probably about learning is our guess, because that can't be about communication. Can it be just noise? Can it be just the the

messiness of the development of a language? Well, if it were just a communication, then we should have languages which have very, very free word order. And we don't have that. We have free error, but not free. Like there's always. Well, no, but what I mean by noise is like cultural, like sticky, cultural things like the way the way you communicate, just there's a stickiness to it. That it's it's an imperfect, it's a noisy optimist, the castic. Yeah. The function over which

you're optimizing is very noisy. Yeah. So because I don't it feels weird to say that learning is part of the objective function, because some languages are way harder to learn than others. Right? Or is that that's not true? That's interesting. I mean, that's the public perception, right? Yes, that's true. For a second language. For a second language. But that depends on what you started with. Right. So it's it really depends on how close that second language is to the first language you've

got. And so yes, it's very, very hard to learn Arabic if you've started with English or Riz Harder to you know, Harder to learn Japanese or if you've started with think of Chinese, I think, is the worst in the there's like defense language institute in the United States has like a list of how hard it is to learn what language from English. I think Chinese is the second language. I say you're saying babies don't care. No, no evidence that there's anything harder,

easier, but any baby any language learned like three or four, they speak that language. And so there's no evidence of any anything harder or easier about any human language. They're all kind of equal. To what degree is language? This is returning to Chomsky a little bit is is a Nate. You said that for Chomsky, he used the idea that language is some aspect of the language or a Nate to explain away certain things that are observed. But to how much are we born with language at the core of our

mind brain? I mean, I you know, the answers I don't know of course, but the I mean, I like to I'm an engineer hard, I guess, and I sort of think it's fine to postulate that a lot of it's learned. And so I'm guessing that a lot of it's learned. So I think the reason Chomsky went with the Nateness is because he, he hypothesized movement in his grammar. He was interested in grammar and movements hard to learn. I think he's right movement is a hard, it's a hard thing to learn to

learn these two things together and how to interact. And there's like a lot of ways in which you might generate exactly the same sentences and it's like really hard. And so he's like, oh, I guess it's learned. Sorry, so I guess it's not learned as to Nate. And if you just throw at the movement and just think about that in a different way, you know, then you get some messiness. But the messiness is human language, which it's actually fits better. It's that messiness isn't a problem. It's actually

it's a valuable asset of of of the theory. And so, so I think I don't really see a reason to postulate much much in Nate's structure. And that's kind of why I think these large language models are learning so well is because I think you can learn the form, the forms of human language from the input. I think that's like it's likely to be true. So that part of the brain that lights up when you're doing all the comprehension that could be learned. That could be just you don't need

to be in a so like lots of stuff is modular in the brain that's learned. It doesn't have to, you know, so there's something called the visual word form area in the back. And so it's in the back of your head when you're the, you know, the visual cortex. Okay. And that is very specialized language. Sorry, very specialized brain area, which does visual word processing if you read, if you're a reader. Okay.

If you don't read, you don't have it. Okay. Guess what? You spend some time learning to read and you develop that that brain area, which does exactly that. And so these the modularization is not evidence for inateness. So the modularization of a language area doesn't mean we're born with it. We could have easily learned that. I we might have been born with it. I, we just don't know at this point. We might very well have been born with this left lateralized area. I mean that there's

like a lot of other interesting components here, features of this kind of argument. So some people get a stroke or something goes really wrong on the left side. Where the left left language area would be. And that and that isn't there. It's not not available. And it develops just fine the right. So it's no longer so it's not about the left. It goes to the left. Like this is a very interesting question. It's like why is the why are any of the brain areas the way that they are?

And how how how did they come to be that way? And you know, there's these natural experiments which happen where people get these you know, strange events in their brains at very young ages, which wipe out sections of their brain. And and they behave totally normally and no one knows anything was wrong. And we find out later because they happen to be accidentally scanned for some

reason. It's like what what happened to your left hemisphere? It's missing. There's not many people missed their whole left hemisphere, but they'll be missing some other section of their left or their right. And they behave absolutely normally would never know. So that's like a very interesting you know, current research. You know, this is another project that this person in Federico is working on. She's got all these people contacting her because she's scanned some people who have

been missing sections. One person missing missing misdisection of her brain and was scanned in her lab. And she and she happened to be a writer for the New York Times. And it was an article in New York Times about the just about the scanning procedure and about what might be learned about by sort of the general process of MRI and language and that's their language. And and because she's writing for

the New York Times and there's all these people started writing to her. But we also have similar similar kinds of deficits because they've been you know accidentally, you know, to scan for some reason and and found out they're missing some section. And then they they volunteer to be scanned. These are natural experiments. Natural experiments. They're kind of messy, but natural experiments kind of cool. The cause of interesting brains. The first few hours, days, months of human life

are fascinating. Like yeah, well, inside the womb actually like that development. That machinery, whatever that is, seems to create powerful humans that are able to speak, comprehend, think all that kind of stuff, no matter what happened, not no matter what, but robust to the different ways that the brain might be damaged and so on. That's really that's really interesting.

But what would Chomsky say about the fact the thing you're saying now that language is is seems to be happening separate from thought because as far as I understand maybe you can correct me, he thought that language under pins. Yeah, he thinks so. I don't know what he'd say. He'll be surprised because for him, the idea is that language is the sort of the foundation of thought. That's right. Absolutely. And it's pretty mind blowing to think that it could be completely separate

from thought. That's right. But so you know, he's basically a philosopher, philosopher of language in a way, thinking about these things. It's a fine thought. You can't test it in his methods. You can't do a thought experiment to figure that out. You need a scanner. You need brain damage people. You need something. You need ways to measure that. And that's what FMRI offers as a and and you know, patients are a little messier. FMRI is pretty unambiguous, I'd say. It's like

very unambiguous. There's no way to say that the language network is doing any of these tasks. There's like you should look at those data. It's like there's no chance that you can say that those networks are overlapping. They're not overlapping. They're just like completely different. So you can always make, it's only two people. It's four people or something for the patients. And there's something special about them. We don't know. But these are just random people.

And with lots of them and you find always the same effects. And it's very robust, I'd say. What's the fassing effect? What's the you mentioned, Bolivia? What's the connection between culture and language? You've also mentioned that much of our study of language comes from WERD, Weird People, Western Educated, Industrialized, Rich and Democratic. So when you study remote cultures such as around the Amazon jungle, what can you learn about

language? So that term Weird is from Joe Henrich. He's at Harvard. He's a Harvard evolutionary biologist. And so he works on lots of different topics. And he basically was pushing that observation that we should be careful about the inferences we want to make when we're talking in psychology or social, yeah, most things psychology, I guess about humans if we're talking about, you know,

undergrads at MIT and Harvard. Those aren't the same, right? These aren't the same things. And so if you want to make inferences about language, for instance, there's a lot of very, a lot of other kinds of languages in the world than English and French and Chinese, you know. And so maybe for language, we care about how culture, because cultures can be varied. I mean, of course, English and Chinese cultures are very different, but in a hunter-gatherer is much more different

in some ways. And so if culture has an effect on what language is, then we kind of want to look there as well as looking. It's not like the industrialized cultures aren't interesting. Of course, they are. But we want to look at non-industrialized cultures as well. And so I worked with two, I've worked with Chimani, which are in Bolivia and in Amazon, both in the Amazon, these cases.

And they are so-called farmer foragers, which is not hunter-gatherers. It's sort of one up from hunter-gatherers and that they do a little bit of farming as well, a lot of hunting as well, but a little bit of farming. And the kind of farming they do is the kind of farming that I might do, if I ever were to grow tomatoes or something in my backyard. It's not like so it's not like big field farming. It's just a farming for a family, a few things you do that. And so that's the kind

of farming they do. And the other group I've worked with are the Pieter Ha, which are also in the Amazon, and it happened to be in Brazil. And that's with a guy called Dan Everett, who was a linguist, anthropologist, who actually lived and worked in the, I mean, he was a missionary, actually, initially, back in the 70s, working with, trying to translate languages so they could

teach them the Bible, teach them Christianity. What can you say about that? Yeah, so the two groups I've worked with, the Chimani and the Pieter Ha, are both isolate languages, meaning there's no known connected languages at all, like just like on their own. Yeah, there's a lot of those. And most of the isolates occur in the Amazon or in Papua New Guinea in these places where the world has sort of stayed still for a long enough. And they're ha, like so there aren't earthquakes. There aren't,

certainly no earthquakes in the Amazon jungle. And the climate isn't bad, so you don't have droughts. And so in Africa, you've got a lot of moving of people because there's drought problems. And so they get a lot of language contact when you have, when people have to, if you got to move, because you've got no water, then you've got to get going. And then you run into contact with other tribes, other groups. In the Amazon, that's not the case. And so people can stay there for hundreds

and hundreds and probably thousands of years, I guess. And so these groups have, the Chimani and the Pionahar, both isolates in that. And they just, I guess they've just lived there for ages and ages with minimal contact with other outside groups. And so I mean, I'm interested in them because they are, I mean, I, you know, in these cases, I'm interested in their words. I would love to study their syntax, their orders of words. But I'm mostly just interested in how languages are connected to

their, their cultures in this way. And so with the Pionahar, they're most interesting. I was working, I was working on number there, number information. And so the basic idea is I think language is invented. Right. So I get from the words here is that I think language is invented. We talked about color earlier. It's the same idea. So that what you need to talk about with someone else is

what you're going to invent words for. Okay. And so we invent labels for colors that I need, not that I, that I can see, but that, but that things I need to tell you about so that I can get objects from you or get you to give me the right objects. And I just don't need a word for teal or, or, a word for acrimorine in the, in the Amazon jungle for the most part because I don't have two things

which differ on those colors. I just don't have that. And so, and so numbers are really another fascinating, infirm source of information here where you might, you know, naively, I certainly thought that all humans would have words for exact counting. And the pionohad don't. Okay. So they don't have any words for even one. There's not a word for one in their, in their language. And so there's still not word for two, three or four. So, so that kind of blows people's minds on.

Yeah, that's boy, my boy. That's pretty weird. How are you, how are you going to ask, I want two of those? You just don't. And so that's just not a thing you can possibly ask in the pionohad. It's not possible. That is there's no words for that. So here's how we found this out. Okay. So, so it was thought to be a one, two, many language. There are three words for quantifiers for, for, for sets. But, and the people had thought that those meant one, two, and many. But what they

really mean is few some and many, many is correct. It's few some and many. And so, and so the way we figured this out. And this is kind of cool is that we gave people, we had a set of objects. Okay. These are having to be spools of thread. Doesn't really matter what they are identical objects. And, and, and I sort of start off here. I just give, you know, give you one of those and say, what's that? Okay. So, you're a pionohad speaker and you tell me what it is. And, and then I give you

two and say, what's that? And, and nothing's changing in this set except for the number. Okay. And then I just ask you to label these things. We just do this for a bunch of different people. And, and frankly, it's a, I did this task and it's a weird, it's a little bit weird. So you say, the word that they thought that we thought was one, it's few, but for the first one. And then maybe they say few or maybe they say some for the second. And then for the third or the fourth, they

start using the word many for the set. And then five, six, seven, eight, I go all the way to 10. And it's always the same word. And they look at me like I'm stupid because they told me what the word was for six, seven, eight. And I'm going to continue asking them at nine and 10. I'm like, I'm sorry, I just, I just, they understand that I want to know their language. That's the point of the task is like I'm pronouning their language. And so that's okay. But it does seem like I'm a little

slow because I, they already told me what the word for many was five, six, seven. And I keep asking. So it's a little funny to do this task over and over. We did this with the guy called Dan was the, our translator. He's the only one who really speaks Pieter Ha, fluently. He's a good bilingual for a bunch of languages, but also in English and in Pieter Ha. And then what guy called Mike Frank was a, also a student with me down there. He and I did these things. And so you do that. Okay.

And everyone does the same thing. They all, all, you know, we asked like 10 people and they all do exactly the same labeling for one up. And then we just do the same thing down on like random order. Actually, we do some of them up, some of them down first. Okay. And so we do, instead of one to 10, we do 10 down to one. And so so I give them 10, nine, eight. They start saying the word for some. And then down when you get to four, everyone is saying the word for few, which we thought was one.

So it's like it's the context determined what word, what, what that quantifier they used was. So it's not a count word. They're not, they're not count words. They're, they're just approximate words. And they're going to be noisy when you interview a bunch of people with the definition of few. And there's going to be a threshold in the context. Yeah. Yeah. Yeah. I don't know what that means. That's, that's going to be turned on the context. I think it's from English too, right? If you ask

an English person what a few is, I mean, that's completely on the context. And that might actually be at first hard to discover. Yeah. Because for a lot of people, the jump from one to two will be few. Right. So it's the jump. Yeah. It might be. It might still be there. Yeah. Like it's, I mean, that's fascinating. That's fascinating. The numbers don't present themselves. Yeah. So the words aren't there. And then and so then we do these other things. Well, if they,

if they don't have the words, can they do exact matching kinds of tasks? Can they even do those tasks? And and and the answer is sort of yes and no. And so yes, they can do them. So here's the tasks that we did. We put out those spools of thread again. Okay. So anyway, put like three out here. And then that we gave them some objects and those happened to be uninflated red balloons. It doesn't really matter what they are. It's just their bunch of exactly the same thing. And it was

easy to put down right next to these spools of thread. Okay. And so then I put out three of these. And your task was to just put one against each of my three things. And they can do that perfectly. So I mean, I would actually do that. It was a very easy task to explain to them because I have, I did this with this guy, Mike Frank, and he would be my, I'd be the experimenter telling him to do this and showing him to do this. And then we just like just do it. He did you'll copy him.

All we had to I didn't have to speak. Except for know what copy him like do what he did is like all we had to be able to say. And and then they would do that just perfectly. And so we'd move it up. We do some sort of random number of items up to 10. And they basically do perfectly on that. They never get that wrong. I mean, that's not accounting task. Great. That is just a match. You just put one against it. It doesn't matter how many I don't need to know how many there are

there to do that correctly. And they would make mistakes, but very, very few and no more than MIT undergrads. Just going to say like this is no, these are low stakes. So you know, you make mistakes. Counting is not required to complete the matching. That's right. Not at all. Okay. And so, and so that's our control. And this guy had gone down there before and said that they couldn't do this task. But I just don't know what he did wrong there because they can do this task perfectly well.

And you know, I can train my dog to do this task. So of course they can do this task. And so, you know, it's not a hard task. But the other task that was sort of more interesting is like so then we do a bunch of tasks where you need some way to encode the set. So like one of them is just I just put a opaque sheet in front of the other things I put down a bunch of set of these things and I put no big sheet down. And so you can't see them anymore. And I tell you do the same thing

you were doing before, right? You know, and it's easy if it's two or three. It's very easy. But if I don't have the words for eight, it's a little harder like maybe, you know, with practice went well, no. Because you have to tell us for us it's easy because we just we just count them. It's just so easy to count them. But they don't they can't count them because they don't count. They don't have words for this thing.

And so they would do approximate. It's totally fascinating. So they would get them approximately right. You know, you know, after four or five, you know, because you can basically always get four right, three or four that looks that's something we can visually see. But after that you kind of have it's approximate number. And so then there's a bunch of tasks we did and they all failed as I mean failed. They did approximate after five on all those tasks. And it kind of shows that the words,

you kind of need the words, you know, to be able to do these these kinds of tasks. Because it's a little bit of a chicken and egg thing there. Because if you don't have the words, then maybe they'll limit you in the kind of like a little baby Einstein there won't be able to come up with a counting task. You know what I mean? Like a the ability to count enables you to come up with interesting things probably. So yes, you develop counting because you

need it. But then once you have counting, you can probably come up with a bunch of different inventions. Like how to I don't know. What kind of thing they do matching really well for building purposes, building some kind of hut or something like this. So it's interesting that language is a a limiter on what you're able to do. Yeah, here's language is just is the words. Here is the words. Like the words for exact count is the limiting factor here. They just don't have them. Yeah.

And this is what I mean. Yeah. Yeah. Yeah. Yeah. The limit that limit is also limit on the society in what they're able to build. That's going to be true. Yeah. So it's problem. I mean, we don't know, this is one of those problems with the snapshot of just current languages is that we don't know what causes a culture to discover slash invent accounting system. But the hypothesis is the guess out there

is something to do with farming. So if you have a bunch of goats and you want to keep track of them. And you have saved 17 goats and you go to bed at night and you get up in the morning. Boy, it's easier to have a count system to do that. You know, I have that's an abstract abstraction over a set. So they don't have like people often ask me when I talk to tell them about this kind of work. And they say, well, don't these people. I don't think I have kids. Don't have a lot of

children. I'm like, yeah, they have a lot of children. And they do. They often have families of three or four or five kids. And they go, well, they don't they need the numbers to keep track of their kids. And I always ask this person who says this, like, do you have children? And the answer is always no because that's not how you keep track of your kids. You care about their identities. It's very important to me when I go, I think I have five children. It's, it's, it's, it's,

it doesn't matter which, yeah, it matters, which five. It's like, if you replaced one with someone else, I would, I would care. A goat, maybe not, right? That's the kind of point. It's an abstraction. Something that looks very similar to the one wouldn't matter to me probably. But if you care

about goats, you're going to know them actually individually also. Yeah, you will. I mean, cows, goats, if there's a source of food and milk and all that kind of stuff, you're going to actually, but, but I'm saying it is abstraction such that you don't have to care about their identities to do this thing fast. That's, that's the hypothesis. Not mine. From anthropologists are guessing about where words for counting came from is from farming, maybe. Yeah. Do you have

a sense why universal languages like Esperanto have not taken off? Like, why do we have all these different languages? Yeah. Well, my guess is that the function of a language is to do something in a community. I mean, unless there's some function to that language in the community, it's not going to survive. It's not going to be useful. So here's a great example. So what I'm, like language death is super common. Language is our dying all around the world. And here's how,

here's why they're dying. And it's like, yeah, I see this in, you know, in, it's not happening right now. And I, the Tremonti or the, or the Piedhung, but it probably will. And so there's a neighboring group called most of the time, which is I say, I, I, I said that it's a isolate. It's actually, there's a dual. There's two of them. Okay. So it's actually two languages, which are really close, which are most of the time and, and Tremonti, which are unrelated to anything else.

And most of the time is unlike Tremonti in that it has a lot of contact with Spanish. And it's dying. So that language is dying. The reason it's dying is there's not a lot of value for the local people in their native language. So there's much more value in knowing Spanish, like because they want to feed their families. And how do you feed your family? You learn Spanish so you can make money, so you can get a job and do these things. And then you can, and then you make money. And so they

want Spanish things they want. And so, so most of the time is, is in danger and is dying. And that's normal. And so basically the problem is that people, the reason we learn languages to communicate, and we need to, we use it to, to make money and to do whatever it is to feed our families. And if that's not happening, then it won't take off. It's not like a game or something. This is like something we, like why is English so popular? It's, it's not because it's an easy language to

learn. Maybe it is. I don't really know. It's, but that's not why it's popular. But because it's the jg, the United States is gigantic economy and therefore, speak economies that do this. It's all it is. It's all about money and that's what, and so, you know, there's a motivation to learn Mandarin. There's a motivation to learn Spanish. There's a motivation to learn English. These languages are very valuable

to know because there's so, so many speakers all over the world. There's less of a value, economically. It's like kind of what drives this. It's not a, it's not a, you know, it's not just for fun. I mean, there are these groups that do want to learn language just for language is sake and they want, and then, and there's something, you know, to that. But those, those are rare, there's a rareities in general. Those are a few small groups that do that. Not most people don't do

that. Well, if that was a primary driver, then everybody was speaking English or speaking one language. There's also attention. That's happening. And that, well, well, two words fewer and fewer languages. We are. I wonder if you're right. Maybe, maybe, you know, this is slow, but maybe that's where we're moving. But there is attention. You're saying languages at the fringes. But if you look at geopolitics and superpowers, it does seem that there's another thing attention, which is

a language is a national identity sometimes. Oh, you're a certain nation. I mean, that's the war in Ukraine. Language, Ukrainian language is a symbol of that war in many ways, like country fighting for its own identity. So it's not merely the convenience. I mean, those two things are attention. Is the convenience of trade and the economics and be able to communicate with neighboring countries and trade more efficiently with neighboring countries, all that kind of stuff. But also

identity of the group. I completely agree. As languages the way, for every community, like dialects that emerge are a kind of identity for people. Sometimes a way for people to say, if you to the more powerful people. That's interesting. So in that way, language can't be used as that tool. Yeah, I completely agree. And there's a lot of work to try to create that identity. So people want to do that speak, you know, as a cognitive scientist and language expert. I hope

that continues because I don't want languages to die. I want languages to survive because I think because they're so interesting for for so many reasons. But I mean, I find the fascinating just for the language part, but I think they, you know, there's a lot of connections to culture as well, which is also very important. Do you have hope for machine translation that can break down

the barriers of language? So while all these different diverse languages exist, I guess there's many ways of asking this question, but basically how hard is to translate in an automated way for one language to turn another? There's going to be cases where it's going to be really hard, right? So there are concepts that are in one language and not another, like the most extreme kinds of cases are these cases of number information. So exactly like good luck translating a lot of

English into Peter, huh? It's just impossible. There's no way to do it because there are no words for these concepts that we're talking about. There's probably the flip side, right? There's probably stuff in Peter, huh? Which is going to be hard to translate into English on the other side. And so I just don't know what those concepts are. I mean, you know, the space, the world space is

a little is different from my world space. And so I don't know what, like, so that the things they talk about, things are, you know, it's going to have to do with their life as opposed to, you know, my industrial life, which is going to be different. And so there's going to be problems like that always. You know, there's like, it's not maybe it's not so bad in the case of some of these spaces. And maybe it's going to be harder than others. And so it's pretty bad in number. It's like,

you know, extreme, I'd say, in the number space, you know, exact number space. But in the color dimension, right? So that's not so bad. There's, I mean, but it's a problem that you don't have ways to talk about the concepts. And there might be entire concepts that are missing. So to you, it's more about the space of concepts versus the space of form. Like form, you can probably map. Yes. Yeah. But so you were talking earlier about translation and about how translations,

there's good and bad translations. I mean, now we're talking about translations of form, right? So what makes writing good, right? It's not just the content. It's, you know, it's how it's written. And translating that, I, you know, that's, that sounds difficult. We should say that there is like, I don't know, it has a day to say meaning, but there's a music and a rhythm to the form. When you look at the broad picture, like the Prince of the East, the Yaskin, Tolstoy, or Hemingway, Bikowski,

James Joyce, like I mentioned, there's a beat to it. There's an edge to it that it's like, is in the form. We can probably get measures of those. Yeah. I don't know. I'm optimistic that we could get measures of those things. And so maybe that's translatable. I don't know. I don't know though. I haven't done that. I would love to see translating translation to Hemingway's probably the lowest, I would love to see different authors, but the average per sentence dependency, like for Hemingway's

probably the shortest. That's your sense. It's simple sentences. Simple sentences. Yeah. Yeah. I mean, that's one, if you have really long sentences, even if they don't have center, like they can have longer connections. Yeah. They can have long connections. You don't have to. Right. You can't have a long, long sentence with a bunch of local words. Yeah. But it's, but it is much more likely to have the possibility of long dependencies with long sentences. Yeah.

Yeah. I met a guy named Azaraskin who, who does a lot of cool stuff. Really works with Tristan Harris and a bunch of stuff. But he was talking to me about communicating with animals. He co-founded Earth Species Project, where you're trying to find the common language between whales,

crows, and humans. And he was saying that there is a, there's a lot of promising work that even though the signals are very different, like the actual like, if you have embeddings of the languages, they're actually trying to communicate similar type things. Is there something you can comment on that, like where is there promise to that? And everything you've seen in different cultures, especially like remote cultures, that this is a possibility?

No. Like we can talk to whales. I would say yes. I think it's not crazy at all. I think it's quite reasonable. There's this sort of weird view, well, odd view, I think, that to think that human language is somehow special. I mean, it is, maybe it is. We can certainly do more than any of the other species. And maybe our language system is part of that. It's possible. But people do, have often talked about how human, like Chomsky, in fact, is talk about how human only human language

has this compositionality thing that he thinks is sort of key in language. And it's the problem with that argument is he doesn't speak whale. And he doesn't speak crow, and he doesn't speak monkey. You know, he's like, they say things like, well, they're making a bunch of grunts and squeaks. And the reasoning is like, that's bad reasoning. Like, you know, I'm pretty sure if you ask to whale what we're saying, they'd say, well, I'm making a bunch of weird noises. Exactly.

And so it's like, this is a very odd reasoning to be making that human language is special, because we're the only ones who have human language. I'm like, well, we don't know what those other, we just don't, we can't talk to them yet. And so there probably is signal in there. And it might very well be something complicated like human language. I mean, sure, with a small brain

in lower species, there's probably not a very good communication system. But in these higher higher species where you have, you know, what seems to be, you know, abilities to communicate something, there might very well be a lot more signal there than we're, than we might have otherwise thought. But also if we have a lot of intellectual humility here, is somebody formally from my teen area, oxman who I admire very much, has talked a lot about, has worked on

communicating with plants. So like, yes, the signal there is even less than, well, like it's not out of the realm of possibility that all nature has a way of communicating. And it's a very different language, but they do develop a kind of language through the chemistry,

through some way of communicating with each other. And if you have enough humility about that possibility, I think you can, I think it would be a very interesting in a few decades, maybe centuries, hopefully not, a humbling possibility of being able to communicate not just between humans effectively, but between all of living things on earth. Well, I mean, I think some of them are not going to have much interesting to say. But you just still, we don't know, we certainly don't know.

I think if we're humble, there could be some interesting trees out there. Well, they're probably talking to other trees, right? They're not talking to us. And so to the extent they're talking, they're saying something interesting to some other, you know, conspicuous archives of people to us, right? And so they probably is, there may be some signal there. I, I, you know, so there are people out there. Actually, it's pretty common to say that language,

that human language is special and different from any other animal communication system. And I, I just, I just don't think the evidence is there for that claim. I think it's not obvious. You know, we just don't know what, because we don't speak these other communication systems until we get better. You know, I do think there's, there are people working on that as you point it out that people working on whale speak, for instance, like that's really fascinating.

Let me ask you a wild out there sci-fi question. If we make contact with an intelligent alien civilization, and you get to meet them, how hard do you think of, like how surprised you'd be about their way of communicating? Do you think it would be recognizable? Maybe there's some parallels here to when you go to the remote tribes? I mean, I would want Dan Everett with me. He is like amazing at learning foreign languages. And so he, like, this is an amazing feat, right, to be able to

go, this is a language, which has no translators before him. I mean, there were, he was just very, well, there was a guy that had been there before, but he wasn't very good. And so he learned the language far better than anyone else had learned before him. He's like good at, he's just, he's a very social person. I think that's a big part of it is being able to interact. So I don't know, it kind of depends on these, these, this species from outer space, how, how much they want

to talk to us. Is there something you can say about the process he follows? Like, what, how do you show up to a tribe and socialize? I mean, I guess colors and counting is one of the most basic things you figure out. Yeah, you start that. You actually start with like objects. And just say, you know, just throw a stick down and say stick. And you say, well, you call this and stick to this feature. And then they'll say the word, whatever. And he says a standard thing to do is to throw two sticks,

two sticks. And then, you know, he learned pretty quick that there weren't any count words. This language, because they didn't know this wasn't interesting. I mean, it was kind of weird. They'd say some or something the same word over and over again. And so, but that is a standard thing. You just like try to, but you have to be pretty out there socially, like willing to talk to random people, which these are, you know, really very different people from you. And he was, and he's,

he's very social. And so I think that's a big part of this is like, that's how, you know, a lot of people know a lot of languages that they're willing to talk to other people. That's a tough one. We just show up knowing nothing. Yeah. Oh, God. That's a beautiful, as beautiful that humans are able to connect in that way. Yeah. Yeah. You've had an incredible career exploring this fascinating topic. What advice would you give to young people about how to have a career

like that or a life that they can be proud of? When you see something interesting, just go and do it. Like I do that. Like that's something I do, which is kind of unusual for most people. So like, when I saw the Peter, like Peter, how was available to go and visit? I was like, yes, yes, I'll go. And then when we couldn't go back, we had some trouble with the Brazilian government, there's some corrupt people there. It was very difficult to get go back in there. And so I was like,

hi, I got to find another group. And so we searched around and we were able to find the Chaman, because I wanted to keep working on this kind of problem. And so we found the Chaman and just go there. I didn't really have, we didn't have content. We had a little bit of contact and brought someone. And that was, you know, we just, you just kind of just try things. I say it's like a lot of that's just like ambition. Just try to do something that other people haven't done.

Just give it a shot. It's what I, I mean, I do that all the time. I love it. And I love the fact that your pursuit of fun has landed you here talking to me. This was an incredible conversation. That you're, you're, you're just a fascinating human being. Thank you for taking a journey through human language with me today. This was awesome. Thank you very much. It's been pleasure. Thanks for listening to this conversation with Edward Gibson. The support that's podcast,

please check out our sponsors in the description. And now let me leave you with some words from Woodkins Thine. The limits of my language mean the limits of my world. Thank you for listening. And hope to see you next time.