← Lex Fridman

Noam Chomsky: Language, Cognition, and Deep Learning

Episode 2019-11-292019-11-29English

Guests: Noam Chomsky

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Contents4 chapters§01Transcript · 00:00–10:01§02Transcript · 10:03–20:13§03Transcript · 20:15–29:58§04Transcript · 30:00–35:39

§01Transcript · 00:00–10:01

00:00The following is a conversation with Noam Chomsky.

00:03He's truly one of the great minds of our time

00:06and is one of the most cited scholars

00:08in the history of our civilization.

00:10He has spent over 60 years at MIT

00:13and recently also joined the University of Arizona

00:16where we met for this conversation.

00:18But it was at MIT about four and a half years ago

00:21when I first met Noam.

00:23My first few days there,

00:24I remember getting into an elevator at Stata Center,

00:27pressing the button for whatever floor,

00:29looking up and realizing it was just me and Noam Chomsky

00:33riding the elevator.

00:35Just me and one of the seminal figures of linguistics,

00:38cognitive science, philosophy,

00:40and political thought in the past century, if not ever.

00:43I tell that silly story because I think

00:46life is made up of funny little defining moments

00:49that you never forget for reasons that may be too poetic

00:53to try and explain.

00:54That was one of mine.

00:57Noam has been an inspiration to me and millions of others.

01:00It was truly an honor for me to sit down with him in Arizona.

01:04I traveled there just for this conversation.

01:07And in a rare heartbreaking moment,

01:10after everything was set up and tested,

01:12the camera was moved and accidentally

01:14the recording button was pressed, stopping the recording.

01:17So I have good audio of both of us, but no video of Noam.

01:22Just the video of me and my sleep deprived,

01:25but excited face that I get to keep

01:28as a reminder of my failures.

01:30Most people just listen to this audio version

01:32for the podcast as opposed to watching it on YouTube.

01:35But still, it's heartbreaking for me.

01:39I hope you understand and still enjoy this conversation

01:41as much as I did.

01:43The depth of intellect that Noam showed

01:45and his willingness to truly listen to me,

01:48a silly looking Russian in a suit,

01:51it was humbling and something I'm deeply grateful for.

01:55As some of you know, this podcast is a side project for me

01:59where my main journey and dream is to build AI systems

02:03that do some good for the world.

02:05This latter effort takes up most of my time,

02:07but for the moment has been mostly private.

02:10But the former, the podcast,

02:12is something I put my heart and soul into.

02:15And I hope you feel that even when I screw things up.

02:18I recently started doing ads at the end of the introduction.

02:22I'll do one or two minutes after introducing the episode

02:25and never any ads in the middle

02:27that break the flow of the conversation.

02:29I hope that works for you.

02:31It doesn't hurt the listening experience.

02:34This is the Artificial Intelligence Podcast.

02:37If you enjoy it, subscribe on YouTube,

02:39give it five stars on Apple Podcast,

02:41support it on Patreon,

02:43or simply connect with me on Twitter,

02:45Alex Friedman, spelled F-R-I-D-M-A-N.

02:49This show is presented by Cash App,

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03:50and boys to dream of engineering a better world.

03:54And now, here's my conversation with Noam Chomsky.

03:58I apologize for the absurd philosophical question,

04:04but if an alien species were to visit Earth,

04:07do you think we would be able to find a common language

04:10or protocol of communication with them?

04:13- There are arguments to the effect that we could.

04:18In fact, one of them was Marvin Minsky's.

04:22Back about 20 or 30 years ago,

04:24he performed a brief experiment

04:29with a student of his, Dan Bobrow.

04:31They essentially ran the simplest possible

04:35Turing machines, just free to see what would happen.

04:39And most of them crashed,

04:42either got into an infinite loop or stopped.

04:47The few that persisted essentially gave

04:52something like arithmetic.

04:54And his conclusion from that was that

04:58if some alien species developed higher intelligence,

05:05they would at least have arithmetic.

05:07They would at least have what the simplest computer would do.

05:12And in fact, he didn't know that at the time,

05:16but the core principles of natural language

05:20are based on operations which yield something

05:25like arithmetic in the limiting case and the minimal case.

05:29So it's conceivable that a mode of communication

05:34could be established based on the core properties

05:38of human language and the core properties of arithmetic,

05:41which maybe are universally shared.

05:44So it's conceivable.

05:46- What is the structure of that language,

05:50of language as an internal system inside our mind

05:55versus an external system as it's expressed?

05:58- It's not an alternative.

06:00It's two different concepts of language.

06:02- Different.

06:03- It's a simple fact that there's something about you,

06:07a trait of yours, part of the organism, you,

06:11that determines that you're talking English

06:14and not Tagalog, let's say.

06:17So there is an inner system.

06:18It determines the sound and meaning

06:23of the infinite number of expressions of your language.

06:27It's localized.

06:28It's not on your foot, obviously.

06:30It's in your brain.

06:31If you look more closely,

06:32it's in specific configurations of your brain.

06:36And that's essentially like the internal structure

06:40of your laptop, whatever programs it has are in there.

06:45Now, one of the things you can do with language,

06:47it's a marginal thing, in fact,

06:50is use it to externalize what's in your head.

06:54Actually, most of your use of language

06:56is thought, internal thought.

06:58But you can do what you and I are now doing.

07:00We can externalize it.

07:02Well, the set of things that we're externalizing

07:05are an external system.

07:07They're noises in the atmosphere.

07:11And you can call that language

07:12in some other sense of the word,

07:14but it's not a set of alternatives.

07:16These are just different concepts.

07:19- So how deep do the roots of language go in our brain?

07:22Our mind, is it yet another feature like vision,

07:26or is it something more fundamental

07:28from which everything else springs in the human mind?

07:31- Well, in a way, it's like vision.

07:33There's something about our genetic endowment

07:38that determines that we have a mammalian

07:41rather than an insect visual system.

07:44And there's something in our genetic endowment

07:47that determines that we have a human language faculty.

07:51No other organism has anything remotely similar.

07:55So in that sense, it's internal.

07:58Now, there is a long tradition,

07:59which I think is valid, going back centuries

08:03to the early scientific revolution, at least,

08:06that holds that language is the core of human life.

08:11The core of human cognitive nature.

08:13It's the source.

08:14It's the mode for constructing thoughts and expressing them.

08:19That is what forms thought.

08:22And it's got fundamental creative capacities.

08:27It's free, independent, unbounded, and so on.

08:31And undoubtedly, I think, the basis

08:34for our creative capacities

08:38and the other remarkable human capacities

08:43that lead to the unique achievements

08:47and not-so-great achievements of the species.

08:51- The capacity to think and reason.

08:53Do you think that's deeply linked with language?

08:56Do you think the way we, the internal language system

08:59is essentially the mechanism

09:01by which we also reason internally?

09:04- It is undoubtedly the mechanism by which we reason.

09:06There may also be other,

09:09there are undoubtedly other faculties involved in reasoning.

09:13We have a kind of scientific faculty.

09:17Nobody knows what it is.

09:18But whatever it is that enables us

09:20to pursue certain lines of endeavor and inquiry

09:25and to decide what makes sense and doesn't make sense

09:29and to achieve a certain degree

09:32of understanding of the world,

09:33that uses language but goes beyond it.

09:37Just as using our capacity for arithmetic

09:42is not the same as having the capacity.

09:44- The idea of capacity, our biology, evolution,

09:49you've talked about it defining, essentially,

09:51our capacity, our limit, and our scope.

09:55Can you try to define what limit and scope are?

09:58And the bigger question,

10:01do you think it's possible to find

§02Transcript · 10:03–20:13

10:03the limit of human cognition?

10:06- Well, that's an interesting question.

10:09It's commonly believed, most scientists believe,

10:13that human intelligence can answer

10:16any question in principle.

10:19I think that's a very strange belief.

10:21If we're biological organisms, which are not angels,

10:26then we, our capacities ought to have scope and limits,

10:33which are interrelated.

10:34- Can you define those two terms?

10:36- Well, let's take a concrete example.

10:40Your genetic endowment determines

10:44that you can have a male and visual system,

10:47arms and legs and so on,

10:48and therefore become a rich, complex organism.

10:53But if you look at that same genetic endowment,

10:56it prevents you from developing in other directions.

11:00There's no kind of experience

11:01which would yield the embryo

11:05to develop an insect visual system,

11:08or to develop wings instead of arms.

11:12So the very endowment that confers richness and complexity

11:17also sets bounds on what can be attained.

11:23Now, I assume that our cognitive capacities

11:27are part of the organic world,

11:29therefore they should have the same properties.

11:32If they had no built-in capacity

11:35to develop a rich and complex structure,

11:39we would have understand nothing.

11:41Just as if your genetic endowment

11:46did not compel you to develop arms and legs,

11:50you would just be some kind of a random amoeboid creature

11:54with no structure at all.

11:56So I think it's plausible to assume

11:58that there are limits,

12:00and I think we even have some evidence as to what they are.

12:03So for example, there's a classic moment

12:06in the history of science at the time of Newton.

12:10There was a, from Galileo to Newton,

12:13modern science developed on a fundamental assumption

12:17which Newton also accepted,

12:20namely that the world, the entire universe,

12:24is a mechanical object.

12:26And by mechanical, they meant something like

12:29the kinds of artifacts that were being developed

12:31by skilled artisans all over Europe,

12:34gears, levers, and so on.

12:37And their belief was, well,

12:39the world is just a more complex variant of this.

12:42Newton, to his astonishment and distress,

12:47proved that there are no machines,

12:51that there's interaction without contact.

12:54His contemporaries like Leibniz and Huygens

12:57just dismissed this as returning to the mysticism

13:02of the neo-scholastics.

13:04And Newton agreed.

13:05He said it is totally absurd.

13:08No person of any scientific intelligence

13:11could ever accept this for a moment.

13:13In fact, he spent the rest of his life

13:15trying to get around it somehow,

13:17as did many other scientists.

13:20That was the very criterion of intelligibility

13:24for say, Galileo or Newton.

13:26Theory did not produce an intelligible world

13:31unless you could duplicate it in a machine.

13:34He showed you can't.

13:35There are no machines, any.

13:37Finally, after a long struggle, took a long time,

13:41scientists just accepted this as common sense.

13:45But that's a significant moment.

13:47That means they abandoned the search

13:49for an intelligible world.

13:51And the great philosophers of the time

13:54understood that very well.

13:57So for example, David Hume, in his encomium to Newton,

14:02wrote that, who was the greatest thinker ever and so on,

14:05he said that he unveiled many of the secrets of nature,

14:10but by showing the imperfections

14:13of the mechanical philosophy, mechanical science,

14:17he left us with, he showed that there are mysteries

14:21which ever will remain.

14:23And science just changed its goals.

14:26It abandoned the mysteries, it can't solve it,

14:29it will put it aside.

14:31We only look for intelligible theories.

14:34Newton's theories were intelligible.

14:36It's just what they described wasn't.

14:39Well, Locke said the same thing.

14:42I think they're basically right.

14:44And if so, that showed something

14:47about the limits of human cognition.

14:49We cannot attain the goal of understanding the world,

14:54of finding an intelligible world.

14:58This mechanical philosophy, Galileo to Newton,

15:02there's a good case that can be made

15:05that that's our instinctive conception of how things work.

15:10So if say infants are tested with things,

15:16if this moves and then this moves,

15:18they kind of invent something that must be invisible

15:22that's in between them that's making them move.

15:24- Yeah, we like physical contact.

15:26Something about our brain seeks--

15:28- Makes us want a world like that,

15:31just like it wants a world

15:32that has regular geometric figures.

15:36So for example, Descartes pointed this out,

15:38that if you have an infant

15:41who's never seen a triangle before,

15:45and you draw a triangle,

15:47the infant will see a distorted triangle,

15:52not whatever crazy figure it actually is.

15:55You know, three lines not coming quite together,

15:58one of them a little bit curved and so on.

16:00We just impose a conception of the world

16:04in terms of perfect geometric objects.

16:09It's now been shown that it goes way beyond that,

16:12that if you show on a tachistoscope,

16:15let's say a couple of lights shining,

16:18you do it three or four times in a row,

16:20what people actually see is a rigid object in motion,

16:25not whatever's there.

16:26We all know that from a television set, basically.

16:31- So that gives us hints of potential limits

16:34to our cognition.

16:35- I think it does, but it's a very contested view.

16:39If you do a poll among scientists,

16:42it's impossible we can understand anything.

16:45- Let me ask and give me a chance with this.

16:48So I just spent a day at a company called Neuralink,

16:52and what they do is try to design

16:56what's called the brain-computer interface.

16:59So they try to do thousands readings in the brain,

17:03be able to read what the neurons are firing,

17:05and then stimulate back, so two-way.

17:08Do you think their dream is to expand the capacity

17:12of the brain to attain information,

17:16sort of increase the bandwidth

17:18at which we can search Google kind of thing?

17:22Do you think our cognitive capacity might be expanded,

17:26our linguistic capacity, our ability to reason

17:29might be expanded by adding a machine into the picture?

17:33- Can be expanded in a certain sense,

17:35but a sense that was known thousands of years ago.

17:39A book expands your cognitive capacity.

17:43Okay, so this could expand it too.

17:46- But it's not a fundamental expansion.

17:47It's not totally new things could be understood.

17:50- Well, nothing that goes beyond

17:53our native cognitive capacities.

17:56Just like you can't turn the visual system

17:58into an insect system.

18:00- Well, I mean, the thought is,

18:04the thought is perhaps you can't directly,

18:06but you can map.

18:08- You could, but we already,

18:10we know that without this experiment.

18:12You could map what a bee sees,

18:15and present it in a form so that we could follow it.

18:17In fact, every bee scientist does that.

18:19- But you don't think there's something greater than bees

18:24that we can map, and then all of a sudden discover

18:28something, be able to understand a quantum world,

18:32quantum mechanics, be able to start

18:34to be able to make sense.

18:36- Students at MIT study and understand quantum mechanics.

18:40- But they always reduce it to the infant, the physical.

18:45I mean, they don't really understand.

18:46- Oh, you don't, there's things,

18:48that may be another area where there's just a limit

18:51to understanding.

18:52We understand the theories,

18:54but the world that it describes doesn't make any sense.

18:58So, you know, the experiment,

19:00Schrodinger's cat, for example,

19:02can understand the theory,

19:03but as Schrodinger pointed out,

19:05it's an unintelligible world.

19:07One of the reasons why Einstein was always very skeptical

19:13about quantum theory,

19:14he described himself as a classical realist,

19:19once in one's intelligibility.

19:23- He has something in common with infants, in that way.

19:27So, back to linguistics.

19:30If you could humor me,

19:32what are the most beautiful or fascinating aspects

19:35of language, or ideas in linguistics,

19:37or cognitive science that you've seen

19:39in a lifetime of studying language

19:42and studying the human mind?

19:44- Well, I think the deepest property of language

19:49and puzzling property that's been discovered

19:52is what is sometimes called structure dependence.

19:57We now understand it pretty well,

19:59but it was puzzling for a long time.

20:01I'll give you a concrete example.

20:03So, suppose you say,

20:05the guy who fixed the car carefully packed his tools.

20:11It's ambiguous.

20:13He could fix the car carefully,

§03Transcript · 20:15–29:58

20:15or carefully pack his tools.

20:17Suppose you put carefully in front,

20:21carefully the guy who fixed the car packed his tools.

20:25Then it's carefully packed, not carefully fixed.

20:29And in fact, you do that even if it makes no sense.

20:32So, suppose you say, carefully,

20:34the guy who fixed the car is tall.

20:38You have to interpret it as carefully he's tall,

20:41even though that doesn't make any sense.

20:44And notice that that's a very puzzling fact,

20:47because you're relating carefully,

20:50not to the linearly closest verb,

20:53but to the linearly more remote verb.

20:57A linear closeness is an easy computation,

21:02but here you're doing a much more,

21:03what looks like a more complex computation.

21:06You're doing something that's taking you

21:09essentially to the more remote thing.

21:11If you look at the actual structure of the sentence,

21:17where the phrases are and so on,

21:20turns out you're picking out

21:21the structurally closest thing,

21:24but the linearly more remote thing.

21:27But notice that what's linear is 100% of what you hear.

21:32You never hear structure, can't.

21:35So what you're doing is,

21:37and incidentally this is universal,

21:39all constructions, all languages.

21:42And what we're compelled to do is carry out

21:45what looks like the more complex computation

21:48on material that we never hear,

21:52and we ignore 100% of what we hear

21:55and the simplest computation.

21:57By now there's even a neural basis for this

22:00that's somewhat understood.

22:02And there's good theories by now

22:04that explain why it's true.

22:06That's a deep insight into the surprising

22:10nature of language with many consequences.

22:13- Let me ask you about a field of machine learning,

22:17deep learning.

22:18There's been a lot of progress in neural networks based,

22:22neural network based machine learning in the recent decade.

22:26Of course neural network research goes back many decades.

22:29What do you think are the limits of deep learning,

22:35of neural network based machine learning?

22:38- Well to give a real answer to that,

22:41you'd have to understand the exact processes

22:44that are taking place, and those are pretty opaque.

22:47So it's pretty hard to prove a theorem

22:50about what can be done and what can't be done.

22:54But I think it's reasonably clear.

22:56I mean putting technicalities aside,

22:59what deep learning is doing is taking huge numbers

23:03of examples and finding some patterns.

23:07Okay, that could be interesting.

23:10In some areas it is, but we have to ask here

23:13a certain question.

23:15Is it engineering or is it science?

23:18Engineering in the sense of just trying to build something

23:21that's useful, or science in the sense that it's trying

23:24to understand something about elements of the world.

23:28So it takes a Google parser.

23:31We can ask that question.

23:33Is it useful?

23:35Yeah, it's pretty useful.

23:36You know, I use a Google translator.

23:39So on engineering grounds it's kind of worth having,

23:43like a bulldozer.

23:44Does it tell you anything about human language?

23:48Zero.

23:49Nothing.

23:51And in fact, it's very striking.

23:54It's from the very beginning,

23:56it's just totally remote from science.

24:00So what is a Google parser doing?

24:02It's taking an enormous text,

24:05let's say the Wall Street Journal corpus,

24:07and asking how close can we come to getting

24:11the right description of every sentence in the corpus.

24:16Well, every sentence in the corpus

24:18is essentially an experiment.

24:21Each sentence that you produce is an experiment

24:24which is, am I a grammatical sentence?

24:27The answer is usually yes.

24:29So most of the stuff in the corpus is grammatical sentences.

24:33But now ask yourself, is there any science

24:36which takes random experiments

24:40which are carried out for no reason whatsoever

24:43and tries to find out something from them?

24:46Like if you're, say, a chemistry PhD student,

24:49you wanna get a thesis, can you say,

24:51well, I'm just gonna do a lot of,

24:53mix a lot of things together, no purpose,

24:56just maybe I'll find something.

24:59You'd be laughed out of the department.

25:01Science tries to find critical experiments,

25:06ones that answer some theoretical question.

25:09Doesn't care about coverage of millions of experiments.

25:12So it just begins by being very remote from science,

25:16and it continues like that.

25:18So the usual question that's asked about,

25:21say, a Google parser, is how well does it do,

25:25or some parser, how well does it do on a corpus?

25:28But there's another question that's never asked.

25:31How well does it do on something

25:32that violates all the rules of language?

25:36So for example, take the structure dependence case

25:38that I mentioned.

25:39Suppose there was a language in which you used

25:43linear proximity as the mode of interpretation.

25:48These deep learning would work very easily on that.

25:51In fact, much more easily than an actual language.

25:54Is that a success?

25:55No, that's a failure.

25:57From a scientific point of view, it's a failure.

26:00It shows that we're not discovering

26:03the nature of the system at all,

26:05'cause it does just as well or even better

26:07on things that violate the structure of the system.

26:10And it goes on from there.

26:12It's not an argument against doing it.

26:14It is useful to have devices like this.

26:17- So yes, so neural networks are kind of approximators

26:20that look, there's echoes of the behavioral debates,

26:24right, behavioralism.

26:26- More than echoes.

26:27Many of the people in deep learning say they've vindicated.

26:31(Lex laughs)

26:32Terry Sanyoski, for example, in his recent books says,

26:36this vindicates Skinnerian behaviors.

26:39It doesn't have anything to do with it.

26:41- Yes, but I think there's something

26:43actually fundamentally different when the data set is huge.

26:48But your point is extremely well taken.

26:51But do you think we can learn, approximate

26:55that interesting complex structure of language

26:58with neural networks that will somehow help us

27:01understand the science?

27:02- It's possible.

27:04I mean, you find patterns that you hadn't noticed,

27:07let's say, could be.

27:09In fact, it's very much like a kind of linguistics

27:13that's done, what's called corpus linguistics.

27:18When you, suppose you have some language

27:21where all the speakers have died out,

27:23but you have records.

27:25So you just look at the records

27:28and see what you can figure out from that.

27:30It's much better to have actual speakers

27:33where you can do critical experiments.

27:36But if they're all dead, you can't do them.

27:38So you have to try to see what you can find out

27:40from just looking at the data that's around.

27:43You can learn things.

27:45Actually, paleoanthropology is very much like that.

27:48You can't do a critical experiment

27:50on what happened two million years ago.

27:53So you're kind of forced just to take what data's around

27:56and see what you can figure out from it.

27:59Okay, it's a serious study.

28:01- So let me venture into another whole body of work

28:05and philosophical question.

28:07You've said that evil in society arises from institutions,

28:13not inherently from our nature.

28:15Do you think most human beings are good,

28:17they have good intent,

28:19or do most have the capacity for intentional evil

28:22that depends on their upbringing,

28:24depends on their environment, on context?

28:27- I wouldn't say that they don't arise from our nature.

28:30Anything we do arises from our nature.

28:34And the fact that we have certain institutions, not others,

28:38is one mode in which human nature has expressed itself.

28:43But as far as we know,

28:45human nature could yield

28:47many different kinds of institutions.

28:50The particular ones that have developed

28:53have to do with historical contingency,

28:56the who conquered whom and that sort of thing.

28:59They're not rooted in our nature

29:03in the sense that they're essential to our nature.

29:06So it's commonly argued that these days

29:10that something like market systems

29:12is just part of our nature.

29:15But we know from a huge amount of evidence

29:18that that's not true.

29:19There's all kinds of other structures.

29:21It's a particular fact about a moment of modern history.

29:26Others have argued that the roots of classical liberalism

29:30actually argue that what's called sometimes

29:34an instinct for freedom,

29:36instinct to be free of domination by illegitimate authority

29:42is the core of our nature.

29:43That would be the opposite of this.

29:45And we don't know.

29:47We just know that human nature can accommodate both kinds.

29:50- If you look back at your life,

29:54is there a moment in your intellectual life

29:58or life in general that jumps from memory

§04Transcript · 30:00–35:39

30:00that brought you happiness,

30:02that you would love to relive again?

30:04- Sure.

30:06Falling in love, having children.

30:10- What about, so you have put forward into the world

30:13a lot of incredible ideas in linguistics,

30:17in cognitive science.

30:20In terms of ideas that just excites you

30:24when they first came to you,

30:26that you would love to relive those moments.

30:28- Well, I mean, when you make a discovery

30:32about something that's exciting,

30:34like say, even the observation of structure dependence

30:39and on from that, the explanation for it.

30:44But the major things just seem like common sense.

30:49So if you go back to,

30:52take your question about external and internal language,

30:55you go back to say the 1950s,

30:58almost entirely language is regarded an external object,

31:03something outside the mind.

31:06It just seemed obvious that that can't be true.

31:09Like I said, there's something about you

31:13that determines you're talking English,

31:15not Swahili or something.

31:18But that's not really a discovery,

31:20that's just an observation, what's transparent.

31:24You might say it's kind of like the 17th century,

31:29the beginnings of modern science, 17th century.

31:33They came from being willing to be puzzled

31:37about things that seemed obvious.

31:40So it seems obvious that a heavy ball of lead

31:44will fall faster than a light ball of lead.

31:47But Galileo was not impressed

31:50by the fact that it seemed obvious.

31:52So he wanted to know if it's true.

31:54Carried out experiments, actually thought experiments,

31:59never actually carried them out,

32:01which showed that it can't be true.

32:03And out of things like that, observations of that kind,

32:09why does a ball fall to the ground

32:14instead of rising, let's say?

32:16Seems obvious, until you start thinking about it.

32:20Because why does steam rise, let's say?

32:23And I think the beginnings of modern linguistics,

32:27roughly in the 50s, are kind of like that,

32:30just being willing to be puzzled about phenomena

32:33that looked, from some point of view, obvious.

32:38And for example, a kind of doctrine,

32:41almost official doctrine of structural linguistics

32:44in the 50s was that languages can differ

32:49from one another in arbitrary ways,

32:52and each one has to be studied on its own

32:56without any presuppositions.

32:58In fact, there were similar views among biologists

33:02about the nature of organisms,

33:04that each one is, they're so different

33:06when you look at them that almost anything,

33:09you could be almost anything.

33:10Well, in both domains, it's been learned

33:13that that's very far from true.

33:15There are very narrow constraints

33:16on what could be an organism or what could be a language.

33:20But these are, that's just the nature of inquiry.

33:26- Science in general, yeah, inquiry.

33:29So one of the peculiar things about us human beings

33:33is our mortality.

33:35Ernest Becker explored it in general.

33:38Do you ponder the value of mortality?

33:40Do you think about your own mortality?

33:42- I used to when I was about 12 years old.

33:46I wondered, I didn't care much about my own mortality,

33:51but I was worried about the fact

33:53that if my consciousness disappeared,

33:57would the entire universe disappear?

34:00That was frightening.

34:01- Did you ever find an answer to that question?

34:03- No, nobody's ever found an answer,

34:05but I stopped being bothered by it.

34:07It's kind of like Woody Allen in one of his films,

34:10you may recall, he starts, he goes to a shrink

34:14when he's a child and the shrink asks him,

34:16"What's your problem?"

34:17He says, "I just learned that the universe is expanding.

34:21"I can't handle that."

34:22- And then another absurd question is,

34:27what do you think is the meaning of our existence here,

34:32our life on Earth, our brief little moment in time?

34:35- That's something we answer by our own activities.

34:39There's no general answer.

34:42We determine what the meaning of it is.

34:44- The action determined the meaning.

34:48- Meaning in the sense of significance,

34:50not meaning in the sense that chair means this,

34:54but the significance of your life is something you create.

34:58- Noam, thank you so much for talking today.

35:02It was a huge honor.

35:04Thank you so much.

35:05Thanks for listening to this conversation with Noam Chomsky

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— A LITTLE MORE TO THINK ABOUT. —