Chomsky has felt a little off to me for a while but lacking quite a lot of background it's difficult to argue why exactly apart from some of his very recent interviews.
I am very interested in language specifically in its uhm, inter-relationship with other scientific disciplines and particularly philosophy and metaphysical considerations of the description of things interacting with their state. Aside from some base level quantum waffle, Chris Langan comes to mind immediately as being the only person i know of to tie these two things together in a seemingly wholesome way thus far, but i digress on that particular avenue.
I have written a few posts in the past about information theory and everything computation related starting to weave into this topic wondering what it means and where it is heading, and whether we are heading in the right direction, thinking of Turing, Shannon, then everyone that followed 'recently' and i would assume a still on-going debate about the differences and overlaps between natural languages and formal/ constructed ones.
Who should i be reading to gain some insights from minds that did not get enticed by materialist reductionism in these fields, if that would indeed be the main criticism, and just in general too?
I am very interested in language specifically in its uhm, inter-relationship with other scientific disciplines and particularly philosophy and metaphysical considerations of the description of things interacting with their state.
i would assume a still on-going debate about the differences and overlaps between natural languages and formal/ constructed ones.
Thank you so much for the indepth and detailed replies that are honestly slightly more then i bargained for. First of all there is this thread that ties into things directly in a very important way, we assume that human communication can be purely logical or rational but that is probably just nonsense: Mystery and Order: the left and right hemispheres, it is not a binary question and therein lies a huge part of the problem (comment of mine at the very bottom, but nonsensical without the full post).
The post i was referring to primarily is this Curt Jaimungal: Humor and Free Will -
For context, the whole article is:It’s tempting to think that monkeys have hidden linguistic depths to rival those of humans but as Ouattara says, “This system pales in contrast to the communicative power of grammar.” They monkeys’ repertoire may be rich, but it’s still relatively limited and they don’t take full advantage of their vocabulary. They can create new meanings by chaining calls together, but never by inverting their order (e.g. KB rather than BK). Our language is also symbolic. I can tell you about monkeys even though none are currently scampering about my living room, but Ouattara only found that Campbell’s monkeys “talk” about things that they actually see.
Boom-boom-krak-oo – Campbell’s monkeys combine just six ‘words’ into rich vocabulary
PUBLISHED DECEMBER 7, 2009
• 7 MIN READ
Many human languages achieve great diversity by combining basic words into compound ones – German is a classic example of this. We’re not the only species that does this. Campbell’s monkeys have just six basic types of calls but they have combined them into one of the richest and most sophisticated of animal vocabularies.
By chaining calls together in ways that drastically alter their meaning, they can communicate to each other about other falling trees, rival groups, harmless animals and potential threats. They can signal the presence of an unspecified threat, a leopard or an eagle, and even how imminent the danger is. It’s a front-runner for the most complex example of animal “proto-grammar” so far discovered.
Many studies have shown that the chirps and shrieks of monkeys are rich in information, ever since Dorothy Cheney and Robert Seyfarth’s seminal research on vervet monkeys. They showed that vervets have specific calls for different predators – eagles, leopards and snakes – and they’ll take specific evasive manoeuvres when they hear each alarm.
Campbell’s monkeys have been equally well-studied. Scientists used to think that they made two basic calls – booms and hacks – and that the latter were predator alarms. Others then discovered that the order of the calls matters, so adding a boom before a hack cancels out the predator message. It also turned out that there were five distinct types of hack, including some that were modified with an -oo suffix. So Campbell’s monkeys not only have a wider repertoire of calls than previously thought, but they can also combine them in meaningful ways.
Now, we know that the males make six different types of calls, comically described as boom (B), krak (K), krak-oo (K+), hok (H), hok-oo (H+) and wak-oo (W+). To decipher their meaning, Karim Ouattara spent 20 months in the Ivory Coast’s Tai National Park studying the wild Campbell’s monkeys from six different groups. Each consists of a single adult male together with several females and youngsters. And it’s the males he focused on.
With no danger in sight, males make three call sequences. The first – a pair of booms – is made when the monkey is far away from the group and can’t see them. It’s a summons that draws the rest of the group towards him. Adding a krak-oo to the end of the boom pair changes its meaning. Rather than “Come here”, the signal now means “Watch out for that branch”. Whenever the males cried “Boom-boom-krak-oo”, other monkeys knew that there were falling trees or branches around (or fighting monkeys overhead that could easily lead to falling vegetation).
Interspersing the booms and krak-oos with some hok-oos changes the meaning yet again. This call means “Prepare for battle”, and it’s used when rival groups or strange males have showed up. In line with this translation, the hok-oo calls are used far more often towards the edge of the monkeys’ territories than they are in the centre. The most important thing about this is that hok-oo is essentially meaningless. The monkeys never say it in isolation – they only use it to change the meaning of another call.
But the most complex calls are reserved for threats. When males know that danger is afoot but don’t have a visual sighting (usually because they’ve heard a suspicious growl or an alarm from other monkeys), they make a few krak-oos.
If they know it’s a crowned eagle that endangers the group, they combine krak-oo and wak-oo calls. And if they can actually see the bird, they add hoks and hok-oos into the mix – these extra components tell other monkeys that the peril is real and very urgent. Leopard alarms were always composed of kraks, and sometimes krak-oos. Here, it’s the proportion of kraks that signals the imminence of danger – the males don’t make any if they’ve just heard leopard noises, but they krak away if they actually see the cat.
The most important part of these results is the fact that calls are ordered in very specific ways. So boom-boom-krak-oo means a falling branch, but boom-krak-oo-boom means nothing. Some sequences act as units that can be chained together to more complicated ones – just as humans use words, clauses and sentences. They can change meaning by adding meaningless calls onto meaningful ones (BBK+ for falling wood but BBK+H+ for neighbours) or by chaining meaningful sequences together (K+K+ means leopard but W+K+ means eagle).
It’s tempting to think that monkeys have hidden linguistic depths to rival those of humans but as Ouattara says, “This system pales in contrast to the communicative power of grammar.” They monkeys’ repertoire may be rich, but it’s still relatively limited and they don’t take full advantage of their vocabulary. They can create new meanings by chaining calls together, but never by inverting their order (e.g. KB rather than BK). Our language is also symbolic. I can tell you about monkeys even though none are currently scampering about my living room, but Ouattara only found that Campbell’s monkeys “talk” about things that they actually see.
Nonetheless, you have to start somewhere, and the complexities of human syntax probably have their evolutionary origins in these sorts of call combinations. So far, the vocabulary of Campbell’s monkeys far outstrips those of other species, but this may simply reflect differences in research efforts. Other studies have started to find complex vocabularies in other forest-dwellers like Diana monkeys and putty-nosed monkeys. Ouattara thinks that forest life, with many predators and low visibility, may have provided strong evolutionary pressures for monkeys to develop particularly sophisticated vocal skills.
And there are probably hidden depths to the sequences of monkey calls that we haven’t even begun to peer into yet. For instance, what calls do female Campbell’s monkeys make? Even for the males, the meanings in this study only become apparent after months of intensive field work and detailed statistical analysis. The variations that happen on a call-by-call basis still remain a mystery to us. The effect would be like looking at Jane Austen’s oeuvre and concluding, “It appears that these sentences signify the presence of posh people”.
Reference: PNAS doi:10.1073/pnas.0908118106
Genetic language of life
So, you're looking at like, this is life itself a bit, right? And so I also wanna make a very quick link now 31.52 to your first question, the tree of life. When we link, when we try 31.58 to understand ancient languages, right? Or the cultures of the, 32.03 or the cultures that use these extinct languages. We start with the modern languages, right?
32.10 So, we look at Indo-European languages and try to understand certain words 32.17 and make trees to understand, you know, this is what Slavic word is for snow, something like "snig."
32.25 - Now we jumped to languages that human spoke throughout human history.
- Exactly, so, we make trees to understand what is 32.31 the original ancestor, what did they use to say snow? And if you have a lot of cultures who use the word snow, 32.38 you can can imagine that it was snowy. That's why they needed that word. It's the same thing for biology, right? 32.46 If they have some, if we understand some function about that enzyme, we can understand 32.51 the environment that they lived in. It's similar in that sense. So, now you're looking at the alphabet for of life. 32.59 In this case it's not 20 or 25 letters, it's you have four letters. 33.04 So, what is really interesting, that stands out to me when I look at this on the other shell, you're looking 33.11 at the 20 amino acids that's composed life, right? The one, the methionine that you see, that's the start. 33.19
So, the start is always the same.
- Got it.
- To me that is fascinating that all life starts with the same starts. 33.24 There is no other start code. So, you send the AG, you know, AUG to the cell that when that information arrives, 33.33 the translation knows, right? I gotta start function is coming, following this 33.39 is a chain of information until the stop code arrives, which are highlighted in black squares.
33.46 - So, for people just listening, we're looking at a standard RNA colored table organized in a wheel. 33.51 There's an outer shell and there's an inner shell all used in the four letters that we're talking about. With that we can compose all of the amino acids 33.58
and there's a start and there's a stop. And presumably you put together the, with these letters, 34.06 you walk around the wheel to put together the words, the sentences that make-
- Yeah, the words, the sentences, and you, again, 34.14 you get one start, you get three, there are three different ways to stop this. One way to start it. 34.19 And for each letter you have multiple options. So, you say you have a code A, 34.26 the second code can be another A, and even if you mess that up, you still can rescue yourself. 34.32 So, you can get a, for instance, I'm looking at the lysine, the K, you get an A and you get an A and then 34.37 you get an A, that gives you the lysine, right? If you get an A and if you get an A and then get a G, you still get the lysine. 34.44 So, there are different combinations. So, even if there's an error, we don't know if these 34.49 are selected because they were erroneous and somehow they got locked down. We don't know if there's a mechanism behind us to, 34.56 or we certainly don't know this definitively, but this is informatic part of this 35.03 and notice that the colors, and in some tables too, the colors will be coded in a way that the type 35.10 of the nucleotide can be similar chemically. But the point is that you will still end up 35.16 with the same amino acids or something similar to it, even if you mess up the code.
- Do we understand the mechanism how natural selection 35.23 interplays with this resilience to error?
- Which errors result in 35.30 the same output, like the same function and which don't, 35.37 which actually results in a dysfunction or which are?
- We understand to some degree how translation 35.44 and the rest of the cell work together. How an error at the translation level, 35.50 this is a really core level, can impact entire cell, but we understand very little about the evolutionary 35.57 mechanisms behind the selection of the system. It's thought to be as one of the hardest problems 36.04 in biology and it is still the dark side of biology. Even though it is so essential. 36.11 So, this is, yeah, you're looking at the language of life, so to speak, and how it can 36.19 found ways rather to tolerate its own mistakes.
- So, the entire phylogenetic tree can be 36.28 like deconstructed with this wheel of language.
- Because all the final letters, 36.35 those are, that's the 20 amino acids, that's our alphabet.
- They're all brought together 36.40 with these bits of information, right? So, when you look at the genes, you're looking at those four letters. 36.46 When you look at the proteins, you're looking at the 20 amino acids, which may be a little easier way to track 36.52 the information when we create the tree.
- So, using this language, 36.57 we can describe all life that's lived on earth. It's one perspective-
- I wish, it's not, 37.04 we are not that good at it yet, right? So-
- So, in theory, this is one way to look at life on earth.
37.11 - If you are a biologist and you want to understand how life evolved from a molecular perspective, 37.18 this would be the way to do it, and this is what nature narrowed its code down to. 37.25
So, when we think of nitrogen, when we think of carbon, when we think of sulfur, it's all in this that the, 37.31 all these nucleotides are built based on those elements.
- And this is fundamental to the informatic perspective 37.39 on this whole mechanism.
- Exactly. That's the informatic perspective. And it's important to emphasize that this is not engineered by humans. 37.47 This is this evolved by itself.
- Like, right. Humans didn't invent this just because, 37.53 we're just describing, we're trying to find, trying to describe the language of life.
- Yeah, it appears to be 37.59 a highly optimized chemical and information code. 38.05 It may indicate that a great deal of chemical evolution 38.10 and this may indicate that a lot of selection pressure 38.16 and Darwinian evolution happened with prior to the rise of last universal common ancestor because this is almost a bridge that connects 38.24 the earliest cells to the last universal common ancestor.
- Okay. Can you describe what the heck you just said? 38.29 So, this mechanism evolved before the what common ancestor? 38.36
- So, there's the-
- The last universal common ancestor.
- So, when we talk about the tree, when we think about the root, if you, 38.42 ideally included all the living information 38.48 or all the available information that comes from living organisms on your tree. Then it on the root of your tree lies 38.54 the last universal common ancestor, LUCA, right?
- Why the last? Last universal? 39.00 Because the earlier universal, it also had trees, but they all died off.
- We call it the last because it is sort 39.07 of the first one that we can track because we cannot, 39.13 we don't know what we cannot track, right?
- So, there was one organism that started the whole thing. 39.19
- It's more like a, I would think of it as more like a population, a group of organisms than a single.
- Hold on one second, I tweeted this. 39.24 So, I wanna know the accuracy of my tweet. All right, sometimes early in the morning 39.31 I tweet very pothead-like things, I said that we all evolved from one common ancestor 39.41 that was a single cell organism 3.5 billion years ago. 39.47
Something like this. How true is that tweet? Do I need to delete it? Not that it's actually correct. 39.54 I mean, I think of course there's a lot to say, which is like, we don't know exactly 40.01 but to what degrees the single organism aspect, is that true versus multiple organisms?
Here is the outline from the YouTube description:40.08 - Do you want me to be-
- Brutally honest? Yes, please.
40.15 - So-
- There's still time. This is how we get like caveated tweets. We'll just-
- All right, so first of all, 40.21 it's not, 3.5 is still a very conservative estimate, that's first for-
- In which direction? 40.27
- I would say it's 3.8 is probably safer to say at this point.
- A bunch of people said it probably was before that.
40.34 - If you put an approximately, I'll take that.
- I didn't, I just love the idea that I was once, 40.42 first of all as a single organism, I was once a cell.
- Well you're still as, you're a group of cells. 40.49
- No, but I started from a single cell. Me, Lex.
- You mean like you versus LUCA? 40.55 Are you relating to LUCA right now? Or you as a-
- No, no, no. I'm relating to my like-
- Your own development.
- My own development. I started from a single cell. 41.02 It's like, (Lex vocalizing)
- It like built up and stuff. Okay. That and then, so that's for single biologic organism. 41.09 And then from an evolutionary perspective, the LUCA, like I start, like my ancestor is a single cell 41.16 and then here I am sitting half asleep, tweeting, 41.21 like I started from a single cell, evolved, a ton of murder along the way to this 41.28 like brutal search for adaptation through 41.34 the 3.5, .8 billion years.
- So, you defy the code 41.39 of Douglas Adams, you are proud of your ancestors and you invite them over to dinner and you invite them over to your Twitter.
41.44 - Yeah.
- And it's amazing that this intelligence, to the degree you can call it intelligence emerged 41.50 to be able to tweet whatever the heck I want.
- I mean it's a bit-
- It's almost intelligence at the chemical level. 41.55 And this is also probably one of the first chemical intelligent system 42.01 that evolved by itself in nature's translations.
- Yeah, so you see that translation as a fundamentally like intelligent mechanism. 42.09
- In its own way, and again, if we manage to figure out 42.16 how to drive life's evolution and it can, 42.21 if it can evolve a sophisticated sort of informatic processing system like this, you may 42.30 ask yourself what might chemical systems be capable of independently doing under different circumstances? 42.40
- Yeah so, like locally, they're intelligent locally, they don't need the rest of the shebang. Like they don't need the big picture.
42.47 - They need, so that's a great segue into what makes this biological, right? 42.53 The heart of the cellular activities are translation. You kill translation, you kill the cell. 42.59
- You not only, the translation itself, you kill the component 43.04
that initiates it, you kill the cell, you kill, you remove the component that elongates it, you kill the cell. 43.10 So, there are many different ways to disrupt this machinery. They all the part, all the parts are important. 43.16 Now, it can vary across different organisms. We see variation between bacteria 43.21 versus eukaryotes versus archaea, right? So, it is not the same exact steps, but it can get more crowded as we get closer 43.30 to eukaryotes for instance. But you are still computing about 20 amino acids per second, right? 43.37
This is what you're generating every second.
- That single machinery is doing 20 a second? 43.43
- 20, 21 for bacteria, I believe eight for eukaryotes, or nine.
- 21 a second? 43.49 I mean that's super inefficient or super efficient depending on how you think about it.
- I think it's great. I mean I cannot-
43.55 - Yeah, but it's way slower than a computer could generate through simulation.
- I think if you can show me a computer 44.02 that does this, we are done here.
- Well, this is the big, this includes the five things, 44.08 not just, but I could show you a computer that's doing the informatic, right?
- Like yes, you can show me that, 44.14 but you cannot show me the one that has all.
- For now.
- For now.
- I will ask you about probably what alpha fold, right?
44.23 - I think the more we learn about, and this is why early life and origin is also very fascinating 44.29 and applicable to many different disciplines. There is no way you see this, the way we just described it,
44.35 unless you think about early life in early geochemistry and earliest emergent systems.
I still have to listen to what comes after minute 45, but posted in case I will not have time for the rest.0:00 - Introduction
0:56 - History of life on Earth
9:00 - Origin of life
31:47 - Genetic language of life
44:43 - Life and energy
55:26 - Ancient DNA
1:14:24 - Evolution
1:25:55 - Alien life
1:53:55 - Panspermia
2:00:17 - Restarting life on Earth
2:12:58 - Where ideas come from
2:20:30 - Science and language
2:29:07 - Love
2:30:30 - Advice to young people
2:35:04 - Meaning of life