Switch Statement

082: Gödel, Escher, Bach - Ch. 16 - The Human OS

• Jon Bedard

Share episode
Matt:

Hello everyone And welcome to the switch statement podcast It's a podcast for investigations into miscellaneous tech topics

Matthew:

Hey, John, how you doing?

Jon:

Hey, Matt, how are you?

Matthew:

I am doing all right. I didn't realize we were reading a book about molecular biology.

Jon:

Yeah, no, this chapter had some wild turns

Matthew:

Literally, there's just all sorts of, twists in it.

Jon:

Yeah, no, I thought this was, I mean, this is kind of like, first of all, this is just a completely fascinating topic. Like I read a book, uh, a while ago now called the song of the cell, which was a very good book, which kind of really dive deeply into this stuff. Uh, and you do get this sense. I don't want to completely reveal the purpose of this chapter, but you get the sense that biology is a computer, like it's performing instructions. It has, you know, it has data and functions, which is kind of like the DNA of all computer languages.

Matthew:

The data is functions,

Jon:

Maybe the data is functions.

Matthew:

data.

Jon:

Yeah,

Matthew:

Um, so we start, we start this chapter off kind of where we left off talking about self reference and, and, and then we immediately get into self replication. Um, I guess as, as a more sneaky way to. Accomplished self reference, I guess?

Jon:

yeah, um, yeah, and it was an interesting transition because he sort of starts with a little bit of like linguistic exercises.

Matthew:

Right.

Jon:

I actually thought this chapter was going to be like light reading at first, which is hilarious, but, you know, he started just sort of describing like this sentence is false for the millionth time in this book, but he didn't describe a concept. That I thought was interesting, which is like how much of the self reference is encoded in the language itself

Matthew:

Yeah.

Jon:

versus like the processing or parsing of the sentence. Like, in other words, you could create a programming language and this was an example he gave, but you could create a programming language where you just type in asterisk. And like, by simply typing that asterisk, you've created a program that prints itself. But that's like completely uninteresting, right? Because all of the self reference is kind of encoded into the compiler. And he's sort of getting at, you want something that more fundamentally self represents itself within the procedure itself, sort of the abstract procedure itself. Which, which he sort of, I don't know if you read into this the same way, but it was like, I feel like his claim was that that's what Gödel's incompleteness is. Like, it's a fully agnostic, self, referential, uh, construct that's fully agnostic of, like, the language it's being represented in.

Matthew:

I guess this ties into his inner message and outer message. Like, this is this

Jon:

Yeah,

Matthew:

message that's completely divorced from any outer representation of it.

Jon:

exactly. At least this was my reading. Is that he gave all these linguistic constructs where Like, he even criticizes the use of the phrase, this sentence, because it's kind of like, as an English reader, you have to like, do all this processing in your brain to sort of understand what this sentence means. even means, you know, you have to read the whole sentence for one thing in order to understand what this sentence even means. So a lot of the encoding of the self reference is like within the processing of, you know, the English words, as opposed to like within the, you know, the algorithm itself.

Matthew:

But

Jon:

It's hard to explain.

Matthew:

in these, like, recursive You know, recursive situations where, like, you know, and we actually have experienced this firsthand. We wrote a tiny little toy programming language you need to build up a certain amount of structure before you can do things like reference a function from. Um, I think it's not, it's not too different from the amount of like English language understanding you need to the phrase, the sentence.

Jon:

Yeah. I mean, that's, that's definitely true. You need some amount of substrate. In order to build these constructs, um, but yeah, I don't know. It was interesting. It was the first time I'd seen him talk about this whole, like, where, you know, it was, it's almost like, where is the self reference? Like, is it in the execution of the, you know, of the procedure? Or is it within syntax of the procedure? I don't even know what to call it. Like,

Matthew:

it's funny because, and maybe this isn't true, but there always has to be a context in order for self reference to. Have any effect, right? Because it's like the context for us is our brain

Jon:

uh,

Matthew:

this sentence and then like, we stick it somewhere and then we keep on like consuming tokens and then, then we arrive at some complete understanding of what the sentence means.

Jon:

so we, so there is a significant amount of context there.

Matthew:

So I think is that, and you're saying the self reference. Exists in both. You need both parts, I guess, in order for there to actually be a self reference.

Jon:

I think so. Cause he gave two, Analogies. One was an iceberg where you have, you know, the iceberg I think is the asterisk program where it's like you have this little tip, which is self reference, but in order to like fully represent the self reference, you need this like lower part of the iceberg, which is the compiler. And then he gave this soap example where there's like a bar of soap floating in the water where most of the soap is above the water. And that was more, I can't remember what that example was. Maybe it was,

Matthew:

to Yield's falsehood when preceded by its quotation, Yield's falsehood when preceded by its quotation, which is so funny because he's like, Yeah, you, you, you only need half a bar of soaps worth of processing to understand Yield's falsehood when preceded by its quotation, Yield's falsehood when preceded by its quotation. And I'm like, I'm not sure that's true. I

Jon:

yeah, it's interesting. You kind of have to like really take what he's saying about the phrase, this sentence, it's like, you have to kind of agree with his premise that like this sentence is a highly costly. Construct.

Matthew:

think it's that it's, uh, A much more abstract,

Jon:

Right. Yeah.

Matthew:

like that, there's no, there's no abstract references that you need to unpack.

Jon:

Yeah, exactly.

Matthew:

yeah.

Jon:

Right. It's like this sentence is that lower part of the iceberg. Whereas yields falsehood. Uh, I can't remember what it is. Yields falsehood when.

Matthew:

quotation,

Jon:

Yeah. Uh, so anyway, this is how the chapter started with these sort of like linguistic wordplay type things. Um, he, he also gave this Nickelodeon example, which I kind of liked. Where, and I guess a Nickelodeon are those things where you like press a letter and a number and it plays a song?

Matthew:

dude, I wanted to ask you, were you young enough to like, did you watch Nickelodeon, the channel when you were

Jon:

Oh yeah. Oh yeah. Yeah. Stick Stickly?

Matthew:

Stick sickly. I don't know. I don't get that one.

Jon:

Pete and Marie Pete? Was that? Or Pete and Pete?

Matthew:

was definitely Pete and Pete, which I know.

Jon:

No way.

Matthew:

school with his sister. Yeah. Little Pete. His sister was in my grade.

Jon:

No way, dude. You know Little Pete. How's Little Pete doing?

Matthew:

he's doing well. He wrote a book. Uh, I hadn't read it, but I went to one of the book signings. I have a picture of my girlfriend with little Pete. I mean, he's, he's old now, but was little Pete.

Jon:

He's Big Pete now.

Matthew:

anyway, yeah, Pete and Pete, good show.

Jon:

Good show, kind of a precursor of like, I want to say it had, it shared a lot of similar DNA with shows that I love, like Twin Peaks, like it was just kind of this really weird, you know, realism, surrealism mixture.

Matthew:

it was weird. Yeah. There was a lot of unusual stuff going on there. Um, but so we have, we have a Nickelodeon in the original sense, uh, like you're talking about.

Jon:

Oh right, okay, so yeah, sorry, I don't want to take too long with this, this anecdote, but basically Nickelodeon, where you press a letter and a number and it plays a song, and if there's a song in there that says, The song itself, the lyrics of the song says, press 11 u to play this song. And then you listen to that song and so you press 11 u and then it plays that song at, so anyway, this is self-reference, like it just repeats the song over and over again, using you as sort of like a mesmerized automaton to keep playing it. Uh, but he, he mentions how this is more, uh, where the 11 U is like a trigger. Of the self reference where and he's sort of, you know, comparing that to Godel's and completeness where the self reference is in the procedure itself. So I think it was just another example of that whole, like, you know, where actually is the self reference? Like, is it? Yeah, go ahead.

Matthew:

And it's interesting because. The self reference, like the things that are being reproduced are the song and the action, but you still have, I think if we're kind of trying to keep on going back to this context, you still have the person and the jukebox are enabling those replications

Jon:

Yes.

Matthew:

and they're just triggering each other.

Jon:

Yeah, yeah. And so there's a massive amount of, like, iceberg happening here.

Matthew:

Yeah,

Jon:

but still, you know, an interesting example. Hey, one last example on this, like, compiler, uh, self reference stuff. Kimian self representation, which I, which I thought was fun. Uh, Kimian is where As your computer program, you basically type the error that the compiler produces when it reads your program. So like, I think when you have an invalid program in C it just says like syntax error when you try to compile it. So if you literally just opened a file and type syntax error and then did GCC, it would print syntax error. So it's like you've. You've used a detail of the error handling of the compiler to create self representation, which I just thought was kind of awesome.

Matthew:

Yeah, it's kind of getting out of quine from the side but uh, oh there was it I had a point here about internet messages versus outer messages where I was thinking about Well, you actually you referenced Twin Peaks. I feel like all of David Lynch's works Are masterclasses of like the outer message, not really, or it just being very unclear, like what the inner message is from the outer message.

Jon:

Yeah.

Matthew:

I don't know. I'm curious what you think about that.

Jon:

Yeah. I mean, for one, I completely agree. I think David Lynch is one of the most like, well, first of all, he's one of the most hard to understand filmmakers. He has, he has several films where you can watch the whole entire thing. Like a racer head. Perfect example. You watch the whole film and you're like, what did I just watch? Like, it doesn't make any sense. Um, and so, you, you can read about the film, you can sort of read what he said about it, you can read what critics have said about it. And there's a lot of different potential meanings for that film. You know, like, uh, farm to the environment, or like, even, I've even heard people talk about climate change. Uh, people talk about, like, love, how love works, because there's obviously the element of Eraserhead, where There's like the child

Matthew:

Yeah.

Jon:

and abortion and all these, you know, highly weighty and interesting topics, but just watching the film, you don't really get that unless you're sort of guessing at what the meaning might be.

Matthew:

Yeah. I mean, you need to really take a step back from what you're watching and. And you obviously you just start to get all these themes when you're like removing the particulars and you're just like, okay, what am I looking at? And then now this is this theme that you're, that he kind of wants you to think about. It's like, Oh, like what is he experiencing when he sees this like crazy looking child, you know, or what have you? Uh,

Jon:

Yeah, which I think, I mean, he did his job as a filmmaker. He's like challenging the viewer, which I don't, I think a lot of filmmakers. Intentionally don't do that. Like certainly, you know, if you think about movies like the Marvel franchise or whatever, like those movies aren't trying to challenge you at all. They're just trying to give you a spectacle. Uh, and I think David Lynch, you know, he's a, he's a true artist. He did his job, he challenged us.

Matthew:

He explicitly disavows conclusion. he's like, nope, you should there are resolute, maybe resolution. There might be a conclusion, nothing's resolved. Uh, but,

Jon:

way it should be, because that's how life is, that's how reality is.

Matthew:

Well, yeah, but yeah, so many things, right? It's like, uh, what the hell, like the JFK assassination. resolution. Um, all right, but let's maybe we get back, get back, start to dive in heads first into the, into the chemistry.

Jon:

Yeah, the, the real meat of this chapter, and in some ways, the culmination of so many little seeds he's laid along the way, I feel like these last few chapters we've been reading, he's been really calling back to earlier chapters, and sort of showing us how he's tying everything together. But we're talking about typogenetics, which is kind of I guess this was his term for it, where, and this is sort of hearkening back to typographical number theory, but this is what genetics where you have strands of, you know, letter codes like Gattaca, G A T C. Um, those are the four right? G A T C.

Matthew:

Yeah. And I mean, you for messenger RNA, but you know, we'll get to that.

Jon:

Right? And then you have enzymes, which are a sequence of amino acids, which he likens to commands. So each, you know, there's this series of, I want to say 16 or so amino acids, which are each commands that when they. You know, when this command encounters one of these strands of like GATC characters, it performs certain actions, like it deletes one of the characters or it converts one of the characters to another. Um, and then these, uh, oh, and then basically, so you have those enzymes, they operate on a strand of, uh, is it of DNA? Is that what he was calling it?

Matthew:

this is what's confusing. Uh, I don't. Yeah. I mean, you're, you are operating on this strand of DNA, but the operations are these fundamentally typographical. So he's kind of like mixing metaphors a little bit here. Um, but yeah, it is, it is a strand of, uh, of DNA, those like four, uh, nucleotides.

Jon:

Okay. Okay, cool. Yeah. I feel like I'm going to use the wrong terminology a few times and yeah, feel free to correct me. But this strand of DNA goes into this amino, this series of amino acids, which is an enzyme and based on those commands that each amino acid represents, it produces a new strand of DNA, but then that new strand of DNA gets broken up into what he calls base pairs. And depending on the base pair, you can use this little table that he made. to determine which command that base pair represents. So basically what's happening here is you have this strand, it's going into an enzyme, it's producing a new strand, but that new strand actually represents a new enzyme or program that can then be run against other strands.

Matthew:

And I mean, so we're immediately, we're getting into this. mean, this is kind of the building of the context as we're talking about before, where is now how you have. Something that both represents a program, but also like can, you know, operate on itself essentially.

Jon:

Yeah, is it data or behavior? It's both kind of like what you were saying earlier. Um, and yeah, I guess one last thing is also in this table, uh, that he gave, which is basically the base pair, you know, where the, the rows of the table is like the first thing in the base pair and the columns is the second thing in each of the cells of this table is also, um, I can't remember what he called it, like a winding parameter almost. Um, which basically tells you how that strand twists. And so you also end up with what he calls this tertiary structure. Which, by the way, we'll learn in just a moment that all of these terms that he's introducing are also terms in biology. He's giving us, like, this very high level of abstraction. Of how actual biology works

Matthew:

right. Which, which is funny because in this context, the tertiary structure doesn't do anything though. Is that, is that correct? Like we haven't been shown anything that the tertiary structure effect that a tertiary structure has.

Jon:

exactly? Yeah, he just sort of mentions that like, oh, there's this tertiary this tertiary structure and then he sort of moves on. But we will get to and this was something I thought was hilarious about this chapter where he sort of he mentioned this type of genetics. And he was like, Oh, I'm just going to give you a very high level, like description of how biology works because biology is complicated and I don't want to talk about it. And then he immediately starts talking about like actual biology and really getting into the nitty gritty detail of like how actual biology functions, which is like incredibly complex.

Matthew:

Right. And I guess, I don't know, I'm not sure I needed type of typo, whatever it was. Sorry. I mean, what's the word

Jon:

Hypergenetics.

Matthew:

I'm not sure that he adds that much by, by going through this process. I mean, I guess if you have someone who is not at all familiar with biology, like never took biology and hasn't learned like the fundamentals of genetics, like this might be, might be a useful step along the way, I, especially given that he then goes on to talk about the real science of,

Jon:

Yeah,

Matthew:

genetics, like why spend that time, just. Go jump there immediately.

Jon:

right, especially since like, everything he talks about in type of genetics is basically like A vastly simplified version of what he then goes on in great detail to describe, you know, so it's like, why? I mean, I, I understand, you know, introduce a concept in a simple way and then talk about the actual complexities and nuance of it, but yeah, I think he could have just skipped right to actual biology, but in any case, oh, yeah, go ahead.

Matthew:

one interesting point he makes, and I'm not sure I completely understand what he's saying, but he talks about the fact that old information in this type of genetics, like old information is flowing upwards, it's kind of saying like, okay, the old information is sticking around. But then new information, like one of these procedures is just like, I guess, modifying old information in such a way that like. It all kind of remains there, then the other one is creating new information.

Jon:

Yeah.

Matthew:

And, and I think that's actually kind of this essential part of self reference and how any of this actually works is because at some point in time, something gets created and it sticks around, like it is still floating around

Jon:

Yeah.

Matthew:

as soon as the other thing was created

Jon:

Yep.

Matthew:

That would kind of put a limitation on how because then you lose, you know, you lose the code But now it's like, okay cool well, we have both the the thing that it created and we have the code too and so this is kind of Essential to continuing this the cycle

Jon:

Yeah, yeah, I feel like he touches on this a little bit like there's a few like subheadings towards the end of his description of biology. One that was really interesting is he sort of talks about like, okay, now we have all these rules of biology, but like, how do you end up with an elephant? You know, it's like, how does this simple processing of DNA, like ladder up basically to this like utterly complex organism where parts of it are, you know, sort of static, like you're saying, like organs stick around, they're not constantly being destroyed and regenerated. I mean, obviously, little parts of them are, but how does that happen? Uh, and he gives a couple examples of, I think he calls it repression where, uh, biology has these mechanisms where it can basically stop. Certain processes from happening and he also mentions what I think is one of the most important details of this chapter and I think you dropped a hint to it earlier where he talks about how biology is very context aware. Like there's, you know, he gives these rules. that sort of feel like they can just happen in isolation. But the reality is that, like, these proteins, uh, which, by the way, the, the pro, a protein is the actual outcome of, like, actual biology, uh, where you get this You know, you, you pass a, what is it, a MR, R, R, or sorry, MRNA passes through a ribosome and amino acids are produced, which then produce a protein based on these folding rules. Proteins are these utterly complicated things. In fact, he mentions in this book that it's an unsolved problem to determine how a protein is actually folding, which is kind of hilarious because Google DeepMind just a few years ago basically solved this problem.

Matthew:

solved it. Yeah alpha fold

Jon:

Yeah. So this was one of the greatest problems on the frontiers of biology. And this problem has been solved, you know, newsflash, I guess. Uh, but in any case, based on these tertiary structures, Molecules can land on these proteins at what are called these activation sites. And when molecules land on these proteins, these extremely complex physical interactions take place. Like, literally molecular interactions. Some are chemical interactions, but some of them are actually molecular. You know, like, um, Electrons have being negatively charged and whatever, attracting or pushing other electrons away and whatnot. Um, but but the key is that all of this takes place. within an environment which that environment is having an effect on the outcome of these interactions. Uh, so you might have cells in your liver that are extremely similar to cells in your lung, but they actually behave in a way that's quite different because their, their sort of respective environments are completely different. Yeah,

Matthew:

about what you're describing is You wind up with a system like just, just, you know, you, you talk, talk about the context. One of the things you need, this to take place in is water, right? It's kind of, all of this stuff is happening in the, I guess they call it the cytoplasm of the cell. And, and it's so wild because All of these cells are working together, and they are, through their collaboration, like, making this enormous organism feel thirsty, and then like, go drink, tell it to go drink water so that it can have, know, it can have water that, like, to have these subjects, so it's like, very indirectly, like, extremely indirectly, indirectly. It's providing its own context, you know, uh, which is just wild, wild to me. Cause you know, you need to have this like self reinforced, you need the context, it needs water, you need this thing to. To drink.

Jon:

right.

Matthew:

so

Jon:

It's insane. Uh, this is, I mean, I'll mention again, song of the cell just because it's a great book, but this is what song of the cell was all about. It's just like how these utterly complicated. You know, rules and systems like biology is just insanely complicated. Like it has got to be one of the, I mean, I've seen, I've observed computing, computing systems that have like layer upon layer upon layer, where there's all these like, you know, interdependent interactions where the state of some server can affect the calling of some end point, but it pales in comparison to biology.

Matthew:

hold a candle. Like it's like, yeah, same. just about to repeat basically what you were saying, but it's like, I've worked on incredibly complicated systems. I spent a little bit of time looking this up this morning and I can just tell that it's just two different animals in terms of complexity.

Jon:

Well, and the other thing is like biology is. Is governed by natural selection, right? So it's not like biology is making decisions. Like it's just whatever survives lives on. So the, the quote unquote design of biology is utterly organic. You know, it's not like in a, in a software system, you typically have like a human being who's. Sort of architecting that system, like taking parts of the system that are complex and trying to simplify them, taking parts of the system that, you know, people struggle to use and trying to make them more user friendly. That doesn't happen in biology. Biology is just stack upon stack of, like, ridiculously complex things that all layer up to make the elephant drink water.

Matthew:

I don't know. I've seen some of Google's systems. Feels like a, like a lot of stacks of, of random shit on top of each other. In a, in a pretty organic way.

Jon:

Yep. No. Well, and then maybe this is what happens when you have a company of 115, 000. Engineers all creating interdependent systems is you basically achieve biology,

Matthew:

a super organism. Yeah.

Jon:

but this is part of and it's funny because this is I feel like this is the crux of this chapter is. You know, Doug Douglas Hofstetter is he's trying to show that like we are computers like biology is a set of formal rules which can be fully understood and you know we've we've mentioned all of this context aware stuff which makes it extremely difficult to understand it but it's like we know how these subatomic particles work we we sort of know how proteins work we know that if a protein is folded in this way that it'll have these activation sites all of this can be found out and these rules can be sort of like you You know, run in a program and, and there's like determinism there. So, so I think that's what he's, he's getting at. It's like, we are computers.

Matthew:

Yeah, yeah. Well, actually, and to that, to that point, so, so I guess, I guess it makes sense to take a step back and just explain how real biology is just subtly different from, um, and I think you already did this, but like, so we had a typo genetics, while real genetics, instead of these, each one of these operations being like slicing up a long, slicing up a word or duplicating a letter. you're folding this pro you know, it's, you have three, DNA base pairs. so it's called a codon, and that maps onto a particular, uh, amino acid. then, and I think you said this, those amino acids, they link together in this long chain, and they fold at these different angles, and they make this shape with these activation sites, right? So,

Jon:

Right. Yeah. The tertiary structure.

Matthew:

the, the tertiary structure. So, the thing that was crazy to me is, where is the mapping? Like the part that seems the most arbitrary to me, like that, it just seems like some random thing is how those three letter sequences, those codons are mapping onto any one of the amino acids. Cause it wasn't, clear to me how that was happening. It's just like, okay, this is just a lookup table. It's literally

Jon:

Yeah.

Matthew:

table in the, you know,

Jon:

Well, and I think that's where it starts to just get into how chemistry works or how physics works even. Where it's like, if you have this, you know, this, like, atom, and you put it next to this atom, like, this thing will just happen. Like, it's just a universal law of the, of the universe.

Matthew:

I don't think that's true actually, because we've seen or in, in. Experiments, scientists have made organisms with modified codons. Like, I think it's actually just, this, this happened, this particular mapping happened, and, and what happened is, started to start relying, yeah, things were relying on this particular mapping, and so if, if a, if an organism came along that started to use a different mapping, they would just die, or they would just never exist, because all the proteins would be wrong.

Jon:

So you're saying that mapping is somewhere in, like, the nucleus or something, or, like, it just is a, and it'll use that mapping as a lookup table with these, these codons.

Matthew:

essentially, essentially. But, but in, in, uh, experimental organisms, scientists have modified that mapping. They've picked different because, and, and I spent way too long and we, and I don't want to spend too much time talking, talking about this. I've spent a bunch of time like trying to figure out where that mapping is, but basically the way this looks is it's this long, it's this long molecule, which there's the anti, there's the code on here. And then there's the, Um, and I'm like making, there's, there's the code on at the bottom or they

Jon:

Yeah.

Matthew:

code on at the bottom. And then there's the, uh, there's the amino acid at the top there's no, there's really no interaction between the two. And it's just by dint of the fact that there's some, there's actually another. another enzyme that's sticking these two things together somewhere else. And then it's just so happens that these two things the only ones that they have to, they like that this thing is designed to connect together. But there's nothing to say that you couldn't design something that would hook together. You know, you GC goes to. Tryptophan. Uh,

Jon:

Yeah.

Matthew:

uh, no, no, UGC goes to, uh, 16, I guess. Anyway, um, but so the point being that there, as far as I've been able to determine, there's nothing to say that you couldn't design a system that maps to different you know, different codons to different, uh, different man, amino acids. This is really been

Jon:

Yeah, no, the terminology is, is, is hard.

Matthew:

different amino acids. Um, so, but anyway, so that's, uh, so that's the

Jon:

But would that be,

Matthew:

like magical to me,

Jon:

is that like saying, you know, like we're, I feel like humans are referred to as like carbon based life forms, or I guess all, all life on earth or most life on earth is referred to as carbon based. Is that similar to saying like, you could, you could just use a different element as like a basis?

Matthew:

maybe, but I think what it is is it's It's kind of just like this, this agreement that happened incidentally, where these three letters translate to this thing. And then once you, once that agreement has been like that, once that pact has been formed, like now you have biological pressure, like reinforcing that that stays the same.

Jon:

Right. So you, so you could start a brand new, you know, life and just, but just use different mappings and it would be fine. It would, it would figure it out.

Matthew:

Right.

Jon:

That mapping would get reinforced over and over again over millennia.

Matthew:

that's, that's my understanding. Now I like all this stuff is in, and this goes back to why I was saying like, it's so fantastically complicated is because like. you know, there's a 10 minute YouTube video just explaining process by which tRNA, which is like, that gets helicated or whatever the hell, you know, like I'm making up a word here. That's not the right word, but, um, so, so anyway, uh, but, but as far as I was able to determine, there's nothing to say that you couldn't design an enzyme that would arbitrarily hook some three, uh, three base pair. coon, you know, some codon to an arbitrary, uh, amino acid, which, which that blows my mind. And like, this is the

Jon:

Yeah.

Matthew:

like, I'm an atheist, but this feels like the most compelling argument for intelligent design that I've seen. Like, it seems like someone just like picked this at some point, this, this, uh, mapping, but

Jon:

Well, but I guess if the mapping was different, like, wouldn't, wouldn't we have still eventually been created? I mean, obviously we would be quite different, but like, it would still life would get more and more complicated until you'd have these higher level organisms, like, it doesn't really matter what the mapping is. You can still sort of build more and more complexity. And yeah, like evolve.

Matthew:

Exactly. But I guess the point is like that, um, that a mapping emerged is interesting to me. You know what I mean? Like

Jon:

Yeah, that is interesting.

Matthew:

Um, but so, and, and like, yeah, so I don't want to, I don't like, I think we can, we can move on from this point. But, um,

Jon:

Nah, I mean, that's super interesting.

Matthew:

these mechanisms are, and again, like it's, it must have always been like, these things started as something simpler and they performed that essential function just poorly at first, and then they got more sophisticated and when you look at them, you're like, oh wow, this is so sophisticated, it looks like it was created out of whole cloth, but actually

Jon:

Yeah,

Matthew:

This evolution literally of of something that was kind of doing something similar. There was already maybe some. Simpler mapping. Um, and then it translated into this. So, um,

Jon:

Yeah. The one thing that, so I, I feel like I get this chapter. I do love this discussion of biology and sort of these like almost formal rules that govern. how biology works. I found that very interesting. And you know, he does compare it to a bunch of topics that he brought up before. Like he gives, he gives this table, which he calls a dog map, which is basically mapping from one dogma to another. So he maps this, the dogma of biology onto the dogma of mathematical logic, where you have like strands of DNA can be equated to. strands of TNT, typographical number theory,

Matthew:

right.

Jon:

and proteins can be equated to statements of TNT. So there's all these, it's like, this is why he's been introducing all these things earlier in the book, was to sort of give us a basis so that he can discuss, you know, this biological dog map. Um, and I really like that. What I didn't really understand Was like where Guttal's incompleteness comes in to this. He did, he had this section where he mentioned viruses. And he mentioned how, you know, viruses are these fascinating organisms that basically like break into your system and release their own DNA, which then starts performing these weird calculations within your biology. And it almost sounded like he was comparing that to Godot's incompleteness, but I just didn't fully get the, the comparison, but it was interesting,

Matthew:

I agree. I, I wasn't a hundred percent sure how good those yeah. Like how G for example, was similar to that. I mean, unless, yeah, I mean, in a way, like, I guess it's. It's atmospherically simple, similar because like if the cell is like the system and G comes in and then like it, it breaks down.

Jon:

right? It gives it this, this statement that like breaks the system.

Matthew:

right, um, there was something that was also very interesting, was, he talked about how, uh, one defense mechanism that cells produce or created. To defend themselves against these invaders is they started like tagging their DNA and they said that tagging is kind of like serifs on a font where they don't actually change the, they don't change the function of it, but it's like something you can look for basically if, if something had DNA that wasn't tagged, it would just chop it up into small, small bits, um, which I thought that that it just, you know, I would say clever, but it was just like, okay. It feels clever,

Jon:

Yeah.

Matthew:

but it's just cool and and this just goes to show that there's so many things that you can learn from biology because I feel like there's, I mean, that's kind of like a security. It's like, it's like providing a token to

Jon:

Yeah.

Matthew:

back end system. Um,

Jon:

Yeah. No, it's, it is crazy how many sort of analogs there are between software and biology. And he makes, he makes a ton of them. He also talks about some of them in the dialogue, which I did read. Uh, he talks about, yeah. You know, the turntable that destroys records and how crab came up with a mechanism for like labeling records that would like prevent them from. Basically, Tortoise couldn't play his records on Crab's turntable, because all of Crab's records were labeled. They went into this little discussion of like, oh, can Tortoise fake the label? Uh, but, anyway, sort of similar to what you were talking about.

Matthew:

yeah, exactly. I did. I did not read the dialogue.

Jon:

Yeah, dude, if I'd have known how long this chapter was, I would not have read that dialogue. Because, man, this was a long chapter. Not really, I felt like the meat of this chapter, I found it really interesting. There was a few interludes.

Matthew:

sentences felt like, I'm, I wasn't really sure what, what exactly his point, it almost felt like the opposite of a, uh, uh, girdle sentence where you're like, this statement is provable in this system. Um, so, but that felt, I don't know, uninteresting, but I, I wasn't sure if you got more, got more from that.

Jon:

Uh, that like, I, I really liked the interlude where he talked about how, how does it ladder up to an elephant? He talked about another element of, which is like cascading where, you know, we, we briefly mentioned how proteins have these activation sites, which cause chemistry and biology to happen, maybe produces new materials. But then that new material then can land on other proteins. And so you get this cascading effect. Um, and a lot of biology. Or a lot of the reason biology is so complicated is because of this cascading effect. Um, and I thought that was really interesting, but yeah, that's, that's about all I had for this chapter. I mean, it was a super long chapter, so I feel like we could talk about it for hours, but I think that was, you know, kind of an overview.

Matthew:

Oh, yeah. Um, no, I mean, just to your point about, I mean, proteins just sound like these super power that can at any, at any layer of abstraction, they can operate on DNA, they can operate on other like, and like you're saying, with these chains, like, sometimes proteins are like, they're getting generated, and then they're creating new enzymes, which are then are operating on proteins. enzymes. So like, um, it's all of these, this is the part where, you know, you were talking about how, like, there's no rational, there's no rationality. It's like, yeah, it's just extremely chaotic, chaotic thing. The other

Jon:

Yeah.

Matthew:

to me is like, if you're, if you're decode or like, if, if the ribosome, right, it's creating a protein. All of the little codon things, all of the possible codons that could like attach, they just need to be like around, they just need to be like floating around, and it just

Jon:

Yeah. Yeah.

Matthew:

it's like the probability just needs to be high enough that it clicks into place, it's not like it can go out and and actually grab one, it's just like by probability, there's just like, but so, so is there just a trillion of every single possible, just like floating around in the cell all the time?

Jon:

That's why you gotta eat your vegetables.

Matthew:

Yeah. Okay. Yeah, make sure you, uh, yeah, to eat some protein powder,

Jon:

Yeah. Some omega 3 fatty acids.

Matthew:

Yeah, it's important. All right, well, that is, that is all I had as well. Um, so, but this was, this was a good one.

Jon:

Yeah. Good one. Yeah. Looking forward to the next one.

Matthew:

All right, I will see you next time. And so, uh, we are doing something slightly different for the rest of them, rest of the chapters. it sounds like instead of going one by one, We're going to of do broad overviews over a couple of chapters, uh, at a time for, for the next couple, a couple episodes, just as, as a heads up. So, um,

Jon:

Yeah. We love this book, uh, but it is vast and we, we think you should read it.

Matthew:

a vast home. Yes, you should definitely read it. And we think we want to start talking about something other than, uh, good little theory of incompleteness. Uh,

Jon:

Yeah.

Matthew:

all right, well, I will see you next time, John.

Jon:

See you next time, Matt.