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Speaker 1: website apart. Welcome to Stuff you Should Know from House
Speaker 1: Stuff Works dot com. Hey, and welcome to the podcast.
Speaker 1: I'm Josh Clark with Charles W. Chuck Bryant, and there's
Speaker 1: Jerry over there. So this is Stuff you Should Know,
Speaker 1: the podcast about chaos theory. Like, uh, have you ever
Speaker 1: seen Event Horizon? I did not bad great movie? Are
Speaker 1: you crazy? I don't think it was great? Oh so
Speaker 1: imagine it it. I thought it was okay. It was
Speaker 1: like a love crafty and thing in our space. Loved
Speaker 1: it all right, I love crafted it. I liked it. Um.
Speaker 1: That's what I think of when I think of k US.
Speaker 1: You know, there's that one part where they kind of
Speaker 1: give you like a glimpse behind, like the dimension that
Speaker 1: this action is taking place in, to see the chaos underneath,
Speaker 1: and you should check that out again. I think about
Speaker 1: Jurassic Park and Jeff Goldblum as as the creep. Dr
Speaker 1: Malcolm explaining chaos in the little auto driving suv or
Speaker 1: whatever that was. Yeah, that's what it was called in
Speaker 1: the script, the auto driving suv scene. Yeah, and you
Speaker 1: know what, he actually rewatched that scene and it confirmed
Speaker 1: two things. One is that he uh, he actually did
Speaker 1: a pretty decent job for a Hollywood movie with a
Speaker 1: very rudimentary explanation of chaos. Um, and you watched it
Speaker 1: for this Yeah, yeah, just that scene. And then it
Speaker 1: also confirmed of what a creep that character was. Yeah,
Speaker 1: if you watched that scene, he's like, you know, he's
Speaker 1: all gross and flirty with her right in front of
Speaker 1: her ex but there's just you know, he's talking to her.
Speaker 1: I didn't even notice this at first. He like he
Speaker 1: just like touches her hair out of nowhere for no reason.
Speaker 1: He's just talking to her and he just like grabs
Speaker 1: her hair and touches it. And I'm like, what a creep.
Speaker 1: I know, if you look closely, you can see the
Speaker 1: hormones emerging through his chest hair. Yeah, and I love
Speaker 1: Jeff Goldblum. It's not a reflection on him. He was
Speaker 1: basically doing Jeff Goldblum. Well that's what Yeah, sure he's
Speaker 1: Jeff Goldblum, but I don't think that's how in the
Speaker 1: manner in which he speaks. But I don't think he's
Speaker 1: a creep, do you. Wow, I've got nothing against Jeff
Speaker 1: gold Okay, I think he's a I think he's doing
Speaker 1: Jeff Goldblum. It was also a sign of the times,
Speaker 1: like if that movie were made today, doctor what was
Speaker 1: her name in the movie? Yeah, Dr Sutler would be like,
Speaker 1: it's very inappropriate to stroke my hair, like, don't touch me.
Speaker 1: But this was the nineties. Was eight No, it was nineties.
Speaker 1: It was the early mid ninety. Other thing, the book
Speaker 1: came out and in the book, uh Ian Malcolm, who's
Speaker 1: a Kayetician creep Kaetician? Right he um he he goes
Speaker 1: into even more depth about chaos. But that was I mean,
Speaker 1: that was the first time I ever heard of chaos
Speaker 1: theory was from Jurassic Parks, and um it really it
Speaker 1: was really misleading. I think the entire term chaos is
Speaker 1: very misleading as far as the general public goes as
Speaker 1: from what I researched in this this for this article. Well, yeah,
Speaker 1: I mean you hear the word chaos as an English
Speaker 1: speaker and you think frenetic and crazy, out of control. Yeah,
Speaker 1: and that's not what it means in terms of science
Speaker 1: like this, right. What it means, I guess we can
Speaker 1: say up front is is basically the idea that complex
Speaker 1: systems do not behave in very neat ways that we
Speaker 1: can easily grasp, understand, ander measure, right, and not even
Speaker 1: even simple systems don't. Sometimes it doesn't always have to
Speaker 1: be complex. But um, I want to give a shout
Speaker 1: out in addition to our own article to UH when
Speaker 1: you know, when it comes to stuff like this, the
Speaker 1: brain breaking stuff. For me, this is a breaker. You
Speaker 1: know how I always go to like blank blank for
Speaker 1: kids because it always helps. If there's a dinosaur mascot
Speaker 1: on the page, it's a sure thing we can understand it.
Speaker 1: But the best explanation for all this stuff that I
Speaker 1: found on the internet was from a website called a
Speaker 1: bar Um A B A R I. M. Publications, which
Speaker 1: turns out to be a website about biblical patterns and
Speaker 1: sandwiched in the middle, there is a really great, easy
Speaker 1: to understand UH series of pages on chaos there. So
Speaker 1: I was like, man, I get it now in a
Speaker 1: rudimentary way, right, Well, yeah, um, I think even a
Speaker 1: lot of people who deal with systems that display chaotic behavior,
Speaker 1: which I guess is to say basically all systems eventually
Speaker 1: under the right conditions, Um, don't necessarily understand chaos. Yeah,
Speaker 1: And they defined a complex system is specifically. It doesn't
Speaker 1: mean just like, oh it's complex, I mean it is,
Speaker 1: but specifically, Um, they define it in a way that
Speaker 1: helped me understand it's a system that has so much motion,
Speaker 1: so many elements that are in motion, moving parts. Yeah,
Speaker 1: that it takes like a computer to calculate all the
Speaker 1: possibilities of like what that could look like five minutes
Speaker 1: from now, ten years from now. So before computers came around,
Speaker 1: we before the quantum mechanical revolution, it was there's a
Speaker 1: lot more basic. It was like what comes up must
Speaker 1: come down, stuff like that. Let's talk about that, Chuckers,
Speaker 1: because when you're talking about chaos theory, it helps to
Speaker 1: understand how it revolutionized the universe by getting a clear
Speaker 1: picture of how we understood the universe leading up to
Speaker 1: the discovery of chaos. Right, So, prior to the um
Speaker 1: the scientific revolution, everybody was like, oh, well, it's it's God.
Speaker 1: The Earth is at the center of the universe and
Speaker 1: God is spinning everything around like a top right, it
Speaker 1: was all a theistic explanation. Then the scientific revolution happens
Speaker 1: and people start applying things like math and making like
Speaker 1: mathematical discoveries and and and figuring out that there are
Speaker 1: there's order. They're finding order in patterns and predictability to
Speaker 1: the universe if you can apply mathematics to it, specifically,
Speaker 1: if you can apply mathematics to the starting point, right, right,
Speaker 1: So if you can, if you can um figure out
Speaker 1: how a system works mathematically speaking, right, you can go
Speaker 1: in and plug in whatever coordinates you want to and
Speaker 1: watch it go. You can predict what what the outcomes
Speaker 1: can be and what this is the it's based on
Speaker 1: what at the time was a totally revolutionary idea um
Speaker 1: By Initially, I think the cart was the first one
Speaker 1: to kind of say, causing effect is a pretty big
Speaker 1: part of our universe, right. Yeah. It was sort of
Speaker 1: like where this is sixteen hundreds, where early science met philosophy.
Speaker 1: They kind of complimented one another as far as something
Speaker 1: that's we're talking about determinism, right, So that was the
Speaker 1: kind of the seeds of determinism. Was the scientific revolution,
Speaker 1: And like you said, where philosophy and science came together
Speaker 1: in the form of Descartes, right, and then Newton came
Speaker 1: along and we did a whole episode on him. Yeah,
Speaker 1: January of this year. That was a good one. It
Speaker 1: was really good. Like I think you said in that
Speaker 1: episode that there's possibly no scienists that changed the world
Speaker 1: more than Newton has. He's he's got legs. People shouted
Speaker 1: out others and email, but I'll just say he's at
Speaker 1: the near the top for sure with some other people.
Speaker 1: The Cream. Yeah, so Newton came along and Newton said
Speaker 1: that was his name, Isaac the Cream new anytime he
Speaker 1: don't to be like cream. Yeah, you just got creamed.
Speaker 1: I thought he was a boxer. He's a basketball player.
Speaker 1: He was much more well known as a boxer, but
Speaker 1: he definitely could dunk as a as a B baller. So,
Speaker 1: um man, that threw me off a little bit. Yeah,
Speaker 1: the Cream comes along and uh, he basically says, watch this, dude,
Speaker 1: this causing effect thing you're talking about, I can express
Speaker 1: it in quantifiable terms. And he comes up with all
Speaker 1: of these great laws and and basically sets the stage
Speaker 1: the foundation for science for the next three centuries or so. Yeah,
Speaker 1: these these laws that were so rock solid and powerful
Speaker 1: that scientists kind of got ahead of themselves a little
Speaker 1: and said we're done. Like with Newton's laws, we can predict, uh,
Speaker 1: we can predict everything if we have a good enough
Speaker 1: beginning accurate value to plug into his equations, and they weren't.
Speaker 1: I think there was a little hubrius and a little
Speaker 1: just excitement about like, well we figured it all out
Speaker 1: right that that you could take Newton's laws and if
Speaker 1: you had accurate enough measurements, uh, you could predict what
Speaker 1: the outcome would be of that system that you plug
Speaker 1: those measurements into using these formula. And at the time,
Speaker 1: a lot of this was like planetary, like, well, we
Speaker 1: know that these planets are here and they're moving and
Speaker 1: their orbiting, So if we know these things, we can
Speaker 1: plug it into an equation and we can figure out
Speaker 1: what it's going to be like in a hundred years exactly.
Speaker 1: And they figured out the basis of determinism is what
Speaker 1: we just said, that if you have accurate measurements, you
Speaker 1: can take those measurements and use them to predict um
Speaker 1: how a system is going to change over time using
Speaker 1: differential equations. Right, yeah, so this is what this is
Speaker 1: what Newton comes along and figures out that you can
Speaker 1: describe the uni us in these mathematical terms using differential
Speaker 1: equations and um like, you said there was a tremendous
Speaker 1: amount of hubris, and well, I think you said there's
Speaker 1: some hubris. I think there's a tremendous amount of hubris
Speaker 1: where science basically said, we've mastered the universe, We've uncovered
Speaker 1: the blueprint of the universe, and now we understand everything.
Speaker 1: It's just a matter now of getting our scientific measurements
Speaker 1: more and more and more exact. Because again, the hallmark
Speaker 1: of determinism is that if you have exact measurements, you
Speaker 1: can predict an outcome accurately, like the pool queue example
Speaker 1: or the pool table example. Right right, So if you've
Speaker 1: got a pool table, let's say you're playing some nine ball.
Speaker 1: You have that beautiful little diamond set up, you got
Speaker 1: your cue ball, you put that cue ball, and you
Speaker 1: you crack it with the queue, and if you are
Speaker 1: super accurate with your initial measurements, you should be able
Speaker 1: to mathematically plot out via angles where the balls will
Speaker 1: end up, right exactly, like you can say, this is
Speaker 1: what the table will look like after the break. If
Speaker 1: you know the force the angle, all those little variable temperature,
Speaker 1: if there's wind in the room, like the felt on
Speaker 1: the table, like everything. The more specific you are, the
Speaker 1: more accurate your end result will be. Right. And then
Speaker 1: one of the other hallmarks of determinism is that if
Speaker 1: you take those exact same initial conditions and do them again,
Speaker 1: the table, the pool table will look exactly the same
Speaker 1: after the break, which is pretty much impossible for like
Speaker 1: a human to do with their hands. Sure, but the
Speaker 1: idea at the time of science is that if you
Speaker 1: could build a perfect machine, sure that could recreate these conditions,
Speaker 1: it will happen the same way every time. Right, And this,
Speaker 1: I mean this led to they had hubris, but you
Speaker 1: could understand it when like literally in two people predicted
Speaker 1: Neptune would exist within months, that would exist, but does exist.
Speaker 1: And this is not by looking up in the sky
Speaker 1: like they did it with math and they were right.
Speaker 1: So imagine in eighty when that happens, they're like, yeah,
Speaker 1: we kinda we've got the math down, so we're pretty
Speaker 1: much all knowing well. Plus also, for the most part,
Speaker 1: these not just with Neptune, they were finding um that
Speaker 1: this stuff really panned out. It held true for everything
Speaker 1: from um you know, the investigation into electricity to new
Speaker 1: chemical reactions and understanding those and it it laid the
Speaker 1: scientific revolution, laid the basis for the industrial revolution, and
Speaker 1: just the change that came out of the world like that.
Speaker 1: It definitely there. It is understandable how science kind of
Speaker 1: was like we got it all figured out well, and
Speaker 1: like you said, they even Galileo was smart enough to
Speaker 1: know there's uncertainty in these measurements, like the precision is key.
Speaker 1: So they spent what does the article say, a lot
Speaker 1: of the much of an enth and twenty century just
Speaker 1: trying to build better instrumentation to get more and more
Speaker 1: smaller and smaller and more precise measurements. Right, That was
Speaker 1: like basically the goal of it, right, Yeah, which was
Speaker 1: the right direction. That's like exactly what they should have
Speaker 1: been doing. The problem is there, Like you said, Galileo
Speaker 1: knew that there was some sort of there there gonna
Speaker 1: be some flaws and measurement that we just didn't have
Speaker 1: those great scientific instruments yet. It's called the uncertainty principle.
Speaker 1: It's accuracy, right, But the idea is that if you
Speaker 1: have a good enough instruments, you can overcome that, and
Speaker 1: that the the more you shrink the um error in
Speaker 1: measuring the initial conditions, the more you're gonna shrink the
Speaker 1: error in the outcome. It would be proportionate. Right. They
Speaker 1: were correct. The thing is they were also aware but
Speaker 1: ignoring in a lot a lot of ways some outstanding problems,
Speaker 1: specifically something called the end body problem. You know what,
Speaker 1: I'm so excited about this. I need to take a break.
Speaker 1: I think that's a good idea. I need to go
Speaker 1: check out my end body in the bathroom. Okay, and
Speaker 1: we'll be back. All right, check, we're back. So there's
Speaker 1: some there's some issues right with determinism. There's some some
Speaker 1: weird problems out there that are saying like, hey, pay
Speaker 1: attention to me because I'm not sure determinism works. Uh.
Speaker 1: And one of the one is the end body problem. Yeah.
Speaker 1: How this came about was that was King Oscar number
Speaker 1: two of Sweet and Norway. Yeah, I don't want to
Speaker 1: leave out Norway both. Uh. He said, you know what,
Speaker 1: let's offer a prize to anyone who can prove the
Speaker 1: stability of the solar system something that has been stable
Speaker 1: for a long time before that and a lot of
Speaker 1: the most brilliant minds on planet Earth got together and
Speaker 1: tried to do this, uh with mathematical proofs, and no
Speaker 1: one could do it. Uh. And then a dude name Honoree.
Speaker 1: You gotta help me there with that, Oh, say, the
Speaker 1: whole thing very nice. He was French, believe it or not,
Speaker 1: and he was a mathematician, and he said, you know what,
Speaker 1: I'm not gonna look at this big picture of all
Speaker 1: the planets in the sun and all their orbits. You'd
Speaker 1: have to be a fool to try that. Sure, he said,
Speaker 1: I'm gonna shrink this down, Like we talked about shrinking
Speaker 1: that initial value, you know, and um, that initial condition.
Speaker 1: He shrunk it down. He said, I'm gonna look at
Speaker 1: just a couple of bodies orbiting one another, uh, with
Speaker 1: a common center of gravity. And I'm gonna look at this.
Speaker 1: And this was called the N body problem. Yeah, which
Speaker 1: was smart to do, because the more variables you factor
Speaker 1: into um a nonlinear equation like that, just the harder
Speaker 1: it's gonna be. So he shrunk it down. So the
Speaker 1: N body problem has to do with three or more
Speaker 1: celestial bodies orbiting one another. So Plank said, I'll just
Speaker 1: start with three. Smart and what he found from doing
Speaker 1: his equations for this this King Oscar. The sequel prize
Speaker 1: um was that shrinking the initial conditions um measurement or
Speaker 1: rate of error right, did not really shrink the the
Speaker 1: error in the outcome, which flies in the face of determinism.
Speaker 1: What he found was that just very very minute different
Speaker 1: is in the initial conditions fed into a system produced
Speaker 1: wildly different outcomes after a fairly short time. Yeah, like,
Speaker 1: let me just round off the mass of this planet
Speaker 1: at like the eighth decimal point, and you know who cares?
Speaker 1: Who cares at that point? Let me just round that
Speaker 1: one to a two, and that would throw everything off
Speaker 1: at a at a pretty high rate. And he said,
Speaker 1: wait a minute, I think this contest is in polsib right,
Speaker 1: He said, there is no way to prove prove, to
Speaker 1: prove the stability of the Solar system, because he just
Speaker 1: uncovered the idea that it's impossible for us to predict
Speaker 1: the the the rate of change among celestial bodies. Yeah,
Speaker 1: it's such a complex system. There are far too many
Speaker 1: variables that, uh, it's impossible to start with something so
Speaker 1: minute to get the equation whatever the sum that you want. Well,
Speaker 1: not only that, but the result, not only that and
Speaker 1: this is what really undermined determinism was that he figured
Speaker 1: out that you would have to have an infinitely precise measurement,
Speaker 1: which even if you build a perfect machine that could
Speaker 1: take the infinitely or a perfect machine that could take
Speaker 1: a measurement of like the the movement of a celestial
Speaker 1: body around another, you, it's literally impossible to get infinite
Speaker 1: and infinitely precise measurement, which means that we could never
Speaker 1: predict out to a certain degree the movement of the
Speaker 1: celestial bodies. Like he was saying, like, no, you you
Speaker 1: can't get You can't build a machine that that gets
Speaker 1: measurements enough that we can overcome this, like determinism is wrong,
Speaker 1: Like you can't just say, uh, we have the understanding
Speaker 1: to predict everything. There's a lot of stuff out there
Speaker 1: that we're not able to predict. And he uncovered it
Speaker 1: trying to figure out this end body problem. Yeah, and
Speaker 1: King Oscar the sequel said you win, Yeah, bring me
Speaker 1: another rack of lamb and uh, here's your prize. And
Speaker 1: he won by proving that it was impossible, which is
Speaker 1: pretty interesting. And they utterly and completely changed not just math,
Speaker 1: but like our our our understanding of the universe, and
Speaker 1: our understanding of our understanding of the universe, which is
Speaker 1: even more kind of earth shaking. Yeah, he discovered dynamical
Speaker 1: instability or chaos, and um, they didn't have supercomputers at
Speaker 1: the time, so it would be a little while, about
Speaker 1: seventy years at m I T until uh we could
Speaker 1: actually kind of feed these things into machines capable of
Speaker 1: plotting these things out in a way that we could see,
Speaker 1: which was really incredible. So there is this dude um
Speaker 1: seven years later, uh named um Edward Lawrence Lawrence. Yeah. Well,
Speaker 1: first of all, we should set the stage the reason
Speaker 1: this guy he was a meteorologist and scientists, right, not
Speaker 1: that those are not the same thing, right, He's a
Speaker 1: scientist who dabbled the meteorology. Here was a mathematician, Yeah,
Speaker 1: but he was really into meteorology because it was there
Speaker 1: was a weird juxtaposition at the time where we were
Speaker 1: sending people into outer space but we couldn't predict the weather. Yeah,
Speaker 1: and it was it was definitely a blot on the
Speaker 1: field of meteorology. People were like, do you guys know
Speaker 1: what you're doing? And and meteorologists are like, you have
Speaker 1: no idea how hard this is? Like yeah, we can
Speaker 1: predict it a couple of days out, but after that,
Speaker 1: it's just it's totally unpredictable. It drives as mad and
Speaker 1: it's not. It wasn't just there. Um, their reputations that
Speaker 1: were at stake, like people were losing their lives because
Speaker 1: of it, right, Yeah. N two there were two notorious storms,
Speaker 1: one on the East coast and one on the west. Uh,
Speaker 1: the ash Wednesday storm in the East and the big
Speaker 1: blow on the West that of a lot of people,
Speaker 1: cost hundreds of millions of dollars in damage. And people
Speaker 1: were like, you know, we need to be able to
Speaker 1: see these things coming a little more because it's a problem.
Speaker 1: And meteorologists were like, why did you do it then?
Speaker 1: So they thought the key was these big supercomputers. Remember
Speaker 1: the supercomputers. When they came out the big rooms full
Speaker 1: of hardware, it was amazing, and they were finally able
Speaker 1: to do like these incredible calculations that we could never
Speaker 1: do before. I know, they were able to like crunch
Speaker 1: sixty four bites a second. Yeah, we had the advocates
Speaker 1: and then the supercomputer. There was nothing in between. Um.
Speaker 1: I looked up the computer that Lawrence was working with
Speaker 1: the Whopper Royal McBee. What was the Whopper board games?
Speaker 1: Was it? It called the Whopper w a PR I
Speaker 1: can't believe they called it that. So the guy just
Speaker 1: nicknamed it Joshua. No, Joshua was the the software Falcon
Speaker 1: was the old man who designed all the stuff up
Speaker 1: and his son was Joshua. And that was the password. Oh,
Speaker 1: that was the password. Yeah, I guess I was too
Speaker 1: young to understand what a password was. Okay, you didn't
Speaker 1: even there weren't passwords at the time. Shouted it at
Speaker 1: the computer and they're like, okay, access granted. Yeah. Still
Speaker 1: that movie holds up. Does it really check it out? Yeah,
Speaker 1: it's still very very fun. Young Ali Sheety boy had
Speaker 1: a crush on her from that movie. She was great. Yeah.
Speaker 1: What else was she in recently? Wasn't she in something? Well?
Speaker 1: I mean she kind of went away for a while
Speaker 1: and then had her big comeback with the indie movie
Speaker 1: High Art, But that was a while ago. Has she
Speaker 1: been in anything else recently? Sure? I think I saw
Speaker 1: something and something recently and I didn't realize that was her.
Speaker 1: She looks familiar and I was like, oh, that's Ali Sheety.
Speaker 1: I don't know, all right, I could look it up,
Speaker 1: but I won't. It doesn't matter anyway. I still crushed
Speaker 1: on her. So the the Royal mcbeebe was not quite
Speaker 1: the whopper. You could actually sit down at it. The
Speaker 1: Royal McBee that's the name of that sounds like a
Speaker 1: hamburger too. It was by the Royal Typewriter Company. And
Speaker 1: they got into computers for a second. And this is
Speaker 1: the kind of computer that Lawrence was working with, and
Speaker 1: it was a huge deal, Like you were saying, Avacus supercomputer. Um.
Speaker 1: But it was still pretty dumb as far as what
Speaker 1: we have today is concerned. But it was enough that
Speaker 1: Lawrence is like Lawrence and his ILK, where like, finally
Speaker 1: we can start running models and actually predict the weather. Yeah,
Speaker 1: he started doing just that. He did. So he started
Speaker 1: off with UM, a computational model of twelve meteorological meteorological
Speaker 1: I like how you calculations, which is very basic because
Speaker 1: they're infinite meteorological calculations, probably depending to stay wrong again,
Speaker 1: like it sounds like you're about to say it wrong
Speaker 1: and then you pull it out at the last second.
Speaker 1: Maybe it's really impressive, but uh so that's very basic.
Speaker 1: But he wanted to start out you know with something
Speaker 1: at Hannibal. So he narrowed it down to twelve conditions,
Speaker 1: basically twelve calculations that had you know, temperature, wind, speed, pressure,
Speaker 1: stuff like that started forecasting weather. Uh. And then he said,
Speaker 1: you know, it'd be great if you could see this,
Speaker 1: So I'm gonna spit it into my wonder machine, the
Speaker 1: McWhopper Royal MCB, and I'm gonna get a print out
Speaker 1: so you can visualize what this looks like. So things
Speaker 1: were going well and you had this print out, and
Speaker 1: everyone was amazed because these these calculations never seemed to
Speaker 1: repeat themselves. He was making like, um, like like word art.
Speaker 1: You remember that. That was the first thing anybody did
Speaker 1: on a computer. Oh yeah, it was to make word
Speaker 1: art like a butterfly, right you would print out. Yeah.
Speaker 1: I never could do that. I couldn't either, Like you
Speaker 1: have to be able to visualize things spatially that you
Speaker 1: have to that right kind of brain for that, right
Speaker 1: or you have to be following a guy book that
Speaker 1: you have you ever seen? Me? You and everyone we know. Yeah,
Speaker 1: I love that movie. That's a great movie. Those little
Speaker 1: kids in there they were doing that. Oh yeah, yeah, forever,
Speaker 1: back and forth, poop. Well, I haven't. I haven't seen
Speaker 1: that since it came out. It's been a while. Oh
Speaker 1: you gotta see it again? Yeah, great movie. Ali's not
Speaker 1: in it. It's a Miranda July right, and she like
Speaker 1: wrote and directed to right. She did a great job.
Speaker 1: It's like it's one of those rare movies where like
Speaker 1: there's just the right amount of whimsy, because whimsy so
Speaker 1: easily overpowers everything else and becomes like, yeah, this is
Speaker 1: like the most perfectly balanced amount of like whimsy you've
Speaker 1: ever seen in a movie. Yeah, there's too much whimsy.
Speaker 1: I just like terrible Garden State. I just want to
Speaker 1: punch in the face terrible. Although I like Garden State,
Speaker 1: but I haven't seen it since it came out. It
Speaker 1: hasn't aged. Well, it's just when you look at it now,
Speaker 1: it's just so cute and whimsical. Oh yeah, it's like
Speaker 1: come on, yeah, boy, we're getting to a lot of
Speaker 1: movies today. Oh yeah, well we're stalling. We haven't even
Speaker 1: talked about butterfly Effect yet, which is coming and I'm
Speaker 1: dreading it. That's why I'm stalling. All right, So where
Speaker 1: were we? He was running his calculations, printing out his
Speaker 1: values so people could see it, and then he got
Speaker 1: a little lazy one day. In this output he noticed
Speaker 1: was interesting, so he said, you know, I'm gonna repeat
Speaker 1: this calculation see it again, but I'm gonna save time.
Speaker 1: I'm just gonna kind of pick up in the middle,
Speaker 1: and I'm not gonna input as many numbers, but I'm
Speaker 1: still using the same values, just I'm not going out
Speaker 1: to six decimal points. So the print out he had
Speaker 1: went to three decimal points. So he was working from
Speaker 1: the print out and didn't take into account that the
Speaker 1: computer accepted six decimal points, so he was just getting
Speaker 1: in three correct and expecting that the outcome would be
Speaker 1: the same, right, yes, but the outcome was way different.
Speaker 1: He went, whoa, whoa what? Yeah, he's like, what's going
Speaker 1: on here? It was a big deal. I mean, someone
Speaker 1: would have come up with this eventually, probably, yeah, but
Speaker 1: I sort of accidentally came upon it. It's neat that
Speaker 1: this guy did this because it changed his career. I
Speaker 1: think he went from emphasis on meteorology to an emphasis
Speaker 1: on chaos math to stud scientists basically. So look, I mean,
Speaker 1: the guy's got an attractor named after him, you know
Speaker 1: what I mean. Yeah, Well, let's get to that. So
Speaker 1: Lorenz starts looking at this and he's like, wait a minute,
Speaker 1: this is this is weird, this is worth investigating, and
Speaker 1: like uh, like uh, what was his name? Plankara? He said,
Speaker 1: I need fewer variables, So I'm not going to try
Speaker 1: to predict weather with these twelve differential equations that you
Speaker 1: have to take into Account'm just gonna take one aspect
Speaker 1: of weather called the rolling convection current, and I'm going
Speaker 1: to see how I can write it down in formula form.
Speaker 1: So a rolling convection current, chuck, is where you know,
Speaker 1: how the wind is created where air at the surface
Speaker 1: is heated and then starts to rise and suddenly cool
Speaker 1: air from higher above comes in to fill that that
Speaker 1: vacuum that's left, and that creates a rolling um or
Speaker 1: vertically based convection current. Okay you could. I would describe
Speaker 1: it as oven oven boiling water, a cup of coffee.
Speaker 1: Wherever there's a temperature differential based on a vertical alignment,
Speaker 1: you're going to have a rolling convection current. Okay, yeah,
Speaker 1: it sounds complex, but he just picked out one thing, basically,
Speaker 1: one condition, and this is the one he picked out.
Speaker 1: But had you seen my hands moving listeners, you would
Speaker 1: be like, oh, yeah, I know. So um He's like, okay,
Speaker 1: I can figure this out. So he comes up with
Speaker 1: three three formula that kind of describe a rolling invection current,
Speaker 1: and he starts trying to figure out how to describe
Speaker 1: this rolling convection current. Right, and so, like I said,
Speaker 1: he got this these three formula, which we're basically three
Speaker 1: variables that he calculated over time, and he plugged him
Speaker 1: in and he found three variables that changed over time.
Speaker 1: And he found that after a certain point, when you
Speaker 1: graph these things out, and since there're three, you graph
Speaker 1: them out on a three dimensional graph. So x, Y
Speaker 1: and Z. Again, he wanted to just be able to
Speaker 1: visualize this because it's easier for people to understand. He
Speaker 1: was a very visual guy. All of a sudden, it
Speaker 1: made this crazy graph that where the the line as
Speaker 1: it progressed forward through time, went all over the place.
Speaker 1: It went from this access to another access to the
Speaker 1: other axis, and it would spend some time over here,
Speaker 1: and then it would suddenly loop over to the other one,
Speaker 1: and it followed no rhyme or reason. It never retraced
Speaker 1: its path. And it was describing how a convey action
Speaker 1: current changes over time, right, and Lorenz is looking at this,
Speaker 1: he was expecting these three things to equalize and eventually
Speaker 1: form a line, because that's what determinism says, things are
Speaker 1: going to fall into a certain amount of equilibrium and
Speaker 1: just even out over time. That is not what he
Speaker 1: found now, And what he discovered was what pan quar
Speaker 1: A discovered, which was that some systems, even relatively simple systems,
Speaker 1: exhibit very complex, unpredictable behavior, which you could call chaos. Yeah.
Speaker 1: And when you say things were going all over like
Speaker 1: if you look at the graph, it it's not just
Speaker 1: lines going in straight lines bouncing all over the place randomly,
Speaker 1: like there was an order to it, but the lines
Speaker 1: were not on top of one another. Like let's say
Speaker 1: you draw a figure eight with your pencil, and then
Speaker 1: you continue drawing that figure eight, it's gonna slip outside
Speaker 1: those curves every time unless you're a robot. Um. And
Speaker 1: that's what it ended up looking like. Yeah, yeah, it
Speaker 1: never retraced the same path twice ever. Um. It had
Speaker 1: a lot of really surprising properties, and at the time
Speaker 1: it just fell completely outside the understanding of science, right. Yeah.
Speaker 1: Luckily this happened to Lawrence, who was curious enough to
Speaker 1: be like, what is going on here? And again he
Speaker 1: sat down and started to do the math and thinking
Speaker 1: about this and especially how it applied to the weather right,
Speaker 1: and he came up with something very famous. Yes, the
Speaker 1: butterfly effect. Yes, uh a, this thing kind of looked
Speaker 1: like butterfly wings a little bit, uh and be When
Speaker 1: he went to present his findings, he basically had the
Speaker 1: notion He's like, I'm gonna I'm gonna wile these people
Speaker 1: in the crowd in No, it's a conference that I'm
Speaker 1: going to and I'm gonna I'm gonna say something like,
Speaker 1: you know, the seagull flaps his wings and it starts
Speaker 1: a small turbulence that can one that can affect whether
Speaker 1: on the other side the world, the small little thing
Speaker 1: will just grow and grow in snowball and effective things.
Speaker 1: And he had a colleague goes like, seagull wings, that's nice,
Speaker 1: and he said, how about this, and this is the title.
Speaker 1: They ended up with, predictability Colin does the flap of
Speaker 1: a butterfly's wings in Brazil set off a tornado and
Speaker 1: Texas and everyone was like, WHOA mind's blown? Should we
Speaker 1: take a break? All right, We'll be right back, all right.
Speaker 1: So the lawns attractor. Uh, is that picture that he
Speaker 1: ended up with? The Laurens attractor? And this biblical pattern
Speaker 1: website that I found described attractors and strange attractors in
Speaker 1: a way that even dumb old me could understand. So
Speaker 1: if I may, he says, all right, here's the cycle
Speaker 1: of chaos. He said, Actually, I don't know who wrote this.
Speaker 1: A woman could have been a small child, could have
Speaker 1: been no of undetermined gender. I have no idea. So
Speaker 1: the gender neutral narrator, they said, he's sorry. I think
Speaker 1: about a town that has like ten thousand people living
Speaker 1: in it. To make that town work, you gotta have
Speaker 1: like a gas station, a grocery store, a library, um,
Speaker 1: whatever you need to sustain that town. So all these
Speaker 1: things are built, everyone's happy. You have equilibrium, he said.
Speaker 1: So that's great. Then let's say you build some Someone
Speaker 1: comes and build a factory on the outskirts of that town,
Speaker 1: and there's gonna be ten thousand more people living there
Speaker 1: and they don't go to church. Maybe so, uh, did
Speaker 1: I say church? They needed a church? Okay? I was
Speaker 1: just assuming this is what's called no. But you just
Speaker 1: have more people. So there's you need another gas station
Speaker 1: and another grocery store. Let's say, so they build all
Speaker 1: these things, and then you reach equilibrium. Again, it's maintained
Speaker 1: because you build all these other systems up. That equilibrium
Speaker 1: is called an attractor. Okay, so then he said it's said,
Speaker 1: they said he capital he the royal. He said, all right, now,
Speaker 1: let's say instead of that that factory being built, and
Speaker 1: you have those original tin bowls, and let's say three
Speaker 1: thousand those people just up and leave one day, and
Speaker 1: the grocery store guy says, well, there's only seven thousand
Speaker 1: people here. We need eight thousand people living here to
Speaker 1: to make a profit. So I'm shutting down this grocery store.
Speaker 1: Then all of a sudden, you have demand for groceries.
Speaker 1: So things go on a little while, and someone comes
Speaker 1: in and say, hey, this town needs a grocery store.
Speaker 1: They build a grocery store, they can't sustain, they shut down.
Speaker 1: Someone else comes along because the demand and it is
Speaker 1: this search for equilibrium, this dyna Well, you reach equal
Speaker 1: delibrium here and there as the store opens, periods of stability,
Speaker 1: periods of stability, and that dynamic equilibrium is called a
Speaker 1: strange attractor. So, an attractor is the state which a
Speaker 1: system settles on. Stranger attractor is the trajectory on which
Speaker 1: it never settles down but tries to reach the equilibrium
Speaker 1: with periods of stability. Does that make sense that Bible
Speaker 1: based explanation was dynamite. I understand it better than I
Speaker 1: did before, and I understood it okay before. That's great.
Speaker 1: Surely can add yeah, yeah, now you're gonna add to it. No,
Speaker 1: that's it, No, I mean like it. Yeah. An attractor
Speaker 1: is where if you raft something and eventually it reaches equilibrium,
Speaker 1: it's a regular attractor. If it never reaches equilibrium, is
Speaker 1: constantly trying to and has periods of stability. Strange attractor.
Speaker 1: I can't. I can't top that, alright, grocery store, small town.
Speaker 1: That was great. So um Lorenz, a strange attractor was
Speaker 1: named a Lorenz attractor named after him. Big deal. They
Speaker 1: weren't using the word chaos yet. No. But he published
Speaker 1: that paper about butterfly wings, right, the butterfly effect, and
Speaker 1: it coupled with his pictures the picture of a strange attractor,
Speaker 1: which is almost the aside from fractals, almost the the
Speaker 1: the um emblem or the logo for chaos theory. The
Speaker 1: Laurens attractor is um. It got attention off the bat.
Speaker 1: It wasn't like plan cares findings where he got neglected
Speaker 1: for seventy years. Almost immediately everybody was talking about this
Speaker 1: because again, what Lorenz had uncovered, which is the same
Speaker 1: thing that plan Care had uncovered, is that determinism is
Speaker 1: possibly based on an illusion that the universe isn't stable,
Speaker 1: that the universe isn't predictable, and that what we are
Speaker 1: seeing as stable and predictable are these little periods windows
Speaker 1: of stability that are found in strange attractor graphs, that
Speaker 1: that's what we think the order of the universe is,
Speaker 1: but that that is actually the abnormal aspect of the universe,
Speaker 1: and that instability unpredictability, as far as we're concerned, is
Speaker 1: the actual state of affairs in in nature. And I
Speaker 1: think as far as we're concerned, is a really important
Speaker 1: point to Chuck, because it doesn't mean that nature is
Speaker 1: unstable chaotic. It means that our picture of what we
Speaker 1: understand as order doesn't jibe with how the universe actually functions.
Speaker 1: It's just our understanding of it, and we're's just so
Speaker 1: um anthropocentric that you know, we we see it as
Speaker 1: chaos and disorder and something to be feared, when really
Speaker 1: it's just complexity that we don't have the capability of
Speaker 1: predicting after a certain degree. Yeah, I think that makes
Speaker 1: me feel a little better, because when you read stuff
Speaker 1: like this, you start to feel like, well, the Earth
Speaker 1: could just throw us all off of its face at
Speaker 1: any moment because it starts spinning so fast that gravity
Speaker 1: becomes undone. And I know that's not right. By the way,
Speaker 1: I've always loved that kind of science that shows we
Speaker 1: don't know anything. Like Robert Hume, who I know, I
Speaker 1: understand was a philosopher, but he was a philosopher scientist. Um.
Speaker 1: His whole jam was like causing effect as an illusion
Speaker 1: that like we all we it's it's just an assumption,
Speaker 1: like that if you drop a pencil, it will always
Speaker 1: fall down, and it's an illusion. And this is pretty
Speaker 1: um gravity understanding gravity. But he makes a good pet
Speaker 1: gravity when everyone's just floating around. Yeah, going this pencils
Speaker 1: got me wacky. But but the point was that you know,
Speaker 1: we we are. We base a lot of our assumptions
Speaker 1: um or a lot of stuff that we take as
Speaker 1: law are actually based on assumptions that are made from
Speaker 1: observations over time, and that we're just making predictions that
Speaker 1: causing effect as an illusion. I love that guy, and
Speaker 1: this this definitely supports that idea for sure. Yeah. Sorry,
Speaker 1: I'm I'm excited about chaos theory. Believe it. Well, I
Speaker 1: mean I like that I'm able to understand it and
Speaker 1: enough of a rudimentary way that I can talk about
Speaker 1: it at a dinner party. Well, thank your Bible website. Well,
Speaker 1: once you take the formulas out for people like us,
Speaker 1: we're like, okay, we can understand chaos. Then when somebody says, good,
Speaker 1: do a differential equation, just like, what a different equation? Right?
Speaker 1: All right? So earlier I said that chaos had not
Speaker 1: been used the word chaos to describe all this junk, uh,
Speaker 1: And that didn't happen until later on and well actually
Speaker 1: about ten years, you know, but it was kind of
Speaker 1: at the same time this other stuff was going on
Speaker 1: with rinds. Yeah, late sixties, early seventies. There was a
Speaker 1: guy named Steven Smile Uh fields metal recipients. So you know,
Speaker 1: he's good at math, and um, he describes something that
Speaker 1: we now know as the small horseshoe, and it goes
Speaker 1: a little something like this. Uh So, all right, take
Speaker 1: a piece of dough with like bread dough, and you
Speaker 1: smash it out into a big flat rectangle. So you're
Speaker 1: looking at that thing and you're like, boy, I hope
Speaker 1: this makes some good bread. This is gonna be so good.
Speaker 1: So then you do a little rosemary on it. Yeah
Speaker 1: maybe so yeah, and then um lick it before you
Speaker 1: bake it, so you know it's yours. No one else
Speaker 1: can happen. Uh So, you you have that flat rectangle
Speaker 1: of dough, you roll it up into a tube, and
Speaker 1: then you smash that down kind of flat, and then
Speaker 1: you bend that down to where it eventually looks like
Speaker 1: a horse shoe. So now how you take that horseshoe.
Speaker 1: You take another rectangle of dough and you throw that
Speaker 1: horseshoe onto that, and then you do the same thing.
Speaker 1: The smell horseshoe basically says you cannot predict where the
Speaker 1: two points of that horseshoe will end up. You can
Speaker 1: roll it a million times and they'll end up in
Speaker 1: a million different places, totally random, different places to totally random.
Speaker 1: You never know. It's like a box of chocolates. You
Speaker 1: never know what you're gonna get. You have to say it,
Speaker 1: and that became known. You have to say it. Oh
Speaker 1: what imitate Forrest Gumps? Now I can't do that. That's fine.
Speaker 1: He's not one. He's not in my repertoire. That's fine.
Speaker 1: Although I did see that again part of it recently.
Speaker 1: Does it hold up well? I mean, take out forty
Speaker 1: minutes of it and it would have been a better movie,
Speaker 1: like all of that coincidence stuff that that and he
Speaker 1: also did the smile t shirt like it was just
Speaker 1: too much, Like he really hammered it too much was
Speaker 1: the basis of the movie. I know. But see it
Speaker 1: again and I guarantee you, like an hour and a
Speaker 1: half into it, you'll be like, I get it. You know.
Speaker 1: It was a good Tom Hanks movie that was overlooked.
Speaker 1: Road to Perdition, Yeah, that was a good one. Great
Speaker 1: Sam Indees. Oh man, that guy is awesome. Yeah, Oh
Speaker 1: what is he gonna do? He might do something he
Speaker 1: did the James bo he did Skyfall. Yeah, yeah, I
Speaker 1: know he's gonna also that last one that wasn't so great.
Speaker 1: He's got a potential project coming up and he would
Speaker 1: be amazing for and I don't remember what it was.
Speaker 1: Did you see Revolutionary Road? Yes? God have it was
Speaker 1: just like, yeah, you want to jump off a bridge
Speaker 1: like every five minutes during that movie. That was hardcore.
Speaker 1: H he did that one too. Huh yeah, And don't
Speaker 1: see that if you're like engaged to be married or
Speaker 1: thinking about it, yeah, or if you're blue already. I'm yeah,
Speaker 1: just take a really good good mood and be like
Speaker 1: I'm sick of being in a good mood, sit down
Speaker 1: and watch Revolutionary Road. Watch Joe Versus of Volcano instead. Uh?
Speaker 1: Where was I smell? Horseshoe? Is what that's called? And? Um?
Speaker 1: That was he was the first person to actually use
Speaker 1: the word chaos. Oh he was? I think so? No? No, No,
Speaker 1: York was Tom York's dad. Yeah, you're right, he wasn't
Speaker 1: the first person York correct. But it's male's horseshoe. Illustrates
Speaker 1: a really good point, Chuck, is it Tom York's dad? No?
Speaker 1: But they're both British, sure, York. Actually one's Australian. No,
Speaker 1: they're British. Um. So those two points, which should which
Speaker 1: started out right by each other and then end up
Speaker 1: in two totally different places. That applies not just a
Speaker 1: bread dough, but also too, things like water molecules that
Speaker 1: are right next to each other at some point and
Speaker 1: then months later they're in two different oceans, even though
Speaker 1: you would assume that they would go through all the
Speaker 1: same motions and everything, but they're not. There's so many
Speaker 1: different variables with things like ocean currents that two water
Speaker 1: molecules that were once side by side end up in
Speaker 1: totally random different places. And that's part of chaos. It's
Speaker 1: basically chaos personified or chaos molecule fied. So we mentioned York.
Speaker 1: Where I was going with that was, Um, there was
Speaker 1: an Australian named Robert May and he was a population biologist.
Speaker 1: So he was using math to model how animal populations
Speaker 1: would change over time, giving certain starting conditions. Uh. So
Speaker 1: he started using uh these equations is differential equations, and
Speaker 1: he came up with a formula known as the logistic
Speaker 1: difference equation that basically enabled him to predict these animal
Speaker 1: populations pretty well. Yeah, and it was working pretty well
Speaker 1: for a while, but he noticed something really really weird, right, Yeah,
Speaker 1: he had this formula. Um, the logistic difference equation is
Speaker 1: the name of it. Sure, Okay, So we had of
Speaker 1: that formula, and he figured out that if you took
Speaker 1: our which in this case was the reproductive rate of
Speaker 1: a animal population, and you pushed it past three, the
Speaker 1: number three, so that meant that the average animal in
Speaker 1: this population of animals had three offspring in its lifetime
Speaker 1: or in a season, whatever. If you pushed the past three,
Speaker 1: all of a sudden, the number of the population would diverge.
Speaker 1: If you pushed it equal to three, actually or more,
Speaker 1: it would diverge, which is weird because a population of
Speaker 1: animals can't be two different numbers, you know, like that
Speaker 1: herd of antelope is not there's not thirty, but there's
Speaker 1: also forty five of them at the same time. That's
Speaker 1: called the superposition, and that has to do with quantum states,
Speaker 1: not herds of antelopes. That was kind of weird. And
Speaker 1: then he found if you pushed it a little further,
Speaker 1: if you made the productive rate like three point oh
Speaker 1: five seven or something like that. I think it was
Speaker 1: a different number, but you just tweaked it a little bit,
Speaker 1: not even to four. We're talking like millions of a
Speaker 1: of a of a degree um, all of a sudden
Speaker 1: it would turn into four so there'd be four different
Speaker 1: numbers for that was the animal population, and then would
Speaker 1: turn into sixteen, and then all of a sudden, after
Speaker 1: a certain point, it would turn into chaos. The number
Speaker 1: would be everything at once, all over the place, just
Speaker 1: totally random numbers that it oscillated between. But in all
Speaker 1: that chaos, there would be periods of stability. Right, you
Speaker 1: push it a little further, and all of a sudden
Speaker 1: it would just go to two again. But beyond that
Speaker 1: it didn't go back to the original two numbers. It
Speaker 1: went to another two. So if you looked at it
Speaker 1: on a graph, it went line divided into two, divided
Speaker 1: into four eight sixteen chaos, two four sixteen sixteen chaos,
Speaker 1: all before you even got to the number four of
Speaker 1: the reproductive right. And he was working with Mr York
Speaker 1: because he was a little confounded, so he was a
Speaker 1: mathematician buddy of his, James Yorke from the University of Maryland,
Speaker 1: so they worked together on this. In the nine they
Speaker 1: co authored a paper called Period three implies Chaos and man,
Speaker 1: finally somebody said the word. I kept thinking it was
Speaker 1: all these other people. Yeah, and the this this paper
Speaker 1: where they first debut the name chaos. Um. They they
Speaker 1: based it. UM. Tom York's Dead based it on Edward
Speaker 1: Lawrences paper. He was like, you know what, I have
Speaker 1: a feeling that has something to do with the Lawrens attractor.
Speaker 1: So that, um, that that provided chaos to the world.
Speaker 1: And it it was the basically the third the third
Speaker 1: time a scientist had said we don't understand the universe
Speaker 1: like we think we do, and determinism is based on
Speaker 1: an illusion of order, a really chaotic universe. And this, uh,
Speaker 1: this established chaos. It took off like a rocket. And
Speaker 1: the eighties and the nineties, you know, as you know
Speaker 1: from Jurassic Park, chaos was everything. Everybody's like, chaos, this
Speaker 1: is totally awesome. It's the new frontier science. And then
Speaker 1: it just went It just went away, And a lot
Speaker 1: of people said, well, it was a little overhyped, but
Speaker 1: I think more than anything, and I think this is
Speaker 1: kind of the current understanding of chaos because it didn't
Speaker 1: actually go away. It became a deeper and deeper field.
Speaker 1: As you'll see, Um, people mistook what chaos meant. It
Speaker 1: wasn't the a new the new type of science. It
Speaker 1: was a new understanding of the universe. It was saying like, yes,
Speaker 1: you can still use new Tony in physics, like don't
Speaker 1: throw everything out the window. You can still try and
Speaker 1: predict weather and still try and build more accurate instruments
Speaker 1: and get you know, decent results. But you can't with
Speaker 1: absolute perfection. Complex systems like determinism. The the ultimate goal
Speaker 1: of determinism is false. It can never be it can
Speaker 1: never be done because we can't have an infinitely precise
Speaker 1: measurement for every variable or any variable. Therefore, we can't
Speaker 1: predict these outcomes. Right, So you would expect science to
Speaker 1: be like, what's the point, what's the point of anything? Well,
Speaker 1: some some chaos people have said, no, this is this
Speaker 1: is great, this is good. We'll take this. Will take
Speaker 1: the universe as it is, rather than trying to force
Speaker 1: it into our pretty little equations and saying like if
Speaker 1: the ocean temperature is this at this time of year, uh,
Speaker 1: and the fish population is this at that time, then
Speaker 1: this is how many offspring this fish stole. This fish
Speaker 1: population is going to have. Um, say, okay, here is
Speaker 1: the fish population, here is the ocean temperature, here all
Speaker 1: these other variables. Let's feed it into a model and
Speaker 1: see what happens. Not this is going to happen. What
Speaker 1: happens instead? And this is kind of the understanding of
Speaker 1: chaos theory. Now. It's taking raw data, as much data
Speaker 1: as you can possibly get your hands on, as precise
Speaker 1: data as you could possibly get your hands on, and
Speaker 1: just feeding it into a model and seeing what patterns emerge.
Speaker 1: Rather than making assumptions, it's saying, what's the outcome? What
Speaker 1: comes out of this model? Yeah, And that's why, like
Speaker 1: when you see some things, like you know, fifty years
Speaker 1: ago they predicted this animal be its extinct and it's not. Well,
Speaker 1: it's because the variations were too complex they tried to predict. Uh.
Speaker 1: And that's why if you look at a ten day forecast, you, sir,
Speaker 1: are a fool. All right, Well, ten days from now
Speaker 1: says it's going to rain in the afternoon. Come on.
Speaker 1: But if you take if you took enough variables for
Speaker 1: weather for like a city, and fed it into a
Speaker 1: model of the weather for that city, you could find, uh,
Speaker 1: you could find a time when it was similar to
Speaker 1: what it is now, and you could conceivably make some
Speaker 1: assumptions based on that. You can say, well, actually we
Speaker 1: can we can predict a little further out than we think.
Speaker 1: But um, it's it's based on this, this theory, this
Speaker 1: understanding of chaos, of unpredictability, of not just not forcing
Speaker 1: nature into our formulas, but putting data into a model
Speaker 1: and seeing what comes out of it. Yeah, and then
Speaker 1: at the end of that you learn like when that
Speaker 1: animal is not extinct like you thought it would be,
Speaker 1: you go back and look at the original thing and
Speaker 1: you have a more accurate picture of how the you know,
Speaker 1: data could have been off slightly this one value, and
Speaker 1: then you have more buffalo than you think. Yeah, sure
Speaker 1: you got buffaloed by chaos. And we're not even getting
Speaker 1: into fractals. It's a whole other thing. And we did
Speaker 1: a whole other podcast in June about fractals and Mandel Bena,
Speaker 1: mandel Brett, mendel Brett, mandel Brett. Yeah, and go listen
Speaker 1: to that one and hear me clinging to the edge
Speaker 1: of a clift. Man. We we should end this, but first, um,
Speaker 1: I want to say, there is a really interesting article
Speaker 1: it's pretty understandable on Quanta magazine about a guy named
Speaker 1: George and he is a chaos theory dude who's got
Speaker 1: a whole lab and is applying it to real life.
Speaker 1: So it's a really good picture of chaos theory and action.
Speaker 1: Go check it out. Okay, Uh, if you want to
Speaker 1: know more about chaos theory, I hope your brain is
Speaker 1: not broken. Yeah, go take some LSD and look attical that. Um,
Speaker 1: you can type those words into how stuff works in
Speaker 1: the search bar any of those fractals LSD chaos. It'll
Speaker 1: bring up some good stuff. And since I said good stuff,
Speaker 1: it's time for a listener. Now, I'm gonna call this
Speaker 1: rare shout out get requests all the time. I bet
Speaker 1: I know which one is really dude his girlfriend? Yeah no,
Speaker 1: so far, so good. Hey, guys, just want to say
Speaker 1: I think you're doing a wonderful job with the show.
Speaker 1: To this date. My first time listening was during my
Speaker 1: first deployment. Uh yeah, when I listened to your list
Speaker 1: on famous and influential films and I was hooked after that.
Speaker 1: Since I came back State Side has spent many hours
Speaker 1: driving to and fro uh see my girlfriend, to my barracks,
Speaker 1: and I can happily say that they've been made all
Speaker 1: the more enjoyable by listening to you guys. Even my
Speaker 1: girlfriend Rachel has warmed up to you dudes, which was
Speaker 1: not a pleasant I'm sorry, which was a pleasant shock
Speaker 1: to me that she has told me repeatedly that she
Speaker 1: cannot listen to audiobooks because quote hearing people talk on
Speaker 1: the radio gives me a headache end quote. Anyway, I
Speaker 1: hope you guys continue to make awesome podcasts as I'm
Speaker 1: headed out on my next deployment. And if you could
Speaker 1: give a show it out to Rachel, I'm sure it
Speaker 1: would make her feel a little better that I got
Speaker 1: the pleasant people on the podcast to reaffirm how much
Speaker 1: I love her. That is John Rachel hanging there, John,
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