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The Stuff You Should Know Doin’ Science Playlist: How Chaos Theory Changed the Universe · Stuff You Should Know

• 0:01
  Speaker 1: Hey, everybody.
• 0:02
  Speaker 2: Chuck here and welcome to our sciencey playlist. Super excited
• 0:05
  Speaker 2: about this one, and I'm going to kick it off
• 0:07
  Speaker 2: everybody with this episode on how chaos theory changed the universe.
• 0:14
  Speaker 3: Welcome to Stuff you Should Know from HowStuffWorks dot Com.
• 0:23
  Speaker 4: Hey, and welcome to the podcast. I'm Josh Clark with
• 0:27
  Speaker 4: Charles W. Chuck Bryant, and there's Jerry over there. So
• 0:30
  Speaker 4: this is Stuff you should Know, the podcast about chaos theory. Like,
• 0:36
  Speaker 4: have you ever seen Event Horizon?
• 0:39
  Speaker 1: I did not bad?
• 0:41
  Speaker 4: Great movie?
• 0:42
  Speaker 1: Are you crazy? Do you think it was great?
• 0:43
  Speaker 4: Oh? It was so imaginative.
• 0:45
  Speaker 1: I thought it was okay.
• 0:46
  Speaker 4: It was like a LOVECRAFTI and thing in outer space. Yeah,
• 0:50
  Speaker 4: I loved it.
• 0:51
  Speaker 3: It was all right.
• 0:52
  Speaker 4: I love crafted it.
• 0:53
  Speaker 3: Yeah, I liked it.
• 0:56
  Speaker 4: That's what I think of when I think of chaos.
• 0:58
  Speaker 4: You know, there's that one part where they kind of
• 1:00
  Speaker 4: give you like a glimpse behind, like the dimension that
• 1:03
  Speaker 4: this action is taking place in, to see the chaos underneath.
• 1:07
  Speaker 1: And I should check that out again.
• 1:09
  Speaker 4: Yeah.
• 1:10
  Speaker 2: I think about Jurassic Park and Jeff Goldblum as the
• 1:17
  Speaker 2: creep doctor Malcolm explaining chaos in the little auto driving.
• 1:25
  Speaker 1: Suv or whatever that was.
• 1:27
  Speaker 4: Yeah, that's what I was calling the script, the auto
• 1:29
  Speaker 4: driving suv scene.
• 1:30
  Speaker 2: Yeah, and you know what, he actually rewatched that scene
• 1:33
  Speaker 2: and it confirmed two things. One is that he actually
• 1:37
  Speaker 2: did a pretty decent job for a Hollywood movie of
• 1:40
  Speaker 2: a very rudimentary explanation of chaos.
• 1:43
  Speaker 4: And you watched it for this.
• 1:45
  Speaker 2: Yeah, yeah, just that scene. And then it also confirmed
• 1:48
  Speaker 2: of what a creep that character was. Yeah, if you
• 1:51
  Speaker 2: watch that scene, he's like, you know, he was all
• 1:54
  Speaker 2: gross and flirty with her right in front of her
• 1:56
  Speaker 2: ex but there's this you know, he's talking to her.
• 1:59
  Speaker 2: I didn't even know notice this at first. He like
• 2:02
  Speaker 2: he just like touches her hair out of nowhere for
• 2:04
  Speaker 2: no reason. Really, He's just talking to her and he
• 2:06
  Speaker 2: just like grabs her hair and touches it.
• 2:08
  Speaker 1: Hu And I'm like, what a creep.
• 2:10
  Speaker 4: I know, if you look closely, you can see the
• 2:11
  Speaker 4: hormones emerging through his chest hair.
• 2:14
  Speaker 1: Yeah, it's groady, And I love Jeff Goldblum. It's not
• 2:16
  Speaker 1: a reflection on him.
• 2:19
  Speaker 4: He was basically doing Jeff Goldbloom.
• 2:21
  Speaker 1: Well that's what he Yeah, sure he's Jeff Goldbloom, but
• 2:23
  Speaker 1: I don't.
• 2:23
  Speaker 2: Think that's how in the manner in which he speaks.
• 2:26
  Speaker 2: But I don't think he's a creep, do you.
• 2:30
  Speaker 4: Wow, I've got nothing against Jeff gold Okay, I think
• 2:33
  Speaker 4: he's a I think he's doing Jeff Goldblum.
• 2:36
  Speaker 2: It was also a sign of the times, like if
• 2:38
  Speaker 2: that movie were made today, doctor.
• 2:41
  Speaker 4: What was her name in the movie, at least Satler,
• 2:43
  Speaker 4: I think.
• 2:43
  Speaker 2: Yeah, doctor Satler would be like, it's very inappropriate to
• 2:46
  Speaker 2: stroke my hair, dude.
• 2:48
  Speaker 4: Yeah, like, don't touch me, right.
• 2:51
  Speaker 1: But this was the nineties or yeah, nineties. Free Wheeling
• 2:55
  Speaker 1: was eight, No, it was nineties.
• 2:56
  Speaker 4: It was the early mid nineties, I think, yeah, ninety
• 2:58
  Speaker 4: twee ninety four. The book came out in nineteen ninety
• 3:01
  Speaker 4: and in the book Ian Malcolm, who's a Chaotician.
• 3:06
  Speaker 1: Yeah, a cre chaotian, right he.
• 3:10
  Speaker 4: He goes into even more depth about chaos there. But
• 3:13
  Speaker 4: that was I mean, that was the first time I
• 3:15
  Speaker 4: ever heard of chaos theory was from Jurassic Part Yeah,
• 3:17
  Speaker 4: me too, probably, and it really it was really misleading.
• 3:22
  Speaker 4: I think the entire term chaos is very misleading as
• 3:26
  Speaker 4: far as the general public goes as from what I
• 3:30
  Speaker 4: researched in this for this article.
• 3:32
  Speaker 2: Well, yeah, I mean you hear the word chaos as
• 3:35
  Speaker 2: an English speaker and you think frenetic and crazy.
• 3:39
  Speaker 4: Out of control.
• 3:40
  Speaker 1: Yeah, and that's not what it means in terms of
• 3:43
  Speaker 1: science like this, right.
• 3:44
  Speaker 4: What it means, I guess we can say up front
• 3:47
  Speaker 4: is basically the idea that complex systems do not behave
• 3:53
  Speaker 4: in very neat ways that we can easily grasp, understand,
• 3:57
  Speaker 4: or measure.
• 3:59
  Speaker 2: Right, and not even simple systems don't. Sometimes it doesn't
• 4:04
  Speaker 2: always have to be complex. But I want to give
• 4:06
  Speaker 2: a shout out in addition to our own article to
• 4:10
  Speaker 2: when you know, when it comes to stuff like this,
• 4:12
  Speaker 2: the brain breaking stuff.
• 4:14
  Speaker 4: For me, man, this was a brain breaker.
• 4:16
  Speaker 2: You know. I always go to like blank blank for
• 4:19
  Speaker 2: kids because it always helps.
• 4:21
  Speaker 4: If there's a dinosaur mascot on the page, it's a
• 4:24
  Speaker 4: sure thing we can understand it.
• 4:26
  Speaker 2: But the best explanation for all this stuff that I
• 4:29
  Speaker 2: found on the internet was from a website called a
• 4:33
  Speaker 2: barm Aba Rim Publications, which turns out to be a
• 4:38
  Speaker 2: website about biblical patterns and sandwiched in the middle, there
• 4:43
  Speaker 2: is a really great, easy to understand series of pages
• 4:48
  Speaker 2: on chaos.
• 4:48
  Speaker 3: They're nice, So.
• 4:49
  Speaker 2: I was like, man, I get it now. I mean
• 4:51
  Speaker 2: in a rudimentary way.
• 4:52
  Speaker 4: Right, Well, yeah, yeah, I think even a lot of
• 4:58
  Speaker 4: people who deal with that display chaotic behavior, which I
• 5:03
  Speaker 4: guess is to say basically all systems, eventually, under the
• 5:05
  Speaker 4: right conditions, Yeah, don't necessarily understand chaos.
• 5:10
  Speaker 2: Yeah, And they defined a complex system as specifically. It
• 5:12
  Speaker 2: doesn't mean just like, oh it's complex, I mean it is, right,
• 5:17
  Speaker 2: but specifically they define it in a way that helped
• 5:20
  Speaker 2: me understand it's a system that has so much motion,
• 5:24
  Speaker 2: so many elements that are in.
• 5:25
  Speaker 5: Motion, moving parts.
• 5:26
  Speaker 2: Yeah, that it takes like a computer to calculate all
• 5:30
  Speaker 2: the possibilities of like what that could look like five
• 5:33
  Speaker 2: minutes from now, ten years from now. So before computers
• 5:37
  Speaker 2: came around, before the quantum mechanical revolution, it was it
• 5:43
  Speaker 2: was a lot more basic.
• 5:44
  Speaker 1: It was like what comes up must come down, stuff
• 5:47
  Speaker 1: like that.
• 5:48
  Speaker 4: Let's talk about that, Chuckers, because when you're talking about
• 5:51
  Speaker 4: chaos theory, it helps to understand how it revolutionized the
• 5:55
  Speaker 4: universe by getting a clear picture of how we understood
• 5:59
  Speaker 4: the uni versus leading up to the discovery of chaos. Right, So,
• 6:04
  Speaker 4: prior to the the scientific revolution, everybody was like, oh,
• 6:10
  Speaker 4: well it's God, the Earth is at the center of
• 6:13
  Speaker 4: the universe and God is spinning everything around like a top. Right, Yeah,
• 6:16
  Speaker 4: it was all a theistic explanation. Then scientific revolution happens
• 6:20
  Speaker 4: and people start applying things like math and making like
• 6:24
  Speaker 4: mathematical discoveries and and figuring out that there are there's order.
• 6:31
  Speaker 4: They're finding order in patterns and predictability to the universe
• 6:36
  Speaker 4: if you can apply mathematics to it.
• 6:39
  Speaker 2: Yes, specifically, if you can apply mathematics to the starting.
• 6:43
  Speaker 4: Point, right, right, So if you can, if you can
• 6:47
  Speaker 4: figure out how a system works mathematically speaking, right, you
• 6:50
  Speaker 4: can go in and plug in whatever coordinates you want
• 6:54
  Speaker 4: to and watch it go. You can predict what the
• 6:57
  Speaker 4: outcome is going to be. And what this is that
• 6:59
  Speaker 4: it's base don what at the time was a totally
• 7:02
  Speaker 4: revolutionary idea by Initially, I think Descartes was the first
• 7:09
  Speaker 4: one to kind of say cause and effect is a
• 7:11
  Speaker 4: pretty big part of our universe, right.
• 7:13
  Speaker 1: Yeah.
• 7:14
  Speaker 2: It was sort of like where this is sixteen hundred's
• 7:16
  Speaker 2: where early science met philosophy, Right, they kind of complimented
• 7:21
  Speaker 2: one another as far as something that's we're talking about determinism.
• 7:25
  Speaker 4: Right, So that was the kind of the seeds of
• 7:28
  Speaker 4: determinism was the scientific revolution, and like you said, where
• 7:31
  Speaker 4: philosophy and science came together in the form of Descartes.
• 7:34
  Speaker 3: Right.
• 7:34
  Speaker 1: Yeah.
• 7:35
  Speaker 4: And then Newton came along and we did a whole
• 7:37
  Speaker 4: episode on him.
• 7:38
  Speaker 1: Yeah, January of this year.
• 7:40
  Speaker 4: That was a good one.
• 7:41
  Speaker 1: It was really good.
• 7:42
  Speaker 4: Like I think you said in that episode that there's
• 7:45
  Speaker 4: possibly no scientists that's changed the world more than Newton has.
• 7:48
  Speaker 4: Maybe he's got.
• 7:50
  Speaker 2: Legs people shouted out others an email, but I'll just
• 7:54
  Speaker 2: say he's at the near the top for sure with
• 7:56
  Speaker 2: some other people.
• 7:57
  Speaker 4: The Cream. Yeah, so Newton came along and knew and said.
• 8:00
  Speaker 1: That was his name, Isaac the Kream Newton.
• 8:02
  Speaker 4: Right. I think anytime he dunked to be like cream. Yeah,
• 8:06
  Speaker 4: you just got creamed.
• 8:07
  Speaker 1: Oh I thought he was a boxer. He's a basketball player.
• 8:10
  Speaker 4: He was much more well known as a boxer, but
• 8:13
  Speaker 4: he definitely could dunk as a as a B baller.
• 8:16
  Speaker 1: Yeah.
• 8:17
  Speaker 4: So, man, that threw me off a.
• 8:20
  Speaker 5: Little bit, that's right.
• 8:21
  Speaker 1: The Cream.
• 8:22
  Speaker 4: Yeah, the Cream comes along and he basically says, watch this, dude,
• 8:27
  Speaker 4: does this cause and effect thing you're talking about? I
• 8:29
  Speaker 4: can express it in quantifiable terms. And he comes up
• 8:33
  Speaker 4: with all of these great laws and basically sets the
• 8:36
  Speaker 4: stage the foundation for science for the next three centuries
• 8:40
  Speaker 4: or so.
• 8:41
  Speaker 2: Yeah, these these laws that were so rock solid and
• 8:45
  Speaker 2: powerful that scientists kind of got ahead of themselves a
• 8:49
  Speaker 2: little and said, we're done, like done with Newton's laws.
• 8:52
  Speaker 2: We can predict we can predict everything if we have
• 8:56
  Speaker 2: a good enough beginning accurate value to plug into his equations,
• 9:02
  Speaker 2: and they weren't. I think there was a little hubris
• 9:04
  Speaker 2: and a little just excitement about like, will we figured
• 9:08
  Speaker 2: it all out right.
• 9:09
  Speaker 4: That you could take Newton's laws and if you had
• 9:13
  Speaker 4: accurate enough measurements you could predict what the outcome would
• 9:18
  Speaker 4: be of that system that you plug those measurements into
• 9:21
  Speaker 4: using these formula.
• 9:22
  Speaker 2: And at the time, a lot of this was like planetary, like, well,
• 9:26
  Speaker 2: we know that these planets are here and they're moving
• 9:29
  Speaker 2: and they're orbiting. So if we know these things, we
• 9:31
  Speaker 2: can plug it into an equation and we can figure
• 9:34
  Speaker 2: out what it's going to be like in a hundred
• 9:35
  Speaker 2: years exactly.
• 9:36
  Speaker 4: And they figured out and the basis of determinism is
• 9:41
  Speaker 4: what we just said, that if you have accurate measurements,
• 9:45
  Speaker 4: you can take those measurements and use them to predict
• 9:48
  Speaker 4: how a system is going to change over time using
• 9:51
  Speaker 4: differential equations. Right, Yeah, so this is what Newton comes
• 9:55
  Speaker 4: along and figures out that you can describe the universe
• 9:57
  Speaker 4: in these mathematical terms using differential equations. And like you said,
• 10:03
  Speaker 4: there was a tremendous amount of hubris, and well, I
• 10:07
  Speaker 4: think you said there were some hubris. I think there
• 10:09
  Speaker 4: was a tremendous amount of hubris where science basically said,
• 10:12
  Speaker 4: we've mastered the universe, We've uncovered the blueprint of the universe,
• 10:16
  Speaker 4: and now we understand everything. It's just a matter now
• 10:20
  Speaker 4: of getting our scientific measurements more and more and more exact. Yeah,
• 10:24
  Speaker 4: because again, the hallmark of determinism is that if you
• 10:27
  Speaker 4: have exact measurements, you can predict an outcome accurately, like
• 10:31
  Speaker 4: the pool queue example or the pool table example. Right.
• 10:35
  Speaker 2: Right, So if you've got a pool table, let's say
• 10:38
  Speaker 2: you're playing some nine ball, right, so you have that
• 10:41
  Speaker 2: beautiful little diamond. Yeah, set up, you got your cueball,
• 10:44
  Speaker 2: you put that que ball, and you crack it with
• 10:47
  Speaker 2: the queue. And if you are super accurate with your
• 10:51
  Speaker 2: initial measurements, you should be able to mathematically plot out
• 10:54
  Speaker 2: via angles where the balls will end up.
• 10:57
  Speaker 4: Right exactly, Like you can say, this is what the
• 10:59
  Speaker 4: table will look like after the break, if you know
• 11:02
  Speaker 4: the force, the angle, all those little.
• 11:04
  Speaker 2: Variable temperature, if there's wind in the room, sure, like
• 11:07
  Speaker 2: the felt on the table, like everything. The more specific
• 11:11
  Speaker 2: you are, the more accurate your end result will be.
• 11:13
  Speaker 5: Right.
• 11:13
  Speaker 4: And then one of the other hallmarks of determinism is
• 11:16
  Speaker 4: that if you take those exact same initial conditions and
• 11:20
  Speaker 4: do them again, the table, the pool table will look
• 11:23
  Speaker 4: exactly the same after the break.
• 11:24
  Speaker 2: Yeah, which is pretty much impossible for like a human
• 11:27
  Speaker 2: to do with their hands.
• 11:29
  Speaker 4: Sure, but the idea at the time of science was
• 11:32
  Speaker 4: that if you could build a perfect machine, sure that
• 11:35
  Speaker 4: could recreate these conditions, it will happen the same way
• 11:38
  Speaker 4: every time, right.
• 11:39
  Speaker 2: Yeah, And this, I mean this led to they had hubris,
• 11:42
  Speaker 2: but you could understand it when like literally in eighteen
• 11:46
  Speaker 2: forty six, two people predicted Neptune would exist.
• 11:52
  Speaker 5: Yeah, within months of that would exist.
• 11:54
  Speaker 1: But does exist?
• 11:55
  Speaker 5: Right?
• 11:55
  Speaker 2: And this is not by looking up in the sky
• 11:57
  Speaker 2: like they did it with math, right, and they were right. Yeah,
• 12:00
  Speaker 2: So imagine in eighteen forty six when that happens, they're like, yeah,
• 12:05
  Speaker 2: we kind of we've got the math down, so we're
• 12:07
  Speaker 2: pretty much all knowing well.
• 12:08
  Speaker 4: Plus also, for the most part, these not just with Neptune,
• 12:13
  Speaker 4: they were finding that this stuff really panned out. It
• 12:17
  Speaker 4: held true for everything from you know, the investigation into
• 12:21
  Speaker 4: electricity to new chemical reactions and understanding those, and it
• 12:27
  Speaker 4: laid the scientific revolution, laid the basis for the industrial
• 12:31
  Speaker 4: revolution and just the change that came out of the
• 12:34
  Speaker 4: world like that. It definitely it is understandable how science
• 12:38
  Speaker 4: kind of was like we got it all figured out.
• 12:40
  Speaker 2: Well, and like you said, they even Galileo was smart
• 12:46
  Speaker 2: enough to know there's uncertainty in these measurements, like the
• 12:52
  Speaker 2: precision is key. So they spent what does the article say,
• 12:56
  Speaker 2: a lot of the much of the nineteenth and twentieth
• 12:58
  Speaker 2: century just trying to build better instrumentation to get more
• 13:02
  Speaker 2: and more smaller and smaller and more precise measurements.
• 13:06
  Speaker 4: Right, That was like basically the goal of it, right.
• 13:08
  Speaker 2: Yeah, which was the right direction, and that's like exactly
• 13:10
  Speaker 2: what they should have been doing.
• 13:11
  Speaker 5: Yeah.
• 13:13
  Speaker 4: The problem is they, like you said, Galileo knew that
• 13:16
  Speaker 4: there was some sort of there there are gonna be
• 13:19
  Speaker 4: some flaws and measurement that we just didn't have those
• 13:23
  Speaker 4: great scientific instruments yet.
• 13:25
  Speaker 1: Right, Yeah, it's called the uncertainty principle. Okaybi of its accuracy.
• 13:30
  Speaker 4: Right, But the idea is that if you have a
• 13:34
  Speaker 4: good enough instruments, you can overcome that, and that the
• 13:38
  Speaker 4: the more you shrink the error in measuring the initial conditions. Yeah,
• 13:46
  Speaker 4: the more you're going to shrink the error in the
• 13:49
  Speaker 4: outcome be proportionate.
• 13:51
  Speaker 1: Right. They were correct.
• 13:52
  Speaker 4: The thing is they were also aware but ignoring in
• 13:57
  Speaker 4: a lot of ways some out standing problems, specifically something
• 14:03
  Speaker 4: called the N body problem.
• 14:05
  Speaker 1: Yet, you know what, I'm so excited about this. I
• 14:07
  Speaker 1: need to take a break.
• 14:08
  Speaker 4: I think that's a good idea.
• 14:09
  Speaker 1: I need to go check out my end body in
• 14:13
  Speaker 1: the bathroom.
• 14:13
  Speaker 3: Okay, and we'll be back, all.
• 14:33
  Speaker 4: Right, Chuck, We're back. So there's some there's some issues
• 14:35
  Speaker 4: right with determinism. There's some some weird problems out there
• 14:40
  Speaker 4: that are saying like, hey, pay attention to me because
• 14:45
  Speaker 4: I'm not sure determinism works. Right, And one is the
• 14:49
  Speaker 4: N body problem.
• 14:50
  Speaker 1: Yeah.
• 14:51
  Speaker 2: How this came about was in eighteen eighty five. That
• 14:53
  Speaker 2: was King Oscar number two of Sweden and Norway.
• 14:59
  Speaker 1: Yeah, don't want to leave out Norway.
• 15:01
  Speaker 2: Both he said, you know what, let's offer a prize
• 15:04
  Speaker 2: to anyone who can prove the stability of the Solar system,
• 15:08
  Speaker 2: something that has been stable for a long time before that.
• 15:12
  Speaker 2: And a lot of the most brilliant minds on planet
• 15:15
  Speaker 2: Earth got together and tried to do this with mathematical proofs,
• 15:20
  Speaker 2: and no one could do it. And then a dude
• 15:24
  Speaker 2: name Henri. You got to help me there with that? Oh,
• 15:30
  Speaker 2: say the whole thing.
• 15:31
  Speaker 5: Henri pon Care very nice.
• 15:34
  Speaker 2: He was French, believe it or not, and he was
• 15:36
  Speaker 2: a mathematician, and he said, you know what, I'm not
• 15:39
  Speaker 2: going to look at this big picture of all the
• 15:41
  Speaker 2: planets in the Sun and all their orbits.
• 15:43
  Speaker 4: You'd have to be a fool to try that.
• 15:45
  Speaker 2: Sure, he said, I'm going to shrink this down, like
• 15:47
  Speaker 2: we talked about shrinking that initial value, right, you know,
• 15:52
  Speaker 2: and that initial condition. He shrunk it down. He said,
• 15:54
  Speaker 2: I'm going to look at just a couple of bodies
• 15:57
  Speaker 2: orbiting one another with a common center of gravity, and
• 16:02
  Speaker 2: I'm going to look at this and this was called
• 16:05
  Speaker 2: the end body problem.
• 16:06
  Speaker 4: Yeah, which was smart to do because the more variables
• 16:09
  Speaker 4: you factor into a nonlinear equation like that, just the
• 16:15
  Speaker 4: harder it's going to be, so he shrunk it down.
• 16:17
  Speaker 4: So the n body problem has to do with three
• 16:21
  Speaker 4: or more celestial bodies orbiting one another. So Plonkarre said,
• 16:25
  Speaker 4: let's just start with three.
• 16:26
  Speaker 1: Yeah, smart.
• 16:28
  Speaker 4: And what he found from doing his equations for this
• 16:31
  Speaker 4: king Oscar the sequel prize, was that shrinking the initial
• 16:38
  Speaker 4: conditions measurements or rate of error, right, yeah, did not
• 16:45
  Speaker 4: really shrink the error in the outcome, which flies in
• 16:50
  Speaker 4: the face of determinism. What he found was that just
• 16:54
  Speaker 4: very very minute differences in the initial conditions fed into
• 17:00
  Speaker 4: a system produced wildly different outcomes. Yeah after a fairly
• 17:05
  Speaker 4: short time.
• 17:06
  Speaker 2: Yeah, Like, let me just round off the mass of
• 17:08
  Speaker 2: this planet at like the eighth decimal point, and you.
• 17:12
  Speaker 1: Know who cares? Who cares at that point?
• 17:15
  Speaker 2: I mean just round that one to a two, and
• 17:17
  Speaker 2: that would throw everything off at a pretty high rate.
• 17:21
  Speaker 2: And he said, wait a minute, I think this contest
• 17:25
  Speaker 2: is impossible.
• 17:27
  Speaker 4: Right, He said, there is no way to prove the
• 17:32
  Speaker 4: stability of the Solar System because he just uncovered the
• 17:38
  Speaker 4: idea that it's impossible for us to predict the rate
• 17:44
  Speaker 4: of change among celestial bodies.
• 17:47
  Speaker 2: Yeah, it's such a complex system. There are far too
• 17:50
  Speaker 2: many variables that it's impossible to start with something so
• 17:56
  Speaker 2: minute to get the equation whatever the that you want.
• 18:00
  Speaker 5: Right, well, not only that, but the result.
• 18:03
  Speaker 4: Not only that. And this is what really undermined determinism
• 18:07
  Speaker 4: was that he figured out that you would have to
• 18:10
  Speaker 4: have an infinitely precise measurement, which even if you build
• 18:16
  Speaker 4: a perfect machine that could take the infinitely or a
• 18:20
  Speaker 4: perfect machine that could take a measurement of like the
• 18:22
  Speaker 4: the movement of a celestial body around another, it's literally
• 18:29
  Speaker 4: impossible to get infinite an infinitely precise measurement, which means
• 18:34
  Speaker 4: that we could never predict out to a certain degree
• 18:38
  Speaker 4: the movement of these celestial bodies. Like he was saying, like, no,
• 18:42
  Speaker 4: you can't get you can't build a machine that gets
• 18:46
  Speaker 4: measurements enough that we can overcome this, Like determinism is wrong,
• 18:51
  Speaker 4: Like you can't just say we have the understanding to
• 18:55
  Speaker 4: predict everything. There's a lot of stuff out there that
• 18:59
  Speaker 4: were not able to predict and he uncovered it trying
• 19:02
  Speaker 4: to figure out this n body problem.
• 19:03
  Speaker 2: Yeah, and King Oscar the sequel said you win, Yeah,
• 19:07
  Speaker 2: bring me another rack of lamb and here's your prize. Yeah,
• 19:11
  Speaker 2: and he won by proving that it was impossible, which
• 19:13
  Speaker 2: is pretty interesting.
• 19:14
  Speaker 4: And they utterly and completely changed not just math, but
• 19:19
  Speaker 4: like our understanding of the universe and our understanding of
• 19:22
  Speaker 4: our understanding of the universe, which is even more kind
• 19:25
  Speaker 4: of earthshaking.
• 19:26
  Speaker 2: Yeah, he discovered dynamical instability or chaos. And they didn't
• 19:32
  Speaker 2: have supercomputers at the time, so it would be a
• 19:34
  Speaker 2: little while, about seventy years at MIT until we could
• 19:40
  Speaker 2: actually kind of feed these things into machines capable of
• 19:45
  Speaker 2: plotting these things out in a way that we could see, right,
• 19:48
  Speaker 2: which was really incredible.
• 19:49
  Speaker 4: So there was this dude seventy years later named Edward
• 19:56
  Speaker 4: Lawrence or Lorenz.
• 19:58
  Speaker 5: Yeah.
• 19:59
  Speaker 2: Well, first of all, we should set the stage the
• 20:01
  Speaker 2: reason this guy he was a meteorologist, yeah, and scientists, right,
• 20:05
  Speaker 2: not that those are not the same thing, right. He's
• 20:08
  Speaker 2: a scientist who dabbled a meteorology. He was a mathematician, yeah,
• 20:13
  Speaker 2: but he was really into meteorology because it was there
• 20:16
  Speaker 2: was a weird juxtaposition at the time where we were
• 20:19
  Speaker 2: sending people into outer space but we couldn't predict the weather.
• 20:23
  Speaker 4: Yeah, and it was it was definitely a blot on
• 20:25
  Speaker 4: the field of meteorology. People were like, do you guys
• 20:28
  Speaker 4: know what you're doing? Yeah, And meteorologists, you're like, you
• 20:32
  Speaker 4: have no idea how hard this is? Yeah, Like, yeah,
• 20:35
  Speaker 4: we can predict it a couple of days out, but
• 20:37
  Speaker 4: after that, it's just it's totally unpredictable. It drives us mad.
• 20:41
  Speaker 4: And it wasn't just their their reputations that were at stake,
• 20:46
  Speaker 4: like people were losing their lives because of it.
• 20:47
  Speaker 2: Right, Yeah, nineteen sixty two there were two notorious storms,
• 20:51
  Speaker 2: one on the East coast and one on the west,
• 20:53
  Speaker 2: the ash Wednesday storm in the East and the big
• 20:55
  Speaker 2: blow on the West that killed a lot of people,
• 20:58
  Speaker 2: cost hundreds of millions of dollars and damage, and people
• 21:02
  Speaker 2: were like, you know, we need to be able to
• 21:03
  Speaker 2: see these things coming a little more, right, because it's
• 21:05
  Speaker 2: a problem.
• 21:06
  Speaker 4: And meteorologists were like, why did you do it?
• 21:08
  Speaker 5: Then?
• 21:11
  Speaker 2: So they thought the key was these big supercomputers. Remember
• 21:14
  Speaker 2: the supercomputers. When they came out the big rooms full
• 21:18
  Speaker 2: of hardware, it was amazing, and they were finally able
• 21:21
  Speaker 2: to do like these incredible calculations that we could never
• 21:24
  Speaker 2: do before.
• 21:25
  Speaker 4: I know, they were able to like crunch sixty four
• 21:27
  Speaker 4: bytes a second.
• 21:28
  Speaker 1: Yeah, we had the abacus and then the supercomputer. There's
• 21:31
  Speaker 1: nothing in between.
• 21:33
  Speaker 4: I looked up the computer that Lareen's was working.
• 21:36
  Speaker 1: Was it the Whopper?
• 21:37
  Speaker 4: A Royal McBee? What was the Whopper?
• 21:39
  Speaker 1: War Games?
• 21:40
  Speaker 4: Was it called the Whopper?
• 21:41
  Speaker 3: Yeah?
• 21:42
  Speaker 1: Wopright, I can't believe they called it that.
• 21:46
  Speaker 4: So the guy just nicknamed it Joshua.
• 21:49
  Speaker 2: No, Joshua was the h the software falcon was the
• 21:55
  Speaker 2: old man who designed all this stuff, and his son
• 21:58
  Speaker 2: was Joshua. And that was the password to get in.
• 22:00
  Speaker 4: Oh, that was the password. Yeah, I guess I was
• 22:03
  Speaker 4: too young to understand what a password was.
• 22:05
  Speaker 1: Yeah, okay, you didn't even there weren't passwords at the time.
• 22:08
  Speaker 4: No, shouted it at the computer and they're like, okay,
• 22:12
  Speaker 4: access granted.
• 22:12
  Speaker 3: Yeah.
• 22:14
  Speaker 1: Still that movie holds up, does it really? Oh? Totally?
• 22:16
  Speaker 4: You got to check it out.
• 22:17
  Speaker 1: Yeah. Still very very fun. Young Ali sheety boy had
• 22:21
  Speaker 1: a crush on her from that movie.
• 22:22
  Speaker 4: She was great.
• 22:23
  Speaker 1: Yeah.
• 22:24
  Speaker 4: What else was she in recently? Wasn't she in something?
• 22:27
  Speaker 3: Well?
• 22:27
  Speaker 2: I mean she kind of went away for a while
• 22:30
  Speaker 2: and then had her big comeback with that indie movie
• 22:33
  Speaker 2: High Art, But that was a while ago.
• 22:35
  Speaker 4: Has she been in anything else? Recently.
• 22:37
  Speaker 2: Sure, I think I saw something and something recently and
• 22:41
  Speaker 2: I didn't realize that was her. Oh rightly, she looks familiar.
• 22:44
  Speaker 2: I was like, oh, that's Ali sheety.
• 22:47
  Speaker 1: I don't know all right, I could look it up,
• 22:50
  Speaker 1: but I won't. It doesn't matter anyway. I still crush
• 22:53
  Speaker 1: on her.
• 22:54
  Speaker 4: So the the Royal McBee was not quite the whopper.
• 22:58
  Speaker 4: You could actually sit.
• 22:59
  Speaker 5: Down the Royal McBee.
• 23:01
  Speaker 4: That's the name of it.
• 23:02
  Speaker 1: That sounds like a hamburger too.
• 23:03
  Speaker 4: It was by the Royal Typewriter Company, and they got
• 23:07
  Speaker 4: into computers for a second. And this is the kind
• 23:10
  Speaker 4: of computer that Lawrence was working with, and it was
• 23:14
  Speaker 4: a huge deal, Like you were saying, Abacus supercomputer. Yeah,
• 23:19
  Speaker 4: but it was still pretty dumb as far as what
• 23:22
  Speaker 4: we have today is concerned. But it was enough that
• 23:25
  Speaker 4: Lawrence was like, Lawrence and his ilk were like, finally
• 23:28
  Speaker 4: we can start running models and actually predict the weather. Yeah,
• 23:32
  Speaker 4: he started doing just that.
• 23:34
  Speaker 2: He did, so he started off with a computational model
• 23:38
  Speaker 2: of twelve meteorological meteorological I liked how you said it calculations,
• 23:43
  Speaker 2: which is very basic because they're infinite meteorological calculations. Probably, yeah,
• 23:49
  Speaker 2: depending to say it wrong again.
• 23:50
  Speaker 4: No, no, like it sounds like you're about to say it
• 23:53
  Speaker 4: wrong and then you pull it out at the last second.
• 23:55
  Speaker 4: Maybe it's really impressive.
• 23:57
  Speaker 1: But so that's very basic.
• 23:59
  Speaker 2: But he wanted to start out, you know, with something attainable,
• 24:02
  Speaker 2: so he narrowed it down to twelve conditions, basically twelve
• 24:05
  Speaker 2: calculations that had you know, temperature, wind, speed, pressure, stuff
• 24:09
  Speaker 2: like that, started forecasting weather. And then he said, you know,
• 24:14
  Speaker 2: it'd be great if you could see this, So I'm
• 24:16
  Speaker 2: going to spit it into my wonder machine, the McWhopper,
• 24:20
  Speaker 2: the Royal McBee, and I'm going to get a print
• 24:24
  Speaker 2: out so you can visualize what this looks like. So
• 24:28
  Speaker 2: things were going well and he had this print out
• 24:29
  Speaker 2: and everyone was amazed because these these calculations never seemed
• 24:33
  Speaker 2: to repeat themselves.
• 24:35
  Speaker 4: He was making like like like word art. You remember that,
• 24:41
  Speaker 4: Like that was the first thing anybody did on a computer.
• 24:43
  Speaker 4: Oh yeah, yeah, it was to make word art like
• 24:45
  Speaker 4: a butterfly or.
• 24:46
  Speaker 1: Something, right, you would print out. Yeah, I never could
• 24:49
  Speaker 1: do that.
• 24:49
  Speaker 4: I couldn't either.
• 24:50
  Speaker 2: Like you have to be able to visualize things spatially.
• 24:53
  Speaker 2: You have to have that right kind of brain.
• 24:55
  Speaker 4: For that, right or you have to be following a
• 24:57
  Speaker 4: guide book.
• 24:59
  Speaker 3: True.
• 25:00
  Speaker 4: Have you ever seen me? You and everyone? We know?
• 25:03
  Speaker 1: Yeah? I love that movie.
• 25:04
  Speaker 4: That's a great movie.
• 25:05
  Speaker 1: Yeah.
• 25:05
  Speaker 4: Those little kids in there, they were doing that. Oh
• 25:08
  Speaker 4: yeah yeah, the forever back and forth poop boy.
• 25:12
  Speaker 1: I haven't I haven't seen that since it came out.
• 25:13
  Speaker 1: It's been a while.
• 25:14
  Speaker 4: Oh you got to see it again?
• 25:15
  Speaker 1: Yeah, great movie, good movie.
• 25:17
  Speaker 4: Ali Sheet he's not in it. No, it's a Miranda
• 25:20
  Speaker 4: in July.
• 25:20
  Speaker 1: Right, and she like wrote and directed too. Right.
• 25:22
  Speaker 4: She did a great job. It's like it's one of
• 25:25
  Speaker 4: those rare movies where like there's just the right amount
• 25:30
  Speaker 4: of whimsy, because whimsy so easily overpowers everything else and becomes.
• 25:35
  Speaker 1: Like yeah, yeah, yeah.
• 25:37
  Speaker 4: This is like the most perfectly balanced amount of like
• 25:40
  Speaker 4: whimsy you I've ever seen in a movie.
• 25:41
  Speaker 2: Yeah, there's too much whimsy. I just like terrible Garden State.
• 25:44
  Speaker 2: I just want to punch it in the face.
• 25:46
  Speaker 4: Terrible. Although I like Garden State, but I haven't seen
• 25:48
  Speaker 4: it since.
• 25:49
  Speaker 5: It came out.
• 25:49
  Speaker 1: It hasn't aged well. Yeah, it's just when you look
• 25:52
  Speaker 1: at it now, it's just so cutesy and whimsical.
• 25:55
  Speaker 3: Oh yeah, it's like come on.
• 25:56
  Speaker 2: Yeah, boy, We're do a lot of movies today.
• 26:01
  Speaker 4: Oh yeah, well we're stalling.
• 26:03
  Speaker 2: We haven't even talked about butterfly effect yet, which is coming.
• 26:07
  Speaker 4: I'm dreading it. That's why I'm stalling, all right.
• 26:10
  Speaker 1: So where were we?
• 26:11
  Speaker 2: He was running his calculations, printing out his values so
• 26:16
  Speaker 2: people could see it, and then he got a little
• 26:19
  Speaker 2: lazy one day in nineteen sixty one. This output he
• 26:23
  Speaker 2: noticed was interesting, so he said, you know, I'm going
• 26:26
  Speaker 2: to repeat this calculation see it again, but I'm going
• 26:30
  Speaker 2: to to save time. I'm just going to kind of
• 26:32
  Speaker 2: pick up in the middle, and I'm not going to
• 26:35
  Speaker 2: input as many numbers, but I'm still using the same values,
• 26:39
  Speaker 2: just I'm not going out to six decimal points.
• 26:41
  Speaker 4: So the print out he had went to three decimal points. Yeah,
• 26:45
  Speaker 4: so he was working from the print out and didn't
• 26:48
  Speaker 4: take into account that the computer accepted six decimal points.
• 26:51
  Speaker 4: So he was just putting in three correct and expecting
• 26:53
  Speaker 4: that the outcome would be the same.
• 26:55
  Speaker 1: Right, Yes, but the outcome was way different, right, And
• 26:57
  Speaker 1: he went.
• 26:58
  Speaker 5: Whoa, whoa what?
• 27:00
  Speaker 1: Yeah, he's like, what's going on here? It was a
• 27:04
  Speaker 1: big deal.
• 27:05
  Speaker 2: I mean, someone would have come up with this eventually,
• 27:06
  Speaker 2: probably yeah, but I sort of accidentally came upon it.
• 27:09
  Speaker 4: It's neat that this guy did this because it changed
• 27:12
  Speaker 4: his career. I think he went from an emphasis on
• 27:15
  Speaker 4: meteorology to an emphasis on chaos math to stud scientists basically.
• 27:21
  Speaker 4: So I mean, the guy's got an attractor named after him,
• 27:24
  Speaker 4: you know what I mean.
• 27:24
  Speaker 1: Yeah, well, let's get to that.
• 27:26
  Speaker 4: So Lorenz starts looking at this and he's like, wait
• 27:29
  Speaker 4: a minute, this is this is weird, this is worth investigating,
• 27:33
  Speaker 4: and like, what was his name, puon Carre. Yeah, he said,
• 27:38
  Speaker 4: I need fewer variables. So I'm not going to try
• 27:41
  Speaker 4: to predict weather with these twelve differential equations that you
• 27:46
  Speaker 4: have to take into account. I'm just going to take
• 27:48
  Speaker 4: one aspect of weather called the rolling convection current, and
• 27:53
  Speaker 4: I'm going to see how I can write it down
• 27:56
  Speaker 4: in formula form. So a rolling convection current, chuck is
• 27:59
  Speaker 4: where you know, how the wind is created, where air
• 28:04
  Speaker 4: at the surface is heated and then starts to rise
• 28:08
  Speaker 4: and suddenly cool air from higher above comes in to
• 28:12
  Speaker 4: fill that vacuum that's left, and that creates a rolling
• 28:17
  Speaker 4: or vertically based convection current.
• 28:20
  Speaker 3: Yeah, okay, you could.
• 28:21
  Speaker 1: I would describe it as oven.
• 28:24
  Speaker 4: Oven, boiling water, cup of coffee. Wherever there's a temperature
• 28:29
  Speaker 4: differential based on a vertical alignment, you're going to have
• 28:33
  Speaker 4: a rolling convection current.
• 28:35
  Speaker 2: Okay, yeah, it sounds complex, but he just picked out
• 28:38
  Speaker 2: one thing, basically one condition, right, and this is the
• 28:41
  Speaker 2: one he picked out.
• 28:42
  Speaker 4: But had you seen my hands moving listeners, you would
• 28:45
  Speaker 4: be like, oh, yeah.
• 28:46
  Speaker 3: I know.
• 28:47
  Speaker 1: He made little rolling motions.
• 28:48
  Speaker 4: So he's like, okay, I can figure this out. So
• 28:52
  Speaker 4: he comes up with three three formulae that kind of
• 28:56
  Speaker 4: describe a rolling convection current, and he starts trying to
• 29:00
  Speaker 4: figure out how to describe this rolling convection current right, correct,
• 29:06
  Speaker 4: And so, like I said, he got these three formula
• 29:08
  Speaker 4: which were basically three variables that he calculated over time,
• 29:12
  Speaker 4: and he plugged him in and he found three variables
• 29:15
  Speaker 4: that changed over time. And he found that after a
• 29:18
  Speaker 4: certain point when you graph these things out, and since
• 29:21
  Speaker 4: they're three, you graph them out on a three dimensional graph.
• 29:25
  Speaker 4: So X, Y and Z.
• 29:26
  Speaker 2: Again, he wanted to just be able to visualize this, right,
• 29:29
  Speaker 2: because it's easier for people to understand.
• 29:30
  Speaker 4: He was a very visual guy.
• 29:32
  Speaker 1: Totally.
• 29:32
  Speaker 4: All of a sudden, it made this crazy graph that
• 29:36
  Speaker 4: where the line as it progressed forward through time, went
• 29:40
  Speaker 4: all over the place. It went from this axis to
• 29:42
  Speaker 4: another axis, to the other axis, and it would spend
• 29:45
  Speaker 4: some time over here, and then it would suddenly loop
• 29:47
  Speaker 4: over to the other one. And it followed no rhyme
• 29:50
  Speaker 4: or reason. It never retraced its path. And it was
• 29:55
  Speaker 4: describing how a convection current changes over time, right.
• 29:59
  Speaker 1: Yeah, And Lorenzo.
• 30:01
  Speaker 4: Is looking at this, he was expecting these three things
• 30:05
  Speaker 4: to equalize and eventually form a line. Yeah, because that's
• 30:09
  Speaker 4: what determinism says, things are going to fall into a
• 30:12
  Speaker 4: certain amount of equilibrium and just even out over time.
• 30:16
  Speaker 4: That is not what he found. No, and what he
• 30:18
  Speaker 4: discovered was what point care A discovered, which was that
• 30:23
  Speaker 4: some systems, even relatively simple systems, exhibit very complex, unpredictable behavior,
• 30:30
  Speaker 4: which you could call chaos.
• 30:32
  Speaker 1: Yeah.
• 30:32
  Speaker 2: And when you say things were going all over like
• 30:35
  Speaker 2: if you look at the graph, it it's not just
• 30:37
  Speaker 2: lines going in straight lines bouncing all over the place randomly,
• 30:41
  Speaker 2: like there was an order to it, but the lines
• 30:44
  Speaker 2: were not on top of one another. Like let's say
• 30:46
  Speaker 2: you draw a figure eight with your pencil and then
• 30:49
  Speaker 2: you continue drawing that figure eight.
• 30:51
  Speaker 1: It's going to.
• 30:51
  Speaker 2: Slip outside those curves right every time unless you're a robot.
• 30:56
  Speaker 1: Sure, And that's what it ended up looking like.
• 30:59
  Speaker 2: Yeah.
• 30:59
  Speaker 4: Yeah, never retraced the same path twice. Ever, it had
• 31:04
  Speaker 4: a lot of really surprising properties, and at the time
• 31:08
  Speaker 4: it just fell completely outside the understanding of science, right.
• 31:12
  Speaker 1: Yeah.
• 31:12
  Speaker 4: Luckily this happened to Lawrence who was curious enough to
• 31:15
  Speaker 4: be like, what is going on here? And again he
• 31:19
  Speaker 4: sat down and started to do the math and thinking
• 31:21
  Speaker 4: about this and especially how it applied to the weather, right, yeah,
• 31:25
  Speaker 4: and he came up with something very famous.
• 31:28
  Speaker 2: Yes, the butterfly effect. Yes, A, this thing kind of
• 31:34
  Speaker 2: looked like butterfly wings a little bit. Yeah, And b
• 31:37
  Speaker 2: when he went to present his findings, he basically had
• 31:41
  Speaker 2: the notion He's like, I'm gonna I'm gonna wow these
• 31:44
  Speaker 2: people in the crowd in nineteen seventy two. It's a
• 31:47
  Speaker 2: conference that I'm going to and I'm gonna I'm gonna
• 31:50
  Speaker 2: say something like, you know, the seagull flaps his wings
• 31:53
  Speaker 2: and it starts a small turbulence that can one that
• 31:55
  Speaker 2: can affect weather on the other side of the world. Right,
• 31:58
  Speaker 2: the small little thing will just grow and grow and
• 32:01
  Speaker 2: snowball and effect things. And he had a colleague was like,
• 32:05
  Speaker 2: seagull wings, that's nice, and he said, how about this,
• 32:09
  Speaker 2: And this is the title. They ended up with, predictability
• 32:12
  Speaker 2: Colon does the flap of a butterfly's wings in Brazil
• 32:16
  Speaker 2: set off a tornado in Texas? And everyone was like, whoa,
• 32:21
  Speaker 2: WHOA mine's blown?
• 32:24
  Speaker 1: Should we take a break? All right, We'll be right back,
• 32:43
  Speaker 1: all right.
• 32:44
  Speaker 2: So the Lorens Attractor is that picture that he ended
• 32:51
  Speaker 2: up with, The Lorens Attractor. And this biblical pattern website
• 32:59
  Speaker 2: that I found described attractors and strange attractors in a
• 33:03
  Speaker 2: way that even dumb old me could understand what you got.
• 33:06
  Speaker 2: So if I may, he says, all right, here's the
• 33:10
  Speaker 2: cycle of chaos. He said, I actually I don't know
• 33:15
  Speaker 2: who wrote this. A woman could have been a small child, Noah.
• 33:19
  Speaker 1: Of undetermined gender. I have no idea the gender neutral narrator,
• 33:25
  Speaker 1: they said.
• 33:26
  Speaker 2: He said, right, think about a town that has like
• 33:29
  Speaker 2: ten thousand people living in it. To make that town work,
• 33:33
  Speaker 2: you got to have like a gas station, a grocery store,
• 33:36
  Speaker 2: a library, whatever you need to sustain that town. Okay,
• 33:41
  Speaker 2: so all these things are built, everyone's happy, you have equilibrium.
• 33:45
  Speaker 2: He said, So that's great. Then let's say you build
• 33:49
  Speaker 2: some Someone comes and builds a factory on the outskirts
• 33:52
  Speaker 2: of that town, and there's going to be ten thousand
• 33:54
  Speaker 2: more people living there.
• 33:55
  Speaker 4: Right, and they don't go to church?
• 33:57
  Speaker 1: Maybe so, uh, did I say church they needed a church?
• 34:01
  Speaker 4: No? No, okay, I was just assuming this is what's equilibrium.
• 34:05
  Speaker 2: No, no, no, but you just have more people. So there's
• 34:08
  Speaker 2: you need another gas station and another grocery store. Let's
• 34:11
  Speaker 2: say so they build all these things and then you
• 34:14
  Speaker 2: reach equilibrium. Again, it's maintained because you build all these
• 34:18
  Speaker 2: other systems.
• 34:19
  Speaker 3: Up.
• 34:19
  Speaker 2: I see that equilibrium. It's called an attractor. Okay, So
• 34:24
  Speaker 2: then he said it said they said.
• 34:30
  Speaker 5: He capital he the royal.
• 34:33
  Speaker 2: He said, all right, Now, let's say instead of that
• 34:36
  Speaker 2: factory being built and you have those original ten thousand,
• 34:39
  Speaker 2: let's say three thousand. Those people just up and leave
• 34:42
  Speaker 2: one day, and the grocery store guy says, well, there's
• 34:45
  Speaker 2: only seven thousand people here. We need eight thousand people
• 34:47
  Speaker 2: living here to make a profit, so I'm shutting down
• 34:51
  Speaker 2: this grocery store. Then all of a sudden, you have
• 34:54
  Speaker 2: demand for groceries. So things go on for a little while,
• 34:57
  Speaker 2: and someone comes in and say, hey, this town needs
• 34:59
  Speaker 2: a grocery store. They build a grocery store, they can't sustain,
• 35:03
  Speaker 2: they shut down. Someone else comes along because the demand,
• 35:06
  Speaker 2: and it is this search for equilibrium, this dynamic Well,
• 35:12
  Speaker 2: you reach equililibrium here and there as the store opens.
• 35:15
  Speaker 4: Periods of stability, periods.
• 35:17
  Speaker 2: Of stability, and that dynamic equilibrium is called a strange attractor.
• 35:23
  Speaker 2: So an attractor is the state which the system settles on.
• 35:27
  Speaker 2: Stranger attractor is the trajectory on which it never settles
• 35:32
  Speaker 2: down but tries to reach the equilibrium with periods of stability.
• 35:36
  Speaker 1: Does that make.
• 35:37
  Speaker 4: Sense that Bible based explanation was dynamite. I understand it
• 35:42
  Speaker 4: better than I did before, and I understood it okay before.
• 35:46
  Speaker 4: That's great.
• 35:47
  Speaker 1: Surely can add yeah, yeah, no, you're gonna add to it. No,
• 35:52
  Speaker 1: that's it.
• 35:53
  Speaker 4: No, I mean like it. Yeah. An attractor is where
• 35:56
  Speaker 4: if you graph something and eventually it reaches equilibrium, it's
• 36:00
  Speaker 4: a regular attractor. If it never reaches equilibrium and is
• 36:04
  Speaker 4: constantly trying to and has periods of stability, strange attractor.
• 36:07
  Speaker 5: I can't.
• 36:07
  Speaker 1: I can't top that, all right, grocery store, small town.
• 36:10
  Speaker 5: That was great.
• 36:11
  Speaker 4: So Lorenz's strange attractor was named a Lorenz attractor named
• 36:16
  Speaker 4: after him. Big deal.
• 36:17
  Speaker 1: They weren't using the word chaos yet.
• 36:19
  Speaker 4: No, but he published that paper about butterfly wings, right, yeah,
• 36:23
  Speaker 4: the butterfly effect, and it coupled with his picture is
• 36:28
  Speaker 4: the picture of a strange attractor, which is almost the
• 36:32
  Speaker 4: aside from fractals, almost the the emblem or the logo
• 36:39
  Speaker 4: for chaos theory, the Lorens attractor is. It got attention
• 36:43
  Speaker 4: off the bat. It wasn't like puant Caret's findings where
• 36:46
  Speaker 4: it got neglected for seventy years. Almost immediately everybody was
• 36:50
  Speaker 4: talking about this because again, what Lorenzo had uncovered, which
• 36:53
  Speaker 4: is the same thing that puant care had uncovered, is
• 36:55
  Speaker 4: that determinism is possibly based on all that the universe
• 37:01
  Speaker 4: isn't stable, that the universe isn't predictable, and that what
• 37:04
  Speaker 4: we are seeing as stable and predictable are these little
• 37:08
  Speaker 4: periods windows of stability that are found in strange attractor graphs.
• 37:13
  Speaker 4: That that's what we think the order of the universe is,
• 37:16
  Speaker 4: but that that is actually the abnormal aspect of the universe,
• 37:21
  Speaker 4: and that instability unpredictability as far as we're concerned is
• 37:25
  Speaker 4: the actual state of affairs in nature. And I think
• 37:29
  Speaker 4: as far as we're concerned is a really important point too, Chuck,
• 37:33
  Speaker 4: because it doesn't mean that nature is unstable chaotic. It
• 37:40
  Speaker 4: means that our picture of what we understand as order
• 37:44
  Speaker 4: doesn't jibe with how the universe actually functions. It's just
• 37:49
  Speaker 4: our understanding of it, and we're just so anthropocentric that
• 37:55
  Speaker 4: we see it as chaos and disorder and something to
• 37:57
  Speaker 4: be feared, when really it's just come flexity that we
• 38:01
  Speaker 4: don't have the capability of predicting. Yeah, after a certain.
• 38:05
  Speaker 2: Degree, Yeah, I think that makes me feel a little better,
• 38:08
  Speaker 2: because when you read stuff like this, you start to
• 38:10
  Speaker 2: feel like, well, the Earth could just throw us all
• 38:13
  Speaker 2: off of its face at any moment because it starts
• 38:16
  Speaker 2: spinning so fast that gravity becomes undone.
• 38:19
  Speaker 1: And I know that's not right.
• 38:20
  Speaker 4: By the way, I've always loved that kind of science
• 38:22
  Speaker 4: that shows we don't know anything, like Robert Robert Hume,
• 38:27
  Speaker 4: who I know, I understand was a philosopher, but he
• 38:28
  Speaker 4: was a philosopher scientist.
• 38:30
  Speaker 1: Sure.
• 38:31
  Speaker 4: His whole jam was like cause and effect is an
• 38:33
  Speaker 4: illusion that, like we all, it's just an assumption, like
• 38:37
  Speaker 4: that if you drop a pencil, it will always fall down.
• 38:40
  Speaker 4: It's an illusion and this is pre gravity understanding gravity.
• 38:45
  Speaker 4: But he makes a good.
• 38:46
  Speaker 1: Price gravity when everyone's just floating around.
• 38:48
  Speaker 4: Yeah, going this pencils got me wacky. Yeah, but the
• 38:52
  Speaker 4: point was that you know, we are we base a
• 38:55
  Speaker 4: lot of our assumptions or a lot of stuff that
• 38:59
  Speaker 4: we take a law are actually based on assumptions that
• 39:02
  Speaker 4: are made from observations over time, and that we're just
• 39:05
  Speaker 4: making predictions that cause and effect as an illusion. I
• 39:08
  Speaker 4: love that guy, and this definitely supports that idea for sure. Sorry,
• 39:15
  Speaker 4: I'm excited about chaos theory.
• 39:17
  Speaker 1: Can believe it?
• 39:18
  Speaker 2: Well, I mean I like that I'm able to understand
• 39:21
  Speaker 2: it in enough of a rudimentary way that I can
• 39:24
  Speaker 2: talk about it at a dinner party.
• 39:26
  Speaker 4: Well, thank your Bible website. Well once you take the
• 39:30
  Speaker 4: formulas out, Yeah, for people like us, we're like, oh, okay,
• 39:34
  Speaker 4: we can understand chaos.
• 39:35
  Speaker 1: Yeah.
• 39:36
  Speaker 4: Then when somebody says, good, do a differential equation, You're
• 39:38
  Speaker 4: just like.
• 39:39
  Speaker 1: What what a different equation? All right?
• 39:43
  Speaker 2: So earlier I said that chaos had not been used
• 39:46
  Speaker 2: the word chaos to describe all this junk, right, and
• 39:49
  Speaker 2: that didn't happen until.
• 39:51
  Speaker 1: Later on, well actually about ten years, you know, but
• 39:54
  Speaker 1: it was kind of.
• 39:55
  Speaker 2: At the same time this other stuff was going on
• 39:57
  Speaker 2: with Lorenz. Yeah, late sixties, early seventies. There's a guy
• 40:01
  Speaker 2: named Stephen Smale, Fields metal recipient, so you know, he's
• 40:06
  Speaker 2: good at math, and he described something that we now
• 40:12
  Speaker 2: know as the Smale horseshoe. And it goes a little
• 40:16
  Speaker 2: something like this. So, all right, take a piece of
• 40:22
  Speaker 2: dough like bread dough, and you smash it out into
• 40:25
  Speaker 2: a big flat rectangle.
• 40:27
  Speaker 1: Do So you're looking at that thing and you're like, boy,
• 40:30
  Speaker 1: I hope this makes some good bread.
• 40:31
  Speaker 4: This is gonna be so good.
• 40:33
  Speaker 1: So then you go a little roseberry on it, yeah,
• 40:35
  Speaker 1: maybe so well sea salt.
• 40:37
  Speaker 4: Yeah, and then lick it before you bake it so
• 40:40
  Speaker 4: you know it's yours. No one else can have it.
• 40:44
  Speaker 2: So you have that flat rectangle of dough, you roll
• 40:47
  Speaker 2: it up into a tube and then you smash that
• 40:51
  Speaker 2: down kind of flat, and then you bend that down
• 40:54
  Speaker 2: to where it eventually looks like a horseshoe. Okay, so
• 40:57
  Speaker 2: now you take that horseshoe, you take another rectangle dough
• 41:00
  Speaker 2: and you throw that horseshoe onto that and then you
• 41:04
  Speaker 2: do the same thing. The smale horseshoe basically says you
• 41:08
  Speaker 2: cannot predict where the two points of that horseshoe will
• 41:11
  Speaker 2: end up. Yeah, you can roll it a million times
• 41:15
  Speaker 2: and it'll end up in a million different places.
• 41:19
  Speaker 4: Totally random, different places too.
• 41:21
  Speaker 1: Totally random. You never know.
• 41:23
  Speaker 2: It's like a box of chocolates. You never know what
• 41:24
  Speaker 2: you're gonna get.
• 41:26
  Speaker 1: You have to say it, and that became known.
• 41:28
  Speaker 4: You have to say it.
• 41:29
  Speaker 1: Oh what imitate Forrest Gump? Sure, I can't do that.
• 41:32
  Speaker 4: That's fine.
• 41:32
  Speaker 1: He's not one. He's not in my repertoire.
• 41:34
  Speaker 4: That's fine.
• 41:35
  Speaker 1: Although I did see that again part of it recently.
• 41:38
  Speaker 4: Does it hold up well?
• 41:40
  Speaker 2: I mean, take out forty minutes of it and it
• 41:42
  Speaker 2: would have been a better movie, like all of that.
• 41:45
  Speaker 1: Coincidence stuff that.
• 41:47
  Speaker 5: Oh I love that.
• 41:49
  Speaker 2: And he also did the smile T shirt like it
• 41:52
  Speaker 2: was just too much, Like he really hammered it too much.
• 41:55
  Speaker 3: I liked it.
• 41:57
  Speaker 4: That was the basis of the movie.
• 41:58
  Speaker 1: I know.
• 41:59
  Speaker 2: But see it again and I guarantee you, like an
• 42:01
  Speaker 2: hour and a half into it, you'll be like, I
• 42:03
  Speaker 2: get it.
• 42:06
  Speaker 4: You know. It was a good Tom Hanks movie. That
• 42:07
  Speaker 4: was overlooked A Road to Perdition.
• 42:11
  Speaker 1: Yeah, this is a good one. Great Sam Mendez.
• 42:14
  Speaker 4: Oh man, that guy's awesome.
• 42:16
  Speaker 1: Yeah.
• 42:16
  Speaker 4: Oh what is he going to do? He might do something.
• 42:19
  Speaker 2: He did the James Bar he did Skyfall. Yeah, yeah, no,
• 42:22
  Speaker 2: he's gonna also that last one that wasn't so great.
• 42:24
  Speaker 4: He's got a potential project coming up and he would
• 42:27
  Speaker 4: be amazing for it. I don't remember what it was.
• 42:29
  Speaker 1: Did you see Revolutionary Road?
• 42:31
  Speaker 5: Yes?
• 42:32
  Speaker 4: God how it was just like.
• 42:35
  Speaker 1: Yeah, you want to jump off a bridge?
• 42:36
  Speaker 4: Yeah? Movie like every five minutes during that movie.
• 42:40
  Speaker 1: That was hardcore.
• 42:41
  Speaker 4: It is.
• 42:42
  Speaker 5: Uh he did that one too, huh.
• 42:43
  Speaker 2: Yeah, and don't see that if you're like engaged to
• 42:45
  Speaker 2: be married or thinking about.
• 42:47
  Speaker 4: It, yeah, or if you're blue already. Yeah I'm yeah.
• 42:50
  Speaker 4: Just take a really good good mood and be like
• 42:52
  Speaker 4: I'm sick of being in a good mood. Sit down
• 42:55
  Speaker 4: and watch Revolutionary Road.
• 42:56
  Speaker 1: Yeah. Watch Joe versus the Volcano instead.
• 42:58
  Speaker 4: Great movie. Uh.
• 43:01
  Speaker 1: Where was I smale? Horseshoe is what that's called?
• 43:04
  Speaker 2: And that was he was the first person to actually
• 43:08
  Speaker 2: use the word chaos.
• 43:10
  Speaker 5: Oh he was, I think so.
• 43:11
  Speaker 4: No, No, No, York was Tom York's dad.
• 43:14
  Speaker 1: Yeah, you're right, he wasn't the first person York correct.
• 43:16
  Speaker 4: But SMaL's horseshoe illustrates a really good point.
• 43:19
  Speaker 1: Chuck, is it Tom York's dad?
• 43:21
  Speaker 4: No, Okay, no, but they're both British, Sure, Yorky's actually
• 43:26
  Speaker 4: one's Australian. Nope, they're British. So those two points which
• 43:33
  Speaker 4: should which started out right by each other, and then
• 43:35
  Speaker 4: end up in two totally different places.
• 43:37
  Speaker 1: Yeah.
• 43:37
  Speaker 4: That applies not just to bread dough, but also too
• 43:40
  Speaker 4: things like water molecules that are right next to each
• 43:44
  Speaker 4: other at some point and then a month later they're
• 43:47
  Speaker 4: in two different oceans, even though you would assume that
• 43:50
  Speaker 4: they would go through all the same motions and everything.
• 43:53
  Speaker 5: Oh, sure, but they're not.
• 43:54
  Speaker 4: There's so many different variables with things like ocean currents
• 43:57
  Speaker 4: that two water molecules that were one side by side
• 44:00
  Speaker 4: end up in totally random different places. Yeah, and that's
• 44:04
  Speaker 4: part of chaos. It's basically chaos personified yea, or chaos
• 44:09
  Speaker 4: molecule fied.
• 44:12
  Speaker 1: So we mentioned York.
• 44:13
  Speaker 2: Where I was going with that was there was an
• 44:16
  Speaker 2: Australian named Robert May, and he was a population biologist,
• 44:22
  Speaker 2: so he was using math to model how animal populations
• 44:25
  Speaker 2: would change over time giving certain starting conditions. So he
• 44:30
  Speaker 2: started using these equations as differential equations, and he came
• 44:36
  Speaker 2: up with a formula known as the logistic difference equation
• 44:39
  Speaker 2: that basically enabled him to predict these animal populations pretty well.
• 44:44
  Speaker 4: Yeah, and it was working pretty well for a while,
• 44:47
  Speaker 4: but he noticed something really really weird, right, Yeah, he
• 44:51
  Speaker 4: had this formula, the logistic difference in equation is the
• 44:55
  Speaker 4: name of it. Sure, Okay, So we had that formula,
• 44:58
  Speaker 4: and he figured out that if you took R, which
• 45:02
  Speaker 4: in this case was the reproductive rate of an animal population, yeah,
• 45:07
  Speaker 4: and you pushed it past three.
• 45:09
  Speaker 1: The number three.
• 45:10
  Speaker 4: So that meant that the average animal in this population
• 45:14
  Speaker 4: of animals had three offspring in its lifetime or in
• 45:19
  Speaker 4: a season whatever. If you pushed it past three, all
• 45:22
  Speaker 4: of a sudden, the number of the population would diverge.
• 45:28
  Speaker 1: Yeah, if you pushed it equal to three actually, or more.
• 45:31
  Speaker 4: Right, it would diverge. Yeah, which is weird because a
• 45:34
  Speaker 4: population of animals can't be two different numbers, you know,
• 45:39
  Speaker 4: like that herd of antelope is not there's not thirty,
• 45:42
  Speaker 4: but there's also forty five of them at the same time. Yeah,
• 45:45
  Speaker 4: that's called a superposition, and that has to do with
• 45:48
  Speaker 4: quantum states, not herds of antelopes.
• 45:52
  Speaker 5: Sure, that was kind of weird.
• 45:54
  Speaker 4: And then he found if you pushed it a little further,
• 45:56
  Speaker 4: if you made the reproductive rate like three point oh five,
• 46:00
  Speaker 4: five seven or something like that. I think it was
• 46:03
  Speaker 4: a different number, but you just tweaked it a little bit, not.
• 46:05
  Speaker 5: Even to four.
• 46:06
  Speaker 1: We're talking like, oh yeah, millions.
• 46:08
  Speaker 4: Of a of a degree. All of a sudden it
• 46:13
  Speaker 4: would turn into four. So there'd be four different numbers
• 46:16
  Speaker 4: for that was the animal population, and then we would
• 46:18
  Speaker 4: turn into sixteen. And then all of a sudden, after
• 46:20
  Speaker 4: a certain point, it would turn into chaos. The number
• 46:23
  Speaker 4: would be everything at once, all over the place, just
• 46:25
  Speaker 4: totally random numbers that it oscillated between.
• 46:29
  Speaker 2: Yeah, but in all that chaos, there would be periods
• 46:32
  Speaker 2: of stability.
• 46:33
  Speaker 4: Right, you push it a little further, and all of
• 46:34
  Speaker 4: a sudden it would just go to two again. Yeah,
• 46:37
  Speaker 4: but beyond that, it didn't go back to the original
• 46:39
  Speaker 4: two numbers. It went to another two. So if you
• 46:41
  Speaker 4: looked at it on a graph, it went line divided
• 46:45
  Speaker 4: into two, divided into four eight sixteen chaos two four
• 46:49
  Speaker 4: sixteen two four eight sixteen chaos all before you even
• 46:53
  Speaker 4: got to the number four of the reproductive rate.
• 46:57
  Speaker 2: Yeah, And he was working with mister York because he
• 47:00
  Speaker 2: was a little confounded. So he was a mathematician buddy
• 47:02
  Speaker 2: of his, James York from the University of Maryland. So
• 47:06
  Speaker 2: they worked together on this, and in nineteen seventy five
• 47:09
  Speaker 2: they co authored a paper called Period three Implies Chaos
• 47:14
  Speaker 2: and man, finally somebody said the word I kept thinking
• 47:18
  Speaker 2: it was all these other people.
• 47:20
  Speaker 4: Yeah, And this paper where they first debuted the name chaos.
• 47:25
  Speaker 4: They they based it Tom York's dead based it on
• 47:31
  Speaker 4: Edward Lawrence's paper.
• 47:33
  Speaker 3: Yeah.
• 47:33
  Speaker 4: He was like, you know what, I have a feeling
• 47:35
  Speaker 4: that has something to do with the Lawrens attractor.
• 47:37
  Speaker 3: So that.
• 47:40
  Speaker 4: Provided chaos to the world. And it was the basically
• 47:43
  Speaker 4: the third the third time a scientist had said we
• 47:49
  Speaker 4: don't understand the universe like we think we do, and
• 47:52
  Speaker 4: determinism is based on an illusion like don't you get
• 47:56
  Speaker 4: it of order in a really chaotic universe? And this
• 48:01
  Speaker 4: established chaos. It took off like a rocket in the
• 48:04
  Speaker 4: eighties and the nineties. You know, as you know from
• 48:06
  Speaker 4: Jurassic Park, chaos was everything everybody's like chaos, this is
• 48:10
  Speaker 4: totally awesome, it's the new frontier of science. And then
• 48:13
  Speaker 4: it just went It just went away, And a lot
• 48:15
  Speaker 4: of people said, well, it was a little overhyped, but
• 48:19
  Speaker 4: I think more than anything, and I think this is
• 48:21
  Speaker 4: kind of the current understanding of chaos because it didn't
• 48:24
  Speaker 4: actually go away. It became a deeper and deeper field.
• 48:26
  Speaker 4: As you'll see, people mistook what chaos meant. It wasn't
• 48:32
  Speaker 4: the new type of science. It was a new understanding
• 48:38
  Speaker 4: of the universe. It was saying like, yes, you can
• 48:40
  Speaker 4: still use Newtonian physics.
• 48:42
  Speaker 2: Yeah, like don't throw everything out the windows. No, you
• 48:44
  Speaker 2: can still try and predict weather and still try and
• 48:46
  Speaker 2: build more accurate instruments and get you know, decent results.
• 48:50
  Speaker 2: But you can't with absolute perfection predict complex systems like determinism.
• 48:57
  Speaker 4: The ultimate goal of determinism is false. It can never
• 49:01
  Speaker 4: be it can never be done because we can't have
• 49:04
  Speaker 4: an infinitely precise measurement for every variable or any variable. Therefore,
• 49:08
  Speaker 4: we can't predict these outcomes. Right, So you would expect
• 49:11
  Speaker 4: science to be like, what's the point, what's.
• 49:13
  Speaker 5: The point of anything?
• 49:14
  Speaker 1: No, not science.
• 49:15
  Speaker 4: Well, some some chaos people have said no, this is
• 49:19
  Speaker 4: this is great, this is good. We'll take this. We'll
• 49:23
  Speaker 4: take the universe as it is, rather than trying to
• 49:26
  Speaker 4: force it into our pretty little equations and saying like,
• 49:30
  Speaker 4: if the ocean temperature is this at this time of
• 49:33
  Speaker 4: year and the fish population is this at that time,
• 49:36
  Speaker 4: then this is how many offspring this fish stop, this
• 49:40
  Speaker 4: fish population is going to have. Say okay, here is
• 49:45
  Speaker 4: the fish population, Here is the ocean temperature, here are
• 49:48
  Speaker 4: all these other variables. Let's feed it into a model
• 49:51
  Speaker 4: and see what happens. Not this is going to happen.
• 49:55
  Speaker 4: What happens instead, And this is kind of the understanding
• 49:59
  Speaker 4: of chaos there. Now, it's taking raw data, as much
• 50:03
  Speaker 4: data as you can possibly get your hands on, as
• 50:05
  Speaker 4: precise data as you could possibly get your hands on,
• 50:08
  Speaker 4: and just feeding it into a model and seeing what
• 50:10
  Speaker 4: patterns emerge. Rather than making assumptions, it's saying, what's the outcome?
• 50:15
  Speaker 4: What comes out of this model?
• 50:17
  Speaker 2: Yeah, And that's why, like when you see things like
• 50:20
  Speaker 2: you know, fifty years ago they predicted this animal be
• 50:22
  Speaker 2: its extinct and it's not. Well, it's because the variations
• 50:27
  Speaker 2: were too complex, right, They tried to predict And that's
• 50:31
  Speaker 2: why if you look at a ten day forecast, you, sir,
• 50:36
  Speaker 2: are a fool, Right, it's true, Well, ten days from
• 50:40
  Speaker 2: now it says it's going to rain in the afternoon.
• 50:42
  Speaker 1: Come on.
• 50:43
  Speaker 4: But if you take if you took enough variables for
• 50:46
  Speaker 4: weather for like a city, and fed it into a
• 50:50
  Speaker 4: model of the weather for that city, you could find
• 50:55
  Speaker 4: you could find a time when it was similar to
• 50:59
  Speaker 4: what it is now. Yeah, and you could conceivably make
• 51:01
  Speaker 4: some assumptions based on that. You can say, well, actually
• 51:05
  Speaker 4: we can, we can predict a little further out than
• 51:07
  Speaker 4: we think. But it's it's based on this theory, this
• 51:12
  Speaker 4: understanding of chaos, of unpredictability, of not just not forcing
• 51:17
  Speaker 4: nature into our formulas, but putting data into a model
• 51:21
  Speaker 4: and seeing what comes out of it.
• 51:23
  Speaker 1: Yeah.
• 51:23
  Speaker 2: And then at the end of that you learn, like
• 51:25
  Speaker 2: when that animal is not extinct like you thought it
• 51:28
  Speaker 2: would be, you go back and look at the original thing,
• 51:30
  Speaker 2: and you have a more accurate picture of how the
• 51:34
  Speaker 2: data could have been off slightly this one value, and
• 51:38
  Speaker 2: then you have more buffalo than you think.
• 51:41
  Speaker 4: Yeah, sure you got buffaloed by chaos.
• 51:45
  Speaker 1: And we're not even getting into fractals. That's a whole
• 51:47
  Speaker 1: other thing.
• 51:48
  Speaker 2: And we did a whole other podcast in June twenty
• 51:51
  Speaker 2: twelve about fractals and the mandal while mandel Brett mendel Brett, Mandelbrot, Yeah,
• 51:58
  Speaker 2: and go listen to that one and hear me clinging
• 52:01
  Speaker 2: to the edge of a clift.
• 52:02
  Speaker 4: Yet, man, we should end this. But first I want
• 52:07
  Speaker 4: to say, there is a really interesting article it's pretty
• 52:10
  Speaker 4: understandable on Quanta magazine about a guy named George Sugihara
• 52:18
  Speaker 4: and he is a chaos theory dude who's got a
• 52:22
  Speaker 4: whole lab and is applying it to real life. So
• 52:26
  Speaker 4: it's a really good picture of chaos theory and action.
• 52:31
  Speaker 4: Go check it out. Okay, if you want to know
• 52:35
  Speaker 4: more about chaos theory, I hope your brain's not broken.
• 52:39
  Speaker 1: Yeah, go take some LSD and look at fractals. Don't
• 52:42
  Speaker 1: do that.
• 52:43
  Speaker 4: You can type those words into how stuff works in
• 52:47
  Speaker 4: the search bar any of those fractals, LSD chaos. It'll
• 52:51
  Speaker 4: bring up some good stuff. And since I said good stuff,
• 52:53
  Speaker 4: it's time for a listener mail.
• 52:57
  Speaker 1: I'm gonna call this rare shout out get requests all
• 53:01
  Speaker 1: the time.
• 53:01
  Speaker 4: And bet I know each one really Yeah dude and
• 53:05
  Speaker 4: his girlfriend. Yeah, no, so far so good.
• 53:09
  Speaker 2: Hey, guys, just wanted to say I think you're doing
• 53:10
  Speaker 2: a wonderful job with the show to this date. My
• 53:13
  Speaker 2: first time listening was during my first deployment.
• 53:17
  Speaker 5: Yeah yeah, yeah, when.
• 53:18
  Speaker 2: I listened to your list on famous and influential films,
• 53:22
  Speaker 2: I was hooked after that. Since I came back stateside,
• 53:25
  Speaker 2: have spent many hours driving to and fro to see
• 53:28
  Speaker 2: my girlfriend to my barracks, and I can happily say
• 53:31
  Speaker 2: that they've been made all the more enjoyable by listening
• 53:34
  Speaker 2: to you guys.
• 53:35
  Speaker 4: That's good.
• 53:36
  Speaker 2: Even my girlfriend Rachel has warmed up to you dudes,
• 53:39
  Speaker 2: which was not a pleasant I'm sorry, which was a
• 53:41
  Speaker 2: pleasant shock to me. She has told me repeatedly that
• 53:44
  Speaker 2: she cannot listen to audiobooks because quote, hearing people talk
• 53:48
  Speaker 2: on the radio.
• 53:49
  Speaker 1: Gives me a headache end quote.
• 53:51
  Speaker 2: Anyway, I hope you guys continue to make awesome podcasts
• 53:54
  Speaker 2: as I'm headed out on my next deployment. And if
• 53:56
  Speaker 2: you could give a shout out to Rachel, I'm sure
• 53:58
  Speaker 2: it would make her feel a little better that I
• 54:00
  Speaker 2: got the pleasant people on the podcast to reaffirm how
• 54:03
  Speaker 2: much I love her. That is, John, Rachel, hang in there, John,
• 54:08
  Speaker 2: be safe and uh, thanks for listening.
• 54:12
  Speaker 4: Yeah, man, thank you, that is a great email. I
• 54:14
  Speaker 4: love that one.
• 54:14
  Speaker 1: Glad we don't give you a headache. Rachel, Yeah for real,
• 54:16
  Speaker 1: she listens to this one, She's.
• 54:17
  Speaker 4: Like, Okay, oh yeah, everybody's gonna get a headache from
• 54:20
  Speaker 4: this one. Like I came to hate the sound of
• 54:22
  Speaker 4: my own voice from this one.
• 54:24
  Speaker 1: Ah, you'll be right.
• 54:27
  Speaker 4: If you want to get in touch with us, you
• 54:28
  Speaker 4: can hang out with us on Twitter at s y
• 54:31
  Speaker 4: s K podcast. Same goes for Instagram. You can hang
• 54:33
  Speaker 4: out with us on Facebook dot com slash stuff you
• 54:35
  Speaker 4: Should Know. You can send us an email to stuff
• 54:38
  Speaker 4: podcast at HowStuffWorks dot com and has always joined us
• 54:41
  Speaker 4: at our home on the web, Stuff you Should Know
• 54:43
  Speaker 4: dot com.
• 54:49
  Speaker 5: For more on this and thousands of other topics, visit
• 54:52
  Speaker 5: HowStuffWorks dot com.