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Speaker 1: Welcome to Stuff to Blow your mind from how Stuff

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Speaker 1: Works dot Com. Robert Lamb and I'm Julie Douglas, and listeners,

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Speaker 1: do not run away. We are we are talking about

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Speaker 1: math in this episode, but we're not going to talk about, um,

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Speaker 1: you know, a whole bunch of rat We're not gonna

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Speaker 1: rattle off a bunch of equations and talk about, um,

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Speaker 1: you know, the the inner workings of Alderbar geometry or calculus,

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Speaker 1: because if you're like me, you probably never really got

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Speaker 1: into mathematics in school and you're not particularly good at it.

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Speaker 1: And uh, anytime a math question comes up, you try

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Speaker 1: and force it on other people. And you absolutely cannot

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Speaker 1: go to a bowling alley that does not have automated

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Speaker 1: score keeper right right, Or you always say something like

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Speaker 1: I'm an English major yeah to the response of what's

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Speaker 1: seven times three or something ridiculous like that, But I

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Speaker 1: will say yes, stick around absolutely, because nobody has more

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Speaker 1: anxiety about math and I do, and nobody's less of

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Speaker 1: a mathlete than I am. And yet I find this

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Speaker 1: question that we're going to pose to you into ourselves

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Speaker 1: absolutely fascinating. Yeah, we're getting into the philosophical area of

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Speaker 1: mathematics to a certain degree and just talking about what

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Speaker 1: it is and uh and indeed the question is mathematics

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Speaker 1: a human creation, a human invention, or is it a

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Speaker 1: human discovery? So think think about that. Mathematics this thing

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Speaker 1: that powers everything pretty much everything that we do, uh

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Speaker 1: that from the device that you're listening to this podcast

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Speaker 1: on right now to the the science that has enabled

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Speaker 1: civilization to reach the point that it has reached. But

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Speaker 1: what is math? Right? Yeah, what is math? We should

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Speaker 1: back up. We're gonna back up. We're gonna we're gonna start.

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Speaker 1: You know, we're not gonna really go back into the

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Speaker 1: history books and talk about like who invented this in

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Speaker 1: that because a lot of mathematics is pretty ancient to

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Speaker 1: the point where we can't really place uh, you know,

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Speaker 1: attributed to one particular individual or another. But we can

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Speaker 1: back up to birth to you, when you were a

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Speaker 1: baby and you were just you know, spit out on

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Speaker 1: the world, uh gleaming with with goog um. Even then,

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Speaker 1: your brain had some mathematics and you didn't even know it. Yeah,

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Speaker 1: a minor mathlete at least. Yeah, because you were you

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Speaker 1: were born with something that we call number since all right, Um,

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Speaker 1: and a number. Just to to break it down, a

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Speaker 1: number is a word and a symbol representing a count. Okay, uh,

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Speaker 1: Like that's the basic though you may call it too,

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Speaker 1: you may attribute it with the numeral two, or you

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Speaker 1: may have some other system of referring to it um.

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Speaker 1: For instance, in China they have they have small they

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Speaker 1: have shorter words for their numbers, which is one of

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Speaker 1: the reasons they're supposedly better at at mathematics because there's

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Speaker 1: s less just you know, it's kind of like cutting

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Speaker 1: a penny off of every transaction. It adds up and

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Speaker 1: that allows everything to to move. You don't have to

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Speaker 1: have a new word for two hundred um. You just

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Speaker 1: well anyway, we won't get into that math system. But

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Speaker 1: it's pretty cool. But but this is necessary because it's

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Speaker 1: imagine yourself in the wild and you encounter a dog,

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Speaker 1: like a wild dog that's barking at you. And then

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Speaker 1: and then you were to encounter two wild dogs barking

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Speaker 1: at you. Being able to differentiate between the two is essential.

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Speaker 1: It's it's all about navigating a world full of multiple

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Speaker 1: objects and moving objects. You know, there are a lot

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Speaker 1: of like if you're just walking around the forest, there

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Speaker 1: are a lot of trees, there are um wild dogs

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Speaker 1: moving around, There may be other humans. I mean, just

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Speaker 1: think all the things in your life that are in flux,

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Speaker 1: and you have to be able to navigate that world.

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Speaker 1: And that is what mathematics helps us do well. And

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Speaker 1: this is your brain in action, right, I mean, when

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Speaker 1: you enter a room, of what you are seeing is

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Speaker 1: not coming in through your eyes, it's actually what your

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Speaker 1: brain is inferring. And so we're pointing to here is

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Speaker 1: our brain structures and this idea that we might be

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Speaker 1: hardwired to to spatially differentiate as much as possible. That's

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Speaker 1: what you're talking about. Right, When you come into a room,

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Speaker 1: you're looking at the height whether or not you know

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Speaker 1: you're looking at the height of the walls. You're you're

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Speaker 1: sort of creating this pattern in your brain of pattern recognition, right,

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Speaker 1: And other animals do this too. But but those other animals,

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Speaker 1: like the gooey infant that you once were, uh, they

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Speaker 1: have wiped that off actually after a while. Yea, all

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Speaker 1: the infants on encounter gooey, some of the adults are

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Speaker 1: gooi um. But such a child will have no grasp

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Speaker 1: of the human number system. They are not going to

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Speaker 1: know what two is, or what three is, or what

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Speaker 1: three times eight is. Those are things are going to

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Speaker 1: come with in time via education, but they still have

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Speaker 1: that number. Since then they can identify changes in quantity

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Speaker 1: and this basically equates to something called logarithmic counting. And

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Speaker 1: neuroimaging research is actually studied the brains of infants and uh,

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Speaker 1: I mean scientists have studied the brains of infants through

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Speaker 1: near imaging research and they've they've registered these, uh the

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Speaker 1: mental activity going on as they identify integral increases in

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Speaker 1: physical quantity. So like a baby, for instance, wouldn't be

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Speaker 1: able to tell that much difference between five and six

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Speaker 1: heady bears, but five and ten they'll definitely see because

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Speaker 1: there there's a definite um logarithmic increase in quantity. Yeah,

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Speaker 1: and I thought this is really fascinating. Um uh To

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Speaker 1: that point, there's an article from futurity dot org and

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Speaker 1: it's called babies can compt before they can communicate, And

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Speaker 1: what they say is our findings indicate that humans use

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Speaker 1: information about quantity to organize their experience of the world

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Speaker 1: from the first few months of life. Quantity appears to

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Speaker 1: be a powerful tool for making predictions about how objects

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Speaker 1: should behave And what I think is really important about

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Speaker 1: that is predictions because that is what math is about

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Speaker 1: at the end of the day. Right If I'm sitting

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Speaker 1: there adding one in one, then then I'm trying to

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Speaker 1: predict what the following number is going to be. And

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Speaker 1: that's at the most basic level. I mean, we know

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Speaker 1: this is applied throughout physics, throughout every single field that

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Speaker 1: you can think of, in order for us to try

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Speaker 1: to make sense and categorize our lives and predict future outcomes.

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Speaker 1: Even chaos theory is an attempt to predict the unpredictable.

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Speaker 1: Now here's the thing, though, and this is where math begins,

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Speaker 1: because we're not even really dealing with math yet. We're

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Speaker 1: dealing with numbers. We're dealing with with numbers. Since but

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Speaker 1: as we're navigating this environment around us, um, the the

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Speaker 1: higher the higher the math gets, the more the larger

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Speaker 1: the numbers become, the harder it is for us to

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Speaker 1: to to to process it like even um like humans

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Speaker 1: are systematically slower to compute four plus five than they

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Speaker 1: are to compute two plus three. Like when I was

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Speaker 1: going over that in my notes, I could actually notice

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Speaker 1: it since I'm not good at math. I mean I

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Speaker 1: could actually feel the difference in in computing four place,

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Speaker 1: you could feel the brain power. Yeah. But but this actually,

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Speaker 1: this is something that everyone experiences, everybody because we are

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Speaker 1: not evolved to do arithmetic, higher arithmetics, certainly not geometry

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Speaker 1: or anything. But I do think this is actually started

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Speaker 1: looking through some information about autism UM, in particular savants

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Speaker 1: UM and ten percent of autistic population is savant, by

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Speaker 1: the way, and estimated one percent of non autistic population

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Speaker 1: is a savant. But when you think about that, and

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Speaker 1: the reason I bring it up is because it's clear

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Speaker 1: in those instances that the brain is working at a

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Speaker 1: much higher cognitive function level than than what we would

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Speaker 1: normally be used to, right. So that's why you've got

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Speaker 1: someone like Daniel Talmot. He's an autistic savant UM And

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Speaker 1: from the Guardian article Genius explains Talmont, they say that

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Speaker 1: Talmot broke the European record for recalling pie the mathematical

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Speaker 1: constant to the furthest decimal point to twenty two thousand,

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Speaker 1: five hundred and fourteen decimal places. He yes, I mean,

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Speaker 1: you know what I'm saying, and we most of us

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Speaker 1: can just say three point one, four and then yea

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Speaker 1: two decimal points right right. Um, So he says that

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Speaker 1: he found it easy because he didn't even have to think.

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Speaker 1: To him, pie is an abstract set of digits. It's

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Speaker 1: a visual story. Um. It's almost like a film projected

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Speaker 1: in front of his eyes. So in that case, we

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Speaker 1: know that he has a brain that is hardwired for

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Speaker 1: this kind of thinking. Um, and this really complex level. Right.

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Speaker 1: But even he, even someone is is gifted uh for numbers,

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Speaker 1: UM has has to turn to an outside system. They

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Speaker 1: have to augment their their number since even if it's

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Speaker 1: particularly phenomenal one And that's where mathematics begins to play. Well, actually,

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Speaker 1: even before mathematics really becomes into play, we have we

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Speaker 1: was we've discussed in our five Fingered Evolutionary podcast. We

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Speaker 1: start using our fingers. That's why we are so much

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Speaker 1: of our our number system is based on us on

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Speaker 1: units of five, tune or twenty, because that's what the

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Speaker 1: tools that we had. It's like, you know, you can

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Speaker 1: easily imagine this ancient and you know, prehistoric individual doing

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Speaker 1: mathemis like, well goodness, I'm having a hard time processing

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Speaker 1: numbers beyond like three or four in my head, What

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Speaker 1: can I turn to to help me? Oh, look at

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Speaker 1: these things? And uh, and you know, starts using his fingers,

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Speaker 1: starts using his toes. From there starts using other things rocks, twigs.

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Speaker 1: Uh and before long you have a an emerging mathematical system.

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Speaker 1: And yeah, and that's why we have these like ten

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Speaker 1: based twenty based mathematics decimal system or the other twenty

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Speaker 1: is of vegesimal system twenty base right. Yeah, even our

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Speaker 1: even our numerals um, like the the Phoenician symbols that

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Speaker 1: are number numbers are are based on those were in

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Speaker 1: their original archaic forms, which are our current system is

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Speaker 1: derived from. If you look back at the the the

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Speaker 1: ancient versions of it, the number um, you could tell

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Speaker 1: what the number was by the number of angles in

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Speaker 1: the symbol. I remember this from your article how math works. Yeah,

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Speaker 1: And a lot of it doesn't stack up because like

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Speaker 1: our nine is a lot different from the the ancient

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Speaker 1: Phoenician nine. Take the number zero. Zero zero has no

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Speaker 1: angles in it because it is nothing. The numeral one

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Speaker 1: has one angle in it, right because it is one.

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Speaker 1: Two has two angles in it in the arcade version,

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Speaker 1: et cetera. So that that's that's fascinating. But that's another

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Speaker 1: example of of you know, the human brain can only

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Speaker 1: do so much and you have to build outward. It's

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Speaker 1: like that the trailer that one is building onto and

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Speaker 1: creating all these additional rooms. Um, we're the trailer and

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Speaker 1: we've begin building the system and out from ourselves to

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Speaker 1: help us to better compute the world around us. Well.

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Speaker 1: And again it's this idea of symbols and abstraction, which

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Speaker 1: is pretty fascinating, right because math becomes something that's much

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Speaker 1: more than just you know, trigonometry or even one plus one.

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Speaker 1: It is a system of symbols that we use to

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Speaker 1: you know, extrapolate our existence in a sense. But I

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Speaker 1: did I didn't want to talk a little bit about

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Speaker 1: pattern recognition. I know we've already brought it up, but

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Speaker 1: I wanted to say a little bit more about the

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Speaker 1: brain and the fact that we're pretty much pattern recognition machines. Um.

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Speaker 1: So you know, you all have all these causal connections

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Speaker 1: between A and B. And this is from a Scientific

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Speaker 1: American article called Turn Me On, dead Man. We can

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Speaker 1: talk about that TI if you want to. Um, But

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Speaker 1: they say that as when our ancestors. Uh so, the

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Speaker 1: causal connection between A and B are like when our

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Speaker 1: ancestors associated the seasons with the migration of game animals.

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Speaker 1: We are skilled enough at it to have survived and

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Speaker 1: passed on the genes for the capacity of association learning.

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Speaker 1: So again, what we're doing is attributing some sort of

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Speaker 1: symbol to these associations, these patterns that we see to

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Speaker 1: document our world and try to navigate it in a

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Speaker 1: better way. Yeah, And I mean that's why pattern recognition

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Speaker 1: is one of the pillars of artificial communication. Yeah, I mean,

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Speaker 1: and that's why I um, that's and that's why pattern

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Speaker 1: recognition is one of the pillars of artificial intelligence, like

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Speaker 1: being able to instill that in a machine, which incidentally

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Speaker 1: is essentially made out of math. Yeah, right, And because

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Speaker 1: it's a it's a type of communicating right um. And

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Speaker 1: and AI we know was sort of based on the

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Speaker 1: way that we think, right um. And even I was

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Speaker 1: thinking even like morse code, something like the sort binary code.

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Speaker 1: These are again attempts to communicate ideas, um. And there's

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Speaker 1: this idea that that math is fundamentally universal. Right, So

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Speaker 1: what you end up with is basically a tower of mathematics,

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Speaker 1: which we're going to discuss, right after this quick break.

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Speaker 1: This presentation is brought to you by Intel Sponsors of Tomorrow,

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Speaker 1: and we're back a tower of mathematics. Now, hang with

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Speaker 1: me on this particular analogy, um, which I came up

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Speaker 1: with for the out Stuff Works article how Math Works,

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Speaker 1: which is a broad, um, you know, a broad look

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Speaker 1: at what math is for generally, you know, for people

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Speaker 1: who are not super into math. It's you know, more

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Speaker 1: about the philosophy of and what it is and the

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Speaker 1: kind of stuff we're discussing here. Um. I was really

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Speaker 1: proud of this analogy until I realized that other people

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Speaker 1: had also developed it years before. Your intuition, your pattern recognition, right, yeah, exactly, So, yeah,

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Speaker 1: you can think of math is this tower that we've built.

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Speaker 1: Imagine a human standing on a plane, all right, a

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Speaker 1: big grassy plane. He can only see so far he

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Speaker 1: or she he, I don't know, she whatever, whatever the gender,

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Speaker 1: This human can only see so far. Given there there

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Speaker 1: their natural born height and the site. Now, if they're

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Speaker 1: going to see better, they're gonna want to climb on

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Speaker 1: top of something, right, climb a tree or something. There

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Speaker 1: no trees around, so they need to build something, and

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Speaker 1: they build a tower, all right. So and in this analogy,

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Speaker 1: the natural born height equates to one's natural born limited

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Speaker 1: mathematical abilities. And then the tower that we build is

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Speaker 1: the system of mathematics. Each level of the mathematical tower

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Speaker 1: enables humans to see farther and achieve more UM. And

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Speaker 1: this this tower like just as a structured you know,

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Speaker 1: physical tower is built of materials and systems. You know,

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Speaker 1: you would you would, you know, have the guys bring out,

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Speaker 1: you know, some stone for it, some woold et cetera.

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Speaker 1: And then you have you know, you probably need plumbers,

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Speaker 1: electricians and other various uh specialists. I'll come out to

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Speaker 1: build the systems that make up the tower. Well, our

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Speaker 1: tower of mathematics would be made of of integers, would

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Speaker 1: be made of rational numbers, irrational numbers, complex numbers, um,

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Speaker 1: real numbers, and these are explained in that article that

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Speaker 1: I reference to how math works. You would also have

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Speaker 1: such systems as arithmetic, algebra, geometry, trigonometry, calculus. Uh. In

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Speaker 1: each of these you can think of as a different

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Speaker 1: level of complexity. Yeah, building block building on the last.

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Speaker 1: And the higher the tower gets, the more humans are

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Speaker 1: able to achieve to they you know, they reached the

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Speaker 1: point where they're able to use mathematics to better navigate

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Speaker 1: the physical world. They're able to use it to better

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Speaker 1: navigate and understand the world beyond our planet, to build

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Speaker 1: artificial machines and artificial intelligences, and create the computer world

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Speaker 1: that we have today. All of these things become possible

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Speaker 1: by building building this tower, working on the backs of

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Speaker 1: other geniuses, as we can sending on the shoulders of giants, right.

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Speaker 1: And I think that's really interesting, even like you're talking

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Speaker 1: about using this system to to get outside of ourselves, right,

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Speaker 1: to get outside of our our particular planet, our universe. Um.

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Speaker 1: And we can see that proven out through math managed

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Speaker 1: throughout history, right. And I was thinking at the very

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Speaker 1: basic level, and we began to understand, uh, you know,

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Speaker 1: pattern recognition in nature. You know, something like the Fibonacci sequence,

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Speaker 1: which you have an excellent article on as well. Um.

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Speaker 1: And if for those who are not familiar with it,

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Speaker 1: Fibonacci sequences essentially like a number wherein each number is

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Speaker 1: that some of the previous two? Yeah, and they in

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Speaker 1: this number sequence is not It's not like the secret

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Speaker 1: code of everything, like it doesn't it doesn't correspond with everything,

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Speaker 1: but it corresponds with the alarming number of things from

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Speaker 1: like propagation numbers in various uh uh species. You know,

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Speaker 1: the number of rabbits for instance of the classic example,

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Speaker 1: like if you can predict how many rabbits will be born, right,

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Speaker 1: and how the population will grow based on that growth

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Speaker 1: points and trees, pedal counts, sunflower seed arrangements. Uh. They're

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Speaker 1: just expressed in multiple ways in nature. And this is

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Speaker 1: called the golden ratio to write this number. Um. And

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Speaker 1: what I thought is when, of course, when us vain humans,

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Speaker 1: when we apply to ourselves, we can see it, right, Um.

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Speaker 1: We can see this um in the number of body

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Speaker 1: parts that we have, the way that our body parts

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Speaker 1: are arranged in spaced they all follow this golden ratio.

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Speaker 1: So there's there's that aspect of math. You know that

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Speaker 1: it it helps us understand the world. It helps us,

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Speaker 1: um predict things in nature that we haven't actually observed

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Speaker 1: yet or proven. Um. You know, certainly when it comes

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Speaker 1: to things like dark matter. Well in dark matter is

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Speaker 1: this problematic thing, right, um? And what I think is

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Speaker 1: interesting about math it's contribution to physics is that we

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Speaker 1: again we arrive at this understanding because we have this

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Speaker 1: universal language and many different uh, epochs of time, people,

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Speaker 1: cultures have all contributed to this. So we have this understanding.

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Speaker 1: But then you get to something like dark matter, and

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Speaker 1: it is purely a result of math. And if I'm

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Speaker 1: understanding it correctly, our computational models of the universe weren't

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Speaker 1: really washing and it was cosmologists who finally figured, you know,

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Speaker 1: via math, that in order are for the mathematical models

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Speaker 1: to make sense, there had to be some sort of

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Speaker 1: matter not seen, known or measured really to us that

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Speaker 1: was occupying the space of the universe. And this is

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Speaker 1: dark matter. It's like an accountant looking at the books

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Speaker 1: for a business and saying, hey, we've got some money

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Speaker 1: missing here. Somebody's embezzling, you know, But in this case

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Speaker 1: the embezzler it's the universe itself, which apparently has the

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Speaker 1: right to embezzle. And then it's just figuring out, well,

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Speaker 1: what does that what where does this money go? What

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Speaker 1: is it paying for? Well? And I love this idea

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Speaker 1: of dark matter um as an example of what what

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Speaker 1: are the limits of our knowledge? You know? What's noble

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Speaker 1: because it's still very much a mystery, but now it's

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Speaker 1: a known mystery, right, It's a known quantity of mystery

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Speaker 1: and um. It furthers us to the edge of understanding,

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Speaker 1: just as the theory of relativity did and every other

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Speaker 1: mental constract that helped us to define something like say,

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Speaker 1: quantum mechanics that now we are beginning to use in

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Speaker 1: a very concrete way, right Like you've got the Hadron

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Speaker 1: large clider, and we're hoping to answer some really fundamental

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Speaker 1: questions about physics through that. Here's the other thing about math.

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Speaker 1: Look back through the history books and show me one

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Speaker 1: war that was waged over disagreements about mathematics. You know.

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Speaker 1: It's it's like, mathematics is the is like the one

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Speaker 1: thing that we have where everybody's like like, yeah, yeah,

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Speaker 1: we can agree. I mean, you can get into disagreements

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Speaker 1: about certain things with with mathematical theory or mathematical philosophy,

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Speaker 1: and you know, you'll have scholarly debates and I'm sure

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Speaker 1: in some cases some bitter rivalries among math mathematicians. But

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Speaker 1: for the most part, this is the thing that we

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Speaker 1: we all understand and we can agree on. And while

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Speaker 1: we may use it to uh, you know, to to

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Speaker 1: prove or or or dictate science, which can at times,

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Speaker 1: as we we know, can can become a little problematic

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Speaker 1: and uh, and there'll be disagreements about things that are scientific,

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Speaker 1: but the mathematics you cannot. You can all you can

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Speaker 1: argue with mathematics, but the reason behind it, the mathematics

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Speaker 1: itself is pure. It's an elegant system, right, and it's

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Speaker 1: not saddled with and I don't know, as far as

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Speaker 1: I can tell, it is not saddled with um a

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Speaker 1: lot of the problems that we have culturally, right and

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Speaker 1: in communicating, because it is universal. So in every single culture,

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Speaker 1: this number system is going to represent the same thing

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Speaker 1: um and maybe just a little bit differently, but you know,

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Speaker 1: certainly and where we are in history right now, it's

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Speaker 1: widely used. And so to your point, you know, it's

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Speaker 1: how can you sit there and argue about the following

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Speaker 1: equation when it is bearing out at least in theory.

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Speaker 1: So we come to the inevitable, inevitable question about mathematics.

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Speaker 1: We've talked about this, this thing that composes the tower

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Speaker 1: by which we achieved everything we've achieved. You know, our

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Speaker 1: our culture, also our science, everything rests on it. Our

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Speaker 1: ability to command as much of the physical world as

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Speaker 1: we seem to be able to command. It comes down

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Speaker 1: to mathematics. So is this something that we created. Did

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Speaker 1: we create something that that that that that corresponds to

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Speaker 1: the natural world so well that it allows us to

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Speaker 1: control it. Or is it something that we discovered. Did

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Speaker 1: we discover, you know, in Galileo's words, the language of God,

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Speaker 1: the language of the universe. Is this? Is it a

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Speaker 1: human creation or a human discovery? Now? Both both possibilities

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Speaker 1: are equally awesome and and humans wind up looking pretty

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Speaker 1: good both equations because because either we're just you know,

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Speaker 1: either we are just so awesome that we created something

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Speaker 1: that that the universe corresponds to and and unlocks the

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Speaker 1: hidden mysteries of the universe, or we discovered like we

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Speaker 1: it's like under uncovering the bones of God in your

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Speaker 1: backyard and saying, look what I found. It enables me

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Speaker 1: to understand And it was always there, whether or not

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Speaker 1: you noticed, right, whether or not. That's the other way

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Speaker 1: of looking at it. Does math exist independently of humans? Like,

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Speaker 1: is a planet out there that we've never we we

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Speaker 1: don't even know about, we haven't heard, we haven't discovered,

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Speaker 1: we haven't been there. Does math exist there? Okay, So

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Speaker 1: that's where I see parallels with like the Copernican principle, right,

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Speaker 1: which basically says that humans are not privileged observers of

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Speaker 1: the universe, Like the universe is going to sit out

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Speaker 1: there and exist regardless of whether or not our gaze

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Speaker 1: is directed at the universe, which I think is pretty interesting.

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Speaker 1: And I think that you know, math is inherently on

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Speaker 1: the one on that one side existing and it's for

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Speaker 1: us to discover. On the other hand, the human brain,

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Speaker 1: it's obviously has obviously developed to a point where it

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Speaker 1: is hardwired to make these observations, right, Like we know

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Speaker 1: that the neo cortex is a new thing for at

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Speaker 1: least the mammalion brain. It was lumped on there on

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Speaker 1: on top of the reptilian brain, and it deals with

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Speaker 1: these higher cognitive functions um like spatial reasoning, like logarithmics.

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Speaker 1: So it's kind of a chicken egg proposition to me, Yeah,

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Speaker 1: and uh and and as will continue to discuss here,

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Speaker 1: you can you can sort of go with both sides. Now,

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Speaker 1: my power analogy definitely sort of lends itself more to

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Speaker 1: the idea that we build something and it's a human creation.

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Speaker 1: But but on the other hand, is was pointed out

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Speaker 1: to me by actually by a DJ by the name

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Speaker 1: of the d J Eric, who actually is a has

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Speaker 1: a PhD in mathematics. I've interviewed him recently on the blocks.

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Speaker 1: You can look that up. But he pointed out that

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Speaker 1: U two hydrogen atoms floating beside two other hydrogen atoms,

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Speaker 1: can still be called four hydrogen atoms, regardless of you know,

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Speaker 1: I fear on Earth in another galaxy that there there

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Speaker 1: is a there is a there's a number system at

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Speaker 1: work in the universe, just an inherent intrinsic number system. Yeah.

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Speaker 1: To to actually throw in, uh, you know, the words

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Speaker 1: of to invoke the words of Plato, who and this

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Speaker 1: is actual Plato, not a DJ named Plato, um argue.

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Speaker 1: He argued that method is this is a discovery system,

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Speaker 1: discoverable system that underlines the structure of the universe, all right,

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Speaker 1: So in other words, the universe has made a math,

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Speaker 1: and the more we understand this vast interplay of numbers,

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Speaker 1: the more we can understand nature itself. So math exists

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Speaker 1: to the observer. But then you know, then the of course,

428
00:24:12,119 --> 00:24:13,680
Speaker 1: the other side again is that math is a man

429
00:24:13,720 --> 00:24:18,600
Speaker 1: made tool, um, and that and that it's an abstraction

430
00:24:18,720 --> 00:24:21,080
Speaker 1: it's free of time and space and merely corresponds with

431
00:24:21,119 --> 00:24:24,200
Speaker 1: the universe. Uh, and that and and not. It doesn't

432
00:24:24,200 --> 00:24:28,560
Speaker 1: always correspond, you know, completely, like a consider elliptical planetary orbits. Uh.

433
00:24:28,840 --> 00:24:32,520
Speaker 1: An elliptical trajectory provides astronoers with a close approximation of

434
00:24:32,560 --> 00:24:35,240
Speaker 1: a planet's movement, but it's not a perfect one. Okay.

435
00:24:35,280 --> 00:24:37,520
Speaker 1: See what I love about that is again you get

436
00:24:37,560 --> 00:24:39,520
Speaker 1: into this sort of gray area that yes, you've got.

437
00:24:39,560 --> 00:24:44,040
Speaker 1: Math is an elegant uh thing unto itself. It's very straightforward,

438
00:24:44,040 --> 00:24:48,600
Speaker 1: it's universal, and yet there it doesn't provide all the answers.

439
00:24:48,600 --> 00:24:52,280
Speaker 1: The mystery still remains the logistic theory. There are a

440
00:24:52,359 --> 00:24:54,440
Speaker 1: number of theories about this, which I'm not going to

441
00:24:54,520 --> 00:24:57,639
Speaker 1: mention all of them. But the logistic theory holds that

442
00:24:57,720 --> 00:24:59,920
Speaker 1: math is an extension of human reason and a lot

443
00:25:00,840 --> 00:25:04,200
Speaker 1: so again, it's the the idea, that's the system. It's

444
00:25:04,200 --> 00:25:07,320
Speaker 1: an extension of our our problem solving abilities, and it's

445
00:25:07,359 --> 00:25:10,399
Speaker 1: just the extension of that that allows us to candle

446
00:25:10,480 --> 00:25:13,480
Speaker 1: even larger problems. It's just an extrapolation of our own

447
00:25:13,720 --> 00:25:17,720
Speaker 1: cogitating minds. Okay, yeah, and then then there's the instant

448
00:25:18,119 --> 00:25:21,679
Speaker 1: the intuitional theory, which defines math is a system of

449
00:25:21,720 --> 00:25:26,440
Speaker 1: purely mental constructs that are internally consistent. So the reason

450
00:25:26,480 --> 00:25:28,800
Speaker 1: math works so well is because it's internally consistent. That

451
00:25:28,880 --> 00:25:31,920
Speaker 1: the system itself works well. And then it but it

452
00:25:31,960 --> 00:25:36,160
Speaker 1: happens to correspond to nature, So you're intuiting pattern recognition. Right.

453
00:25:36,520 --> 00:25:41,480
Speaker 1: The the extrapolation of of the intuitional theory and one

454
00:25:41,520 --> 00:25:45,000
Speaker 1: that is less accepted as one called fictionalist theory, which

455
00:25:45,000 --> 00:25:48,400
Speaker 1: says that math is essentially a fairy tale. Uh success

456
00:25:48,760 --> 00:25:51,960
Speaker 1: that are just scientifically useful fictions, which is uh. And

457
00:25:52,000 --> 00:25:54,160
Speaker 1: again this is an extreme version, but it helps eliminate

458
00:25:54,240 --> 00:25:56,919
Speaker 1: this whole idea of math is a human creation. It's

459
00:25:57,160 --> 00:26:00,480
Speaker 1: it's kind of the idea that you have outgo biblical

460
00:26:00,520 --> 00:26:03,040
Speaker 1: for a second. So you have Jesus setting around on

461
00:26:03,080 --> 00:26:05,360
Speaker 1: a log, right, I don't know why it's on a log,

462
00:26:05,480 --> 00:26:08,520
Speaker 1: but he he's telling parables. Right. There are no like

463
00:26:08,560 --> 00:26:11,840
Speaker 1: fancy seats, right, and the parables that he's telling in

464
00:26:11,880 --> 00:26:15,359
Speaker 1: this situation, they're not true stories. Uh, there're you know,

465
00:26:15,480 --> 00:26:18,800
Speaker 1: some some story about someone's kind of like subs fables, right.

466
00:26:18,920 --> 00:26:23,320
Speaker 1: The subs fables are not real stories. They didn't actually happen,

467
00:26:24,160 --> 00:26:28,399
Speaker 1: but there's a truth to them that that resonates throughout

468
00:26:28,480 --> 00:26:32,000
Speaker 1: human culture, you know. So it's kind of that idea

469
00:26:32,040 --> 00:26:35,720
Speaker 1: of math. It's like math is an internally consistent story

470
00:26:36,640 --> 00:26:41,840
Speaker 1: that is not true, but it's real. Okay. And and

471
00:26:41,920 --> 00:26:45,600
Speaker 1: did we talk about formalist theory yet, because I want

472
00:26:45,640 --> 00:26:47,240
Speaker 1: to talk about that one and then and then I

473
00:26:47,240 --> 00:26:49,520
Speaker 1: want to sort of see if we can locate if

474
00:26:49,560 --> 00:26:52,280
Speaker 1: it's possible dark matter in one of these Okay, I

475
00:26:52,320 --> 00:26:54,120
Speaker 1: don't know, just as like a little quiz for us,

476
00:26:54,840 --> 00:26:58,840
Speaker 1: which um, just a fun game. But the formalist theory,

477
00:26:58,840 --> 00:27:01,720
Speaker 1: and this is from your article, argues that mathematics boils

478
00:27:01,760 --> 00:27:05,120
Speaker 1: down to the manipulation of man made symbols. In other words,

479
00:27:05,200 --> 00:27:07,760
Speaker 1: these theories propose that math as a kind of analogy,

480
00:27:08,240 --> 00:27:11,480
Speaker 1: um that draws a line between concepts and real events.

481
00:27:12,119 --> 00:27:14,240
Speaker 1: And I thought it was interesting because I began to think,

482
00:27:14,480 --> 00:27:16,639
Speaker 1: what is the line between art and math? Then, because

483
00:27:16,640 --> 00:27:21,919
Speaker 1: you're communicating through a system of symbols some sort of experience, right,

484
00:27:22,640 --> 00:27:24,639
Speaker 1: So I find I find that really fascinating for for

485
00:27:24,680 --> 00:27:27,680
Speaker 1: that aspect of it. But I began to think about

486
00:27:27,680 --> 00:27:30,600
Speaker 1: the fictionalist theory, and I'm begin to think about dark matter,

487
00:27:30,960 --> 00:27:33,920
Speaker 1: and and I don't want to call it a fairy tale.

488
00:27:33,920 --> 00:27:35,960
Speaker 1: I don't want anybody to to misconstrue that. But I

489
00:27:36,000 --> 00:27:39,440
Speaker 1: did think that if even though we've got the mathematical

490
00:27:39,480 --> 00:27:42,280
Speaker 1: model that sets one plus one equals too it is,

491
00:27:42,440 --> 00:27:46,040
Speaker 1: it may not necessarily be a true statement, right, because

492
00:27:46,040 --> 00:27:50,080
Speaker 1: it's still an unknowable quantity. It's still a mystery to

493
00:27:50,400 --> 00:27:54,639
Speaker 1: a certain degree. I don't know. Yeah, I think that's valid.

494
00:27:55,560 --> 00:27:59,159
Speaker 1: The when when you take this even farther though, you

495
00:27:59,200 --> 00:28:03,399
Speaker 1: get into question of okay, regardless of whether math is

496
00:28:03,640 --> 00:28:06,399
Speaker 1: something we created or discovered, like how far does it go?

497
00:28:06,520 --> 00:28:09,800
Speaker 1: What are the limits of mathematics? Um, there's a cosmologist,

498
00:28:10,200 --> 00:28:12,960
Speaker 1: contemporary dude by the name of Max teg Mark. Yes,

499
00:28:13,040 --> 00:28:16,040
Speaker 1: he has a website and everything, so you know he's yes,

500
00:28:16,119 --> 00:28:18,320
Speaker 1: string theory guy. So this is the idea of of

501
00:28:18,400 --> 00:28:21,240
Speaker 1: math is the ultimate, like like, yeah, math is the

502
00:28:21,320 --> 00:28:24,800
Speaker 1: universe and math is the understanding of the universe, and

503
00:28:24,800 --> 00:28:26,800
Speaker 1: and hey, we can probably figure it out in time. Well,

504
00:28:26,840 --> 00:28:31,040
Speaker 1: I think that's fascinating because, uh, in the neuroscience field,

505
00:28:31,080 --> 00:28:32,880
Speaker 1: they're trying to figure out a theory of the brain,

506
00:28:32,920 --> 00:28:36,160
Speaker 1: which is very similar to the theory of everything right,

507
00:28:36,240 --> 00:28:39,200
Speaker 1: it's very difficult to figure out how the brain works,

508
00:28:39,480 --> 00:28:43,040
Speaker 1: the one cohesive theory of the brain. But we know

509
00:28:43,120 --> 00:28:45,600
Speaker 1: because we've we've researched this before that there's the Blue

510
00:28:45,600 --> 00:28:48,640
Speaker 1: Brain project, in which they're trying to re engineer the

511
00:28:48,720 --> 00:28:53,920
Speaker 1: human brain, essentially map it's one trillion synapses and to

512
00:28:54,080 --> 00:28:59,560
Speaker 1: get some sort of understanding of how it works, much

513
00:28:59,640 --> 00:29:02,760
Speaker 1: like the universe. Because if what they're saying to what

514
00:29:02,840 --> 00:29:05,280
Speaker 1: they're proposing is that the universe is the brain, it

515
00:29:05,400 --> 00:29:10,040
Speaker 1: is a construct of the brain. So let's just imagine

516
00:29:10,040 --> 00:29:12,000
Speaker 1: that these these you know, side by side, they are

517
00:29:12,000 --> 00:29:15,000
Speaker 1: going down the same rails, and then within ten years

518
00:29:15,000 --> 00:29:17,480
Speaker 1: we'd be able to answer this question. I mean, what

519
00:29:17,680 --> 00:29:22,680
Speaker 1: would we just vaporize with, you know, because we've we've

520
00:29:22,720 --> 00:29:25,640
Speaker 1: reached some sort of final end of the meaning you know,

521
00:29:25,920 --> 00:29:28,720
Speaker 1: it's like the semantic apocalypse. Like we've discussed before, the

522
00:29:28,760 --> 00:29:31,960
Speaker 1: idea that if you explain the way the magic trick,

523
00:29:32,160 --> 00:29:34,240
Speaker 1: then it's no longer a magic trick, and that and

524
00:29:34,360 --> 00:29:38,280
Speaker 1: we are the magic tricks. So um. Then but then

525
00:29:38,280 --> 00:29:42,960
Speaker 1: there's also something we call Godal's first incompleteness theorem, and

526
00:29:43,000 --> 00:29:47,000
Speaker 1: this is the work of Austrian mathematician Kurt Godel, and

527
00:29:47,720 --> 00:29:52,160
Speaker 1: he basically said in this theorem that any theory that's

528
00:29:52,200 --> 00:29:57,640
Speaker 1: based on self evident but unprovable proofs is incomplete or inconsistent.

529
00:29:57,760 --> 00:30:01,200
Speaker 1: So the the implication here is that um and and

530
00:30:01,240 --> 00:30:03,200
Speaker 1: this is something that this is something that also this

531
00:30:03,280 --> 00:30:06,000
Speaker 1: is what keeps us from vaporizing. Yeah. Yeah, So so

532
00:30:06,040 --> 00:30:08,880
Speaker 1: basically the idea here is that mathematics is inexhaustible. All right,

533
00:30:09,080 --> 00:30:11,520
Speaker 1: no matter how many problems we solve, we're inevitably going

534
00:30:11,560 --> 00:30:17,120
Speaker 1: to encounter more unsolvable problems within the existing rules. Um

535
00:30:17,200 --> 00:30:19,920
Speaker 1: So this would seem to discount the idea of of

536
00:30:19,960 --> 00:30:23,760
Speaker 1: a of a theory of everything, because math is is

537
00:30:23,760 --> 00:30:27,880
Speaker 1: this system that whether we created it or or discovered it,

538
00:30:27,880 --> 00:30:31,160
Speaker 1: it goes on forever. It's like the how many to

539
00:30:31,200 --> 00:30:34,239
Speaker 1: what decimal point can we carry out pie? Uh? You know,

540
00:30:34,280 --> 00:30:35,800
Speaker 1: you can carry it out to the billions, you can

541
00:30:35,800 --> 00:30:38,160
Speaker 1: carry it out to the trillions, But can you carry

542
00:30:38,160 --> 00:30:40,080
Speaker 1: it out to the end. No, because it's infinite, because

543
00:30:40,120 --> 00:30:43,120
Speaker 1: there is no end. Well, but that's what's so interesting too.

544
00:30:43,320 --> 00:30:45,880
Speaker 1: You know, if if the Blue Brain Project does have

545
00:30:45,920 --> 00:30:48,440
Speaker 1: some sort of breakthrough about our understanding of the hum

546
00:30:48,520 --> 00:30:52,160
Speaker 1: mbringing and if string theory begins to prove itself out

547
00:30:52,840 --> 00:30:56,280
Speaker 1: in a more concrete way, then does it just spiral

548
00:30:56,720 --> 00:31:00,320
Speaker 1: other questions that we need to answer into the you know,

549
00:31:00,760 --> 00:31:05,000
Speaker 1: into the effor um or you know, which is probably

550
00:31:05,000 --> 00:31:06,920
Speaker 1: the case, right. I don't think it just closes down

551
00:31:07,080 --> 00:31:10,640
Speaker 1: our understanding and we finally say we are complete, we

552
00:31:10,680 --> 00:31:13,040
Speaker 1: know it all. Yeah, it's like we get new you know.

553
00:31:13,040 --> 00:31:15,080
Speaker 1: It's it's like it's like life. You you solve one problem,

554
00:31:15,160 --> 00:31:18,200
Speaker 1: there's going to be another one. You. You know, if

555
00:31:18,240 --> 00:31:20,760
Speaker 1: you're you see that one item in the store, that

556
00:31:20,880 --> 00:31:23,200
Speaker 1: one game, that one book you you know you really need,

557
00:31:23,240 --> 00:31:24,520
Speaker 1: and you finally get it, and it's just gonna be

558
00:31:24,560 --> 00:31:27,239
Speaker 1: another one you you end up setting your heart on.

559
00:31:27,320 --> 00:31:30,160
Speaker 1: So yeah, and at the end today, it's not going

560
00:31:30,200 --> 00:31:34,000
Speaker 1: to actually make me become a scrabble champ. I don't think.

561
00:31:34,320 --> 00:31:36,640
Speaker 1: I don't know. Maybe maybe we could take some theories

562
00:31:36,640 --> 00:31:41,160
Speaker 1: of the brain and in some string theory and we

563
00:31:41,200 --> 00:31:45,000
Speaker 1: should do something of scrabble. Sometimes it's pretty yeah, word

564
00:31:45,040 --> 00:31:48,240
Speaker 1: freaking well, Hey, there you go. So that's math. Um.

565
00:31:48,280 --> 00:31:51,920
Speaker 1: You know, from a very broad level, it's like like

566
00:31:52,000 --> 00:31:54,800
Speaker 1: math is a city and we're flying over it at

567
00:31:54,800 --> 00:31:57,360
Speaker 1: a fairly high altitude and trying to make out as

568
00:31:57,440 --> 00:31:59,680
Speaker 1: much as we can of it through the clouds. That's right,

569
00:31:59,720 --> 00:32:03,800
Speaker 1: we're we're just tourists of math cool. I have a

570
00:32:03,800 --> 00:32:07,920
Speaker 1: couple of listener mail here for us and nonmath related,

571
00:32:07,960 --> 00:32:10,680
Speaker 1: because it would be impossible for someone to respond to

572
00:32:10,760 --> 00:32:13,440
Speaker 1: the podcast that you just record. I don't know, multi

573
00:32:13,560 --> 00:32:17,239
Speaker 1: versus maybe stream theory um. This first one comes from

574
00:32:17,240 --> 00:32:19,880
Speaker 1: a listener by the name of Peyton, and Peyton says, hey,

575
00:32:20,000 --> 00:32:22,760
Speaker 1: Robert and Julie, this is Peyton from Hendersville, North Carolina.

576
00:32:23,240 --> 00:32:25,280
Speaker 1: I lived just up the road apiece from you guys

577
00:32:25,320 --> 00:32:28,080
Speaker 1: in Atlanta. Actually it's more it's about three hours away.

578
00:32:28,120 --> 00:32:30,280
Speaker 1: I just wanted to say that I enjoyed the Nuclear

579
00:32:30,320 --> 00:32:33,440
Speaker 1: Fallout podcast and it actually reminded me of a very

580
00:32:33,440 --> 00:32:35,400
Speaker 1: funny episode of The Office from a few years ago.

581
00:32:35,640 --> 00:32:38,240
Speaker 1: It was the one where Pam's old boyfriend Roy comes

582
00:32:38,240 --> 00:32:41,280
Speaker 1: into the office and attacks Jim. Fortunately, Dwight quickly Pepper

583
00:32:41,280 --> 00:32:44,480
Speaker 1: sprays him, and everyone in the office has immediately bent over,

584
00:32:44,560 --> 00:32:47,480
Speaker 1: coughing and rubbing their eyes. A similar thing would happen

585
00:32:47,520 --> 00:32:50,240
Speaker 1: in the case of a nuclear strike. It's true that

586
00:32:50,320 --> 00:32:54,040
Speaker 1: only one concentrated area would receive the full destruction of

587
00:32:54,080 --> 00:32:56,240
Speaker 1: the bomb, but its effects would be felt all over

588
00:32:56,240 --> 00:32:58,840
Speaker 1: the world. Just that i'd share the analogy with you guys,

589
00:32:58,920 --> 00:33:02,280
Speaker 1: keep up the great podcast. So, yeah, this is the

590
00:33:02,400 --> 00:33:06,200
Speaker 1: example he's he's bringing up here is a small application

591
00:33:06,400 --> 00:33:10,800
Speaker 1: of um of fluid dynamics. The way um these particles

592
00:33:10,840 --> 00:33:16,000
Speaker 1: of pepper spray, uh, particles of pepper or whatever would

593
00:33:16,520 --> 00:33:21,080
Speaker 1: would distribute through a closed environment in moving air and

594
00:33:21,160 --> 00:33:24,520
Speaker 1: moving fluid and then nuclear fallout. As we discussed in

595
00:33:24,760 --> 00:33:27,840
Speaker 1: the previous podcast, A lot of that depends on you know,

596
00:33:27,880 --> 00:33:30,440
Speaker 1: how is how is air, how is this fluid moving on,

597
00:33:30,800 --> 00:33:33,320
Speaker 1: you know, around the globe, in a local area, in

598
00:33:33,320 --> 00:33:35,880
Speaker 1: an urban environment, et cetera. I just like the fact

599
00:33:35,960 --> 00:33:38,240
Speaker 1: that in this analogy, Dwight, it's kind of like the

600
00:33:38,520 --> 00:33:44,160
Speaker 1: enriched uranium. I think that's appropriate. Yeah, we received another

601
00:33:44,200 --> 00:33:46,680
Speaker 1: one here. This is from Eric, and Eric writes in

602
00:33:46,720 --> 00:33:49,520
Speaker 1: about the dog podcast that's my dogs really loved me

603
00:33:49,560 --> 00:33:53,120
Speaker 1: that we did and uh, actually he's responding to an

604
00:33:53,120 --> 00:33:56,000
Speaker 1: email with most of respond he's actually responding to a response.

605
00:33:56,560 --> 00:33:59,360
Speaker 1: He says, he says, Hey, you had an email from

606
00:33:59,360 --> 00:34:01,800
Speaker 1: a dog owner who felt that maybe his dog had

607
00:34:01,800 --> 00:34:05,160
Speaker 1: Stockholm syndrome, having adopted more than one rescue dog. I've

608
00:34:05,160 --> 00:34:07,960
Speaker 1: noticed many dogs who have been abused are very skittish

609
00:34:07,960 --> 00:34:10,520
Speaker 1: at first, but when they realized they will no longer

610
00:34:10,600 --> 00:34:12,959
Speaker 1: be a hit, they showed quite a lot more love.

611
00:34:13,200 --> 00:34:16,000
Speaker 1: My current dog than Austie, named Ghost, was very skittish

612
00:34:16,000 --> 00:34:17,879
Speaker 1: at first. If you reached down to give him a rub,

613
00:34:17,920 --> 00:34:21,360
Speaker 1: he'd always he'd always flinch. Uh. He was also always

614
00:34:21,440 --> 00:34:23,880
Speaker 1: very skittish around new people. We started having all the

615
00:34:24,200 --> 00:34:27,239
Speaker 1: HouseGuests give him a treat when they arrived. Last week,

616
00:34:27,520 --> 00:34:30,320
Speaker 1: while walking him off leash, we came upon another couple

617
00:34:30,400 --> 00:34:33,600
Speaker 1: walking their dogs. Ghost walk right up to them for

618
00:34:33,640 --> 00:34:36,200
Speaker 1: a rub. Personally, I think this this person's dog was

619
00:34:36,200 --> 00:34:38,840
Speaker 1: simply afraid of him at first, but soon realized his

620
00:34:38,920 --> 00:34:42,040
Speaker 1: new owner was okay. Uh. It's something whether there's an

621
00:34:42,040 --> 00:34:45,920
Speaker 1: account of dogs. I don't know if it's love, but

622
00:34:46,160 --> 00:34:48,680
Speaker 1: you know it certainly it shows that dogs are able

623
00:34:48,680 --> 00:34:51,359
Speaker 1: to get over trauma a lot easier to humans. Yeah.

624
00:34:51,480 --> 00:34:53,399
Speaker 1: But do you think Ghost has something to do with

625
00:34:53,800 --> 00:34:56,680
Speaker 1: with maybe being a most skittish I mean the names Ghost.

626
00:34:57,000 --> 00:34:59,279
Speaker 1: He occured to send us a picture but did not.

627
00:34:59,680 --> 00:35:03,720
Speaker 1: I'm not, you know, questioning the existence of the dog. Yeah,

628
00:35:03,880 --> 00:35:06,080
Speaker 1: or even that the name choice. I'm just wondering, don't

629
00:35:06,120 --> 00:35:09,320
Speaker 1: ever know what dogs really understand. I think i'd be

630
00:35:09,320 --> 00:35:12,040
Speaker 1: a little skinnish if I was name ghost I did.

631
00:35:12,120 --> 00:35:13,560
Speaker 1: I think that's a lot to put it on the

632
00:35:13,680 --> 00:35:17,800
Speaker 1: naming of the dog, I know, but still I have heard, um,

633
00:35:17,840 --> 00:35:19,719
Speaker 1: I have heard the argument that the name you give

634
00:35:19,719 --> 00:35:22,200
Speaker 1: the dog does have a huge play a role in

635
00:35:22,239 --> 00:35:26,359
Speaker 1: how that dog is, Like it's just about like how like, um,

636
00:35:26,760 --> 00:35:29,080
Speaker 1: I forget which dog expert this was, but they pointed

637
00:35:29,080 --> 00:35:31,359
Speaker 1: out that if you have a big, scary pit bull

638
00:35:31,400 --> 00:35:35,640
Speaker 1: and you name it Kujo, then you're already, as you know,

639
00:35:35,840 --> 00:35:38,920
Speaker 1: ascribing a certain energy to that animal, you know, and

640
00:35:39,120 --> 00:35:43,399
Speaker 1: uh and and you're like, I'm just a subconscious level,

641
00:35:43,480 --> 00:35:46,000
Speaker 1: you're already making the dog the scary thing that you're

642
00:35:46,000 --> 00:35:48,520
Speaker 1: going to be submissive to. And is is you know,

643
00:35:48,560 --> 00:35:51,520
Speaker 1: maybe not your friend, that's the so psychologically on the

644
00:35:51,520 --> 00:35:54,799
Speaker 1: part of that the person who's perceiving the dog, right,

645
00:35:55,160 --> 00:35:57,360
Speaker 1: maybe it's kind of I don't know if it actually

646
00:35:57,360 --> 00:35:59,839
Speaker 1: crosses up like human names as well, because you've heard

647
00:35:59,880 --> 00:36:01,880
Speaker 1: like if you if you name a child like Eggberg

648
00:36:01,960 --> 00:36:05,040
Speaker 1: or something, it's not really a name like Cuban Hubert

649
00:36:05,160 --> 00:36:07,440
Speaker 1: or Hubert. That's kind of you're kind of setting them

650
00:36:07,480 --> 00:36:10,280
Speaker 1: up to be, you know, a bookish nerd, I guess.

651
00:36:11,160 --> 00:36:13,160
Speaker 1: And if you're kind of call them brutus or something,

652
00:36:13,200 --> 00:36:15,440
Speaker 1: then there they've kind of been. They're kind of destined

653
00:36:15,440 --> 00:36:19,520
Speaker 1: to be on the football team, right, unless they're taking

654
00:36:19,560 --> 00:36:23,000
Speaker 1: some sort of like classical like antiquities interpretation from that,

655
00:36:23,120 --> 00:36:25,080
Speaker 1: you know, I don't know. Yeah, it's certainly not the

656
00:36:25,120 --> 00:36:27,040
Speaker 1: only factor. But you know, you wonder to what extent

657
00:36:27,040 --> 00:36:30,319
Speaker 1: you're you're you're you're forecasting their their future. You're gonna

658
00:36:30,320 --> 00:36:32,000
Speaker 1: have to check in with Apple in a couple of

659
00:36:32,040 --> 00:36:37,640
Speaker 1: years see how that's working for her. Gwyneth paultrows kid, right,

660
00:36:37,719 --> 00:36:42,160
Speaker 1: that's right, Yeah, that's all I got. All Right, Well, hey,

661
00:36:42,200 --> 00:36:43,759
Speaker 1: if you guys have anything to share with us, you

662
00:36:43,800 --> 00:36:47,160
Speaker 1: want to check out what we're into. You can find

663
00:36:47,239 --> 00:36:50,120
Speaker 1: us online. We are blow the Mind on both Twitter

664
00:36:50,200 --> 00:36:54,279
Speaker 1: and Facebook, and do check out how stuff Works dot com.

665
00:36:54,560 --> 00:36:57,120
Speaker 1: You can find that math article we talked about, um,

666
00:36:57,120 --> 00:37:00,480
Speaker 1: how math works. You can find the Fibonacci of Nassis

667
00:37:00,600 --> 00:37:04,360
Speaker 1: numbers article, and uh, there's also some really cool stuff

668
00:37:04,360 --> 00:37:08,560
Speaker 1: about fractals. Right, Yeah, we have an incredible Fractal Image Gallery, um,

669
00:37:08,560 --> 00:37:10,359
Speaker 1: which is I mean, if you would like to see

670
00:37:10,520 --> 00:37:15,200
Speaker 1: math is interpreted in in these um incredible figures, then

671
00:37:15,200 --> 00:37:17,920
Speaker 1: you should check that out. It's on our homepage and

672
00:37:18,000 --> 00:37:20,120
Speaker 1: it's definitely worth a look. Um. It's not something that

673
00:37:20,120 --> 00:37:22,080
Speaker 1: we were able to get to today, but Mandel brought

674
00:37:22,200 --> 00:37:25,600
Speaker 1: set one of the fractals is just amazing. Yeah, and

675
00:37:25,640 --> 00:37:27,920
Speaker 1: we and if you don't know what fractals are, guess what.

676
00:37:27,960 --> 00:37:30,520
Speaker 1: They have an article about how fractals work as well,

677
00:37:30,520 --> 00:37:33,160
Speaker 1: and it's excellent. And yeah, if you want to drop

678
00:37:33,239 --> 00:37:35,120
Speaker 1: us line, please do so at blow the Mind that

679
00:37:35,200 --> 00:37:41,200
Speaker 1: has to Works dot com. For more on this and

680
00:37:41,239 --> 00:37:44,000
Speaker 1: thousands of other topics, visit how stuff Works dot com.

681
00:37:44,160 --> 00:37:46,760
Speaker 1: To learn more about the podcast, click on the podcast

682
00:37:47,000 --> 00:37:49,960
Speaker 1: icon in the upper right corner of our homepage. The

683
00:37:50,000 --> 00:37:52,640
Speaker 1: How Stuff Works iPhone app has a ride. Download it

684
00:37:52,680 --> 00:37:53,960
Speaker 1: today on iTunes