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Speaker 1: Brought to you by the reinvented two thousand twelve Camray.

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Speaker 1: It's ready. Are you get in touch with technology with

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Speaker 1: tech Stuff from how stuff works dot com. Hello again, everyone,

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Speaker 1: and welcome to tech stuff. My name is Chris Poette,

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Speaker 1: and I am an editor here at how stuff works

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Speaker 1: dot com. Sitting across from me, as he always does

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Speaker 1: on these occasions, is senior writer Jonathan Strickland. Hey there,

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Speaker 1: all right, then, so we have some listener mail to

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Speaker 1: start us off. Okay, this listener mail comes from Harry

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Speaker 1: from the University of North Carolina, Chapel Hill. He says, Hey,

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Speaker 1: as a table top gamer, I have been known to

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Speaker 1: generate a few random numbers via my trustee D twenty. However,

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Speaker 1: I know computers are able to do this without tossing

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Speaker 1: around an ico hay, drawn, Just how do digital randomer

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Speaker 1: generator's work and are they truly random? So? D twenty,

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Speaker 1: for those of you who are not entrenched in the

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Speaker 1: geek world as I am, that is a twenty sided die. Yeah. That,

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Speaker 1: As it turns out, that's a far more effective tool

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Speaker 1: for generating random numbers than a computer can be. Yeah,

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Speaker 1: and uh, here's here's the basics of why that is

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Speaker 1: a computer follows instructions. So you have to be able

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Speaker 1: to give a computer instructions in order for it to

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Speaker 1: do anything. Yes, in order for you to be able

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Speaker 1: to tell it to generate a number, you have to

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Speaker 1: tell it how to generate that number. And it is very,

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Speaker 1: very challenging to create a way to tell a computer

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Speaker 1: to generate a number so that that number is truly random.

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Speaker 1: And when we're talking about truly random, or it may

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Speaker 1: seem that a random number generator that you might use

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Speaker 1: online is random, but it's random to you and not

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Speaker 1: the computer, depending on what system they're using. Well, that's true,

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Speaker 1: but a lot of the ones that I've seen for

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Speaker 1: free are using an algorithm to determine a random number. Right,

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Speaker 1: And there are a couple of different ways that computers

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Speaker 1: tried to generate random numbers. Okay, so in one version,

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Speaker 1: the computer actually just has a table of numbers that

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Speaker 1: have been pregenerated through some complex means, and that the

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Speaker 1: numbers themselves appear to be random because it's a string

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Speaker 1: of numbers that don't repeat in any sort of in

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Speaker 1: any source of sequence, no matter how long the number is.

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Speaker 1: And we're talking about numbers that could have thousands and

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Speaker 1: thousands of digits within them, right, I mean it's it

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Speaker 1: could be as simple as generating numbers at random and

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Speaker 1: making a list, and it's going down and saying, okay, number,

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Speaker 1: this is the number, right, three, six, this is the number.

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Speaker 1: But it also might also is likely to be using

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Speaker 1: an algorithm it says, okay, well, add twenty to this one. Okay,

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Speaker 1: at fourteen to this one, add one to that one. Right,

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Speaker 1: So you if you know where you are in the sequence,

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Speaker 1: you can predict what the next theoretically random number is. Right.

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Speaker 1: So if it's if it's the list version that that

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Speaker 1: we were talking about before, let's let's make it really simple.

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Speaker 1: Let's say you have a list of ten numbers and

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Speaker 1: each of those numbers is ten digits long, but the

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Speaker 1: digits are apparently random. Yes, all right, so there's no

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Speaker 1: like easy sequence to follow with you, and we have

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Speaker 1: just completed, Uh, we've just used random number number eight, Okay,

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Speaker 1: all right. That means that we would have random number

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Speaker 1: number nine and random number number ten, and then it

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Speaker 1: would go back to random number number one. So if

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Speaker 1: you had the table in front of you, and you

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Speaker 1: had you knew how many of those random numbers there were,

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Speaker 1: and you knew where it was. In the previous iteration

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Speaker 1: of generating a random number, you would know the next

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Speaker 1: quote unquote random number. So the number itself may appear

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Speaker 1: to be random, but you would still be able to

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Speaker 1: predict what it was because you had the list. Right.

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Speaker 1: So that's a problem because random numbers are used in

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Speaker 1: things like encryption, so you want you want those numbers

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Speaker 1: to be as true as close to true random as

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Speaker 1: possible so that it is not easy or or preferably

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Speaker 1: possible to break that encryption. I think this is a

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Speaker 1: good time to introduce two concepts. Okay, uh, that would

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Speaker 1: be the the pseudo random number generator, sure, and the

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Speaker 1: true random number generator. Right. So true random number generators

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Speaker 1: are actually kind of easier to to explain. Yeah, I

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Speaker 1: think so too. So a true random number generator is

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Speaker 1: something that can generate a random number excluding any minor

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Speaker 1: quantum effects that you want to imagine. So let's talk

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Speaker 1: about that D twenty. Yeah, that's an excellent example of

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Speaker 1: a true random number generator, right when you roll that

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Speaker 1: D twenty. Theoretically, anytime you roll that D twenty, there

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Speaker 1: should be an equal chance of any number between one

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Speaker 1: and twenty popping up as the the eventual result. That is,

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Speaker 1: assuming that your D twenty is equally sharp on all sides,

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Speaker 1: and I mean the edges are the exact same and

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Speaker 1: the surface. That's what I'm talking about, elimiting the quantum effects, Okay,

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Speaker 1: because when I when I'm talking about quantum effects in

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Speaker 1: this case, I'm talking about things that are so small

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Speaker 1: that we can't really observe them anyway. So it doesn't

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Speaker 1: you know, if you eliminate all that and you say that,

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Speaker 1: with all things being equal, rolling on the same surface

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Speaker 1: with a perfectly uh intact D twenty, you should get

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Speaker 1: a random result every time you roll it, right, right,

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Speaker 1: I have a couple of D twenties that look like

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Speaker 1: a dog chewed on them. Yes, and in those cases,

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Speaker 1: there might be some numbers that might roll up more

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Speaker 1: often than others just because of the shape of that

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Speaker 1: D twenty exactly. But a true random number generator would

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Speaker 1: not give any preference to any particular result. Uh. Pseudo

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Speaker 1: random number generators are different. Yes, they We've already been

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Speaker 1: talking about pseudorandom number generators, which is the number generators

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Speaker 1: that are using the algorithms or a predetermined list of

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Speaker 1: numbers that that are being used to theoretically generate random number. Now,

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Speaker 1: the the the algorithms use something called a seedy, and

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Speaker 1: a seed is a predetermined figure that gets plugged into

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Speaker 1: the algorithm that helps generate the seemingly random numbers. But

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Speaker 1: if you know what the seed is, then theoretically you

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Speaker 1: can eventually determine what the algorithm is and then break

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Speaker 1: the encryption. Now this is this is not as easy

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Speaker 1: as I'm making it sound. It's not like this is

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Speaker 1: a trivial task. No, No, it's actually incredibly difficult. Yeah,

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Speaker 1: as long as uh. I mean, if if you have

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Speaker 1: no clue what the seed is or what the algorithm is,

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Speaker 1: it's it's going to appear to you to be random, right,

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Speaker 1: it's going to seem like an impossible test to break it.

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Speaker 1: But if you were to find one of those two elements,

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Speaker 1: then theoretically, over several results, you would be able to

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Speaker 1: eventually derive the other. You would either be able to

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Speaker 1: derive the seed or you'd be able to derive the algorithm.

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Speaker 1: And again, this is it's not easy, but it is

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Speaker 1: theoretically at any rate possible, which is why you want

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Speaker 1: to get as close to a true random number generator

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Speaker 1: as possible, because then you can't predict it because it

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Speaker 1: is random. Now, what I think is interesting is the

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Speaker 1: way again we're talking about when you get back to

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Speaker 1: the computers, if you're if you're doing the pseudo random

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Speaker 1: number generator, that's a lot easier. Yes, because that's something

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Speaker 1: you can do within the realm of computing. Yes, And

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Speaker 1: and there are quite a few, as I found out,

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Speaker 1: pseudo random number generators available online. You can you can

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Speaker 1: say hey, I need a random number, and you can

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Speaker 1: go find a website somewhere that will give you something

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Speaker 1: that appears to be a completely random number. Yeah. And

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Speaker 1: it's um it's a really efficient, is some I mean,

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Speaker 1: it doesn't take long at all for a pseudo random

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Speaker 1: number generator to come up with a number. True random

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Speaker 1: number generators tend to take longer to to generate that number.

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Speaker 1: So uh. And when I'm saying longer, I'm talking about

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Speaker 1: maybe a matter of seconds. But in computing terms, that's

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Speaker 1: an eternity. Now, you would want a true number random

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Speaker 1: number generator for for certain tasks, like like a lottery.

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Speaker 1: You don't want people to be able to predict your lottery. Um,

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Speaker 1: that would be bad because then it would no longer

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Speaker 1: be a gamble. Yeah, well those are those are the

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Speaker 1: machines that they used to pull the ping pong balls

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Speaker 1: out of her true random numbers exactly. So again assuming

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Speaker 1: all the ping pong balls are in the same shape

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Speaker 1: and blah blah blah, and they are the same weights, exactly. Uh.

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Speaker 1: But things like random sampling if you wanted to do,

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Speaker 1: if you wanted to do true random sampling, and I

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Speaker 1: can give you an example of where this would be

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Speaker 1: really important. Excellent airports. Yes, so in an airport security line.

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Speaker 1: You know, supposedly airport security are going to single out

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Speaker 1: people for random screening. Now to make that truly a

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Speaker 1: random event where it's not based upon anyone's appearance, you know,

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Speaker 1: you want to take all of that out because you

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Speaker 1: don't want to do the profiling thing, because that has

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Speaker 1: its own set of problems. If you're truly random, you

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Speaker 1: need to have a system that's going to just tell

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Speaker 1: you a really random result, like say the third person,

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Speaker 1: the third person to go through the line next, that

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Speaker 1: will be someone you single out, and then seventh will

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Speaker 1: be the next one or whatever. But you would want

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Speaker 1: a true random number generator to come up with that

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Speaker 1: so that you could show that there was no preference,

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Speaker 1: there was no bias that went into that sample. Plus

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Speaker 1: it makes it a little more frightening for someone who

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Speaker 1: might be thinking about trying something. Is the idea that

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Speaker 1: you might be selected completely at random for a check.

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Speaker 1: But yeah, it doesn't matter how quote unquote normal you

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Speaker 1: look or unassuming you look, you could still be picked indeed. Um.

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Speaker 1: Whereas with a pseudo random number generator, you might want

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Speaker 1: that for something like if you're uh doing some sort

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Speaker 1: of simulation, because it's going to be much more efficient

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Speaker 1: and a lot of When we're talking simulation, we're talking

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Speaker 1: about simulation of complex systems, like let's say atmospheric systems.

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Speaker 1: That's incredibly complex, and if you were to rely on

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Speaker 1: true random number generators to generate the numbers you need

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Speaker 1: to run that simulation, it would not be nearly as

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Speaker 1: responsive as what you would need to to get a

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Speaker 1: true simulation. True simulation is kind of a oxymoron, but

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Speaker 1: at any rate, um, But there's some really cool ways

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Speaker 1: that various organizations and and and mathematicians have come up

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Speaker 1: with to try and create as random a number generator

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Speaker 1: as possible. Using computers. Yes, I figured, since you just

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Speaker 1: mentioned it, the use of atmospheric noise would be a

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Speaker 1: good place to start in that it's a random dot org.

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Speaker 1: It's a great resource as far as putting this podcast together.

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Speaker 1: And yes, yes, definitely, Um, that's that's how they try

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Speaker 1: to generate random numbers. Right, So let's say, let's say

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Speaker 1: that what they do is it's a combination of the

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Speaker 1: pseudo random number generator and the true random number generator method. Uh.

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Speaker 1: They use observations of atmospheric phenomena to generate a random

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Speaker 1: number and um, and they do that by putting it

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Speaker 1: through I suppose it's like putting it through an algorithm.

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Speaker 1: But at any rate that they depending on what's going

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Speaker 1: on the atmosphere at any given moment, that's what's going

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Speaker 1: to generate that random number. Uh. Now, now we start

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Speaker 1: to enter a philosophical debate about whether or not that

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Speaker 1: atmospheric conditions are truly random. And the reason why there's

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Speaker 1: a debate is that there's still a debate on is

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Speaker 1: everything deterministic, meaning that things happen and those things cause

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Speaker 1: other things to happen, and those things cause other things

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Speaker 1: to happen. And if you know the whole system. You

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Speaker 1: can predict everything that's going to happen. Versus nondeterministic which

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Speaker 1: allows for random occurrences. Fish still not not aplicy I

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Speaker 1: knew that was going to happen, so that's real, that's

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Speaker 1: not random. Good point. So now in a deterministic system,

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Speaker 1: what you could argue is that the conditions of the

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Speaker 1: atmosphere are in fact predictable if you know all the

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Speaker 1: factors that are going into making that condition. But that's

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Speaker 1: if there is a low pressure system. You could say, well,

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Speaker 1: you know, there is a good possibility that is going

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Speaker 1: to rain today, and so I by that I could

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Speaker 1: say it is more likely to rain than not rain

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Speaker 1: because the pressure is low. So there are things factors

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Speaker 1: taken a new cap, but what about the smog in

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Speaker 1: the area, or whether there is dust from any interference

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Speaker 1: from electromagnetic radiation Exactly these things can factor into it

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Speaker 1: to uh to change the probability that something will happen.

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Speaker 1: That's why we're talking about these incredibly complex systems. Now,

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Speaker 1: if there were some way for you to know all

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Speaker 1: the factors that were going into making the atmosphere behave

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Speaker 1: a particular way at any particular time, you could no

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Speaker 1: longer say that that was a random number. It's almost

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Speaker 1: just an academic argument because there's no way you can

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Speaker 1: know all those factors. It's just too complex, and especially

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Speaker 1: if you start to pull in things like chaos theory. Now,

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Speaker 1: if you've heard of chaos theory, you know that chaos

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Speaker 1: theory states that very small events can contribute to enormous events.

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Speaker 1: And the thought experiment that is always referred to is

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Speaker 1: that a butterfly flaps its wings and John Travolta gets

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Speaker 1: a movie no, I'm sorry, tsunami wipes out some city

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Speaker 1: somewhere across the world. So the wind uh generated, the

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Speaker 1: tiny little movement of air molecules generated by that butterfly's

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Speaker 1: wings flapping in Brazil sets off a chain of events

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Speaker 1: that ultimately leads to a catastrophically huge weather phenomena somewhere

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Speaker 1: across the globe. UH. And of course, not like, not

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Speaker 1: like instantaneously. Not I'm not suggesting that Brazilians go out

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Speaker 1: in massacre butterflies, but rather that these tiny events are

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Speaker 1: what contribute to enormous events. So in that sense, anything

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Speaker 1: that's going on in the atmosphere at any given time

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Speaker 1: is is the product of so many different tiny factors

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Speaker 1: that it's mind boggling right, right, So that uh, it's

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Speaker 1: it's so complex essentially then that while it is not

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Speaker 1: that is essentially random, right, it may or may not

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Speaker 1: be truly random. But one, we are not capable of

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Speaker 1: knowing that because it's so complex. And two it doesn't

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Speaker 1: matter because we're not capable of knowing that. So even

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Speaker 1: if even if you were to somehow philosophically argue that

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Speaker 1: it's not truly random, it's as close to truly random

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Speaker 1: as it needs to be, or in order for us

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Speaker 1: to go ahead and say, you know, it's just an

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Speaker 1: academic argument. Um, And that's not the only the the atmosphere,

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Speaker 1: observing the atmosphere is not the only method that people

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Speaker 1: have used to try and generate a random number. No,

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Speaker 1: not at all. And it's um. And as a matter

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Speaker 1: of fact, the one I think you were thinking of

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Speaker 1: would be, uh, the quantum mechanics version of determining random numbers.

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Speaker 1: That one that was a cool one. It's a really

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Speaker 1: cool idea. Uh, we're talking about by using particles that

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Speaker 1: are smaller than an atom to determine the randomness of

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Speaker 1: an event or in this case, generate random numbers. Yeah,

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Speaker 1: we're talking here about these quantum particles are behaving in

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Speaker 1: a way that we cannot predict at this time. Now, again,

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Speaker 1: this could mean that either the quantum particles behave in

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Speaker 1: a truly random fashion, or it may mean that we

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Speaker 1: don't understand them well enough to be able to recognize

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Speaker 1: the patterns or a series of events that are going on.

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Speaker 1: Or it may be that we we just don't have

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Speaker 1: the instruments capable of measuring that. So it may be

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Speaker 1: that the behavior is in fact predictable if we have

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Speaker 1: enough information instant of enough uh instruments. But as it

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Speaker 1: stands right now, it appears to be completely random. So

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Speaker 1: if you base a random number generator off of quantum events,

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Speaker 1: then you would get results that to us appear to

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Speaker 1: be completely random, and you could generate enormous numbers, I

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Speaker 1: mean numbers that are so big that you know you

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Speaker 1: you would if you were to try and write one

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Speaker 1: down on a sheet of paper, would take up the

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Speaker 1: entire sheet and and it would have no apparent repeating

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Speaker 1: integers at all. As you're going through there. Um Yeah,

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Speaker 1: hot fits actually is a website um a project from

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Speaker 1: a an organization in Switzerland Formulab, and UH they have

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Speaker 1: connected a Geiger Mueller tube to a computer to basically

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Speaker 1: UH their drag racing decaying atoms. Right, because adams decay

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Speaker 1: at an unpredictable rate, right, So that so by measuring

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Speaker 1: the decay rate, UH, that can in turn generate a

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Speaker 1: random number. Yeah, they take two, they take a pair

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Speaker 1: of decaying atoms and UH basically when when when one

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Speaker 1: of them decays and and releases particles, then that helps

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Speaker 1: them generate random numbers. And pretty amazing stuff really to

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Speaker 1: to be measuring that and to be using that for

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Speaker 1: a number. But the thing is, as as Jonathan mentioned earlier,

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Speaker 1: it is not something that happens very quickly. You have

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Speaker 1: to submit a series of numbers. You say, I want

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Speaker 1: fifty six random numbers, and they have to be no

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Speaker 1: larger than you know, forty three digits long, and things

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Speaker 1: like that. You have to you have to program this

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Speaker 1: in advance, and they will return and set of numbers

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Speaker 1: to you. But it will not happen instantaneously. Now do

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Speaker 1: you know what my favorite, uh version favorite way of

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Speaker 1: generating random numbers is? What's that? My favorite one was

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Speaker 1: a project and unfortunately don't have the name in front

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Speaker 1: of me. But my favorite one was a project where

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Speaker 1: it was using a webcam pointed at a lava lamp

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Speaker 1: and by measuring the shapes of the lava quote unquote

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Speaker 1: in the lava lamp, it generated random numbers. So the

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Speaker 1: yes as the lava lamp, as the the wax inside

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Speaker 1: the lava lamp changed shape, the webcam would measure that,

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Speaker 1: you know, it would get an image of it, and

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Speaker 1: it would be analyzed by the computer to create a

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Speaker 1: random number. And because the shape was constantly changing, you

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Speaker 1: could generate random numbers at any given time. And I

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Speaker 1: love that. The simplicity and the elegance of it is amazing.

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Speaker 1: Oh yes, so you've got it in front of you. Yes,

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Speaker 1: I don't have my glasses on, so I can't read it.

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Speaker 1: Lava Lava RAND, Lava RAND, Yes, l A v A

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Speaker 1: r n D. That project is still still exists, although

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Speaker 1: they are no longer generating random numbers based off lava lamps.

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Speaker 1: As far as I understand, that's pretty neat. Though I

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Speaker 1: had never heard of it before. Now it is a

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Speaker 1: neat neat concept. I thought that was you know again,

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Speaker 1: mathematicians are wacky, crazy, awesome people and we're talking about

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Speaker 1: like mathematicians, who are you know, studying math for things

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Speaker 1: like number theory. Yes, stuff that's so far beyond my understanding,

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Speaker 1: as the Aida Love Lace podcast illustrated to Great Lakes. Um.

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Speaker 1: But yeah, that these are the folks who are are

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Speaker 1: coming up with the various theories about how to generate

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Speaker 1: random numbers if in fact it is truly possible. Yes,

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Speaker 1: and I think that their work proves that our days

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Speaker 1: are numbered. Well, with that, how about we move on

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Speaker 1: to a little listener mail? Is it? This really isn't

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Speaker 1: listener mail. This is a message that comes to us

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Speaker 1: courtesy of our Facebook group. So, uh, Elizabeth, Elizabeth, we're

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Speaker 1: gonna call this Facebook facts and then uh putting put

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Speaker 1: in the sound of a hand slapping someone's face. Owl.

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Speaker 1: So this comes from Dan, and Dan says, uh, says,

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Speaker 1: how about late a history of broken English and long

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Speaker 1: forgotten keyboard keys. I would like to see how it

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Speaker 1: came about. Also popular terms like pound. Thanks all right, Dan,

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Speaker 1: So you're talking about let speak leap being short for

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Speaker 1: elite and really this kind of grew out of the

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Speaker 1: bulletin board system culture. So you had on bulletin board systems,

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Speaker 1: you normally had not Normally, a lot of bulletin boards

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Speaker 1: had multiple um levels of access, and there might be

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Speaker 1: a general access where you can log in and you

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Speaker 1: can access certain things that everyone has access to. But

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Speaker 1: then you might have to pay or be invited to

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Speaker 1: become part of a more restricted access community, and that

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Speaker 1: might get you stuff like access to two different files

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Speaker 1: or games or whatever. And because you're part of an

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Speaker 1: inner community, you of course would began to develop a

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Speaker 1: sense of elitism because that's just that's kind of how

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Speaker 1: we humans work. Well, yes, we uh, we do tend

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Speaker 1: to enjoy being part of an exclusive club. Yes, and uh.

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Speaker 1: When you are part of an exclusive club and you're

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Speaker 1: interested in being part of an exclusive club, you might

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Speaker 1: try to find ways to maybe disguise what you're doing

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Speaker 1: so that not everyone could find it via simple keyword search. Right,

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Speaker 1: So you start to develop your own language, and we

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Speaker 1: see this across human societies outside of the computer realm,

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Speaker 1: of course. Well slang, I mean, if you find a

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Speaker 1: slang dictionary and and the slang terms go from generation

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Speaker 1: to generation and region the region, and region the region.

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Speaker 1: So you might find a certain group has their own

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Speaker 1: kind of vocabulary. Well, the same thing with lead speak.

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Speaker 1: Lead speak, as Chris was pointing out, part of it

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Speaker 1: was to kind of obvious skate what they were talking

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Speaker 1: about so that you couldn't easily find it with a search. UM.

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Speaker 1: This was particularly important if they were trying to share

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Speaker 1: things like software that had been under copyright. If you

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Speaker 1: had an a copy of a program and you had

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Speaker 1: cracked it so that you didn't need any kind of UH,

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Speaker 1: there was no longer any DRM attached to it, there

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Speaker 1: was no sort of copy protection attached to it, and

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Speaker 1: you wanted to share this with all of your lead friends,

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Speaker 1: but you didn't want people to figure out that you

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Speaker 1: were doing something illegal. You might go through and change

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Speaker 1: a few little figures, a little some of the letters

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Speaker 1: to different symbols so that you know, you could still

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Speaker 1: read it, because the you know, we look at it

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Speaker 1: and we're like, okay, well that that that UH dollar

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Speaker 1: sign is supposed to stand for an S or three

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Speaker 1: for any yeah, or five for an s um or

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Speaker 1: four for an A, that kind of thing. But you know,

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Speaker 1: if you were to search for that term, it wouldn't

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Speaker 1: come up because the computer doesn't know that the uh

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Speaker 1: the symbols are standing in for letters UH that that's

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Speaker 1: a human thing. That's one of those things that humans

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Speaker 1: do really well that computers don't do well unless you

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Speaker 1: go through in program and a whole new database of words.

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Speaker 1: I mean, anybody who's who's done the sorry, go ahead,

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Speaker 1: anyone who's done the the calculator thing where you type in,

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Speaker 1: you know, certain numbers and then flip it over and

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Speaker 1: realize that they look a lot like you know, the

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Speaker 1: numbers actually look sort of like a word. You can

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Speaker 1: spell very few simple words using a calculator, but um,

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Speaker 1: you know, it's it's instantly recognizable to somebody who has

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Speaker 1: a grasp of the language, which is why a lot

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Speaker 1: of the words that they used gradually shifted from just

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Speaker 1: using numbers in place of letters to actually being spelled

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Speaker 1: differently as well. Right, uh so the first lead speaks

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Speaker 1: probably there for again, off you skate what they're doing.

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Speaker 1: But then it kind of came a way to communicate

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Speaker 1: with other LEAK members and to exclude neubs, the new folks,

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Speaker 1: even if you were in the public areas, because the

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Speaker 1: new folks were they were it was completely foreign to them.

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Speaker 1: And so if you first start looking at a page

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Speaker 1: of Lead speak and you're not familiar with. It just

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Speaker 1: looks like gibberish at first glance. You know, you actually

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Speaker 1: have to take an effort to kind of say, oh, wait,

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Speaker 1: that symbol probably means a you, and that one looks

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Speaker 1: like an S, so that's a nest, you know, and

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Speaker 1: a casual glance it looks like it's meaningless, which it

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Speaker 1: serves the purpose of the lead group very well. But

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Speaker 1: then they started to do other things, because these are

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Speaker 1: the same sort of folks who kind of have a

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Speaker 1: mischief streak a mile wide, and so they began to

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Speaker 1: incorporate things like common typos. Typos would become the the

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Speaker 1: typo version of a word would become the official version

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Speaker 1: of a word, which is why you would see things

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Speaker 1: like t e H standing in for the so and

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Speaker 1: and you. Misusing words on purpose also became very common,

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Speaker 1: so it began to develop its own grammar. So, for example,

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Speaker 1: instead of instead of using awesome as a a an adjective,

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Speaker 1: you'd use it as a noun and call it too awesome,

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Speaker 1: that is too awesome, and uh, things like pooned that

430
00:25:16,920 --> 00:25:20,520
Speaker 1: of course came from the TYPEO version of owned and you,

431
00:25:20,520 --> 00:25:24,280
Speaker 1: you know, owning as in I owned him. In this argument,

432
00:25:24,359 --> 00:25:29,159
Speaker 1: I completely dominated him by showing him that he is

433
00:25:29,200 --> 00:25:33,280
Speaker 1: stupid and is a poor debater, and I am awesome

434
00:25:33,400 --> 00:25:36,359
Speaker 1: and he is not, or I am too awesome and

435
00:25:36,440 --> 00:25:41,240
Speaker 1: he is not um so I pooned him. And most

436
00:25:41,359 --> 00:25:43,920
Speaker 1: of the lead speak is based off one of these

437
00:25:43,920 --> 00:25:46,960
Speaker 1: two things, or or a combination of the two, where

438
00:25:47,040 --> 00:25:51,040
Speaker 1: you substitute a letter for either a symbol or a number,

439
00:25:51,359 --> 00:25:54,199
Speaker 1: something that that resembles it physically but is not it,

440
00:25:55,119 --> 00:25:58,560
Speaker 1: or it is some sort of misspelling or TYPEO version

441
00:25:58,720 --> 00:26:03,119
Speaker 1: of the original word. And I mean most of it

442
00:26:03,160 --> 00:26:06,880
Speaker 1: can be can be pretty easily translated if you if

443
00:26:06,880 --> 00:26:10,239
Speaker 1: you just give yourself a little time. Sometimes if they

444
00:26:10,280 --> 00:26:13,160
Speaker 1: get really, really, really complex, it gets to a point

445
00:26:13,200 --> 00:26:16,719
Speaker 1: where you're like, uh, now, certain symbols are standing in

446
00:26:16,760 --> 00:26:20,000
Speaker 1: for entire um are symbols are standing in for an

447
00:26:20,119 --> 00:26:24,480
Speaker 1: entire syllable? Right? And then some of the others who

448
00:26:24,520 --> 00:26:29,359
Speaker 1: were really into lead speak also incorporated. You could also

449
00:26:29,400 --> 00:26:33,160
Speaker 1: make a t out of certain lines on the keyboard,

450
00:26:33,240 --> 00:26:36,440
Speaker 1: making it a little bit even more obfuscatory, could even

451
00:26:36,560 --> 00:26:38,960
Speaker 1: could tell you'd have to really scrutinize it to go,

452
00:26:39,000 --> 00:26:42,520
Speaker 1: oh wait, those three characters are making up another letter

453
00:26:42,920 --> 00:26:45,119
Speaker 1: for let's say that you wanted to use, use the

454
00:26:45,160 --> 00:26:49,560
Speaker 1: ampersand to stand for the sound and okay, and then

455
00:26:49,560 --> 00:26:50,880
Speaker 1: you put a B in front of it, so it's

456
00:26:50,920 --> 00:26:54,280
Speaker 1: be ampersand and it's banned, right, so you can say, I,

457
00:26:54,359 --> 00:26:57,280
Speaker 1: you know, don't do that, you'll get banned be ampersand

458
00:26:57,320 --> 00:27:00,000
Speaker 1: meaning that, Well, if you really want to go even

459
00:27:00,000 --> 00:27:02,480
Speaker 1: one step further, you would not even use the ampersand

460
00:27:02,600 --> 00:27:05,000
Speaker 1: you would use the seven, which of course is the

461
00:27:05,040 --> 00:27:07,480
Speaker 1: same key that the ampersand is found on, so B

462
00:27:07,720 --> 00:27:10,600
Speaker 1: seven would stand for band. So now now you've gone

463
00:27:10,640 --> 00:27:13,879
Speaker 1: from just replacing letters to actually creating a little minor

464
00:27:13,920 --> 00:27:17,080
Speaker 1: bit of a cipher. Um. It's a pretty simple cipher,

465
00:27:17,960 --> 00:27:20,040
Speaker 1: but it can be very confusing to someone who has

466
00:27:20,080 --> 00:27:23,440
Speaker 1: never encountered it before. Yep, yep. And gradually more people

467
00:27:23,480 --> 00:27:29,480
Speaker 1: became acquainted with it as uh the lead people with

468
00:27:29,600 --> 00:27:34,280
Speaker 1: lead skills UM started playing games online, and they would

469
00:27:34,320 --> 00:27:39,359
Speaker 1: start using lead language in the communication part of the

470
00:27:39,400 --> 00:27:43,000
Speaker 1: game when you were typed comments along with the game. Uh,

471
00:27:43,040 --> 00:27:45,360
Speaker 1: you know, and could interact with the other players online.

472
00:27:45,880 --> 00:27:48,760
Speaker 1: And then uh, from the research I did on it,

473
00:27:48,800 --> 00:27:53,240
Speaker 1: apparently the the comic Mega Tokyo basically made it more

474
00:27:53,320 --> 00:27:58,120
Speaker 1: even more of a popular phenomenon to to speak lead um,

475
00:27:58,560 --> 00:28:00,640
Speaker 1: just to bring it out that much on the open, which,

476
00:28:00,640 --> 00:28:04,840
Speaker 1: of course, for the people who are truly lead uh

477
00:28:05,000 --> 00:28:07,919
Speaker 1: probably drove them absolutely bonkers because suddenly they were no

478
00:28:07,960 --> 00:28:11,400
Speaker 1: longer exclusive, right yeah, so, and of course now it's

479
00:28:11,400 --> 00:28:14,919
Speaker 1: it's pretty much just common parlance on a lot of

480
00:28:14,920 --> 00:28:17,560
Speaker 1: the web, where you know, it's almost it's almost just

481
00:28:17,600 --> 00:28:20,600
Speaker 1: become a parody of itself, um, which is kind of

482
00:28:20,600 --> 00:28:23,280
Speaker 1: funny because it was already sort of the parody at

483
00:28:23,280 --> 00:28:26,119
Speaker 1: any rate. That's pretty much the lowdown on lead speak

484
00:28:26,280 --> 00:28:31,640
Speaker 1: and uh and and why people funny yep. Uh. If

485
00:28:31,680 --> 00:28:34,800
Speaker 1: you have any questions that you would like to pose

486
00:28:35,119 --> 00:28:39,640
Speaker 1: to the tech Stuff group, the grouping Chris um, you

487
00:28:39,680 --> 00:28:42,600
Speaker 1: can do so at our email address, which is tech

488
00:28:42,640 --> 00:28:45,400
Speaker 1: Stuff at how stuff works dot com, or like Dan,

489
00:28:45,480 --> 00:28:47,400
Speaker 1: you can leave us a message on our Facebook group

490
00:28:47,440 --> 00:28:50,440
Speaker 1: because we are checking that every day, or you can

491
00:28:50,840 --> 00:28:53,360
Speaker 1: contact us on our Twitter handle and you'll hear all

492
00:28:53,360 --> 00:28:57,800
Speaker 1: that information at our handy dandy PostScript announcement done by

493
00:28:57,800 --> 00:29:01,440
Speaker 1: our very own Chris Pallette. Hey. Then in the meantime,

494
00:29:01,880 --> 00:29:04,080
Speaker 1: I hope you guys enjoyed this show and we will

495
00:29:04,160 --> 00:29:10,520
Speaker 1: taught to you again. Really soon If you're a tech

496
00:29:10,600 --> 00:29:12,680
Speaker 1: stuff and be sure to check us out on Twitter

497
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Speaker 1: tech Stuff hs WSR handle, and you can also find

498
00:29:16,600 --> 00:29:19,719
Speaker 1: us on Facebook at Facebook dot com slash tech Stuff

499
00:29:19,880 --> 00:29:23,520
Speaker 1: h s W. For more on this and thousands of

500
00:29:23,560 --> 00:29:26,480
Speaker 1: other topics, visit how stuff Works dot com and be

501
00:29:26,560 --> 00:29:28,760
Speaker 1: sure to check out the new tech stuff blog now

502
00:29:28,800 --> 00:29:35,680
Speaker 1: on the House stuff Works homepage. Brought to you by

503
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