June 14, 2010 • 29 mins
Many random number generators you'll find online don't produce truly random values. Jonathan and Chris explain the difference between true random number generators and pseudo-random number generators in this episode.
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Speaker 1 (00:00):
Brought to you by the reinvented two thousand twelve Camray.
It's ready. Are you get in touch with technology with
tech Stuff from how stuff works dot com. Hello again, everyone,
and welcome to tech stuff. My name is Chris Poette,
(00:21):
and I am an editor here at how stuff works
dot com. Sitting across from me, as he always does
on these occasions, is senior writer Jonathan Strickland. Hey there,
all right, then, so we have some listener mail to
start us off. Okay, this listener mail comes from Harry
(00:41):
from the University of North Carolina, Chapel Hill. He says, Hey,
as a table top gamer, I have been known to
generate a few random numbers via my trustee D twenty. However,
I know computers are able to do this without tossing
around an ico hay, drawn, Just how do digital randomer
generator's work and are they truly random? So? D twenty,
(01:04):
for those of you who are not entrenched in the
geek world as I am, that is a twenty sided die. Yeah. That,
As it turns out, that's a far more effective tool
for generating random numbers than a computer can be. Yeah,
and uh, here's here's the basics of why that is
(01:24):
a computer follows instructions. So you have to be able
to give a computer instructions in order for it to
do anything. Yes, in order for you to be able
to tell it to generate a number, you have to
tell it how to generate that number. And it is very,
very challenging to create a way to tell a computer
to generate a number so that that number is truly random.
(01:46):
And when we're talking about truly random, or it may
seem that a random number generator that you might use
online is random, but it's random to you and not
the computer, depending on what system they're using. Well, that's true,
but a lot of the ones that I've seen for
free are using an algorithm to determine a random number. Right,
(02:08):
And there are a couple of different ways that computers
tried to generate random numbers. Okay, so in one version,
the computer actually just has a table of numbers that
have been pregenerated through some complex means, and that the
numbers themselves appear to be random because it's a string
of numbers that don't repeat in any sort of in
(02:31):
any source of sequence, no matter how long the number is.
And we're talking about numbers that could have thousands and
thousands of digits within them, right, I mean it's it
could be as simple as generating numbers at random and
making a list, and it's going down and saying, okay, number,
this is the number, right, three, six, this is the number.
(02:52):
But it also might also is likely to be using
an algorithm it says, okay, well, add twenty to this one. Okay,
at fourteen to this one, add one to that one. Right,
So you if you know where you are in the sequence,
you can predict what the next theoretically random number is. Right.
So if it's if it's the list version that that
(03:15):
we were talking about before, let's let's make it really simple.
Let's say you have a list of ten numbers and
each of those numbers is ten digits long, but the
digits are apparently random. Yes, all right, so there's no
like easy sequence to follow with you, and we have
just completed, Uh, we've just used random number number eight, Okay,
(03:37):
all right. That means that we would have random number
number nine and random number number ten, and then it
would go back to random number number one. So if
you had the table in front of you, and you
had you knew how many of those random numbers there were,
and you knew where it was. In the previous iteration
of generating a random number, you would know the next
(03:57):
quote unquote random number. So the number itself may appear
to be random, but you would still be able to
predict what it was because you had the list. Right.
So that's a problem because random numbers are used in
things like encryption, so you want you want those numbers
to be as true as close to true random as
possible so that it is not easy or or preferably
(04:20):
possible to break that encryption. I think this is a
good time to introduce two concepts. Okay, uh, that would
be the the pseudo random number generator, sure, and the
true random number generator. Right. So true random number generators
are actually kind of easier to to explain. Yeah, I
think so too. So a true random number generator is
(04:42):
something that can generate a random number excluding any minor
quantum effects that you want to imagine. So let's talk
about that D twenty. Yeah, that's an excellent example of
a true random number generator, right when you roll that
D twenty. Theoretically, anytime you roll that D twenty, there
should be an equal chance of any number between one
and twenty popping up as the the eventual result. That is,
(05:06):
assuming that your D twenty is equally sharp on all sides,
and I mean the edges are the exact same and
the surface. That's what I'm talking about, elimiting the quantum effects, Okay,
because when I when I'm talking about quantum effects in
this case, I'm talking about things that are so small
that we can't really observe them anyway. So it doesn't
you know, if you eliminate all that and you say that,
(05:29):
with all things being equal, rolling on the same surface
with a perfectly uh intact D twenty, you should get
a random result every time you roll it, right, right,
I have a couple of D twenties that look like
a dog chewed on them. Yes, and in those cases,
there might be some numbers that might roll up more
often than others just because of the shape of that
(05:51):
D twenty exactly. But a true random number generator would
not give any preference to any particular result. Uh. Pseudo
random number generators are different. Yes, they We've already been
talking about pseudorandom number generators, which is the number generators
that are using the algorithms or a predetermined list of
(06:12):
numbers that that are being used to theoretically generate random number. Now,
the the the algorithms use something called a seedy, and
a seed is a predetermined figure that gets plugged into
the algorithm that helps generate the seemingly random numbers. But
(06:32):
if you know what the seed is, then theoretically you
can eventually determine what the algorithm is and then break
the encryption. Now this is this is not as easy
as I'm making it sound. It's not like this is
a trivial task. No, No, it's actually incredibly difficult. Yeah,
as long as uh. I mean, if if you have
no clue what the seed is or what the algorithm is,
(06:55):
it's it's going to appear to you to be random, right,
it's going to seem like an impossible test to break it.
But if you were to find one of those two elements,
then theoretically, over several results, you would be able to
eventually derive the other. You would either be able to
derive the seed or you'd be able to derive the algorithm.
And again, this is it's not easy, but it is
(07:19):
theoretically at any rate possible, which is why you want
to get as close to a true random number generator
as possible, because then you can't predict it because it
is random. Now, what I think is interesting is the
way again we're talking about when you get back to
the computers, if you're if you're doing the pseudo random
number generator, that's a lot easier. Yes, because that's something
you can do within the realm of computing. Yes, And
(07:42):
and there are quite a few, as I found out,
pseudo random number generators available online. You can you can
say hey, I need a random number, and you can
go find a website somewhere that will give you something
that appears to be a completely random number. Yeah. And
it's um it's a really efficient, is some I mean,
it doesn't take long at all for a pseudo random
(08:03):
number generator to come up with a number. True random
number generators tend to take longer to to generate that number.
So uh. And when I'm saying longer, I'm talking about
maybe a matter of seconds. But in computing terms, that's
an eternity. Now, you would want a true number random
number generator for for certain tasks, like like a lottery.
(08:26):
You don't want people to be able to predict your lottery. Um,
that would be bad because then it would no longer
be a gamble. Yeah, well those are those are the
machines that they used to pull the ping pong balls
out of her true random numbers exactly. So again assuming
all the ping pong balls are in the same shape
and blah blah blah, and they are the same weights, exactly. Uh.
But things like random sampling if you wanted to do,
(08:49):
if you wanted to do true random sampling, and I
can give you an example of where this would be
really important. Excellent airports. Yes, so in an airport security line.
You know, supposedly airport security are going to single out
people for random screening. Now to make that truly a
random event where it's not based upon anyone's appearance, you know,
(09:11):
you want to take all of that out because you
don't want to do the profiling thing, because that has
its own set of problems. If you're truly random, you
need to have a system that's going to just tell
you a really random result, like say the third person,
the third person to go through the line next, that
will be someone you single out, and then seventh will
be the next one or whatever. But you would want
(09:34):
a true random number generator to come up with that
so that you could show that there was no preference,
there was no bias that went into that sample. Plus
it makes it a little more frightening for someone who
might be thinking about trying something. Is the idea that
you might be selected completely at random for a check.
But yeah, it doesn't matter how quote unquote normal you
(09:54):
look or unassuming you look, you could still be picked indeed. Um.
Whereas with a pseudo random number generator, you might want
that for something like if you're uh doing some sort
of simulation, because it's going to be much more efficient
and a lot of When we're talking simulation, we're talking
about simulation of complex systems, like let's say atmospheric systems.
(10:15):
That's incredibly complex, and if you were to rely on
true random number generators to generate the numbers you need
to run that simulation, it would not be nearly as
responsive as what you would need to to get a
true simulation. True simulation is kind of a oxymoron, but
at any rate, um, But there's some really cool ways
(10:37):
that various organizations and and and mathematicians have come up
with to try and create as random a number generator
as possible. Using computers. Yes, I figured, since you just
mentioned it, the use of atmospheric noise would be a
good place to start in that it's a random dot org.
It's a great resource as far as putting this podcast together.
(10:59):
And yes, yes, definitely, Um, that's that's how they try
to generate random numbers. Right, So let's say, let's say
that what they do is it's a combination of the
pseudo random number generator and the true random number generator method. Uh.
They use observations of atmospheric phenomena to generate a random
(11:21):
number and um, and they do that by putting it
through I suppose it's like putting it through an algorithm.
But at any rate that they depending on what's going
on the atmosphere at any given moment, that's what's going
to generate that random number. Uh. Now, now we start
to enter a philosophical debate about whether or not that
(11:43):
atmospheric conditions are truly random. And the reason why there's
a debate is that there's still a debate on is
everything deterministic, meaning that things happen and those things cause
other things to happen, and those things cause other things
to happen. And if you know the whole system. You
can predict everything that's going to happen. Versus nondeterministic which
(12:04):
allows for random occurrences. Fish still not not aplicy I
knew that was going to happen, so that's real, that's
not random. Good point. So now in a deterministic system,
what you could argue is that the conditions of the
atmosphere are in fact predictable if you know all the
(12:27):
factors that are going into making that condition. But that's
if there is a low pressure system. You could say, well,
you know, there is a good possibility that is going
to rain today, and so I by that I could
say it is more likely to rain than not rain
because the pressure is low. So there are things factors
taken a new cap, but what about the smog in
(12:50):
the area, or whether there is dust from any interference
from electromagnetic radiation Exactly these things can factor into it
to uh to change the probability that something will happen.
That's why we're talking about these incredibly complex systems. Now,
if there were some way for you to know all
the factors that were going into making the atmosphere behave
a particular way at any particular time, you could no
(13:12):
longer say that that was a random number. It's almost
just an academic argument because there's no way you can
know all those factors. It's just too complex, and especially
if you start to pull in things like chaos theory. Now,
if you've heard of chaos theory, you know that chaos
theory states that very small events can contribute to enormous events.
(13:35):
And the thought experiment that is always referred to is
that a butterfly flaps its wings and John Travolta gets
a movie no, I'm sorry, tsunami wipes out some city
somewhere across the world. So the wind uh generated, the
tiny little movement of air molecules generated by that butterfly's
(13:56):
wings flapping in Brazil sets off a chain of events
that ultimately leads to a catastrophically huge weather phenomena somewhere
across the globe. UH. And of course, not like, not
like instantaneously. Not I'm not suggesting that Brazilians go out
in massacre butterflies, but rather that these tiny events are
(14:18):
what contribute to enormous events. So in that sense, anything
that's going on in the atmosphere at any given time
is is the product of so many different tiny factors
that it's mind boggling right, right, So that uh, it's
it's so complex essentially then that while it is not
(14:39):
that is essentially random, right, it may or may not
be truly random. But one, we are not capable of
knowing that because it's so complex. And two it doesn't
matter because we're not capable of knowing that. So even
if even if you were to somehow philosophically argue that
it's not truly random, it's as close to truly random
(14:59):
as it needs to be, or in order for us
to go ahead and say, you know, it's just an
academic argument. Um, And that's not the only the the atmosphere,
observing the atmosphere is not the only method that people
have used to try and generate a random number. No,
not at all. And it's um. And as a matter
of fact, the one I think you were thinking of
(15:20):
would be, uh, the quantum mechanics version of determining random numbers.
That one that was a cool one. It's a really
cool idea. Uh, we're talking about by using particles that
are smaller than an atom to determine the randomness of
an event or in this case, generate random numbers. Yeah,
(15:40):
we're talking here about these quantum particles are behaving in
a way that we cannot predict at this time. Now, again,
this could mean that either the quantum particles behave in
a truly random fashion, or it may mean that we
don't understand them well enough to be able to recognize
(16:01):
the patterns or a series of events that are going on.
Or it may be that we we just don't have
the instruments capable of measuring that. So it may be
that the behavior is in fact predictable if we have
enough information instant of enough uh instruments. But as it
stands right now, it appears to be completely random. So
(16:23):
if you base a random number generator off of quantum events,
then you would get results that to us appear to
be completely random, and you could generate enormous numbers, I
mean numbers that are so big that you know you
you would if you were to try and write one
down on a sheet of paper, would take up the
entire sheet and and it would have no apparent repeating
(16:46):
integers at all. As you're going through there. Um Yeah,
hot fits actually is a website um a project from
a an organization in Switzerland Formulab, and UH they have
connected a Geiger Mueller tube to a computer to basically
(17:07):
UH their drag racing decaying atoms. Right, because adams decay
at an unpredictable rate, right, So that so by measuring
the decay rate, UH, that can in turn generate a
random number. Yeah, they take two, they take a pair
of decaying atoms and UH basically when when when one
(17:28):
of them decays and and releases particles, then that helps
them generate random numbers. And pretty amazing stuff really to
to be measuring that and to be using that for
a number. But the thing is, as as Jonathan mentioned earlier,
it is not something that happens very quickly. You have
to submit a series of numbers. You say, I want
(17:51):
fifty six random numbers, and they have to be no
larger than you know, forty three digits long, and things
like that. You have to you have to program this
in advance, and they will return and set of numbers
to you. But it will not happen instantaneously. Now do
you know what my favorite, uh version favorite way of
(18:13):
generating random numbers is? What's that? My favorite one was
a project and unfortunately don't have the name in front
of me. But my favorite one was a project where
it was using a webcam pointed at a lava lamp
and by measuring the shapes of the lava quote unquote
in the lava lamp, it generated random numbers. So the
(18:33):
yes as the lava lamp, as the the wax inside
the lava lamp changed shape, the webcam would measure that,
you know, it would get an image of it, and
it would be analyzed by the computer to create a
random number. And because the shape was constantly changing, you
could generate random numbers at any given time. And I
(18:55):
love that. The simplicity and the elegance of it is amazing.
Oh yes, so you've got it in front of you. Yes,
I don't have my glasses on, so I can't read it.
Lava Lava RAND, Lava RAND, Yes, l A v A
r n D. That project is still still exists, although
they are no longer generating random numbers based off lava lamps.
As far as I understand, that's pretty neat. Though I
(19:16):
had never heard of it before. Now it is a
neat neat concept. I thought that was you know again,
mathematicians are wacky, crazy, awesome people and we're talking about
like mathematicians, who are you know, studying math for things
like number theory. Yes, stuff that's so far beyond my understanding,
as the Aida Love Lace podcast illustrated to Great Lakes. Um.
(19:40):
But yeah, that these are the folks who are are
coming up with the various theories about how to generate
random numbers if in fact it is truly possible. Yes,
and I think that their work proves that our days
are numbered. Well, with that, how about we move on
to a little listener mail? Is it? This really isn't
(20:02):
listener mail. This is a message that comes to us
courtesy of our Facebook group. So, uh, Elizabeth, Elizabeth, we're
gonna call this Facebook facts and then uh putting put
in the sound of a hand slapping someone's face. Owl.
So this comes from Dan, and Dan says, uh, says,
(20:23):
how about late a history of broken English and long
forgotten keyboard keys. I would like to see how it
came about. Also popular terms like pound. Thanks all right, Dan,
So you're talking about let speak leap being short for
elite and really this kind of grew out of the
bulletin board system culture. So you had on bulletin board systems,
(20:45):
you normally had not Normally, a lot of bulletin boards
had multiple um levels of access, and there might be
a general access where you can log in and you
can access certain things that everyone has access to. But
then you might have to pay or be invited to
become part of a more restricted access community, and that
(21:06):
might get you stuff like access to two different files
or games or whatever. And because you're part of an
inner community, you of course would began to develop a
sense of elitism because that's just that's kind of how
we humans work. Well, yes, we uh, we do tend
to enjoy being part of an exclusive club. Yes, and uh.
(21:30):
When you are part of an exclusive club and you're
interested in being part of an exclusive club, you might
try to find ways to maybe disguise what you're doing
so that not everyone could find it via simple keyword search. Right,
So you start to develop your own language, and we
see this across human societies outside of the computer realm,
(21:50):
of course. Well slang, I mean, if you find a
slang dictionary and and the slang terms go from generation
to generation and region the region, and region the region.
So you might find a certain group has their own
kind of vocabulary. Well, the same thing with lead speak.
Lead speak, as Chris was pointing out, part of it
was to kind of obvious skate what they were talking
about so that you couldn't easily find it with a search. UM.
(22:12):
This was particularly important if they were trying to share
things like software that had been under copyright. If you
had an a copy of a program and you had
cracked it so that you didn't need any kind of UH,
there was no longer any DRM attached to it, there
was no sort of copy protection attached to it, and
you wanted to share this with all of your lead friends,
(22:33):
but you didn't want people to figure out that you
were doing something illegal. You might go through and change
a few little figures, a little some of the letters
to different symbols so that you know, you could still
read it, because the you know, we look at it
and we're like, okay, well that that that UH dollar
sign is supposed to stand for an S or three
(22:54):
for any yeah, or five for an s um or
four for an A, that kind of thing. But you know,
if you were to search for that term, it wouldn't
come up because the computer doesn't know that the uh
the symbols are standing in for letters UH that that's
a human thing. That's one of those things that humans
do really well that computers don't do well unless you
(23:14):
go through in program and a whole new database of words.
I mean, anybody who's who's done the sorry, go ahead,
anyone who's done the the calculator thing where you type in,
you know, certain numbers and then flip it over and
realize that they look a lot like you know, the
numbers actually look sort of like a word. You can
spell very few simple words using a calculator, but um,
(23:37):
you know, it's it's instantly recognizable to somebody who has
a grasp of the language, which is why a lot
of the words that they used gradually shifted from just
using numbers in place of letters to actually being spelled
differently as well. Right, uh so the first lead speaks
probably there for again, off you skate what they're doing.
(23:58):
But then it kind of came a way to communicate
with other LEAK members and to exclude neubs, the new folks,
even if you were in the public areas, because the
new folks were they were it was completely foreign to them.
And so if you first start looking at a page
of Lead speak and you're not familiar with. It just
looks like gibberish at first glance. You know, you actually
(24:21):
have to take an effort to kind of say, oh, wait,
that symbol probably means a you, and that one looks
like an S, so that's a nest, you know, and
a casual glance it looks like it's meaningless, which it
serves the purpose of the lead group very well. But
then they started to do other things, because these are
the same sort of folks who kind of have a
mischief streak a mile wide, and so they began to
(24:43):
incorporate things like common typos. Typos would become the the
typo version of a word would become the official version
of a word, which is why you would see things
like t e H standing in for the so and
and you. Misusing words on purpose also became very common,
so it began to develop its own grammar. So, for example,
(25:05):
instead of instead of using awesome as a a an adjective,
you'd use it as a noun and call it too awesome,
that is too awesome, and uh, things like pooned that
of course came from the TYPEO version of owned and you,
you know, owning as in I owned him. In this argument,
I completely dominated him by showing him that he is
(25:29):
stupid and is a poor debater, and I am awesome
and he is not, or I am too awesome and
he is not um so I pooned him. And most
of the lead speak is based off one of these
two things, or or a combination of the two, where
you substitute a letter for either a symbol or a number,
(25:51):
something that that resembles it physically but is not it,
or it is some sort of misspelling or TYPEO version
of the original word. And I mean most of it
can be can be pretty easily translated if you if
you just give yourself a little time. Sometimes if they
get really, really, really complex, it gets to a point
(26:13):
where you're like, uh, now, certain symbols are standing in
for entire um are symbols are standing in for an
entire syllable? Right? And then some of the others who
were really into lead speak also incorporated. You could also
make a t out of certain lines on the keyboard,
(26:33):
making it a little bit even more obfuscatory, could even
could tell you'd have to really scrutinize it to go,
oh wait, those three characters are making up another letter
for let's say that you wanted to use, use the
ampersand to stand for the sound and okay, and then
you put a B in front of it, so it's
be ampersand and it's banned, right, so you can say, I,
(26:54):
you know, don't do that, you'll get banned be ampersand
meaning that, Well, if you really want to go even
one step further, you would not even use the ampersand
you would use the seven, which of course is the
same key that the ampersand is found on, so B
seven would stand for band. So now now you've gone
from just replacing letters to actually creating a little minor
bit of a cipher. Um. It's a pretty simple cipher,
(27:17):
but it can be very confusing to someone who has
never encountered it before. Yep, yep. And gradually more people
became acquainted with it as uh the lead people with
lead skills UM started playing games online, and they would
start using lead language in the communication part of the
(27:39):
game when you were typed comments along with the game. Uh,
you know, and could interact with the other players online.
And then uh, from the research I did on it,
apparently the the comic Mega Tokyo basically made it more
even more of a popular phenomenon to to speak lead um,
just to bring it out that much on the open, which,
(28:00):
of course, for the people who are truly lead uh
probably drove them absolutely bonkers because suddenly they were no
longer exclusive, right yeah, so, and of course now it's
it's pretty much just common parlance on a lot of
the web, where you know, it's almost it's almost just
become a parody of itself, um, which is kind of
funny because it was already sort of the parody at
(28:23):
any rate. That's pretty much the lowdown on lead speak
and uh and and why people funny yep. Uh. If
you have any questions that you would like to pose
to the tech Stuff group, the grouping Chris um, you
can do so at our email address, which is tech
Stuff at how stuff works dot com, or like Dan,
(28:45):
you can leave us a message on our Facebook group
because we are checking that every day, or you can
contact us on our Twitter handle and you'll hear all
that information at our handy dandy PostScript announcement done by
our very own Chris Pallette. Hey. Then in the meantime,
I hope you guys enjoyed this show and we will
taught to you again. Really soon If you're a tech
(29:10):
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(29:35):
the reinvented two thousand twelve camera. It's ready, are you
Brought to you by the reinvented two thousand twelve Camray.
It's ready. Are you get in touch with technology with
tech Stuff from how stuff works dot com. Hello again, everyone,
and welcome to tech stuff. My name is Chris Poette,
(00:21):
and I am an editor here at how stuff works
dot com. Sitting across from me, as he always does
on these occasions, is senior writer Jonathan Strickland. Hey there,
all right, then, so we have some listener mail to
start us off. Okay, this listener mail comes from Harry
(00:41):
from the University of North Carolina, Chapel Hill. He says, Hey,
as a table top gamer, I have been known to
generate a few random numbers via my trustee D twenty. However,
I know computers are able to do this without tossing
around an ico hay, drawn, Just how do digital randomer
generator's work and are they truly random? So? D twenty,
(01:04):
for those of you who are not entrenched in the
geek world as I am, that is a twenty sided die. Yeah. That,
As it turns out, that's a far more effective tool
for generating random numbers than a computer can be. Yeah,
and uh, here's here's the basics of why that is
(01:24):
a computer follows instructions. So you have to be able
to give a computer instructions in order for it to
do anything. Yes, in order for you to be able
to tell it to generate a number, you have to
tell it how to generate that number. And it is very,
very challenging to create a way to tell a computer
to generate a number so that that number is truly random.
(01:46):
And when we're talking about truly random, or it may
seem that a random number generator that you might use
online is random, but it's random to you and not
the computer, depending on what system they're using. Well, that's true,
but a lot of the ones that I've seen for
free are using an algorithm to determine a random number. Right,
(02:08):
And there are a couple of different ways that computers
tried to generate random numbers. Okay, so in one version,
the computer actually just has a table of numbers that
have been pregenerated through some complex means, and that the
numbers themselves appear to be random because it's a string
of numbers that don't repeat in any sort of in
(02:31):
any source of sequence, no matter how long the number is.
And we're talking about numbers that could have thousands and
thousands of digits within them, right, I mean it's it
could be as simple as generating numbers at random and
making a list, and it's going down and saying, okay, number,
this is the number, right, three, six, this is the number.
(02:52):
But it also might also is likely to be using
an algorithm it says, okay, well, add twenty to this one. Okay,
at fourteen to this one, add one to that one. Right,
So you if you know where you are in the sequence,
you can predict what the next theoretically random number is. Right.
So if it's if it's the list version that that
(03:15):
we were talking about before, let's let's make it really simple.
Let's say you have a list of ten numbers and
each of those numbers is ten digits long, but the
digits are apparently random. Yes, all right, so there's no
like easy sequence to follow with you, and we have
just completed, Uh, we've just used random number number eight, Okay,
(03:37):
all right. That means that we would have random number
number nine and random number number ten, and then it
would go back to random number number one. So if
you had the table in front of you, and you
had you knew how many of those random numbers there were,
and you knew where it was. In the previous iteration
of generating a random number, you would know the next
(03:57):
quote unquote random number. So the number itself may appear
to be random, but you would still be able to
predict what it was because you had the list. Right.
So that's a problem because random numbers are used in
things like encryption, so you want you want those numbers
to be as true as close to true random as
possible so that it is not easy or or preferably
(04:20):
possible to break that encryption. I think this is a
good time to introduce two concepts. Okay, uh, that would
be the the pseudo random number generator, sure, and the
true random number generator. Right. So true random number generators
are actually kind of easier to to explain. Yeah, I
think so too. So a true random number generator is
(04:42):
something that can generate a random number excluding any minor
quantum effects that you want to imagine. So let's talk
about that D twenty. Yeah, that's an excellent example of
a true random number generator, right when you roll that
D twenty. Theoretically, anytime you roll that D twenty, there
should be an equal chance of any number between one
and twenty popping up as the the eventual result. That is,
(05:06):
assuming that your D twenty is equally sharp on all sides,
and I mean the edges are the exact same and
the surface. That's what I'm talking about, elimiting the quantum effects, Okay,
because when I when I'm talking about quantum effects in
this case, I'm talking about things that are so small
that we can't really observe them anyway. So it doesn't
you know, if you eliminate all that and you say that,
(05:29):
with all things being equal, rolling on the same surface
with a perfectly uh intact D twenty, you should get
a random result every time you roll it, right, right,
I have a couple of D twenties that look like
a dog chewed on them. Yes, and in those cases,
there might be some numbers that might roll up more
often than others just because of the shape of that
(05:51):
D twenty exactly. But a true random number generator would
not give any preference to any particular result. Uh. Pseudo
random number generators are different. Yes, they We've already been
talking about pseudorandom number generators, which is the number generators
that are using the algorithms or a predetermined list of
(06:12):
numbers that that are being used to theoretically generate random number. Now,
the the the algorithms use something called a seedy, and
a seed is a predetermined figure that gets plugged into
the algorithm that helps generate the seemingly random numbers. But
(06:32):
if you know what the seed is, then theoretically you
can eventually determine what the algorithm is and then break
the encryption. Now this is this is not as easy
as I'm making it sound. It's not like this is
a trivial task. No, No, it's actually incredibly difficult. Yeah,
as long as uh. I mean, if if you have
no clue what the seed is or what the algorithm is,
(06:55):
it's it's going to appear to you to be random, right,
it's going to seem like an impossible test to break it.
But if you were to find one of those two elements,
then theoretically, over several results, you would be able to
eventually derive the other. You would either be able to
derive the seed or you'd be able to derive the algorithm.
And again, this is it's not easy, but it is
(07:19):
theoretically at any rate possible, which is why you want
to get as close to a true random number generator
as possible, because then you can't predict it because it
is random. Now, what I think is interesting is the
way again we're talking about when you get back to
the computers, if you're if you're doing the pseudo random
number generator, that's a lot easier. Yes, because that's something
you can do within the realm of computing. Yes, And
(07:42):
and there are quite a few, as I found out,
pseudo random number generators available online. You can you can
say hey, I need a random number, and you can
go find a website somewhere that will give you something
that appears to be a completely random number. Yeah. And
it's um it's a really efficient, is some I mean,
it doesn't take long at all for a pseudo random
(08:03):
number generator to come up with a number. True random
number generators tend to take longer to to generate that number.
So uh. And when I'm saying longer, I'm talking about
maybe a matter of seconds. But in computing terms, that's
an eternity. Now, you would want a true number random
number generator for for certain tasks, like like a lottery.
(08:26):
You don't want people to be able to predict your lottery. Um,
that would be bad because then it would no longer
be a gamble. Yeah, well those are those are the
machines that they used to pull the ping pong balls
out of her true random numbers exactly. So again assuming
all the ping pong balls are in the same shape
and blah blah blah, and they are the same weights, exactly. Uh.
But things like random sampling if you wanted to do,
(08:49):
if you wanted to do true random sampling, and I
can give you an example of where this would be
really important. Excellent airports. Yes, so in an airport security line.
You know, supposedly airport security are going to single out
people for random screening. Now to make that truly a
random event where it's not based upon anyone's appearance, you know,
(09:11):
you want to take all of that out because you
don't want to do the profiling thing, because that has
its own set of problems. If you're truly random, you
need to have a system that's going to just tell
you a really random result, like say the third person,
the third person to go through the line next, that
will be someone you single out, and then seventh will
be the next one or whatever. But you would want
(09:34):
a true random number generator to come up with that
so that you could show that there was no preference,
there was no bias that went into that sample. Plus
it makes it a little more frightening for someone who
might be thinking about trying something. Is the idea that
you might be selected completely at random for a check.
But yeah, it doesn't matter how quote unquote normal you
(09:54):
look or unassuming you look, you could still be picked indeed. Um.
Whereas with a pseudo random number generator, you might want
that for something like if you're uh doing some sort
of simulation, because it's going to be much more efficient
and a lot of When we're talking simulation, we're talking
about simulation of complex systems, like let's say atmospheric systems.
(10:15):
That's incredibly complex, and if you were to rely on
true random number generators to generate the numbers you need
to run that simulation, it would not be nearly as
responsive as what you would need to to get a
true simulation. True simulation is kind of a oxymoron, but
at any rate, um, But there's some really cool ways
(10:37):
that various organizations and and and mathematicians have come up
with to try and create as random a number generator
as possible. Using computers. Yes, I figured, since you just
mentioned it, the use of atmospheric noise would be a
good place to start in that it's a random dot org.
It's a great resource as far as putting this podcast together.
(10:59):
And yes, yes, definitely, Um, that's that's how they try
to generate random numbers. Right, So let's say, let's say
that what they do is it's a combination of the
pseudo random number generator and the true random number generator method. Uh.
They use observations of atmospheric phenomena to generate a random
(11:21):
number and um, and they do that by putting it
through I suppose it's like putting it through an algorithm.
But at any rate that they depending on what's going
on the atmosphere at any given moment, that's what's going
to generate that random number. Uh. Now, now we start
to enter a philosophical debate about whether or not that
(11:43):
atmospheric conditions are truly random. And the reason why there's
a debate is that there's still a debate on is
everything deterministic, meaning that things happen and those things cause
other things to happen, and those things cause other things
to happen. And if you know the whole system. You
can predict everything that's going to happen. Versus nondeterministic which
(12:04):
allows for random occurrences. Fish still not not aplicy I
knew that was going to happen, so that's real, that's
not random. Good point. So now in a deterministic system,
what you could argue is that the conditions of the
atmosphere are in fact predictable if you know all the
(12:27):
factors that are going into making that condition. But that's
if there is a low pressure system. You could say, well,
you know, there is a good possibility that is going
to rain today, and so I by that I could
say it is more likely to rain than not rain
because the pressure is low. So there are things factors
taken a new cap, but what about the smog in
(12:50):
the area, or whether there is dust from any interference
from electromagnetic radiation Exactly these things can factor into it
to uh to change the probability that something will happen.
That's why we're talking about these incredibly complex systems. Now,
if there were some way for you to know all
the factors that were going into making the atmosphere behave
a particular way at any particular time, you could no
(13:12):
longer say that that was a random number. It's almost
just an academic argument because there's no way you can
know all those factors. It's just too complex, and especially
if you start to pull in things like chaos theory. Now,
if you've heard of chaos theory, you know that chaos
theory states that very small events can contribute to enormous events.
(13:35):
And the thought experiment that is always referred to is
that a butterfly flaps its wings and John Travolta gets
a movie no, I'm sorry, tsunami wipes out some city
somewhere across the world. So the wind uh generated, the
tiny little movement of air molecules generated by that butterfly's
(13:56):
wings flapping in Brazil sets off a chain of events
that ultimately leads to a catastrophically huge weather phenomena somewhere
across the globe. UH. And of course, not like, not
like instantaneously. Not I'm not suggesting that Brazilians go out
in massacre butterflies, but rather that these tiny events are
(14:18):
what contribute to enormous events. So in that sense, anything
that's going on in the atmosphere at any given time
is is the product of so many different tiny factors
that it's mind boggling right, right, So that uh, it's
it's so complex essentially then that while it is not
(14:39):
that is essentially random, right, it may or may not
be truly random. But one, we are not capable of
knowing that because it's so complex. And two it doesn't
matter because we're not capable of knowing that. So even
if even if you were to somehow philosophically argue that
it's not truly random, it's as close to truly random
(14:59):
as it needs to be, or in order for us
to go ahead and say, you know, it's just an
academic argument. Um, And that's not the only the the atmosphere,
observing the atmosphere is not the only method that people
have used to try and generate a random number. No,
not at all. And it's um. And as a matter
of fact, the one I think you were thinking of
(15:20):
would be, uh, the quantum mechanics version of determining random numbers.
That one that was a cool one. It's a really
cool idea. Uh, we're talking about by using particles that
are smaller than an atom to determine the randomness of
an event or in this case, generate random numbers. Yeah,
(15:40):
we're talking here about these quantum particles are behaving in
a way that we cannot predict at this time. Now, again,
this could mean that either the quantum particles behave in
a truly random fashion, or it may mean that we
don't understand them well enough to be able to recognize
(16:01):
the patterns or a series of events that are going on.
Or it may be that we we just don't have
the instruments capable of measuring that. So it may be
that the behavior is in fact predictable if we have
enough information instant of enough uh instruments. But as it
stands right now, it appears to be completely random. So
(16:23):
if you base a random number generator off of quantum events,
then you would get results that to us appear to
be completely random, and you could generate enormous numbers, I
mean numbers that are so big that you know you
you would if you were to try and write one
down on a sheet of paper, would take up the
entire sheet and and it would have no apparent repeating
(16:46):
integers at all. As you're going through there. Um Yeah,
hot fits actually is a website um a project from
a an organization in Switzerland Formulab, and UH they have
connected a Geiger Mueller tube to a computer to basically
(17:07):
UH their drag racing decaying atoms. Right, because adams decay
at an unpredictable rate, right, So that so by measuring
the decay rate, UH, that can in turn generate a
random number. Yeah, they take two, they take a pair
of decaying atoms and UH basically when when when one
(17:28):
of them decays and and releases particles, then that helps
them generate random numbers. And pretty amazing stuff really to
to be measuring that and to be using that for
a number. But the thing is, as as Jonathan mentioned earlier,
it is not something that happens very quickly. You have
to submit a series of numbers. You say, I want
(17:51):
fifty six random numbers, and they have to be no
larger than you know, forty three digits long, and things
like that. You have to you have to program this
in advance, and they will return and set of numbers
to you. But it will not happen instantaneously. Now do
you know what my favorite, uh version favorite way of
(18:13):
generating random numbers is? What's that? My favorite one was
a project and unfortunately don't have the name in front
of me. But my favorite one was a project where
it was using a webcam pointed at a lava lamp
and by measuring the shapes of the lava quote unquote
in the lava lamp, it generated random numbers. So the
(18:33):
yes as the lava lamp, as the the wax inside
the lava lamp changed shape, the webcam would measure that,
you know, it would get an image of it, and
it would be analyzed by the computer to create a
random number. And because the shape was constantly changing, you
could generate random numbers at any given time. And I
(18:55):
love that. The simplicity and the elegance of it is amazing.
Oh yes, so you've got it in front of you. Yes,
I don't have my glasses on, so I can't read it.
Lava Lava RAND, Lava RAND, Yes, l A v A
r n D. That project is still still exists, although
they are no longer generating random numbers based off lava lamps.
As far as I understand, that's pretty neat. Though I
(19:16):
had never heard of it before. Now it is a
neat neat concept. I thought that was you know again,
mathematicians are wacky, crazy, awesome people and we're talking about
like mathematicians, who are you know, studying math for things
like number theory. Yes, stuff that's so far beyond my understanding,
as the Aida Love Lace podcast illustrated to Great Lakes. Um.
(19:40):
But yeah, that these are the folks who are are
coming up with the various theories about how to generate
random numbers if in fact it is truly possible. Yes,
and I think that their work proves that our days
are numbered. Well, with that, how about we move on
to a little listener mail? Is it? This really isn't
(20:02):
listener mail. This is a message that comes to us
courtesy of our Facebook group. So, uh, Elizabeth, Elizabeth, we're
gonna call this Facebook facts and then uh putting put
in the sound of a hand slapping someone's face. Owl.
So this comes from Dan, and Dan says, uh, says,
(20:23):
how about late a history of broken English and long
forgotten keyboard keys. I would like to see how it
came about. Also popular terms like pound. Thanks all right, Dan,
So you're talking about let speak leap being short for
elite and really this kind of grew out of the
bulletin board system culture. So you had on bulletin board systems,
(20:45):
you normally had not Normally, a lot of bulletin boards
had multiple um levels of access, and there might be
a general access where you can log in and you
can access certain things that everyone has access to. But
then you might have to pay or be invited to
become part of a more restricted access community, and that
(21:06):
might get you stuff like access to two different files
or games or whatever. And because you're part of an
inner community, you of course would began to develop a
sense of elitism because that's just that's kind of how
we humans work. Well, yes, we uh, we do tend
to enjoy being part of an exclusive club. Yes, and uh.
(21:30):
When you are part of an exclusive club and you're
interested in being part of an exclusive club, you might
try to find ways to maybe disguise what you're doing
so that not everyone could find it via simple keyword search. Right,
So you start to develop your own language, and we
see this across human societies outside of the computer realm,
(21:50):
of course. Well slang, I mean, if you find a
slang dictionary and and the slang terms go from generation
to generation and region the region, and region the region.
So you might find a certain group has their own
kind of vocabulary. Well, the same thing with lead speak.
Lead speak, as Chris was pointing out, part of it
was to kind of obvious skate what they were talking
about so that you couldn't easily find it with a search. UM.
(22:12):
This was particularly important if they were trying to share
things like software that had been under copyright. If you
had an a copy of a program and you had
cracked it so that you didn't need any kind of UH,
there was no longer any DRM attached to it, there
was no sort of copy protection attached to it, and
you wanted to share this with all of your lead friends,
(22:33):
but you didn't want people to figure out that you
were doing something illegal. You might go through and change
a few little figures, a little some of the letters
to different symbols so that you know, you could still
read it, because the you know, we look at it
and we're like, okay, well that that that UH dollar
sign is supposed to stand for an S or three
(22:54):
for any yeah, or five for an s um or
four for an A, that kind of thing. But you know,
if you were to search for that term, it wouldn't
come up because the computer doesn't know that the uh
the symbols are standing in for letters UH that that's
a human thing. That's one of those things that humans
do really well that computers don't do well unless you
(23:14):
go through in program and a whole new database of words.
I mean, anybody who's who's done the sorry, go ahead,
anyone who's done the the calculator thing where you type in,
you know, certain numbers and then flip it over and
realize that they look a lot like you know, the
numbers actually look sort of like a word. You can
spell very few simple words using a calculator, but um,
(23:37):
you know, it's it's instantly recognizable to somebody who has
a grasp of the language, which is why a lot
of the words that they used gradually shifted from just
using numbers in place of letters to actually being spelled
differently as well. Right, uh so the first lead speaks
probably there for again, off you skate what they're doing.
(23:58):
But then it kind of came a way to communicate
with other LEAK members and to exclude neubs, the new folks,
even if you were in the public areas, because the
new folks were they were it was completely foreign to them.
And so if you first start looking at a page
of Lead speak and you're not familiar with. It just
looks like gibberish at first glance. You know, you actually
(24:21):
have to take an effort to kind of say, oh, wait,
that symbol probably means a you, and that one looks
like an S, so that's a nest, you know, and
a casual glance it looks like it's meaningless, which it
serves the purpose of the lead group very well. But
then they started to do other things, because these are
the same sort of folks who kind of have a
mischief streak a mile wide, and so they began to
(24:43):
incorporate things like common typos. Typos would become the the
typo version of a word would become the official version
of a word, which is why you would see things
like t e H standing in for the so and
and you. Misusing words on purpose also became very common,
so it began to develop its own grammar. So, for example,
(25:05):
instead of instead of using awesome as a a an adjective,
you'd use it as a noun and call it too awesome,
that is too awesome, and uh, things like pooned that
of course came from the TYPEO version of owned and you,
you know, owning as in I owned him. In this argument,
I completely dominated him by showing him that he is
(25:29):
stupid and is a poor debater, and I am awesome
and he is not, or I am too awesome and
he is not um so I pooned him. And most
of the lead speak is based off one of these
two things, or or a combination of the two, where
you substitute a letter for either a symbol or a number,
(25:51):
something that that resembles it physically but is not it,
or it is some sort of misspelling or TYPEO version
of the original word. And I mean most of it
can be can be pretty easily translated if you if
you just give yourself a little time. Sometimes if they
get really, really, really complex, it gets to a point
(26:13):
where you're like, uh, now, certain symbols are standing in
for entire um are symbols are standing in for an
entire syllable? Right? And then some of the others who
were really into lead speak also incorporated. You could also
make a t out of certain lines on the keyboard,
(26:33):
making it a little bit even more obfuscatory, could even
could tell you'd have to really scrutinize it to go,
oh wait, those three characters are making up another letter
for let's say that you wanted to use, use the
ampersand to stand for the sound and okay, and then
you put a B in front of it, so it's
be ampersand and it's banned, right, so you can say, I,
(26:54):
you know, don't do that, you'll get banned be ampersand
meaning that, Well, if you really want to go even
one step further, you would not even use the ampersand
you would use the seven, which of course is the
same key that the ampersand is found on, so B
seven would stand for band. So now now you've gone
from just replacing letters to actually creating a little minor
bit of a cipher. Um. It's a pretty simple cipher,
(27:17):
but it can be very confusing to someone who has
never encountered it before. Yep, yep. And gradually more people
became acquainted with it as uh the lead people with
lead skills UM started playing games online, and they would
start using lead language in the communication part of the
(27:39):
game when you were typed comments along with the game. Uh,
you know, and could interact with the other players online.
And then uh, from the research I did on it,
apparently the the comic Mega Tokyo basically made it more
even more of a popular phenomenon to to speak lead um,
just to bring it out that much on the open, which,
(28:00):
of course, for the people who are truly lead uh
probably drove them absolutely bonkers because suddenly they were no
longer exclusive, right yeah, so, and of course now it's
it's pretty much just common parlance on a lot of
the web, where you know, it's almost it's almost just
become a parody of itself, um, which is kind of
funny because it was already sort of the parody at
(28:23):
any rate. That's pretty much the lowdown on lead speak
and uh and and why people funny yep. Uh. If
you have any questions that you would like to pose
to the tech Stuff group, the grouping Chris um, you
can do so at our email address, which is tech
Stuff at how stuff works dot com, or like Dan,
(28:45):
you can leave us a message on our Facebook group
because we are checking that every day, or you can
contact us on our Twitter handle and you'll hear all
that information at our handy dandy PostScript announcement done by
our very own Chris Pallette. Hey. Then in the meantime,
I hope you guys enjoyed this show and we will
taught to you again. Really soon If you're a tech
(29:10):
stuff and be sure to check us out on Twitter
tech Stuff hs WSR handle, and you can also find
us on Facebook at Facebook dot com slash tech Stuff
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(29:35):
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