April 10, 2015 • 45 mins
We hear about "big data" more and more these days - but how much of it is sensationalism, and how much is true? The answers may surprise you.
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Speaker 1 (00:01):
From UFOs, two, ghosts and government cover ups. History is
riddled with unexplained events. You can turn back now or
learn the stuff they don't want you to now. Hello,
welcome back to the show. My name is Matt and
i'm Ben. We're here with our super producer Noel, which
(00:22):
makes this stuff they moge want you to know. Close
snug quite there. You're right, I said something wrong. I
can't figure out what it was. Yeah, we're pretty excited
about this new gadget or I guess new to us
gadget that's here in the studio to day, Matt, do
you want to tell everybody a little bit about it?
It's just a couple of old school synthesizers from the
(00:43):
seventies and eighties, and hopefully no one's going to be
bringing those in the mix. Maybe on this show, maybe not.
I don't know. I don't know. He looks like it
could go either way. So let's start today's episode with
an anecdote that you may have heard if you watched
our video series earlier this week two thousand twelve. Minnesota,
(01:04):
and this family starts getting ads from Target. You know
every people get ads from Target, right, You're used to it.
It happens, but this time something's different. They've received ads before,
but this time something's off. The ads are for stuff
like diapers, strollers and baby lotion and stuff. You get
the idea, like for someone who's expecting lovely thing to be.
(01:27):
But there's a catch here. Nobody at that house is expecting.
So the dad is livid, and the especially about the
ads addressed specifically to his teenage daughter. That's the whole
point to his high school aged daughter. Yeah, yep. And
so he goes to Target in person on in some
(01:47):
beast mode WTF kind of stuff trending towards w w E.
Possibly when he goes back, has a chat with his daughter,
comes back to Target and says, oh, yeah, she's expecting
in August. Whoops on all accounts. This comes to us
(02:07):
through a New York Times piece that was published on
February nineteenth, two thousand twelve, about how obsessively companies track
your shopping habits and every little piece of information they
can about you to hopefully make you more likely to
buy something. And the guy who was interviewed, the statistics
(02:29):
man working for Target interviewed in this New York Times
article was shut down after he mentioned this because what
we're talking about is something that many many companies do
but is incredibly controversial, and that is big data. Yeah,
it's something that it's not necessarily something they don't want
(02:51):
you to know, but they don't want you to know
about it, Like it's kind of a known thing now
that you are being tracked in all these different ways
and looked at. But this is something that we feel
you should know more about, right, Yeah, it's something that
it's it's strange that you say it's not necessarily something
they don't want you to know, because they certainly don't
(03:12):
want other people to know the techniques used to gather
this information. Absolutely, they might not want you to know
some of the things that we can't tell you because
we don't know, right, because the data sets themselves are important,
But what maybe even more important and even more secretive
would be the techniques used to parson analyze this information
(03:35):
and even where they're getting some of this data from. Ah. Yes, yeah,
So what what is big data? If we have to
define it, it's not like it's not the same thing
as uh, big agribusiness or big bell or something big pomegranate. Right, No,
big data is just as in definition, any extremely large
(03:57):
data set or data set so that can be analyzed
usually now in the modern day computationally is the only
way to even get your wrap your head around it
um to reveal patterns and trends all kinds of associations,
especially those relating to human behavior and or the interactions
between humans. So, for instance, this big data would not
(04:20):
be the GPS of one person. It would be the
GPS of a city, or people who all work at
a large company or yeah, one one GPS companies data.
Yeah or yeah, But I love the idea of the
GPS information of one like certain area and just all
(04:40):
of that data stacked vertically together from everyone that's been
in that area. Right. There are different ways to parse this, uh.
This big data usually include sets that are so large
there beyond the ability of typical software tools Like you couldn't,
for instance, just take uh, some data set that is
(05:02):
big data size and put it in an Excel spreadsheet
on a Google travel. No, there's no way, and if
you did, it would be the largest file in the
history of XL. So when we look at the range
of this, we also know that the size of data
is a constantly moving target. Because it seems that there's
just more and more and more out there. There's a
(05:23):
really weird statistic here somewhere that I think is like
nine percent of all the data that exists was made
in the last twenty or ten years or something. Oh. Absolutely,
it's exponential. And that's one of the things we're gonna
talk about here. Just the number of devices that collect
data or that you can collect data upon, and just
everything that's connected to the Internet nowadays. It's insane. And again,
(05:48):
it doesn't go away once. Once you have a data set,
it's not like it's irrelevant. You're still going to need
that maybe for future, you know, whatever endeavor is that
you're going to do as a big business. Right for gamers, uh,
this would be sort of the equivalent of an inventory
that doesn't register weight. How role playing games turn and
(06:09):
you're more familiar with this, and I am, how role
playing games turn so many people into orders. We are
data orders as well, like the NSA clearly is. Uh. So,
what we'll do is will walk through some history of
big data, and then we'll also talk about some of
the controversy surrounding it, the ways it could be used,
(06:30):
some of the conspiracies theoretical and factual about this practice.
So one way that one way there's a group called
Meta Group or Gardner. Uh. They they have an analyst
there named Doug Laney, and he said, one great way
to measure big data would be in the three vs.
(06:51):
That would be increasing volume, uh, just how much data
you have, Uh, the velocity of the data, the speed
of the in and out right, and variety that type
of stuff, where is it coming from? So not just
all homogeneous GPS records, but also like what something else
people would collect on another set of data that's really
interesting to look at when when combined with GPS data,
(07:14):
our phone records, that's always a fun thing. Um. And
these these three vs that you're mentioning, this is now
an industry standard the way the whole industry looks at
big data with what is it? Volume? Velocity? What was
the third one? That's volume, velocity and variety. So uh,
GPS and phone records would be something that that helps
(07:36):
you flesh out a virtual persona, but it comes from
the same device probably for most people. True. So another
thing that would be more very would be stuff like
medical records, stuff like recent purchases on your financial records,
so you could part it down to uh people who
let's get a little bit dark with it. You could
parse it down to uh some We have these four
(07:58):
sets of data out this person, right, So we know
that once every week or something they go to a
clinic right and specializes in some kind of treatment, right.
And then we know that the medical records that they
have some kind of uh, debilitating condition. And then we
see that one of their recent purchases is a skydiving suit.
(08:23):
So we have built this picture with just the very
little information about someone who probably has a terminal disease
and wants to skydive because it was on their bucket list.
Now we know exactly the kind of things to sell
to this person, and we know exactly the kind of
things to sell, which is frightening. Uh. So this is
I mean, this is an example that you and I
(08:44):
just made up now, right, Matt, this is not we
don't know any more and that this has happened to
so this is a new information age. It's tough to
stress how quickly personal privacy has eroded, right, um, and
we the people of the information age, have committed um,
(09:08):
have have lost our privacy due to acts of negligence,
not due to aggressively rooting for this loss of privacy.
It's just who reads terms and conditions, right, Yeah, And
we're all very happy about some of the things that
providing this type of data gives to us. The GPS
(09:29):
thing alone, I think is a massive I mean a
lot of people would I don't. I can't speak for everyone,
but for myself to have the ability to use the
GPS when I'm out on the road somewhere instead of
having to look at a physical map is one of
those things where sometimes I just go, you know what,
screw it, turn my GPS on. I need it, right,
(09:49):
And even if they know that this, were highly aware
that this is being tracked. But but there's also there's
such a a sort of narcissism in paranoia, this idea
that yes, of course they care where I get my pickles.
Of course, of course someone does. And don't get me wrong,
(10:11):
I mean sure grocery stores for sure do. Every time
you swipe a loyalty card, you are generating more information
for them to uh target ads toward you, which we'll
talk about whether or not that is, you know, morally
wrong or ethically sticky or whatever or if you would,
would someone want that? Like, are there people out there
(10:31):
that want Kroger to know exactly what the order so
they can just say, hey, here's all the things that
you want and hear the coupons right there? And there
are some people I would definitely take some coupons for things.
But also, this is okay, this is so unrelated. This
is a side conspiracy here. And Matt, uh, So you
and I are just old enough to remember before these
(10:52):
loyalty cards came out everywhere, right, Yeah, when I was
in college, they didn't exist. So the way that they
were instant you did was that for for all the
young guns out there, the way that these cards were
instituted was at first they gave you discounts on things. Right.
But one of the conspiracies I've heard is that this
(11:13):
artificial price shenaniganting if I can make up the word, uh,
ultimately became something where having the card didn't really get
you a discount. It just got you the the regular price.
The regular price, yes, and the money that they're making
on top of selling your information to third parties. CBS
is like yeah, thanks, yeah, it's and and so you're
(11:36):
you're being penalized at least, this theory goes for not
participating in this program. And that's because again, the overwhelming
majority of companies, uh, don't just do this. They do
this with relish and it is profitable. But it is
a very very old idea. Is that I have to
say this, here's the worst news about that whole situation.
(11:57):
What's that even if you go somewhere and shop. I'm
just gonna use an example, but I'm aware of like
publics that I don't have a loyalty card for and
I don't know that you can get one. Maybe you
can now, but and you you try and save money there,
if you pay with a card of any type, then
that stuff is being tracked, maybe by a separate company,
a third party, or maybe just your credit card company.
(12:18):
But the same thing is happening. And we'll see a
couple of different reasons that this happens, you know, or
or a couple of different motivations for this collection. But
let's walk through the history first. Okay, so this is
not a new thing. Trying to track data, trying to
understand numbers about things that are happening in the world
(12:40):
around you. Goes back seven thousand years to something that
we've mentioned before in Mesopotamia during the birth of agriculture,
when you had to keep track of seeds, you had
to keep track of crops and soil, just everything you
needed to know data about the ground and the plants
that you're trying to put in there. So yeah, So,
(13:02):
for example, there's an accounting system that goes in to
monitor the growth of a I don't know, uh, an
olive sure, an olive tree, right, and so, um, you
know some farmer named John Stamos, a very common name
in Mesopotamia at the time. I imagine, Uh, this farmer,
(13:23):
John Stamos has uh, a bunch of olives, and they
have to have a way to track year over year
the performance of that crop. Right. So this is when
they begin saying, Okay, you know John Stamos has X
amount of trees. They yield y amount of olives each year.
This is what we can expect. This is what we
(13:44):
can expect, this is what we can bet against, this
is what we can sell an advance before it's made.
Then you get into we're gonna jump forward pretty far here,
all the way to sixteen sixty three. Nothing else happened, yeah, zero,
nothing between seven thousand years ago. Let's just say improvements
(14:05):
are being in incrementally, you know, are happening up until
this point. But then on the next big change, the
sixteen sixty three when John Grant, I think that's how
you correctly spell it. Grant very British. Uh. He recorded
an examined information about mortality rates because the bubonic plague
(14:25):
was just ravaging just the entire area at the time,
and he decided he wanted to know more information about,
like how what is happening here, exactly how many people
are getting affected? Why are they getting affected? Let's get
information and we can start solving this. Yeah, and that
sounds that sounds sensible. It's the least you could do,
right we I don't know, have we ever talked on
(14:47):
this show about just how profoundly the plague or the
series of things known as the plague change the world. No,
I've listened to a little too much stuff you missed
in history class about it. So the information, Saan sometimes
gets muddled between what we've talked about and what I've
just heard. These plagues have played such a profound role
(15:07):
in uh, the global evolution of the human species. It's
just crazy it's the kind of stuff you would want
to keep track on. So this guy, uh, John Grant becomes,
uh the father of statistics, or he's considered that because
he does the first statistical data analysis that we have
recordings of, uh, and he has a book about it
(15:28):
called Natural and Political Observations Made upon the Bills of Mortality.
Just kind of a dry name, right, but it's it's
not a feel good subject, and people continue to work
off this statistical analysis. So let's fast forward to the
twenty century. Okay, so we're going to fast forward all
the way to eighteen eight seven. This is when the
(15:52):
modern the age of modern data, modern data is when
it is born. So this gentleman named Herman Hollerith invented
a computing machine that could read these holes punched holes
in cards that paper cards in order to organize census data.
And this is a huge change because you have to
(16:14):
imagine just collecting data, of going door to door getting information,
then trying to compile that just with humans in rooms.
That it was taking so they would do one one
census every ten years, I believe at the time, and
it was taking almost nine years before you would get
the results from the census of the previous the previous census.
(16:35):
So it was almost I don't not worthless, but it
was just felt like they were running backwards almost right. Yeah,
they had they had a data set that would be
when it was finally complete, useful for a little less
than a year. Yeah, exactly so. Uh. The first data
processing machine appeared in nineteen forty three. This was, of course,
(16:56):
it came out of war. A lot of technological in
vations come out of war. Uh. And it was meant
to decipher codes from the Nationalist Socialists or the Nazis. Uh.
This thing was named Colossus, which is a pretty cool name.
And they would intercept messages. They would feed things to
Colossus and would search for patterns in these characters. And
(17:17):
it worked pretty quickly. Yeah. It would go five thousand
characters per second, which is huge. It reduced the time
from weeks two hours. Let's stroll through some other stuff here,
just kind of laundry listed so you can get to
the good stuff. Uh. Nineteen fifty two, everybody's favorite, the
n s A, the National Security Agency is created, and
within ten years they have more than twelve thousand cryptologists
(17:41):
on contract. That's huge. Then you've got. In nineteen sixty five,
the US government builds the first data center which can
store seven hundred and forty two million tax returns and
also a hundred and seventy five million sets of fingerprints.
Now this is pretty interesting here because this is this
(18:02):
is something that was recorded on the magnetic tape and
computer tape. You may, I don't know if anybody listening
would know what that is. Hopefully maybe you've heard of
this magnetic tape. My father is a controller controlling account
and they have a like a newer version of this
magnetic tape, but it's still all of their stuff is
backed up to this magnetic tape because it's so well,
(18:25):
it's supposedly so reliable. Well, also, people will recognize the
magnetic tape if you've never seen it before. Um, every
time you see an old computer with reels on it,
that's magnetic tape. In Captain America the Winter Soldier, the
sequel to the First Captain America, Uh, there is a
scene which I won't spoil for you if you haven't
(18:46):
gotten around the scene it yet, but there is a
scene which involves a gigantic computer and that is magnetic tape. Awesome, Okay,
good reference. Now we understand. But here here's the thing. Though,
This whole project was scrapped because of fears of quote
big brother right, being a little bit too big brotherish
little Orwellian. So this, however, changed everything because people were thinking,
(19:11):
what if we centralize, um, the location of data, you know,
no more paper, just electronically store it. A British guy,
so you may have heard of tim Berners Lee of
Invince what will go on to become the World Wide Web?
And with this foom, we're going like gangbusters because people
(19:34):
are able to generate massive amounts of information, much more
so than anybody could plausibly read. And when it's connected
up to this uh interweb, if you will, it can
be it can be sent somewhere else, right. And then
if you do have a data center, doesn't matter where
the data is collected, you can send it directly over
(19:54):
there almost immediately. Yeah, it's it's bizarre when we think
about that, and especially we think about um, how just
let's have a John Henry moment, and can you compare
matt the the ability of a supercomputer to a person.
Oh God, can I m hmm. Let's say okay, let's
(20:17):
say in the first supercomputer. Let's use that one as
our example. It could do as much work in one
second then a single human being operating a calculator could
do in thirty thousand years. Thirty thousand years. And that's
not even the twenty one century, ladies and gentlemen. Now
(20:39):
we are at the modern age. And two thousand five,
a guy from O'Reilly media coined the term big data
for the first time. Uh and this was, you know,
a successor to a another less fortunate buzzword, which was
web two point oh yeah, yeah, the new coke of
web words. But yeah, so this this idea here is
(21:03):
a little bit more bad of the idea of data
set that is just so massive and complex and interwoven
that you can't use the traditional business intelligence tools to
figure out what's going on. Right And oh five is
also the year that Hoddup was created by Yahoo, which
was built on the back of Google's map produce. And
these are just softwares that can basically take data from
(21:28):
using a bunch of different computers to crunched numbers like
and huge amounts um And it was the goal to
index the entire or the entire worldwide Web. That's why
these things were created and uh, it's the open source
to dupe. It was used by a lot of organizations
to crunch through data. That just is it's almost in
(21:50):
quantifiable how huge it is. Right, and this is not
just a private industry thing, of course, And the line
is blurry. We seem to talk about these two events
in isolation, as if the n s A using phone
records and social media contacts. Who do you know that
knows who? That knows who on Facebook? Right on the list?
Now you're on the list right. Uh, they're they're not
(22:13):
just using that, Uh and target or another private organization, um,
a data broker. There are companies that just broker data. Uh.
These do not exist in a vacuum. There's interplay between them,
and uh, they're increasingly merging to do just some amazing things.
(22:34):
We're getting very very good at seeing the present as
never before. And other governments are involved in this as well.
In two thousand nine, the Indian government did something just
ambitious is like the most reasonable word for this. They
decided to take an Irish scan, fingerprint and photo of
everyone in India, every single person in India. There are
(22:59):
so many people, Yes, can you imagine listener Uh, if
the government came to you and said, Okay, we're going
to need we're gonna need an Irish skin of fingerprint,
and we're also going to need a photograph, a really
nicely framed photograph with good contrast. We're gonna put it
in of you and everyone in your family put it
(23:21):
into this database. Don't worry. We're gonna make sure it's secure,
and we're not going to use it for anything but
for good things. Right, Yeah, and that's one point to
billion people to bill. Yeah, this is the largest biometric
database in the world. So there's a great thing that
Eric Schmidt from Google also said, right, just another sense
(23:44):
of perspective here, yeah, he he stated at the Techonomy
conference in Lake Tahoe. He he stated, quote, there were
five exabytes of information created by the entire world between
the dawn of civilization and two thousand three. Now that
same amount is created every two days. Boom. Take take
(24:07):
that history of the universe. I can't wait till the
aliens land and say uh that they're like, Wow, these
guys figured out how to make uh pizza into burritos
and there's a blog about it. It's sort of like
when you know, when people modern times find ancient Greek
or Roman or African ruins from these empires of bygone days.
(24:31):
And there's always some jerk like hundreds of years ago
who wrote like Tim was here, yeah and true, like
dick butt from Reddit. They're just like, what is a
precursor right there? Right? So there's but there's so much
information being made and you know, I'm being a little
crass here, but the point I'm hoping to make is
that this information is not you know, noble stuff or
(24:55):
even stuff that would really make sense to a human
being you didn't know they were looking for. These are metrics,
these are movements. These are little breadcrumbs of you scattered
around the Internet at large, and then you're just bringing
them together to make another picture. It's like pointali is um,
Really that's a wonderful, wonderful image. Yeah, that's really good.
(25:16):
On Everything you do online today or in any electronic
medium is recorded by somebody. Yep. If you're typing on
a keyboard or on a touchpad, it's getting recorded, So
enjoy it. I'm like, hey, unless you're on an air
gas computer, and we've all learned now what that is,
so okay, so let's just another example here. Uh, Fortunately
(25:43):
for some of us, I'm not going to name names,
but for some of us, it is not a crime
to be drunk on the internet. Well yeah, okay, sure.
And there are people who you know, have maybe in
a fit of passion or maybe they had some drinks
and they wrote something crazy on the Internet and they
almost sent it and they said, no, wait, I'm gonna
(26:04):
sleep on this. Let me think about this before I
write anything. Well, those ghost movements, those drafts that you make,
are also part of this. So just the act of typing,
especially in Facebook. Oh you know, yeah, don't goos draft
in Facebook, right, yeah, and all of these pieces of
information assemble. Again, that's such a beautiful image, man, a
(26:26):
point list portrait of you and who you are. And
the big worry that a lot of people have, and
we see it through sci fi and pop culture, and
we've seen this for decades, is that this will be
able to go beyond just a a panopoly view of
the present or a panopticon kind of view of the present,
(26:47):
to become predictive. Oh yeah, so eventually you just have
an understanding of what each one of these people is
going to do throughout their daily lives, what what the
corporation is going to profit from in the next two
three years? You can. I mean, it's crazy to imagine
(27:07):
all the information that we will eventually be able to
get from this big data and whose hands will it
be in well? And who can who can accurately understand this?
So we we also talk about these controversies. This stuff
is around to stay until the lights go out on humanity. Yeah,
this this stuff will be around to stay. Oh yeah,
(27:30):
it is here to stay unless there's some kind of
weird fight club moment and all the buildings holding all
the stuff the data centers blow up, which is probably
not gonna happen. There's really good security at those buildings, right,
and it's a distributed network, so it would be hard
to take the head off. It would hard be hard
to take all of the heads off the hydrant that
(27:51):
is the information age. Of course, this doesn't come without controversy.
We have we have a video about four creepy things
about big aida, or this umbrella term, which can again
apply to government as well as industry. It can learn
big data can be used to learn your secrets. That's scary.
So if you think, uh, nobody knows that you routinely
(28:13):
order three large cheese pizzas with ham and sit in
the dark in your house at two am every Thursday
night eating and crying. Nope, sorry, getching reruns A Firefly,
watching reruns a Firefly? Nope, sorry. Somebody knows. Papa John's knows. Comcast,
probably knows, Podcast, probably knows. Maybe Netflix. Uh so. The
(28:37):
another thing it doesn't have to tell you what it knows.
There is a surprising lack of transparency on the part
of companies collecting your information. Yeah, that, Um, what is
the name of the company Axiom? Axiom? Oh yeah, yeah, yeah,
they're scary. Huh uh dude, Yeah, I've been We're making
(28:57):
this video, and the way it works is usually Ben
will Ben will write an outline for what he's going
to present in the vlog. I will shoot it. Then
as I'm editing, I end up doing a lot of
research and oh my god, Ben, I've just been scouring
their website and nothing against you, Axiom. If you're listening
your employees, Um, it's just a murky world. Well, it's pervasive.
(29:22):
Like the the amount of information that Axiom has is
is impressive. Yeah, and the way they talk about it
sometimes their offline data that they have used for online purposes.
I don't know, it's fascinating if you go to the website.
So we we've talked a little bit about just the
(29:42):
science fiction elements and how we see some science fact generating.
But we knew know that this is a very old story.
We've seen things like Isaac Asmov's Foundation series, which deal
with the fictional at this point fictional science of psychohistory
predicting the future on a large scale event. We dealt
with gatica, where personal information medical information is used to gosh,
(30:05):
I'm trying to not to spoil things, yeah, but I mean,
what's the limit with spoilers? Like, at what point it
kind of stinks? I guess you just have to put
an alert at the very beginning of something that might
have a spoiler in it and just say don't listen
to this if you have not seen these movies, read
these books, or listen to these songs. Wow. Pretty okay,
(30:28):
all right, So that's that's pretty complicated. But we've also
seen it in of course Minority Report, which we mentioned.
But the controversy surrounding this, which is expressed in our
culture is uh oh oh and Another one would be
the dark night. Oh yes, yeah, we're Lucius Fox. Yeah,
(30:48):
and the cell phone system monitoring system. So, uh, we see,
we see this expressed in the culture of UM multiple nations.
But what what's the real stuff? What's the real controversy?
Could could it damage your credit report if you had
GPS data that said you were I don't know, going
(31:11):
to a pawn shop often or something? You know, Yeah,
I don't know, And that's I don't want to scare anybody.
That's a that's a made up example there, But we
do know that the we do know that the possibilities
for that kind of stuff. Again, just possibilities are there.
And then I love that you pointed out the other
side of the argument, which is, well, maybe this is
(31:32):
more convenient, Maybe this is the way things should be. Uh,
it's a personalized experience for me wherever I go. Yeah,
it's certainly sold to us that way, and I think
maybe some people by that. I I am, unfortunately somewhere
in the middle. I don't know if you ask for
my opinion, Ben, but guess what I'm giving. I have
(31:55):
fall somewhere in the middle where I'm I appreciate what
the I appreciate the attempt of what's trying to happen,
but ultimately, really the bottom line is that these companies
are trying to make a profit by targeting. Sure, right,
and that's the way you sell things. Nowadays, we're so
inundative with advertising that the only real way to get
(32:17):
a message across is to send it with an arrow
straight at your eyeball in your ear like, uh hi,
super producer Noel Brown, we understand that you have recently
begun working with a move. Here are some products that
might interest you exactly. I mean, that's the only way
you're gonna do it now. But yeah, that's that's probably true, man.
(32:39):
But I personally, my biggest concern about this, this encroaching analysis.
My biggest concern is that a can function as a
kind of inherent censorship due to like the search bubble,
the idea that based on your past search history, you
only receive results that are quote unquote relevant to you.
(33:00):
I don't want to receive the results that are relevant
to me. I want to see both sides of any argument.
I want to see um news that would be in
another language. There's gonna be some kind of college course
that shows you how to get the most balanced Google
search results, and it will all it will be an
(33:20):
entire course that just shows you how to develop over
time by searching for certain things at certain times. Well,
there are things like scroogle and Duck duck go that
are supposed to not track your search. There are places
to go currently, but you know, we've seen as acquisitions
continue and certain corporations continue to get more and more monolithic,
(33:44):
I can see a future where there is one place
to search for things. Well. And there's also this idea
that that I think is tremendously positive, even noble almost,
that if we had enough data, and we had enough
sophisticated part seeing algorithms or software, then we might be
able to address global problems that ordinarily wouldn't have been
(34:07):
able to be solved, Like what if there were a
way to uh stop the massive extinction of Earth's wildlife? Right, agreed,
And that's amazing and I love that view. But conversely,
you could also with that same data stomp out all
and every form of resistance against a certain movement. That's true, right,
(34:30):
and uh, this this becomes a matter of uh, I
don't know, the short term stuff versus the long term problems.
With that being said, let's go straight to the crazy stuff,
the conspiracies, both theoretical and actual. Uh, there's a there's
a this theory, the troubling possibility, the kind of thing
(34:54):
that a sci fi writer would make a dystopian novel about,
that we could eventually arrive at a world in which
circumstance and accident has fallen to the statistical hand of
faith and certitude, so that something very much like an
artificial god knows how you will live your days from
(35:15):
the cradle to the grave. Yeah. I don't like that, Ben,
It's a scary thing. There is some silver lining here, though. Uh,
there are many competitors right now in this space. There's
not just one big data right company. Right, Um, so
at least, you know, much in the same way that
there are shadowy forces trying to control the world, there
(35:36):
are a lot of them. There's not just one group. Yeah.
And then these groups might not necessarily work together, especially
if they're competing in a private sphere, right, not unless
it's you know, helpful for the bottom line. Right. They
gather their data sets and and they guard their techniques
pretty jealously. Also, here's the one thing when we talk
about this super all knowing Wizard of Oz type computer.
(36:00):
The fact of the matter is that we still apparently
can't build a computer that can predict the weather. No,
because there there's always with the weather. There's always that
um I don't know, that chaos factor almost to where
you can't. There's so it's so complex, all of the
different moving parts that create the weather. But what ben
(36:21):
what Yeah, I guess it would be. I guess the
same would be true for big data. There's so many
different moving parts and it's so complex. Yeah, it seems
like it would be. I don't know. Is it easier
to build something that can predict the weather or something
that can predict the passage of time in a country?
You know, we've talked before off air about this and
(36:43):
maybe on air too, but uh, you know, I had
a professor a long time ago and in different life
who was working with DARPA to build an artificial model
of a country, with the idea that if they programmed
enough data points together and assign them to these individuals,
(37:04):
then they could measure kind of like foundation in real life.
They could measure the likelihood of trends, you know, like
if if police, if support for police goes up by
x percent, what will be the effect upon the livelihood
or the likelihood of the regime's collapse or stability. And
(37:25):
I don't know where he went with it, but it
is some amazing, terrifying and inspiring stuff. Either way, he
can't talk about it anymore, right, and this and still
at least our knowledge listeners. Uh, this remains a theoretical thing,
but it is a fact that big business is doing
stuff like this all the time, and not necessarily. You know,
(37:48):
it's not like there's somebody out there just rubbing their
hands together, supervillain style, waiting for you to slip so
they can, you know, tell tell your mom that you
are smoking cigarettes. There's something there. What what it is
more about is um not immoral but amoral um providing
(38:09):
of a better service or being a better um service
to the consumer. But now consumers are increasingly the product
as well, right exactly, Your information is the thing the commodity,
which is so weird. Information as commodity. I guess it's
been coming for a long time and it has been
that way for a long time. It's just strange to
think about it on this scale. It's almost like the
(38:32):
information what I'm seeing it is the access and ability
to collect massive amounts of information. Is this new gold
rush thing? Ah, that's good. Yeah, you're killing it with
the comparisons. So I don't know if that's just what
I'm seeing. Where all these large corporations that are building
you know, massive supercomputer complexes of supercomputers that can just
(38:54):
crunch numbers. Man, well we're but we're at a point where,
you know, they're could be some positives for this if
if it was able to if these data sets were
able to, for instance, help humanity combat I don't know,
over fishing, or help humanity figure out the best way
(39:16):
to prevent mass starvation or disease. But again, you know,
those things are also they seem to be more complex
than weather patterns. They are, and I just it's hard
for me to imagine someone looking at those problems and
making a profit from it, or you know, devising a
way to make a profit from it and use all
(39:36):
these assets in order to do something good. I'm sorry, man,
my faith in humanity just like got ticked down a
couple of notches for some reason in thinking about all
this stuff. Because this is all really what we're talking
about here, are ways to sell things, right, That's what
this whole thing is about. Well, what I would say,
what I feel like, what we're talking about is a
(39:58):
little bit further than that. It's ways to predict future events.
So selling something is trying to predict what will trigger
a purchase, right, So it's it's still predictive, or hopefully predictive.
But the question though right now, and the answer it seems,
(40:18):
is that overall, while we know that people are working
fervently to build a machine that can read the future, right,
a modern day fortune teller, uh, we we do not
yet have that oracle, at least in the public sphere.
(40:38):
We don't know about it, but we do know that
people are working on it, and that brings us to um,
I guess one of the things we can close with today.
Huh Yeah, a little something called anomaly detection at multiple
scales or atoms. It's brought to you by DARPA. Friends
over at DARPA, good people. Um, they were card over
(41:00):
at DARPA. So this comes directly from the DARPA website.
And I'm just going to read you this quote's a
little along bear with me quote. The anomaly detection at
multiple scales or atoms program creates, adapts, and applies technology
to anomaly characterization and detection in massive data sets. Anomalies
(41:22):
in data cue the collection of additional actionable information in
a wide variety of real world contexts. The initial application
domain is insider threat detection, in which malevolent or possibly
inadvertent actions by a trusted individual are detected against a
background of everyday network activity. So they're looking, they're looking
(41:47):
at a person, a trusted insider person, and and then
they are going, oh, well, here is an anomalous action
or an anomalous piece of data inside this. Sure, Like
Mrs Cunningham works for a Wall Street investment firm, and
(42:08):
every day Mrs Cunningham has lunch at twelve thirty and
goes back to work until six thirty, at which point
she leaves and it takes her an hour and a
half to get home because of traffic. And then one
day she goes to lunch, but instead of going to
a restaurant, she goes down to you know, um, a
(42:33):
gun store, or she goes to a you know her
her anomalous change, right yeah, Or she seems to be
in closer contact with the competitors, so they could they
could call these anomalies. And we've we've heard people talk
about this sometimes, like if you haven't checked out the
website zero hedge, that's a it's a very interesting read
and it's well done. One of the things that they've
(42:54):
talked about before is anomaly detection and these theories about
you know, UM market forces or investor actions right before
calamitous events. Oh yeah, there's some great analysis of that
stuff by Tyler Dirton actually speaking of fight Club. Yes, yes,
uh so what Okay, this is part of one one
(43:16):
of the things I wanted to ask you about. Have
you heard the theory that um not only is Tyler
Dirden not real, but the girlfriend character is not real either.
I know that it's another person he made up. Oh wow, okay,
oh wow, okay. I'm trying to check it out. It
(43:38):
has nothing to do with what we're talking about it,
but we do want to hear from you, not just
with your Fight Club theories. But send him if you want.
I think it's an interesting movie too. I won't go
into some of the plothole parts. All right, Well, let
us know what you think about it, but more importantly,
let us know what you think about big data. Is
it possible to get off of the grid? How difficult
(44:00):
is it? What do you think, um, what do you
think people are doing with all this information the public
and private sphere. One of the big concerns that we
hear a lot about is the idea that the surveillance state,
at least in the US and the West, has grown
because um, the intelligence agencies and departments are able to
(44:22):
use the dirt they have on the elected officials to
prevent the elected officials from uh, you know, slowing the
growth of the surveillance state, the idea of a deep
state or shadow government. Okay, so to make me feel better,
please send in your suggestions like Ben had earlier, of
positive things that could be used that big data could
be used for. Please please send those to us, just
(44:43):
to make me feel better. Thank you, all right, and
you can you can hit us up on Facebook and
Twitter where we are conspiracy stuff. You can also drop
a drop by our website stuff they don't want you
to know dot com which has a bunch of stuff
on it. And if you want to send us an
email directly, please knock yourself out. It doesn't have to
(45:03):
be answering, just the questions, we asked, it could be
I don't know a joke during the jokes. Sure. Our
email address is conspiracy at how stuff works dot com.
From more on this topic, another unexplained phenomenon, visit YouTube
dot com slash conspiracy stuff. You can also get in
(45:24):
touch on Twitter at the handle at conspiracy stuff
From UFOs, two, ghosts and government cover ups. History is
riddled with unexplained events. You can turn back now or
learn the stuff they don't want you to now. Hello,
welcome back to the show. My name is Matt and
i'm Ben. We're here with our super producer Noel, which
(00:22):
makes this stuff they moge want you to know. Close
snug quite there. You're right, I said something wrong. I
can't figure out what it was. Yeah, we're pretty excited
about this new gadget or I guess new to us
gadget that's here in the studio to day, Matt, do
you want to tell everybody a little bit about it?
It's just a couple of old school synthesizers from the
(00:43):
seventies and eighties, and hopefully no one's going to be
bringing those in the mix. Maybe on this show, maybe not.
I don't know. I don't know. He looks like it
could go either way. So let's start today's episode with
an anecdote that you may have heard if you watched
our video series earlier this week two thousand twelve. Minnesota,
(01:04):
and this family starts getting ads from Target. You know
every people get ads from Target, right, You're used to it.
It happens, but this time something's different. They've received ads before,
but this time something's off. The ads are for stuff
like diapers, strollers and baby lotion and stuff. You get
the idea, like for someone who's expecting lovely thing to be.
(01:27):
But there's a catch here. Nobody at that house is expecting.
So the dad is livid, and the especially about the
ads addressed specifically to his teenage daughter. That's the whole
point to his high school aged daughter. Yeah, yep. And
so he goes to Target in person on in some
(01:47):
beast mode WTF kind of stuff trending towards w w E.
Possibly when he goes back, has a chat with his daughter,
comes back to Target and says, oh, yeah, she's expecting
in August. Whoops on all accounts. This comes to us
(02:07):
through a New York Times piece that was published on
February nineteenth, two thousand twelve, about how obsessively companies track
your shopping habits and every little piece of information they
can about you to hopefully make you more likely to
buy something. And the guy who was interviewed, the statistics
(02:29):
man working for Target interviewed in this New York Times
article was shut down after he mentioned this because what
we're talking about is something that many many companies do
but is incredibly controversial, and that is big data. Yeah,
it's something that it's not necessarily something they don't want
(02:51):
you to know, but they don't want you to know
about it, Like it's kind of a known thing now
that you are being tracked in all these different ways
and looked at. But this is something that we feel
you should know more about, right, Yeah, it's something that
it's it's strange that you say it's not necessarily something
they don't want you to know, because they certainly don't
(03:12):
want other people to know the techniques used to gather
this information. Absolutely, they might not want you to know
some of the things that we can't tell you because
we don't know, right, because the data sets themselves are important,
But what maybe even more important and even more secretive
would be the techniques used to parson analyze this information
(03:35):
and even where they're getting some of this data from. Ah. Yes, yeah,
So what what is big data? If we have to
define it, it's not like it's not the same thing
as uh, big agribusiness or big bell or something big pomegranate. Right, No,
big data is just as in definition, any extremely large
(03:57):
data set or data set so that can be analyzed
usually now in the modern day computationally is the only
way to even get your wrap your head around it
um to reveal patterns and trends all kinds of associations,
especially those relating to human behavior and or the interactions
between humans. So, for instance, this big data would not
(04:20):
be the GPS of one person. It would be the
GPS of a city, or people who all work at
a large company or yeah, one one GPS companies data.
Yeah or yeah, But I love the idea of the
GPS information of one like certain area and just all
(04:40):
of that data stacked vertically together from everyone that's been
in that area. Right. There are different ways to parse this, uh.
This big data usually include sets that are so large
there beyond the ability of typical software tools Like you couldn't,
for instance, just take uh, some data set that is
(05:02):
big data size and put it in an Excel spreadsheet
on a Google travel. No, there's no way, and if
you did, it would be the largest file in the
history of XL. So when we look at the range
of this, we also know that the size of data
is a constantly moving target. Because it seems that there's
just more and more and more out there. There's a
(05:23):
really weird statistic here somewhere that I think is like
nine percent of all the data that exists was made
in the last twenty or ten years or something. Oh. Absolutely,
it's exponential. And that's one of the things we're gonna
talk about here. Just the number of devices that collect
data or that you can collect data upon, and just
everything that's connected to the Internet nowadays. It's insane. And again,
(05:48):
it doesn't go away once. Once you have a data set,
it's not like it's irrelevant. You're still going to need
that maybe for future, you know, whatever endeavor is that
you're going to do as a big business. Right for gamers, uh,
this would be sort of the equivalent of an inventory
that doesn't register weight. How role playing games turn and
(06:09):
you're more familiar with this, and I am, how role
playing games turn so many people into orders. We are
data orders as well, like the NSA clearly is. Uh. So,
what we'll do is will walk through some history of
big data, and then we'll also talk about some of
the controversy surrounding it, the ways it could be used,
(06:30):
some of the conspiracies theoretical and factual about this practice.
So one way that one way there's a group called
Meta Group or Gardner. Uh. They they have an analyst
there named Doug Laney, and he said, one great way
to measure big data would be in the three vs.
(06:51):
That would be increasing volume, uh, just how much data
you have, Uh, the velocity of the data, the speed
of the in and out right, and variety that type
of stuff, where is it coming from? So not just
all homogeneous GPS records, but also like what something else
people would collect on another set of data that's really
interesting to look at when when combined with GPS data,
(07:14):
our phone records, that's always a fun thing. Um. And
these these three vs that you're mentioning, this is now
an industry standard the way the whole industry looks at
big data with what is it? Volume? Velocity? What was
the third one? That's volume, velocity and variety. So uh,
GPS and phone records would be something that that helps
(07:36):
you flesh out a virtual persona, but it comes from
the same device probably for most people. True. So another
thing that would be more very would be stuff like
medical records, stuff like recent purchases on your financial records,
so you could part it down to uh people who
let's get a little bit dark with it. You could
parse it down to uh some We have these four
(07:58):
sets of data out this person, right, So we know
that once every week or something they go to a
clinic right and specializes in some kind of treatment, right.
And then we know that the medical records that they
have some kind of uh, debilitating condition. And then we
see that one of their recent purchases is a skydiving suit.
(08:23):
So we have built this picture with just the very
little information about someone who probably has a terminal disease
and wants to skydive because it was on their bucket list.
Now we know exactly the kind of things to sell
to this person, and we know exactly the kind of
things to sell, which is frightening. Uh. So this is
I mean, this is an example that you and I
(08:44):
just made up now, right, Matt, this is not we
don't know any more and that this has happened to
so this is a new information age. It's tough to
stress how quickly personal privacy has eroded, right, um, and
we the people of the information age, have committed um,
(09:08):
have have lost our privacy due to acts of negligence,
not due to aggressively rooting for this loss of privacy.
It's just who reads terms and conditions, right, Yeah, And
we're all very happy about some of the things that
providing this type of data gives to us. The GPS
(09:29):
thing alone, I think is a massive I mean a
lot of people would I don't. I can't speak for everyone,
but for myself to have the ability to use the
GPS when I'm out on the road somewhere instead of
having to look at a physical map is one of
those things where sometimes I just go, you know what,
screw it, turn my GPS on. I need it, right,
(09:49):
And even if they know that this, were highly aware
that this is being tracked. But but there's also there's
such a a sort of narcissism in paranoia, this idea
that yes, of course they care where I get my pickles.
Of course, of course someone does. And don't get me wrong,
(10:11):
I mean sure grocery stores for sure do. Every time
you swipe a loyalty card, you are generating more information
for them to uh target ads toward you, which we'll
talk about whether or not that is, you know, morally
wrong or ethically sticky or whatever or if you would,
would someone want that? Like, are there people out there
(10:31):
that want Kroger to know exactly what the order so
they can just say, hey, here's all the things that
you want and hear the coupons right there? And there
are some people I would definitely take some coupons for things.
But also, this is okay, this is so unrelated. This
is a side conspiracy here. And Matt, uh, So you
and I are just old enough to remember before these
(10:52):
loyalty cards came out everywhere, right, Yeah, when I was
in college, they didn't exist. So the way that they
were instant you did was that for for all the
young guns out there, the way that these cards were
instituted was at first they gave you discounts on things. Right.
But one of the conspiracies I've heard is that this
(11:13):
artificial price shenaniganting if I can make up the word, uh,
ultimately became something where having the card didn't really get
you a discount. It just got you the the regular price.
The regular price, yes, and the money that they're making
on top of selling your information to third parties. CBS
is like yeah, thanks, yeah, it's and and so you're
(11:36):
you're being penalized at least, this theory goes for not
participating in this program. And that's because again, the overwhelming
majority of companies, uh, don't just do this. They do
this with relish and it is profitable. But it is
a very very old idea. Is that I have to
say this, here's the worst news about that whole situation.
(11:57):
What's that even if you go somewhere and shop. I'm
just gonna use an example, but I'm aware of like
publics that I don't have a loyalty card for and
I don't know that you can get one. Maybe you
can now, but and you you try and save money there,
if you pay with a card of any type, then
that stuff is being tracked, maybe by a separate company,
a third party, or maybe just your credit card company.
(12:18):
But the same thing is happening. And we'll see a
couple of different reasons that this happens, you know, or
or a couple of different motivations for this collection. But
let's walk through the history first. Okay, so this is
not a new thing. Trying to track data, trying to
understand numbers about things that are happening in the world
(12:40):
around you. Goes back seven thousand years to something that
we've mentioned before in Mesopotamia during the birth of agriculture,
when you had to keep track of seeds, you had
to keep track of crops and soil, just everything you
needed to know data about the ground and the plants
that you're trying to put in there. So yeah, So,
(13:02):
for example, there's an accounting system that goes in to
monitor the growth of a I don't know, uh, an
olive sure, an olive tree, right, and so, um, you
know some farmer named John Stamos, a very common name
in Mesopotamia at the time. I imagine, Uh, this farmer,
(13:23):
John Stamos has uh, a bunch of olives, and they
have to have a way to track year over year
the performance of that crop. Right. So this is when
they begin saying, Okay, you know John Stamos has X
amount of trees. They yield y amount of olives each year.
This is what we can expect. This is what we
(13:44):
can expect, this is what we can bet against, this
is what we can sell an advance before it's made.
Then you get into we're gonna jump forward pretty far here,
all the way to sixteen sixty three. Nothing else happened, yeah, zero,
nothing between seven thousand years ago. Let's just say improvements
(14:05):
are being in incrementally, you know, are happening up until
this point. But then on the next big change, the
sixteen sixty three when John Grant, I think that's how
you correctly spell it. Grant very British. Uh. He recorded
an examined information about mortality rates because the bubonic plague
(14:25):
was just ravaging just the entire area at the time,
and he decided he wanted to know more information about,
like how what is happening here, exactly how many people
are getting affected? Why are they getting affected? Let's get
information and we can start solving this. Yeah, and that
sounds that sounds sensible. It's the least you could do,
right we I don't know, have we ever talked on
(14:47):
this show about just how profoundly the plague or the
series of things known as the plague change the world. No,
I've listened to a little too much stuff you missed
in history class about it. So the information, Saan sometimes
gets muddled between what we've talked about and what I've
just heard. These plagues have played such a profound role
(15:07):
in uh, the global evolution of the human species. It's
just crazy it's the kind of stuff you would want
to keep track on. So this guy, uh, John Grant becomes,
uh the father of statistics, or he's considered that because
he does the first statistical data analysis that we have
recordings of, uh, and he has a book about it
(15:28):
called Natural and Political Observations Made upon the Bills of Mortality.
Just kind of a dry name, right, but it's it's
not a feel good subject, and people continue to work
off this statistical analysis. So let's fast forward to the
twenty century. Okay, so we're going to fast forward all
the way to eighteen eight seven. This is when the
(15:52):
modern the age of modern data, modern data is when
it is born. So this gentleman named Herman Hollerith invented
a computing machine that could read these holes punched holes
in cards that paper cards in order to organize census data.
And this is a huge change because you have to
(16:14):
imagine just collecting data, of going door to door getting information,
then trying to compile that just with humans in rooms.
That it was taking so they would do one one
census every ten years, I believe at the time, and
it was taking almost nine years before you would get
the results from the census of the previous the previous census.
(16:35):
So it was almost I don't not worthless, but it
was just felt like they were running backwards almost right. Yeah,
they had they had a data set that would be
when it was finally complete, useful for a little less
than a year. Yeah, exactly so. Uh. The first data
processing machine appeared in nineteen forty three. This was, of course,
(16:56):
it came out of war. A lot of technological in
vations come out of war. Uh. And it was meant
to decipher codes from the Nationalist Socialists or the Nazis. Uh.
This thing was named Colossus, which is a pretty cool name.
And they would intercept messages. They would feed things to
Colossus and would search for patterns in these characters. And
(17:17):
it worked pretty quickly. Yeah. It would go five thousand
characters per second, which is huge. It reduced the time
from weeks two hours. Let's stroll through some other stuff here,
just kind of laundry listed so you can get to
the good stuff. Uh. Nineteen fifty two, everybody's favorite, the
n s A, the National Security Agency is created, and
within ten years they have more than twelve thousand cryptologists
(17:41):
on contract. That's huge. Then you've got. In nineteen sixty five,
the US government builds the first data center which can
store seven hundred and forty two million tax returns and
also a hundred and seventy five million sets of fingerprints.
Now this is pretty interesting here because this is this
(18:02):
is something that was recorded on the magnetic tape and
computer tape. You may, I don't know if anybody listening
would know what that is. Hopefully maybe you've heard of
this magnetic tape. My father is a controller controlling account
and they have a like a newer version of this
magnetic tape, but it's still all of their stuff is
backed up to this magnetic tape because it's so well,
(18:25):
it's supposedly so reliable. Well, also, people will recognize the
magnetic tape if you've never seen it before. Um, every
time you see an old computer with reels on it,
that's magnetic tape. In Captain America the Winter Soldier, the
sequel to the First Captain America, Uh, there is a
scene which I won't spoil for you if you haven't
(18:46):
gotten around the scene it yet, but there is a
scene which involves a gigantic computer and that is magnetic tape. Awesome, Okay,
good reference. Now we understand. But here here's the thing. Though,
This whole project was scrapped because of fears of quote
big brother right, being a little bit too big brotherish
little Orwellian. So this, however, changed everything because people were thinking,
(19:11):
what if we centralize, um, the location of data, you know,
no more paper, just electronically store it. A British guy,
so you may have heard of tim Berners Lee of
Invince what will go on to become the World Wide Web?
And with this foom, we're going like gangbusters because people
(19:34):
are able to generate massive amounts of information, much more
so than anybody could plausibly read. And when it's connected
up to this uh interweb, if you will, it can
be it can be sent somewhere else, right. And then
if you do have a data center, doesn't matter where
the data is collected, you can send it directly over
(19:54):
there almost immediately. Yeah, it's it's bizarre when we think
about that, and especially we think about um, how just
let's have a John Henry moment, and can you compare
matt the the ability of a supercomputer to a person.
Oh God, can I m hmm. Let's say okay, let's
(20:17):
say in the first supercomputer. Let's use that one as
our example. It could do as much work in one
second then a single human being operating a calculator could
do in thirty thousand years. Thirty thousand years. And that's
not even the twenty one century, ladies and gentlemen. Now
(20:39):
we are at the modern age. And two thousand five,
a guy from O'Reilly media coined the term big data
for the first time. Uh and this was, you know,
a successor to a another less fortunate buzzword, which was
web two point oh yeah, yeah, the new coke of
web words. But yeah, so this this idea here is
(21:03):
a little bit more bad of the idea of data
set that is just so massive and complex and interwoven
that you can't use the traditional business intelligence tools to
figure out what's going on. Right And oh five is
also the year that Hoddup was created by Yahoo, which
was built on the back of Google's map produce. And
these are just softwares that can basically take data from
(21:28):
using a bunch of different computers to crunched numbers like
and huge amounts um And it was the goal to
index the entire or the entire worldwide Web. That's why
these things were created and uh, it's the open source
to dupe. It was used by a lot of organizations
to crunch through data. That just is it's almost in
(21:50):
quantifiable how huge it is. Right, and this is not
just a private industry thing, of course, And the line
is blurry. We seem to talk about these two events
in isolation, as if the n s A using phone
records and social media contacts. Who do you know that
knows who? That knows who on Facebook? Right on the list?
Now you're on the list right. Uh, they're they're not
(22:13):
just using that, Uh and target or another private organization, um,
a data broker. There are companies that just broker data. Uh.
These do not exist in a vacuum. There's interplay between them,
and uh, they're increasingly merging to do just some amazing things.
(22:34):
We're getting very very good at seeing the present as
never before. And other governments are involved in this as well.
In two thousand nine, the Indian government did something just
ambitious is like the most reasonable word for this. They
decided to take an Irish scan, fingerprint and photo of
everyone in India, every single person in India. There are
(22:59):
so many people, Yes, can you imagine listener Uh, if
the government came to you and said, Okay, we're going
to need we're gonna need an Irish skin of fingerprint,
and we're also going to need a photograph, a really
nicely framed photograph with good contrast. We're gonna put it
in of you and everyone in your family put it
(23:21):
into this database. Don't worry. We're gonna make sure it's secure,
and we're not going to use it for anything but
for good things. Right, Yeah, and that's one point to
billion people to bill. Yeah, this is the largest biometric
database in the world. So there's a great thing that
Eric Schmidt from Google also said, right, just another sense
(23:44):
of perspective here, yeah, he he stated at the Techonomy
conference in Lake Tahoe. He he stated, quote, there were
five exabytes of information created by the entire world between
the dawn of civilization and two thousand three. Now that
same amount is created every two days. Boom. Take take
(24:07):
that history of the universe. I can't wait till the
aliens land and say uh that they're like, Wow, these
guys figured out how to make uh pizza into burritos
and there's a blog about it. It's sort of like
when you know, when people modern times find ancient Greek
or Roman or African ruins from these empires of bygone days.
(24:31):
And there's always some jerk like hundreds of years ago
who wrote like Tim was here, yeah and true, like
dick butt from Reddit. They're just like, what is a
precursor right there? Right? So there's but there's so much
information being made and you know, I'm being a little
crass here, but the point I'm hoping to make is
that this information is not you know, noble stuff or
(24:55):
even stuff that would really make sense to a human
being you didn't know they were looking for. These are metrics,
these are movements. These are little breadcrumbs of you scattered
around the Internet at large, and then you're just bringing
them together to make another picture. It's like pointali is um,
Really that's a wonderful, wonderful image. Yeah, that's really good.
(25:16):
On Everything you do online today or in any electronic
medium is recorded by somebody. Yep. If you're typing on
a keyboard or on a touchpad, it's getting recorded, So
enjoy it. I'm like, hey, unless you're on an air
gas computer, and we've all learned now what that is,
so okay, so let's just another example here. Uh, Fortunately
(25:43):
for some of us, I'm not going to name names,
but for some of us, it is not a crime
to be drunk on the internet. Well yeah, okay, sure.
And there are people who you know, have maybe in
a fit of passion or maybe they had some drinks
and they wrote something crazy on the Internet and they
almost sent it and they said, no, wait, I'm gonna
(26:04):
sleep on this. Let me think about this before I
write anything. Well, those ghost movements, those drafts that you make,
are also part of this. So just the act of typing,
especially in Facebook. Oh you know, yeah, don't goos draft
in Facebook, right, yeah, and all of these pieces of
information assemble. Again, that's such a beautiful image, man, a
(26:26):
point list portrait of you and who you are. And
the big worry that a lot of people have, and
we see it through sci fi and pop culture, and
we've seen this for decades, is that this will be
able to go beyond just a a panopoly view of
the present or a panopticon kind of view of the present,
(26:47):
to become predictive. Oh yeah, so eventually you just have
an understanding of what each one of these people is
going to do throughout their daily lives, what what the
corporation is going to profit from in the next two
three years? You can. I mean, it's crazy to imagine
(27:07):
all the information that we will eventually be able to
get from this big data and whose hands will it
be in well? And who can who can accurately understand this?
So we we also talk about these controversies. This stuff
is around to stay until the lights go out on humanity. Yeah,
this this stuff will be around to stay. Oh yeah,
(27:30):
it is here to stay unless there's some kind of
weird fight club moment and all the buildings holding all
the stuff the data centers blow up, which is probably
not gonna happen. There's really good security at those buildings, right,
and it's a distributed network, so it would be hard
to take the head off. It would hard be hard
to take all of the heads off the hydrant that
(27:51):
is the information age. Of course, this doesn't come without controversy.
We have we have a video about four creepy things
about big aida, or this umbrella term, which can again
apply to government as well as industry. It can learn
big data can be used to learn your secrets. That's scary.
So if you think, uh, nobody knows that you routinely
(28:13):
order three large cheese pizzas with ham and sit in
the dark in your house at two am every Thursday
night eating and crying. Nope, sorry, getching reruns A Firefly,
watching reruns a Firefly? Nope, sorry. Somebody knows. Papa John's knows. Comcast,
probably knows, Podcast, probably knows. Maybe Netflix. Uh so. The
(28:37):
another thing it doesn't have to tell you what it knows.
There is a surprising lack of transparency on the part
of companies collecting your information. Yeah, that, Um, what is
the name of the company Axiom? Axiom? Oh yeah, yeah, yeah,
they're scary. Huh uh dude, Yeah, I've been We're making
(28:57):
this video, and the way it works is usually Ben
will Ben will write an outline for what he's going
to present in the vlog. I will shoot it. Then
as I'm editing, I end up doing a lot of
research and oh my god, Ben, I've just been scouring
their website and nothing against you, Axiom. If you're listening
your employees, Um, it's just a murky world. Well, it's pervasive.
(29:22):
Like the the amount of information that Axiom has is
is impressive. Yeah, and the way they talk about it
sometimes their offline data that they have used for online purposes.
I don't know, it's fascinating if you go to the website.
So we we've talked a little bit about just the
(29:42):
science fiction elements and how we see some science fact generating.
But we knew know that this is a very old story.
We've seen things like Isaac Asmov's Foundation series, which deal
with the fictional at this point fictional science of psychohistory
predicting the future on a large scale event. We dealt
with gatica, where personal information medical information is used to gosh,
(30:05):
I'm trying to not to spoil things, yeah, but I mean,
what's the limit with spoilers? Like, at what point it
kind of stinks? I guess you just have to put
an alert at the very beginning of something that might
have a spoiler in it and just say don't listen
to this if you have not seen these movies, read
these books, or listen to these songs. Wow. Pretty okay,
(30:28):
all right, So that's that's pretty complicated. But we've also
seen it in of course Minority Report, which we mentioned.
But the controversy surrounding this, which is expressed in our
culture is uh oh oh and Another one would be
the dark night. Oh yes, yeah, we're Lucius Fox. Yeah,
(30:48):
and the cell phone system monitoring system. So, uh, we see,
we see this expressed in the culture of UM multiple nations.
But what what's the real stuff? What's the real controversy?
Could could it damage your credit report if you had
GPS data that said you were I don't know, going
(31:11):
to a pawn shop often or something? You know, Yeah,
I don't know, And that's I don't want to scare anybody.
That's a that's a made up example there, But we
do know that the we do know that the possibilities
for that kind of stuff. Again, just possibilities are there.
And then I love that you pointed out the other
side of the argument, which is, well, maybe this is
(31:32):
more convenient, Maybe this is the way things should be. Uh,
it's a personalized experience for me wherever I go. Yeah,
it's certainly sold to us that way, and I think
maybe some people by that. I I am, unfortunately somewhere
in the middle. I don't know if you ask for
my opinion, Ben, but guess what I'm giving. I have
(31:55):
fall somewhere in the middle where I'm I appreciate what
the I appreciate the attempt of what's trying to happen,
but ultimately, really the bottom line is that these companies
are trying to make a profit by targeting. Sure, right,
and that's the way you sell things. Nowadays, we're so
inundative with advertising that the only real way to get
(32:17):
a message across is to send it with an arrow
straight at your eyeball in your ear like, uh hi,
super producer Noel Brown, we understand that you have recently
begun working with a move. Here are some products that
might interest you exactly. I mean, that's the only way
you're gonna do it now. But yeah, that's that's probably true, man.
(32:39):
But I personally, my biggest concern about this, this encroaching analysis.
My biggest concern is that a can function as a
kind of inherent censorship due to like the search bubble,
the idea that based on your past search history, you
only receive results that are quote unquote relevant to you.
(33:00):
I don't want to receive the results that are relevant
to me. I want to see both sides of any argument.
I want to see um news that would be in
another language. There's gonna be some kind of college course
that shows you how to get the most balanced Google
search results, and it will all it will be an
(33:20):
entire course that just shows you how to develop over
time by searching for certain things at certain times. Well,
there are things like scroogle and Duck duck go that
are supposed to not track your search. There are places
to go currently, but you know, we've seen as acquisitions
continue and certain corporations continue to get more and more monolithic,
(33:44):
I can see a future where there is one place
to search for things. Well. And there's also this idea
that that I think is tremendously positive, even noble almost,
that if we had enough data, and we had enough
sophisticated part seeing algorithms or software, then we might be
able to address global problems that ordinarily wouldn't have been
(34:07):
able to be solved, Like what if there were a
way to uh stop the massive extinction of Earth's wildlife? Right, agreed,
And that's amazing and I love that view. But conversely,
you could also with that same data stomp out all
and every form of resistance against a certain movement. That's true, right,
(34:30):
and uh, this this becomes a matter of uh, I
don't know, the short term stuff versus the long term problems.
With that being said, let's go straight to the crazy stuff,
the conspiracies, both theoretical and actual. Uh, there's a there's
a this theory, the troubling possibility, the kind of thing
(34:54):
that a sci fi writer would make a dystopian novel about,
that we could eventually arrive at a world in which
circumstance and accident has fallen to the statistical hand of
faith and certitude, so that something very much like an
artificial god knows how you will live your days from
(35:15):
the cradle to the grave. Yeah. I don't like that, Ben,
It's a scary thing. There is some silver lining here, though. Uh,
there are many competitors right now in this space. There's
not just one big data right company. Right, Um, so
at least, you know, much in the same way that
there are shadowy forces trying to control the world, there
(35:36):
are a lot of them. There's not just one group. Yeah.
And then these groups might not necessarily work together, especially
if they're competing in a private sphere, right, not unless
it's you know, helpful for the bottom line. Right. They
gather their data sets and and they guard their techniques
pretty jealously. Also, here's the one thing when we talk
about this super all knowing Wizard of Oz type computer.
(36:00):
The fact of the matter is that we still apparently
can't build a computer that can predict the weather. No,
because there there's always with the weather. There's always that
um I don't know, that chaos factor almost to where
you can't. There's so it's so complex, all of the
different moving parts that create the weather. But what ben
(36:21):
what Yeah, I guess it would be. I guess the
same would be true for big data. There's so many
different moving parts and it's so complex. Yeah, it seems
like it would be. I don't know. Is it easier
to build something that can predict the weather or something
that can predict the passage of time in a country?
You know, we've talked before off air about this and
(36:43):
maybe on air too, but uh, you know, I had
a professor a long time ago and in different life
who was working with DARPA to build an artificial model
of a country, with the idea that if they programmed
enough data points together and assign them to these individuals,
(37:04):
then they could measure kind of like foundation in real life.
They could measure the likelihood of trends, you know, like
if if police, if support for police goes up by
x percent, what will be the effect upon the livelihood
or the likelihood of the regime's collapse or stability. And
(37:25):
I don't know where he went with it, but it
is some amazing, terrifying and inspiring stuff. Either way, he
can't talk about it anymore, right, and this and still
at least our knowledge listeners. Uh, this remains a theoretical thing,
but it is a fact that big business is doing
stuff like this all the time, and not necessarily. You know,
(37:48):
it's not like there's somebody out there just rubbing their
hands together, supervillain style, waiting for you to slip so
they can, you know, tell tell your mom that you
are smoking cigarettes. There's something there. What what it is
more about is um not immoral but amoral um providing
(38:09):
of a better service or being a better um service
to the consumer. But now consumers are increasingly the product
as well, right exactly, Your information is the thing the commodity,
which is so weird. Information as commodity. I guess it's
been coming for a long time and it has been
that way for a long time. It's just strange to
think about it on this scale. It's almost like the
(38:32):
information what I'm seeing it is the access and ability
to collect massive amounts of information. Is this new gold
rush thing? Ah, that's good. Yeah, you're killing it with
the comparisons. So I don't know if that's just what
I'm seeing. Where all these large corporations that are building
you know, massive supercomputer complexes of supercomputers that can just
(38:54):
crunch numbers. Man, well we're but we're at a point where,
you know, they're could be some positives for this if
if it was able to if these data sets were
able to, for instance, help humanity combat I don't know,
over fishing, or help humanity figure out the best way
(39:16):
to prevent mass starvation or disease. But again, you know,
those things are also they seem to be more complex
than weather patterns. They are, and I just it's hard
for me to imagine someone looking at those problems and
making a profit from it, or you know, devising a
way to make a profit from it and use all
(39:36):
these assets in order to do something good. I'm sorry, man,
my faith in humanity just like got ticked down a
couple of notches for some reason in thinking about all
this stuff. Because this is all really what we're talking
about here, are ways to sell things, right, That's what
this whole thing is about. Well, what I would say,
what I feel like, what we're talking about is a
(39:58):
little bit further than that. It's ways to predict future events.
So selling something is trying to predict what will trigger
a purchase, right, So it's it's still predictive, or hopefully predictive.
But the question though right now, and the answer it seems,
(40:18):
is that overall, while we know that people are working
fervently to build a machine that can read the future, right,
a modern day fortune teller, uh, we we do not
yet have that oracle, at least in the public sphere.
(40:38):
We don't know about it, but we do know that
people are working on it, and that brings us to um,
I guess one of the things we can close with today.
Huh Yeah, a little something called anomaly detection at multiple
scales or atoms. It's brought to you by DARPA. Friends
over at DARPA, good people. Um, they were card over
(41:00):
at DARPA. So this comes directly from the DARPA website.
And I'm just going to read you this quote's a
little along bear with me quote. The anomaly detection at
multiple scales or atoms program creates, adapts, and applies technology
to anomaly characterization and detection in massive data sets. Anomalies
(41:22):
in data cue the collection of additional actionable information in
a wide variety of real world contexts. The initial application
domain is insider threat detection, in which malevolent or possibly
inadvertent actions by a trusted individual are detected against a
background of everyday network activity. So they're looking, they're looking
(41:47):
at a person, a trusted insider person, and and then
they are going, oh, well, here is an anomalous action
or an anomalous piece of data inside this. Sure, Like
Mrs Cunningham works for a Wall Street investment firm, and
(42:08):
every day Mrs Cunningham has lunch at twelve thirty and
goes back to work until six thirty, at which point
she leaves and it takes her an hour and a
half to get home because of traffic. And then one
day she goes to lunch, but instead of going to
a restaurant, she goes down to you know, um, a
(42:33):
gun store, or she goes to a you know her
her anomalous change, right yeah, Or she seems to be
in closer contact with the competitors, so they could they
could call these anomalies. And we've we've heard people talk
about this sometimes, like if you haven't checked out the
website zero hedge, that's a it's a very interesting read
and it's well done. One of the things that they've
(42:54):
talked about before is anomaly detection and these theories about
you know, UM market forces or investor actions right before
calamitous events. Oh yeah, there's some great analysis of that
stuff by Tyler Dirton actually speaking of fight Club. Yes, yes,
uh so what Okay, this is part of one one
(43:16):
of the things I wanted to ask you about. Have
you heard the theory that um not only is Tyler
Dirden not real, but the girlfriend character is not real either.
I know that it's another person he made up. Oh wow, okay,
oh wow, okay. I'm trying to check it out. It
(43:38):
has nothing to do with what we're talking about it,
but we do want to hear from you, not just
with your Fight Club theories. But send him if you want.
I think it's an interesting movie too. I won't go
into some of the plothole parts. All right, Well, let
us know what you think about it, but more importantly,
let us know what you think about big data. Is
it possible to get off of the grid? How difficult
(44:00):
is it? What do you think, um, what do you
think people are doing with all this information the public
and private sphere. One of the big concerns that we
hear a lot about is the idea that the surveillance state,
at least in the US and the West, has grown
because um, the intelligence agencies and departments are able to
(44:22):
use the dirt they have on the elected officials to
prevent the elected officials from uh, you know, slowing the
growth of the surveillance state, the idea of a deep
state or shadow government. Okay, so to make me feel better,
please send in your suggestions like Ben had earlier, of
positive things that could be used that big data could
be used for. Please please send those to us, just
(44:43):
to make me feel better. Thank you, all right, and
you can you can hit us up on Facebook and
Twitter where we are conspiracy stuff. You can also drop
a drop by our website stuff they don't want you
to know dot com which has a bunch of stuff
on it. And if you want to send us an
email directly, please knock yourself out. It doesn't have to
(45:03):
be answering, just the questions, we asked, it could be
I don't know a joke during the jokes. Sure. Our
email address is conspiracy at how stuff works dot com.
From more on this topic, another unexplained phenomenon, visit YouTube
dot com slash conspiracy stuff. You can also get in
(45:24):
touch on Twitter at the handle at conspiracy stuff
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