September 21, 2021 • 47 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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Episode Transcript
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Speaker 1 (00:00):
So every so often when we're exploring a classic episode,
we run into something that is true but unfortunately prescient.
And folks, this week we're sharing with you our episode
called Big Data and You. It kind of makes me
(00:22):
think of like an after school special on like you know,
how to like protect yourself against big data. It's not
too far off from what it is. Well, it's it's
weird to think this was put out originally six years ago,
I believe, and just how much the landscape has changed
since then. It hasn't really changed, it's these big data
(00:42):
has just gotten stronger, basically, and there are several companies
that have just kind of strengthened their positions. Yeah, and
there's there's no turning back the clock on this. But
on one positive note, assurely depositive note, more people are
(01:03):
aware of the dangers of this sort of ubiquitous observation
and surveillance and how it can be used to nudge
your decisions. It's gone past, by the way, figuring out
who might want to buy some new ray bands or something.
This is this is about making people do things they
(01:25):
may not have ordinarily done at this point. But if
you have someone that in your life, that you feel
like needs to know a little bit more about the
basics of what people mean when they say big data.
This is the episode for you. And also it goes
into the idea that if you don't know what the
product is, it's probably you. So, without further ado, turn
(01:47):
your ad trackers on and tune in to this classic episode. Disagree,
turn your phones off. Even if you're listening to this
on your phone, just go ahead and turn it off
right now. Don't even listen. Just up into a bucket
of water to cover ups. History is riddled with unexplained events.
You can turn back now or learn the stuff they
(02:08):
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 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,
(02:29):
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
seventies and eighties, and hopefully no one's going to be
bringing those into the mix. Maybe on this show, maybe not.
I don't know. I don't know. He looks like he
(02:50):
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,
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,
(03:15):
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.
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,
(03:39):
to his high school aged daughter. Yeah, yep. And so
he goes to Target in person on in some 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
(04:02):
in August. Whoops on all accounts. This comes to us
through a New York Times piece that was published on
February nineteen, 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
(04:24):
buy something. And the guy who was interviewed, the statistics
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,
(04:45):
it's something that it's not necessarily something they don't want
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
(05:06):
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
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.
(05:27):
But what maybe even more important and even more secretive
would be the techniques used to parse and analyze this
information 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
(05:49):
or something big pomegranate. Right, No, big data is just
as in definition, any extremely large data set or data
sets 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
(06:13):
behavior and or the interactions between humans. So, for instance,
this big data would not 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
(06:35):
the idea of the GPS information of one like certain
area and just all 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
(06:57):
take some some data set that is 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
(07:20):
more and more out there. There's a 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 going to talk
about here. Just the number of devices that collect data
or that you can collect data upon, and just everything
(07:43):
that's connected to the internet nowadays. It's insane. And again,
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
(08:04):
that doesn't register weight. How role playing games turn and
you're more familiar with this, and I am how role
playing games turn so many people into hoarders. 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
(08:27):
of the controversy surrounding it, the ways it could be used,
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
(08:47):
to measure big data would be in the three vs.
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 the 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? Well, another set of data that's
(09:09):
really interesting to look at when when combined with GPS
data or 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,
(09:32):
GPS and phone records would be something that that helps
you flesh out a virtual persona, but it comes from
the same device probably for most people. 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 parse it down to uh people who let's get
(09:53):
a little bit dark with it. You could parse it
down to uh. Some we have these four sets of
data about this person, right, so we know that once
every week or something they go to a clinic, right
it specializes in some kind of treatment, right. And then
we know that the medical records that they have some
(10:14):
kind of uh, debilitating condition. And then we see that
one of their recent purchases is a skydiving suit. 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
(10:35):
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
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
(11:01):
we the people of the information age, have committed um,
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
(11:25):
providing this type of data gives to us. The GPS
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 a
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,
(11:45):
screw it, turn my GPS on. I need it. Right,
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.
(12:07):
Of course, of course someone does. And don't get me wrong,
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
(12:30):
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
(12:52):
loyalty cards came out everywhere, right, Yeah, when I was
in college, they didn't exist. So the way that they
were instituted is 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 artificial
(13:13):
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, then 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
(13:36):
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.
(13:56):
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.
(14:18):
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
(14:40):
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,
(15:02):
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
(15:24):
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 m olives
each year. This is what we can expect. This is
(15:44):
what we 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. Nothing else happened, yeah, zero,
nothing between seven thousand years ago. Let's just say improvements
(16:05):
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
(16:26):
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,
But we don't know. Have we ever talked on this
(16:48):
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 he missed
in history class about it. So information sometimes gets muddled
between what we've talked about and what I've just heard.
These plagues have played such a profound role in, uh,
(17:09):
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 called Natural and
(17:29):
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 twentie century. Okay,
So we're going to fast forward all the way to
eighteen seven. This is when the modern the age of
(17:54):
modern data. Modern data is when it is born. So
this gentleman named Herman Hollerth 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 imagine just collecting data,
(18:16):
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, so it was almost,
(18:38):
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, it came out of war. A
(18:58):
lot of technology coinnovations come out of war. 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 it worked pretty quickly. Yeah, it
(19:20):
would go five thousand characters per second, which is huge.
It reduced the time from weeks two hours. And let's
stroll through some other stuff here, just kind of laundry
listed so we 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 on contract. That's huge.
(19:45):
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 is something that
was recorded on the magnetic tape and computer tape. You may,
(20:08):
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, it's supposedly so reliable. Well, also,
(20:28):
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 gotten around the scene it yet, But
there is a scene which involves a gigantic computer and
(20:51):
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 years of quote big brother right being
a little bit too big brotherish little Orwellian. So this, however,
changed everything because people were thinking, what if we centralize um,
(21:14):
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 boom,
we're going like gangbusters because people are able to generate
(21:36):
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
to 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 there almost immediately. Yeah,
(21:57):
it's it's bizarre when we think about that, and esp
actually when 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 say in
(22:18):
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 we are
(22:38):
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.
(22:58):
But yeah, so this, this idea here is 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
(23:19):
on the back of Google's map produce. And these are
just softwares that can basically take data from 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.
(23:43):
It was used by a lot of organizations to crunch
through data. That just is it's almost unquantifiable 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
(24:05):
knows who on Facebook? Right now? On the list? Now
you're on the list right Uh, they're they're not 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,
(24:26):
and uh, they're increasingly merging to do just some amazing things.
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
(24:48):
decided to take an Irish scan fingerprint and photo of
everyone in India, every single person in India. There are
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 scan of fingerprint,
(25:10):
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
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
(25:32):
biometric database in the world. So there's a great thing
that Eric Schmidt from Google also said, right, just another
sense 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
(25:54):
between the dawn of civilization and two thousand three. Now
that same amount is created every two days. Boom, take
take 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 pizza into burritos
(26:14):
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,
and there's always some jerk like hundreds of years ago
who wrote like Tim was here, yeah and true, like
(26:36):
dick Butt from Reddit. They're just like that 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
even stuff that would really make sense to a human being.
(26:57):
You didn't know what 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. On
Everything you do online today or in any electronic medium
(27:19):
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 gap computer.
And we've all learned now what that is. So okay,
So let's just another example here. Uh. Fortunately for some
(27:42):
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 sleep on this.
(28:02):
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 goost draft in Facebook, right, yeah,
and all of these pieces of information assemble. Again, that's
such a beautiful image, man, A point list portrait of
(28:26):
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, to become predictive. Yeah,
(28:49):
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 all the information that
we will eventually be able to get from this big
(29:09):
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, it
is here to stay unless there's some kind of weird
(29:31):
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
is the information age. Of course, this doesn't come without controversy.
(29:54):
We have we have a video about four creepy things
about big data, or this umbrella term, which and 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
order three large cheese pizzas with ham and sit in
(30:15):
the dark in your house at two am every Thursday night,
eating and crying. Nope, sorry, catching reruns a firefly, watching
reruns a firefly? Nope, sorry, somebody knows. Papa John's knows.
Comcast probably no podcast, probably knows. Maybe Netflix. Uh So
the another thing, it doesn't have to tell you what
(30:37):
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
this video and the way it works is usually Ben Will.
(30:58):
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. Like the
(31:20):
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 science fiction
(31:42):
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've dealt
with Gaddick uh, where personal information medical information is used
(32:04):
to gosh, I'm trying 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,
(32:27):
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 Knight. Oh yes, yeah, we're Lucius Fox Yeah,
(32:48):
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
(33:10):
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
(33:31):
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
(33:54):
fall somewhere in the middle where I'm I appreciate what
the ipre 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 a message
(34:16):
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.
(34:38):
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.
They idea that based on your past search history, you
only receive results that are quote unquote relevant to you.
(34:59):
I don't want to res 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
(35:19):
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.
(35:43):
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 parsing i'll rhythms or software, then we might be
able to address global problems that ordinarily wouldn't have been
(36:06):
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,
(36:30):
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. There's a there's a
(36:50):
this theory, the troubling possibility, the kind of thing 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
fate and certitude, so that something very much like an
(37:11):
artificial god knows how you will live your days from
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
(37:32):
their shadowy forces trying to control the world. There 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
(37:52):
pretty jealously. Also, here's the one thing when we talk
about this super all knowing Wizard of Oz type computer,
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
(38:13):
you can't there's so it's so complex, all of the
different moving parts that create the weather. But what ben
what Yeah, I guess it would be. I guess the
same would be true for big data. There's so many
different moving parts that 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
(38:35):
that can predict the passage of time in a country?
You know, we've talked before off air about this and
maybe on air too. But uh, you know, I had
a professor a long time ago, in a different life
who was working with DARPA to build an artificial model
of a country with with the idea that if they
(38:58):
programmed enough data points together and assign them to these individuals,
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
(39:19):
or the likelihood of the regime's collapse or stability. And
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
(39:43):
stuff like this all the time, and not necessarily. You know,
It's not like there's somebody out there just rubbing their
hands together super villain style, waiting for you to slip
so they can you know, tell tell your mom that
you are smoking cigarettes or something. You know. There what
what it is more about is is um not immoral
(40:05):
but a moral um providing 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
(40:26):
long time. It's just strange to think about it on
this scale. It's almost like the 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 comparison. So I don't know
if that's just what I'm seeing. Where all these large
(40:46):
corporations that are building, you know, massive supercomputer complexes of
supercomputers that can just crunch numbers. Man, well we're but
we're at a point where, you know, there could be
some positives for this if if it was able to
if these data sets were able to for instance, help
(41:08):
humanity combat I don't know, over fishing, or help humanity
figure out the best way 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
(41:29):
at those problems and making a profit from it, or
you know, devising a way to make a profit from
it and use all 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
(41:50):
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 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,
(42:15):
right now, and the answer, it seems, 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. We don't
(42:38):
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 work hard over
(42:59):
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 anomally characterization and detection in massive data sets. Anomalies
(43:21):
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
(43:46):
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 your data set.
Sure Like, Mrs Cunningham works for a Wall Street investment firm,
(44:07):
and 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,
(44:32):
a 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
(44:53):
they've 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
(45:15):
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
(45:37):
has nothing to do with what it's 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 plithhole 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
(45:59):
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
(46:21):
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
(46:42):
to make me feel better. Thank you, and that's the
end of this classic episode. If you have any thoughts
or questions about this episode, you can get into contact
with us in a number of different ways. One of
the best is to give us a call. Our number
is one eight three three std to b y t K.
If you don't want to do that, you can send
(47:03):
us a good old fashioned email. We are conspiracy at
i heart radio dot com. Stuff they don't want you
to know is a production of I heart Radio. For
more podcasts from my heart Radio, visit the i heart
Radio app, Apple Podcasts, or wherever you listen to your
favorite shows.
So every so often when we're exploring a classic episode,
we run into something that is true but unfortunately prescient.
And folks, this week we're sharing with you our episode
called Big Data and You. It kind of makes me
(00:22):
think of like an after school special on like you know,
how to like protect yourself against big data. It's not
too far off from what it is. Well, it's it's
weird to think this was put out originally six years ago,
I believe, and just how much the landscape has changed
since then. It hasn't really changed, it's these big data
(00:42):
has just gotten stronger, basically, and there are several companies
that have just kind of strengthened their positions. Yeah, and
there's there's no turning back the clock on this. But
on one positive note, assurely depositive note, more people are
(01:03):
aware of the dangers of this sort of ubiquitous observation
and surveillance and how it can be used to nudge
your decisions. It's gone past, by the way, figuring out
who might want to buy some new ray bands or something.
This is this is about making people do things they
(01:25):
may not have ordinarily done at this point. But if
you have someone that in your life, that you feel
like needs to know a little bit more about the
basics of what people mean when they say big data.
This is the episode for you. And also it goes
into the idea that if you don't know what the
product is, it's probably you. So, without further ado, turn
(01:47):
your ad trackers on and tune in to this classic episode. Disagree,
turn your phones off. Even if you're listening to this
on your phone, just go ahead and turn it off
right now. Don't even listen. Just up into a bucket
of water to cover ups. History is riddled with unexplained events.
You can turn back now or learn the stuff they
(02:08):
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 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,
(02:29):
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
seventies and eighties, and hopefully no one's going to be
bringing those into the mix. Maybe on this show, maybe not.
I don't know. I don't know. He looks like he
(02:50):
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,
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,
(03:15):
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.
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,
(03:39):
to his high school aged daughter. Yeah, yep. And so
he goes to Target in person on in some 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
(04:02):
in August. Whoops on all accounts. This comes to us
through a New York Times piece that was published on
February nineteen, 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
(04:24):
buy something. And the guy who was interviewed, the statistics
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,
(04:45):
it's something that it's not necessarily something they don't want
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
(05:06):
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
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.
(05:27):
But what maybe even more important and even more secretive
would be the techniques used to parse and analyze this
information 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
(05:49):
or something big pomegranate. Right, No, big data is just
as in definition, any extremely large data set or data
sets 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
(06:13):
behavior and or the interactions between humans. So, for instance,
this big data would not 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
(06:35):
the idea of the GPS information of one like certain
area and just all 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
(06:57):
take some some data set that is 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
(07:20):
more and more out there. There's a 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 going to talk
about here. Just the number of devices that collect data
or that you can collect data upon, and just everything
(07:43):
that's connected to the internet nowadays. It's insane. And again,
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
(08:04):
that doesn't register weight. How role playing games turn and
you're more familiar with this, and I am how role
playing games turn so many people into hoarders. 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
(08:27):
of the controversy surrounding it, the ways it could be used,
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
(08:47):
to measure big data would be in the three vs.
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 the 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? Well, another set of data that's
(09:09):
really interesting to look at when when combined with GPS
data or 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,
(09:32):
GPS and phone records would be something that that helps
you flesh out a virtual persona, but it comes from
the same device probably for most people. 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 parse it down to uh people who let's get
(09:53):
a little bit dark with it. You could parse it
down to uh. Some we have these four sets of
data about this person, right, so we know that once
every week or something they go to a clinic, right
it specializes in some kind of treatment, right. And then
we know that the medical records that they have some
(10:14):
kind of uh, debilitating condition. And then we see that
one of their recent purchases is a skydiving suit. 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
(10:35):
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
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
(11:01):
we the people of the information age, have committed um,
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
(11:25):
providing this type of data gives to us. The GPS
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 a
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,
(11:45):
screw it, turn my GPS on. I need it. Right,
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.
(12:07):
Of course, of course someone does. And don't get me wrong,
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
(12:30):
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
(12:52):
loyalty cards came out everywhere, right, Yeah, when I was
in college, they didn't exist. So the way that they
were instituted is 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 artificial
(13:13):
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, then 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
(13:36):
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.
(13:56):
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.
(14:18):
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
(14:40):
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,
(15:02):
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
(15:24):
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 m olives
each year. This is what we can expect. This is
(15:44):
what we 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. Nothing else happened, yeah, zero,
nothing between seven thousand years ago. Let's just say improvements
(16:05):
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
(16:26):
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,
But we don't know. Have we ever talked on this
(16:48):
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 he missed
in history class about it. So information sometimes gets muddled
between what we've talked about and what I've just heard.
These plagues have played such a profound role in, uh,
(17:09):
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 called Natural and
(17:29):
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 twentie century. Okay,
So we're going to fast forward all the way to
eighteen seven. This is when the modern the age of
(17:54):
modern data. Modern data is when it is born. So
this gentleman named Herman Hollerth 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 imagine just collecting data,
(18:16):
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, so it was almost,
(18:38):
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, it came out of war. A
(18:58):
lot of technology coinnovations come out of war. 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 it worked pretty quickly. Yeah, it
(19:20):
would go five thousand characters per second, which is huge.
It reduced the time from weeks two hours. And let's
stroll through some other stuff here, just kind of laundry
listed so we 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 on contract. That's huge.
(19:45):
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 is something that
was recorded on the magnetic tape and computer tape. You may,
(20:08):
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, it's supposedly so reliable. Well, also,
(20:28):
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 gotten around the scene it yet, But
there is a scene which involves a gigantic computer and
(20:51):
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 years of quote big brother right being
a little bit too big brotherish little Orwellian. So this, however,
changed everything because people were thinking, what if we centralize um,
(21:14):
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 boom,
we're going like gangbusters because people are able to generate
(21:36):
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
to 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 there almost immediately. Yeah,
(21:57):
it's it's bizarre when we think about that, and esp
actually when 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 say in
(22:18):
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 we are
(22:38):
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.
(22:58):
But yeah, so this, this idea here is 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
(23:19):
on the back of Google's map produce. And these are
just softwares that can basically take data from 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.
(23:43):
It was used by a lot of organizations to crunch
through data. That just is it's almost unquantifiable 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
(24:05):
knows who on Facebook? Right now? On the list? Now
you're on the list right Uh, they're they're not 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,
(24:26):
and uh, they're increasingly merging to do just some amazing things.
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
(24:48):
decided to take an Irish scan fingerprint and photo of
everyone in India, every single person in India. There are
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 scan of fingerprint,
(25:10):
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
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
(25:32):
biometric database in the world. So there's a great thing
that Eric Schmidt from Google also said, right, just another
sense 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
(25:54):
between the dawn of civilization and two thousand three. Now
that same amount is created every two days. Boom, take
take 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 pizza into burritos
(26:14):
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,
and there's always some jerk like hundreds of years ago
who wrote like Tim was here, yeah and true, like
(26:36):
dick Butt from Reddit. They're just like that 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
even stuff that would really make sense to a human being.
(26:57):
You didn't know what 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. On
Everything you do online today or in any electronic medium
(27:19):
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 gap computer.
And we've all learned now what that is. So okay,
So let's just another example here. Uh. Fortunately for some
(27:42):
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 sleep on this.
(28:02):
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 goost draft in Facebook, right, yeah,
and all of these pieces of information assemble. Again, that's
such a beautiful image, man, A point list portrait of
(28:26):
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, to become predictive. Yeah,
(28:49):
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 all the information that
we will eventually be able to get from this big
(29:09):
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, it
is here to stay unless there's some kind of weird
(29:31):
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
is the information age. Of course, this doesn't come without controversy.
(29:54):
We have we have a video about four creepy things
about big data, or this umbrella term, which and 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
order three large cheese pizzas with ham and sit in
(30:15):
the dark in your house at two am every Thursday night,
eating and crying. Nope, sorry, catching reruns a firefly, watching
reruns a firefly? Nope, sorry, somebody knows. Papa John's knows.
Comcast probably no podcast, probably knows. Maybe Netflix. Uh So
the another thing, it doesn't have to tell you what
(30:37):
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
this video and the way it works is usually Ben Will.
(30:58):
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. Like the
(31:20):
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 science fiction
(31:42):
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've dealt
with Gaddick uh, where personal information medical information is used
(32:04):
to gosh, I'm trying 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,
(32:27):
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 Knight. Oh yes, yeah, we're Lucius Fox Yeah,
(32:48):
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
(33:10):
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
(33:31):
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
(33:54):
fall somewhere in the middle where I'm I appreciate what
the ipre 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 a message
(34:16):
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.
(34:38):
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.
They idea that based on your past search history, you
only receive results that are quote unquote relevant to you.
(34:59):
I don't want to res 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
(35:19):
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.
(35:43):
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 parsing i'll rhythms or software, then we might be
able to address global problems that ordinarily wouldn't have been
(36:06):
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,
(36:30):
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. There's a there's a
(36:50):
this theory, the troubling possibility, the kind of thing 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
fate and certitude, so that something very much like an
(37:11):
artificial god knows how you will live your days from
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
(37:32):
their shadowy forces trying to control the world. There 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
(37:52):
pretty jealously. Also, here's the one thing when we talk
about this super all knowing Wizard of Oz type computer,
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
(38:13):
you can't there's so it's so complex, all of the
different moving parts that create the weather. But what ben
what Yeah, I guess it would be. I guess the
same would be true for big data. There's so many
different moving parts that 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
(38:35):
that can predict the passage of time in a country?
You know, we've talked before off air about this and
maybe on air too. But uh, you know, I had
a professor a long time ago, in a different life
who was working with DARPA to build an artificial model
of a country with with the idea that if they
(38:58):
programmed enough data points together and assign them to these individuals,
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
(39:19):
or the likelihood of the regime's collapse or stability. And
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
(39:43):
stuff like this all the time, and not necessarily. You know,
It's not like there's somebody out there just rubbing their
hands together super villain style, waiting for you to slip
so they can you know, tell tell your mom that
you are smoking cigarettes or something. You know. There what
what it is more about is is um not immoral
(40:05):
but a moral um providing 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
(40:26):
long time. It's just strange to think about it on
this scale. It's almost like the 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 comparison. So I don't know
if that's just what I'm seeing. Where all these large
(40:46):
corporations that are building, you know, massive supercomputer complexes of
supercomputers that can just crunch numbers. Man, well we're but
we're at a point where, you know, there could be
some positives for this if if it was able to
if these data sets were able to for instance, help
(41:08):
humanity combat I don't know, over fishing, or help humanity
figure out the best way 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
(41:29):
at those problems and making a profit from it, or
you know, devising a way to make a profit from
it and use all 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
(41:50):
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 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,
(42:15):
right now, and the answer, it seems, 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. We don't
(42:38):
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 work hard over
(42:59):
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 anomally characterization and detection in massive data sets. Anomalies
(43:21):
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
(43:46):
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 your data set.
Sure Like, Mrs Cunningham works for a Wall Street investment firm,
(44:07):
and 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,
(44:32):
a 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
(44:53):
they've 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
(45:15):
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
(45:37):
has nothing to do with what it's 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 plithhole 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
(45:59):
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
(46:21):
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
(46:42):
to make me feel better. Thank you, and that's the
end of this classic episode. If you have any thoughts
or questions about this episode, you can get into contact
with us in a number of different ways. One of
the best is to give us a call. Our number
is one eight three three std to b y t K.
If you don't want to do that, you can send
(47:03):
us a good old fashioned email. We are conspiracy at
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