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