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Speaker 1: With technology with tex Stuff. Hey there, and welcome to

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Speaker 1: tex Stuff. I am your host, Jonathan Strickland, and today

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Speaker 1: I have a very special guest as a guest co

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Speaker 1: host of this episode, then Johnson Marketplace dot Org. Welcome Ben. Hey,

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Speaker 1: how's it going, Jonathan, It's great. Now I have a

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Speaker 1: question for you before we get into the topic of

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Speaker 1: the day. I got to chance to be a guest

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Speaker 1: host on a show you host called code Breaker, And

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Speaker 1: on code Breaker when we talked, I made a reference

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Speaker 1: to Shakespeare. Yes. Now my question for you is, are

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Speaker 1: you ever confused with the seventeenth century British playwright Ben Johnson.

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Speaker 1: Oh man all the time, yapens all the time, embarrassing. Yeah,

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Speaker 1: I've been you know, I have been to Stratford upon Avon.

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Speaker 1: I have done that whole thing, and and and I

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Speaker 1: used to you know, I never get confused for that

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Speaker 1: Ben Johnson. But in the past, at least people made

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Speaker 1: jokes about steroids because of the other Ben Johnson. Nadian

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Speaker 1: Runner and I used to have a T shirt that

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Speaker 1: said I'm not on steroids, but thanks for asking, which

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Speaker 1: was a kind of inside joke among my friends. But

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Speaker 1: it was, um, you know, I've never been. No, I've

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Speaker 1: never been that poetic. I've never been that good at sonnets,

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Speaker 1: so usually I'm not not confused. It's it's sad. It's

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Speaker 1: really funny because, uh, I'm in in an upcoming episode

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Speaker 1: of tech stuff. I'm going to have my friend i

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Speaker 1: as actar from on and his his Twitter handle is

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Speaker 1: at i as, so he gets a lot of hip

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Speaker 1: hop and rap questions. So yeah, it's like, not so much.

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Speaker 1: I I'm okay with that. But the reason I have

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Speaker 1: been on today is because we're doing something kind of

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Speaker 1: a complementary episode to the Codebreaker episode I was on,

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Speaker 1: which was all about data tracking and data mining, and

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Speaker 1: to kind of talk about what that is, what it's

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Speaker 1: used for, what is the best case scenario, like what

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Speaker 1: why do we want it? And what are some really

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Speaker 1: really good reasons why we may not want it, despite

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Speaker 1: the fact that at this point it's here to stay.

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Speaker 1: I think a spoiler alert, data tracking is not going away,

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Speaker 1: right until the sort of posted post apocalyptic world sent

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Speaker 1: until we're all in um fallout for world whatever it is,

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Speaker 1: or or we reached singularity and we're sharing the same

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Speaker 1: consciousness anyway, right Yeah, and then it doesn't matter there's

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Speaker 1: no such thing as an individual at that point, so

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Speaker 1: until then we're stuck with it. So data tracking and

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Speaker 1: data mining, these are terms that kind of relate to

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Speaker 1: another buzz term that you've probably heard over and over again,

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Speaker 1: which is big data. Um. And the reason why we're

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Speaker 1: even talking about this at all is because information has value,

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Speaker 1: but it's hard to pin down exactly what the value

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Speaker 1: is because it may be very different from one person

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Speaker 1: to another, or between the individual whose data it is

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Speaker 1: and some other organization or a chain of organizations, and

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Speaker 1: the whole thing gets really messy. And uh yeah, we'll

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Speaker 1: be talking about information and information about information like metadata.

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Speaker 1: I mean this, it becomes a rabbit hole that you

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Speaker 1: can easily get lost in as you start to try

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Speaker 1: and unravel everything and figure out, okay, well, what's the

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Speaker 1: heart of this story. And to be fair, data trekking

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Speaker 1: is something that's been going on for ages. It's not new.

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Speaker 1: It's just that the Internet and the rules we use

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Speaker 1: today allow us to generate, gather, and sift through more

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Speaker 1: information than we've ever been able to do before. It's

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Speaker 1: kind of a catch all phrase, right, Jonathan, It's it's

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Speaker 1: this kind of catch all phrase for for computers in

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Speaker 1: software to build a really comprehensive um data profile about

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Speaker 1: you in some ways. But it's something that credit card

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Speaker 1: companies and banks and advertisers have endeavored to do for

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Speaker 1: decades exactly, I mean. And this can be done for

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Speaker 1: very simple, quote unquote innocent reasons. Like one of the

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Speaker 1: examples I like to say is you might have a

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Speaker 1: shop and you in your shop, you sell ice cream,

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Speaker 1: and you pay attention to see which flavors of ice

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Speaker 1: cream sell the best, and that informs you of which

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Speaker 1: flavors of ice cream you need to stock up on.

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Speaker 1: You are tracking the data of sales. In this case,

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Speaker 1: you're not necessarily looking at individuals. You don't care who

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Speaker 1: it is that buys the vanilla, but if everyone's buying vanilla,

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Speaker 1: you want to make sure you have more vanilla than

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Speaker 1: the other flavors that aren't selling as well. So that's

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Speaker 1: one that's like the very simplest version of data tracking

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Speaker 1: there where you know you've scrubbed the identity of the individuals,

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Speaker 1: that's not even that doesn't even come up, it's not important.

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Speaker 1: But it can go all the way to the other extreme,

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Speaker 1: where you want to create targeted advertising. That is going

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Speaker 1: to appeal to each specific person that visits a particular

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Speaker 1: site or use as a particular service, and the whole

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Speaker 1: point of that is to attempt to get them to

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Speaker 1: buy something or to enroll in something. It's it's the

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Speaker 1: goal of all advertising. It's to get you to act

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Speaker 1: in a certain way. And the idea is that, well,

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Speaker 1: if we can narrow the target down to the most

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Speaker 1: likely candidates, will get a better return on our efforts.

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Speaker 1: And this is theoretically good for the user, right Jonathan.

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Speaker 1: Theoretically it's it's sort of like, we want to give

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Speaker 1: you the thing that you really want, and we want

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Speaker 1: to give it to you in the most efficient way

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Speaker 1: and in the most sort of sympatico way with with

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Speaker 1: what you are interested in doing already. Um, but it

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Speaker 1: can sort of like data tracting to me means so

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Speaker 1: many things, Jonathan, Right. It means your online browsing behavior,

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Speaker 1: or the things you choose to buy, the platforms, the

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Speaker 1: software you use, the things you download, your name, age, race,

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Speaker 1: sexual orientation, when you're sick, what what you're sick with,

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Speaker 1: your eating habits, your television watching habits, your fears, hopes, dreams.

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Speaker 1: Right when it really means so many things when you're awake,

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Speaker 1: when you're asleep. I mean, like we we're talking about

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Speaker 1: a world now where where we we want these wearables.

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Speaker 1: They could give us real time data feedback on how

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Speaker 1: we are doing throughout the day. Maybe you are an

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Speaker 1: extreme type a personality and you are trying to schedule

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Speaker 1: your day so that when you're hitting those peak moments

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Speaker 1: of productivity, that's when you're tackling the most important tasks

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Speaker 1: to you, and you're wearing wearables, you're getting that feedback

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Speaker 1: and you're understanding all this stuff. That data may also

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Speaker 1: be going to other places and there may not be

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Speaker 1: any use for it right now. Like right now, as

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Speaker 1: you're generating that data, that's fine, no one's looking at it.

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Speaker 1: But down the road that might not be the case

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Speaker 1: for various reasons, some of which are terrifying. But but

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Speaker 1: you know, you were talking about the different types of data.

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Speaker 1: There are actually three kind of broad categories we can

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Speaker 1: classify the data that we are creating that that various

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Speaker 1: entities are really interested in. There's there's volunteered data, and

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Speaker 1: that's the stuff we share, right that's when we go

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Speaker 1: on facebooks exactly if that's my tweets, your tweets, your

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Speaker 1: your YouTube's, your facebooks, your Pinterests, it's it's the stuff

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Speaker 1: where we are participating in the conversation and generating that information.

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Speaker 1: We're not just doing that for the people that we

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Speaker 1: are trying to impress or our friends or sometimes are

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Speaker 1: enemies if we want to rub their noses and stuff

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Speaker 1: because they think they're so big. But that's that's the

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Speaker 1: stuff that we are actively sharing. Then there's the observed data.

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Speaker 1: That's the stuff that companies and other entities can can

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Speaker 1: figure out about us just by watching our behavior. So

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Speaker 1: this would be kind of the stuff we buy, the

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Speaker 1: browsing habits we have where we log in from. Like

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Speaker 1: if you are doing most of your browsing on a

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Speaker 1: mobile device, you better bet there their companies out there

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Speaker 1: that want to know that. They want to know that

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Speaker 1: you access stuff on your phone more than you do

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Speaker 1: on a laptop or desktop. That's important information. And then

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Speaker 1: there's the inferred data, and that's the stuff that companies

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Speaker 1: guess is relevant to you based upon the information they

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Speaker 1: gather from the other two types of sources. And it's

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Speaker 1: it's this collection that's important. And you know I mentioned

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Speaker 1: about value and how value is different for different people.

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Speaker 1: So the Telegraph did a survey UH, in which they

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Speaker 1: asked people what they felt their personal information was worth

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Speaker 1: to them personally, Like, what is your identity worth to you? Uh?

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Speaker 1: Based upon the kind of information to get shared around

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Speaker 1: these entities? Can you make a ballpark? Guess I'll tell

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Speaker 1: you this. It's it's it's less than ten thousand dollars,

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Speaker 1: but it's more than five dollars. Man, man, I'm gonna

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Speaker 1: go with I'm gonna go with twenty bucks or no, no, no, eight, wait,

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Speaker 1: hundred bucks. You know it's probably because we share so

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Speaker 1: much ben that we value it so little. But the

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Speaker 1: average person five thousand dollars. Oh well, that's good. That

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Speaker 1: that makes and that makes the average person a lot

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Speaker 1: smarter than me. But to be fair, it was three

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Speaker 1: thousand two pounds sterling. But I did the conversion. Yeah,

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Speaker 1: and and uh. And they also mentioned the fact that,

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Speaker 1: um that while they were doing this, they they found

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Speaker 1: that there was a disparity that that women in the

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Speaker 1: survey tended to answer more frequently that their personal information

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Speaker 1: was priceless, that there was no price you could put

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Speaker 1: on it that they would feel comfortable selling it. And

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Speaker 1: men were less likely to do that. They also said

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Speaker 1: an interesting data point, if you will, Yeah, I also

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Speaker 1: mentioned that older people were less likely to want to

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Speaker 1: sell their information than younger people, which kind of young

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Speaker 1: younger people are just giving it away. Yeah they are,

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Speaker 1: I mean they you know, we're seeing that more and

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Speaker 1: more with the the the very cavalier behavior of certain

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Speaker 1: executives like Zuckerberg who have said that privacy is dead. No,

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Speaker 1: privacy is no longer a thing. That seems to be

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Speaker 1: kind of the message that younger generations have not only

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Speaker 1: absorbed but adopted at least to some extent. Say you

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Speaker 1: and I asked old kajure, so we claim, yeah, no,

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Speaker 1: if your generation X or older, you're like, get your dirty,

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Speaker 1: grubby hands off my personal information, you damn millennial, right,

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Speaker 1: so now here, that's so, that's how much the person

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Speaker 1: values their personal information in the thousands of dollars range. Okay,

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Speaker 1: I know what you're gonna do next, Right, You're gonna

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Speaker 1: give me the average company's value on our data. Ye's

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Speaker 1: gonnas exactly what's gonna happen? So here's the deal. Your

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Speaker 1: personal information to a company at best, like at its

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Speaker 1: peak for average amount of information. I'm not talking about

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Speaker 1: very specialized stuff like if you were talking about that

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Speaker 1: wearable example. I was saying earlier that would that would

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Speaker 1: be worth more because it's more comprehensive data. But you're

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Speaker 1: average stuff, like you're the stuff you might find on

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Speaker 1: a Facebook profile page. It is worth point zero zero

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Speaker 1: five dollars. So half a cent is how much your

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Speaker 1: data is. We're at at the peak average is closer

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Speaker 1: to point zero zero zero five dollars. To know that.

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Speaker 1: I'm you know, a boring, middle aged white guy who's

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Speaker 1: probably going to see the Star Wars movie probably, I

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Speaker 1: mean I would have paid you for definitely, Okay, I

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Speaker 1: mean it's seven days away, dude, it is seven days

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Speaker 1: away as we record that. So so at any rate, yeah, exactly,

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Speaker 1: Like the company is like, that's how much your information

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Speaker 1: is worth to them, So to you, it's in the

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Speaker 1: thousands of dollars. To them, it doesn't even equal a

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Speaker 1: penny for the but an aggregate, right, it's gonna be

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Speaker 1: worth so much more. Well, exactly because because when a

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Speaker 1: company is buying information, they're not buying one person's information.

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Speaker 1: They're buying bundles of thousands upon thousands of people's information.

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Speaker 1: Then it's that company's job to either sell that information

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Speaker 1: off to someone else. These are data brokers. This is

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Speaker 1: all they do. That's that's their job. That's their business.

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Speaker 1: They buy and sell info. They don't make stuff, they

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Speaker 1: don't provide any services. They aggregate data and sell it

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Speaker 1: off to other people. This is a fascinating part of

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Speaker 1: this whole thing to me, Jonathan, because it's like they're

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Speaker 1: these companies are really I mean, they're they're kind of

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Speaker 1: like in the shadows, you know, they're they're they're like

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Speaker 1: companies with like weird names that that that have you know,

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Speaker 1: data centers in in in the middle of nowhere, and

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Speaker 1: and they're really hard to find. And you you know,

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Speaker 1: I mean you and I probably both remember the days

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Speaker 1: of like do not call me at home, you know,

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Speaker 1: getting getting that phone call from marketers, the pre do

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Speaker 1: not call list days. Right, And we don't really have

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Speaker 1: a do not call list for data yet, right. We

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Speaker 1: have some tools, but we don't really have that. And

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Speaker 1: I feel like we're in this kind of um, I

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Speaker 1: don't know, gold rush on data exactly. We don't have

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Speaker 1: we don't have the power to say here is what

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Speaker 1: you can use my data to do, and here is

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Speaker 1: what you cannot use my data to do. Uh. You know,

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Speaker 1: if you're lucky, you will be or if you're very

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Speaker 1: um you know, careful, and you're and you're paying really

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Speaker 1: close attention, then you might use services where you're looking

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Speaker 1: at the user agreement and looking to see do they

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Speaker 1: have a policy? Do they do they specifically state they

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Speaker 1: will not share your information with third parties. Um, you

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Speaker 1: know you can find some of those, but honestly, that's

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Speaker 1: not the way business works for most of these companies.

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Speaker 1: You are the product, right, like like Google is free. Yep,

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Speaker 1: you're the one the product. That's that's what Google, your

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Speaker 1: Google's product, your Facebook product. You are what they are

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Speaker 1: selling to other other interested parties. And uh, even if

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Speaker 1: you look at ones that say that, oh, don't worry,

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Speaker 1: we scrub all the identifiable stuff from your day, animize

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Speaker 1: your data if you will. Yeah, that, Um, that don't

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Speaker 1: work so good, Ben, it doesn't work very good. There

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Speaker 1: have been studies about this in the last few years, right.

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Speaker 1: I think there was a study that came out of

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Speaker 1: the UK that suggested, um that really in order to

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Speaker 1: identify a person, um, you really only needed like one

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Speaker 1: or two pieces of information about them, right, Do you

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Speaker 1: remember that study that came out in the last I

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Speaker 1: don't know year or so. It was like, I remember

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Speaker 1: that one. And there's one that Latania Sweeney Harvard she

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Speaker 1: did one where she proved that with three points of data,

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Speaker 1: which was gender, birth date, and ZIP code, she could

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Speaker 1: identify of US residents. So so nearly of all people

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Speaker 1: living in the United States she could identify just with

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Speaker 1: those three pieces of information. That One of the things

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Speaker 1: I like to stress to people is your name, which

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Speaker 1: you try to protect when you are when you think

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Speaker 1: you're being anonymous on the Internet and you use a handle,

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Speaker 1: so you're not using your name like I use my

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Speaker 1: name everywhere as my handle. That's just but most of

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Speaker 1: the time, but a lot of people like, no, I'm

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Speaker 1: going to use an anonymous handle. Your name is the

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Speaker 1: least important thing about you. I hate to break it

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Speaker 1: to you that because honestly, most of these companies don't care.

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Speaker 1: Just like I was saying, the shop owner with the

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Speaker 1: ice cream doesn't care who you are. Most of these

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Speaker 1: companies don't really care who you are. They care about

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Speaker 1: what you do. Yeah, and it's really interesting too, Like Google,

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Speaker 1: for instance, leverages large amounts of money and even some

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Speaker 1: of their best algorithmic computing power to make sure that

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Speaker 1: advertisers and marketers, for instance, don't engage in dishonest practices

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Speaker 1: or break Google's own rules about tracking data. And even

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Speaker 1: for Google, this is becoming like a herculean task. We

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Speaker 1: I talked to in for Cobreaker, I talked to some

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Speaker 1: people at Columbia's and Columbia grad students who are studying this,

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Speaker 1: and it's really interesting to see what gets pulled from

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Speaker 1: even like emails that you might send in Gmail, for instance. Yeah, yeah,

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Speaker 1: this is this is kind of um, kind of terrifying.

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Speaker 1: Also if you if you think about because you think

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Speaker 1: of email as being this this uh, private means of

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Speaker 1: communication that you know, just as you wouldn't expect someone

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Speaker 1: else to read your snail mail, that that would be

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Speaker 1: a violation of trust to find out that stuff is

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Speaker 1: popping up based on things that you've typed. Like if

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Speaker 1: you've written an email recently and then something that is

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Speaker 1: related to that email starts seemingly to pop up in

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Speaker 1: places you weren't expecting, you might think at first that

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Speaker 1: that's a weird coincidence. But if this happens a few

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Speaker 1: times like, Okay, this is not a coincidence. What is

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Speaker 1: going on? Yeah, it's really interesting. I think that's something Jonathan,

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Speaker 1: that so many people can relate to, right, this idea

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Speaker 1: of like, oh I looked at this use once or

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Speaker 1: I looked at you know, whatever, the whatever item that

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Speaker 1: you might buy online once and that advertisement follows you

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Speaker 1: around forever. Yeah, you had that great moment and codebreaker

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Speaker 1: in fact, about the sandals. Yes, the sandals that followed

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Speaker 1: Molly around. Every time I logged into Facebook for a

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Speaker 1: straight week, they would pop up, and I was so

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Speaker 1: scared because I was like, oh my god, I'm gonna

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Speaker 1: be sitting next to my boyfriend and I'm gonna log

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Speaker 1: onto Facebook and these sandals are going to come up,

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Speaker 1: and it's gonna reopen the wound, and he's going to

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Speaker 1: know that I was sending them to people asking for

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Speaker 1: an opinion. Did you change your behavior when you were

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Speaker 1: hanging out with him? Yeah, Like if we were loafing around,

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Speaker 1: I would open so many tabs and none of them

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Speaker 1: would be Facebook. This is a really funny story. What

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Speaker 1: does it make you think about? It made me wish

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Speaker 1: that I could go back to the days where Facebook

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Speaker 1: was just showing me things that I wanted to buy

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Speaker 1: and not things that I wanted to run away from.

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Speaker 1: That's uh, that's such a I mean, I've seen the

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Speaker 1: same sort of stuff and I've seen advertisements that it's odd,

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Speaker 1: like things that do not interest me at all. They're

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Speaker 1: just completely off base and I can't It's one of

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Speaker 1: those things where I think, well, maybe now, it's just

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Speaker 1: that the system hasn't gathered enough information about me to

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Speaker 1: get a good feel right, So I'm seeing stuff. It's

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Speaker 1: like this is the shotgun approach. I'm throwing everything I

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Speaker 1: can and hopefully stuff will work. Uh. Systems like Facebook,

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Speaker 1: for example, where you can actually go in to an

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Speaker 1: ad and say I am not interested in this, you know,

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Speaker 1: then maybe you'll end up getting a different selection of stuff,

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Speaker 1: but sometimes you'll just get different versions of the same

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Speaker 1: thing you already said. Uh, that's not it's not me man.

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Speaker 1: I appreciate the muscles on this dude, but I am

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Speaker 1: not going to be that guy. So I'm into I'm

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Speaker 1: into my little pony, but I I think I've outgrown it.

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Speaker 1: I look, my collection, my collection can only be so complete.

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Speaker 1: That's you know, there's you gotta draw the line after

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Speaker 1: like series five, I gotta say I'm done. I have

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Speaker 1: no more shelf space. Um, yeah, this is this is

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Speaker 1: one of those those amazing stories, like the story about

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Speaker 1: the sandals. Just just one of those things that has

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Speaker 1: happened to pretty much anyone who has been active online

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Speaker 1: over the last couple of years. And it's probably one

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Speaker 1: of those stories I would imagine that people start to

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Speaker 1: share not realizing that this is something that other people

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Speaker 1: have experienced. Yeah, right, it is. It feels like a

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Speaker 1: very personal experience, but it's something that seems to happen

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Speaker 1: to everyone. And it was interesting to see relatively recently Facebook,

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Speaker 1: I believe UM made it easier for people to control

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Speaker 1: how much of their ex girlfriend or a friend or

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Speaker 1: significant others information surfaced on their own timeline. Did you

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Speaker 1: see that story that came out relatively recently. He was

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Speaker 1: interesting this idea that like you know, you know, even

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Speaker 1: the tech companies they're really really good at data tracking

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Speaker 1: at this point, are still trying to figure out when

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Speaker 1: it goes wrong and how to help the user. In

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Speaker 1: some cases, UM make it a more kind of custom experience, right,

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Speaker 1: And and we should also stress that that the overwhelming

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Speaker 1: majority of companies out there do not want it to

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Speaker 1: go wrong, because that's that's counterproductive to what the end

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Speaker 1: end goal is, at least for the person that actually

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Speaker 1: bought the advertisement and the person who owns the place

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Speaker 1: where that ad is being shown. In the case of

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Speaker 1: targeted advertising, at least like they don't want that to

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Speaker 1: go badly. But in other cases, you know, they don't

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Speaker 1: want to go badly either that that's counterproductive. But it

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Speaker 1: is possible for this to go wrong even if everyone

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Speaker 1: is trying to use it to to people's benefit. And

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Speaker 1: on Codebreaker, you had a heartbreaking story about about a

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Speaker 1: father who shared some information, not not knowing, not even

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Speaker 1: realizing that that information was somehow going to make its

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Speaker 1: way into a database and then came back to haunt him. Yeah,

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Speaker 1: this guy, Mike say, and and this was happened a

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Speaker 1: few years back. He you know, unfortunately, um his his

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Speaker 1: one of his children, his daughter, died in a car accident.

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Speaker 1: And he went to buy some uh you know, he

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Speaker 1: went to buy some some actually some some um some

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Speaker 1: stuff for photos, Like he was putting together some photo albums,

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Speaker 1: and he went to buy some frames for those photos.

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Speaker 1: And the place that he called to do that Um,

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Speaker 1: he just in passing men sinned that his daughter had

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Speaker 1: died in this car accident. And the person that he

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Speaker 1: was talking to actually took down that information. And it's

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Speaker 1: still not clear how it how all of this happened,

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Speaker 1: but apparently they took down the information and that information

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Speaker 1: ended up getting sold to a data broker that sold

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Speaker 1: the information from the company he originally called to Office Max,

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Speaker 1: and then Office Max sent a letter to him. Um,

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Speaker 1: there was addressed to owner of the house or Mike,

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Speaker 1: say daughter died in a car accident. Um, And that's

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Speaker 1: when it goes really really wrong, right. The picture frames

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Speaker 1: I was was buying with through my children in the

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Speaker 1: bottom it had Ashley's name and her birth and death

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Speaker 1: date and that she loved them, and you know what

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Speaker 1: I mean, that's how she the conversation came about that

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Speaker 1: what is this for? And she took you know, the

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Speaker 1: information that would go on the bottom and put that,

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Speaker 1: you know, on whatever to get it stamp. But they

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Speaker 1: had nothing that it didn't say killed in car crashing

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Speaker 1: out of that that was all on her own. And

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Speaker 1: this is a totally separate company from Office Max. And

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Speaker 1: somehow Office Max got the information they bought the information

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Speaker 1: from a data group. So that information was sold from

401
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Speaker 1: things remembered to a data group that collects data and

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Speaker 1: then taken and sold to Office Max. And an Office

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Speaker 1: Max used without looking at it. What an an emotional

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Speaker 1: uh punch to the gut to see something like that.

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Speaker 1: And and honestly, you know, I mean, you know, Office

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Speaker 1: Max did not intend that. There's no sane reason anyone

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Speaker 1: would do at So it's clearly an epic mistake that

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Speaker 1: that could result in true emotional trauma, even if it's

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Speaker 1: just a momentary thing. You don't want any person to

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Speaker 1: experience that kind of thing. And uh, it was a

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Speaker 1: pure accident, I imagine based upon way back in the day,

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Speaker 1: I used to work for consultants. And when when I

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Speaker 1: worked for consultants, one of the things I had to

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Speaker 1: do was arranged giant mailing lists and print out tons

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Speaker 1: and tons and tons of mailing labels. And so you're

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Speaker 1: you're working with these big spreadsheets that are possibly thousands

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Speaker 1: of names long. So I'm guessing what happened was you

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Speaker 1: had the data entered into some sort of spreadsheet, and

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Speaker 1: probably the spreadsheet was converted at least once, and in

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Speaker 1: that conversion, a tab that had been or you know,

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Speaker 1: a cell that had just been labeled miscellaneous got put

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Speaker 1: into address instead of miscellany or title or something along

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Speaker 1: those lines. And that's when it went from one point

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Speaker 1: where it was a piece of data, which, as as

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Speaker 1: Mike said, like, why do people even need to know this?

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Speaker 1: No one needs to know this. This is this is

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Speaker 1: not a relevant piece of information. It should never have

428
00:26:18,119 --> 00:26:21,680
Speaker 1: gone into a database. But for that to then accidentally

429
00:26:21,680 --> 00:26:25,040
Speaker 1: get transposed into a cell that is going to show

430
00:26:25,160 --> 00:26:32,280
Speaker 1: up on a mailing label is incredibly it feels incredibly callous, yeah,

431
00:26:32,320 --> 00:26:35,600
Speaker 1: and invasive. And it also, you know, it reminds you that,

432
00:26:37,760 --> 00:26:39,520
Speaker 1: I mean, it reminds us all that the way we

433
00:26:39,720 --> 00:26:43,119
Speaker 1: view ourselves isn't always the way we're viewed by the world,

434
00:26:43,400 --> 00:26:47,720
Speaker 1: right and and and and that's an interesting aspect of

435
00:26:47,800 --> 00:26:52,879
Speaker 1: this too, this idea that basically a data profile of

436
00:26:53,640 --> 00:26:57,320
Speaker 1: most users on the Internet is being slowly put together

437
00:26:57,440 --> 00:27:01,959
Speaker 1: over time, and and you might be we all might

438
00:27:02,040 --> 00:27:07,000
Speaker 1: be really curious to know, um or disappointed to know, um,

439
00:27:08,119 --> 00:27:11,439
Speaker 1: what that data profile looks like from the other side. Now,

440
00:27:12,119 --> 00:27:14,399
Speaker 1: then let me ask you this. I'm gonna paint a

441
00:27:14,480 --> 00:27:19,720
Speaker 1: picture for you of a dystopia. Okay, that's that's sadly

442
00:27:20,480 --> 00:27:24,480
Speaker 1: not difficult to imagine based on our current events. All Right,

443
00:27:24,560 --> 00:27:28,000
Speaker 1: so I'm sure you heard, I mean, I'm absolutely certain

444
00:27:28,040 --> 00:27:31,040
Speaker 1: you heard about the app people p E E P

445
00:27:31,359 --> 00:27:34,480
Speaker 1: l E. Right, Yes, this was the one that was

446
00:27:34,760 --> 00:27:37,440
Speaker 1: supposed to be the yelp for people where you would

447
00:27:37,480 --> 00:27:42,399
Speaker 1: be able to rate other human beings, and so then

448
00:27:42,480 --> 00:27:44,880
Speaker 1: other human beings could see your rating and could see

449
00:27:44,960 --> 00:27:47,359
Speaker 1: ratings of other people, and it just seemed like it

450
00:27:47,520 --> 00:27:51,679
Speaker 1: was going to lead to fallout four. That's what literally

451
00:27:51,920 --> 00:27:55,920
Speaker 1: an idea, an idea whose time should have never come exactly. Now,

452
00:27:56,160 --> 00:27:59,399
Speaker 1: let's take that same basic concept, but instead of you

453
00:27:59,640 --> 00:28:03,320
Speaker 1: rating people, imagine that you get to look up what

454
00:28:03,560 --> 00:28:07,320
Speaker 1: that person's data profile is worth compared to your own.

455
00:28:09,320 --> 00:28:12,760
Speaker 1: Because guess what you could do that you could build

456
00:28:13,640 --> 00:28:17,080
Speaker 1: something where you start to look at the the robustness

457
00:28:17,200 --> 00:28:20,960
Speaker 1: of a data profile, at least for a certain given

458
00:28:21,200 --> 00:28:23,960
Speaker 1: set of transactions. Because keep in mind, there are a

459
00:28:24,000 --> 00:28:26,639
Speaker 1: lot of data brokers out there, and your profile in

460
00:28:26,720 --> 00:28:29,480
Speaker 1: one data broker's database may be very different from another

461
00:28:29,560 --> 00:28:34,280
Speaker 1: because it may be less or more complete or from

462
00:28:34,520 --> 00:28:38,400
Speaker 1: totally different sources than the other one. But that would

463
00:28:38,440 --> 00:28:42,240
Speaker 1: be a weird world where you say, wow, my life

464
00:28:42,720 --> 00:28:47,720
Speaker 1: is worth this much to a potential marketer, and my

465
00:28:47,920 --> 00:28:50,840
Speaker 1: friends life is worth twice as much. What am I

466
00:28:50,960 --> 00:28:53,680
Speaker 1: doing wrong in my life where I'm worth less? I mean,

467
00:28:53,920 --> 00:28:57,240
Speaker 1: it's this is what this is how companies are looking

468
00:28:57,280 --> 00:29:00,479
Speaker 1: at us. Yeah, and it is scary. Mean, I think

469
00:29:00,520 --> 00:29:03,160
Speaker 1: it's also fair to say that this is as scary

470
00:29:03,200 --> 00:29:05,520
Speaker 1: as it is to imagine that it is. It does

471
00:29:05,680 --> 00:29:08,320
Speaker 1: also have some connection to reality. I mean, when you

472
00:29:08,400 --> 00:29:11,320
Speaker 1: go into a bank and apply for a loan, um,

473
00:29:11,960 --> 00:29:15,600
Speaker 1: you're also being sort of sized up, right, um, And

474
00:29:16,400 --> 00:29:20,000
Speaker 1: over time, um, you know, we've we've we've seen some

475
00:29:20,200 --> 00:29:25,000
Speaker 1: actually some really big problems arise from how banks look

476
00:29:25,040 --> 00:29:28,360
Speaker 1: at people and look at people's ability to pay things back,

477
00:29:28,480 --> 00:29:30,720
Speaker 1: and how credit card companies do this, how your net

478
00:29:30,800 --> 00:29:34,280
Speaker 1: worth is calculated, how you're um, you know, your your

479
00:29:34,360 --> 00:29:38,000
Speaker 1: likelihood of paying a loan back is calculated. And but

480
00:29:38,080 --> 00:29:40,960
Speaker 1: at the same time, over time, there have been some

481
00:29:41,160 --> 00:29:45,600
Speaker 1: safeguards put in against discrimination, right, and a lot of

482
00:29:45,680 --> 00:29:48,640
Speaker 1: people have actually talked about this idea of data discrimination

483
00:29:48,720 --> 00:29:52,760
Speaker 1: over time and how um, we really need to think

484
00:29:52,800 --> 00:29:55,360
Speaker 1: about as we go forward and as more and more

485
00:29:55,440 --> 00:29:58,480
Speaker 1: data gets collected on on on users over time. I

486
00:29:58,560 --> 00:30:01,280
Speaker 1: mean people that were born, you know, in the last

487
00:30:01,320 --> 00:30:04,000
Speaker 1: ten years are going to have a very different life

488
00:30:04,280 --> 00:30:07,000
Speaker 1: on the Internet than even you or I have had, right,

489
00:30:07,080 --> 00:30:09,200
Speaker 1: I mean I wasn't on the Internet until I was

490
00:30:09,720 --> 00:30:12,800
Speaker 1: a teenager. So it's interesting to think about it. And

491
00:30:12,880 --> 00:30:15,680
Speaker 1: it's also you know, you would you would hope some

492
00:30:15,800 --> 00:30:19,760
Speaker 1: people are talking about this idea of like, you know,

493
00:30:20,080 --> 00:30:25,840
Speaker 1: if you are perceived to be a a person of

494
00:30:25,880 --> 00:30:31,040
Speaker 1: a certain race, a certain education level, tax bracketing like

495
00:30:31,160 --> 00:30:36,520
Speaker 1: that the tax bracket, then you might actually see a

496
00:30:36,680 --> 00:30:41,080
Speaker 1: web that is different from the web that someone else

497
00:30:41,360 --> 00:30:44,480
Speaker 1: with a different perceived data profile would see. Yeah, this

498
00:30:44,600 --> 00:30:47,400
Speaker 1: is this is like a dark twisted version of what

499
00:30:47,640 --> 00:30:51,760
Speaker 1: the semantic web is supposed to be. The semantic web

500
00:30:51,920 --> 00:30:55,640
Speaker 1: is supposed to learn about you and then respond to

501
00:30:55,840 --> 00:30:58,880
Speaker 1: what you want, so that you don't have to play

502
00:30:59,000 --> 00:31:01,960
Speaker 1: the game of the web search or the web browser.

503
00:31:02,320 --> 00:31:04,720
Speaker 1: You just say what you want and it returns exactly

504
00:31:04,760 --> 00:31:08,080
Speaker 1: what you want, possibly from multiple sources, so you're not

505
00:31:08,160 --> 00:31:11,240
Speaker 1: even going to a web page in the semantic web necessarily.

506
00:31:11,320 --> 00:31:14,479
Speaker 1: It may be that it's pulling things from various sources

507
00:31:14,600 --> 00:31:18,040
Speaker 1: and presenting you kind of a united view. That's one

508
00:31:18,240 --> 00:31:21,400
Speaker 1: realization of what those semantic web would look like. This

509
00:31:21,720 --> 00:31:24,640
Speaker 1: this is similar to that, but a very dark version

510
00:31:24,760 --> 00:31:28,680
Speaker 1: where it's not responding to what you want, it's responding

511
00:31:28,760 --> 00:31:32,280
Speaker 1: to what the other side wants based upon what they

512
00:31:32,320 --> 00:31:35,320
Speaker 1: think they can get out of you. Yeah, and that's

513
00:31:35,360 --> 00:31:39,280
Speaker 1: a really scary thing. I mean there's also some potential

514
00:31:39,400 --> 00:31:42,640
Speaker 1: good from that can come from this, right. Um And

515
00:31:42,840 --> 00:31:45,840
Speaker 1: and you know, other people are talking about this idea

516
00:31:46,360 --> 00:31:51,440
Speaker 1: of you know, a time at which maybe people actually

517
00:31:51,520 --> 00:31:54,960
Speaker 1: start to you know, the data is more valuable big data.

518
00:31:55,480 --> 00:31:57,960
Speaker 1: You know, it's all perceived to be more valuable to

519
00:31:58,200 --> 00:32:02,160
Speaker 1: companies and marketers and and and and certain organizations in

520
00:32:02,240 --> 00:32:06,120
Speaker 1: the aggregate. Right, So what if we actually reached a

521
00:32:06,240 --> 00:32:10,680
Speaker 1: time where um not I won't say like data gets

522
00:32:10,840 --> 00:32:14,560
Speaker 1: unionized necessarily, but like, what if you reach the time

523
00:32:14,680 --> 00:32:20,920
Speaker 1: where users actually banded together and did collective bargaining on

524
00:32:21,080 --> 00:32:25,080
Speaker 1: behalf of their data in order to say, hey, okay,

525
00:32:25,160 --> 00:32:30,240
Speaker 1: my data maybe only half of a penny value, but

526
00:32:30,800 --> 00:32:33,200
Speaker 1: when I get together with this large group of other

527
00:32:33,400 --> 00:32:37,240
Speaker 1: users that I that I'm aligned with, it's actually worth

528
00:32:37,280 --> 00:32:40,520
Speaker 1: a lot more. And I want to align myself with

529
00:32:40,600 --> 00:32:44,800
Speaker 1: this large group and then sell that um and actually

530
00:32:45,120 --> 00:32:48,000
Speaker 1: gained some agency in that way and gain maybe some

531
00:32:48,440 --> 00:32:51,959
Speaker 1: some money. You're you're like creating a brand new career

532
00:32:52,360 --> 00:33:00,960
Speaker 1: a data agent, Like they're like Dave Walkers. When if

533
00:33:01,000 --> 00:33:04,320
Speaker 1: you want to go the vampire route, Yeah, that's that,

534
00:33:04,640 --> 00:33:08,160
Speaker 1: that's I I honestly had never considered that, but that

535
00:33:08,400 --> 00:33:12,000
Speaker 1: is a really compelling idea, especially when you think even

536
00:33:12,080 --> 00:33:13,720
Speaker 1: if you go on a route where you're like, all right,

537
00:33:13,760 --> 00:33:15,760
Speaker 1: we're not doing this to make money, We're doing this

538
00:33:15,840 --> 00:33:19,000
Speaker 1: so that we're only opting into the things that we

539
00:33:19,280 --> 00:33:22,880
Speaker 1: agree with that we find acceptable. One of the other

540
00:33:22,960 --> 00:33:28,080
Speaker 1: things about data tracking that I think is problematic is

541
00:33:28,160 --> 00:33:32,960
Speaker 1: that sure, uh, you might be getting stuff that's reacting

542
00:33:33,040 --> 00:33:35,760
Speaker 1: to your behavior, but a lot of that is going

543
00:33:35,800 --> 00:33:38,760
Speaker 1: to be based on guesswork. So for example, if I

544
00:33:39,280 --> 00:33:42,800
Speaker 1: have posted a lot about hiking, it maybe that I

545
00:33:42,920 --> 00:33:46,920
Speaker 1: get a lot of advertisements for outdoor supply stores like

546
00:33:47,040 --> 00:33:49,280
Speaker 1: camping gear, that kind of stuff. And it may be

547
00:33:49,400 --> 00:33:52,280
Speaker 1: that that doesn't fully overlap with my interests. That's not

548
00:33:52,800 --> 00:33:55,840
Speaker 1: terrible or whatever, but it also could mean that I

549
00:33:56,000 --> 00:34:00,760
Speaker 1: start to have a narrower selection of things presented to me,

550
00:34:01,040 --> 00:34:03,200
Speaker 1: which in one way is good, but it also means

551
00:34:03,760 --> 00:34:10,040
Speaker 1: it takes away the possibility of discovery. Yeah, this is

552
00:34:10,120 --> 00:34:14,400
Speaker 1: a huge I mean, this is a really interesting idea

553
00:34:14,400 --> 00:34:17,480
Speaker 1: and a really interesting topic, right. I mean, Amazon is

554
00:34:17,760 --> 00:34:20,680
Speaker 1: is kind of thinking about, I think trying to do this.

555
00:34:20,920 --> 00:34:23,279
Speaker 1: I mean, there there are companies that would would love

556
00:34:23,360 --> 00:34:25,319
Speaker 1: to be able to basically when you go to their

557
00:34:25,440 --> 00:34:28,400
Speaker 1: homepage they basically say or not that we would ever

558
00:34:28,520 --> 00:34:31,640
Speaker 1: necessarily do that in the future, but you you, whatever,

559
00:34:31,800 --> 00:34:36,240
Speaker 1: you you engage with their platform, they would say, here's

560
00:34:36,360 --> 00:34:41,040
Speaker 1: the three products you can choose from today. Yeah, right, right,

561
00:34:41,640 --> 00:34:46,320
Speaker 1: And that does, as you say, really reduce this idea

562
00:34:46,360 --> 00:34:48,680
Speaker 1: of discovery. And this is you know, this happens in

563
00:34:48,719 --> 00:34:51,960
Speaker 1: the media world to write this idea of like UM

564
00:34:52,200 --> 00:34:55,640
Speaker 1: and in the social media world, this idea of UM

565
00:34:56,239 --> 00:35:00,719
Speaker 1: being able to curate so in such a you know,

566
00:35:00,920 --> 00:35:05,120
Speaker 1: a focused way to the individual user also makes it

567
00:35:05,239 --> 00:35:09,080
Speaker 1: so that user doesn't i don't know, read the New

568
00:35:09,160 --> 00:35:13,120
Speaker 1: York Times and and learn anything about what goes on

569
00:35:13,320 --> 00:35:16,080
Speaker 1: outside of their own interest. Here it becomes it becomes

570
00:35:16,160 --> 00:35:20,560
Speaker 1: like the ultimate echo chamber, where where you know you're

571
00:35:20,640 --> 00:35:24,520
Speaker 1: you're you're stuck there seeing the same stuff and maybe

572
00:35:24,560 --> 00:35:27,200
Speaker 1: you don't even realize that we're we're heading to like

573
00:35:27,640 --> 00:35:30,200
Speaker 1: this is almost a Terry Gilliam film what we're describing

574
00:35:30,239 --> 00:35:33,440
Speaker 1: at this right, Like we're heading to Brazil right now,

575
00:35:34,719 --> 00:35:37,279
Speaker 1: let's head. Let's say I'm ready for that movie. I mean,

576
00:35:37,320 --> 00:35:40,400
Speaker 1: I don't want to be in the reality that it depicts.

577
00:35:40,440 --> 00:35:42,680
Speaker 1: I don't want to be I don't want it to

578
00:35:42,760 --> 00:35:45,600
Speaker 1: be reality. But I would totally watch that movie. Yeah,

579
00:35:45,760 --> 00:35:48,680
Speaker 1: me too. When we're talking big data, when we're talking

580
00:35:48,760 --> 00:35:51,000
Speaker 1: about all the information that's going out there, and we

581
00:35:51,120 --> 00:35:54,480
Speaker 1: mentioned the fact that companies are buying profiles in the

582
00:35:55,000 --> 00:35:59,160
Speaker 1: tens or hundreds of thousands or more. Here's why. Let

583
00:35:59,239 --> 00:36:01,960
Speaker 1: me give you a few statistics on how much information

584
00:36:02,719 --> 00:36:06,600
Speaker 1: is hitting the Internet on a per minute basis. This

585
00:36:06,840 --> 00:36:09,759
Speaker 1: is based off of research that was conducted by a

586
00:36:09,960 --> 00:36:13,480
Speaker 1: data company called Domo, and this was published in August

587
00:36:13,600 --> 00:36:16,240
Speaker 1: of two thousand fifteen, So keep in mind these numbers

588
00:36:16,280 --> 00:36:22,120
Speaker 1: are already old. But every single minute, Amazon receives four

589
00:36:22,200 --> 00:36:27,279
Speaker 1: thousand three unique visitors. So Amazon is one of those

590
00:36:27,320 --> 00:36:32,000
Speaker 1: companies that's really good at doing this focused data tracking

591
00:36:32,080 --> 00:36:34,800
Speaker 1: that you know, this is where you buy a product

592
00:36:34,880 --> 00:36:36,919
Speaker 1: and it says, hey, people who bought this product also

593
00:36:37,040 --> 00:36:41,080
Speaker 1: bought these other things, and then you think, oh, right, right,

594
00:36:41,239 --> 00:36:43,480
Speaker 1: I should get that. It's like it's like the perfect

595
00:36:43,880 --> 00:36:50,880
Speaker 1: impulse by shelf, and it's customized for every single purchase. Right,

596
00:36:50,960 --> 00:36:55,640
Speaker 1: I do need batteries and dog biscuits. How did you know, Well,

597
00:36:55,680 --> 00:36:59,239
Speaker 1: there's there's there's there's this other level two uh that

598
00:36:59,760 --> 00:37:02,640
Speaker 1: that people are starting to imagine when you think about

599
00:37:02,920 --> 00:37:07,600
Speaker 1: companies like nest Um, and you think about smart televisions

600
00:37:07,680 --> 00:37:10,279
Speaker 1: that actually interact with your refrigerator. And one of the

601
00:37:10,360 --> 00:37:15,280
Speaker 1: most convincing sort of imagined scenarios that was ever presented

602
00:37:15,360 --> 00:37:18,520
Speaker 1: to me in recent years was this idea that essentially,

603
00:37:18,600 --> 00:37:23,960
Speaker 1: over time, your refrigerator learns that you go for ice cream.

604
00:37:24,200 --> 00:37:27,400
Speaker 1: You go in for that vanilla ice cream, um, right

605
00:37:27,480 --> 00:37:33,160
Speaker 1: around eight pm, and um, it tells your television that.

606
00:37:33,760 --> 00:37:37,120
Speaker 1: And then in the world of streaming that you know,

607
00:37:37,239 --> 00:37:39,800
Speaker 1: more and more people are doing on their actual television

608
00:37:39,880 --> 00:37:42,040
Speaker 1: instead of having it connected to a you know, a

609
00:37:42,120 --> 00:37:47,000
Speaker 1: cable channel. Um, you actually get at eight forty five,

610
00:37:47,239 --> 00:37:51,800
Speaker 1: you get an advertisement for ice cream, which reminds you

611
00:37:52,280 --> 00:37:54,360
Speaker 1: to go and eat some ice cream, and then you

612
00:37:54,480 --> 00:37:56,640
Speaker 1: end up eating more ice cream, and then you end

613
00:37:56,760 --> 00:37:59,600
Speaker 1: up buying This is the world where the appliances are

614
00:37:59,640 --> 00:38:03,920
Speaker 1: working against you. Yes, they're not telling you to eat

615
00:38:03,960 --> 00:38:07,560
Speaker 1: a kale sale, or maybe they are. Yeah, that's also possible.

616
00:38:07,640 --> 00:38:09,480
Speaker 1: Like if you have if you have this thing where

617
00:38:09,520 --> 00:38:11,920
Speaker 1: you're going out and buying kale salads and then you're

618
00:38:11,960 --> 00:38:14,400
Speaker 1: watching a lot of you know, food type stuff, and

619
00:38:14,520 --> 00:38:17,680
Speaker 1: you're you might end up getting Hey, here's a quick recipe,

620
00:38:17,880 --> 00:38:20,080
Speaker 1: and here are the fourteen ingredients you don't have that

621
00:38:20,200 --> 00:38:23,759
Speaker 1: you need to go to Whole Foods and buy. Um. Yeah,

622
00:38:24,239 --> 00:38:26,879
Speaker 1: it's a world. I host a show. I host another

623
00:38:26,920 --> 00:38:30,520
Speaker 1: show called Forward Thinking, and in that show it's it's

624
00:38:30,560 --> 00:38:32,959
Speaker 1: an optimistic view of the future. And I have often

625
00:38:33,040 --> 00:38:35,800
Speaker 1: looked at this side from the optimist side, and it

626
00:38:36,040 --> 00:38:38,960
Speaker 1: is similar to what you were saying about. Every single

627
00:38:39,080 --> 00:38:43,719
Speaker 1: person's web experience could be unique to them based upon

628
00:38:43,840 --> 00:38:46,719
Speaker 1: these factors. Uh. And and there's a way of doing

629
00:38:46,840 --> 00:38:50,680
Speaker 1: that where it is not kind of the scary, oppressive

630
00:38:50,960 --> 00:38:53,520
Speaker 1: type of way. But I I talk about how with

631
00:38:53,600 --> 00:38:57,279
Speaker 1: the Internet of things and with this connectivity of different appliances,

632
00:38:58,120 --> 00:38:59,920
Speaker 1: it's not that far of a stretch to a man,

633
00:39:00,360 --> 00:39:05,640
Speaker 1: not just the web, your experience of reality could get

634
00:39:05,680 --> 00:39:07,800
Speaker 1: to a point where a lot of it is catered

635
00:39:07,960 --> 00:39:12,080
Speaker 1: specifically to you. Yeah, and there could be a lot

636
00:39:12,120 --> 00:39:14,600
Speaker 1: of good things that come out of that, right, um

637
00:39:15,080 --> 00:39:19,239
Speaker 1: and and and and I totally believe in that possibility.

638
00:39:19,280 --> 00:39:22,319
Speaker 1: I mean, like like we like you said, I think

639
00:39:22,440 --> 00:39:26,960
Speaker 1: so um so thoughtfully when you came in and and

640
00:39:27,320 --> 00:39:30,000
Speaker 1: and uh talked about this stuff with me on Codebreaker,

641
00:39:30,719 --> 00:39:35,280
Speaker 1: was was basically this idea that look, it's um I'm

642
00:39:35,320 --> 00:39:38,080
Speaker 1: not going to pull out the quotes so eloquently as

643
00:39:38,160 --> 00:39:42,160
Speaker 1: you did, but look, this technology can it's us that

644
00:39:42,400 --> 00:39:45,279
Speaker 1: makes it good or bad? Right we We we have

645
00:39:45,560 --> 00:39:49,000
Speaker 1: the power theoretically too to make all of this stuff

646
00:39:49,040 --> 00:39:51,680
Speaker 1: and make data tracking work for us as users and

647
00:39:51,800 --> 00:39:55,120
Speaker 1: work for us as organizations and companies. I mean, I

648
00:39:55,200 --> 00:39:58,040
Speaker 1: think a lot about the possibility of data tracking being

649
00:39:58,200 --> 00:40:01,520
Speaker 1: really good in a few sure where our climate is changing.

650
00:40:02,320 --> 00:40:06,320
Speaker 1: Um and and the fact that we really understand on

651
00:40:06,400 --> 00:40:10,680
Speaker 1: a global level a lot more about our climate than

652
00:40:10,920 --> 00:40:15,040
Speaker 1: we did even thirty years ago thanks to an increased

653
00:40:15,120 --> 00:40:17,400
Speaker 1: level of data tracking, right right, And and that is

654
00:40:17,440 --> 00:40:19,880
Speaker 1: an important point to make, Ben, is that data tracking

655
00:40:20,320 --> 00:40:24,360
Speaker 1: does not necessarily mean it's about just people. Although people

656
00:40:24,400 --> 00:40:27,000
Speaker 1: are an important factor when you're looking even at climate change.

657
00:40:27,040 --> 00:40:29,520
Speaker 1: Because you want to see, well, how is this affecting

658
00:40:29,600 --> 00:40:33,120
Speaker 1: actual human beings as well as the environment in general.

659
00:40:33,200 --> 00:40:35,640
Speaker 1: You need to know the whole picture. But data tracking

660
00:40:35,760 --> 00:40:40,040
Speaker 1: is really it's it's agnostic as far as what the

661
00:40:40,160 --> 00:40:45,480
Speaker 1: data actually represents. Like we're talking about data about everything,

662
00:40:46,200 --> 00:40:48,120
Speaker 1: you know, anything that you can imagine that can be

663
00:40:48,239 --> 00:40:51,440
Speaker 1: quantified in the form of data that is being tracked

664
00:40:51,840 --> 00:40:55,719
Speaker 1: by someone for some reason. Some of those reasons are good,

665
00:40:56,160 --> 00:41:00,920
Speaker 1: some of those reasons are way not good. But but

666
00:41:01,040 --> 00:41:05,040
Speaker 1: it's not all just about people. And it's interesting too

667
00:41:05,080 --> 00:41:07,360
Speaker 1: to think about I wonder what you think about this,

668
00:41:07,480 --> 00:41:13,400
Speaker 1: But it's interesting too to think about just how I

669
00:41:13,480 --> 00:41:15,680
Speaker 1: think a lot of it feels out of control at

670
00:41:15,719 --> 00:41:18,680
Speaker 1: this point for the user. It feels like we don't

671
00:41:18,840 --> 00:41:22,440
Speaker 1: fully understand it. We don't fully understand how it's being used.

672
00:41:22,920 --> 00:41:25,359
Speaker 1: Um and what data about us is being tracked as

673
00:41:25,480 --> 00:41:29,560
Speaker 1: users and um and you know, government, I mean the

674
00:41:29,680 --> 00:41:32,440
Speaker 1: FTC in the past few years has become much more

675
00:41:32,560 --> 00:41:37,840
Speaker 1: involved in this idea of helping consumers protect themselves against

676
00:41:38,200 --> 00:41:41,399
Speaker 1: you know, abusive forms of data traction um if you will,

677
00:41:42,040 --> 00:41:43,880
Speaker 1: um and and there are a lot of qui and

678
00:41:44,000 --> 00:41:46,200
Speaker 1: and and also it's fair to say that. I think

679
00:41:46,239 --> 00:41:49,719
Speaker 1: a lot of tech companies have endeavored to put not

680
00:41:49,880 --> 00:41:53,240
Speaker 1: only in terms of service agreements, but in the actual

681
00:41:53,360 --> 00:41:56,520
Speaker 1: tools that they give to the user when they manage

682
00:41:56,520 --> 00:41:59,120
Speaker 1: their own accounts, as you know, to put the power

683
00:41:59,200 --> 00:42:02,160
Speaker 1: in the user's hand ends to control this. But you know,

684
00:42:02,640 --> 00:42:04,879
Speaker 1: let's be honest, right, I mean, you and I don't

685
00:42:04,960 --> 00:42:08,440
Speaker 1: necessarily read all of the terms of services. You're telling

686
00:42:08,600 --> 00:42:12,359
Speaker 1: me you don't read every end user license agreement. You don't.

687
00:42:12,480 --> 00:42:17,680
Speaker 1: You can't recite the you know, it's it's tough. It's tough.

688
00:42:17,880 --> 00:42:20,840
Speaker 1: I can't remember the last time, Honestly, I cannot remember

689
00:42:20,880 --> 00:42:25,160
Speaker 1: the last time I actually read ULA because who who

690
00:42:25,320 --> 00:42:29,120
Speaker 1: has time for that? I want to use your shiny

691
00:42:29,239 --> 00:42:32,440
Speaker 1: new toy. I don't care that I've given up all

692
00:42:32,560 --> 00:42:35,080
Speaker 1: my privacy and if I as long as I get

693
00:42:35,160 --> 00:42:37,640
Speaker 1: to play with the shiny new toy. If my b

694
00:42:37,800 --> 00:42:42,080
Speaker 1: B eight remote control droid is secretly telling Disney exactly

695
00:42:42,200 --> 00:42:45,840
Speaker 1: how to make me buy more BB eight remote control droids,

696
00:42:46,120 --> 00:42:50,960
Speaker 1: so be it. Um. It all comes back to Star Wars. Well,

697
00:42:51,040 --> 00:42:54,400
Speaker 1: I mean, you know, the force is strong in my family.

698
00:42:54,560 --> 00:42:57,480
Speaker 1: My father has it, I have it, my sister has it.

699
00:42:57,760 --> 00:43:00,759
Speaker 1: So uh, you know. This is. This has been a

700
00:43:00,840 --> 00:43:04,800
Speaker 1: great conversation, great talk about this topic. And uh, you

701
00:43:04,880 --> 00:43:07,920
Speaker 1: know again, if you guys haven't checked out Code Breaker,

702
00:43:08,000 --> 00:43:12,400
Speaker 1: you definitely need to remedy that immediately. It is a

703
00:43:12,480 --> 00:43:16,000
Speaker 1: phenomenal series that covers various topics. And this is your

704
00:43:16,040 --> 00:43:19,840
Speaker 1: first season, right, this is our first season, Jonathan, and

705
00:43:20,160 --> 00:43:22,600
Speaker 1: we're looking forward to having you back on for our

706
00:43:22,680 --> 00:43:26,600
Speaker 1: second season, which is coming soon. Um. But but yeah,

707
00:43:26,640 --> 00:43:28,400
Speaker 1: it's it's been a lot of fun and and it

708
00:43:28,520 --> 00:43:30,640
Speaker 1: was it was great fun to talk to you on

709
00:43:30,760 --> 00:43:34,239
Speaker 1: Codebreaker and um, and have you be a part of

710
00:43:34,280 --> 00:43:37,799
Speaker 1: the show, and and we hope you'll you'll keep listening absolutely,

711
00:43:37,880 --> 00:43:40,760
Speaker 1: And guys, you also have to know this thing about

712
00:43:40,840 --> 00:43:45,080
Speaker 1: Code Breaker because it's a gimmick that's so good. I

713
00:43:45,360 --> 00:43:50,000
Speaker 1: was mad at myself for never thinking about it. This

714
00:43:50,200 --> 00:43:53,879
Speaker 1: is it was brilliant you you produced, you had all

715
00:43:53,920 --> 00:43:56,279
Speaker 1: of the shows produced and ready to go, and you

716
00:43:56,520 --> 00:44:01,440
Speaker 1: hid codes in the episodes. And if you're able to

717
00:44:01,560 --> 00:44:04,320
Speaker 1: break the code, you can listen to the next episode

718
00:44:04,400 --> 00:44:08,800
Speaker 1: before it publishes. Yes, well we should start, you know,

719
00:44:09,040 --> 00:44:13,320
Speaker 1: I mean, you know I'm ready to have we embedded

720
00:44:13,360 --> 00:44:16,000
Speaker 1: a secret code in this episode, John, you know I

721
00:44:16,440 --> 00:44:19,759
Speaker 1: like to say yes just to drive people crazy, but

722
00:44:19,840 --> 00:44:23,719
Speaker 1: I'm not gonna do that. I was just so proud

723
00:44:23,800 --> 00:44:28,560
Speaker 1: of myself for for breaking the the Morse code. In

724
00:44:28,680 --> 00:44:31,960
Speaker 1: the you did an episode about internet porn, which is

725
00:44:32,600 --> 00:44:36,520
Speaker 1: an incredible episode. Probably not safe for you to listen

726
00:44:36,600 --> 00:44:40,960
Speaker 1: to on speakers if you have, not safe for work

727
00:44:41,080 --> 00:44:43,640
Speaker 1: or for little ones, obviously for for obvious reasons. But

728
00:44:43,680 --> 00:44:48,000
Speaker 1: it's a real honest look at what is not only

729
00:44:48,120 --> 00:44:53,640
Speaker 1: a big industry online but legitimately it can make or

730
00:44:53,800 --> 00:44:59,560
Speaker 1: break certain types of of on the edge technologies. Like

731
00:45:00,440 --> 00:45:03,640
Speaker 1: if you wonder why Blu ray one over h D

732
00:45:03,880 --> 00:45:08,600
Speaker 1: d v D, it's one of the big reasons. I mean,

733
00:45:08,960 --> 00:45:11,680
Speaker 1: you know, it's it's crazy to think about that. If

734
00:45:11,719 --> 00:45:17,320
Speaker 1: you wonder why the A v N conference overlapped with

735
00:45:17,440 --> 00:45:22,600
Speaker 1: CS for so many years, this is why. It's an

736
00:45:22,640 --> 00:45:24,919
Speaker 1: incredible episode and I managed to actually break the code,

737
00:45:25,000 --> 00:45:28,240
Speaker 1: and I was probably a little more proud of myself

738
00:45:28,320 --> 00:45:32,920
Speaker 1: than I should have been. Well, now you should, I

739
00:45:33,000 --> 00:45:35,640
Speaker 1: mean you should be proud. It's Morris code. It's it's

740
00:45:35,880 --> 00:45:39,560
Speaker 1: it's not used, um, you know, very very widely anymore

741
00:45:39,719 --> 00:45:42,719
Speaker 1: by by at least most people. But it's uh, it's

742
00:45:42,719 --> 00:45:44,520
Speaker 1: always a good thing to know. Yeah, it turns out

743
00:45:44,680 --> 00:45:48,160
Speaker 1: you know, once you once you commit that scout stuff

744
00:45:48,280 --> 00:45:52,400
Speaker 1: to memory and have and have a web page opened

745
00:45:52,480 --> 00:45:54,239
Speaker 1: up that tells you what the dots and dashes me

746
00:45:54,360 --> 00:45:58,000
Speaker 1: because I didn't remember, but but it's still it's it's

747
00:45:58,040 --> 00:46:00,920
Speaker 1: a it's a great way to engage the listeners on

748
00:46:01,120 --> 00:46:05,480
Speaker 1: top of this interesting take you have on the different topics.

749
00:46:05,560 --> 00:46:08,960
Speaker 1: So guys, make sure you go and check that out. Ben,

750
00:46:09,440 --> 00:46:12,200
Speaker 1: Thank you so much for agreeing to be part of this.

751
00:46:12,360 --> 00:46:15,040
Speaker 1: This was I think a really fun kind of complimentary

752
00:46:15,200 --> 00:46:21,000
Speaker 1: piece to your Codebreaker episode, which again, uh, I really

753
00:46:21,120 --> 00:46:23,320
Speaker 1: enjoyed you guys. Let me listen to it before I

754
00:46:23,480 --> 00:46:27,480
Speaker 1: was even I recorded the final segment on that episode

755
00:46:27,760 --> 00:46:30,239
Speaker 1: and I got to listen to everything up to that point,

756
00:46:30,320 --> 00:46:32,319
Speaker 1: and I was just I was like, are you sure

757
00:46:32,360 --> 00:46:35,560
Speaker 1: you want me? You're bat and clean up man, you

758
00:46:35,640 --> 00:46:38,120
Speaker 1: are are close up. We love im. Glad, glad I

759
00:46:38,160 --> 00:46:41,560
Speaker 1: could could be of service. It was an honor. So well,

760
00:46:41,640 --> 00:46:43,440
Speaker 1: it was great to have you. It was great to

761
00:46:43,520 --> 00:46:46,160
Speaker 1: have you on and when we're big fans of tech stuff,

762
00:46:46,239 --> 00:46:49,239
Speaker 1: so we're like, um, it was thank you so much

763
00:46:49,280 --> 00:46:51,560
Speaker 1: for for having me and for for talking to me

764
00:46:51,600 --> 00:46:54,279
Speaker 1: about this stuff, and I think, I don't know, man,

765
00:46:54,360 --> 00:46:57,440
Speaker 1: I you know, I end up feeling you know, I

766
00:46:57,520 --> 00:46:59,759
Speaker 1: think so many of us end up healing lost right

767
00:47:00,000 --> 00:47:02,400
Speaker 1: about when it when it comes to data tracking. And

768
00:47:02,719 --> 00:47:05,400
Speaker 1: my only hope is that is that I mean not

769
00:47:05,520 --> 00:47:07,120
Speaker 1: to say that there should be an app for that,

770
00:47:07,320 --> 00:47:09,279
Speaker 1: but you know, I'm my only hope is that we

771
00:47:09,400 --> 00:47:12,479
Speaker 1: will at some point reach a reach a stage where

772
00:47:13,400 --> 00:47:17,239
Speaker 1: it's easier for the user to to really have control

773
00:47:17,440 --> 00:47:20,600
Speaker 1: over what they're sharing and and and also users are

774
00:47:20,680 --> 00:47:25,160
Speaker 1: more more educated about what they're sharing. Um, and I

775
00:47:25,280 --> 00:47:27,759
Speaker 1: think I think we're all, you know, we're all a

776
00:47:27,840 --> 00:47:29,920
Speaker 1: little bit on the hook for for not being more

777
00:47:30,080 --> 00:47:33,480
Speaker 1: educated about what we're sharing. But we're also you know,

778
00:47:33,640 --> 00:47:37,319
Speaker 1: we also need better tools to understand how we are

779
00:47:37,680 --> 00:47:39,640
Speaker 1: sharing our data and how it's being trapped and we

780
00:47:39,840 --> 00:47:44,839
Speaker 1: and we definitely need to hold government accountable for making

781
00:47:44,920 --> 00:47:50,640
Speaker 1: sure they create and foster departments that are specialized in

782
00:47:51,520 --> 00:47:55,080
Speaker 1: informing other bodies of government about these things, keeping up

783
00:47:55,120 --> 00:47:58,040
Speaker 1: to date, because as we know, I mean, we're we're

784
00:47:58,320 --> 00:48:01,320
Speaker 1: still seeing an era where people who are in positions

785
00:48:01,360 --> 00:48:05,560
Speaker 1: of power often they have limited to no experience with

786
00:48:05,920 --> 00:48:07,880
Speaker 1: a lot of the things that are affecting us on

787
00:48:07,960 --> 00:48:11,920
Speaker 1: a daily basis. Oh man, those those those uh, those

788
00:48:11,960 --> 00:48:15,960
Speaker 1: Supreme Court transcripts. Man, it's a sad world. So if

789
00:48:16,000 --> 00:48:19,480
Speaker 1: we're gonna like, we can't wait until the millennials are

790
00:48:19,520 --> 00:48:22,840
Speaker 1: in the Supreme Court for one thing, I'm gonna be

791
00:48:23,000 --> 00:48:26,080
Speaker 1: dead by then, So I would like to see things

792
00:48:26,120 --> 00:48:28,359
Speaker 1: improve before that. But that's that's one of those real

793
00:48:28,520 --> 00:48:33,000
Speaker 1: challenges is that we have a different population and positions

794
00:48:33,040 --> 00:48:37,000
Speaker 1: of power who they may have every desire to help

795
00:48:37,160 --> 00:48:40,520
Speaker 1: us make sure that these things are being used in

796
00:48:40,560 --> 00:48:44,680
Speaker 1: a responsible way, a non predatory, and an ethical way,

797
00:48:45,760 --> 00:48:48,560
Speaker 1: but they have, you know, to them, it's it's like

798
00:48:48,719 --> 00:48:50,840
Speaker 1: if you were to present me with a page written

799
00:48:50,840 --> 00:48:53,080
Speaker 1: in Greek, I would have no idea what to do

800
00:48:53,200 --> 00:48:58,920
Speaker 1: with that. So what technology moves faster than policy? And

801
00:48:59,160 --> 00:49:01,440
Speaker 1: and and and I think it's it's as true with

802
00:49:01,560 --> 00:49:05,000
Speaker 1: data tracking as anything else. And it's just, um, it's

803
00:49:05,000 --> 00:49:06,880
Speaker 1: a reality that we all have to deal with. But

804
00:49:07,000 --> 00:49:10,439
Speaker 1: it's it's it's something that hopefully will will change over time,

805
00:49:10,760 --> 00:49:15,239
Speaker 1: and it's not not not before, not before. A lot

806
00:49:15,280 --> 00:49:18,120
Speaker 1: of stuff happens that many people may regret. So it's

807
00:49:18,239 --> 00:49:21,520
Speaker 1: it's always good to be thinking about it. Yeah, absolutely, Uh,

808
00:49:21,760 --> 00:49:23,520
Speaker 1: you know, I'm gonna have to have you back on

809
00:49:23,640 --> 00:49:25,520
Speaker 1: the show. Maybe we can talk a little bit more

810
00:49:25,600 --> 00:49:30,160
Speaker 1: about some of these other elements where technology has severely

811
00:49:30,239 --> 00:49:35,560
Speaker 1: outpaced policy. I mean, autonomous cars is easily, that's easily

812
00:49:35,680 --> 00:49:37,600
Speaker 1: one of the top ones to talk about, right Like

813
00:49:38,239 --> 00:49:40,080
Speaker 1: they're and they're not even really out yet, and they're

814
00:49:40,120 --> 00:49:43,920
Speaker 1: already outpacing policy. I mean, it's and and I mean

815
00:49:44,120 --> 00:49:47,000
Speaker 1: I I am very much of the selfish opinion that

816
00:49:47,960 --> 00:49:50,520
Speaker 1: I want autonomous cars here now. I want to be

817
00:49:50,600 --> 00:49:54,279
Speaker 1: able to do right now. But but even then I recognize,

818
00:49:54,360 --> 00:49:57,680
Speaker 1: like it's it's not it's not as easy an answer

819
00:49:57,760 --> 00:50:00,120
Speaker 1: as saying, let's wait till the technology is ready. The

820
00:50:00,200 --> 00:50:08,319
Speaker 1: technology is ready, we've got other things we have to fix. Yeah, yeah, yeah, absolutely, Well, happy,

821
00:50:08,480 --> 00:50:10,759
Speaker 1: happy to talk to you any time, and thanks so

822
00:50:10,920 --> 00:50:13,560
Speaker 1: much for for having me up. Yeah now, Ben, tell

823
00:50:13,640 --> 00:50:15,880
Speaker 1: tell my listeners where they can find you so that

824
00:50:16,000 --> 00:50:21,359
Speaker 1: they can actually seek out your brilliance apart from this, well,

825
00:50:22,160 --> 00:50:24,800
Speaker 1: I don't know about that, but I'm I'm on Twitter

826
00:50:24,960 --> 00:50:29,120
Speaker 1: at the brock Johnson are our show Codebreakers at Codebreaker

827
00:50:29,239 --> 00:50:32,920
Speaker 1: dot Codes or you can find it on iTunes or

828
00:50:33,120 --> 00:50:37,279
Speaker 1: wherever you get your podcasts. Uh and you know, right

829
00:50:37,320 --> 00:50:41,279
Speaker 1: along tech Stuff and How Stuff Works. We're we're in

830
00:50:41,320 --> 00:50:44,440
Speaker 1: there with you guys, so so you can find us,

831
00:50:44,560 --> 00:50:47,400
Speaker 1: we're there, Thanks so much, and guys listen. If you

832
00:50:47,520 --> 00:50:50,560
Speaker 1: have any suggestions for future episodes of tech Stuff, whether

833
00:50:50,640 --> 00:50:54,719
Speaker 1: it's another guest host, someone I should interview, whether it's

834
00:50:54,760 --> 00:50:58,880
Speaker 1: a company or technology, whatever it may be, write me

835
00:50:59,440 --> 00:51:02,560
Speaker 1: let me know what you think. Text stuff at how

836
00:51:02,600 --> 00:51:05,239
Speaker 1: stuff works dot com. Is that email address. You can

837
00:51:05,239 --> 00:51:08,560
Speaker 1: also drop me a line on Facebook, on Twitter, or

838
00:51:08,719 --> 00:51:11,880
Speaker 1: on Tumblr. We are tech stuff h s W at

839
00:51:11,960 --> 00:51:14,200
Speaker 1: all three of those and I'll talk to you guys

840
00:51:14,239 --> 00:51:22,600
Speaker 1: again really soon for more on this and thousands of

841
00:51:22,680 --> 00:51:24,919
Speaker 1: other topics because a stock works dot com