1 00:00:00,240 --> 00:00:03,520 Speaker 1: So every so often when we're exploring a classic episode, 2 00:00:03,680 --> 00:00:11,160 Speaker 1: we run into something that is true but unfortunately prescient. 3 00:00:11,480 --> 00:00:16,000 Speaker 1: And folks, this week we're sharing with you our episode 4 00:00:16,400 --> 00:00:22,080 Speaker 1: called Big Data and You. It kind of makes me 5 00:00:22,120 --> 00:00:24,640 Speaker 1: think of like an after school special on like you know, 6 00:00:24,680 --> 00:00:27,639 Speaker 1: how to like protect yourself against big data. It's not 7 00:00:27,720 --> 00:00:30,000 Speaker 1: too far off from what it is. Well, it's it's 8 00:00:30,040 --> 00:00:33,360 Speaker 1: weird to think this was put out originally six years ago, 9 00:00:33,800 --> 00:00:38,639 Speaker 1: I believe, and just how much the landscape has changed 10 00:00:38,720 --> 00:00:42,839 Speaker 1: since then. It hasn't really changed, it's these big data 11 00:00:42,960 --> 00:00:48,680 Speaker 1: has just gotten stronger, basically, and there are several companies 12 00:00:48,720 --> 00:00:53,319 Speaker 1: that have just kind of strengthened their positions. Yeah, and 13 00:00:53,360 --> 00:00:57,560 Speaker 1: there's there's no turning back the clock on this. But 14 00:00:57,800 --> 00:01:03,280 Speaker 1: on one positive note, assurely depositive note, more people are 15 00:01:03,440 --> 00:01:09,280 Speaker 1: aware of the dangers of this sort of ubiquitous observation 16 00:01:09,360 --> 00:01:13,160 Speaker 1: and surveillance and how it can be used to nudge 17 00:01:13,360 --> 00:01:16,720 Speaker 1: your decisions. It's gone past, by the way, figuring out 18 00:01:17,280 --> 00:01:21,280 Speaker 1: who might want to buy some new ray bands or something. 19 00:01:21,400 --> 00:01:25,840 Speaker 1: This is this is about making people do things they 20 00:01:25,840 --> 00:01:28,560 Speaker 1: may not have ordinarily done at this point. But if 21 00:01:28,600 --> 00:01:31,160 Speaker 1: you have someone that in your life, that you feel 22 00:01:31,160 --> 00:01:34,080 Speaker 1: like needs to know a little bit more about the 23 00:01:34,080 --> 00:01:37,480 Speaker 1: basics of what people mean when they say big data. 24 00:01:37,640 --> 00:01:39,720 Speaker 1: This is the episode for you. And also it goes 25 00:01:39,720 --> 00:01:42,040 Speaker 1: into the idea that if you don't know what the 26 00:01:42,080 --> 00:01:47,920 Speaker 1: product is, it's probably you. So, without further ado, turn 27 00:01:47,960 --> 00:01:53,160 Speaker 1: your ad trackers on and tune in to this classic episode. Disagree, 28 00:01:53,240 --> 00:01:55,720 Speaker 1: turn your phones off. Even if you're listening to this 29 00:01:55,760 --> 00:01:57,240 Speaker 1: on your phone, just go ahead and turn it off 30 00:01:57,360 --> 00:02:00,720 Speaker 1: right now. Don't even listen. Just up into a bucket 31 00:02:00,720 --> 00:02:05,160 Speaker 1: of water to cover ups. History is riddled with unexplained events. 32 00:02:05,640 --> 00:02:08,240 Speaker 1: You can turn back now or learn the stuff they 33 00:02:08,280 --> 00:02:17,800 Speaker 1: don't want you to now. Hello, welcome back to the show. 34 00:02:17,840 --> 00:02:19,920 Speaker 1: My name is Matt and I'm Ben. We're here with 35 00:02:20,000 --> 00:02:23,240 Speaker 1: our super producer Noel, which makes this stuff they moge 36 00:02:23,280 --> 00:02:26,320 Speaker 1: want you to know. Close snug quite there. You're right. 37 00:02:26,480 --> 00:02:29,720 Speaker 1: I said something wrong. I can't figure out what it was. Yeah, 38 00:02:29,880 --> 00:02:34,120 Speaker 1: we're pretty excited about this new gadget or I guess 39 00:02:34,160 --> 00:02:36,880 Speaker 1: new to us gadget that's here in the studio to day. Matt, 40 00:02:36,919 --> 00:02:38,799 Speaker 1: do you want to tell everybody a little bit about it? 41 00:02:38,800 --> 00:02:42,160 Speaker 1: It's just a couple of old school synthesizers, from the 42 00:02:42,200 --> 00:02:45,600 Speaker 1: seventies and eighties, and hopefully no one's going to be 43 00:02:45,960 --> 00:02:48,840 Speaker 1: bringing those into the mix. Maybe on this show, maybe not. 44 00:02:48,960 --> 00:02:50,760 Speaker 1: I don't know. I don't know. He looks like he 45 00:02:50,840 --> 00:02:54,560 Speaker 1: could go either way. So let's start today's episode with 46 00:02:54,720 --> 00:02:57,720 Speaker 1: an anecdote that you may have heard if you watched 47 00:02:57,760 --> 00:03:03,120 Speaker 1: our video series earlier this week. Two thousand twelve, Minnesota, 48 00:03:03,360 --> 00:03:06,880 Speaker 1: and this family starts getting ads from Target. You know, 49 00:03:07,040 --> 00:03:10,720 Speaker 1: every people get ads from Target, right, You're used to it. 50 00:03:10,720 --> 00:03:14,960 Speaker 1: It happens, but this time something's different. They've received ads before, 51 00:03:15,040 --> 00:03:17,720 Speaker 1: but this time something's off. The ads are for stuff 52 00:03:17,760 --> 00:03:22,200 Speaker 1: like diapers, strollers and baby lotion and stuff. You get 53 00:03:22,240 --> 00:03:26,280 Speaker 1: the idea, like for someone who's expecting lovely thing to be. 54 00:03:26,400 --> 00:03:30,239 Speaker 1: But there's a catch here. Nobody at that house is expecting. 55 00:03:30,639 --> 00:03:34,120 Speaker 1: So the dad is livid and the especially about the 56 00:03:34,160 --> 00:03:39,760 Speaker 1: ads addressed specifically to his teenage daughter. That's the whole point, 57 00:03:39,800 --> 00:03:43,200 Speaker 1: to his high school aged daughter. Yeah, yep. And so 58 00:03:43,280 --> 00:03:47,120 Speaker 1: he goes to Target in person on in some beast 59 00:03:47,160 --> 00:03:51,520 Speaker 1: mode WTF kind of stuff trending towards w w E 60 00:03:51,680 --> 00:03:57,240 Speaker 1: possibly When he goes back, has a chat with his daughter, 61 00:03:57,760 --> 00:04:02,320 Speaker 1: comes back to Target, and says, Oh, yeah, she's expecting 62 00:04:02,360 --> 00:04:06,840 Speaker 1: in August. Whoops on all accounts. This comes to us 63 00:04:07,000 --> 00:04:10,120 Speaker 1: through a New York Times piece that was published on 64 00:04:10,240 --> 00:04:16,560 Speaker 1: February nineteen, two thousand twelve, about how obsessively companies track 65 00:04:16,640 --> 00:04:20,640 Speaker 1: your shopping habits and every little piece of information they 66 00:04:20,680 --> 00:04:24,120 Speaker 1: can about you to hopefully make you more likely to 67 00:04:24,279 --> 00:04:29,039 Speaker 1: buy something. And the guy who was interviewed, the statistics 68 00:04:29,040 --> 00:04:31,679 Speaker 1: man working for Target interviewed in this New York Times 69 00:04:31,800 --> 00:04:37,760 Speaker 1: article was shut down after he mentioned this because what 70 00:04:38,080 --> 00:04:41,080 Speaker 1: we're talking about is something that many many companies do, 71 00:04:41,320 --> 00:04:45,880 Speaker 1: but is incredibly controversial, and that is big data. Yeah, 72 00:04:45,920 --> 00:04:50,080 Speaker 1: it's something that it's not necessarily something they don't want 73 00:04:50,120 --> 00:04:51,560 Speaker 1: you to know, but they don't want you to know 74 00:04:51,680 --> 00:04:55,160 Speaker 1: about it. Like it's kind of a known thing now 75 00:04:55,240 --> 00:04:58,080 Speaker 1: that you are being tracked in all these different ways 76 00:04:58,120 --> 00:05:01,400 Speaker 1: and looked at. But this is something that we feel 77 00:05:01,480 --> 00:05:05,760 Speaker 1: you should know more about, right. Yeah, it's something that 78 00:05:06,240 --> 00:05:09,120 Speaker 1: it's it's strange that you say it's not necessarily something 79 00:05:09,160 --> 00:05:12,040 Speaker 1: they don't want you to know, because they certainly don't 80 00:05:12,080 --> 00:05:15,480 Speaker 1: want other people to know the techniques used to gather 81 00:05:15,600 --> 00:05:18,680 Speaker 1: this information. Absolutely, they might not want you to know 82 00:05:18,800 --> 00:05:22,440 Speaker 1: some of the things that we can't tell you because 83 00:05:22,520 --> 00:05:27,800 Speaker 1: we don't know, right, because the data sets themselves are important. 84 00:05:27,800 --> 00:05:30,599 Speaker 1: But what maybe even more important and even more secretive 85 00:05:30,600 --> 00:05:33,479 Speaker 1: would be the techniques used to parse and analyze this 86 00:05:33,600 --> 00:05:36,960 Speaker 1: information and even where they're getting some of this data from. 87 00:05:37,240 --> 00:05:41,400 Speaker 1: Ah yes, yeah, So what what is big data? If 88 00:05:41,440 --> 00:05:43,760 Speaker 1: we have to define it, It's not like it's not 89 00:05:43,800 --> 00:05:49,320 Speaker 1: the same thing as uh, big agribusiness or big bell 90 00:05:49,520 --> 00:05:53,279 Speaker 1: or something big pomegranate. Right, No, big data is just 91 00:05:53,960 --> 00:05:58,839 Speaker 1: as in definition, any extremely large data set or data 92 00:05:58,880 --> 00:06:01,839 Speaker 1: sets that can be analyzed usually now in the modern 93 00:06:01,920 --> 00:06:05,040 Speaker 1: day computationally is the only way to even get your 94 00:06:05,640 --> 00:06:09,000 Speaker 1: wrap your head around it um to reveal patterns and 95 00:06:09,040 --> 00:06:13,200 Speaker 1: trends all kinds of associations, especially those relating to human 96 00:06:13,279 --> 00:06:17,400 Speaker 1: behavior and or the interactions between humans. So, for instance, 97 00:06:17,520 --> 00:06:22,000 Speaker 1: this big data would not be the GPS of one person. 98 00:06:22,400 --> 00:06:26,520 Speaker 1: It would be the GPS of a city, or people 99 00:06:26,560 --> 00:06:29,680 Speaker 1: who all work at a large company or yeah, one 100 00:06:30,040 --> 00:06:35,080 Speaker 1: one GPS companies data yeah or yeah, But I love 101 00:06:35,120 --> 00:06:38,760 Speaker 1: the idea of the GPS information of one like certain 102 00:06:38,800 --> 00:06:41,920 Speaker 1: area and just all of that data stacked vertically together 103 00:06:42,080 --> 00:06:44,480 Speaker 1: from everyone that's been in that area. Right, There are 104 00:06:44,520 --> 00:06:48,520 Speaker 1: different ways to parse this, uh this Big data usually 105 00:06:49,040 --> 00:06:53,920 Speaker 1: include sets that are so large there beyond the ability 106 00:06:54,160 --> 00:06:57,840 Speaker 1: of typical software tools like you couldn't, for instance, just 107 00:06:57,960 --> 00:07:02,280 Speaker 1: take some some data set that is big data size 108 00:07:02,360 --> 00:07:04,839 Speaker 1: and put it in an Excel spreadsheet on a Google 109 00:07:04,920 --> 00:07:07,520 Speaker 1: travel No, there's no way, and if you did, it 110 00:07:07,560 --> 00:07:10,119 Speaker 1: would be the largest file in the history of XL. 111 00:07:11,000 --> 00:07:13,600 Speaker 1: So when we look at the range of this, we 112 00:07:13,640 --> 00:07:15,960 Speaker 1: also know that the size of data is a constantly 113 00:07:16,040 --> 00:07:20,640 Speaker 1: moving target because it seems that there's just more and 114 00:07:20,720 --> 00:07:23,960 Speaker 1: more and more out there. There's a really weird statistic 115 00:07:24,040 --> 00:07:27,200 Speaker 1: here somewhere that I think is like nine percent of 116 00:07:27,280 --> 00:07:29,680 Speaker 1: all the data that exists was made in the last 117 00:07:30,360 --> 00:07:33,840 Speaker 1: twenty or ten years or something. Oh. Absolutely, it's exponential. 118 00:07:33,880 --> 00:07:35,200 Speaker 1: And that's one of the things we're going to talk 119 00:07:35,200 --> 00:07:39,600 Speaker 1: about here. Just the number of devices that collect data 120 00:07:39,720 --> 00:07:43,200 Speaker 1: or that you can collect data upon, and just everything 121 00:07:43,200 --> 00:07:47,119 Speaker 1: that's connected to the internet nowadays. It's insane. And again, 122 00:07:47,280 --> 00:07:50,280 Speaker 1: it doesn't go away once. Once you have a data set, 123 00:07:50,640 --> 00:07:53,560 Speaker 1: it's not like it's irrelevant. You're still going to need 124 00:07:53,640 --> 00:07:57,080 Speaker 1: that maybe for future. You know, whatever endeavor is that 125 00:07:57,120 --> 00:08:00,480 Speaker 1: you're going to do as a big business right for gamers, Uh, 126 00:08:00,560 --> 00:08:04,120 Speaker 1: this would be sort of the equivalent of an inventory 127 00:08:04,160 --> 00:08:09,000 Speaker 1: that doesn't register weight. How role playing games turn and 128 00:08:09,040 --> 00:08:10,760 Speaker 1: you're more familiar with this, and I am how role 129 00:08:10,800 --> 00:08:14,800 Speaker 1: playing games turn so many people into hoarders. We are 130 00:08:15,520 --> 00:08:19,560 Speaker 1: data orders as well, like the NSA clearly is. Uh. 131 00:08:19,720 --> 00:08:23,400 Speaker 1: So what we'll do is will walk through some history 132 00:08:23,800 --> 00:08:26,840 Speaker 1: of big data and then we'll also talk about some 133 00:08:27,000 --> 00:08:29,880 Speaker 1: of the controversy surrounding it, the ways it could be used, 134 00:08:29,880 --> 00:08:36,000 Speaker 1: some of the conspiracies theoretical and factual about this practice. 135 00:08:36,559 --> 00:08:40,040 Speaker 1: So one way that one way. There's a group called 136 00:08:40,040 --> 00:08:43,400 Speaker 1: Meta Group or Gardner. Uh. They they have an analyst 137 00:08:43,440 --> 00:08:47,000 Speaker 1: there named Doug Laney, and he said one great way 138 00:08:47,040 --> 00:08:50,920 Speaker 1: to measure big data would be in the three vs. 139 00:08:50,960 --> 00:08:54,559 Speaker 1: That would be increasing volume, Uh, just how much data 140 00:08:54,600 --> 00:08:57,480 Speaker 1: you have, uh, the velocity of the data, the speed 141 00:08:57,520 --> 00:08:59,800 Speaker 1: of the in and out right, and the variety that 142 00:09:00,200 --> 00:09:02,600 Speaker 1: type of stuff. Where is it coming from? So not 143 00:09:02,679 --> 00:09:06,559 Speaker 1: just all homogeneous GPS records, but also like what something 144 00:09:06,559 --> 00:09:09,040 Speaker 1: else people would collect? Well, another set of data that's 145 00:09:09,080 --> 00:09:12,800 Speaker 1: really interesting to look at when when combined with GPS 146 00:09:12,880 --> 00:09:17,240 Speaker 1: data or phone records, that's always a fun thing. Um. 147 00:09:17,280 --> 00:09:20,480 Speaker 1: And these these three vs. That you're mentioning. This is 148 00:09:20,920 --> 00:09:24,840 Speaker 1: now an industry standard, the way the whole industry looks 149 00:09:24,840 --> 00:09:28,080 Speaker 1: at big data with what is it volume velocity? What 150 00:09:28,120 --> 00:09:32,600 Speaker 1: was the third one? That's volume, velocity and variety? So UH, 151 00:09:32,679 --> 00:09:35,920 Speaker 1: GPS and phone records would be something that that helps 152 00:09:35,960 --> 00:09:39,600 Speaker 1: you flesh out a virtual persona, but it comes from 153 00:09:39,760 --> 00:09:43,000 Speaker 1: the same device probably for most people. So another thing 154 00:09:43,000 --> 00:09:46,000 Speaker 1: that would be more very would be stuff like medical records, 155 00:09:46,040 --> 00:09:49,680 Speaker 1: stuff like recent purchases on your financial records, so you 156 00:09:49,679 --> 00:09:53,520 Speaker 1: could parse it down to uh people who let's get 157 00:09:53,559 --> 00:09:55,120 Speaker 1: a little bit dark with it. You could parse it 158 00:09:55,120 --> 00:09:58,520 Speaker 1: down to uh. Some we have these four sets of 159 00:09:58,679 --> 00:10:02,160 Speaker 1: data about this person, right, so we know that once 160 00:10:02,640 --> 00:10:06,959 Speaker 1: every week or something they go to a clinic, right 161 00:10:07,240 --> 00:10:10,080 Speaker 1: it specializes in some kind of treatment, right. And then 162 00:10:10,200 --> 00:10:14,400 Speaker 1: we know that the medical records that they have some 163 00:10:14,520 --> 00:10:17,920 Speaker 1: kind of uh, debilitating condition. And then we see that 164 00:10:17,960 --> 00:10:23,360 Speaker 1: one of their recent purchases is a skydiving suit. So 165 00:10:24,000 --> 00:10:26,679 Speaker 1: we have built this picture with just the very little 166 00:10:26,679 --> 00:10:31,040 Speaker 1: information about someone who probably has a terminal disease and 167 00:10:31,120 --> 00:10:33,360 Speaker 1: wants to skydive because it was on their bucket list. 168 00:10:33,480 --> 00:10:35,680 Speaker 1: Now we know exactly the kind of things to sell 169 00:10:35,720 --> 00:10:37,800 Speaker 1: to this person, and we know exactly the kind of 170 00:10:37,800 --> 00:10:41,040 Speaker 1: things to sell, which is frightening. Uh. So this is 171 00:10:41,120 --> 00:10:43,240 Speaker 1: I mean, this is an example that you and I 172 00:10:43,440 --> 00:10:46,280 Speaker 1: just made up now, right Matt, this is not we 173 00:10:46,600 --> 00:10:49,200 Speaker 1: don't know any more and that this has happened to 174 00:10:49,960 --> 00:10:55,480 Speaker 1: so this is a new information age. It's tough to 175 00:10:55,520 --> 00:11:01,880 Speaker 1: stress how quickly personal privacy has eroded, right, um, And 176 00:11:01,920 --> 00:11:07,560 Speaker 1: we the people of the information age, have committed um, 177 00:11:07,920 --> 00:11:11,599 Speaker 1: have have lost our privacy due to acts of negligence, 178 00:11:11,679 --> 00:11:17,079 Speaker 1: not due to aggressively rooting for this loss of privacy. 179 00:11:17,240 --> 00:11:21,040 Speaker 1: It's just who reads terms and conditions, right, Yeah. And 180 00:11:21,720 --> 00:11:24,880 Speaker 1: we're all very happy about some of the things that 181 00:11:25,000 --> 00:11:28,440 Speaker 1: providing this type of data gives to us. The GPS 182 00:11:28,520 --> 00:11:31,040 Speaker 1: thing alone, I think is a massive I mean a 183 00:11:31,040 --> 00:11:34,280 Speaker 1: lot of people would I don't. I can't speak for everyone, 184 00:11:34,320 --> 00:11:36,719 Speaker 1: but for myself to have the ability to use a 185 00:11:36,800 --> 00:11:39,640 Speaker 1: GPS when I'm out on the road somewhere instead of 186 00:11:39,640 --> 00:11:42,040 Speaker 1: having to look at a physical map is one of 187 00:11:42,080 --> 00:11:45,440 Speaker 1: those things where sometimes I just go, you know what, 188 00:11:45,520 --> 00:11:48,400 Speaker 1: screw it, turn my GPS on. I need it. Right, 189 00:11:48,600 --> 00:11:52,480 Speaker 1: And even if they know that this were highly aware 190 00:11:53,000 --> 00:11:56,800 Speaker 1: that this is being tracked but but there's also there's 191 00:11:56,840 --> 00:12:02,920 Speaker 1: such a a sort of narcissism in paranoia, this idea 192 00:12:03,080 --> 00:12:06,960 Speaker 1: that yes, of course they care where I get my pickles. 193 00:12:07,800 --> 00:12:10,560 Speaker 1: Of course, of course someone does. And don't get me wrong, 194 00:12:10,600 --> 00:12:13,320 Speaker 1: I mean sure grocery stores for sure do. Every time 195 00:12:13,360 --> 00:12:17,360 Speaker 1: you swipe a loyalty card, you are generating more information 196 00:12:17,440 --> 00:12:21,280 Speaker 1: for them to uh target ads toward you, which we'll 197 00:12:21,320 --> 00:12:24,320 Speaker 1: talk about whether or not that is, you know, morally 198 00:12:24,360 --> 00:12:27,560 Speaker 1: wrong or ethically sticky or whatever, or if you would 199 00:12:27,679 --> 00:12:30,800 Speaker 1: would someone want that? Like, are there people out there 200 00:12:30,840 --> 00:12:33,000 Speaker 1: that want Kroger to know exactly what the order so 201 00:12:33,040 --> 00:12:35,000 Speaker 1: they can just say, hey, here's all the things that 202 00:12:35,040 --> 00:12:37,480 Speaker 1: you want and hear the coupons right there. And there 203 00:12:37,520 --> 00:12:40,480 Speaker 1: are some people I would definitely take some coupons for things. 204 00:12:40,800 --> 00:12:43,199 Speaker 1: But also this is okay, this is so unrelated. This 205 00:12:43,280 --> 00:12:47,320 Speaker 1: is a side conspiracy here. And Matt, uh So, you 206 00:12:47,440 --> 00:12:52,040 Speaker 1: and I are just old enough to remember before these 207 00:12:52,080 --> 00:12:54,840 Speaker 1: loyalty cards came out everywhere, right, Yeah, when I was 208 00:12:54,880 --> 00:12:58,319 Speaker 1: in college, they didn't exist. So the way that they 209 00:12:58,360 --> 00:13:02,079 Speaker 1: were instituted is that for for all the young guns 210 00:13:02,120 --> 00:13:04,160 Speaker 1: out there, the way that these cards were instituted was 211 00:13:04,200 --> 00:13:08,679 Speaker 1: at first they gave you discounts on things, right. But 212 00:13:09,200 --> 00:13:13,520 Speaker 1: one of the conspiracies I've heard is that this artificial 213 00:13:13,880 --> 00:13:17,680 Speaker 1: price shenaniganting, if I can make up the word, uh, 214 00:13:17,960 --> 00:13:22,240 Speaker 1: ultimately became something where having the card didn't really get 215 00:13:22,280 --> 00:13:25,679 Speaker 1: you a discount, it just got you the the regular price. 216 00:13:25,760 --> 00:13:28,360 Speaker 1: The regular price. Yes, then the money that they're making 217 00:13:28,400 --> 00:13:32,800 Speaker 1: on top of selling your information to third parties. CBS 218 00:13:32,920 --> 00:13:36,000 Speaker 1: is like, yeah, thanks, yeah, it's and and so you're 219 00:13:36,120 --> 00:13:39,439 Speaker 1: you're being penalized at least. This theory goes for not 220 00:13:39,559 --> 00:13:44,840 Speaker 1: participating in this program. And that's because again, the overwhelming 221 00:13:44,880 --> 00:13:48,120 Speaker 1: majority of companies UH don't just do this. They do 222 00:13:48,200 --> 00:13:51,120 Speaker 1: this with relish and it is profitable. But it is 223 00:13:51,160 --> 00:13:53,600 Speaker 1: a very very old idea. Is that I have to 224 00:13:53,640 --> 00:13:56,240 Speaker 1: say this, here's the worst news about that whole situation. 225 00:13:56,280 --> 00:13:59,000 Speaker 1: What's that even if you go somewhere and shop. I'm 226 00:13:59,040 --> 00:14:00,840 Speaker 1: just gonna use an example, but I'm aware of like 227 00:14:00,920 --> 00:14:04,000 Speaker 1: publics that I don't have a loyalty card for, and 228 00:14:04,000 --> 00:14:05,680 Speaker 1: I don't know that you can get one. Maybe you 229 00:14:05,720 --> 00:14:09,079 Speaker 1: can now, but and you you try and save money there, 230 00:14:09,120 --> 00:14:12,320 Speaker 1: if you pay with a card of any type, then 231 00:14:12,520 --> 00:14:15,160 Speaker 1: that stuff is being tracked. Maybe by a separate company, 232 00:14:15,240 --> 00:14:17,960 Speaker 1: a third party, or maybe just your credit card company. 233 00:14:18,000 --> 00:14:22,360 Speaker 1: But the same thing is happening, and we'll see a 234 00:14:22,400 --> 00:14:25,400 Speaker 1: couple of different reasons that this happens, you know, or 235 00:14:25,800 --> 00:14:29,760 Speaker 1: or a couple of different motivations for this collection. But 236 00:14:29,880 --> 00:14:32,560 Speaker 1: let's walk through the history first. Okay, So this is 237 00:14:32,600 --> 00:14:35,960 Speaker 1: not a new thing, trying to track data, trying to 238 00:14:36,200 --> 00:14:40,480 Speaker 1: understand numbers about things that are happening in the world 239 00:14:40,480 --> 00:14:43,320 Speaker 1: around you. Goes back seven thousand years to something that 240 00:14:43,320 --> 00:14:49,080 Speaker 1: we've mentioned before in Mesopotamia during the birth of agriculture, 241 00:14:50,120 --> 00:14:52,240 Speaker 1: when you had to keep track of seeds, you had 242 00:14:52,280 --> 00:14:55,880 Speaker 1: to keep track of crops and soil, just everything you 243 00:14:55,920 --> 00:14:59,200 Speaker 1: needed to know data about the ground and the plants 244 00:14:59,200 --> 00:15:02,240 Speaker 1: that you're trying to put in there. So yeah, so, 245 00:15:02,400 --> 00:15:07,120 Speaker 1: for example, there's an accounting system that goes in to 246 00:15:07,440 --> 00:15:12,600 Speaker 1: monitor the growth of a I don't know, uh, an 247 00:15:12,640 --> 00:15:17,280 Speaker 1: olive sure, an olive tree, right, and so um, you 248 00:15:17,320 --> 00:15:21,040 Speaker 1: know some farmer named John Stamos, a very common name 249 00:15:21,080 --> 00:15:24,080 Speaker 1: in Mesopotamia at the time. I imagine, Uh, this farmer 250 00:15:24,160 --> 00:15:28,080 Speaker 1: John Stamos has uh a bunch of olives and they 251 00:15:28,120 --> 00:15:30,720 Speaker 1: have to have a way to track year over year 252 00:15:30,840 --> 00:15:34,200 Speaker 1: the performance of that crop. Right, So this is when 253 00:15:34,240 --> 00:15:37,240 Speaker 1: they begin saying, Okay, you know John Stamos has x 254 00:15:37,280 --> 00:15:41,920 Speaker 1: amount of trees. They yield y amount of m olives 255 00:15:41,960 --> 00:15:44,320 Speaker 1: each year. This is what we can expect. This is 256 00:15:44,320 --> 00:15:46,920 Speaker 1: what we can expect. This is what we can bet against. 257 00:15:47,160 --> 00:15:50,720 Speaker 1: This is what we can sell an advance before it's made. 258 00:15:51,840 --> 00:15:55,360 Speaker 1: Then you get into we're gonna jump forward pretty far here, 259 00:15:55,520 --> 00:15:59,680 Speaker 1: all the way to sixteen. Nothing else happened, yeah, zero, 260 00:16:00,800 --> 00:16:05,400 Speaker 1: nothing between seven thousand years ago. Let's just say improvements 261 00:16:05,840 --> 00:16:10,560 Speaker 1: are being in incrementally, you know, are happening up until 262 00:16:10,640 --> 00:16:12,960 Speaker 1: this point. But then on the next big change, the 263 00:16:13,000 --> 00:16:16,600 Speaker 1: sixteen sixty three when John Grant, I think that's how 264 00:16:16,640 --> 00:16:20,920 Speaker 1: you correctly spell it. Grant very British. Uh. He recorded 265 00:16:20,920 --> 00:16:26,120 Speaker 1: an examined information about mortality rates because the bubonic plague 266 00:16:26,200 --> 00:16:30,240 Speaker 1: was just ravaging just the entire area at the time, 267 00:16:30,480 --> 00:16:32,760 Speaker 1: and he decided he wanted to know more information about 268 00:16:32,840 --> 00:16:35,360 Speaker 1: like how what is happening here, exactly how many people 269 00:16:35,360 --> 00:16:37,760 Speaker 1: are getting affected? Why are they getting affected? Let's get 270 00:16:37,800 --> 00:16:41,680 Speaker 1: information and we can start solving this. Yeah, and that 271 00:16:41,720 --> 00:16:44,840 Speaker 1: sounds that sounds sensible. It's the least you could do, right, 272 00:16:45,400 --> 00:16:48,080 Speaker 1: But we don't know. Have we ever talked on this 273 00:16:48,120 --> 00:16:51,720 Speaker 1: show about just how profoundly the plague or the series 274 00:16:51,760 --> 00:16:54,440 Speaker 1: of things known as the plague change the world. No, 275 00:16:54,800 --> 00:16:58,120 Speaker 1: I've listened to a little too much stuff he missed 276 00:16:58,120 --> 00:17:01,760 Speaker 1: in history class about it. So information sometimes gets muddled 277 00:17:01,760 --> 00:17:04,480 Speaker 1: between what we've talked about and what I've just heard. 278 00:17:04,520 --> 00:17:09,159 Speaker 1: These plagues have played such a profound role in, uh, 279 00:17:09,240 --> 00:17:13,200 Speaker 1: the global evolution of the human species. It's just crazy. 280 00:17:13,240 --> 00:17:14,760 Speaker 1: It's the kind of stuff you would want to keep 281 00:17:14,800 --> 00:17:19,680 Speaker 1: track on. So this guy, uh, John Grant becomes uh 282 00:17:19,720 --> 00:17:22,639 Speaker 1: the father of statistics, or he's considered that because he 283 00:17:22,680 --> 00:17:27,440 Speaker 1: does the first statistical data analysis that we have recordings of, uh, 284 00:17:27,480 --> 00:17:29,760 Speaker 1: and he has a book about it called Natural and 285 00:17:29,760 --> 00:17:33,880 Speaker 1: Political Observations Made upon the Bills of Mortality. Just kind 286 00:17:33,880 --> 00:17:35,960 Speaker 1: of a dry name, right, but it's it's not a 287 00:17:36,000 --> 00:17:41,439 Speaker 1: feel good subject, and people continue to work off this 288 00:17:41,520 --> 00:17:46,560 Speaker 1: statistical analysis. So let's fast forward to the twentie century. Okay, 289 00:17:46,560 --> 00:17:48,480 Speaker 1: So we're going to fast forward all the way to 290 00:17:49,320 --> 00:17:54,639 Speaker 1: eighteen seven. This is when the modern the age of 291 00:17:54,720 --> 00:17:59,640 Speaker 1: modern data. Modern data is when it is born. So 292 00:18:00,040 --> 00:18:04,000 Speaker 1: this gentleman named Herman Hollerth invented a computing machine that 293 00:18:04,080 --> 00:18:08,240 Speaker 1: could read these holes punched holes in cards, that paper 294 00:18:08,280 --> 00:18:12,480 Speaker 1: cards in order to organize census data. And this is 295 00:18:12,520 --> 00:18:16,200 Speaker 1: a huge change because you have to imagine just collecting data, 296 00:18:16,240 --> 00:18:18,480 Speaker 1: of going door to door getting information, then trying to 297 00:18:18,520 --> 00:18:23,159 Speaker 1: compile that just with humans in rooms that it was taking. 298 00:18:24,240 --> 00:18:26,920 Speaker 1: So they would do one one census every ten years, 299 00:18:26,920 --> 00:18:30,080 Speaker 1: I believe at the time, and it was taking almost 300 00:18:30,160 --> 00:18:32,919 Speaker 1: nine years before you would get the results from the 301 00:18:32,960 --> 00:18:37,600 Speaker 1: census of the previous the previous census, so it was almost, 302 00:18:38,320 --> 00:18:40,359 Speaker 1: I don't not worthless, but it was just felt like 303 00:18:40,480 --> 00:18:43,640 Speaker 1: they were running backwards almost right. Yeah, they had they 304 00:18:43,640 --> 00:18:45,359 Speaker 1: had a data set that would be when it was 305 00:18:45,440 --> 00:18:50,879 Speaker 1: finally complete, useful for a little less than a year, Yeah, exactly. So. Uh. 306 00:18:51,359 --> 00:18:55,480 Speaker 1: The first data processing machine appeared in nineteen forty three. 307 00:18:56,000 --> 00:18:58,439 Speaker 1: This was, of course, it came out of war. A 308 00:18:58,520 --> 00:19:02,920 Speaker 1: lot of technology coinnovations come out of war. And it 309 00:19:02,960 --> 00:19:06,639 Speaker 1: was meant to decipher codes from the Nationalist Socialists or 310 00:19:06,720 --> 00:19:10,600 Speaker 1: the Nazis. Uh. This thing was named Colossus, which is 311 00:19:10,640 --> 00:19:13,639 Speaker 1: a pretty cool name, and they would intercept messages. They 312 00:19:13,640 --> 00:19:16,720 Speaker 1: would feed things to Colossus and would search for patterns 313 00:19:16,720 --> 00:19:20,399 Speaker 1: in these characters and it worked pretty quickly. Yeah, it 314 00:19:20,440 --> 00:19:24,399 Speaker 1: would go five thousand characters per second, which is huge. 315 00:19:24,440 --> 00:19:27,960 Speaker 1: It reduced the time from weeks two hours. And let's 316 00:19:27,960 --> 00:19:30,119 Speaker 1: stroll through some other stuff here, just kind of laundry 317 00:19:30,160 --> 00:19:32,000 Speaker 1: listed so we can get to the good stuff. Uh. 318 00:19:32,119 --> 00:19:35,040 Speaker 1: Nineteen fifty two, everybody's favorite, the n s A, the 319 00:19:35,119 --> 00:19:38,720 Speaker 1: National Security Agency is created, and within ten years they 320 00:19:38,800 --> 00:19:44,600 Speaker 1: have more than twelve thousand cryptologists on contract. That's huge. 321 00:19:45,480 --> 00:19:48,240 Speaker 1: Then you've got in nineteen sixty five, the US government 322 00:19:48,520 --> 00:19:52,760 Speaker 1: builds the first data center which can store seven hundred 323 00:19:52,760 --> 00:19:56,520 Speaker 1: and forty two million tax returns and also a hundred 324 00:19:56,520 --> 00:19:59,680 Speaker 1: and seventy five million sets of fingerprints. Now this is 325 00:19:59,720 --> 00:20:03,639 Speaker 1: pretty interesting here because this is this is something that 326 00:20:03,720 --> 00:20:08,120 Speaker 1: was recorded on the magnetic tape and computer tape. You may, 327 00:20:08,280 --> 00:20:11,720 Speaker 1: I don't know if anybody listening would know what that is. 328 00:20:11,960 --> 00:20:15,040 Speaker 1: Hopefully maybe you've heard of this magnetic tape. My father 329 00:20:15,359 --> 00:20:18,800 Speaker 1: is a controller controlling account and they have a like 330 00:20:18,840 --> 00:20:22,000 Speaker 1: a newer version of this magnetic tape, but it's still 331 00:20:22,080 --> 00:20:24,240 Speaker 1: all of their stuff is backed up to this magnetic 332 00:20:24,240 --> 00:20:28,080 Speaker 1: tape because it's so well, it's supposedly so reliable. Well, also, 333 00:20:28,560 --> 00:20:31,760 Speaker 1: people will recognize the magnetic tape if you've never seen 334 00:20:31,760 --> 00:20:35,960 Speaker 1: it before. Um, every time you see an old computer 335 00:20:36,200 --> 00:20:39,920 Speaker 1: with reels on it, that's magnetic tape. In Captain America 336 00:20:40,080 --> 00:20:43,800 Speaker 1: the Winter Soldier, the sequel to the first Captain America, Uh, 337 00:20:43,840 --> 00:20:46,520 Speaker 1: there is a scene which I won't spoil for you 338 00:20:46,560 --> 00:20:48,359 Speaker 1: if you haven't gotten around the scene it yet, But 339 00:20:48,440 --> 00:20:51,720 Speaker 1: there is a scene which involves a gigantic computer and 340 00:20:51,760 --> 00:20:56,119 Speaker 1: that is magnetic tape. Awesome, Okay, good reference, now we understand. 341 00:20:56,760 --> 00:20:58,960 Speaker 1: But here here's the thing though. This whole project was 342 00:20:59,000 --> 00:21:03,040 Speaker 1: scrapped because of years of quote big brother right being 343 00:21:03,040 --> 00:21:08,280 Speaker 1: a little bit too big brotherish little Orwellian. So this, however, 344 00:21:09,600 --> 00:21:14,480 Speaker 1: changed everything because people were thinking, what if we centralize um, 345 00:21:14,520 --> 00:21:18,119 Speaker 1: the location of data, you know, no more paper, just 346 00:21:18,200 --> 00:21:23,199 Speaker 1: electronically store it. A British guy so you may have 347 00:21:23,280 --> 00:21:26,919 Speaker 1: heard of Tim berners Lee of Invince what will go 348 00:21:26,960 --> 00:21:32,040 Speaker 1: on to become the World Wide Web? And with this boom, 349 00:21:32,400 --> 00:21:36,040 Speaker 1: we're going like gangbusters because people are able to generate 350 00:21:36,119 --> 00:21:39,760 Speaker 1: massive amounts of information, much more so than anybody could 351 00:21:39,800 --> 00:21:44,959 Speaker 1: plausibly read. And when it's connected up to this uh interweb, 352 00:21:45,280 --> 00:21:48,600 Speaker 1: if you will, it can be it can be sent 353 00:21:48,680 --> 00:21:50,760 Speaker 1: to somewhere else right, and then if you do have 354 00:21:50,800 --> 00:21:53,520 Speaker 1: a data center, doesn't matter where the data is collected, 355 00:21:53,560 --> 00:21:57,359 Speaker 1: you can send it directly over there almost immediately. Yeah, 356 00:21:57,440 --> 00:21:59,840 Speaker 1: it's it's bizarre when we think about that, and esp 357 00:22:00,000 --> 00:22:04,080 Speaker 1: actually when we think about, um, how just let's have 358 00:22:04,119 --> 00:22:06,960 Speaker 1: a John Henry moment, and can you compare, matt the 359 00:22:06,960 --> 00:22:10,200 Speaker 1: the ability of a supercomputer to a person? Oh god, 360 00:22:11,000 --> 00:22:16,720 Speaker 1: can I m hmm. Let's say, okay, let's say in 361 00:22:18,040 --> 00:22:21,160 Speaker 1: the first supercomputer, let's use that one as our example. 362 00:22:21,960 --> 00:22:25,440 Speaker 1: It could do as much work in one second, then 363 00:22:25,760 --> 00:22:29,919 Speaker 1: a single human being operating a calculator could do in 364 00:22:30,560 --> 00:22:35,880 Speaker 1: thirty thousand years. Thirty thousand years. And that's not even 365 00:22:35,880 --> 00:22:38,360 Speaker 1: the twenty one century, ladies and gentlemen. Now we are 366 00:22:38,400 --> 00:22:40,760 Speaker 1: at the modern age. And two thousand five, a guy 367 00:22:40,800 --> 00:22:44,240 Speaker 1: from O'Reilly Media coined the term big data for the 368 00:22:44,280 --> 00:22:48,040 Speaker 1: first time. Uh and this was, you know, a successor 369 00:22:48,119 --> 00:22:52,359 Speaker 1: to a another less fortunate buzzword, which was web two 370 00:22:52,359 --> 00:22:58,760 Speaker 1: point oh yeah, yeah, the new coke of web words. 371 00:22:58,760 --> 00:23:02,480 Speaker 1: But yeah, so this, this idea here is a little 372 00:23:02,480 --> 00:23:05,280 Speaker 1: bit more bad of the idea of data set that 373 00:23:05,440 --> 00:23:09,080 Speaker 1: is just so massive and complex and interwoven that you 374 00:23:09,160 --> 00:23:13,960 Speaker 1: can't use the traditional business intelligence tools to figure out 375 00:23:14,040 --> 00:23:16,879 Speaker 1: what's going on. Right And oh five is also the 376 00:23:16,960 --> 00:23:19,800 Speaker 1: year that hoddup was created by Yahoo, which was built 377 00:23:19,880 --> 00:23:22,440 Speaker 1: on the back of Google's map produce. And these are 378 00:23:22,480 --> 00:23:27,240 Speaker 1: just softwares that can basically take data from using a 379 00:23:27,240 --> 00:23:32,160 Speaker 1: bunch of different computers to crunched numbers like and huge 380 00:23:32,200 --> 00:23:36,040 Speaker 1: amounts um. And it was the goal to index the 381 00:23:36,200 --> 00:23:39,880 Speaker 1: entire or the entire worldwide Web. That's why these things 382 00:23:39,880 --> 00:23:42,960 Speaker 1: were created. And uh, it's the open source to dupe. 383 00:23:43,040 --> 00:23:46,080 Speaker 1: It was used by a lot of organizations to crunch 384 00:23:46,080 --> 00:23:50,400 Speaker 1: through data. That just is it's almost unquantifiable how huge 385 00:23:50,400 --> 00:23:52,440 Speaker 1: it is, right, And this is not just a private 386 00:23:52,440 --> 00:23:55,399 Speaker 1: industry thing, of course, And the line is blurry. We 387 00:23:55,400 --> 00:23:57,919 Speaker 1: seem to talk about these two events in isolation, as 388 00:23:58,000 --> 00:24:02,640 Speaker 1: if the n s A using phone records and social 389 00:24:02,680 --> 00:24:05,840 Speaker 1: media contacts. Who do you know that knows who? That 390 00:24:05,920 --> 00:24:09,040 Speaker 1: knows who on Facebook? Right now? On the list? Now 391 00:24:09,080 --> 00:24:12,160 Speaker 1: you're on the list right Uh, they're they're not just 392 00:24:12,240 --> 00:24:18,040 Speaker 1: using that, uh and target or another private organization, UM 393 00:24:18,240 --> 00:24:21,639 Speaker 1: a data broker. There are companies that just broker data. Uh. 394 00:24:21,840 --> 00:24:25,560 Speaker 1: These do not exist in a vacuum. There's interplay between them, 395 00:24:26,040 --> 00:24:32,520 Speaker 1: and uh, they're increasingly merging to do just some amazing things. 396 00:24:32,600 --> 00:24:36,840 Speaker 1: We're getting very very good at seeing the present as 397 00:24:36,880 --> 00:24:39,640 Speaker 1: never before. And other governments are involved in this as well. 398 00:24:39,680 --> 00:24:43,040 Speaker 1: In two thousand nine, the Indian government did something just 399 00:24:44,680 --> 00:24:48,119 Speaker 1: ambitious is like the most reasonable word for this. They 400 00:24:48,160 --> 00:24:51,840 Speaker 1: decided to take an Irish scan fingerprint and photo of 401 00:24:52,400 --> 00:24:57,479 Speaker 1: everyone in India, every single person in India. There are 402 00:24:57,760 --> 00:25:04,000 Speaker 1: so many people. Yes, can you imagine listener, Uh, if 403 00:25:04,400 --> 00:25:06,680 Speaker 1: the government came to you and said, Okay, we're going 404 00:25:06,720 --> 00:25:09,960 Speaker 1: to need we're gonna need an Irish scan of fingerprint, 405 00:25:10,040 --> 00:25:12,000 Speaker 1: and we're also going to need a photograph, a really 406 00:25:12,119 --> 00:25:16,679 Speaker 1: nicely framed photograph with good contrast. We're gonna put it 407 00:25:16,720 --> 00:25:19,600 Speaker 1: in of you and everyone in your family put it 408 00:25:19,600 --> 00:25:21,640 Speaker 1: into this database. Don't worry. We're gonna make sure it's 409 00:25:21,640 --> 00:25:23,760 Speaker 1: secure and we're not going to use it for anything 410 00:25:24,359 --> 00:25:28,159 Speaker 1: but for good things. Right. Yeah, and that's one point 411 00:25:28,200 --> 00:25:32,439 Speaker 1: to billion people to bill. Yeah, this is the largest 412 00:25:32,480 --> 00:25:37,720 Speaker 1: biometric database in the world. So there's a great thing 413 00:25:38,040 --> 00:25:41,399 Speaker 1: that Eric Schmidt from Google also said, right, just another 414 00:25:41,560 --> 00:25:45,040 Speaker 1: sense of perspective here, yeah, he he stated at the 415 00:25:45,080 --> 00:25:50,600 Speaker 1: Techonomy conference in Lake Tahoe, he he stated, quote, there 416 00:25:50,600 --> 00:25:54,639 Speaker 1: were five exabytes of information created by the entire world 417 00:25:54,720 --> 00:25:58,920 Speaker 1: between the dawn of civilization and two thousand three. Now 418 00:25:58,960 --> 00:26:03,840 Speaker 1: that same amount is created every two days. Boom, take 419 00:26:04,640 --> 00:26:07,040 Speaker 1: take that history of the universe. I can't wait till 420 00:26:07,040 --> 00:26:10,560 Speaker 1: the aliens land and say uh that. They're like, Wow, 421 00:26:10,720 --> 00:26:14,760 Speaker 1: these guys figured out how to make pizza into burritos 422 00:26:14,760 --> 00:26:19,239 Speaker 1: and there's a blog about it. It's sort of like 423 00:26:19,520 --> 00:26:24,639 Speaker 1: when you know, when people modern times find ancient Greek 424 00:26:24,840 --> 00:26:29,200 Speaker 1: or Roman or African ruins from these empires of bygone days, 425 00:26:29,359 --> 00:26:32,359 Speaker 1: and there's always some jerk like hundreds of years ago 426 00:26:32,600 --> 00:26:36,560 Speaker 1: who wrote like Tim was here, yeah and true, like 427 00:26:36,640 --> 00:26:39,720 Speaker 1: dick Butt from Reddit. They're just like that is a 428 00:26:39,840 --> 00:26:43,239 Speaker 1: precursor right there, right. So there's but there's so much 429 00:26:43,320 --> 00:26:46,240 Speaker 1: information being made and you know, I'm being a little 430 00:26:46,280 --> 00:26:48,879 Speaker 1: crass here, But the point I'm hoping to make is 431 00:26:48,960 --> 00:26:53,480 Speaker 1: that this information is not you know, noble stuff or 432 00:26:53,520 --> 00:26:57,160 Speaker 1: even stuff that would really make sense to a human being. 433 00:26:57,200 --> 00:26:59,600 Speaker 1: You didn't know what they were looking for. These are metrics, 434 00:26:59,640 --> 00:27:03,640 Speaker 1: these are movements. These are little breadcrumbs of you scattered 435 00:27:03,640 --> 00:27:06,480 Speaker 1: around the Internet at large, and then you're just bringing 436 00:27:06,520 --> 00:27:09,880 Speaker 1: them together to make another picture. It's like pointali is um. Really, 437 00:27:10,440 --> 00:27:14,720 Speaker 1: that's a wonderful, wonderful image. Yeah, that's really good. On 438 00:27:15,200 --> 00:27:19,320 Speaker 1: Everything you do online today or in any electronic medium 439 00:27:19,400 --> 00:27:24,040 Speaker 1: is recorded by somebody. Yep. If you're typing on a 440 00:27:24,119 --> 00:27:29,000 Speaker 1: keyboard or on a touchpad, it's getting recorded, So enjoy it. 441 00:27:29,400 --> 00:27:31,560 Speaker 1: I'm like, hey, unless you're on an air gap computer. 442 00:27:31,720 --> 00:27:35,480 Speaker 1: And we've all learned now what that is. So okay, 443 00:27:35,560 --> 00:27:42,040 Speaker 1: So let's just another example here. Uh. Fortunately for some 444 00:27:42,280 --> 00:27:44,359 Speaker 1: of us, I'm not going to name names, but for 445 00:27:44,400 --> 00:27:46,680 Speaker 1: some of us, it is not a crime to be 446 00:27:46,920 --> 00:27:50,520 Speaker 1: drunk on the internet. Well yeah, okay, sure. And there 447 00:27:50,520 --> 00:27:54,320 Speaker 1: are people who you know, have maybe in a fit 448 00:27:54,359 --> 00:27:56,600 Speaker 1: of passion or maybe they had some drinks and they 449 00:27:56,640 --> 00:27:59,359 Speaker 1: wrote something crazy on the Internet and they almost sent 450 00:27:59,440 --> 00:28:02,800 Speaker 1: it and they said, no, wait, I'm gonna sleep on this. 451 00:28:02,920 --> 00:28:06,240 Speaker 1: Let me think about this before I write anything. Well, 452 00:28:06,800 --> 00:28:10,840 Speaker 1: those ghost movements, those drafts that you make, are also 453 00:28:10,960 --> 00:28:13,679 Speaker 1: part of this. So just the act of typing, especially 454 00:28:13,720 --> 00:28:18,639 Speaker 1: in Facebook. Oh you know, yeah, don't goost draft in Facebook, right, yeah, 455 00:28:18,920 --> 00:28:22,560 Speaker 1: and all of these pieces of information assemble. Again, that's 456 00:28:22,560 --> 00:28:26,080 Speaker 1: such a beautiful image, man, A point list portrait of 457 00:28:26,200 --> 00:28:29,760 Speaker 1: you and who you are. And the big worry that 458 00:28:29,840 --> 00:28:31,480 Speaker 1: a lot of people have, and we see it through 459 00:28:31,520 --> 00:28:34,200 Speaker 1: sci fi and pop culture, and we've seen this for decades, 460 00:28:34,600 --> 00:28:38,200 Speaker 1: is that this will be able to go beyond just 461 00:28:38,360 --> 00:28:43,720 Speaker 1: a a panopoly view of the present or a panopticon 462 00:28:43,880 --> 00:28:48,720 Speaker 1: kind of view of the present, to become predictive. Yeah, 463 00:28:49,320 --> 00:28:53,480 Speaker 1: oh yeah. So eventually you just have an understanding of 464 00:28:53,520 --> 00:28:55,800 Speaker 1: what each one of these people is going to do 465 00:28:55,960 --> 00:29:00,120 Speaker 1: throughout their daily lives, what what the corporation is going 466 00:29:00,120 --> 00:29:03,760 Speaker 1: to profit from in the next two three years. You can, 467 00:29:03,800 --> 00:29:06,440 Speaker 1: I mean, it's crazy to imagine all the information that 468 00:29:06,520 --> 00:29:09,760 Speaker 1: we will eventually be able to get from this big 469 00:29:09,840 --> 00:29:13,280 Speaker 1: data and whose hands will it be in well, and 470 00:29:13,720 --> 00:29:18,200 Speaker 1: who can who can accurately understand this? So we we 471 00:29:18,280 --> 00:29:21,120 Speaker 1: also talk about these controversies. This stuff is around to 472 00:29:21,240 --> 00:29:25,920 Speaker 1: stay until the lights go out on humanity. Yeah, this 473 00:29:25,920 --> 00:29:28,560 Speaker 1: this stuff will be around to stay. Oh yeah, it 474 00:29:28,720 --> 00:29:31,440 Speaker 1: is here to stay unless there's some kind of weird 475 00:29:31,520 --> 00:29:34,400 Speaker 1: fight club moment and all the buildings holding all the 476 00:29:34,400 --> 00:29:37,720 Speaker 1: stuff the data centers blow up, which is probably not 477 00:29:37,800 --> 00:29:41,280 Speaker 1: gonna happen. There's really good security at those buildings, right, 478 00:29:41,360 --> 00:29:44,040 Speaker 1: and it's a distributed network so it would be hard 479 00:29:44,080 --> 00:29:46,680 Speaker 1: to take the head off. It would hard be hard 480 00:29:46,720 --> 00:29:49,200 Speaker 1: to take all of the heads off the hydrant. That 481 00:29:49,400 --> 00:29:54,160 Speaker 1: is the information age. Of course, this doesn't come without controversy. 482 00:29:54,240 --> 00:29:56,920 Speaker 1: We have we have a video about four creepy things 483 00:29:56,960 --> 00:30:00,400 Speaker 1: about big data, or this umbrella term, which and again 484 00:30:00,440 --> 00:30:03,200 Speaker 1: apply to government as well as industry. It can learn 485 00:30:03,440 --> 00:30:06,440 Speaker 1: big data can be used to learn your secrets. That's scary. 486 00:30:06,520 --> 00:30:10,920 Speaker 1: So if you think, uh, nobody knows that you routinely 487 00:30:11,120 --> 00:30:15,440 Speaker 1: order three large cheese pizzas with ham and sit in 488 00:30:15,440 --> 00:30:19,080 Speaker 1: the dark in your house at two am every Thursday night, 489 00:30:19,120 --> 00:30:23,120 Speaker 1: eating and crying. Nope, sorry, catching reruns a firefly, watching 490 00:30:23,160 --> 00:30:27,800 Speaker 1: reruns a firefly? Nope, sorry, somebody knows. Papa John's knows. 491 00:30:28,600 --> 00:30:33,880 Speaker 1: Comcast probably no podcast, probably knows. Maybe Netflix. Uh So 492 00:30:34,520 --> 00:30:37,239 Speaker 1: the another thing, it doesn't have to tell you what 493 00:30:37,320 --> 00:30:41,920 Speaker 1: it knows. There is a surprising lack of transparency on 494 00:30:42,040 --> 00:30:47,680 Speaker 1: the part of companies collecting your information. Yeah that, um, 495 00:30:47,720 --> 00:30:51,600 Speaker 1: what is the name of the company axiom? Axiom? Oh yeah, yeah, yeah, 496 00:30:51,600 --> 00:30:55,560 Speaker 1: they're scary. Huh uh dude, Yeah, I've been We're making 497 00:30:55,560 --> 00:30:58,760 Speaker 1: this video and the way it works is usually Ben Will. 498 00:30:58,920 --> 00:31:01,880 Speaker 1: Ben Will write an outline for what he's going to 499 00:31:01,960 --> 00:31:04,840 Speaker 1: present in the vlog. I will shoot it. Then as 500 00:31:04,880 --> 00:31:07,520 Speaker 1: I'm editing, I end up doing a lot of research 501 00:31:08,080 --> 00:31:11,120 Speaker 1: and oh my god, Ben, I've just been scouring their 502 00:31:11,160 --> 00:31:16,800 Speaker 1: website and nothing against you Axiom. If you're listening your employees, Um, 503 00:31:16,880 --> 00:31:20,600 Speaker 1: it's just a murky world. Well, it's pervasive. Like the 504 00:31:20,600 --> 00:31:26,680 Speaker 1: the amount of information that Axiom has is is impressive. Yeah, 505 00:31:26,880 --> 00:31:29,480 Speaker 1: and the way they talk about it sometimes their offline 506 00:31:29,560 --> 00:31:34,160 Speaker 1: data that they have used for online purposes. I don't know, 507 00:31:34,720 --> 00:31:38,760 Speaker 1: it's fascinating if you go to the website. So we 508 00:31:38,920 --> 00:31:42,400 Speaker 1: we've talked a little bit about just the science fiction 509 00:31:42,560 --> 00:31:45,680 Speaker 1: elements and how we see some science fact generating. But 510 00:31:45,760 --> 00:31:48,120 Speaker 1: we knew know that this is a very old story. 511 00:31:48,520 --> 00:31:52,320 Speaker 1: We've seen things like Isaac Asmov's Foundation series, which deal 512 00:31:52,360 --> 00:31:56,160 Speaker 1: with the fictional at this point fictional science of psychohistory 513 00:31:56,240 --> 00:31:59,360 Speaker 1: predicting the future on a large scale event. We've dealt 514 00:31:59,400 --> 00:32:03,680 Speaker 1: with Gaddick uh, where personal information medical information is used 515 00:32:04,000 --> 00:32:08,560 Speaker 1: to gosh, I'm trying not to spoil things. Yeah, but 516 00:32:08,680 --> 00:32:11,320 Speaker 1: I mean, what's the limit with spoilers, Like, at what 517 00:32:11,480 --> 00:32:15,239 Speaker 1: point it kind of stinks? I guess you just have 518 00:32:15,320 --> 00:32:17,960 Speaker 1: to put an alert at the very beginning of something 519 00:32:18,000 --> 00:32:19,960 Speaker 1: that might have a spoiler in it and just say 520 00:32:20,080 --> 00:32:22,719 Speaker 1: don't listen to this if you have not seen these movies, 521 00:32:22,840 --> 00:32:27,640 Speaker 1: read these books, or listen to these songs. Wow, pretty okay, 522 00:32:27,760 --> 00:32:30,160 Speaker 1: all right, So that's that's pretty complicated. But we've also 523 00:32:30,240 --> 00:32:33,680 Speaker 1: seen it in of course Minority Report, which we mentioned. 524 00:32:34,200 --> 00:32:37,520 Speaker 1: But the controversy surrounding this, which is expressed in our 525 00:32:37,560 --> 00:32:40,959 Speaker 1: culture is uh oh oh. And another one would be 526 00:32:41,240 --> 00:32:47,680 Speaker 1: the Dark Knight. Oh yes, yeah, we're Lucius Fox Yeah, 527 00:32:48,040 --> 00:32:52,080 Speaker 1: and the cell phone system monitoring system. So uh, we see, 528 00:32:52,280 --> 00:32:57,720 Speaker 1: we see this expressed in the culture of UM multiple nations. 529 00:32:57,760 --> 00:33:02,040 Speaker 1: But what what's the real stuff? What's the real controversy? 530 00:33:02,120 --> 00:33:06,960 Speaker 1: Could could it damage your credit report if you had 531 00:33:07,240 --> 00:33:10,560 Speaker 1: GPS data that said you were, I don't know, going 532 00:33:10,560 --> 00:33:13,440 Speaker 1: to a pawn shop often or something? You know, Yeah, 533 00:33:13,480 --> 00:33:16,120 Speaker 1: I don't know, And that's I don't want to scare anybody. 534 00:33:16,120 --> 00:33:18,800 Speaker 1: That's a that's a made up example there, But we 535 00:33:18,880 --> 00:33:22,880 Speaker 1: do know that the we do know that the possibilities 536 00:33:22,920 --> 00:33:26,520 Speaker 1: for that kind of stuff. Again, just possibilities are there. 537 00:33:26,560 --> 00:33:28,280 Speaker 1: And then I love that you pointed out the other 538 00:33:28,320 --> 00:33:31,240 Speaker 1: side of the argument, which is well, maybe this is 539 00:33:31,280 --> 00:33:35,400 Speaker 1: more convenient, Maybe this is the way things should be. Uh, 540 00:33:35,520 --> 00:33:39,720 Speaker 1: it's a personalized experience for me wherever I go. Yeah, 541 00:33:40,240 --> 00:33:43,000 Speaker 1: it's certainly sold to us that way, and I think 542 00:33:43,080 --> 00:33:48,520 Speaker 1: maybe some people by that. I I am unfortunately somewhere 543 00:33:48,520 --> 00:33:50,080 Speaker 1: in the middle. I don't know if you ask for 544 00:33:50,080 --> 00:33:54,440 Speaker 1: my opinion, Ben, but guess what I'm giving. I have 545 00:33:54,520 --> 00:33:59,120 Speaker 1: fall somewhere in the middle where I'm I appreciate what 546 00:33:59,240 --> 00:34:03,640 Speaker 1: the ipre the attempt of what's trying to happen, but ultimately, 547 00:34:03,720 --> 00:34:06,840 Speaker 1: really the bottom line is that these companies are trying 548 00:34:06,880 --> 00:34:10,279 Speaker 1: to make a profit by targeting. Sure, right, and that's 549 00:34:10,280 --> 00:34:13,000 Speaker 1: the way you sell things nowadays. We're so inundative with 550 00:34:13,040 --> 00:34:16,880 Speaker 1: advertising that the only real way to get a message 551 00:34:16,880 --> 00:34:20,839 Speaker 1: across is to send it with an arrow straight at 552 00:34:20,920 --> 00:34:25,000 Speaker 1: your eyeball in your ear like, uh, hi, super producer 553 00:34:25,120 --> 00:34:30,080 Speaker 1: Noel Brown. We understand that you have recently begun working 554 00:34:30,120 --> 00:34:33,160 Speaker 1: with a move. Here are some products that might interest 555 00:34:33,200 --> 00:34:35,680 Speaker 1: you exactly. I mean, that's the only way you're gonna 556 00:34:35,719 --> 00:34:38,759 Speaker 1: do it now. But yeah, that's that's probably true, man. 557 00:34:38,800 --> 00:34:45,640 Speaker 1: But I personally, my biggest concern about this, this encroaching analysis. 558 00:34:46,040 --> 00:34:48,560 Speaker 1: My biggest concern is that a can function as a 559 00:34:48,640 --> 00:34:52,080 Speaker 1: kind of inherent censorship due to like the search bubble. 560 00:34:52,640 --> 00:34:55,719 Speaker 1: They idea that based on your past search history, you 561 00:34:55,840 --> 00:34:58,839 Speaker 1: only receive results that are quote unquote relevant to you. 562 00:34:59,200 --> 00:35:01,320 Speaker 1: I don't want to res the results that are relevant 563 00:35:01,400 --> 00:35:04,160 Speaker 1: to me. I want to see both sides of any argument. 564 00:35:04,280 --> 00:35:07,840 Speaker 1: I want to see um news that would be in 565 00:35:07,880 --> 00:35:12,440 Speaker 1: another language. There's gonna be some kind of college course 566 00:35:12,600 --> 00:35:17,000 Speaker 1: that shows you how to get the most balanced Google 567 00:35:17,040 --> 00:35:19,719 Speaker 1: Search results, and it will all it will be an 568 00:35:19,800 --> 00:35:22,880 Speaker 1: entire course that just shows you how to develop over 569 00:35:22,960 --> 00:35:26,759 Speaker 1: time by searching for certain things at certain times. Well, 570 00:35:26,760 --> 00:35:29,840 Speaker 1: there are things like scroogle and Duck Duck Go that 571 00:35:29,880 --> 00:35:32,480 Speaker 1: are supposed to not track your search. There are places 572 00:35:32,520 --> 00:35:37,400 Speaker 1: to go currently, but you know, we've seen as acquisitions 573 00:35:37,440 --> 00:35:42,160 Speaker 1: continue and certain corporations continue to get more and more monolithic. 574 00:35:43,160 --> 00:35:45,680 Speaker 1: I can see a future where there is one place 575 00:35:45,719 --> 00:35:49,680 Speaker 1: to search for things. Well. And there's also this idea 576 00:35:49,880 --> 00:35:53,720 Speaker 1: that that I think is tremendously positive, even noble almost 577 00:35:53,800 --> 00:35:57,000 Speaker 1: that if we had enough data, and we had enough 578 00:35:57,120 --> 00:36:02,400 Speaker 1: sophisticated parsing i'll rhythms or software, then we might be 579 00:36:02,440 --> 00:36:06,160 Speaker 1: able to address global problems that ordinarily wouldn't have been 580 00:36:06,239 --> 00:36:08,359 Speaker 1: able to be solved, Like what if there were a 581 00:36:08,400 --> 00:36:15,680 Speaker 1: way to uh stop the massive extinction of Earth's wildlife? Right, agreed, 582 00:36:15,880 --> 00:36:19,319 Speaker 1: And that's amazing and I love that view. But conversely, 583 00:36:19,800 --> 00:36:24,320 Speaker 1: you could also with that same data stomp out all 584 00:36:24,600 --> 00:36:29,680 Speaker 1: and every form of resistance against a certain movement. That's true, right, 585 00:36:30,000 --> 00:36:33,400 Speaker 1: And Uh, this this becomes a matter of, Uh, I 586 00:36:33,400 --> 00:36:37,560 Speaker 1: don't know the short term stuff versus the long term problems. 587 00:36:38,080 --> 00:36:42,880 Speaker 1: With that being said, let's go straight to the crazy stuff, 588 00:36:43,680 --> 00:36:49,480 Speaker 1: the conspiracies, both theoretical and actual. There's a there's a 589 00:36:50,040 --> 00:36:53,920 Speaker 1: this theory, the troubling possibility, the kind of thing that 590 00:36:54,640 --> 00:36:57,720 Speaker 1: a sci fi writer would make a dystopian novel about, 591 00:36:58,120 --> 00:37:01,759 Speaker 1: that we could eventually arrive at a world in which 592 00:37:01,800 --> 00:37:06,040 Speaker 1: circumstance and accident has fallen to the statistical hand of 593 00:37:06,200 --> 00:37:11,480 Speaker 1: fate and certitude, so that something very much like an 594 00:37:11,600 --> 00:37:14,520 Speaker 1: artificial god knows how you will live your days from 595 00:37:14,520 --> 00:37:19,319 Speaker 1: the cradle to the grave. Yeah, I don't like that, Ben, 596 00:37:19,520 --> 00:37:23,280 Speaker 1: It's a scary thing. There is some silver lining here, though, Uh, 597 00:37:23,320 --> 00:37:26,239 Speaker 1: there are many competitors right now in this space. There's 598 00:37:26,239 --> 00:37:30,080 Speaker 1: not just one big data right company, Right, Um, so 599 00:37:30,160 --> 00:37:32,160 Speaker 1: at least, you know, much in the same way that 600 00:37:32,200 --> 00:37:35,799 Speaker 1: their shadowy forces trying to control the world. There are 601 00:37:35,840 --> 00:37:39,000 Speaker 1: a lot of them, there's not just one group. Yeah, 602 00:37:39,080 --> 00:37:43,440 Speaker 1: and then these groups might not necessarily work together, especially 603 00:37:43,480 --> 00:37:46,680 Speaker 1: if they're competing in a private sphere, right, not unless 604 00:37:46,719 --> 00:37:49,080 Speaker 1: it's you know, helpful for the bottom line. Right. They 605 00:37:49,120 --> 00:37:52,320 Speaker 1: gather their data sets and and they guard their techniques 606 00:37:52,320 --> 00:37:55,279 Speaker 1: pretty jealously. Also, here's the one thing when we talk 607 00:37:55,360 --> 00:37:59,080 Speaker 1: about this super all knowing Wizard of Oz type computer, 608 00:37:59,400 --> 00:38:02,080 Speaker 1: the fact of the matter is that we still apparently 609 00:38:02,120 --> 00:38:05,080 Speaker 1: can't build a computer that can predict the weather. No, 610 00:38:05,360 --> 00:38:08,799 Speaker 1: because there there's always with the weather, there's always that 611 00:38:09,880 --> 00:38:13,120 Speaker 1: um I don't know, that chaos factor almost to where 612 00:38:13,200 --> 00:38:16,600 Speaker 1: you can't there's so it's so complex, all of the 613 00:38:16,600 --> 00:38:20,480 Speaker 1: different moving parts that create the weather. But what ben 614 00:38:20,719 --> 00:38:23,840 Speaker 1: what Yeah, I guess it would be. I guess the 615 00:38:23,840 --> 00:38:26,839 Speaker 1: same would be true for big data. There's so many 616 00:38:26,960 --> 00:38:30,200 Speaker 1: different moving parts that it's so complex. Yeah, it seems 617 00:38:30,239 --> 00:38:32,880 Speaker 1: like it would be. I don't know. Is it easier 618 00:38:32,920 --> 00:38:35,520 Speaker 1: to build something that can predict the weather or something 619 00:38:35,560 --> 00:38:39,240 Speaker 1: that can predict the passage of time in a country? 620 00:38:39,320 --> 00:38:42,879 Speaker 1: You know, we've talked before off air about this and 621 00:38:43,040 --> 00:38:46,080 Speaker 1: maybe on air too. But uh, you know, I had 622 00:38:46,120 --> 00:38:49,759 Speaker 1: a professor a long time ago, in a different life 623 00:38:49,760 --> 00:38:54,640 Speaker 1: who was working with DARPA to build an artificial model 624 00:38:54,920 --> 00:38:58,040 Speaker 1: of a country with with the idea that if they 625 00:38:58,160 --> 00:39:03,040 Speaker 1: programmed enough data points together and assign them to these individuals, 626 00:39:03,280 --> 00:39:06,719 Speaker 1: then they could measure kind of like foundation in real life. 627 00:39:06,760 --> 00:39:10,080 Speaker 1: They could measure the likelihood of trends, you know, like 628 00:39:10,760 --> 00:39:14,879 Speaker 1: if if police, if support for police goes up by 629 00:39:15,080 --> 00:39:19,440 Speaker 1: x percent, what will be the effect upon the livelihood 630 00:39:19,480 --> 00:39:23,959 Speaker 1: or the likelihood of the regime's collapse or stability. And 631 00:39:24,760 --> 00:39:26,520 Speaker 1: I don't know where he went with it, but it 632 00:39:26,600 --> 00:39:30,840 Speaker 1: is some amazing, terrifying and inspiring stuff. Either way, he 633 00:39:30,880 --> 00:39:33,960 Speaker 1: can't talk about it anymore, right, and this and still 634 00:39:34,480 --> 00:39:38,279 Speaker 1: at least our knowledge listeners. Uh, this remains a theoretical thing, 635 00:39:38,640 --> 00:39:43,040 Speaker 1: but it is a fact that big business is doing 636 00:39:43,080 --> 00:39:47,239 Speaker 1: stuff like this all the time, and not necessarily. You know, 637 00:39:47,280 --> 00:39:50,080 Speaker 1: It's not like there's somebody out there just rubbing their 638 00:39:50,120 --> 00:39:53,920 Speaker 1: hands together super villain style, waiting for you to slip 639 00:39:54,040 --> 00:39:57,200 Speaker 1: so they can you know, tell tell your mom that 640 00:39:57,280 --> 00:40:01,600 Speaker 1: you are smoking cigarettes or something. You know. There what 641 00:40:01,600 --> 00:40:05,239 Speaker 1: what it is more about is is um not immoral 642 00:40:05,360 --> 00:40:09,360 Speaker 1: but a moral um providing of a better service or 643 00:40:09,400 --> 00:40:13,720 Speaker 1: being a better um service to the consumer. But now 644 00:40:13,920 --> 00:40:17,839 Speaker 1: consumers are increasingly the product as well, right, exactly, Your 645 00:40:17,880 --> 00:40:22,040 Speaker 1: information is the thing the commodity, which is so weird. 646 00:40:22,080 --> 00:40:24,680 Speaker 1: Information as commodity. I guess it's been coming for a 647 00:40:24,719 --> 00:40:26,400 Speaker 1: long time, and it has been that way for a 648 00:40:26,400 --> 00:40:28,880 Speaker 1: long time. It's just strange to think about it on 649 00:40:28,920 --> 00:40:34,080 Speaker 1: this scale. It's almost like the information. What I'm seeing, 650 00:40:34,080 --> 00:40:38,760 Speaker 1: it is the access and ability to collect massive amounts 651 00:40:38,760 --> 00:40:42,959 Speaker 1: of information. Is this new gold rush thing? Ah, that's good. Yeah, 652 00:40:43,000 --> 00:40:44,840 Speaker 1: you're killing it with the comparison. So I don't know 653 00:40:44,840 --> 00:40:46,799 Speaker 1: if that's just what I'm seeing. Where all these large 654 00:40:46,800 --> 00:40:51,040 Speaker 1: corporations that are building, you know, massive supercomputer complexes of 655 00:40:51,080 --> 00:40:56,719 Speaker 1: supercomputers that can just crunch numbers. Man, well we're but 656 00:40:56,840 --> 00:40:59,520 Speaker 1: we're at a point where, you know, there could be 657 00:40:59,600 --> 00:41:02,920 Speaker 1: some positives for this if if it was able to 658 00:41:04,280 --> 00:41:07,320 Speaker 1: if these data sets were able to for instance, help 659 00:41:08,080 --> 00:41:13,520 Speaker 1: humanity combat I don't know, over fishing, or help humanity 660 00:41:13,840 --> 00:41:18,880 Speaker 1: figure out the best way to prevent mass starvation or disease. 661 00:41:19,000 --> 00:41:21,520 Speaker 1: But again, you know, those things are also they seem 662 00:41:21,560 --> 00:41:25,080 Speaker 1: to be more complex than weather patterns. They are, and 663 00:41:25,200 --> 00:41:28,960 Speaker 1: I just it's hard for me to imagine someone looking 664 00:41:29,000 --> 00:41:32,480 Speaker 1: at those problems and making a profit from it, or 665 00:41:32,640 --> 00:41:35,080 Speaker 1: you know, devising a way to make a profit from 666 00:41:35,120 --> 00:41:37,880 Speaker 1: it and use all these assets in order to do 667 00:41:37,960 --> 00:41:41,640 Speaker 1: something good. I'm sorry, man, my faith in humanity just 668 00:41:41,840 --> 00:41:43,960 Speaker 1: like got ticked down a couple of notches for some 669 00:41:44,000 --> 00:41:46,840 Speaker 1: reason in thinking about all this stuff. Because this is 670 00:41:46,840 --> 00:41:50,040 Speaker 1: all really what we're talking about. Here are ways to 671 00:41:50,160 --> 00:41:53,880 Speaker 1: sell things, right, that's what this whole thing is about. Well, 672 00:41:54,040 --> 00:41:56,239 Speaker 1: what I would say, what I feel like what we're 673 00:41:56,280 --> 00:41:58,919 Speaker 1: talking about is a little bit further than that. It's 674 00:41:59,160 --> 00:42:05,520 Speaker 1: ways to predict future events. So selling something is trying 675 00:42:05,600 --> 00:42:09,120 Speaker 1: to predict what will trigger a purchase. Right, So it's 676 00:42:09,160 --> 00:42:15,279 Speaker 1: it's still predictive, or hopefully predictive. But the question though, 677 00:42:15,360 --> 00:42:20,160 Speaker 1: right now, and the answer, it seems, is that overall, 678 00:42:21,440 --> 00:42:25,560 Speaker 1: while we know that people are working fervently to build 679 00:42:25,600 --> 00:42:30,080 Speaker 1: a machine that can read the future, right, a modern 680 00:42:30,160 --> 00:42:34,839 Speaker 1: day fortune teller, uh, we we do not yet have 681 00:42:35,120 --> 00:42:38,160 Speaker 1: that oracle, at least in the public sphere. We don't 682 00:42:38,239 --> 00:42:41,040 Speaker 1: know about it, but we do know that people are 683 00:42:41,160 --> 00:42:43,680 Speaker 1: working on it, and that brings us to um, I 684 00:42:43,719 --> 00:42:46,120 Speaker 1: guess one of the things we can close with today. 685 00:42:46,200 --> 00:42:50,800 Speaker 1: Huh Yeah, A little something called Anomaly Detection at Multiple 686 00:42:50,840 --> 00:42:55,520 Speaker 1: Scales or atoms. It's brought to you by DARPA. Friends 687 00:42:55,520 --> 00:42:59,520 Speaker 1: over at DARPA, good people. Um, they work hard over 688 00:42:59,560 --> 00:43:03,960 Speaker 1: at DARPA. So this comes directly from the DARPA website. 689 00:43:04,400 --> 00:43:05,920 Speaker 1: And I'm just going to read you this quote's a 690 00:43:05,920 --> 00:43:11,120 Speaker 1: little along bear with me quote. The Anomaly Detection at 691 00:43:11,200 --> 00:43:16,160 Speaker 1: Multiple Scales or Atoms program creates, adapts, and applies technology 692 00:43:16,200 --> 00:43:21,799 Speaker 1: to anomally characterization and detection in massive data sets. Anomalies 693 00:43:21,840 --> 00:43:25,799 Speaker 1: in data cue the collection of additional actionable information in 694 00:43:25,840 --> 00:43:30,879 Speaker 1: a wide variety of real world contexts. The initial application 695 00:43:30,960 --> 00:43:35,880 Speaker 1: domain is insider threat detection, in which malevolent or possibly 696 00:43:35,960 --> 00:43:41,400 Speaker 1: inadvertent actions by a trusted individual are detected against a 697 00:43:41,480 --> 00:43:46,760 Speaker 1: background of everyday network activity. So they're looking, they're looking 698 00:43:46,880 --> 00:43:53,319 Speaker 1: at a person, a trusted insider person, and and then 699 00:43:53,400 --> 00:43:56,560 Speaker 1: they are going, oh, well, here is an anomalous action 700 00:43:56,640 --> 00:44:00,719 Speaker 1: or an anomalous piece of data inside this your data set. 701 00:44:01,000 --> 00:44:07,080 Speaker 1: Sure Like, Mrs Cunningham works for a Wall Street investment firm, 702 00:44:07,320 --> 00:44:12,320 Speaker 1: and every day Mrs Cunningham has lunch at twelve thirty 703 00:44:12,560 --> 00:44:16,600 Speaker 1: and goes back to work until six thirty, at which 704 00:44:16,600 --> 00:44:18,600 Speaker 1: point she leaves and it takes her an hour and 705 00:44:18,640 --> 00:44:21,359 Speaker 1: a half to get home because of traffic. And then 706 00:44:21,440 --> 00:44:27,200 Speaker 1: one day she goes to lunch, but instead of going 707 00:44:27,200 --> 00:44:32,000 Speaker 1: to a restaurant, she goes down to you know, um, 708 00:44:32,040 --> 00:44:35,200 Speaker 1: a gun store, or she goes to a you know, 709 00:44:35,280 --> 00:44:38,719 Speaker 1: her her anomalous change, right, yeah, or she seems to 710 00:44:38,719 --> 00:44:41,280 Speaker 1: be in closer contact with the competitors, so they could 711 00:44:41,440 --> 00:44:44,640 Speaker 1: they could call these anomalies. And we've we've heard people 712 00:44:45,120 --> 00:44:47,480 Speaker 1: talk about this sometimes, like if you haven't checked out 713 00:44:47,480 --> 00:44:50,760 Speaker 1: the website zero hedge, that's a it's a very interesting 714 00:44:50,800 --> 00:44:53,400 Speaker 1: read and it's well done. One of the things that 715 00:44:53,440 --> 00:44:57,320 Speaker 1: they've talked about before is anomaly detection and these theories 716 00:44:57,360 --> 00:45:02,120 Speaker 1: about you know, um market forces or investor actions right 717 00:45:02,120 --> 00:45:06,680 Speaker 1: before calamitous events. Oh yeah, there's some great analysis of 718 00:45:06,719 --> 00:45:11,120 Speaker 1: that stuff by Tyler Dirton actually speaking of fight club. Yes, yes, 719 00:45:11,800 --> 00:45:15,600 Speaker 1: uh so what okay, this is part of one one 720 00:45:15,640 --> 00:45:17,279 Speaker 1: of the things I wanted to ask you about. Have 721 00:45:17,400 --> 00:45:22,400 Speaker 1: you heard the theory that, um, not only is Tyler 722 00:45:22,560 --> 00:45:26,200 Speaker 1: Dirden not real, but the girlfriend character is not real either. 723 00:45:26,719 --> 00:45:32,799 Speaker 1: I know that it's another person he made up. Oh wow, okay, 724 00:45:33,840 --> 00:45:37,320 Speaker 1: oh wow, okay, I'm trying to check it out. It 725 00:45:37,400 --> 00:45:38,960 Speaker 1: has nothing to do with what it's talking about it. 726 00:45:40,000 --> 00:45:41,920 Speaker 1: But we do want to hear from you, not just 727 00:45:41,960 --> 00:45:44,000 Speaker 1: with your fight club theories. But send him if you want. 728 00:45:44,080 --> 00:45:48,080 Speaker 1: I think it's an interesting movie too. I won't go 729 00:45:48,120 --> 00:45:51,239 Speaker 1: into some of the plithhole parts. All right, well, let 730 00:45:51,360 --> 00:45:53,600 Speaker 1: us know what you think about it, but more importantly, 731 00:45:53,680 --> 00:45:56,319 Speaker 1: let us know what you think about big data. Is 732 00:45:56,360 --> 00:45:59,640 Speaker 1: it possible to get off of the grid? How difficult 733 00:45:59,840 --> 00:46:03,279 Speaker 1: is it? What do you think, um, what do you 734 00:46:03,280 --> 00:46:05,960 Speaker 1: think people are doing with all this information the public 735 00:46:06,000 --> 00:46:09,160 Speaker 1: and private sphere. One of the big concerns that we 736 00:46:09,200 --> 00:46:12,600 Speaker 1: hear a lot about is the idea that the surveillance state, 737 00:46:12,680 --> 00:46:15,480 Speaker 1: at least in the US and the West, has grown 738 00:46:15,600 --> 00:46:21,080 Speaker 1: because um, the intelligence agencies and departments are able to 739 00:46:21,200 --> 00:46:23,960 Speaker 1: use the dirt they have on the elected officials to 740 00:46:24,080 --> 00:46:27,520 Speaker 1: prevent the elected officials from uh, you know, slowing the 741 00:46:27,560 --> 00:46:30,080 Speaker 1: growth of the surveillance state, the idea of a deep 742 00:46:30,160 --> 00:46:33,319 Speaker 1: state or shadow government. Okay, So to make me feel better, 743 00:46:33,440 --> 00:46:36,640 Speaker 1: please send in your suggestions, like Ben had earlier, of 744 00:46:36,760 --> 00:46:39,640 Speaker 1: positive things that could be used that big data could 745 00:46:39,640 --> 00:46:42,520 Speaker 1: be used for. Please please send those to us, just 746 00:46:42,600 --> 00:46:45,719 Speaker 1: to make me feel better. Thank you, and that's the 747 00:46:45,840 --> 00:46:49,240 Speaker 1: end of this classic episode. If you have any thoughts 748 00:46:49,360 --> 00:46:53,200 Speaker 1: or questions about this episode, you can get into contact 749 00:46:53,280 --> 00:46:55,440 Speaker 1: with us in a number of different ways. One of 750 00:46:55,440 --> 00:46:57,280 Speaker 1: the best is to give us a call. Our number 751 00:46:57,320 --> 00:47:00,920 Speaker 1: is one eight three three std to b y t K. 752 00:47:01,440 --> 00:47:03,200 Speaker 1: If you don't want to do that, you can send 753 00:47:03,280 --> 00:47:06,520 Speaker 1: us a good old fashioned email. We are conspiracy at 754 00:47:06,560 --> 00:47:10,200 Speaker 1: i heart radio dot com. Stuff they don't want you 755 00:47:10,239 --> 00:47:12,880 Speaker 1: to know is a production of I heart Radio. For 756 00:47:12,960 --> 00:47:15,319 Speaker 1: more podcasts from my heart Radio, visit the i heart 757 00:47:15,400 --> 00:47:18,160 Speaker 1: Radio app, Apple Podcasts, or wherever you listen to your 758 00:47:18,200 --> 00:47:18,880 Speaker 1: favorite shows.