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