1 00:00:03,800 --> 00:00:06,680 Speaker 1: Welcome to Stuff to Blow your Mind from how Stuff 2 00:00:06,680 --> 00:00:14,120 Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind. 3 00:00:14,200 --> 00:00:17,200 Speaker 1: My name is Robert Lamb and Julie Douglas. Julie, you know, 4 00:00:17,600 --> 00:00:21,360 Speaker 1: making decisions in this world can be really taxing. We've 5 00:00:21,400 --> 00:00:24,400 Speaker 1: discussed that before. Even the smallest decisions in life are 6 00:00:24,440 --> 00:00:27,040 Speaker 1: often difficult to make. But put yourself in the in 7 00:00:27,120 --> 00:00:31,520 Speaker 1: the boots in the gold studied jewel encrusted boots of 8 00:00:31,160 --> 00:00:37,440 Speaker 1: a king or or an Elvis, somebody who's whose decisions 9 00:00:37,720 --> 00:00:42,800 Speaker 1: are are are sweeping that change the escape of international 10 00:00:42,840 --> 00:00:48,000 Speaker 1: politics and economics. We're talking about huge ripple effect globally, globally, 11 00:00:48,120 --> 00:00:53,840 Speaker 1: huge political leaders, leaders of multinational companies, policymakers, The decisions 12 00:00:53,840 --> 00:00:58,040 Speaker 1: that they make UH can have catastrophic effects. They can 13 00:00:58,080 --> 00:01:00,840 Speaker 1: they can change the world for the better for the worst. 14 00:01:01,480 --> 00:01:03,960 Speaker 1: So in UH, in ancient times, you know, you would 15 00:01:04,000 --> 00:01:06,080 Speaker 1: have the emperor and he would have like a sourcerer 16 00:01:06,160 --> 00:01:08,399 Speaker 1: or a diviner there too. I was gonna say, an 17 00:01:08,440 --> 00:01:10,560 Speaker 1: oracle or an oracle, you know, there would be some 18 00:01:10,600 --> 00:01:13,760 Speaker 1: sort of magical go to man to bounce these ideas 19 00:01:13,800 --> 00:01:16,360 Speaker 1: off of and be like, hey, I'm trying to figure 20 00:01:16,360 --> 00:01:18,560 Speaker 1: out what to do about this. I don't know this 21 00:01:18,600 --> 00:01:22,959 Speaker 1: protest situation in the streets, um, but I'm not really 22 00:01:23,000 --> 00:01:25,759 Speaker 1: sure what to do. Can you look into your magic 23 00:01:25,800 --> 00:01:28,200 Speaker 1: pool of water and see what the future is going 24 00:01:28,280 --> 00:01:32,440 Speaker 1: to be, um, and tell me what I should do? Now? 25 00:01:32,480 --> 00:01:34,600 Speaker 1: Of course, there's no such thing as magic. There is, 26 00:01:35,440 --> 00:01:38,720 Speaker 1: so I'm sorry atually to tell me that my fortune 27 00:01:38,760 --> 00:01:42,520 Speaker 1: cookies are full of bunk, except the ones where the 28 00:01:42,600 --> 00:01:46,039 Speaker 1: fortune is general advice. But but we we love the 29 00:01:46,080 --> 00:01:47,960 Speaker 1: idea of being able to do that. Mainly we want 30 00:01:47,960 --> 00:01:50,520 Speaker 1: to we want to test our assumptions about what should 31 00:01:50,520 --> 00:01:53,560 Speaker 1: be done versus the outcome of those assumptions in the 32 00:01:53,640 --> 00:01:57,200 Speaker 1: real world. So what you're saying is that if leaders 33 00:01:57,240 --> 00:02:01,760 Speaker 1: had some sort of magic ball, right that they could 34 00:02:02,120 --> 00:02:04,600 Speaker 1: ask a question of and it could it actually spit 35 00:02:04,640 --> 00:02:09,079 Speaker 1: out an answer that was valid, we could uh really 36 00:02:09,120 --> 00:02:12,040 Speaker 1: manage our lives on a global scale in the much cleaner, 37 00:02:12,240 --> 00:02:15,400 Speaker 1: better way. That's the idea. But of course, to power 38 00:02:15,440 --> 00:02:18,480 Speaker 1: that magic eight ball, you would need some pretty intense 39 00:02:18,560 --> 00:02:21,080 Speaker 1: computer technology, and you would you would basically have to 40 00:02:21,160 --> 00:02:24,760 Speaker 1: have a computer model of everything under there. Uh. And 41 00:02:24,919 --> 00:02:27,919 Speaker 1: in the same day, in our attempts to understand global 42 00:02:27,960 --> 00:02:30,880 Speaker 1: climate UH, and just not even global climate, just local 43 00:02:30,919 --> 00:02:33,720 Speaker 1: weather to find out whether whether we should have a 44 00:02:33,720 --> 00:02:35,840 Speaker 1: picnic tomorrow. Can I plan on mo in my yard? 45 00:02:35,919 --> 00:02:37,840 Speaker 1: Should I would bring a raincoat with me to the 46 00:02:37,919 --> 00:02:40,359 Speaker 1: to the train station, that kind of thing. We depend 47 00:02:40,400 --> 00:02:44,920 Speaker 1: on these these climate models, which UHL is we've discussed 48 00:02:44,919 --> 00:02:47,000 Speaker 1: in the past. Creating an an accurate climate model is 49 00:02:47,080 --> 00:02:50,320 Speaker 1: very difficult. There's so many factors involved. It's a it's 50 00:02:50,800 --> 00:02:54,400 Speaker 1: largely chaotic system, and it's it's difficult to judge. Like 51 00:02:54,440 --> 00:02:57,400 Speaker 1: every every day that you're into the future that you look, 52 00:02:57,680 --> 00:03:02,200 Speaker 1: the more flawed the model becomes. But but still we 53 00:03:02,200 --> 00:03:04,840 Speaker 1: we depend on the community computer models for for our 54 00:03:04,919 --> 00:03:06,880 Speaker 1: understanding of what the weather is going to be. And 55 00:03:07,320 --> 00:03:11,200 Speaker 1: if conceivably, if we created a complex enough computer model, 56 00:03:12,040 --> 00:03:15,920 Speaker 1: could we not have a a kind of simulation of 57 00:03:15,960 --> 00:03:19,200 Speaker 1: the world in which to test our ideas. So you 58 00:03:19,240 --> 00:03:22,600 Speaker 1: would have this politician or this policymaker somewhere in a 59 00:03:22,639 --> 00:03:26,400 Speaker 1: position of power, gold stilettos, and she's thinking to herself, 60 00:03:26,480 --> 00:03:28,640 Speaker 1: what should I do? Should I enact this policy or 61 00:03:28,680 --> 00:03:31,760 Speaker 1: this policy? Well, bring me the magic eight ball and 62 00:03:31,800 --> 00:03:33,679 Speaker 1: I will ask it in the magic eight ball will 63 00:03:33,720 --> 00:03:37,080 Speaker 1: then run two scenarios in the in its simulation of 64 00:03:37,080 --> 00:03:40,880 Speaker 1: the world, one in which policy A is enacted and 65 00:03:40,960 --> 00:03:43,720 Speaker 1: one in which policy B is enacted. Okay, but this 66 00:03:43,800 --> 00:03:46,640 Speaker 1: magic eight ball would have to aggregate data of our 67 00:03:46,960 --> 00:03:51,000 Speaker 1: entire existence, right, so we're talking about the economic existence, 68 00:03:51,040 --> 00:03:56,800 Speaker 1: are social um existence, the geographical existence um, you know, 69 00:03:56,920 --> 00:04:00,240 Speaker 1: the physics of our existence. All of this, uh, would 70 00:04:00,240 --> 00:04:02,520 Speaker 1: have to be something that we could get our arms around, 71 00:04:02,520 --> 00:04:05,120 Speaker 1: all this data, right, and and and then we have 72 00:04:05,160 --> 00:04:06,760 Speaker 1: to have a name for this, all this data. We 73 00:04:06,800 --> 00:04:10,600 Speaker 1: call it big data. Big data name for it. And 74 00:04:10,640 --> 00:04:12,720 Speaker 1: it's kind of like imagine, like the way I was 75 00:04:12,760 --> 00:04:15,840 Speaker 1: kind of thinking, because I'm writing about some related issues 76 00:04:15,880 --> 00:04:18,320 Speaker 1: here for work, so I've been been researching, and the 77 00:04:18,360 --> 00:04:21,480 Speaker 1: way I tend to think of it is imagine, imagine 78 00:04:21,520 --> 00:04:25,080 Speaker 1: like this this field, all right, this flat plane, right, 79 00:04:25,560 --> 00:04:28,240 Speaker 1: what's not completely flat. There's some slight bumps in it 80 00:04:28,520 --> 00:04:31,400 Speaker 1: enough to where you have numerous puddles of water, and 81 00:04:31,400 --> 00:04:33,880 Speaker 1: then it starts raining, right, those puddles get bigger and 82 00:04:33,880 --> 00:04:35,720 Speaker 1: bigger until those puddles all meet and then you have 83 00:04:35,960 --> 00:04:39,600 Speaker 1: like a giant lake. And so that's the kind of Uh, 84 00:04:39,960 --> 00:04:42,440 Speaker 1: that's how I like I end up imagining big data 85 00:04:42,680 --> 00:04:46,840 Speaker 1: because every day we create an estimated two point five 86 00:04:47,360 --> 00:04:51,080 Speaker 1: quin trillion bytes of data. So to the point of 87 00:04:51,080 --> 00:04:52,919 Speaker 1: the data in the world today has been created in 88 00:04:52,960 --> 00:04:56,160 Speaker 1: the last two years alone. And when I say big data, 89 00:04:56,240 --> 00:04:58,560 Speaker 1: like this is all these little puddles of data that 90 00:04:58,640 --> 00:05:00,839 Speaker 1: form this giant lake of big day. We're talking about 91 00:05:00,880 --> 00:05:04,920 Speaker 1: everything we'll climate sensors, to your to everyone's Facebook and 92 00:05:04,960 --> 00:05:10,200 Speaker 1: Twitter updates, to digital text, digital video and picture upload. 93 00:05:10,200 --> 00:05:12,600 Speaker 1: Stuff you're putting on Flicker, stuff you're putting on YouTube 94 00:05:12,920 --> 00:05:16,360 Speaker 1: to just to show visions of the world, ideas about 95 00:05:16,400 --> 00:05:19,440 Speaker 1: what the world is doing, online transaction records, cell phone 96 00:05:19,480 --> 00:05:22,520 Speaker 1: GPS signals, all of it is coming together into this 97 00:05:22,600 --> 00:05:25,320 Speaker 1: big picture of big data, right and right now it's 98 00:05:25,400 --> 00:05:27,320 Speaker 1: chaos to us, right, I mean, this is not something 99 00:05:27,360 --> 00:05:30,560 Speaker 1: that we've tried to manage before to get a picture 100 00:05:30,640 --> 00:05:33,039 Speaker 1: of what our lives look like using all of this 101 00:05:33,120 --> 00:05:36,919 Speaker 1: big data, although some people, some institutions have tried to 102 00:05:36,920 --> 00:05:39,240 Speaker 1: do it at a smaller scale, like NASA. Right, it's 103 00:05:39,279 --> 00:05:41,960 Speaker 1: kind of like imagine the dude or a lady who 104 00:05:42,040 --> 00:05:44,880 Speaker 1: starts just picking up buying a book every day and 105 00:05:44,920 --> 00:05:47,920 Speaker 1: doesn't doesn't have any kind of accurate library system going 106 00:05:47,920 --> 00:05:49,760 Speaker 1: in their house. They just bring a book or to home. 107 00:05:50,120 --> 00:05:51,520 Speaker 1: They maybe they read a little bit of one, they 108 00:05:51,520 --> 00:05:53,320 Speaker 1: throw on here, throw on here. Eventually they have an 109 00:05:53,440 --> 00:05:56,360 Speaker 1: entire house just filled with books. But they have no 110 00:05:56,440 --> 00:05:59,400 Speaker 1: system of organization to understand how many books they have, 111 00:06:00,080 --> 00:06:03,000 Speaker 1: how their selection in one category stacks up against another, 112 00:06:03,520 --> 00:06:05,440 Speaker 1: or even kind of like just a general idea of 113 00:06:05,440 --> 00:06:09,040 Speaker 1: what kind of books they like. Um. So we were 114 00:06:09,080 --> 00:06:11,839 Speaker 1: in this household of big data where data is just everywhere, 115 00:06:11,880 --> 00:06:15,520 Speaker 1: on everything, but we we tend to lack a complete 116 00:06:15,560 --> 00:06:18,200 Speaker 1: picture of what that data is telling us. What we're 117 00:06:18,240 --> 00:06:21,760 Speaker 1: getting to is that there is actually in the works 118 00:06:22,080 --> 00:06:26,560 Speaker 1: a a proposal and an enactment of this idea. It's 119 00:06:26,560 --> 00:06:30,680 Speaker 1: a billion a half dollar idea computing system to actually 120 00:06:30,680 --> 00:06:34,160 Speaker 1: try to wrangle all of this data. Right. It all 121 00:06:34,440 --> 00:06:38,039 Speaker 1: falls under the this project known as UH Future I 122 00:06:38,120 --> 00:06:41,560 Speaker 1: C T, which is really three parts. There's a planetary 123 00:06:41,720 --> 00:06:45,040 Speaker 1: nervous system and the idea here is you would um, 124 00:06:45,120 --> 00:06:47,479 Speaker 1: it's like a global sensor network throwing in all sorts 125 00:06:47,520 --> 00:06:51,559 Speaker 1: of socio economic, environmental, technological data from around the world. Again, 126 00:06:51,600 --> 00:06:53,760 Speaker 1: like the big day of the big data in the 127 00:06:53,760 --> 00:06:56,440 Speaker 1: world is from the last two years. Although there it's 128 00:06:56,480 --> 00:06:58,760 Speaker 1: the the timiness of this data as it's rolling in. 129 00:06:59,080 --> 00:07:01,240 Speaker 1: So if you put up enough, put up enough sensors, 130 00:07:01,279 --> 00:07:04,679 Speaker 1: you you hooked into enough existing data networks, you would 131 00:07:04,680 --> 00:07:07,840 Speaker 1: have like a real time picture of what is happening 132 00:07:07,839 --> 00:07:10,560 Speaker 1: in the world and all these different spheres. Okay, and 133 00:07:10,720 --> 00:07:13,720 Speaker 1: the guy who is heading it all up is dirt helping. 134 00:07:14,160 --> 00:07:16,480 Speaker 1: He's the Scientific Coordinator of the Future i c T. 135 00:07:16,680 --> 00:07:18,840 Speaker 1: Which is that what you're talking about, this large scale 136 00:07:18,880 --> 00:07:23,559 Speaker 1: European research program to explore and manage our future. And 137 00:07:23,680 --> 00:07:27,760 Speaker 1: he's talking about the necessity to understand complex global, socially 138 00:07:27,840 --> 00:07:31,360 Speaker 1: interactive systems. He's saying that we live in a global 139 00:07:31,360 --> 00:07:35,160 Speaker 1: world and this requires new tools. Yeah, and uh, if 140 00:07:35,240 --> 00:07:37,720 Speaker 1: i'm engine the first tool the planetary nervous system. Another tool, 141 00:07:37,760 --> 00:07:40,640 Speaker 1: which we'll get into later, is the Global Participatory Platform, 142 00:07:41,120 --> 00:07:43,000 Speaker 1: which you can think of in a way sort of 143 00:07:43,040 --> 00:07:47,960 Speaker 1: like this the the interactive aspect of this project. But 144 00:07:47,960 --> 00:07:49,520 Speaker 1: then the really core thing, the thing that we're going 145 00:07:49,560 --> 00:07:53,000 Speaker 1: to talk in detail about here is the Living Earth simulator. 146 00:07:53,160 --> 00:07:55,000 Speaker 1: And this is exactly what we were talking about, the 147 00:07:55,000 --> 00:07:58,960 Speaker 1: engine that would drive this imaginary eight ball magic eight ball. 148 00:07:59,320 --> 00:08:01,560 Speaker 1: I love this idea because to me it seems like 149 00:08:01,560 --> 00:08:04,040 Speaker 1: a souped up second life. Yeah, where it's or it's 150 00:08:04,120 --> 00:08:06,560 Speaker 1: you can't help. But even though it's it's probably not 151 00:08:06,640 --> 00:08:09,800 Speaker 1: kosher to really talk about the matrix anymore after the 152 00:08:09,880 --> 00:08:12,480 Speaker 1: last two films, but it sounds very matrix like the 153 00:08:12,520 --> 00:08:15,600 Speaker 1: idea that here is here, there's a simulated world where 154 00:08:15,600 --> 00:08:17,480 Speaker 1: we would we would bring in all this data to 155 00:08:17,480 --> 00:08:19,640 Speaker 1: create a model of the world on which we can 156 00:08:19,640 --> 00:08:23,360 Speaker 1: test possible choices. Well, and have you ever seen Google's 157 00:08:23,360 --> 00:08:26,200 Speaker 1: Liquid Earth. I don't think I have. It's really cool. 158 00:08:26,280 --> 00:08:30,200 Speaker 1: It's a it's actually created as like someone's project over 159 00:08:30,240 --> 00:08:33,200 Speaker 1: at Google. So the Skuy's time, he decided to dedicate 160 00:08:33,520 --> 00:08:35,760 Speaker 1: to the Google Earth model that they have, you know 161 00:08:35,800 --> 00:08:38,720 Speaker 1: that consuming on the cities, and he created this highly 162 00:08:38,800 --> 00:08:42,680 Speaker 1: immersive program has eight panels surrounding you. She almost feel 163 00:08:42,720 --> 00:08:45,920 Speaker 1: like you're in a video game. And the idea I 164 00:08:45,960 --> 00:08:47,679 Speaker 1: think is that you know, you can zoom in and 165 00:08:47,720 --> 00:08:49,559 Speaker 1: out and you can see the taj Mahal and you 166 00:08:49,600 --> 00:08:52,720 Speaker 1: can see all the details. Um So when I think 167 00:08:52,760 --> 00:08:57,400 Speaker 1: about this Earth simulator, this Living Earth simulator, I think 168 00:08:57,480 --> 00:09:01,000 Speaker 1: about this sort of immersive situation and where you can 169 00:09:01,040 --> 00:09:03,200 Speaker 1: be on a city street, you can zoom in and 170 00:09:03,240 --> 00:09:05,640 Speaker 1: you'll have all of this data overlaid on top of 171 00:09:05,679 --> 00:09:09,120 Speaker 1: it real time, right, And again, real time is key 172 00:09:09,280 --> 00:09:13,120 Speaker 1: when we're talking about all this data. Um so Dr 173 00:09:13,200 --> 00:09:18,040 Speaker 1: Dirk Um, Dr Dirk, Dr Dirk he Um. He's very 174 00:09:18,040 --> 00:09:20,160 Speaker 1: expressive about all of this. He makes the point that 175 00:09:20,200 --> 00:09:22,080 Speaker 1: in the past, we we didn't really have the data 176 00:09:22,120 --> 00:09:25,120 Speaker 1: to come up with a systemic science of how our 177 00:09:25,160 --> 00:09:28,040 Speaker 1: society works. But now we have this data, right, and 178 00:09:28,080 --> 00:09:30,280 Speaker 1: of course who could have conceived of it right? Right? Right? 179 00:09:30,280 --> 00:09:32,040 Speaker 1: Who could have conceived of it in the past. And 180 00:09:32,080 --> 00:09:34,520 Speaker 1: he's saying it's it's necessary to keep up with globalization, 181 00:09:34,600 --> 00:09:37,880 Speaker 1: technological change. You have all these systems like smashing into 182 00:09:37,880 --> 00:09:40,240 Speaker 1: each other. In fact, he often refers to the living 183 00:09:40,320 --> 00:09:43,280 Speaker 1: or simulator is a knowledge collider. The idea that you 184 00:09:43,280 --> 00:09:45,400 Speaker 1: would take all this data and in the same way 185 00:09:45,400 --> 00:09:48,920 Speaker 1: that the the large hypn collider is is slamming particles 186 00:09:48,920 --> 00:09:51,360 Speaker 1: together to try to understand how the universe works. This 187 00:09:51,440 --> 00:09:54,000 Speaker 1: is about like slamming all this information together and seeing 188 00:09:54,080 --> 00:09:57,880 Speaker 1: what happens. Which I like this idea because although I 189 00:09:57,880 --> 00:09:59,520 Speaker 1: will say that it doesn't sound like it's going to 190 00:09:59,559 --> 00:10:02,760 Speaker 1: be quite that dynamic, because this is this is predictive 191 00:10:02,800 --> 00:10:05,760 Speaker 1: modeling that we're talking about, and as and as we 192 00:10:06,160 --> 00:10:11,280 Speaker 1: discussed in the weather example, predictive modeling is uh imperfect 193 00:10:11,360 --> 00:10:15,480 Speaker 1: at best. Uh. There there are various arguments about what 194 00:10:15,600 --> 00:10:18,640 Speaker 1: can be done with computer modeling of of complex systems. 195 00:10:19,040 --> 00:10:21,600 Speaker 1: There are plenty of arguments that state that you cannot 196 00:10:21,640 --> 00:10:25,360 Speaker 1: form a perfect model of a complex system that you're 197 00:10:25,400 --> 00:10:27,280 Speaker 1: you're never gonna be able to to really get down 198 00:10:27,320 --> 00:10:30,080 Speaker 1: into the exact minusha of it. Uh. It's kind of 199 00:10:30,080 --> 00:10:32,560 Speaker 1: like with our our ability to understand whether you can 200 00:10:32,600 --> 00:10:35,400 Speaker 1: look at data telling you what the weather has been 201 00:10:35,480 --> 00:10:38,840 Speaker 1: like at a particular place, particular times of the year, 202 00:10:39,280 --> 00:10:40,840 Speaker 1: and you can use that and you can create a 203 00:10:40,840 --> 00:10:42,439 Speaker 1: general idea of what the weather is going to be 204 00:10:42,440 --> 00:10:45,280 Speaker 1: in the future. You know, I can see, like say 205 00:10:45,400 --> 00:10:49,000 Speaker 1: March third, We can take March third for Atlanta, Georgia, 206 00:10:49,720 --> 00:10:51,679 Speaker 1: run it all the way through the past as far 207 00:10:51,679 --> 00:10:53,559 Speaker 1: as the recorded data goes, and we can get a 208 00:10:53,600 --> 00:10:55,560 Speaker 1: general idea of what March thirds in the future are 209 00:10:55,559 --> 00:10:58,480 Speaker 1: going to be because it's the their their seasonal aspects 210 00:10:58,480 --> 00:11:01,600 Speaker 1: to all of this. There is currents, but it's not 211 00:11:01,720 --> 00:11:04,640 Speaker 1: that far away from Richard's Almanac, right, or Richard's Almanac, 212 00:11:04,880 --> 00:11:07,520 Speaker 1: which is what two hundred years old or something that 213 00:11:07,559 --> 00:11:11,520 Speaker 1: they've been, um, you know, recording all the weather systems 214 00:11:11,600 --> 00:11:14,520 Speaker 1: to try to predict the best time to plant crops 215 00:11:14,559 --> 00:11:17,040 Speaker 1: and some one and so forth. So yeah, there's it's different. 216 00:11:17,160 --> 00:11:20,960 Speaker 1: Um you know, technologies of course to have are in 217 00:11:21,000 --> 00:11:24,440 Speaker 1: play now that inform us, but it's you know, the 218 00:11:24,520 --> 00:11:28,719 Speaker 1: unpredictability factor is still there. But before we talk about that, 219 00:11:28,760 --> 00:11:30,559 Speaker 1: I just wanted to talk about the impetus for this 220 00:11:30,600 --> 00:11:35,800 Speaker 1: whole creation. This the the future I see because because 221 00:11:35,800 --> 00:11:38,600 Speaker 1: it's we can agree that it's a wonderful idea, but 222 00:11:38,640 --> 00:11:42,720 Speaker 1: what potentially gets the money behind this idea? And uh 223 00:11:42,760 --> 00:11:45,719 Speaker 1: and that's what Yeah, billion and a half dollars. The 224 00:11:45,760 --> 00:11:48,840 Speaker 1: European Commission selected the Living Earth Simulator, which is part 225 00:11:48,840 --> 00:11:51,120 Speaker 1: of this project right as a way to help predict 226 00:11:51,120 --> 00:11:53,959 Speaker 1: economic conditions. And this was brought up by the Greek 227 00:11:54,000 --> 00:11:58,240 Speaker 1: financial crisis because as we know now, it's severely undermine 228 00:11:58,240 --> 00:12:02,360 Speaker 1: the European Union and a lot of people are saying, well, perhaps, uh, 229 00:12:02,400 --> 00:12:05,280 Speaker 1: you know, grease should pull out of the euro Zone. 230 00:12:05,320 --> 00:12:07,000 Speaker 1: And if they were to do that, what would be 231 00:12:07,000 --> 00:12:11,599 Speaker 1: the ramifications? You know, you would have a highly devalued 232 00:12:11,640 --> 00:12:14,880 Speaker 1: currency for Greece. But what does this mean on a 233 00:12:14,920 --> 00:12:18,319 Speaker 1: practical level, for for a global economy. Does this mean 234 00:12:18,360 --> 00:12:23,800 Speaker 1: that trade routes would alter? Would there be less disease? Actually, 235 00:12:23,800 --> 00:12:26,200 Speaker 1: because it would be a less tourist, less people traveling 236 00:12:26,240 --> 00:12:29,120 Speaker 1: to Greece. Um, they're saying, I wish we had to 237 00:12:29,120 --> 00:12:32,200 Speaker 1: magic eight ball to ask about this. Dr Dirk says, 238 00:12:32,240 --> 00:12:35,040 Speaker 1: I have one. I can build one for you. Will 239 00:12:35,080 --> 00:12:38,520 Speaker 1: only cost a billion and a half. There you go. Yeah. 240 00:12:38,800 --> 00:12:40,880 Speaker 1: So they have all these different questions about what would 241 00:12:40,880 --> 00:12:43,640 Speaker 1: happen if this were the scenario, because of course they 242 00:12:43,640 --> 00:12:46,280 Speaker 1: don't want Grease necessarily to pull out and you know 243 00:12:46,480 --> 00:12:49,240 Speaker 1: for the EU to crumble, right, I mean these are 244 00:12:49,280 --> 00:12:52,320 Speaker 1: big stakes. It's like a big economic ginga game. Um, yeah, 245 00:12:52,320 --> 00:12:55,200 Speaker 1: it is. It is um. And And then also you 246 00:12:55,200 --> 00:12:59,840 Speaker 1: know you can use this for for other huge situations 247 00:13:00,400 --> 00:13:02,640 Speaker 1: or high impact and situations. I should say, like for 248 00:13:02,720 --> 00:13:06,439 Speaker 1: instance of volcano eruption. UM could tell you what the 249 00:13:06,480 --> 00:13:08,560 Speaker 1: short term economic growth is going to be, as well 250 00:13:08,600 --> 00:13:11,280 Speaker 1: as the effect it may have on everything from education 251 00:13:11,720 --> 00:13:17,280 Speaker 1: to the distribution of vaccines, political unrest. UM. Disease epidemics 252 00:13:17,360 --> 00:13:21,079 Speaker 1: was another one. How disease is spread across the world, 253 00:13:21,080 --> 00:13:23,920 Speaker 1: how we should be prepared for the spread, and that 254 00:13:24,000 --> 00:13:29,080 Speaker 1: was actually modeled on how the dollar bills are circulated 255 00:13:29,120 --> 00:13:33,480 Speaker 1: in the United States, which is really interesting that they 256 00:13:33,559 --> 00:13:35,920 Speaker 1: use this as a model. And again we'll talk about 257 00:13:36,000 --> 00:13:39,320 Speaker 1: the limitations of using these types of models for other 258 00:13:39,360 --> 00:13:43,400 Speaker 1: instances such as disease epidemics. One of the real world 259 00:13:43,480 --> 00:13:46,880 Speaker 1: systems that this is based on is UH as actually 260 00:13:46,960 --> 00:13:51,840 Speaker 1: the urban traffic the idea of typically congested traffic and 261 00:13:52,040 --> 00:13:54,360 Speaker 1: urban area and how do you how do you figure 262 00:13:54,360 --> 00:13:56,400 Speaker 1: out what's going wrong, how do you combat it? How 263 00:13:56,400 --> 00:14:00,280 Speaker 1: do you deal with the little pockets of unrest and 264 00:14:00,400 --> 00:14:05,719 Speaker 1: change that eventually cascade into just complete gridlock, right, and 265 00:14:05,920 --> 00:14:09,280 Speaker 1: and Dirk helping this is really his knowledge center, you know, 266 00:14:09,320 --> 00:14:11,480 Speaker 1: this is something he's been concentrating on in his career. 267 00:14:12,000 --> 00:14:14,880 Speaker 1: Um in this case it's human and machine traffic patterns. 268 00:14:14,880 --> 00:14:17,559 Speaker 1: Helving actually consulted on a project that model the movement 269 00:14:17,600 --> 00:14:21,520 Speaker 1: of pedestrians during the Hajj in Mecca, resulting in a 270 00:14:21,560 --> 00:14:25,320 Speaker 1: billion dollars of street and bridge rejiggering to prevent deaths 271 00:14:25,320 --> 00:14:27,480 Speaker 1: from trampling. So this is yeah, this is of course 272 00:14:27,520 --> 00:14:29,720 Speaker 1: one of the pillars of Islam. Uh and the the 273 00:14:29,760 --> 00:14:32,560 Speaker 1: idea that if you were able, you take this pilgrimage 274 00:14:32,600 --> 00:14:36,440 Speaker 1: to Mecca to see the holy sites. So it creates 275 00:14:36,680 --> 00:14:39,680 Speaker 1: a lot of challenges just for the infrastructure in Saudi 276 00:14:39,720 --> 00:14:42,200 Speaker 1: Arabia to how do you deal with all these these 277 00:14:42,280 --> 00:14:45,160 Speaker 1: visitors coming to the country to to see these sites 278 00:14:45,560 --> 00:14:47,720 Speaker 1: and do so in a way that doesn't, like you said, 279 00:14:47,760 --> 00:14:51,400 Speaker 1: result in trampling, result in starvation. I'm not starvation, but 280 00:14:51,440 --> 00:14:55,320 Speaker 1: the hydration. I watched an interesting video here just back. 281 00:14:55,360 --> 00:14:57,960 Speaker 1: It was actually put up by the Saudi government that 282 00:14:58,240 --> 00:15:00,400 Speaker 1: it was kind of their their video of saying, hey, 283 00:15:00,440 --> 00:15:02,760 Speaker 1: we got it under control, don't worry when you come 284 00:15:02,840 --> 00:15:05,600 Speaker 1: on the hodge. It's like a public service and yeah, yeah, 285 00:15:05,640 --> 00:15:07,920 Speaker 1: and uh, you know, so it was definitely coming from 286 00:15:07,920 --> 00:15:09,320 Speaker 1: from the said of government, but it but it was 287 00:15:09,360 --> 00:15:11,520 Speaker 1: really interesting because they did go into all these various 288 00:15:11,560 --> 00:15:13,520 Speaker 1: things that they are doing and or have done in 289 00:15:13,560 --> 00:15:17,040 Speaker 1: the past to try and and limit the congestion or 290 00:15:17,920 --> 00:15:19,960 Speaker 1: or or things like the hydration making sure there's plenty 291 00:15:19,960 --> 00:15:22,920 Speaker 1: of water. Yeah, and this is a really cool model. 292 00:15:23,400 --> 00:15:25,640 Speaker 1: But of course there are limitations to this type of model, 293 00:15:25,800 --> 00:15:29,120 Speaker 1: and in particular, if you look at the Hajje or highways, 294 00:15:29,160 --> 00:15:31,520 Speaker 1: everyone is moving in the same direction. Right, This is 295 00:15:31,600 --> 00:15:35,760 Speaker 1: highly predictable, which underlies one of the main criticisms of 296 00:15:35,800 --> 00:15:39,880 Speaker 1: trying to predict the future based on these types of models. Um, 297 00:15:39,920 --> 00:15:43,560 Speaker 1: what we know and can predict is actually far less 298 00:15:43,600 --> 00:15:46,560 Speaker 1: than what we don't actually know, because in real life 299 00:15:46,560 --> 00:15:49,760 Speaker 1: there's not just a northbound lane and a southbound lane 300 00:15:49,760 --> 00:15:54,160 Speaker 1: and turn lane there. It's commendously more complex and every 301 00:15:54,160 --> 00:15:58,600 Speaker 1: every layer of complexity, Um, I mean it just makes 302 00:15:58,600 --> 00:16:01,880 Speaker 1: the overall model that much more are difficult to create. Right. 303 00:16:01,880 --> 00:16:04,920 Speaker 1: And there's actually a term for this. Yes, yes, they're 304 00:16:04,920 --> 00:16:09,080 Speaker 1: called black Swan events. And when we return, we shall 305 00:16:09,160 --> 00:16:17,480 Speaker 1: reveal what the black Swan is. The black Swan we're back. 306 00:16:17,880 --> 00:16:21,240 Speaker 1: The black Swan is, uh, is not a crazy ballerina 307 00:16:21,240 --> 00:16:24,560 Speaker 1: who turns into a bird. It's true, Yeah, just to 308 00:16:24,600 --> 00:16:26,880 Speaker 1: get that out there. Uh, but it is actually an 309 00:16:26,880 --> 00:16:30,800 Speaker 1: incredible theory about not only the auliers who change the world, 310 00:16:31,560 --> 00:16:35,280 Speaker 1: but the way that we try, the way the way 311 00:16:35,280 --> 00:16:39,400 Speaker 1: that we don't understand, um, the importance of those outliers 312 00:16:39,480 --> 00:16:42,640 Speaker 1: in retrospect, and the reason why we we it's called 313 00:16:42,640 --> 00:16:45,960 Speaker 1: the black Swan event for black Swan events is because 314 00:16:46,400 --> 00:16:49,880 Speaker 1: for I don't know, decades hundreds of years. Actually people 315 00:16:49,880 --> 00:16:52,680 Speaker 1: thought that there were no black swans because all that 316 00:16:52,720 --> 00:16:57,840 Speaker 1: had been documented were white swans. Right, so people thought 317 00:16:57,920 --> 00:17:00,840 Speaker 1: really like that there are no black swans, um, there 318 00:17:00,840 --> 00:17:02,720 Speaker 1: are only white swans. And in fact they were so 319 00:17:02,760 --> 00:17:06,240 Speaker 1: confident of this information that black swan became sort of 320 00:17:06,520 --> 00:17:10,399 Speaker 1: this this uh code word for for you know, something 321 00:17:10,440 --> 00:17:14,040 Speaker 1: not existing, right, just be something of medical fantasy. Really, yeah, 322 00:17:14,080 --> 00:17:16,080 Speaker 1: there's there's actually a Latin term that talks about this, 323 00:17:16,480 --> 00:17:18,320 Speaker 1: but low and the whole. Some dude in the eighteen 324 00:17:18,400 --> 00:17:22,399 Speaker 1: hundreds visits Australia documents a black swan, and all of 325 00:17:22,440 --> 00:17:26,840 Speaker 1: a sudden, this this uh certainty, this this absolute idea 326 00:17:27,160 --> 00:17:28,960 Speaker 1: that there were no black swans on the white swans 327 00:17:29,040 --> 00:17:31,760 Speaker 1: was turned on its head. Yeah. It reminds me a 328 00:17:31,760 --> 00:17:34,440 Speaker 1: lot of two events in the last several years, one 329 00:17:34,760 --> 00:17:37,879 Speaker 1: being the maybe half hour or so that it seemed 330 00:17:37,880 --> 00:17:40,639 Speaker 1: possible that we had found Bigfoot and Bigfoot's body was 331 00:17:40,680 --> 00:17:44,000 Speaker 1: in a cooler in the in the middle Georgia. Uh. 332 00:17:44,040 --> 00:17:46,760 Speaker 1: And of course that turned out to be complete bunk bunk. 333 00:17:46,880 --> 00:17:48,959 Speaker 1: But but for a for a very brief time, I 334 00:17:49,000 --> 00:17:50,679 Speaker 1: was like, oh my goodness, what if they what if 335 00:17:50,760 --> 00:17:53,159 Speaker 1: this is it? What if if the the world in 336 00:17:53,160 --> 00:17:56,240 Speaker 1: which Bigfoot is an unproven mythical creature is about to end, 337 00:17:56,560 --> 00:17:58,880 Speaker 1: and I am entering into a new world in which 338 00:17:58,880 --> 00:18:01,719 Speaker 1: Bigfoot is a reality, proven reality, what would that be like? 339 00:18:02,000 --> 00:18:04,879 Speaker 1: Another example would be in the last year. And the 340 00:18:04,960 --> 00:18:07,120 Speaker 1: jury is still out on exactly what these findings means. 341 00:18:07,359 --> 00:18:11,480 Speaker 1: But the findings out of cern regarding faster than light particles. 342 00:18:11,640 --> 00:18:13,879 Speaker 1: The idea, I mean the speed of light is is 343 00:18:14,680 --> 00:18:18,320 Speaker 1: based on our understanding of physics, uh a universal speed limit. 344 00:18:18,400 --> 00:18:21,520 Speaker 1: Nothing can go faster than that, um, because I mean 345 00:18:21,520 --> 00:18:23,280 Speaker 1: it would break the universe, to break our understanding of 346 00:18:23,320 --> 00:18:25,800 Speaker 1: the universe, it would be a black swan. And then 347 00:18:25,800 --> 00:18:27,840 Speaker 1: suddenly we have this finding saying, yeah, we we clocked 348 00:18:27,880 --> 00:18:31,040 Speaker 1: some uh some phimotomic particles going fast in the speed 349 00:18:31,040 --> 00:18:33,840 Speaker 1: of light. And everybody's like, whoa, I doubt it. Uh, 350 00:18:33,880 --> 00:18:36,480 Speaker 1: you know, let's let's do some let's study this, let's uh, 351 00:18:36,600 --> 00:18:38,919 Speaker 1: let's find out if if you're if your findings are 352 00:18:38,920 --> 00:18:42,240 Speaker 1: actually accurate here. But if they are, it changes everything. 353 00:18:42,320 --> 00:18:44,719 Speaker 1: It's it just forces us to complete to enter this 354 00:18:44,800 --> 00:18:47,920 Speaker 1: new world where the rules are are different than we 355 00:18:48,160 --> 00:18:51,040 Speaker 1: originally perceived them to be. Right, there's a book by 356 00:18:51,200 --> 00:18:54,600 Speaker 1: Nacineing Nicholas Talub And he's a distinguished professor of Risk 357 00:18:54,680 --> 00:18:57,720 Speaker 1: Engineering at the Polytechnic Institute that n y u UM. 358 00:18:57,760 --> 00:19:00,520 Speaker 1: He has this book black Swan, and he talks about 359 00:19:00,560 --> 00:19:04,840 Speaker 1: these black Swan events is having three attributes, and the 360 00:19:04,880 --> 00:19:09,879 Speaker 1: attributes are rarity, extreme impact in retrospective and what he 361 00:19:09,960 --> 00:19:12,320 Speaker 1: means by that is the first, for for for it 362 00:19:12,359 --> 00:19:14,080 Speaker 1: to be a black Swan event, it has to be 363 00:19:14,119 --> 00:19:18,560 Speaker 1: an outlier. Right, it's outside of our realm of expectations 364 00:19:19,280 --> 00:19:23,120 Speaker 1: because nothing in the past would have predicted that it existed. Right. Second, 365 00:19:23,280 --> 00:19:27,200 Speaker 1: it carries an extreme impact. The ripples of its existence 366 00:19:27,240 --> 00:19:29,600 Speaker 1: are far reaching. Right, and like the photon, I mean 367 00:19:29,600 --> 00:19:32,840 Speaker 1: the subatomic particle example here definitely lines up with those 368 00:19:32,880 --> 00:19:36,240 Speaker 1: two right if true? If true? Right, that's that's the 369 00:19:36,320 --> 00:19:39,560 Speaker 1: key there. For that third, in spite of its outlier status, 370 00:19:39,640 --> 00:19:43,399 Speaker 1: human nature makes us concoct explanations for its occurrence after 371 00:19:43,440 --> 00:19:47,199 Speaker 1: the after the fact, making it explainable to us and 372 00:19:47,359 --> 00:19:51,919 Speaker 1: seemingly predictable. Okay, So he says that there are examples 373 00:19:51,960 --> 00:19:54,639 Speaker 1: of this all around us, like the two thousand and 374 00:19:54,680 --> 00:19:59,399 Speaker 1: four tsunami, the Rise of Hitler nine eleven, UH, the 375 00:19:59,480 --> 00:20:02,720 Speaker 1: advent of the Internet. He says, there all all black 376 00:20:02,760 --> 00:20:08,240 Speaker 1: Swan events that we didn't expect were outliers, changed our culture, 377 00:20:08,320 --> 00:20:13,880 Speaker 1: um forever right, Uh, changed society, changed world events and uh. 378 00:20:14,000 --> 00:20:18,280 Speaker 1: And also the things that we we scrambled afterwards to 379 00:20:18,520 --> 00:20:21,440 Speaker 1: try to explain their existence, to to try to make 380 00:20:21,440 --> 00:20:25,400 Speaker 1: them feel not so much like these voids of knowledge 381 00:20:25,440 --> 00:20:27,760 Speaker 1: to us, because a lot of people would say, oh, well, 382 00:20:27,800 --> 00:20:30,359 Speaker 1: if we could just only have done this, we would 383 00:20:30,359 --> 00:20:34,600 Speaker 1: have you know, avoided the tsunami or voted nine eleven. 384 00:20:34,920 --> 00:20:36,840 Speaker 1: So he actually says, and this is a quote from 385 00:20:36,880 --> 00:20:39,440 Speaker 1: his book, Um, this is from the intro. He says, 386 00:20:39,560 --> 00:20:42,520 Speaker 1: what did people learn from the nine eleven episode? They 387 00:20:42,600 --> 00:20:45,200 Speaker 1: did they learn that some events, owing to their dynamics, 388 00:20:45,200 --> 00:20:49,359 Speaker 1: stand largely outside the realm of the predictable. No, did 389 00:20:49,359 --> 00:20:52,919 Speaker 1: they learn the built in defect of conventional wisdom? No? 390 00:20:53,560 --> 00:20:56,800 Speaker 1: What they what did they figure out? They learned precise 391 00:20:56,960 --> 00:21:00,280 Speaker 1: rules for avoiding Islamic proto terrorists and tall bill links. 392 00:21:00,880 --> 00:21:04,359 Speaker 1: And this, he says, is a real problem because we 393 00:21:04,560 --> 00:21:09,199 Speaker 1: learned so specifically that we try to apply this model again. 394 00:21:09,400 --> 00:21:12,399 Speaker 1: You know, we're talking about model systems over and over again. 395 00:21:13,000 --> 00:21:15,119 Speaker 1: So he's saying Okay, we learned some sort of lesson 396 00:21:15,240 --> 00:21:18,280 Speaker 1: from there, but it's so specific it deals with strong 397 00:21:18,400 --> 00:21:21,520 Speaker 1: predators and tall buildings, that it doesn't necessarily say that 398 00:21:21,840 --> 00:21:24,680 Speaker 1: we learned a lesson there that helps us to avoid 399 00:21:24,760 --> 00:21:27,919 Speaker 1: terrorism altogether. Right, I mean, and that comes down to 400 00:21:28,000 --> 00:21:29,919 Speaker 1: just to wear of our minds work. And we were 401 00:21:29,920 --> 00:21:33,440 Speaker 1: talking about this before the podcast. Inevitably, when we're talking 402 00:21:33,440 --> 00:21:35,000 Speaker 1: about the way we think about the world, we end 403 00:21:35,040 --> 00:21:38,840 Speaker 1: up falling back on on examples and involve our ancestors, 404 00:21:38,840 --> 00:21:41,639 Speaker 1: and like savor tooth tigers, like the sabre tooth tiger 405 00:21:41,960 --> 00:21:44,040 Speaker 1: attacks you, you learn a lesson, but it's gonna be 406 00:21:44,040 --> 00:21:47,879 Speaker 1: a very specific lesson in these cases, how to avoid 407 00:21:47,960 --> 00:21:50,000 Speaker 1: being eaten? How did you avoid being eaten on this 408 00:21:50,040 --> 00:21:54,159 Speaker 1: particular this particular encounter, right we we He would argue 409 00:21:54,320 --> 00:21:57,960 Speaker 1: that um Talent would argue that our brains really aren't 410 00:21:57,960 --> 00:22:01,360 Speaker 1: built for thinking per se, because if our ancestors had 411 00:22:01,359 --> 00:22:04,200 Speaker 1: stopped to think, you know, that probably would have been 412 00:22:04,640 --> 00:22:08,200 Speaker 1: torn apart by the sabre tooth tiger. Right, Um that 413 00:22:08,320 --> 00:22:12,200 Speaker 1: we are so much more invested in pattern recognition and 414 00:22:12,440 --> 00:22:17,120 Speaker 1: UH predictability models than unpredictability. So he's saying that now 415 00:22:17,160 --> 00:22:19,720 Speaker 1: we live in this entirely complex world, and we can't 416 00:22:19,720 --> 00:22:23,240 Speaker 1: really conceive of all the black swans around us. You know, 417 00:22:23,280 --> 00:22:25,280 Speaker 1: we think about eighteen hundreds. Probably it is a lot 418 00:22:25,320 --> 00:22:28,960 Speaker 1: more simple, Right, you were going to turn some butter milk, 419 00:22:29,040 --> 00:22:33,320 Speaker 1: some cows, um things were you have far less choices. Um, 420 00:22:33,520 --> 00:22:36,600 Speaker 1: it was a far lex complex world. So he's saying, really, 421 00:22:36,640 --> 00:22:38,400 Speaker 1: our brains haven't caught up to it, and that's why 422 00:22:38,440 --> 00:22:40,600 Speaker 1: we are actually blind to all of the black swans 423 00:22:40,640 --> 00:22:43,639 Speaker 1: around us. Well, you know, just think back, you know, 424 00:22:43,760 --> 00:22:46,480 Speaker 1: I'm speaking to everyone, to you the listener, Just think 425 00:22:46,520 --> 00:22:49,680 Speaker 1: back on your own life and and just how how 426 00:22:49,760 --> 00:22:53,000 Speaker 1: little of it you could have possibly predicted you could 427 00:22:53,119 --> 00:22:54,679 Speaker 1: you know, all the all the little things that have 428 00:22:55,040 --> 00:22:57,240 Speaker 1: that have led you to this place in your life, 429 00:22:57,760 --> 00:23:00,959 Speaker 1: that the decisions, the the the bits of of just 430 00:23:01,240 --> 00:23:03,680 Speaker 1: fate and blind luck and uh and here you are. 431 00:23:04,440 --> 00:23:06,159 Speaker 1: But you look back on it and you see it, 432 00:23:06,440 --> 00:23:10,720 Speaker 1: uh the way our perspective on it works. Um, you 433 00:23:10,720 --> 00:23:14,360 Speaker 1: don't see those black swans even in our own personal history. Right. 434 00:23:14,920 --> 00:23:17,240 Speaker 1: But but if you really look closely and you see 435 00:23:17,240 --> 00:23:19,680 Speaker 1: that there probably are a series of random events. Right. 436 00:23:19,720 --> 00:23:22,000 Speaker 1: We we plan to the best of our ability, and 437 00:23:22,040 --> 00:23:25,920 Speaker 1: things happen, and life takes us down different roads, right, 438 00:23:26,000 --> 00:23:28,720 Speaker 1: and you know, in retrospect we can probably explain some 439 00:23:28,800 --> 00:23:31,520 Speaker 1: of those away and apply some sort of pattern to them. 440 00:23:31,560 --> 00:23:36,000 Speaker 1: But Talib would say that it's complete randomness. Um. And 441 00:23:36,160 --> 00:23:38,960 Speaker 1: he says, actually, the world is dominated by black swans 442 00:23:39,080 --> 00:23:41,480 Speaker 1: and that they are the norm. And so this is 443 00:23:41,480 --> 00:23:43,679 Speaker 1: interesting when he says this, because if you if you 444 00:23:43,680 --> 00:23:46,840 Speaker 1: take this up face value, it means that this, uh, 445 00:23:46,960 --> 00:23:50,280 Speaker 1: this Earth simulator is probably not going to work in 446 00:23:50,320 --> 00:23:54,000 Speaker 1: the way that that the European Commission actually wants it to. Yeah, 447 00:23:54,040 --> 00:23:56,320 Speaker 1: it's it wouldn't be a situation where, oh, just occasionally 448 00:23:56,359 --> 00:23:58,680 Speaker 1: you have a black swan that throws the monkey wrench 449 00:23:58,760 --> 00:24:01,040 Speaker 1: into the into our our understanding the world. It's not 450 00:24:01,040 --> 00:24:04,159 Speaker 1: like occasionally, oh occasionally, there's been a lot. Occasionally there'sn't 451 00:24:04,160 --> 00:24:06,680 Speaker 1: there's a Hitler or Einstein that kind of changes the 452 00:24:06,720 --> 00:24:08,920 Speaker 1: way it works. You know, he's saying they're everywhere. That 453 00:24:08,920 --> 00:24:12,560 Speaker 1: that that that the world continues to change at a 454 00:24:12,560 --> 00:24:15,040 Speaker 1: at a at a steady rate based on the actions 455 00:24:15,040 --> 00:24:18,639 Speaker 1: of these various black swans, both individuals and just random 456 00:24:18,680 --> 00:24:22,119 Speaker 1: events in the world around us and these very spheres 457 00:24:22,160 --> 00:24:24,880 Speaker 1: of activity. Right, So it's great to have these predictive models, 458 00:24:24,920 --> 00:24:28,480 Speaker 1: but if you can't build in some sort of system 459 00:24:28,600 --> 00:24:32,199 Speaker 1: for ferreting out black swans, or you don't have a 460 00:24:32,240 --> 00:24:34,360 Speaker 1: system in there, and that says, okay, well, we think 461 00:24:34,359 --> 00:24:36,479 Speaker 1: this is going to happen based on what's you know, 462 00:24:36,520 --> 00:24:39,480 Speaker 1: historically the data stream that's coming in. But if you 463 00:24:39,520 --> 00:24:41,840 Speaker 1: can't run that up against something that says, you know, 464 00:24:42,520 --> 00:24:45,480 Speaker 1: forget it, this might actually not happen, or you have 465 00:24:45,680 --> 00:24:50,199 Speaker 1: five other variables, uh, then sort of it sort of 466 00:24:50,400 --> 00:24:53,800 Speaker 1: discounts the system as a whole. And even if you 467 00:24:53,920 --> 00:24:56,720 Speaker 1: did have the black swan effect or events built in, 468 00:24:57,000 --> 00:24:59,679 Speaker 1: you're still not going to get that one answer that 469 00:24:59,760 --> 00:25:02,560 Speaker 1: they so desperately want. This says this is the right answer, 470 00:25:02,880 --> 00:25:05,760 Speaker 1: because you're gonna get four variables, five variables, ten variables 471 00:25:05,800 --> 00:25:08,200 Speaker 1: spit out at you and you're back at square one. 472 00:25:08,600 --> 00:25:10,720 Speaker 1: And and then this is something that really blew my mind. 473 00:25:10,880 --> 00:25:13,840 Speaker 1: Um when you when you think, imagine you did build 474 00:25:13,840 --> 00:25:17,879 Speaker 1: this just enormously complex, uh model of the world, this 475 00:25:18,200 --> 00:25:21,760 Speaker 1: living Earth simulator. What gets me is the feedback loop 476 00:25:21,840 --> 00:25:26,439 Speaker 1: loops you eventually fall into because because because you're building, 477 00:25:27,119 --> 00:25:30,080 Speaker 1: you're building a model of the world that has access 478 00:25:30,160 --> 00:25:32,520 Speaker 1: to a model of the world, that has access to 479 00:25:32,560 --> 00:25:34,199 Speaker 1: a model of the world, that has access to a 480 00:25:34,240 --> 00:25:36,840 Speaker 1: model of the world, and it just like it just 481 00:25:36,960 --> 00:25:38,720 Speaker 1: it blows my mind to think of that. How would 482 00:25:38,720 --> 00:25:41,320 Speaker 1: that pan out? It just would the complexity would just 483 00:25:41,760 --> 00:25:45,960 Speaker 1: would spiral out forever. Well, and there's another point here 484 00:25:46,080 --> 00:25:50,440 Speaker 1: that even if you did have a couple of answers 485 00:25:50,480 --> 00:25:53,320 Speaker 1: spit out in a in a scenario, right, that seemed like, Okay, 486 00:25:53,400 --> 00:25:55,679 Speaker 1: this is the best course of action. Because you have 487 00:25:55,880 --> 00:25:58,720 Speaker 1: such a complex system and you can't even understand how 488 00:25:58,760 --> 00:26:01,880 Speaker 1: that data came to that conclusion, then you're probably less 489 00:26:01,920 --> 00:26:04,800 Speaker 1: likely to trust it in the first place. Yeah. Yeah, 490 00:26:04,800 --> 00:26:07,720 Speaker 1: And in fact, it lines up interestingly with climate change 491 00:26:07,920 --> 00:26:11,080 Speaker 1: Man Maine climate change and our our attempts to understand it. Uh, 492 00:26:11,119 --> 00:26:13,960 Speaker 1: the scientific findings that have come out arguing, uh, the 493 00:26:14,000 --> 00:26:16,439 Speaker 1: point that hey, humans are are altering global climate and 494 00:26:16,440 --> 00:26:18,600 Speaker 1: here are some things we should do to stop it. 495 00:26:18,680 --> 00:26:21,359 Speaker 1: And and just and just how how little of that 496 00:26:21,400 --> 00:26:24,879 Speaker 1: has been has resonated with the decision makers and with 497 00:26:24,960 --> 00:26:27,919 Speaker 1: the general public in some cases. Right, Right, So they 498 00:26:27,920 --> 00:26:30,240 Speaker 1: have a bunch of information there and they still can't act. Yeah, 499 00:26:30,280 --> 00:26:33,439 Speaker 1: we ask our our our biggest reasoning machine that we 500 00:26:33,480 --> 00:26:35,800 Speaker 1: have available to a science what we should do about 501 00:26:35,800 --> 00:26:38,200 Speaker 1: a given situation, we get an answer for it, and 502 00:26:38,240 --> 00:26:40,040 Speaker 1: not not everyone's gonna listen. So is there gonna be 503 00:26:40,080 --> 00:26:43,879 Speaker 1: any better if we have a a complex simulation of 504 00:26:43,880 --> 00:26:46,600 Speaker 1: of existence that we can turn to our people are 505 00:26:46,640 --> 00:26:49,000 Speaker 1: gonna trust that? And indeed, are people are going to 506 00:26:49,080 --> 00:26:51,879 Speaker 1: trust this supercomputer that has that is using reasoning that 507 00:26:51,920 --> 00:26:54,639 Speaker 1: we can't even fathom, uh, to tell us of what 508 00:26:54,760 --> 00:26:56,760 Speaker 1: we should do? And what if what indeed if if 509 00:26:56,760 --> 00:27:00,040 Speaker 1: it's a suggestion is something that seems nonsensical? Well, and 510 00:27:00,080 --> 00:27:02,919 Speaker 1: there's this whole idea too, that all of this is 511 00:27:02,920 --> 00:27:06,199 Speaker 1: predicated on us even understanding our existence in the first place, 512 00:27:06,359 --> 00:27:09,440 Speaker 1: and how our existence is the fact that you and 513 00:27:09,520 --> 00:27:11,359 Speaker 1: I are sitting here and everybody you guys are listening. 514 00:27:11,440 --> 00:27:14,440 Speaker 1: This is a black Swan event in and of itself. 515 00:27:14,800 --> 00:27:17,439 Speaker 1: And what I mean about that is the odds of 516 00:27:17,480 --> 00:27:22,920 Speaker 1: our existence. Um. There's a Harvard professor Dr Ali Benazzar 517 00:27:23,000 --> 00:27:26,960 Speaker 1: who says, so, what's the probability of you existing? This 518 00:27:27,000 --> 00:27:29,400 Speaker 1: is a quote. It says, it's the probability of two 519 00:27:29,440 --> 00:27:32,560 Speaker 1: million people getting together about the population of San Diego, 520 00:27:32,920 --> 00:27:36,040 Speaker 1: each to play a game of dice with trillion sided dice. 521 00:27:36,359 --> 00:27:38,959 Speaker 1: They each roll the dice and they all come up 522 00:27:38,960 --> 00:27:45,040 Speaker 1: with the exact same number. Say, five hundred and fifty trillion, 523 00:27:45,080 --> 00:27:48,199 Speaker 1: three hundred forty three million, two hundred seventy nine thousand 524 00:27:48,240 --> 00:27:51,560 Speaker 1: and one. That's the number. So, you know, we've talked 525 00:27:51,560 --> 00:27:54,320 Speaker 1: about this before to just in terms of the rare 526 00:27:54,400 --> 00:27:58,800 Speaker 1: Earth theory, about how the fact that life on Earth 527 00:27:59,200 --> 00:28:03,320 Speaker 1: came about in and how there's circumstances were just right. 528 00:28:03,640 --> 00:28:06,560 Speaker 1: But this is a rarity as far as we know, right, Yeah, 529 00:28:06,600 --> 00:28:08,760 Speaker 1: so it's you know, we have a whole podcast devoted 530 00:28:08,760 --> 00:28:12,080 Speaker 1: to this. But but the arguments often come down to, um, 531 00:28:12,119 --> 00:28:14,520 Speaker 1: it's such a rare event that this Earth exists, Does 532 00:28:14,560 --> 00:28:18,080 Speaker 1: that mean that we're special or you know? But but 533 00:28:18,160 --> 00:28:20,920 Speaker 1: we can't think scientifically, we can't view ourselves as special. 534 00:28:20,960 --> 00:28:22,640 Speaker 1: So how do we how do we wrap our heads 535 00:28:22,640 --> 00:28:24,760 Speaker 1: around that one? So yeah, yeah, there you go, There 536 00:28:24,760 --> 00:28:27,680 Speaker 1: you go. And then if this if this simulator, if 537 00:28:27,720 --> 00:28:32,360 Speaker 1: this future set, if it actually it works right, if 538 00:28:32,359 --> 00:28:35,480 Speaker 1: it comes to fruition and it's useful in naturally predicting 539 00:28:36,240 --> 00:28:40,080 Speaker 1: uh black swans really, because that's what the end result 540 00:28:40,080 --> 00:28:43,720 Speaker 1: of that should be. Uh, then What does that mean 541 00:28:43,800 --> 00:28:47,160 Speaker 1: about science? What does that mean about thought experiments? You know, 542 00:28:47,200 --> 00:28:50,520 Speaker 1: if everything is answerable and predictable, is at the end 543 00:28:50,520 --> 00:28:52,960 Speaker 1: of science? I don't know. And and then likewise it 544 00:28:53,000 --> 00:28:56,200 Speaker 1: also brings to mind any kind of corporate situation where 545 00:28:56,200 --> 00:28:58,239 Speaker 1: you have a problem. What's the first thing people do? 546 00:28:59,240 --> 00:29:01,960 Speaker 1: Meeting about it? Committee about it. Let's form a task 547 00:29:02,000 --> 00:29:05,320 Speaker 1: force to to talk about this, uh, this situation and 548 00:29:05,360 --> 00:29:08,240 Speaker 1: come up with some recommendations to what extent would this 549 00:29:08,280 --> 00:29:11,560 Speaker 1: simulation this uh, this Living Earth simulator be a version 550 00:29:11,560 --> 00:29:13,840 Speaker 1: of that where we're like, oh, we have a problem. Um, 551 00:29:13,920 --> 00:29:16,680 Speaker 1: you know, there's a there's a there suffering in the 552 00:29:16,680 --> 00:29:19,600 Speaker 1: world somewhere. What should we do about it? Throw into 553 00:29:19,640 --> 00:29:21,520 Speaker 1: the simulator and then we get the results, and then 554 00:29:21,960 --> 00:29:24,280 Speaker 1: the simulator gives us a list of recommendations and we 555 00:29:24,360 --> 00:29:26,440 Speaker 1: end up following none of them. Right, we don't have 556 00:29:26,480 --> 00:29:29,800 Speaker 1: the budget for that, right because we've we've talked about it. 557 00:29:29,840 --> 00:29:32,280 Speaker 1: We we put the the info into the machine. It 558 00:29:32,560 --> 00:29:36,200 Speaker 1: uh it does spit us out some numbers, so we're good. Right, Well, 559 00:29:36,240 --> 00:29:38,840 Speaker 1: at least we can finally uh let the cat out 560 00:29:38,880 --> 00:29:43,440 Speaker 1: of Shreddinger's box, right, yeah, all right, Well, let's have 561 00:29:43,520 --> 00:29:49,160 Speaker 1: the Robot bring us something to read reading Donald, Thank you. 562 00:29:49,280 --> 00:29:53,080 Speaker 1: There we go. All right, Well, we heard from a 563 00:29:53,120 --> 00:29:56,800 Speaker 1: listener by the name of Dustin. Dustin Wright sentences Hi, Robert, 564 00:29:56,880 --> 00:29:59,479 Speaker 1: Julie and the rest of the STB y M staff. 565 00:30:00,000 --> 00:30:02,800 Speaker 1: I wanted to say thanks for your episode on mesophonium. 566 00:30:02,800 --> 00:30:05,840 Speaker 1: My whole life has been driven nuts by chewing, swallowing 567 00:30:05,880 --> 00:30:08,880 Speaker 1: and smacking. I always thought that my reaction wasn't normal, 568 00:30:08,920 --> 00:30:10,840 Speaker 1: but now I realized that I'm not alone out there. 569 00:30:11,200 --> 00:30:13,479 Speaker 1: It was really fascinating to hear about others triggers. At 570 00:30:13,560 --> 00:30:16,280 Speaker 1: least I know when I expect my triggers and can 571 00:30:16,320 --> 00:30:19,400 Speaker 1: take steps to cope with the overwhelming anxiety. If a 572 00:30:19,400 --> 00:30:22,280 Speaker 1: person's trigger was the crunching of leaves, then that must 573 00:30:22,320 --> 00:30:25,560 Speaker 1: be hell. I really hope that the more research that 574 00:30:25,600 --> 00:30:27,400 Speaker 1: develops on this topic. Thanks for all of your hard 575 00:30:27,400 --> 00:30:30,160 Speaker 1: work and the wonderful show. Yeah, that was really great. 576 00:30:30,360 --> 00:30:33,600 Speaker 1: We've had so many people right in about mesophonia. It's 577 00:30:33,600 --> 00:30:38,400 Speaker 1: really interesting. It seems to be just anecdotally, uh more 578 00:30:39,120 --> 00:30:41,520 Speaker 1: widespread than we saw. Yeah. I mean, I find myself 579 00:30:41,520 --> 00:30:43,320 Speaker 1: thinking about it all the time too. Whenever I let 580 00:30:43,800 --> 00:30:47,040 Speaker 1: petty things sounds annoy me, and I sort of have 581 00:30:47,040 --> 00:30:49,080 Speaker 1: to step back and I'm like, maybe have a touch 582 00:30:49,120 --> 00:30:51,160 Speaker 1: of messophonia. And then I'm like, well be conscious of it, 583 00:30:51,400 --> 00:30:54,160 Speaker 1: don't let it irk you in. And luckily I don't 584 00:30:54,200 --> 00:30:59,400 Speaker 1: have severe enough reactions that that doesn't work. Yeah. Yeah, 585 00:30:59,440 --> 00:31:01,040 Speaker 1: you know what, Chris, this was hard for me because 586 00:31:01,040 --> 00:31:02,320 Speaker 1: I've got a three year old and there was a 587 00:31:02,440 --> 00:31:05,720 Speaker 1: lot of start from around and I was constantly stop. 588 00:31:06,120 --> 00:31:08,720 Speaker 1: All right, here's another one just from listener Neil. Neil 589 00:31:08,760 --> 00:31:11,440 Speaker 1: writes and it says, Hi, Robert and Julie's super podcast again, 590 00:31:11,720 --> 00:31:14,200 Speaker 1: didn't know about Terry Pratchett's sword made out of meteor? 591 00:31:14,240 --> 00:31:16,600 Speaker 1: Do they call him sky swords? He's a responding tore 592 00:31:16,640 --> 00:31:19,680 Speaker 1: away of the Sword podcast. Just a note on kindo. 593 00:31:20,120 --> 00:31:23,000 Speaker 1: The foot stomp happens at the time of the strike 594 00:31:23,120 --> 00:31:26,560 Speaker 1: or cut, not before, or at least not as a warning. 595 00:31:26,800 --> 00:31:31,120 Speaker 1: The stomp uh fumi komi is part of the cut. Typically, 596 00:31:31,200 --> 00:31:33,240 Speaker 1: for a cut to be counted, you need to stomp, 597 00:31:33,560 --> 00:31:35,600 Speaker 1: call out what you were cutting, and of course land 598 00:31:35,600 --> 00:31:37,480 Speaker 1: the cut in the place that you have called out. 599 00:31:37,760 --> 00:31:42,240 Speaker 1: This is all to demonstrate what kN Doka call key 600 00:31:42,360 --> 00:31:46,360 Speaker 1: kin ta ichi, the oneness of the spirit key with 601 00:31:46,400 --> 00:31:49,280 Speaker 1: the sword. Can and the body tie. You can, of 602 00:31:49,280 --> 00:31:52,000 Speaker 1: course foot stomp before a cut, but typically this is 603 00:31:52,040 --> 00:31:55,520 Speaker 1: a thing to get a reaction, either a flinch or 604 00:31:55,600 --> 00:31:59,400 Speaker 1: start from the other kim dooka, which might reveal a 605 00:31:59,440 --> 00:32:02,640 Speaker 1: hesitate to exploit immediately or just put them on edge, 606 00:32:02,880 --> 00:32:06,240 Speaker 1: or an attack which you might counterattack or otherwise blunt. 607 00:32:06,640 --> 00:32:08,280 Speaker 1: I am not sure I would call it a warning, 608 00:32:08,320 --> 00:32:11,480 Speaker 1: since the intention is usually not that charitable. That said, 609 00:32:11,560 --> 00:32:13,840 Speaker 1: kendo is a great martial art, no disrespect to other 610 00:32:13,920 --> 00:32:17,400 Speaker 1: sword arts, and well worth the effort. Fun to boot 611 00:32:17,600 --> 00:32:20,480 Speaker 1: and the gear is cool too. Anyway, thanks for the 612 00:32:20,480 --> 00:32:23,320 Speaker 1: super podcast series, Cheers and Neil Cool. Yeah, I was 613 00:32:23,400 --> 00:32:25,680 Speaker 1: so glad to hear the information. I saw some of 614 00:32:25,680 --> 00:32:29,080 Speaker 1: the kendo in the documentary Reclaiming the Blade, and it 615 00:32:29,200 --> 00:32:31,880 Speaker 1: seemed as though they were stomping as a warning. But 616 00:32:31,920 --> 00:32:34,000 Speaker 1: it's great to share that information. It makes it even 617 00:32:34,120 --> 00:32:38,080 Speaker 1: that much more intriguing of muscial art. Yeah, it's kind 618 00:32:38,120 --> 00:32:39,480 Speaker 1: of it sounds a lot like it's kind of like 619 00:32:39,560 --> 00:32:41,960 Speaker 1: a like a lunch like you see. I mean, I 620 00:32:42,240 --> 00:32:43,720 Speaker 1: not that I've ever been in a fight, but you know, 621 00:32:43,720 --> 00:32:45,680 Speaker 1: you see people in like in a fighting situation, and 622 00:32:45,680 --> 00:32:50,440 Speaker 1: one will sort of like Yeah, but I like this idea, 623 00:32:50,520 --> 00:32:53,680 Speaker 1: this one oneness of the spirit and the sword and um, 624 00:32:53,720 --> 00:32:56,320 Speaker 1: and that's why they did a little foot stomping. I 625 00:32:56,360 --> 00:32:58,920 Speaker 1: also I was thinking that the little foot stoping because 626 00:32:58,920 --> 00:33:01,560 Speaker 1: they have tiny little feet with Kendem Marshall artists have 627 00:33:01,680 --> 00:33:05,160 Speaker 1: tiny little fie. Okay, I'm kidding, um, but but I 628 00:33:05,200 --> 00:33:07,520 Speaker 1: mean it made me think of playing pool, like if 629 00:33:07,560 --> 00:33:09,840 Speaker 1: you were to call out a pocket, but you would 630 00:33:09,880 --> 00:33:11,440 Speaker 1: do it at the same time that you were to 631 00:33:11,480 --> 00:33:13,719 Speaker 1: hit the ball, or kind of like Babe Ruth like 632 00:33:13,800 --> 00:33:16,960 Speaker 1: pointing the field is going to hit the ball. Yeah, 633 00:33:17,240 --> 00:33:21,440 Speaker 1: thank you, sucker. Try just try to get it. Well. Hey, 634 00:33:21,640 --> 00:33:23,640 Speaker 1: we would love to hear what anyone has to say 635 00:33:23,680 --> 00:33:28,240 Speaker 1: about the the idea of a living Earth simulator. Um, 636 00:33:28,280 --> 00:33:30,080 Speaker 1: what do you think about it? Depending do you do? 637 00:33:30,120 --> 00:33:32,480 Speaker 1: You do you think it's a great idea, do you 638 00:33:32,480 --> 00:33:35,320 Speaker 1: think it's at all feasible? And how do you imagine 639 00:33:35,360 --> 00:33:38,360 Speaker 1: a future in which we have access to one? Would 640 00:33:38,360 --> 00:33:40,480 Speaker 1: you want an app on your phone? Yeah, because that 641 00:33:40,520 --> 00:33:42,800 Speaker 1: was one of the things that they talk about, is 642 00:33:42,840 --> 00:33:44,760 Speaker 1: the idea that if we had this simulator in place, 643 00:33:44,760 --> 00:33:46,720 Speaker 1: you would basically be able to get apps where you 644 00:33:46,720 --> 00:33:48,680 Speaker 1: could see like and I imagine someone would be kind 645 00:33:48,680 --> 00:33:50,200 Speaker 1: of fun like. Someone will probably be like, what would 646 00:33:50,200 --> 00:33:53,480 Speaker 1: a zombie apocalypse actually look like? Let's load that in. 647 00:33:54,200 --> 00:33:56,160 Speaker 1: What would it be like? If you know, just any 648 00:33:56,200 --> 00:33:58,000 Speaker 1: kind of crazy scenario you could think of, you could 649 00:33:58,080 --> 00:34:01,160 Speaker 1: conceivably have an app for it to to to load 650 00:34:01,200 --> 00:34:04,840 Speaker 1: in to check. And you can just imagine everyone from 651 00:34:04,920 --> 00:34:06,959 Speaker 1: any like every business in the world would have some 652 00:34:07,000 --> 00:34:09,680 Speaker 1: sort of access to this model so they could test 653 00:34:09,680 --> 00:34:15,200 Speaker 1: their various uh, promotional materials, etcetera. But let us know 654 00:34:15,239 --> 00:34:17,520 Speaker 1: what you think. You can find us on Facebook and Twitter. 655 00:34:18,360 --> 00:34:20,120 Speaker 1: We are blow the Mind on Twitter, and you can 656 00:34:20,120 --> 00:34:22,160 Speaker 1: find us on Facebook just by searching for stuff to 657 00:34:22,160 --> 00:34:24,560 Speaker 1: blow the mind. And you can drop us a line 658 00:34:24,640 --> 00:34:32,160 Speaker 1: at blow the Mind at how stuff works dot com. 659 00:34:32,239 --> 00:34:34,800 Speaker 1: Be sure to check out our new video podcast, Stuff 660 00:34:34,840 --> 00:34:37,480 Speaker 1: from the Future. Join how Stuff Work staff as we 661 00:34:37,520 --> 00:34:41,080 Speaker 1: explore the most promising and perplexing possibilities of tomorrow.