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 --> 00:00:14,080 Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind. 3 00:00:14,080 --> 00:00:16,840 Speaker 1: My name is Robert Lamb and I'm Julie Douglas. Do 4 00:00:16,880 --> 00:00:19,639 Speaker 1: we have a hashtag for this podcast? We do? What 5 00:00:19,760 --> 00:00:25,800 Speaker 1: is it? It's called hashtag um blow the mind protest? 6 00:00:26,800 --> 00:00:31,480 Speaker 1: Whoa protesting in a digital age? That's that's really I 7 00:00:31,520 --> 00:00:33,199 Speaker 1: think that's gonna I don't know if we're going to 8 00:00:33,240 --> 00:00:35,760 Speaker 1: have enough room for the rest of the tweet, but 9 00:00:35,760 --> 00:00:38,840 Speaker 1: but it's it's it's pretty good. We'll try that out. Yeah, 10 00:00:39,200 --> 00:00:41,920 Speaker 1: we'll work on that. No, but this we we're talking 11 00:00:41,960 --> 00:00:44,200 Speaker 1: about today, I'm pretty sure we get to own that hashtag. 12 00:00:44,240 --> 00:00:46,240 Speaker 1: We don't know. I don't think anybody. It's not gonna 13 00:00:46,240 --> 00:00:50,200 Speaker 1: be like when I tried to retake blow blew my mind. Yeah, 14 00:00:50,280 --> 00:00:53,680 Speaker 1: from from from the rest of the twitter verse. But 15 00:00:53,680 --> 00:00:58,800 Speaker 1: but yeah, we're talking in this podcast specifically about Occupy 16 00:00:58,840 --> 00:01:02,480 Speaker 1: Wall Street and some of these other um social media 17 00:01:02,720 --> 00:01:07,280 Speaker 1: enhanced protest movements that have taken hold in late two 18 00:01:07,280 --> 00:01:10,480 Speaker 1: thousand ten and then throughout two thousand and eleven, ranging 19 00:01:10,520 --> 00:01:13,760 Speaker 1: from um, you know, from the Arab Spring to uh 20 00:01:13,880 --> 00:01:17,280 Speaker 1: to London to back in the US with the occupied stuff, 21 00:01:17,480 --> 00:01:19,640 Speaker 1: and of course the occupied stuff is has led over 22 00:01:19,640 --> 00:01:21,560 Speaker 1: into other countries as well. But we're talking about what 23 00:01:21,920 --> 00:01:24,480 Speaker 1: is a modern protest? What is it really is the 24 00:01:24,480 --> 00:01:28,280 Speaker 1: modern protests shaping up? I mean, uh, protests have always 25 00:01:29,240 --> 00:01:33,119 Speaker 1: utilized advanced technology to whatever degree it's available. I mean, 26 00:01:33,160 --> 00:01:36,720 Speaker 1: it's just the way we work, um. And so it's 27 00:01:36,720 --> 00:01:39,200 Speaker 1: no surprise that people are going to use modern technology, 28 00:01:39,240 --> 00:01:43,440 Speaker 1: modern social media to to to fuel the movement. Um. 29 00:01:43,480 --> 00:01:45,520 Speaker 1: But let's back up before we we get into two, 30 00:01:45,520 --> 00:01:49,840 Speaker 1: into the technology things. Just talking about numbers because yeah, 31 00:01:49,880 --> 00:01:52,280 Speaker 1: because when we're talking about hashtabs being heard around the 32 00:01:52,320 --> 00:01:55,640 Speaker 1: world to a certain extent, we're talking about the number 33 00:01:55,680 --> 00:01:59,440 Speaker 1: of people involved in that protest, because I mean, the 34 00:01:59,440 --> 00:02:03,440 Speaker 1: the people of serving a protest are arguably as important 35 00:02:04,040 --> 00:02:06,960 Speaker 1: as the individuals that are partaking in it, because it 36 00:02:06,960 --> 00:02:10,040 Speaker 1: has to be seen to really take that effect. That's 37 00:02:10,080 --> 00:02:12,480 Speaker 1: the whole point is to convey the importance of a 38 00:02:12,520 --> 00:02:14,640 Speaker 1: topic and to put a little context behind that. Two. 39 00:02:14,680 --> 00:02:18,239 Speaker 1: And we're talking about the hashtag occupy Wall Street, which 40 00:02:18,280 --> 00:02:21,040 Speaker 1: showed up on July. Right, It is just this sort 41 00:02:21,080 --> 00:02:23,680 Speaker 1: of idea linking to a blog about hey, we should 42 00:02:23,680 --> 00:02:27,240 Speaker 1: occupy Wall Street there. Obviously there was uh no formal 43 00:02:27,280 --> 00:02:29,679 Speaker 1: movement at that time or even intention. So the earth 44 00:02:29,720 --> 00:02:32,840 Speaker 1: we live on is currently home to what seven billion people? Now, 45 00:02:33,480 --> 00:02:36,720 Speaker 1: we just had the seven billion birth yea, like the 46 00:02:36,800 --> 00:02:38,920 Speaker 1: last couple. Can you imagine being like, hey, I'm baby 47 00:02:39,040 --> 00:02:47,400 Speaker 1: seven billion? Well my parents? Um, these billions and billions 48 00:02:47,400 --> 00:02:50,919 Speaker 1: of people, uh, roughly spread across two hundred and forty countries, 49 00:02:51,360 --> 00:02:56,240 Speaker 1: close to seven thousand languages around like documented documented languages, Yes, 50 00:02:56,280 --> 00:03:00,200 Speaker 1: somewhere in the neighborhood of sixty and twelve uh, and 51 00:03:00,360 --> 00:03:04,520 Speaker 1: more branches of religion, personal belief, ideology than you can 52 00:03:04,560 --> 00:03:07,400 Speaker 1: shake a stick at. Yeah, twenty major. But you're right, 53 00:03:07,440 --> 00:03:09,959 Speaker 1: there's there's uh quite a bit more than that. Yeah, 54 00:03:09,960 --> 00:03:12,120 Speaker 1: And we continue to grow, we continue to in some 55 00:03:12,160 --> 00:03:17,399 Speaker 1: cases of splinter uh and and reorganize amid those groups. Um, 56 00:03:17,480 --> 00:03:19,960 Speaker 1: So how are you going to get these people together? 57 00:03:20,160 --> 00:03:23,519 Speaker 1: Because I was I was talking with my life the 58 00:03:23,560 --> 00:03:26,000 Speaker 1: other day about a recent episode of This American Life 59 00:03:26,000 --> 00:03:28,560 Speaker 1: where they're talking about junior high and how junior high 60 00:03:28,680 --> 00:03:30,600 Speaker 1: is horrible, and how one of the things going on 61 00:03:30,639 --> 00:03:32,359 Speaker 1: in junior high is that when you're young, you get 62 00:03:32,360 --> 00:03:35,160 Speaker 1: along with everybody, but then as you get older. Uh, 63 00:03:35,600 --> 00:03:39,240 Speaker 1: just within school you begin you become more judgmental, you 64 00:03:39,280 --> 00:03:42,760 Speaker 1: become more perceptive to Uh, you know, who you want 65 00:03:42,800 --> 00:03:46,000 Speaker 1: to be associated with and who you want to to 66 00:03:46,080 --> 00:03:49,240 Speaker 1: identify with. You created this blueprint of reality. And you're right, 67 00:03:49,400 --> 00:03:51,640 Speaker 1: and so by junior high it's already pretty complicated. So 68 00:03:51,640 --> 00:03:53,880 Speaker 1: how do we ever agree on anything? How do we ever, 69 00:03:53,960 --> 00:03:55,920 Speaker 1: how do we get out of high school mentality? Right? 70 00:03:56,240 --> 00:03:59,720 Speaker 1: And Uh, Historically, wars are generally a pretty good way 71 00:03:59,760 --> 00:04:03,760 Speaker 1: to do it. Um. I mean, wars have long brought 72 00:04:03,840 --> 00:04:08,880 Speaker 1: together millions of individuals and pitched battles. It's thankfully, hopefully 73 00:04:08,920 --> 00:04:11,000 Speaker 1: it seems to be going out of style in recent 74 00:04:11,080 --> 00:04:14,960 Speaker 1: years and doing part well again because technology. Technology changed 75 00:04:15,000 --> 00:04:17,479 Speaker 1: the way that we wage war. Uh. And then maybe 76 00:04:17,480 --> 00:04:20,000 Speaker 1: there's something going on in the in our collective psyches 77 00:04:20,040 --> 00:04:23,320 Speaker 1: as well. But the better angels of ourselves, we can hope. 78 00:04:23,360 --> 00:04:25,400 Speaker 1: I've seen some interesting arguments of that that we you know, 79 00:04:25,440 --> 00:04:27,440 Speaker 1: we live in a time where there's there's less war 80 00:04:27,520 --> 00:04:30,920 Speaker 1: now than than ever before. So let's hope that keeps 81 00:04:31,000 --> 00:04:36,840 Speaker 1: up until the big machine human war. But singularity, Yeah, 82 00:04:36,880 --> 00:04:38,760 Speaker 1: but that's gonna bring us all together even more so, 83 00:04:38,839 --> 00:04:41,080 Speaker 1: Like it's hard to put stats on some of these 84 00:04:41,120 --> 00:04:44,600 Speaker 1: ancient battles, but for a DBC, the Persian army marched 85 00:04:44,600 --> 00:04:49,280 Speaker 1: into the Battle of Thermipoly with between two hundred thousand 86 00:04:49,320 --> 00:04:52,880 Speaker 1: and five hundred thousand men. More than two thousand years 87 00:04:52,960 --> 00:04:55,960 Speaker 1: later and three the Soviet Union's Red Army suffered more 88 00:04:55,960 --> 00:04:59,960 Speaker 1: than a million casualties of the Battle of Stalingrad. UH. 89 00:05:00,200 --> 00:05:03,240 Speaker 1: And then we have other examples where we can see 90 00:05:03,240 --> 00:05:08,440 Speaker 1: people converging UH for a cause that is not bloodthirsty 91 00:05:08,640 --> 00:05:12,840 Speaker 1: and drenched in hardship. UH. January two thousand seven, an 92 00:05:12,960 --> 00:05:16,960 Speaker 1: estimated sixty million Hindu pilgrims gathered at the convergence of 93 00:05:16,960 --> 00:05:21,120 Speaker 1: the Ganges and Yamuna rivers in northern India for the 94 00:05:21,240 --> 00:05:25,120 Speaker 1: um Are Kombla Mella Festival, huge festival. You've probably seen 95 00:05:25,800 --> 00:05:29,800 Speaker 1: clips to this individual's bathing UH in the rivers and 96 00:05:30,400 --> 00:05:32,400 Speaker 1: sort of an active purity, right, Yeah, in active purity. 97 00:05:32,440 --> 00:05:36,000 Speaker 1: Sixty million people coming together for that, which is amazing, amazing. 98 00:05:36,960 --> 00:05:40,479 Speaker 1: So protests also bring people together. And this is the 99 00:05:40,720 --> 00:05:43,840 Speaker 1: and we just recorded a podcast about this, so it 100 00:05:43,839 --> 00:05:47,080 Speaker 1: should already be in your iTunes folder or hanging out 101 00:05:47,080 --> 00:05:48,120 Speaker 1: on the web for you to listen to. If you 102 00:05:48,160 --> 00:05:50,679 Speaker 1: want a lettle more information about why we get into 103 00:05:50,839 --> 00:05:54,000 Speaker 1: into the protest mentality. But once we are there, we 104 00:05:54,040 --> 00:05:57,719 Speaker 1: can gather in in rather large numbers sometimes. And the 105 00:05:57,760 --> 00:06:00,560 Speaker 1: reason why Occupy on All Street has I think some 106 00:06:00,720 --> 00:06:03,599 Speaker 1: legs or has had legs at least over the past 107 00:06:03,640 --> 00:06:06,760 Speaker 1: four months. We don't again know what its fate is now. Um. 108 00:06:06,760 --> 00:06:09,160 Speaker 1: It has a classic earmarks of a protest. So we're 109 00:06:09,200 --> 00:06:15,080 Speaker 1: talking about disenfranchised people, right check. Crappy economy check uh huh, 110 00:06:15,279 --> 00:06:20,320 Speaker 1: and stagnating politics double check. Right. So pretty much this 111 00:06:20,360 --> 00:06:22,960 Speaker 1: is pretty much a global situation right now. Right. So 112 00:06:23,000 --> 00:06:26,080 Speaker 1: it's not just in the United States. Uh, you know 113 00:06:26,080 --> 00:06:28,640 Speaker 1: a group of people in New York starting this. Uh, 114 00:06:29,200 --> 00:06:31,640 Speaker 1: that's why it has, you know, such resonation all over 115 00:06:31,680 --> 00:06:34,720 Speaker 1: the world. Um. And of course we've seen this before. 116 00:06:34,760 --> 00:06:37,920 Speaker 1: We've seen it with Vietnam, right, Uh, a little bit 117 00:06:37,920 --> 00:06:40,400 Speaker 1: different circumstances. But we have people who are saying that 118 00:06:40,440 --> 00:06:42,760 Speaker 1: there are wrongs that need to be righted. We've seen 119 00:06:42,760 --> 00:06:46,000 Speaker 1: it with the Arab Spring. Um. But the Arab Spring 120 00:06:46,040 --> 00:06:50,120 Speaker 1: being just the the string of of protests and in 121 00:06:51,000 --> 00:06:54,960 Speaker 1: many in some cases actual rebellion revolution situations that has 122 00:06:55,000 --> 00:06:58,080 Speaker 1: just spread throughout various countries in the Middle East, ranging 123 00:06:58,120 --> 00:07:01,840 Speaker 1: from we've seen for the protest and I ran Tunisia 124 00:07:02,560 --> 00:07:06,360 Speaker 1: Egypt Um. I mean really, it points of the fact 125 00:07:06,400 --> 00:07:07,839 Speaker 1: that people will put up with a lot of crap 126 00:07:07,920 --> 00:07:10,520 Speaker 1: until it gets to the point where you know, it's 127 00:07:11,240 --> 00:07:15,280 Speaker 1: it's a it's making them powerless in their day to 128 00:07:15,360 --> 00:07:18,640 Speaker 1: day decisions on whether or not to buy groceries or 129 00:07:18,680 --> 00:07:22,520 Speaker 1: you know, buy new shoes, or you know, it's hamstringing them. 130 00:07:22,560 --> 00:07:25,200 Speaker 1: So yeah, we can put it with a lot of corruption, 131 00:07:25,280 --> 00:07:28,920 Speaker 1: a lot of political who until that happens. All right, 132 00:07:28,920 --> 00:07:30,800 Speaker 1: we're gonna take a quick break to hear a word 133 00:07:30,840 --> 00:07:34,160 Speaker 1: from our sponsors, and then we're gonna really break down 134 00:07:34,200 --> 00:07:36,400 Speaker 1: the question what is the largest protest of all time 135 00:07:36,640 --> 00:07:41,280 Speaker 1: and what factor has made it that way? Yeah. This 136 00:07:41,360 --> 00:07:44,240 Speaker 1: podcast is brought to you by Intel, the sponsors of 137 00:07:44,280 --> 00:07:49,400 Speaker 1: Tomorrow and the Discovery Channel. At Intel, we believe curiosity 138 00:07:49,480 --> 00:07:53,320 Speaker 1: is the spark which drives innovation. Join us at curiosity 139 00:07:53,400 --> 00:07:59,600 Speaker 1: dot com and explore the answers to life's questions. All right, 140 00:07:59,600 --> 00:08:02,480 Speaker 1: we're back largest protests. Uh. This is something that is 141 00:08:02,520 --> 00:08:06,640 Speaker 1: sometimes very difficult to gauge because not every protest consists 142 00:08:06,760 --> 00:08:10,400 Speaker 1: of individuals gathering in a in a square, and then 143 00:08:10,720 --> 00:08:14,200 Speaker 1: it's hard to count sizes and and and you're gonna 144 00:08:14,240 --> 00:08:17,880 Speaker 1: get into the wars of information and politics of information 145 00:08:17,920 --> 00:08:21,520 Speaker 1: to where the protesters are may in some cases want 146 00:08:21,560 --> 00:08:24,840 Speaker 1: to inflate the number because it sounds better, and then 147 00:08:24,840 --> 00:08:27,040 Speaker 1: the people cracking down on the protests are likely to 148 00:08:27,080 --> 00:08:29,320 Speaker 1: lower that number for obvious reasons. You know what I 149 00:08:29,680 --> 00:08:31,600 Speaker 1: just occurred to me, Why don't they use the same 150 00:08:31,640 --> 00:08:34,760 Speaker 1: technology that NASA does to count the constellations that they 151 00:08:34,800 --> 00:08:39,200 Speaker 1: then applied to tracking dolphins. Well, true, that's that's true. 152 00:08:39,240 --> 00:08:41,360 Speaker 1: You can do some sort of sophisticated data. But then 153 00:08:41,400 --> 00:08:43,880 Speaker 1: if you're the people who has that in that technology, 154 00:08:44,000 --> 00:08:46,880 Speaker 1: it's gonna tend to be the governmental forces, So they're 155 00:08:46,880 --> 00:08:50,160 Speaker 1: not gonna necessarily want an accurate count anyway, not for 156 00:08:50,280 --> 00:08:53,040 Speaker 1: public consumption. And then the other thing think back to 157 00:08:53,360 --> 00:08:55,800 Speaker 1: in the other podcast we mentioned Mohamma Gandhi and the 158 00:08:55,840 --> 00:08:58,360 Speaker 1: whole u uh. Some of the civil disobedience was going 159 00:08:58,360 --> 00:09:01,079 Speaker 1: on there, with individuals protesting by showing up for work 160 00:09:01,520 --> 00:09:04,600 Speaker 1: or by resigning from their positions. You get into protests 161 00:09:04,600 --> 00:09:06,720 Speaker 1: of that nature and it becomes even more difficult to 162 00:09:06,720 --> 00:09:09,160 Speaker 1: to gauge what the turnout is because they're protesting in 163 00:09:09,240 --> 00:09:11,480 Speaker 1: ways and ruch, they're not visible when you're getting down 164 00:09:11,480 --> 00:09:15,240 Speaker 1: into actual numbers though, and you can say like, well, 165 00:09:15,280 --> 00:09:16,920 Speaker 1: this is the number of individuals that we know of 166 00:09:17,000 --> 00:09:19,079 Speaker 1: the participated in this, and therefore this is a very 167 00:09:19,160 --> 00:09:21,520 Speaker 1: large or the biggest protests. One of the big ones, 168 00:09:21,559 --> 00:09:24,280 Speaker 1: according to Guinness Folk of World Records was it was 169 00:09:24,320 --> 00:09:28,280 Speaker 1: the Iraq protest in Rome UH February two thousand three. 170 00:09:28,720 --> 00:09:32,240 Speaker 1: The event drew a crowd of estimated three million people, 171 00:09:32,320 --> 00:09:34,559 Speaker 1: and on the same day, protesters gathered in nearly six 172 00:09:34,640 --> 00:09:38,240 Speaker 1: hundred cities in a coordinated global effort to express moral 173 00:09:38,280 --> 00:09:41,120 Speaker 1: outrage against the US invasion of Iraq. So you had 174 00:09:41,160 --> 00:09:45,040 Speaker 1: like one point three million protesters in Barcelona, Spain, between 175 00:09:45,120 --> 00:09:49,160 Speaker 1: seven fifty thousand and two million in London, um. All told, 176 00:09:50,440 --> 00:09:54,280 Speaker 1: according to BBC, between six and ten million people participating 177 00:09:54,280 --> 00:09:57,679 Speaker 1: in a global protest, which is amazing because think about 178 00:09:58,160 --> 00:10:00,439 Speaker 1: you know, when we talked about the civil rights movement, 179 00:10:00,920 --> 00:10:04,480 Speaker 1: and we talked about Rosa Parks and how her refusal 180 00:10:04,520 --> 00:10:06,559 Speaker 1: to give up a seat to a white person than 181 00:10:07,400 --> 00:10:11,320 Speaker 1: uh spurred a one day boycott of the bus system, 182 00:10:11,600 --> 00:10:13,920 Speaker 1: right um, And this was something that was in the 183 00:10:13,960 --> 00:10:16,120 Speaker 1: works forever. I mean that this there were a lot 184 00:10:16,160 --> 00:10:18,959 Speaker 1: of people working on this specific plan in other ones 185 00:10:19,679 --> 00:10:22,320 Speaker 1: um for months and months and months, and then you 186 00:10:22,400 --> 00:10:26,280 Speaker 1: have something like this, between six and ten million people protesting. 187 00:10:26,760 --> 00:10:28,760 Speaker 1: And I'm going to wager a bit that it didn't 188 00:10:28,800 --> 00:10:32,200 Speaker 1: take that long given the data that we know about 189 00:10:32,240 --> 00:10:35,640 Speaker 1: when the Iraq War broke out and when the protests happened, 190 00:10:36,240 --> 00:10:39,480 Speaker 1: um that people were mobilized. And so you can obviously 191 00:10:39,640 --> 00:10:43,040 Speaker 1: point to when really one key factor in mobilizing all 192 00:10:43,040 --> 00:10:45,400 Speaker 1: those people, which is the technology we have available today, 193 00:10:46,120 --> 00:10:51,120 Speaker 1: which really does change the landscape of protesting. It's it's 194 00:10:51,120 --> 00:10:53,240 Speaker 1: worth noting real quick before we get even more into 195 00:10:53,280 --> 00:10:56,400 Speaker 1: the technological stuff that that's sometimes the protest is just 196 00:10:56,440 --> 00:11:01,760 Speaker 1: as simple as a naked lady with a fur and 197 00:11:01,920 --> 00:11:04,360 Speaker 1: some fake blood splider on her, you know, or a 198 00:11:04,440 --> 00:11:07,760 Speaker 1: naked man or a naked man probably both, I guess. 199 00:11:07,800 --> 00:11:10,200 Speaker 1: So you get all your demographics covered. But that's the 200 00:11:10,200 --> 00:11:12,720 Speaker 1: shock tactic, that's like, hey, we want to we want 201 00:11:12,720 --> 00:11:14,600 Speaker 1: to the media to cover this. Yeah, and then that's 202 00:11:14,600 --> 00:11:16,320 Speaker 1: the thing like like Peter, no matter what you think 203 00:11:16,320 --> 00:11:20,679 Speaker 1: about Peter, and and certainly there's some very some very 204 00:11:20,720 --> 00:11:24,600 Speaker 1: contrary positions on on what Peter stands for and or 205 00:11:24,679 --> 00:11:28,320 Speaker 1: how they carry out their protests, but their protests tend 206 00:11:28,440 --> 00:11:31,319 Speaker 1: to uh to get attention, they do. And I will say, 207 00:11:31,360 --> 00:11:33,360 Speaker 1: you know, I've I've certainly walked through a Peter protest. 208 00:11:33,440 --> 00:11:36,960 Speaker 1: I was walking through one in Boston with my mom 209 00:11:36,520 --> 00:11:40,520 Speaker 1: h when when we all lived there, um a number 210 00:11:40,520 --> 00:11:42,800 Speaker 1: of years ago, and my mom had a fake fur 211 00:11:43,080 --> 00:11:47,920 Speaker 1: colored line jacket on, and of course she was the target. 212 00:11:48,080 --> 00:11:50,319 Speaker 1: The thing that stood out is that they said, I 213 00:11:50,360 --> 00:11:53,079 Speaker 1: hope that you get cancer and die. Yeah, so I 214 00:11:53,120 --> 00:11:58,199 Speaker 1: mean obviously fake blood. Uh no, they didn't, but that 215 00:11:58,320 --> 00:12:02,200 Speaker 1: was in itself really very jarring. Obviously my mom um, 216 00:12:02,920 --> 00:12:04,880 Speaker 1: she was actually kind of oblivious, just kind of like, 217 00:12:04,920 --> 00:12:07,400 Speaker 1: what did I just say? And I was like nothing, um. 218 00:12:07,440 --> 00:12:10,080 Speaker 1: But you know, there are effective ways to to really 219 00:12:10,120 --> 00:12:13,079 Speaker 1: get at um at the psyche, right, and that's that's 220 00:12:13,120 --> 00:12:15,160 Speaker 1: what protests are trying to do. So you can do 221 00:12:15,200 --> 00:12:17,240 Speaker 1: it in numbers, or you can do it in an 222 00:12:17,240 --> 00:12:19,079 Speaker 1: awful things that you say to pastors by yeah. And 223 00:12:19,120 --> 00:12:22,880 Speaker 1: then of course there's there's art. There's music protests, songs protests. 224 00:12:23,120 --> 00:12:26,040 Speaker 1: In our art episode that we did recently about your 225 00:12:26,040 --> 00:12:28,520 Speaker 1: brain and are we mentioned, um, like the words of 226 00:12:28,520 --> 00:12:32,240 Speaker 1: public Picasso talking about the Spanish Civil War and expressing 227 00:12:32,280 --> 00:12:35,520 Speaker 1: that through art, prep expressing his outrage and varied emotions 228 00:12:35,559 --> 00:12:40,760 Speaker 1: about this political situation through the expression of art, you know. 229 00:12:40,760 --> 00:12:44,800 Speaker 1: And I'm thinking to um about the protests of the 230 00:12:44,840 --> 00:12:49,079 Speaker 1: Iraq War. And obviously this is before Twitter and Facebook, 231 00:12:49,200 --> 00:12:51,559 Speaker 1: and this isn't a question that we can answer today 232 00:12:51,640 --> 00:12:54,600 Speaker 1: or anytime soon. But uh, you know, I do wonder 233 00:12:54,600 --> 00:12:58,000 Speaker 1: how it would impact or would have impacted that protest. 234 00:12:58,160 --> 00:13:00,200 Speaker 1: Do you think more people would have participated? Do you 235 00:13:00,200 --> 00:13:02,640 Speaker 1: think that it would have attenuated it in some way 236 00:13:03,000 --> 00:13:05,640 Speaker 1: because it might have taken focus away? Um, And we 237 00:13:05,920 --> 00:13:08,720 Speaker 1: certainly know that the protests of late have been really 238 00:13:08,760 --> 00:13:12,920 Speaker 1: effective because of these technologies. Yeah, it's it's real effective 239 00:13:13,559 --> 00:13:15,920 Speaker 1: for a number of reasons. But I mean, I mean 240 00:13:15,960 --> 00:13:19,360 Speaker 1: because a it's instant networking and social networking. It's it's 241 00:13:19,480 --> 00:13:22,000 Speaker 1: the ability to throw something out with that hashtag occupy 242 00:13:22,040 --> 00:13:24,720 Speaker 1: Wall Street and then everybody following that hashtag can instantly 243 00:13:24,720 --> 00:13:28,120 Speaker 1: see what's up, real time information in all of it's 244 00:13:28,200 --> 00:13:31,760 Speaker 1: effective and in some cases perplexing manner, you know. I mean, 245 00:13:31,800 --> 00:13:34,120 Speaker 1: it's the real time aspect of it. I think is 246 00:13:34,160 --> 00:13:36,200 Speaker 1: really key because you can you could be like somebody 247 00:13:36,360 --> 00:13:39,920 Speaker 1: suddenly somebody's tweeting, Hey, the police are cracking down. Uh, 248 00:13:39,960 --> 00:13:43,400 Speaker 1: they're moving into this area and everybody knows. And it's 249 00:13:43,400 --> 00:13:46,680 Speaker 1: also allowed certain groups to even It allows you this 250 00:13:46,760 --> 00:13:51,280 Speaker 1: huge voice without necessarily having any huge investment in the megaphone, 251 00:13:51,360 --> 00:13:55,800 Speaker 1: in the metaphorical megaphone or the real megaphone. You know. Um, 252 00:13:56,000 --> 00:13:59,600 Speaker 1: like not quite a protest, but just consider with all 253 00:13:59,640 --> 00:14:02,960 Speaker 1: the crack downs that have occurred on Wiki leaks, a 254 00:14:03,000 --> 00:14:07,040 Speaker 1: lot of their continued momentum has taken place on Twitter, 255 00:14:07,400 --> 00:14:09,200 Speaker 1: where they have continued to be able to speak out 256 00:14:09,240 --> 00:14:12,520 Speaker 1: to all of these followers despite bank accounts and websites 257 00:14:12,880 --> 00:14:15,960 Speaker 1: going down around the world. Yeah, I just I can't 258 00:14:15,960 --> 00:14:18,719 Speaker 1: help but think about the protests in Iraq or the 259 00:14:18,720 --> 00:14:23,000 Speaker 1: protests of the Iraq War and ten million people mobilized 260 00:14:23,120 --> 00:14:26,520 Speaker 1: by via email. Right, that's a huge coordinated global effort. 261 00:14:26,840 --> 00:14:29,280 Speaker 1: Just what it might have looked like with these different 262 00:14:29,280 --> 00:14:33,520 Speaker 1: technologies that we used today. UM, where's people people were 263 00:14:33,720 --> 00:14:38,320 Speaker 1: have been studying the the impact of social media on protests, 264 00:14:38,800 --> 00:14:42,400 Speaker 1: UM and movements prior to all of this. I actually 265 00:14:42,480 --> 00:14:45,800 Speaker 1: spoke to uh, this guy by the name of Dr 266 00:14:45,880 --> 00:14:49,320 Speaker 1: Michael Best, who had Technologies and inter National Development the 267 00:14:49,400 --> 00:14:51,400 Speaker 1: lab at the Georgie Institute of Technology. I thought. I 268 00:14:51,400 --> 00:14:54,520 Speaker 1: spoke to him early two thousand eleven UM for an 269 00:14:54,600 --> 00:14:57,760 Speaker 1: article on Discovery News called our Smartphones Worth It, and 270 00:14:57,800 --> 00:15:00,080 Speaker 1: it ended up being a lot more interesting of an 271 00:15:00,160 --> 00:15:03,000 Speaker 1: article then it sounds, because I got to discuss like 272 00:15:03,040 --> 00:15:06,000 Speaker 1: how does it affect our daily lives? And and then 273 00:15:06,240 --> 00:15:08,920 Speaker 1: by talking to Best, learned a little bit about how 274 00:15:08,960 --> 00:15:14,120 Speaker 1: it was specifically being used by UM Nigerians leading into 275 00:15:14,120 --> 00:15:17,040 Speaker 1: the two thousand eleven Nigerian election, and they were doing 276 00:15:17,040 --> 00:15:20,120 Speaker 1: a lot of political organism organizing. They were planning activist 277 00:15:20,160 --> 00:15:24,160 Speaker 1: meetings and flash mobs all leading up to the election 278 00:15:24,200 --> 00:15:26,080 Speaker 1: that I was at least at that time slated to 279 00:15:26,120 --> 00:15:29,880 Speaker 1: take place in April, and and at the time I 280 00:15:29,920 --> 00:15:31,520 Speaker 1: thought it was really interesting. But then of course this 281 00:15:31,560 --> 00:15:33,520 Speaker 1: turns out to be a major theme of two thousand 282 00:15:33,560 --> 00:15:36,760 Speaker 1: and eleven UM as individuals are are tweeting about the 283 00:15:36,800 --> 00:15:40,520 Speaker 1: various protests that they're involved in uh and and coordinating 284 00:15:40,520 --> 00:15:44,400 Speaker 1: their movements. And in all of this, I also can't 285 00:15:44,400 --> 00:15:48,880 Speaker 1: help but think another advantage of social media, and specifically Twitter, 286 00:15:49,920 --> 00:15:54,120 Speaker 1: has to do with the people observing because I feel 287 00:15:54,160 --> 00:15:56,280 Speaker 1: like with Twitter, one of the things people like about 288 00:15:56,280 --> 00:15:59,200 Speaker 1: celebrities being on Twitter is that you're kind of as 289 00:15:59,280 --> 00:16:02,960 Speaker 1: close to that celebrity as you are to the friend, 290 00:16:03,040 --> 00:16:04,800 Speaker 1: the real life friend that you know on Twitter, Like 291 00:16:04,800 --> 00:16:09,280 Speaker 1: there's there's this um, there's this closeness. Well, there's this 292 00:16:09,320 --> 00:16:12,040 Speaker 1: idea that you're getting insights to how they're living in 293 00:16:12,440 --> 00:16:16,320 Speaker 1: and you're all a part of the same information stream. 294 00:16:16,440 --> 00:16:19,960 Speaker 1: So when you're observing like a real time protest taking 295 00:16:19,960 --> 00:16:22,800 Speaker 1: place on Twitter and you're seeing updates about Occupy Wall 296 00:16:22,840 --> 00:16:26,320 Speaker 1: Street or or the protests in Iran, well whatever it 297 00:16:26,360 --> 00:16:29,040 Speaker 1: may be, I feel like the people observing it on 298 00:16:29,080 --> 00:16:32,760 Speaker 1: Twitter end up feeling closer and a little more personally invested. 299 00:16:33,520 --> 00:16:36,640 Speaker 1: It probably gives people a little bit more empathy, right movement, 300 00:16:36,720 --> 00:16:38,960 Speaker 1: So if someone is sprayed with tear gas, right, and 301 00:16:39,040 --> 00:16:42,040 Speaker 1: let's say that this was a uh the use was 302 00:16:42,040 --> 00:16:45,880 Speaker 1: was just an indiscriminate response by police and that or 303 00:16:46,000 --> 00:16:48,960 Speaker 1: that was how it was tweeted, right, then people are 304 00:16:48,960 --> 00:16:50,880 Speaker 1: going to say, oh my, you know, the depth of 305 00:16:50,920 --> 00:16:54,400 Speaker 1: injustice here is unbearable. Yeah, because you're getting real time 306 00:16:54,480 --> 00:16:56,280 Speaker 1: data about this, right. I think it's like if you 307 00:16:56,520 --> 00:16:58,880 Speaker 1: it's as if you were standing on the edges of 308 00:16:58,920 --> 00:17:01,960 Speaker 1: a protesting crowd in real life, and even if you're 309 00:17:01,960 --> 00:17:04,920 Speaker 1: not actively participating in it, you're gonna feel a little 310 00:17:04,920 --> 00:17:09,040 Speaker 1: more like a protests, You're gonna feel more sympathetic to 311 00:17:09,200 --> 00:17:11,600 Speaker 1: that cause just by virtue of being in close proximity 312 00:17:11,640 --> 00:17:16,639 Speaker 1: to it, and the social media world of Twitter puts 313 00:17:16,640 --> 00:17:19,600 Speaker 1: you on the edges of that crowd in a virtual sense. 314 00:17:20,160 --> 00:17:22,640 Speaker 1: So I think that the same thing ends up happening. Well, 315 00:17:22,640 --> 00:17:24,720 Speaker 1: it's real. It's such a game changer too for the 316 00:17:24,760 --> 00:17:28,160 Speaker 1: government because now that you do have these so much 317 00:17:28,200 --> 00:17:33,199 Speaker 1: information to share, it's very easy to document the missteps 318 00:17:33,400 --> 00:17:35,720 Speaker 1: that the government might take or that a police force 319 00:17:35,800 --> 00:17:39,880 Speaker 1: might take against protesters. So you know, obviously that sort 320 00:17:39,880 --> 00:17:42,959 Speaker 1: of stuff came out before, but now it's it's pretty verifiable, 321 00:17:43,040 --> 00:17:44,960 Speaker 1: particularly if you've got, you know, a cell phone video 322 00:17:45,000 --> 00:17:47,040 Speaker 1: of it. Now. Of course, it's it's worth noting that 323 00:17:47,040 --> 00:17:49,880 Speaker 1: that throughout these these various protests and on Twitter hasn't 324 00:17:49,880 --> 00:17:53,879 Speaker 1: necessarily been the motivating force. It's not like Twitter was 325 00:17:53,920 --> 00:17:58,000 Speaker 1: the like the real power of the movement necessarily. It 326 00:17:58,080 --> 00:18:00,560 Speaker 1: certainly helped, and it was certainly a game changer, but 327 00:18:00,880 --> 00:18:06,919 Speaker 1: take the Egyptian Revolution UM January through February two eleven. UM. 328 00:18:07,000 --> 00:18:09,280 Speaker 1: So you know, Twitter and the various social media outlets 329 00:18:09,320 --> 00:18:12,800 Speaker 1: are made a lot of headlines, but the television newspapers 330 00:18:12,800 --> 00:18:17,240 Speaker 1: and telephone UM specifically like Al Jazeera UH continued to 331 00:18:17,240 --> 00:18:20,880 Speaker 1: to be a major informer for the people even as 332 00:18:20,920 --> 00:18:25,040 Speaker 1: the Egyptians the Egyptian government blocked internet connections in January. 333 00:18:25,200 --> 00:18:27,520 Speaker 1: So UH, you know, it's worth noting that there are 334 00:18:27,520 --> 00:18:31,239 Speaker 1: other media outlets and employ here uh. And then in 335 00:18:31,280 --> 00:18:34,120 Speaker 1: some cases, UM, this was pretty interesting. This is from 336 00:18:34,600 --> 00:18:38,000 Speaker 1: February two th eleven, following the uprisings in the Middle East, 337 00:18:38,400 --> 00:18:42,240 Speaker 1: some individuals in China were arguing for a quote Jasmine revolution, 338 00:18:43,000 --> 00:18:45,720 Speaker 1: and they were talking about on this overseas Chinese community 339 00:18:45,760 --> 00:18:49,320 Speaker 1: news site boxing bo x u n. The Chinese government 340 00:18:49,400 --> 00:18:52,760 Speaker 1: ended up blocking searches for Jasmine for the term jasmine, 341 00:18:53,160 --> 00:18:56,840 Speaker 1: and they also the the website was hacked by a 342 00:18:56,840 --> 00:19:00,280 Speaker 1: denial of service attack. So right, so that you see 343 00:19:00,280 --> 00:19:03,200 Speaker 1: a little bit of this sort of digital virtual social 344 00:19:03,200 --> 00:19:06,680 Speaker 1: media warfare. It ends up taking place like the basically 345 00:19:06,720 --> 00:19:11,680 Speaker 1: the protest testing UH situation of the crowd of protesters 346 00:19:11,760 --> 00:19:16,080 Speaker 1: and the riot control taking place in a vertu. Yeah, 347 00:19:16,080 --> 00:19:18,720 Speaker 1: seeing it play out there. But and actually that's it's 348 00:19:18,720 --> 00:19:23,080 Speaker 1: worth noting to the Jasmine Revolution really didn't have legs, 349 00:19:23,200 --> 00:19:26,879 Speaker 1: right for that very reason that it was shut down. So, 350 00:19:27,480 --> 00:19:29,320 Speaker 1: you know, looking to the future a bit, it would 351 00:19:29,320 --> 00:19:33,080 Speaker 1: be very interesting to see how China and protesting uh 352 00:19:33,400 --> 00:19:36,400 Speaker 1: take place in the future, giving technologies and also given 353 00:19:36,440 --> 00:19:39,280 Speaker 1: the fact that you know, if you can shut down 354 00:19:40,640 --> 00:19:43,840 Speaker 1: a protest that way, you can obviously have people go 355 00:19:43,920 --> 00:19:47,159 Speaker 1: in and hack the other way. Uh. So, I don't know, 356 00:19:47,440 --> 00:19:50,320 Speaker 1: just just interesting thought. With that many people in in 357 00:19:50,480 --> 00:19:54,080 Speaker 1: one country, surely something will come out of it. Yeah, 358 00:19:54,400 --> 00:19:55,960 Speaker 1: But I guess one of the things too, is that 359 00:19:56,000 --> 00:20:01,040 Speaker 1: even even as as we've been increasingly digital, it seems 360 00:20:01,080 --> 00:20:04,080 Speaker 1: like the idea of individuals gathering together to support a 361 00:20:04,119 --> 00:20:06,440 Speaker 1: cause is just is going to remain a vital part 362 00:20:06,440 --> 00:20:09,000 Speaker 1: of protests. Like I can't see I can't see protests 363 00:20:09,040 --> 00:20:12,119 Speaker 1: going completely digital, you know, because it's kind of like 364 00:20:12,359 --> 00:20:15,000 Speaker 1: on Facebook, for instance, for while, there there were a 365 00:20:15,000 --> 00:20:17,600 Speaker 1: string of these things where it's like, uh, speak out 366 00:20:17,640 --> 00:20:21,439 Speaker 1: against I don't know, something like child abuse by changing 367 00:20:21,480 --> 00:20:25,000 Speaker 1: your your profile picture to a cartoon or something like. 368 00:20:25,040 --> 00:20:27,480 Speaker 1: There was there was a there were stringy and and 369 00:20:27,520 --> 00:20:29,600 Speaker 1: then finally people started saying, you know, this is this 370 00:20:29,680 --> 00:20:32,119 Speaker 1: is kind of dumb because we're not doing anything. You're 371 00:20:32,160 --> 00:20:35,840 Speaker 1: not actually doing anything for these causes. You're just it's 372 00:20:35,880 --> 00:20:37,800 Speaker 1: not like you quit paying your taxes, right, right, right, 373 00:20:38,280 --> 00:20:39,720 Speaker 1: It's not like you went out in the street and 374 00:20:39,760 --> 00:20:42,840 Speaker 1: made yourself known. And we're I mean, there's something about 375 00:20:42,880 --> 00:20:45,919 Speaker 1: people gathering in a space generally a space they're not 376 00:20:45,960 --> 00:20:50,040 Speaker 1: really supposed to be in, um and speaking out as 377 00:20:50,119 --> 00:20:52,840 Speaker 1: as as a group and saying, look at these individuals 378 00:20:52,880 --> 00:20:56,200 Speaker 1: all piled here together, look at them, uh, look at us. Yeah, 379 00:20:56,280 --> 00:20:58,240 Speaker 1: but it's not to sound too or well in again, 380 00:20:58,359 --> 00:21:01,800 Speaker 1: looking for the future and thinking about facial recognition software, 381 00:21:01,840 --> 00:21:03,880 Speaker 1: it could be really problematic for people in the near 382 00:21:03,920 --> 00:21:06,879 Speaker 1: future to congregate and um, I was just thinking about 383 00:21:06,880 --> 00:21:10,240 Speaker 1: the app that came out for the Chicago bar scene 384 00:21:10,400 --> 00:21:13,760 Speaker 1: using facial recognition software, which you can you know, go 385 00:21:13,840 --> 00:21:17,040 Speaker 1: buy a bar and it basically is going to tell you, um, 386 00:21:17,080 --> 00:21:19,040 Speaker 1: the age range of the people in there and the 387 00:21:19,080 --> 00:21:22,399 Speaker 1: relative income, which, by the way, if you've ever just 388 00:21:22,480 --> 00:21:25,919 Speaker 1: by looking at faces, yeah, from the facial recognition software, 389 00:21:25,960 --> 00:21:27,720 Speaker 1: like you take a picture of people at the bar 390 00:21:27,880 --> 00:21:30,879 Speaker 1: and then it yeah, because it's because it's indexing some 391 00:21:30,920 --> 00:21:33,399 Speaker 1: sort of database that's basically saying, okay, well, I mean, 392 00:21:33,440 --> 00:21:36,359 Speaker 1: I don't know if someone's wearing pearls, maybe it's looking 393 00:21:36,400 --> 00:21:39,040 Speaker 1: at that. I don't you know. But anyway, the point 394 00:21:39,119 --> 00:21:41,479 Speaker 1: is that this is a software that could be used 395 00:21:42,359 --> 00:21:45,280 Speaker 1: for crowds and you could start, you know, I ding 396 00:21:45,400 --> 00:21:49,320 Speaker 1: certain people UM. You could tag them as a known protester. 397 00:21:49,920 --> 00:21:52,359 Speaker 1: You could begin tracking them, and this is fascinating. I 398 00:21:52,400 --> 00:21:55,520 Speaker 1: could see then a situation where say a protest movement 399 00:21:55,520 --> 00:21:59,639 Speaker 1: begins to um gel together in a public space. They 400 00:21:59,680 --> 00:22:01,960 Speaker 1: send a guy out with one of these devices. All 401 00:22:02,000 --> 00:22:04,679 Speaker 1: he has to do is take a picture of analyze it, 402 00:22:04,720 --> 00:22:07,159 Speaker 1: just a quick scan of the crowd, and then it 403 00:22:07,200 --> 00:22:09,880 Speaker 1: will it will compare faces with the database. It will 404 00:22:10,160 --> 00:22:13,840 Speaker 1: sort of judge, you know, the various socio economic status, 405 00:22:14,160 --> 00:22:17,760 Speaker 1: any possible criminal histories, and then give them a readout 406 00:22:18,400 --> 00:22:20,840 Speaker 1: with advice. On the next step. They could be like, oh, 407 00:22:20,840 --> 00:22:22,840 Speaker 1: this has nothing to worry about. This is going to 408 00:22:22,920 --> 00:22:25,840 Speaker 1: break down in a couple of hours, versus go ahead 409 00:22:25,880 --> 00:22:28,000 Speaker 1: and shut this down now because this is going to 410 00:22:28,080 --> 00:22:30,520 Speaker 1: get bad. Right so, right now, we think about protesters 411 00:22:30,560 --> 00:22:35,000 Speaker 1: facing UM officers who might be armored with kevlar vests 412 00:22:35,000 --> 00:22:38,360 Speaker 1: and plastic shields, and um, you know, instead of bullets, 413 00:22:38,359 --> 00:22:42,520 Speaker 1: they might be hit by rubber bullets, which we've seen before. Um, 414 00:22:42,520 --> 00:22:45,080 Speaker 1: you know you've got snatched squads that will come and 415 00:22:45,119 --> 00:22:48,000 Speaker 1: snatch people. Um and sound cannon. We've talked about this 416 00:22:48,040 --> 00:22:50,320 Speaker 1: to you in our Future of Pain podcast about how 417 00:22:50,400 --> 00:22:53,679 Speaker 1: you can basically just scatter a bunch of people with 418 00:22:53,760 --> 00:22:56,880 Speaker 1: an incredibly loud noises. Okay, so you have all of that, 419 00:22:57,480 --> 00:22:59,840 Speaker 1: but maybe in the future, you know, you don't have 420 00:23:00,040 --> 00:23:03,480 Speaker 1: of uh as much of a police presence, but you 421 00:23:03,520 --> 00:23:06,720 Speaker 1: do have people in the crowd who are trying to 422 00:23:06,720 --> 00:23:09,440 Speaker 1: shut it down by getting the data on the people 423 00:23:09,880 --> 00:23:14,160 Speaker 1: and preventing them for future, you know, and from participating 424 00:23:14,160 --> 00:23:19,600 Speaker 1: in future protests. It's very likely. Uh, this is yeah, 425 00:23:19,800 --> 00:23:21,959 Speaker 1: this is kind of a tangent, but I can't help 426 00:23:22,040 --> 00:23:24,560 Speaker 1: ever be reminded. I think it's a So it's like 427 00:23:24,600 --> 00:23:27,920 Speaker 1: a DJ shadow track UM from an older album where 428 00:23:28,000 --> 00:23:30,760 Speaker 1: he ended up sampling this this bit from a sermon 429 00:23:31,200 --> 00:23:34,520 Speaker 1: who was taking place in the Woodstock era, and uh, 430 00:23:35,240 --> 00:23:37,359 Speaker 1: the preacher giving this sermon was talking like he was 431 00:23:37,440 --> 00:23:40,879 Speaker 1: describing outlandish lee like a situation where you had like 432 00:23:40,960 --> 00:23:44,920 Speaker 1: hippies in uh in kind of a protest environment, and 433 00:23:45,160 --> 00:23:48,320 Speaker 1: the police could not enter, they could not even like 434 00:23:48,440 --> 00:23:51,480 Speaker 1: enter in to like control them, because the marijuana smoke 435 00:23:51,600 --> 00:23:54,119 Speaker 1: was too thick that that it was just there was 436 00:23:54,160 --> 00:23:57,080 Speaker 1: just this crowd of just out of control, sinful hippies 437 00:23:57,320 --> 00:23:59,520 Speaker 1: and they were just just layed in drug smoke so 438 00:23:59,560 --> 00:24:01,600 Speaker 1: that the police couldn't go in. And so the way 439 00:24:01,640 --> 00:24:04,720 Speaker 1: to combat facial recognition software is just to have a 440 00:24:04,760 --> 00:24:10,000 Speaker 1: big thick uh ganja cloud. Well, and according to this guy, 441 00:24:10,080 --> 00:24:13,440 Speaker 1: according to that guy, yeah, I'm not suggesting that you're 442 00:24:13,240 --> 00:24:16,600 Speaker 1: you're you're saying people should do that, but it is 443 00:24:17,119 --> 00:24:19,119 Speaker 1: well but but no, I'm not suggesting people do that. 444 00:24:19,200 --> 00:24:20,760 Speaker 1: But but you know, it just brought it to mind. 445 00:24:20,760 --> 00:24:23,879 Speaker 1: But here's the thing about the facial recognition software. Um, 446 00:24:23,920 --> 00:24:25,920 Speaker 1: everybody could just end up wearing masks, right, I mean 447 00:24:25,920 --> 00:24:28,159 Speaker 1: that's the whole, the whole vif for Vendetta guy fox Man, 448 00:24:28,320 --> 00:24:33,080 Speaker 1: that's true. That become increasingly a symbol really more of 449 00:24:33,080 --> 00:24:35,560 Speaker 1: a symbol of these various movements in various groups like 450 00:24:35,560 --> 00:24:40,679 Speaker 1: anonymous h etcetera, rather than just this movie based on 451 00:24:40,720 --> 00:24:42,960 Speaker 1: this Alan More graphic novel. Yeah, yeah, you could you 452 00:24:42,960 --> 00:24:45,720 Speaker 1: could dial down the protester profiling profiling by doing that. 453 00:24:45,760 --> 00:24:47,560 Speaker 1: For so, in some cases it's illegal to wear a 454 00:24:47,600 --> 00:24:52,119 Speaker 1: mask in public officially, so I can understand why. I 455 00:24:52,200 --> 00:24:53,960 Speaker 1: was on Marto one day and someone had a mask 456 00:24:54,040 --> 00:24:56,240 Speaker 1: on um, but I can't remember. It was maybe like 457 00:24:56,280 --> 00:25:00,840 Speaker 1: Tigger or something. It was so disconcerting I thought, for sure, 458 00:25:01,160 --> 00:25:05,160 Speaker 1: you know, no good will come with this. You know this? Yeah, Well, 459 00:25:05,160 --> 00:25:08,600 Speaker 1: when uh, for the Olympics it took place in China, 460 00:25:09,119 --> 00:25:13,879 Speaker 1: Mexico sent a couple of Lucadors. They masked wrestlers, not 461 00:25:13,920 --> 00:25:15,640 Speaker 1: to actually compete, but to just sort of I think 462 00:25:15,640 --> 00:25:17,840 Speaker 1: they were doing the show. There's goodwill kind of thing. 463 00:25:18,080 --> 00:25:20,480 Speaker 1: But they had to get special permission for them to 464 00:25:20,560 --> 00:25:23,960 Speaker 1: wear their masks because they can't take their masks off. 465 00:25:24,359 --> 00:25:27,040 Speaker 1: I mean, that's their identity, right, I mean right, And 466 00:25:27,440 --> 00:25:29,000 Speaker 1: I mean there's no point in them going over there 467 00:25:29,000 --> 00:25:30,639 Speaker 1: if they can't wear their mass they are their masks. 468 00:25:31,040 --> 00:25:33,000 Speaker 1: So they had to get special permission from the Chinese 469 00:25:33,040 --> 00:25:37,000 Speaker 1: government to to wear these things. Well. Oh, by the way, 470 00:25:37,040 --> 00:25:38,840 Speaker 1: when I speak of MARTA, I'm talking about the train 471 00:25:38,840 --> 00:25:41,480 Speaker 1: system we have here in Atlanta. Yes, okay, Anybody's like, 472 00:25:41,520 --> 00:25:44,680 Speaker 1: what do you mean you are in Marta. Who's Marta? Yeah, 473 00:25:45,119 --> 00:25:47,960 Speaker 1: that's the train system. Alright. So just just a little 474 00:25:47,960 --> 00:25:50,640 Speaker 1: bit of food for thought. Um, the number one indicator 475 00:25:50,720 --> 00:25:54,040 Speaker 1: civil unrest lack of money, right, yes, and then of 476 00:25:54,040 --> 00:25:58,679 Speaker 1: course resources and political corruption. Now keep in mind that 477 00:25:58,760 --> 00:26:03,280 Speaker 1: by thinking about two and a half billion more people, Well, 478 00:26:03,320 --> 00:26:05,879 Speaker 1: I don't know, what do you think is gonna happen? Well, 479 00:26:06,080 --> 00:26:09,879 Speaker 1: I think, as with any age in the past, there 480 00:26:09,920 --> 00:26:12,919 Speaker 1: will continue to be protests, There will continue to be 481 00:26:13,480 --> 00:26:17,879 Speaker 1: situations that are worth protesting about. Uh, and hopefully, like 482 00:26:17,920 --> 00:26:19,840 Speaker 1: I said at the start of the podcast, will continue 483 00:26:19,840 --> 00:26:23,760 Speaker 1: to engage in these type of group activities rather than 484 00:26:24,119 --> 00:26:27,280 Speaker 1: open warfare. Yeah, absolutely, I mean, but it's it's a 485 00:26:27,359 --> 00:26:30,480 Speaker 1: much better prospect for sure. So there you go. What 486 00:26:30,560 --> 00:26:32,840 Speaker 1: do we have in our magic mailbox? I don't know 487 00:26:32,880 --> 00:26:36,560 Speaker 1: where's the robot that's always taken breaks? Here you go, 488 00:26:36,800 --> 00:26:40,800 Speaker 1: bring us some of that sweet listener mail. All right, 489 00:26:40,800 --> 00:26:43,520 Speaker 1: here we go. We have one from a listener by 490 00:26:43,560 --> 00:26:48,280 Speaker 1: the name of Jen. Jen writes in UH and says, Hello, 491 00:26:48,400 --> 00:26:50,280 Speaker 1: Robert and Julie. I hope this finds you Well. I've 492 00:26:50,320 --> 00:26:52,120 Speaker 1: been listening to your podcast for about half a year, 493 00:26:52,359 --> 00:26:54,760 Speaker 1: and I've loved every single one. That's that's great. I 494 00:26:54,800 --> 00:26:58,840 Speaker 1: even I haven't loved every single one. UM, I know 495 00:26:59,040 --> 00:27:00,919 Speaker 1: I love every single one. I have loved all of them, 496 00:27:01,160 --> 00:27:04,959 Speaker 1: from one I want to talk about. Okay, UM. I 497 00:27:05,000 --> 00:27:07,320 Speaker 1: now look forward to my long commutes because I get 498 00:27:07,320 --> 00:27:10,480 Speaker 1: to listen to your entertaining and insightful conversations. I think 499 00:27:10,480 --> 00:27:12,360 Speaker 1: that's why your podcast really stands out for me. You're 500 00:27:12,359 --> 00:27:15,480 Speaker 1: not just delivering information UH to your listeners, but you 501 00:27:15,560 --> 00:27:18,800 Speaker 1: are actively engaging with it and picking apart of some 502 00:27:18,880 --> 00:27:21,720 Speaker 1: quite complimented topics for us. Thank you for hours of entertainment. 503 00:27:21,720 --> 00:27:23,800 Speaker 1: It takes my mind up of how much I'm standing 504 00:27:23,800 --> 00:27:27,800 Speaker 1: on GAS as a graduate student researching medieval gender in 505 00:27:27,800 --> 00:27:31,440 Speaker 1: women's history, which is pretty exciting. I particularly appreciated your 506 00:27:31,480 --> 00:27:35,560 Speaker 1: October eleventh podcast on evolution of and the Orgasm war UM. 507 00:27:35,600 --> 00:27:37,800 Speaker 1: I apologize for this being late. My pod broke and 508 00:27:37,840 --> 00:27:40,720 Speaker 1: I only just replaced it, so I was listening to 509 00:27:40,840 --> 00:27:44,040 Speaker 1: two months worth of podcast last week. Ideas about the 510 00:27:44,119 --> 00:27:46,960 Speaker 1: nature of the female orgasm, especially in regard to its 511 00:27:47,359 --> 00:27:51,359 Speaker 1: UH necessity or lack thereof, for conception, was a topic 512 00:27:51,400 --> 00:27:54,960 Speaker 1: of interest for doctors, church officials, and court officials. Alike 513 00:27:55,000 --> 00:27:57,639 Speaker 1: in the Middle Ages. In fact, it had rather tragic 514 00:27:57,680 --> 00:28:01,280 Speaker 1: consequences for a number of women throughout the period. Um 515 00:28:01,480 --> 00:28:04,840 Speaker 1: galenic medicine, The dominant theory throughout the Middle Ages held 516 00:28:04,840 --> 00:28:08,919 Speaker 1: the female orgasm wasn't necessary requisite for conception. It was 517 00:28:09,240 --> 00:28:12,720 Speaker 1: widely believed that women also produced sperm during intercourse, and 518 00:28:12,800 --> 00:28:17,160 Speaker 1: it could only be released if the woman had enjoyed herself. Consequently, 519 00:28:17,240 --> 00:28:18,960 Speaker 1: a woman in the Middle Ages who was the victim 520 00:28:19,000 --> 00:28:21,959 Speaker 1: of a rape resulting in pregnancy had no legal recourses 521 00:28:22,520 --> 00:28:25,560 Speaker 1: in many areas of Europe. Her pregnancy belied her enjoyment 522 00:28:25,600 --> 00:28:29,960 Speaker 1: of the encounter, which inherently singled her consent to the act. Well, 523 00:28:30,119 --> 00:28:32,639 Speaker 1: that was a bummer anyway, thank you once again for 524 00:28:32,680 --> 00:28:36,120 Speaker 1: your wonderful podcast, and I'm sorry that my gratitude prompt 525 00:28:36,240 --> 00:28:38,400 Speaker 1: need to tell you some really sad history. You guys 526 00:28:38,440 --> 00:28:42,560 Speaker 1: rule best gen Well, that's awesome. That yeah, very interesting 527 00:28:42,560 --> 00:28:46,040 Speaker 1: and certainly kind of sad, rather insight but I mean 528 00:28:46,080 --> 00:28:49,400 Speaker 1: that's that's exploring anything about the Middle Ages. What's true true, 529 00:28:49,440 --> 00:28:51,200 Speaker 1: But it is uplifting in the sense that if you 530 00:28:51,280 --> 00:28:53,640 Speaker 1: think that the female orgasm is still a mystery, just 531 00:28:53,680 --> 00:28:57,040 Speaker 1: look back down and say, ah, the understanding is is 532 00:28:57,680 --> 00:29:00,640 Speaker 1: a little bit better there. Yeah, you gotta love the 533 00:29:00,680 --> 00:29:03,160 Speaker 1: middle Age. So I'm excited that we have a listener 534 00:29:03,160 --> 00:29:06,680 Speaker 1: who is steeped in medieval lore. Yeah, well, especially in 535 00:29:07,320 --> 00:29:09,440 Speaker 1: gender issues. All right, Robot, hand me another one. Here 536 00:29:09,480 --> 00:29:12,880 Speaker 1: we go, Um Jim right then and says, hi, guys, 537 00:29:12,960 --> 00:29:16,320 Speaker 1: your mind. Slash Art show reminded me of an exhibit 538 00:29:16,360 --> 00:29:18,440 Speaker 1: I saw a few years ago. I took my then 539 00:29:18,480 --> 00:29:20,240 Speaker 1: eight year old son to the Moment in New York 540 00:29:20,240 --> 00:29:22,600 Speaker 1: City on a school holiday. They had a really cool 541 00:29:22,640 --> 00:29:25,560 Speaker 1: temporary exhibit. It contained something like sixty four small works 542 00:29:25,560 --> 00:29:28,680 Speaker 1: of art in sequential order. The concept of this exhibit 543 00:29:28,840 --> 00:29:31,920 Speaker 1: was that each work related to the to the next one, 544 00:29:32,400 --> 00:29:34,520 Speaker 1: uh the one next to it. For example, a Rubik's 545 00:29:34,560 --> 00:29:37,280 Speaker 1: cube was next to a drawing of a Rubik's cube, 546 00:29:37,440 --> 00:29:39,840 Speaker 1: which was next to a drawing of a cube shaped building, 547 00:29:39,880 --> 00:29:42,880 Speaker 1: which was next to a round building, et cetera. At 548 00:29:42,960 --> 00:29:46,000 Speaker 1: one place, we came across a photo of rocks. Then 549 00:29:46,200 --> 00:29:49,200 Speaker 1: there were rocks displayed as a sculpture. Then the rocks 550 00:29:49,200 --> 00:29:52,360 Speaker 1: were on paper, Then there was paper, then shredded paper, 551 00:29:52,640 --> 00:29:56,080 Speaker 1: then some scissors, ah, rock paper scissors. The exhibit came 552 00:29:56,080 --> 00:29:58,560 Speaker 1: with a brochure which identified the works and artists, but 553 00:29:58,880 --> 00:30:01,320 Speaker 1: there were no details about how the pieces related. This 554 00:30:01,400 --> 00:30:04,120 Speaker 1: was up to the viewer. The last several items were 555 00:30:04,480 --> 00:30:07,760 Speaker 1: a dangling bear light bulb that talked a set of 556 00:30:08,080 --> 00:30:11,520 Speaker 1: phrases out of out of any meaningful context, a film 557 00:30:11,520 --> 00:30:14,320 Speaker 1: projector a vintage photo from the balcony of a nine 558 00:30:14,920 --> 00:30:17,920 Speaker 1: era movie theater, a sign that said the end. And 559 00:30:17,960 --> 00:30:20,720 Speaker 1: finally you had to check the brochure for this one, 560 00:30:21,000 --> 00:30:23,920 Speaker 1: but the rooms built in exit sign was the final piece. 561 00:30:24,520 --> 00:30:26,360 Speaker 1: It was a really cool and fun to walk through 562 00:30:26,360 --> 00:30:30,160 Speaker 1: the exhibit with my son and figure out the connections. Well, 563 00:30:30,160 --> 00:30:33,040 Speaker 1: that's that's interesting. I've gotten to to look at some 564 00:30:33,160 --> 00:30:36,840 Speaker 1: art with Nissan nephews and uh and it is interesting 565 00:30:36,920 --> 00:30:40,680 Speaker 1: too to sort of prod young minds on and see 566 00:30:40,720 --> 00:30:44,160 Speaker 1: them inspired by kind of figure out what art is about. 567 00:30:44,240 --> 00:30:46,200 Speaker 1: And then, as we discussed in the podcast, it's one 568 00:30:46,200 --> 00:30:48,720 Speaker 1: of the amazing things about art how it engages in 569 00:30:48,880 --> 00:30:52,520 Speaker 1: their brain on both conscious and subconscious levels. Yeah, it 570 00:30:52,720 --> 00:30:55,160 Speaker 1: was that quote from Picasso, something like he you know, 571 00:30:55,240 --> 00:30:59,040 Speaker 1: he could draw like Rembrandt, but it took him, but 572 00:30:59,640 --> 00:31:02,840 Speaker 1: you know, his whole lifetime to draw like a child 573 00:31:03,240 --> 00:31:04,640 Speaker 1: or think like a child. I guess that's what he 574 00:31:04,720 --> 00:31:07,080 Speaker 1: was saying. Um, so, yeah, that's really cool And I 575 00:31:07,160 --> 00:31:10,480 Speaker 1: love the idea that it's this cumulative work that you 576 00:31:10,560 --> 00:31:14,240 Speaker 1: content like, your your perception is forced to reevaluate each 577 00:31:14,280 --> 00:31:20,120 Speaker 1: piece that you're seeing as a whole. Pretty trippy. Yeah. Well, hey, 578 00:31:20,160 --> 00:31:21,720 Speaker 1: if you have anything trip you would like to share 579 00:31:21,760 --> 00:31:24,040 Speaker 1: with us, you can find us on Facebook and Twitter. 580 00:31:24,120 --> 00:31:27,000 Speaker 1: We are blow the Mind on both of those and uh, 581 00:31:27,040 --> 00:31:29,239 Speaker 1: we we find update both of those fees with all 582 00:31:29,240 --> 00:31:32,480 Speaker 1: sorts of cool links, uh, both the stuff that we're 583 00:31:32,600 --> 00:31:35,040 Speaker 1: working on and stuff we've done and you know, podcast stop, 584 00:31:35,320 --> 00:31:37,800 Speaker 1: but also just cool things we find around the net. 585 00:31:38,040 --> 00:31:40,600 Speaker 1: And certainly you guys are invited to share that kind 586 00:31:40,640 --> 00:31:43,560 Speaker 1: of stuff as well. On more than one occasion, something 587 00:31:43,600 --> 00:31:45,760 Speaker 1: you guys have shared with us has turned around and 588 00:31:45,880 --> 00:31:48,360 Speaker 1: ended up being a podcast episode all and you can 589 00:31:48,400 --> 00:31:51,160 Speaker 1: always send us your thoughts via email at blow the 590 00:31:51,200 --> 00:31:58,560 Speaker 1: Mind at how supports dot com. Be sure to check 591 00:31:58,560 --> 00:32:01,680 Speaker 1: out our new video podcast, Stuff from the Future. Join 592 00:32:01,800 --> 00:32:04,280 Speaker 1: House to work staff as we explore the most promising 593 00:32:04,360 --> 00:32:06,680 Speaker 1: and perplexing possibilities of tomorrow.