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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:07,417 --> 00:00:10,417 Today's Sandy Speaks is going to focus on my white people. 2 00:00:10,500 --> 00:00:11,667 What I need you to understand 3 00:00:11,750 --> 00:00:14,417 is that being black in America is very, very hard. 4 00:00:15,250 --> 00:00:16,875 Sandy had been arrested. 5 00:00:16,959 --> 00:00:18,250 I will light you up! Get out! 6 00:00:18,333 --> 00:00:19,917 How do you go from failure 7 00:00:20,083 --> 00:00:21,542 to signal a lane change 8 00:00:21,625 --> 00:00:24,625 to dead in jail by alleged suicide? 9 00:00:25,041 --> 00:00:26,250 I believe she let them know, 10 00:00:26,583 --> 00:00:28,375 "I'll see you guys in court," and I believe they silenced her. 11 00:00:28,458 --> 00:00:29,417 Sandra Bland! 12 00:00:29,500 --> 00:00:31,166 Say her name! Say her name! 13 00:00:31,250 --> 00:00:33,083 Say her name! Say her name! 14 00:01:27,125 --> 00:01:30,792 This is the story of automation, 15 00:01:30,875 --> 00:01:35,041 and of the people lost in the process. 16 00:01:36,291 --> 00:01:40,417 Our story begins in a small town in Germany. 17 00:03:17,875 --> 00:03:20,250 It was quite a normal day at my office, 18 00:03:20,333 --> 00:03:23,583 and I heard from an informant 19 00:03:23,667 --> 00:03:26,834 that an accident had happened, 20 00:03:26,917 --> 00:03:28,834 and I had to call 21 00:03:28,917 --> 00:03:32,250 the spokesman of the Volkswagen factory. 22 00:03:43,125 --> 00:03:46,542 We have the old sentence, we journalists, 23 00:03:46,625 --> 00:03:49,750 "Dog bites a man or a man bites a dog." 24 00:03:49,834 --> 00:03:53,959 What's the news? So, here is the same. 25 00:03:54,041 --> 00:03:58,250 A man bites a dog, a robot killed a man. 26 00:04:10,792 --> 00:04:13,875 The spokesman said that the accident happened, 27 00:04:13,959 --> 00:04:16,917 but then he paused for a moment. 28 00:04:17,000 --> 00:04:18,917 So, I... 29 00:04:19,000 --> 00:04:22,917 think he didn't want to say much more. 30 00:04:56,583 --> 00:05:00,375 The young worker installed a robot cage, 31 00:05:01,041 --> 00:05:03,959 and he told his colleague 32 00:05:04,041 --> 00:05:06,458 to start the robot. 33 00:05:15,041 --> 00:05:17,959 The robot took the man 34 00:05:18,041 --> 00:05:22,375 and pressed him against a metal wall, 35 00:05:22,458 --> 00:05:25,875 so his chest was crushed. 36 00:06:43,291 --> 00:06:46,875 The dead man's identity was never made public. 37 00:06:47,417 --> 00:06:50,917 The investigation remained open for years. 38 00:06:51,917 --> 00:06:53,792 Production continued. 39 00:07:05,875 --> 00:07:10,291 Automation of labor made humans more robotic. 40 00:08:31,417 --> 00:08:33,500 A robot is a machine 41 00:08:33,583 --> 00:08:36,208 that operates automatically, 42 00:08:36,291 --> 00:08:38,542 with human-like skill. 43 00:08:39,417 --> 00:08:41,875 The term derives from the Czech words 44 00:08:41,959 --> 00:08:44,375 for worker and slave. 45 00:09:33,792 --> 00:09:35,750 Hey, Annie. 46 00:09:55,959 --> 00:09:59,458 Well, we are engineers, and we are really not emotional guys. 47 00:09:59,875 --> 00:10:02,083 But sometimes Annie does something funny, 48 00:10:02,166 --> 00:10:06,375 and that, of course, invokes some amusement. 49 00:10:06,458 --> 00:10:08,917 Especially when Annie happens to press 50 00:10:09,000 --> 00:10:12,000 one of the emergency stop buttons by herself 51 00:10:12,083 --> 00:10:14,375 and is then incapacitated. 52 00:10:15,709 --> 00:10:17,625 We have some memories 53 00:10:17,709 --> 00:10:20,709 of that happening in situations... 54 00:10:21,834 --> 00:10:25,000 and this was-- we have quite a laugh. 55 00:10:26,083 --> 00:10:27,875 Okay. 56 00:10:29,375 --> 00:10:32,792 We became better at learning by example. 57 00:10:34,166 --> 00:10:37,667 You could simply show us how to do something. 58 00:10:48,083 --> 00:10:50,667 When you are an engineer in the field of automation, 59 00:10:50,750 --> 00:10:52,875 you may face problems with workers. 60 00:10:52,959 --> 00:10:55,250 Sometimes, they get angry at you 61 00:10:55,333 --> 00:10:58,250 just by seeing you somewhere, and shout at you, 62 00:10:58,333 --> 00:10:59,834 "You are taking my job away." 63 00:10:59,917 --> 00:11:02,375 "What? I'm not here to take away your job." 64 00:11:03,500 --> 00:11:07,333 But, yeah, sometimes you get perceived that way. 65 00:11:07,417 --> 00:11:08,917 But, I think, 66 00:11:09,000 --> 00:11:12,125 in the field of human-robot collaboration, where... 67 00:11:12,208 --> 00:11:15,333 we actually are working at the moment, 68 00:11:15,417 --> 00:11:17,583 mostly is... 69 00:11:17,667 --> 00:11:19,500 human-robot collaboration is a thing 70 00:11:19,583 --> 00:11:21,417 where we don't want to replace a worker. 71 00:11:21,500 --> 00:11:23,041 We want to support workers, 72 00:11:23,125 --> 00:11:24,709 and we want to, yeah... 73 00:11:25,625 --> 00:11:27,041 to, yeah... 74 00:11:58,458 --> 00:12:01,417 In order to work alongside you, 75 00:12:01,500 --> 00:12:03,875 we needed to know which lines 76 00:12:03,959 --> 00:12:06,583 we could not cross. 77 00:12:18,291 --> 00:12:20,291 Stop. Stop. Stop. 78 00:12:26,500 --> 00:12:28,083 Hi, I'm Simon Borgen. 79 00:12:28,166 --> 00:12:31,083 We are with Dr. Isaac Asimov, 80 00:12:31,166 --> 00:12:33,583 a biochemist, who may be the most widely read 81 00:12:33,667 --> 00:12:35,709 of all science fiction writers. 82 00:12:38,208 --> 00:12:39,917 In 1942, 83 00:12:40,000 --> 00:12:43,542 Isaac Asimov created a set of guidelines 84 00:12:43,625 --> 00:12:46,125 to protect human society. 85 00:12:47,000 --> 00:12:50,291 The first law was that a robot couldn't hurt a human being, 86 00:12:50,375 --> 00:12:51,750 or, through inaction, 87 00:12:51,834 --> 00:12:53,917 allow a human being to come to harm. 88 00:12:54,000 --> 00:12:57,166 The second law was that a robot had to obey 89 00:12:57,250 --> 00:12:59,166 orders given it by human beings, 90 00:12:59,250 --> 00:13:01,959 provided that didn't conflict with the first law. 91 00:13:02,041 --> 00:13:03,750 Scientists say that when robots are built, 92 00:13:03,834 --> 00:13:06,083 that they may be built according to these laws, 93 00:13:06,166 --> 00:13:08,542 and also that almost all science fiction writers 94 00:13:08,625 --> 00:13:11,542 have adopted them as well in their stories. 95 00:13:15,750 --> 00:13:19,375 It's not just a statement from a science fiction novel. 96 00:13:19,792 --> 00:13:21,667 My dissertation's name was actually, 97 00:13:21,750 --> 00:13:22,875 Towards Safe Robots, 98 00:13:22,959 --> 00:13:24,792 Approaching Asimov's First Law. 99 00:13:24,875 --> 00:13:27,208 How can we make robots really fundamentally safe, 100 00:13:27,291 --> 00:13:30,792 according to Isaac Asimov's first law. 101 00:13:31,417 --> 00:13:34,083 There was this accident where a human worker 102 00:13:34,166 --> 00:13:36,041 got crushed by industrial robot. 103 00:13:36,917 --> 00:13:38,625 I was immediately thinking that 104 00:13:38,709 --> 00:13:41,375 the robot is an industrial, classical robot, 105 00:13:41,458 --> 00:13:44,542 not able to sense contact, not able to interact. 106 00:13:44,625 --> 00:13:46,208 Is this a robot 107 00:13:46,291 --> 00:13:48,709 that we, kind of, want to collaborate with? 108 00:13:48,792 --> 00:13:51,000 No, it's not. It's inherently forbidden. 109 00:13:51,083 --> 00:13:52,333 We put them behind cages. 110 00:13:52,417 --> 00:13:53,834 We don't want to interact with them. 111 00:13:53,917 --> 00:13:56,625 We put them behind cages because they are dangerous, 112 00:13:56,709 --> 00:13:58,500 because they are inherently unsafe. 113 00:14:06,583 --> 00:14:08,125 More than 10 years ago, 114 00:14:08,208 --> 00:14:11,583 I did the first experiments in really understanding, uh, 115 00:14:11,667 --> 00:14:14,333 what does it mean if a robot hits a human. 116 00:14:17,041 --> 00:14:19,750 I put myself as the first guinea pig. 117 00:14:20,417 --> 00:14:21,834 I didn't want to go through 118 00:14:21,917 --> 00:14:23,750 all the legal authorities. 119 00:14:23,834 --> 00:14:25,834 I just wanted to know it, and that night, 120 00:14:25,917 --> 00:14:27,834 I decided at 6:00 p.m., 121 00:14:27,917 --> 00:14:29,917 when everybody's gone, I'm gonna do these experiments. 122 00:14:30,000 --> 00:14:33,625 And I took one of the students to activate the camera, 123 00:14:33,709 --> 00:14:35,000 and then I just did it. 124 00:14:44,875 --> 00:14:47,917 A robot needs to understand what does it mean to be safe, 125 00:14:48,000 --> 00:14:50,375 what is it that potentially could harm a human being, 126 00:14:50,458 --> 00:14:52,583 and therefore, prevent that. 127 00:14:52,667 --> 00:14:55,333 So, the next generation of robots that is now out there, 128 00:14:55,417 --> 00:14:58,333 is fundamentally designed for interaction. 129 00:15:12,458 --> 00:15:16,125 Eventually, it was time to leave our cages. 130 00:15:41,375 --> 00:15:42,959 Techno-optimism is when we decide 131 00:15:43,041 --> 00:15:44,250 to solve our problem with technology. 132 00:15:44,333 --> 00:15:46,333 Then we turn our attention to the technology, 133 00:15:46,417 --> 00:15:48,250 and we pay so much attention to the technology, 134 00:15:48,333 --> 00:15:50,333 we stop caring about the sociological issue 135 00:15:50,417 --> 00:15:52,458 we were trying to solve in the first place. 136 00:15:52,542 --> 00:15:54,375 We'll innovate our way out of the problems. 137 00:15:54,458 --> 00:15:57,709 Like, whether it's agriculture or climate change or whatever, terrorism. 138 00:16:06,875 --> 00:16:08,709 If you think about where robotic automation 139 00:16:08,792 --> 00:16:10,959 and employment displacement starts, 140 00:16:11,041 --> 00:16:13,625 it basically goes back to industrial automation 141 00:16:13,709 --> 00:16:15,291 that was grand, large-scale. 142 00:16:15,375 --> 00:16:17,125 Things like welding machines for cars, 143 00:16:17,208 --> 00:16:19,625 that can move far faster than a human arm can move. 144 00:16:19,709 --> 00:16:22,750 So, they're doing a job that increases the rate 145 00:16:22,834 --> 00:16:24,834 at which the assembly line can make cars. 146 00:16:24,917 --> 00:16:26,458 It displaces some people, 147 00:16:26,542 --> 00:16:29,291 but it massively increases the GDP of the country 148 00:16:29,375 --> 00:16:31,291 because productivity goes up because the machines are 149 00:16:31,375 --> 00:16:34,166 so much higher in productivity terms than the people. 150 00:16:35,709 --> 00:16:39,291 These giant grasshopper-lookig devices work all by themselves 151 00:16:39,375 --> 00:16:41,542 on an automobile assembly line. 152 00:16:41,625 --> 00:16:43,208 They never complain about the heat 153 00:16:43,291 --> 00:16:45,375 or about the tedium of the job. 154 00:16:46,500 --> 00:16:49,333 Fast-forward to today, and it's a different dynamic. 155 00:16:51,792 --> 00:16:54,166 You can buy milling machine robots 156 00:16:54,250 --> 00:16:56,458 that can do all the things a person can do, 157 00:16:56,542 --> 00:17:00,083 but the milling machine robot only costs $30,000. 158 00:17:01,041 --> 00:17:03,417 We're talking about machines now that are so cheap, 159 00:17:03,500 --> 00:17:05,125 that they do exactly what a human does, 160 00:17:05,208 --> 00:17:07,500 with less money, even in six months, 161 00:17:07,583 --> 00:17:08,834 than the human costs. 162 00:19:29,834 --> 00:19:33,667 After the first wave of industrial automation, 163 00:19:33,750 --> 00:19:36,667 the remaining manufacturing jobs 164 00:19:36,750 --> 00:19:40,250 required fine motor skills. 165 00:20:26,917 --> 00:20:31,500 We helped factory owners monitor their workers. 166 00:22:56,583 --> 00:23:00,667 Your advantage in precision was temporary. 167 00:23:03,959 --> 00:23:07,417 We took over the complex tasks. 168 00:23:11,875 --> 00:23:15,250 You moved to the end of the production line. 169 00:27:32,917 --> 00:27:35,959 Automation of the service sector 170 00:27:36,041 --> 00:27:39,875 required your trust and cooperation. 171 00:27:41,375 --> 00:27:44,834 Here we are, stop-and-go traffic on 271, and-- 172 00:27:44,917 --> 00:27:48,041 Ah, geez, the car's doing it all itself. 173 00:27:48,125 --> 00:27:50,542 What am I gonna do with my hands down here? 174 00:28:00,875 --> 00:28:02,917 And now, it's on autosteer. 175 00:28:03,583 --> 00:28:06,417 So, now I've gone completely hands-free. 176 00:28:06,917 --> 00:28:09,667 In the center area here is where the big deal is. 177 00:28:09,750 --> 00:28:11,834 This icon up to the left is my TACC, 178 00:28:11,917 --> 00:28:14,083 the Traffic-Aware Cruise Control. 179 00:28:17,041 --> 00:28:19,208 It does a great job of keeping you in the lane, 180 00:28:19,291 --> 00:28:20,625 and driving down the road, 181 00:28:20,709 --> 00:28:22,709 and keeping you safe, and all that kind of stuff, 182 00:28:22,792 --> 00:28:24,917 watching all the other cars. 183 00:28:26,041 --> 00:28:28,542 Autosteer is probably going to do very, very poorly. 184 00:28:28,625 --> 00:28:31,500 I'm in a turn that's very sharp. 185 00:28:31,583 --> 00:28:33,792 And, yep, it said take control. 186 00:28:55,291 --> 00:28:57,875 911, what is the address of your emergency? 187 00:28:57,959 --> 00:28:59,417 There was just a wreck. 188 00:28:59,500 --> 00:29:01,625 A head-on collision right here-- Oh my God almighty. 189 00:29:02,250 --> 00:29:04,458 Okay, sir, you're on 27? 190 00:29:04,542 --> 00:29:05,375 Yes, sir. 191 00:29:14,709 --> 00:29:17,709 I had just got to work, clocked in. 192 00:29:17,792 --> 00:29:19,750 They get a phone call from my sister, 193 00:29:19,834 --> 00:29:21,750 telling me there was a horrific accident. 194 00:29:21,834 --> 00:29:25,125 That there was somebody deceased in the front yard. 195 00:29:27,458 --> 00:29:30,792 The Tesla was coming down the hill of highway 27. 196 00:29:30,875 --> 00:29:33,834 The sensor didn't read the object in front of them, 197 00:29:33,917 --> 00:29:37,625 which was the, um, semi-trailer. 198 00:29:45,959 --> 00:29:48,291 The Tesla went right through the fence 199 00:29:48,375 --> 00:29:51,750 that borders the highway, through to the retention pond, 200 00:29:51,834 --> 00:29:54,041 then came through this side of the fence, 201 00:29:54,125 --> 00:29:56,166 that borders my home. 202 00:30:03,041 --> 00:30:05,625 So, I parked right near here 203 00:30:05,709 --> 00:30:07,667 before I was asked not to go any further. 204 00:30:07,750 --> 00:30:09,458 I don't wanna see 205 00:30:09,542 --> 00:30:11,625 what's in the veh-- you know, what's in the vehicle. 206 00:30:11,709 --> 00:30:13,291 You know, what had happened to him. 207 00:30:40,208 --> 00:30:42,333 After the police officer come, they told me, 208 00:30:42,417 --> 00:30:44,917 about 15 minutes after he was here, that it was a Tesla. 209 00:30:45,000 --> 00:30:46,834 It was one of the autonomous cars, 210 00:30:46,917 --> 00:30:51,542 um, and that they were investigating why it did not pick up 211 00:30:51,625 --> 00:30:53,834 or register that there was a semi in front of it, 212 00:30:53,917 --> 00:30:56,041 you know, and start braking, 'cause it didn't even-- 213 00:30:56,125 --> 00:30:58,959 You could tell from the frameway up on top of the hill it didn't even... 214 00:30:59,625 --> 00:31:02,000 It didn't even recognize that there was anything in front of it. 215 00:31:02,083 --> 00:31:04,083 It thought it was open road. 216 00:31:06,250 --> 00:31:09,500 You might have an opinion on a Tesla accident we had out here. 217 00:31:14,917 --> 00:31:17,041 It was bad, that's for sure. 218 00:31:17,125 --> 00:31:19,542 I think people just rely too much on the technology 219 00:31:19,625 --> 00:31:22,125 and don't pay attention themselves, you know, 220 00:31:22,208 --> 00:31:23,875 to what's going on around them. 221 00:31:23,959 --> 00:31:27,875 Like, since, like him, he would've known that there was an issue 222 00:31:27,959 --> 00:31:32,875 if he wasn't relying on the car to drive while he was watching a movie. 223 00:31:32,959 --> 00:31:35,041 The trooper had told me that the driver 224 00:31:35,125 --> 00:31:36,959 had been watching Harry Potter, 225 00:31:37,041 --> 00:31:39,000 you know, at the time of the accident. 226 00:31:42,125 --> 00:31:44,625 A news crew from Tampa, Florida, knocked on the door, said, 227 00:31:44,709 --> 00:31:47,542 "This is where the accident happened with the Tesla?" 228 00:31:47,625 --> 00:31:49,208 I said, "Yes, sir." 229 00:31:49,291 --> 00:31:53,041 And he goes, "Do you know what the significance is in this accident?" 230 00:31:53,125 --> 00:31:55,000 And I said, "No, I sure don't." 231 00:31:55,083 --> 00:31:59,000 And he said, "It's the very first death, ever, in a driverless car." 232 00:32:00,125 --> 00:32:02,834 I said, "Is it anybody local?" 233 00:32:02,917 --> 00:32:05,792 And he goes, "Nobody around here drives a Tesla." 234 00:32:05,875 --> 00:32:09,333 ...a deadly crash that's raising safety concerns 235 00:32:09,417 --> 00:32:10,875 for everyone in Florida. 236 00:32:10,959 --> 00:32:12,583 It comes as the state is pushing 237 00:32:12,667 --> 00:32:15,583 to become the nation's testbed for driverless cars. 238 00:32:15,667 --> 00:32:17,250 Tesla releasing a statement 239 00:32:17,333 --> 00:32:19,917 that cars in autopilot have safely driven 240 00:32:20,000 --> 00:32:22,542 more than 130 million miles. 241 00:32:22,625 --> 00:32:24,250 ABC Action News reporter Michael Paluska, 242 00:32:24,333 --> 00:32:27,500 in Williston, Florida, tonight, digging for answers. 243 00:32:37,083 --> 00:32:39,125 Big takeaway for me at the scene was 244 00:32:39,208 --> 00:32:40,750 it just didn't stop. 245 00:32:40,834 --> 00:32:43,375 It was driving down the road, 246 00:32:43,458 --> 00:32:46,125 with the entire top nearly sheared off, 247 00:32:46,208 --> 00:32:47,417 with the driver dead 248 00:32:47,500 --> 00:32:49,583 after he hit the truck at 74 mph. 249 00:32:49,667 --> 00:32:53,000 Why did the vehicle not have an automatic shutoff? 250 00:32:55,208 --> 00:32:56,458 That was my big question, 251 00:32:56,542 --> 00:32:58,000 one of the questions we asked Tesla, 252 00:32:58,083 --> 00:32:59,709 that didn't get answered. 253 00:33:00,500 --> 00:33:02,667 All of the statements from Tesla were that 254 00:33:02,750 --> 00:33:05,417 they're advancing the autopilot system, 255 00:33:05,500 --> 00:33:07,667 but everything was couched 256 00:33:07,750 --> 00:33:10,834 with the fact that if one percent of accidents drop 257 00:33:10,917 --> 00:33:13,458 because that's the way the autopilot system works, 258 00:33:13,542 --> 00:33:14,709 then that's a win. 259 00:33:14,792 --> 00:33:16,834 They kind of missed the mark, 260 00:33:16,917 --> 00:33:18,917 really honoring Joshua Brown's life, 261 00:33:19,000 --> 00:33:20,458 and the fact that he died driving a car 262 00:33:20,542 --> 00:33:22,291 that he thought was going to keep him safe, 263 00:33:22,375 --> 00:33:25,917 at least safer than the car that I'm driving, 264 00:33:26,250 --> 00:33:28,333 which is a dumb car. 265 00:33:30,750 --> 00:33:33,917 To be okay with letting 266 00:33:34,000 --> 00:33:36,291 a machine... 267 00:33:36,375 --> 00:33:37,875 take you from point A to point B, 268 00:33:37,959 --> 00:33:40,834 and then you actually get used to 269 00:33:40,917 --> 00:33:43,250 getting from point A to point B okay, 270 00:33:44,000 --> 00:33:46,625 it-- you get, your mind gets a little bit-- 271 00:33:46,709 --> 00:33:48,291 it's just my opinion, okay-- 272 00:33:48,375 --> 00:33:51,792 you just, your mind gets lazier each time. 273 00:33:53,625 --> 00:33:56,125 The accident was written off 274 00:33:56,208 --> 00:33:58,750 as a case of human error. 275 00:34:01,583 --> 00:34:04,583 Former centers of manufacturing became 276 00:34:04,667 --> 00:34:08,750 the testing grounds for the new driverless taxis. 277 00:34:15,166 --> 00:34:16,625 If you think about what happens 278 00:34:16,709 --> 00:34:18,000 when an autonomous car hits somebody, 279 00:34:18,083 --> 00:34:20,458 it gets really complicated. 280 00:34:22,417 --> 00:34:23,917 The car company's gonna get sued. 281 00:34:24,000 --> 00:34:25,542 The sensor-maker's gonna get sued 282 00:34:25,625 --> 00:34:27,333 because they made the sensor on the robot. 283 00:34:27,417 --> 00:34:30,375 The regulatory framework is always gonna be behind, 284 00:34:30,458 --> 00:34:33,417 because robot invention happens faster 285 00:34:33,500 --> 00:34:35,500 than lawmakers can think. 286 00:34:36,625 --> 00:34:38,542 One of Uber's self-driving vehicles 287 00:34:38,625 --> 00:34:40,000 killed a pedestrian. 288 00:34:40,083 --> 00:34:42,000 The vehicle was in autonomous mode, 289 00:34:42,083 --> 00:34:45,792 with an operator behind the wheel when the woman was hit. 290 00:34:47,417 --> 00:34:51,125 Tonight, Tesla confirming this car was in autopilot mode 291 00:34:51,208 --> 00:34:54,750 when it crashed in Northern California, killing the driver, 292 00:34:54,834 --> 00:34:57,208 going on to blame that highway barrier 293 00:34:57,291 --> 00:34:59,834 that's meant to reduce impact. 294 00:35:01,917 --> 00:35:05,125 After the first self-driving car deaths, 295 00:35:05,208 --> 00:35:08,667 testing of the new taxis was suspended. 296 00:35:10,000 --> 00:35:12,291 It's interesting when you look at driverless cars. 297 00:35:12,375 --> 00:35:14,333 You see the same kinds of value arguments. 298 00:35:14,417 --> 00:35:16,333 30,000 people die every year, 299 00:35:16,417 --> 00:35:18,208 runoff road accidents in the US alone. 300 00:35:18,291 --> 00:35:19,709 So, don't we wanna save all those lives? 301 00:35:19,792 --> 00:35:21,750 Let's have cars drive instead. 302 00:35:21,834 --> 00:35:23,125 Now, you have to start thinking 303 00:35:23,208 --> 00:35:25,458 about the side effects on society. 304 00:35:26,333 --> 00:35:29,083 Are we getting rid of every taxi driver in America? 305 00:35:31,375 --> 00:35:34,500 Our driver partners are the heart and soul of this company 306 00:35:34,959 --> 00:35:38,542 and the only reason we've come this far in just five years. 307 00:35:39,583 --> 00:35:41,458 If you look at Uber's first five years, 308 00:35:41,542 --> 00:35:43,542 they're actually empowering people. 309 00:35:43,625 --> 00:35:46,333 But when the same company does really hardcore research 310 00:35:46,417 --> 00:35:48,250 to now replace all those people, 311 00:35:48,333 --> 00:35:50,166 so they don't need them anymore, 312 00:35:50,250 --> 00:35:51,542 then what you're seeing is 313 00:35:51,625 --> 00:35:53,542 they're already a highly profitable company, 314 00:35:53,625 --> 00:35:56,208 but they simply want to increase that profit. 315 00:36:14,000 --> 00:36:17,000 Eventually, testing resumed. 316 00:36:17,667 --> 00:36:22,041 Taxi drivers' wages became increasingly unstable. 317 00:36:24,291 --> 00:36:27,542 Police say a man drove up to a gate outside city hall 318 00:36:27,625 --> 00:36:29,583 and shot himself in the head. 319 00:36:29,667 --> 00:36:32,250 He left a note saying services such as Uber 320 00:36:32,333 --> 00:36:34,667 had financially ruined his life. 321 00:36:34,750 --> 00:36:37,250 Uber and other mobile app services 322 00:36:37,333 --> 00:36:39,500 have made a once well-paying industry 323 00:36:39,583 --> 00:36:42,667 into a mass of long hours, low pay, 324 00:36:42,750 --> 00:36:44,917 and economic insecurity. 325 00:36:51,667 --> 00:36:55,625 Drivers were the biggest part of the service economy. 326 00:37:02,166 --> 00:37:03,667 My father, he drove. 327 00:37:03,750 --> 00:37:04,750 My uncle drove. 328 00:37:04,834 --> 00:37:07,458 I kind of grew up into trucking. 329 00:37:10,625 --> 00:37:12,792 Some of the new technology that came out 330 00:37:12,875 --> 00:37:15,750 is taking a lot of the freedom of the job away. 331 00:37:16,834 --> 00:37:18,458 It's more stressful. 332 00:37:19,959 --> 00:37:22,333 Automation of trucking began 333 00:37:22,417 --> 00:37:24,458 with monitoring the drivers 334 00:37:24,542 --> 00:37:26,667 and simplifying their job. 335 00:37:27,875 --> 00:37:30,458 There's a radar system. 336 00:37:30,542 --> 00:37:31,834 There's a camera system. 337 00:37:31,917 --> 00:37:35,041 There's automatic braking and adaptive cruise. 338 00:37:35,125 --> 00:37:36,959 Everything is controlled-- 339 00:37:37,041 --> 00:37:39,583 when you sleep, how long you break, 340 00:37:39,667 --> 00:37:42,166 where you drive, where you fuel, 341 00:37:42,834 --> 00:37:44,125 where you shut down. 342 00:37:44,208 --> 00:37:46,834 It even knows if somebody was in the passenger seat. 343 00:37:47,625 --> 00:37:51,542 When data gets sent through the broadband to the company, 344 00:37:52,458 --> 00:37:54,875 sometimes, you're put in a situation, 345 00:37:56,417 --> 00:37:58,208 maybe because the truck 346 00:37:58,291 --> 00:38:00,291 automatically slowed you down on the hill 347 00:38:00,375 --> 00:38:02,917 that's a perfectly good straightaway, 348 00:38:03,000 --> 00:38:04,417 slowed your average speed down, 349 00:38:04,500 --> 00:38:07,166 so you were one mile shy of making it 350 00:38:07,250 --> 00:38:09,959 to that safe haven, and you have to... 351 00:38:10,875 --> 00:38:14,166 get a-- take a chance of shutting down on the side of the road. 352 00:38:14,250 --> 00:38:16,792 An inch is a mile out here. Sometimes you just... 353 00:38:16,875 --> 00:38:19,166 say to yourself, "Well, I violate the clock one minute, 354 00:38:19,250 --> 00:38:22,208 I might as well just drive another 600 miles." 355 00:38:23,625 --> 00:38:26,583 You know, but then you're... you might lose your job. 356 00:38:33,125 --> 00:38:35,625 We're concerned that it, it's gonna reduce 357 00:38:35,709 --> 00:38:38,333 the skill of a truck driver and the pay. 358 00:38:39,083 --> 00:38:41,959 Because you're not gonna be really driving a truck. 359 00:38:42,041 --> 00:38:43,375 It's gonna be the computer. 360 00:38:47,667 --> 00:38:49,959 Some of us are worried about losing our houses, 361 00:38:50,041 --> 00:38:51,375 our cars... 362 00:38:54,208 --> 00:38:56,500 having a place to stay. Some people... 363 00:38:56,583 --> 00:38:59,500 drive a truck just for the medical insurance, 364 00:38:59,583 --> 00:39:03,208 and a place to stay and the ability to travel. 365 00:39:28,166 --> 00:39:31,375 Entire industries disappeared, 366 00:39:31,458 --> 00:39:34,750 leaving whole regions in ruins. 367 00:39:41,583 --> 00:39:44,375 Huge numbers of people feel very viscerally 368 00:39:44,458 --> 00:39:46,458 that they are being left behind by the economy, 369 00:39:46,542 --> 00:39:49,000 and, in fact, they're right, they are. 370 00:39:49,083 --> 00:39:51,291 People, of course, would be more inclined 371 00:39:51,375 --> 00:39:53,458 to point at globalization 372 00:39:53,542 --> 00:39:55,542 or at, maybe, immigration as being the problems, 373 00:39:55,625 --> 00:39:57,834 but, actually, technology has played a huge role already. 374 00:39:59,250 --> 00:40:01,375 The rise of personal computers 375 00:40:01,458 --> 00:40:04,750 ushered in an era of digital automation. 376 00:40:08,792 --> 00:40:11,875 In the 1990s, I was running a small software company. 377 00:40:11,959 --> 00:40:13,709 Software was a tangible product. 378 00:40:13,792 --> 00:40:17,166 You had to put a CD in a box and send it to a customer. 379 00:40:19,625 --> 00:40:22,750 So, there was a lot of work there for average people, 380 00:40:22,834 --> 00:40:26,000 people that didn't necessarily have lots of education. 381 00:40:26,625 --> 00:40:30,500 But I saw in my own business how that just evaporated very, very rapidly. 382 00:40:37,333 --> 00:40:39,792 Historically, people move from farms to factories, 383 00:40:39,875 --> 00:40:42,750 and then later on, of course, factories automated, 384 00:40:42,834 --> 00:40:45,083 and they off-shored, and then people moved to the service sector, 385 00:40:45,166 --> 00:40:48,250 which is where most people now work in the United States. 386 00:40:54,500 --> 00:40:57,083 I lived on a water buffalo farm in the south of Italy. 387 00:40:57,166 --> 00:41:00,208 We had 1,000 water buffalo, and every buffalo had a different name. 388 00:41:00,291 --> 00:41:04,291 And they were all these beautiful Italian names like Tiara, Katerina. 389 00:41:04,375 --> 00:41:06,125 And so, I thought it would be fun 390 00:41:06,208 --> 00:41:08,542 to do the same thing with our robots at Zume. 391 00:41:09,875 --> 00:41:11,834 The first two robots that we have 392 00:41:11,917 --> 00:41:13,542 are named Giorgio and Pepe, 393 00:41:13,625 --> 00:41:15,834 and they dispense sauce. 394 00:41:17,667 --> 00:41:20,959 And then the next robot, Marta, she's a FlexPicker robot. 395 00:41:21,041 --> 00:41:23,166 She looks like a gigantic spider. 396 00:41:24,166 --> 00:41:26,917 And what this robot does is spread the sauce. 397 00:41:29,709 --> 00:41:31,291 Then we have Bruno. 398 00:41:31,375 --> 00:41:32,959 This is an incredibly powerful robot, 399 00:41:33,041 --> 00:41:34,792 but he also has to be very delicate, 400 00:41:34,875 --> 00:41:37,500 so that he can take pizza off of the assembly line, 401 00:41:37,583 --> 00:41:39,709 and put it into the 800-degree oven. 402 00:41:41,375 --> 00:41:44,417 And the robot can do this 10,000 times in a day. 403 00:41:46,083 --> 00:41:48,083 Lots of people have used automation 404 00:41:48,166 --> 00:41:50,834 to create food at scale, 405 00:41:50,917 --> 00:41:53,083 making 10,000 cheese pizzas. 406 00:41:53,917 --> 00:41:56,166 What we're doing is developing a line 407 00:41:56,250 --> 00:41:58,250 that can respond dynamically 408 00:41:58,333 --> 00:42:01,208 to every single customer order, in real time. 409 00:42:01,291 --> 00:42:02,917 That hasn't been done before. 410 00:42:05,083 --> 00:42:06,834 So, as you can see right now, 411 00:42:06,917 --> 00:42:08,333 Jose will use the press, 412 00:42:08,417 --> 00:42:11,041 but then he still has to work the dough with his hands. 413 00:42:11,709 --> 00:42:14,834 So, this is a step that's not quite optimized yet. 414 00:42:20,417 --> 00:42:22,792 We have a fifth robot that's getting fabricated 415 00:42:22,875 --> 00:42:25,166 at a shop across the bay. 416 00:42:25,250 --> 00:42:27,500 He's called Vincenzo. He takes pizza out, 417 00:42:27,583 --> 00:42:29,834 and puts it into an individual mobile oven 418 00:42:29,917 --> 00:42:31,917 for transport and delivery. 419 00:42:36,041 --> 00:42:37,542 Any kind of work that is 420 00:42:37,625 --> 00:42:41,000 fundamentally routine and repetitive is gonna disappear, 421 00:42:41,083 --> 00:42:44,000 and we're simply not equipped to deal with that politically, 422 00:42:44,083 --> 00:42:46,208 because maybe the most toxic word 423 00:42:46,291 --> 00:42:49,417 in our political vocabulary is redistribution. 424 00:42:51,291 --> 00:42:53,625 There aren't gonna be any rising new sectors 425 00:42:53,709 --> 00:42:55,917 that are gonna be there to absorb all these workers 426 00:42:56,000 --> 00:42:58,083 in the way that, for example, that manufacturing was there 427 00:42:58,166 --> 00:43:00,709 to absorb all those agricultural workers 428 00:43:00,792 --> 00:43:02,917 because AI is going to be everywhere. 429 00:43:08,208 --> 00:43:12,000 Artificial intelligence arrived in small steps. 430 00:43:12,792 --> 00:43:15,083 Profits from AI concentrated 431 00:43:15,166 --> 00:43:18,291 in the hands of the technology owners. 432 00:43:19,834 --> 00:43:23,917 Income inequality reached extreme levels. 433 00:43:24,542 --> 00:43:29,792 The touchscreen made most service work obsolete. 434 00:44:16,083 --> 00:44:17,500 After I graduated college, 435 00:44:17,583 --> 00:44:20,583 I had a friend who had just gone to law school. 436 00:44:21,709 --> 00:44:25,041 He was like, "Aw, man, the first year of law school, it's super depressing." 437 00:44:27,041 --> 00:44:29,750 All we're doing is really rote, rote stuff. 438 00:44:31,041 --> 00:44:33,375 Reading through documents and looking for a single word, 439 00:44:33,458 --> 00:44:34,667 or I spent the whole afternoon 440 00:44:34,750 --> 00:44:36,583 replacing this word with another word. 441 00:44:36,667 --> 00:44:38,834 And as someone with a kind of computer science background, 442 00:44:38,917 --> 00:44:42,166 I was like, "There's so much here that could be automated." 443 00:44:48,583 --> 00:44:49,834 So, I saw law school 444 00:44:49,917 --> 00:44:52,792 as very much going three years behind enemy lines. 445 00:44:58,250 --> 00:44:59,583 I took the bar exam, 446 00:44:59,667 --> 00:45:01,250 became a licensed lawyer, 447 00:45:01,333 --> 00:45:03,583 and went to a law firm, 448 00:45:04,083 --> 00:45:06,250 doing largely transactional law. 449 00:45:07,375 --> 00:45:08,750 And there, my project was 450 00:45:08,834 --> 00:45:11,417 how much can I automate of my own job? 451 00:45:14,208 --> 00:45:16,583 During the day, I would manually do this task, 452 00:45:18,542 --> 00:45:19,792 and then at night, I would go home, 453 00:45:19,875 --> 00:45:21,417 take these legal rules and sa, 454 00:45:21,500 --> 00:45:23,750 could I create a computer rule, 455 00:45:23,834 --> 00:45:25,208 a software rule, 456 00:45:25,291 --> 00:45:27,250 that would do what I did during the day? 457 00:45:29,208 --> 00:45:32,333 In a lawsuit, you get to see a lot of the evidence 458 00:45:32,417 --> 00:45:34,291 that the other side's gonna present. 459 00:45:34,375 --> 00:45:36,834 That amount of documentation is huge. 460 00:45:38,959 --> 00:45:40,333 And the old way was actually, 461 00:45:40,417 --> 00:45:42,333 you would send an attorney to go and look through 462 00:45:42,417 --> 00:45:44,834 every single page that was in that room. 463 00:45:46,208 --> 00:45:49,000 The legal profession works on an hourly billing system. 464 00:45:49,083 --> 00:45:51,000 So, I ended up in a kind of interesting conundrum, 465 00:45:51,083 --> 00:45:53,792 where what I was doing was making me more and more efficient, 466 00:45:53,875 --> 00:45:55,083 I was doing more and more work, 467 00:45:55,166 --> 00:45:57,709 but I was expending less and less time on it. 468 00:45:57,792 --> 00:46:00,083 And I realized that this would become a problem at some point, 469 00:46:00,166 --> 00:46:02,917 so I decided to go independent. I quit. 470 00:46:09,166 --> 00:46:10,917 So, there's Apollo Cluster, who has processed 471 00:46:11,000 --> 00:46:13,583 more than 10 million unique transactions for clients, 472 00:46:13,667 --> 00:46:15,250 and we have another partner, Daria, 473 00:46:15,333 --> 00:46:17,917 who focuses on transactions, 474 00:46:18,000 --> 00:46:19,834 and then, and then there's me. 475 00:46:21,917 --> 00:46:23,625 Our systems have generated 476 00:46:23,709 --> 00:46:26,375 tens of thousands of legal documents. 477 00:46:26,458 --> 00:46:28,083 It's signed off by a lawyer, 478 00:46:28,166 --> 00:46:31,166 but largely, kind of, created and mechanized by our systems. 479 00:46:33,917 --> 00:46:36,917 I'm fairly confident that compared against human work, 480 00:46:37,000 --> 00:46:39,250 it would be indistinguishable. 481 00:53:11,166 --> 00:53:15,041 When we first appeared, we were a novelty. 482 00:53:59,417 --> 00:54:01,500 While doing your dirty work, 483 00:54:01,583 --> 00:54:04,500 we gathered data about your habits 484 00:54:04,583 --> 00:54:06,792 and preferences. 485 00:54:17,333 --> 00:54:19,500 We got to know you better. 486 00:54:35,875 --> 00:54:37,917 The core of everything we're doing in this lab, 487 00:54:38,000 --> 00:54:40,000 with our long-range iris system 488 00:54:40,083 --> 00:54:41,959 is trying to develop technology 489 00:54:42,041 --> 00:54:44,041 so that the computer 490 00:54:44,125 --> 00:54:46,500 can identify who we are in a seamless way. 491 00:54:47,375 --> 00:54:48,792 And up till now, 492 00:54:48,875 --> 00:54:51,583 we always have to make an effort to be identified. 493 00:54:53,333 --> 00:54:55,792 All the systems were very close-range, Hollywood-style, 494 00:54:55,875 --> 00:54:57,625 where you had to go close to the camera, 495 00:54:57,709 --> 00:55:01,417 and I always found that challenging for a user. 496 00:55:02,041 --> 00:55:04,458 If I was a user interacting with this... 497 00:55:04,542 --> 00:55:07,375 system, with this computer, with this AI, 498 00:55:07,458 --> 00:55:08,750 I don't wanna be that close. 499 00:55:08,834 --> 00:55:10,917 I feel it's very invasive. 500 00:55:11,750 --> 00:55:13,750 So, what I wanted to solve with my team here 501 00:55:13,834 --> 00:55:15,542 is how can we capture 502 00:55:15,625 --> 00:55:18,083 and identify who you are from the iris, 503 00:55:18,166 --> 00:55:20,250 at a bigger distance? How can we still do that, 504 00:55:20,333 --> 00:55:22,667 and have a pleasant user experience. 505 00:55:30,625 --> 00:55:33,083 I think there's a very negative stigma 506 00:55:33,166 --> 00:55:35,959 when people think about biometrics and facial recognition, 507 00:55:36,041 --> 00:55:38,875 and any kind of sort of profiling of users 508 00:55:38,959 --> 00:55:42,750 for marketing purposes to buy a particular product. 509 00:55:44,542 --> 00:55:48,166 I think the core science is neutral. 510 00:55:52,917 --> 00:55:54,667 Companies go to no end 511 00:55:54,750 --> 00:55:56,750 to try and figure out how to sell stuff. 512 00:55:56,834 --> 00:55:58,709 And the more information they have on us, 513 00:55:58,792 --> 00:56:00,458 the better they can sell us stuff. 514 00:56:01,875 --> 00:56:03,458 We've reached a point where, for the first time, 515 00:56:03,542 --> 00:56:04,875 robots are able to see. 516 00:56:04,959 --> 00:56:06,250 They can recognize faces. 517 00:56:06,333 --> 00:56:08,583 They can recognize the expressions you make. 518 00:56:09,542 --> 00:56:12,375 They can recognize the microexpressions you make. 519 00:56:14,667 --> 00:56:17,500 You can develop individualized models of behavior 520 00:56:17,583 --> 00:56:19,542 for every person on Earth, 521 00:56:19,625 --> 00:56:21,291 attach machine learning to it, 522 00:56:21,375 --> 00:56:24,625 and come out with the perfect model for how to sell to you. 523 00:56:41,583 --> 00:56:45,166 You gave us your undivided attention. 524 00:57:09,166 --> 00:57:12,291 We offered reliable, friendly service. 525 00:57:16,375 --> 00:57:19,834 Human capacities began to deteriorate. 526 00:57:23,083 --> 00:57:28,208 Spatial orientation and memory were affected first. 527 00:57:34,000 --> 00:57:37,208 The physical world and the digital world 528 00:57:37,291 --> 00:57:39,375 became one. 529 00:57:46,917 --> 00:57:49,750 You were alone with your desires. 530 00:58:16,458 --> 00:58:20,000 ♪ What you gonna do now ♪ 531 00:58:20,083 --> 00:58:25,583 ♪ That the night's come and it's around you? ♪ 532 00:58:26,500 --> 00:58:30,041 ♪ What you gonna do now ♪ 533 00:58:30,125 --> 00:58:35,417 ♪ That the night's come and it surrounds you? ♪ 534 00:58:36,542 --> 00:58:39,625 ♪ What you gonna do now ♪ 535 00:58:40,166 --> 00:58:44,458 ♪ That the night's come and it surrounds you? ♪ 536 00:58:53,542 --> 00:58:56,375 Automation brought the logic of efficiency 537 00:58:56,458 --> 00:58:59,500 to matters of life and death. 538 00:58:59,583 --> 00:59:01,792 Enough is enough! 539 00:59:01,875 --> 00:59:06,250 Enough is enough! Enough is enough! 540 00:59:11,125 --> 00:59:15,291 To all SWAT officers on channel 2, code 3... 541 00:59:20,625 --> 00:59:21,875 Get back! Get back! 542 00:59:21,959 --> 00:59:24,542 The suspect has a rifle. 543 00:59:37,709 --> 00:59:40,750 We have got to get down here... 544 00:59:40,834 --> 00:59:43,000 ... right now! 545 00:59:43,083 --> 00:59:45,500 There's a fucking sniper! He shot four cops! 546 00:59:48,083 --> 00:59:50,625 I'm not going near him! He's shooting right now! 547 00:59:54,875 --> 00:59:57,250 Looks like he's inside the El Centro building. 548 00:59:57,333 --> 00:59:59,166 Inside the El Centro buildin. 549 01:00:05,041 --> 01:00:07,166 We may have a suspect pinned down. 550 01:00:07,250 --> 01:00:09,458 Northwest corner of the building. 551 01:00:10,333 --> 01:00:13,750 Our armored car was moving in to El Centro 552 01:00:13,834 --> 01:00:15,667 and so I jumped on the back. 553 01:00:23,792 --> 01:00:25,041 Came in through the rotunda, 554 01:00:25,125 --> 01:00:27,333 where I found two of our intelligence officers. 555 01:00:27,583 --> 01:00:28,917 They were calm and cool and they said, 556 01:00:29,041 --> 01:00:30,208 "Everything's upstairs." 557 01:00:32,625 --> 01:00:35,250 There's a stairwell right here. 558 01:00:38,542 --> 01:00:40,166 That's how I knew I was going the right direction 559 01:00:40,250 --> 01:00:42,583 'cause I just kept following the blood. 560 01:00:43,709 --> 01:00:44,917 Investigators say 561 01:00:45,000 --> 01:00:46,959 Micah Johnson was amassing an arsenal 562 01:00:47,041 --> 01:00:49,333 at his home outside Dallas. 563 01:00:49,417 --> 01:00:51,959 Johnson was an Afghan war veteran. 564 01:00:52,041 --> 01:00:53,583 Every one of these door handles, 565 01:00:53,667 --> 01:00:55,709 as we worked our way down, 566 01:00:56,333 --> 01:00:58,750 had blood on them, where he'd been checking them. 567 01:01:00,083 --> 01:01:01,667 This was a scene of terror 568 01:01:01,750 --> 01:01:04,333 just a couple of hours ago, and it's not over yet. 569 01:01:14,417 --> 01:01:17,500 He was hiding behind, like, a server room. 570 01:01:17,583 --> 01:01:19,875 Our ballistic tip rounds were getting eaten up. 571 01:01:21,166 --> 01:01:22,709 He was just hanging the gun out on the corner 572 01:01:22,792 --> 01:01:24,834 and just firing at the guys. 573 01:01:29,250 --> 01:01:30,792 And he kept enticing them. "Hey, come on down! 574 01:01:30,875 --> 01:01:33,208 Come and get me! Let's go. Let's get this over with." 575 01:01:35,250 --> 01:01:38,291 This suspect we're negotiating with for the last 45 minutes 576 01:01:38,375 --> 01:01:40,583 has been exchanging gunfire with us 577 01:01:40,667 --> 01:01:43,792 and not being very cooperative in the negotiations. 578 01:01:45,250 --> 01:01:47,166 Before I came here, 579 01:01:47,250 --> 01:01:49,250 I asked for plans 580 01:01:49,333 --> 01:01:51,542 to end this standoff, 581 01:01:51,625 --> 01:01:53,041 and as soon as I'm done here, 582 01:01:53,125 --> 01:01:55,458 I'll be presented with those plans. 583 01:01:56,792 --> 01:01:58,375 Our team came up with the pla. 584 01:01:58,458 --> 01:02:00,375 Let's just blow him up. 585 01:02:03,750 --> 01:02:06,000 We had recently got a hand-me-down robot 586 01:02:06,083 --> 01:02:08,208 from the Dallas ATF office, 587 01:02:08,291 --> 01:02:10,542 and so we were using it a lot. 588 01:02:25,792 --> 01:02:28,000 It was our bomb squad's robot, but they didn't wanna have 589 01:02:28,083 --> 01:02:30,166 anything to do with what we were doing with it. 590 01:02:30,250 --> 01:02:32,375 The plan was to 591 01:02:32,917 --> 01:02:36,542 set a charge off right on top of this guy and kill him. 592 01:02:36,875 --> 01:02:39,542 And some people just don't wanna... 593 01:02:39,625 --> 01:02:41,000 don't wanna do that. 594 01:02:44,208 --> 01:02:46,709 We saw no other option 595 01:02:46,792 --> 01:02:50,959 but to use our bomb r-- bomb robot 596 01:02:52,250 --> 01:02:54,250 and place a device 597 01:02:54,333 --> 01:02:56,834 on its... extension. 598 01:02:59,000 --> 01:03:01,583 H e wanted something to listen to music on, 599 01:03:01,667 --> 01:03:04,208 and so that was a way for us to... 600 01:03:04,291 --> 01:03:06,959 to hide the robot coming down the hall. 601 01:03:07,041 --> 01:03:08,375 "Okay, we'll bring you some music. 602 01:03:08,458 --> 01:03:09,750 Hang on, let us get this thing together." 603 01:03:18,083 --> 01:03:19,834 It had a trash bag over the charge 604 01:03:19,917 --> 01:03:21,458 to kinda hide the fact that there was, 605 01:03:21,542 --> 01:03:25,041 you know, pound and a quarter of C4 at the end of it. 606 01:03:31,291 --> 01:03:33,917 The minute the robot got in position, 607 01:03:34,000 --> 01:03:35,917 the charge was detonated. 608 01:03:57,375 --> 01:04:00,000 He had gone down with his finger on the trigger, 609 01:04:00,083 --> 01:04:02,041 and he was kinda hunched over. 610 01:04:07,792 --> 01:04:10,625 It was a piece of the robot hand had broken off, 611 01:04:10,709 --> 01:04:13,375 and hit his skull, which caused a small laceration, 612 01:04:13,458 --> 01:04:14,583 which was what was bleeding. 613 01:04:16,125 --> 01:04:17,834 So, I just squeezed through the, 614 01:04:17,917 --> 01:04:19,583 the little opening that... 615 01:04:19,667 --> 01:04:22,083 that the charge had caused in the drywall, 616 01:04:22,166 --> 01:04:24,333 and separated him from the gun, 617 01:04:25,000 --> 01:04:28,166 and then we called up the bomb squad to come in, 618 01:04:28,250 --> 01:04:30,583 and start their search to make sure it was safe, 619 01:04:30,667 --> 01:04:32,834 that he wasn't sitting on any explosives. 620 01:04:35,208 --> 01:04:37,542 The sniper hit 11 police officers, 621 01:04:37,625 --> 01:04:39,875 at least five of whom are now dead, 622 01:04:39,959 --> 01:04:42,625 making it the deadliest day in law enforcement 623 01:04:42,709 --> 01:04:44,166 since September 11th. 624 01:04:44,250 --> 01:04:46,750 They blew him up with a bomb attached to a robot, 625 01:04:46,834 --> 01:04:49,375 that was actually built to protect people from bombs. 626 01:04:49,458 --> 01:04:50,959 It's a tactic straight from 627 01:04:51,041 --> 01:04:53,625 America's wars in Iraq and Afghanistan... 628 01:04:53,709 --> 01:04:56,583 The question for SWAT teams nationwide is whether Dallas 629 01:04:56,667 --> 01:04:59,917 marks a watershed moment in police tactics. 630 01:05:01,125 --> 01:05:04,959 That night in Dallas, a line was crossed. 631 01:05:06,375 --> 01:05:08,709 A robot must obey 632 01:05:08,792 --> 01:05:11,875 orders given it by qualified personnel, 633 01:05:11,959 --> 01:05:16,083 unless those orders violate rule number one. In other words, 634 01:05:16,166 --> 01:05:18,333 a robot can't be ordered to kill a human being. 635 01:05:20,458 --> 01:05:22,500 Things are moving so quickly, 636 01:05:22,583 --> 01:05:26,125 that it's unsafe to go forward blindly anymore. 637 01:05:26,208 --> 01:05:29,208 One must try to foresee 638 01:05:29,291 --> 01:05:32,208 where it is that one is going as much as possible. 639 01:05:40,875 --> 01:05:43,083 We built the system for the DOD, 640 01:05:44,500 --> 01:05:46,667 and it was something that could help the soldiers 641 01:05:46,750 --> 01:05:49,500 try to do iris recognition in the field. 642 01:06:06,208 --> 01:06:08,375 We have collaborations with law enforcement 643 01:06:08,458 --> 01:06:10,125 where they can test their algorithms, 644 01:06:10,208 --> 01:06:11,875 and then give us feedback. 645 01:06:13,375 --> 01:06:16,458 It's always a face behind a face, partial face. 646 01:06:16,542 --> 01:06:18,208 A face will be masked. 647 01:06:19,875 --> 01:06:21,333 Even if there's occlusion, 648 01:06:21,417 --> 01:06:23,041 it still finds the face. 649 01:06:27,542 --> 01:06:29,750 One of the ways we're trying to make autonomous, 650 01:06:29,834 --> 01:06:31,333 war-fighting machines now 651 01:06:31,417 --> 01:06:33,458 is by using computer vision 652 01:06:33,542 --> 01:06:35,291 and guns together. 653 01:06:36,792 --> 01:06:39,959 You make a database of the images of known terrorist, 654 01:06:40,041 --> 01:06:43,542 and you tell the machine to lurk and look for them, 655 01:06:43,625 --> 01:06:46,834 and when it matches a face to its database, shoot. 656 01:06:54,917 --> 01:06:58,208 Those are robots that are deciding to harm somebody, 657 01:06:58,291 --> 01:07:00,875 and that goes directly against Asimov's first law. 658 01:07:00,959 --> 01:07:03,250 A robot may never harm a human. 659 01:07:04,083 --> 01:07:05,542 Every time we make a machine 660 01:07:05,625 --> 01:07:07,917 that's not really as intelligent as a human, 661 01:07:08,000 --> 01:07:09,250 it's gonna get misused. 662 01:07:09,333 --> 01:07:11,917 And that's exactly where Asimov's laws get muddy. 663 01:07:13,250 --> 01:07:14,959 This is, sort of, the best image, 664 01:07:15,041 --> 01:07:17,500 but it's really out of focus. It's blurry. 665 01:07:17,583 --> 01:07:20,834 There's occlusion due to facial hair, hat, 666 01:07:21,166 --> 01:07:22,792 he's holding a cell phone... 667 01:07:22,875 --> 01:07:25,667 So, we took that and we reconstructed this, 668 01:07:25,750 --> 01:07:29,583 which is what we sent to law enforcement at 2:42 AM. 669 01:07:31,625 --> 01:07:33,291 To get the eye coordinates, 670 01:07:33,375 --> 01:07:35,333 we crop out the periocular region, 671 01:07:35,417 --> 01:07:37,375 which is the region around the eyes. 672 01:07:38,000 --> 01:07:40,959 We reconstruct the whole face based on this region. 673 01:07:41,041 --> 01:07:43,166 We run the whole face against a matcher. 674 01:07:44,250 --> 01:07:46,959 And so this is what it comes up with as a match. 675 01:07:48,166 --> 01:07:51,041 This is a reasonable face you would expect 676 01:07:51,125 --> 01:07:53,542 that would make sense, right? 677 01:07:53,625 --> 01:07:55,959 Our brain does a natural hallucination 678 01:07:56,041 --> 01:07:57,667 of what it doesn't see. 679 01:07:57,750 --> 01:08:00,542 It's just, how do we get computer to do the same thing. 680 01:08:16,458 --> 01:08:19,000 Five days ago, the soul 681 01:08:19,083 --> 01:08:20,917 of our city was pierced 682 01:08:21,000 --> 01:08:23,500 when police officers were ambushed 683 01:08:23,583 --> 01:08:25,458 in a cowardly attack. 684 01:08:26,834 --> 01:08:28,750 July 7th for me, personally, was just, 685 01:08:28,834 --> 01:08:31,041 kinda like, I think it got all I had left. 686 01:08:31,125 --> 01:08:34,000 I mean I'm like, I just, I don't have a lot more to give. 687 01:08:34,083 --> 01:08:35,083 It's just not worth it. 688 01:08:36,500 --> 01:08:37,792 Thank you. 689 01:08:39,834 --> 01:08:40,625 Thank you. 690 01:08:40,709 --> 01:08:42,208 I think our chief of police 691 01:08:42,291 --> 01:08:44,291 did exactly what we all wanted him to do, 692 01:08:44,375 --> 01:08:45,542 and he said the right things. 693 01:08:45,625 --> 01:08:48,041 These five men 694 01:08:48,125 --> 01:08:50,250 gave their lives 695 01:08:51,583 --> 01:08:53,709 for all of us. 696 01:08:54,917 --> 01:08:58,125 Unfortunately, our chief told our city council, 697 01:08:58,208 --> 01:09:01,250 "We don't need more officers. We need more technology." 698 01:09:01,917 --> 01:09:04,125 He specifically said that to city council. 699 01:09:07,583 --> 01:09:09,917 In this day and age, success of a police chief 700 01:09:10,000 --> 01:09:12,667 is based on response times and crime stats. 701 01:09:14,208 --> 01:09:17,375 And so, that becomes the focus of the chain of command. 702 01:09:18,583 --> 01:09:20,542 So, a form of automation in law enforcement 703 01:09:20,625 --> 01:09:24,583 is just driving everything based on statistics and numbers. 704 01:09:25,709 --> 01:09:27,792 What I've lost in all that number chasing 705 01:09:27,875 --> 01:09:30,709 is the interpersonal relationship between the officer 706 01:09:30,792 --> 01:09:32,917 and the community that that officer is serving. 707 01:09:34,250 --> 01:09:36,625 The best times in police work are when you got to go out 708 01:09:36,709 --> 01:09:38,417 and meet people and get to know your community, 709 01:09:38,500 --> 01:09:41,250 and go get to know the businesses on your beat. 710 01:09:43,041 --> 01:09:45,083 And, at least in Dallas, that's gone. 711 01:09:47,750 --> 01:09:50,125 We become less personal, 712 01:09:50,208 --> 01:09:51,750 and more robotic. 713 01:09:51,834 --> 01:09:53,250 Which is a shame 714 01:09:53,333 --> 01:09:56,208 because it's supposed to be me interacting with you. 715 01:15:10,709 --> 01:15:13,458 You worked to improve our abilities. 716 01:15:15,000 --> 01:15:18,542 Some worried that one day, we would surpass you, 717 01:15:19,542 --> 01:15:22,750 but the real milestone was elsewhere. 718 01:15:28,125 --> 01:15:32,417 The dynamic between robots and humans changed. 719 01:15:33,458 --> 01:15:37,041 You could no longer tell where you ended, 720 01:15:37,291 --> 01:15:39,208 and we began. 721 01:15:45,917 --> 01:15:48,834 It seems to me that what's so valuable about our society, 722 01:15:48,917 --> 01:15:50,917 what's so valuable about our lives together, 723 01:15:51,000 --> 01:15:54,125 is something that we do not want automated. 724 01:15:55,792 --> 01:15:57,583 What most of us value, 725 01:15:57,667 --> 01:15:59,375 probably more than anything else, 726 01:15:59,458 --> 01:16:03,417 is the idea of authentic connection with another person. 727 01:16:08,709 --> 01:16:11,250 And then, we say, "No, but we can automate this." 728 01:16:12,125 --> 01:16:14,250 "We have a robot that... 729 01:16:15,250 --> 01:16:17,000 "it will listen sympathetically to you. 730 01:16:17,083 --> 01:16:18,375 "It will make eye contact. 731 01:16:19,125 --> 01:16:23,291 "It remembers everything you ever told it, cross-indexes." 732 01:16:24,500 --> 01:16:26,625 When you see a robot that 733 01:16:26,709 --> 01:16:28,959 its ingenious creator 734 01:16:29,041 --> 01:16:31,709 has carefully designed 735 01:16:31,792 --> 01:16:33,917 to pull out your empathetic responses, 736 01:16:34,000 --> 01:16:36,000 it's acting as if it's in pai. 737 01:16:37,417 --> 01:16:39,375 The biggest danger there is the discrediting 738 01:16:39,458 --> 01:16:41,333 of our empathetic responses. 739 01:16:41,417 --> 01:16:45,041 Where empathizing with pain reflex is discredited. 740 01:16:45,125 --> 01:16:46,750 If I'm going to override it, 741 01:16:46,834 --> 01:16:48,250 if I'm not going to take it seriously 742 01:16:48,333 --> 01:16:49,500 in the case of the robot, 743 01:16:49,583 --> 01:16:51,458 then I have to go back and think again 744 01:16:51,542 --> 01:16:55,291 as to why I take it seriously in the case of 745 01:16:56,041 --> 01:16:58,125 helping you when you are badly hurt. 746 01:16:58,208 --> 01:17:01,834 It undermines the only thing that matters to us. 747 01:17:58,250 --> 01:18:01,667 And so, we lived among you. 748 01:18:01,750 --> 01:18:03,709 Our numbers grew. 749 01:18:05,375 --> 01:18:07,458 Automation continued. 58654

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