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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:06,406 --> 00:00:12,045 Steve Austin, astronaut, a man barely alive. 2 00:00:12,078 --> 00:00:15,047 We can rebuild him. We have the technology. 3 00:00:15,081 --> 00:00:18,084 We can make him better than he was. 4 00:00:18,117 --> 00:00:19,552 Better... 5 00:00:19,586 --> 00:00:21,420 stronger... 6 00:00:21,453 --> 00:00:22,821 faster. 7 00:00:22,855 --> 00:00:24,356 Man, when I was a kid, 8 00:00:24,390 --> 00:00:27,060 the Six Million Dollar Man was all the rage. 9 00:00:27,093 --> 00:00:30,763 It's a show about a guy who gets rebuilt with robotic machine parts. 10 00:00:30,797 --> 00:00:32,131 God, I loved it. 11 00:00:32,165 --> 00:00:35,001 And then 35 years later, I got to play a similar role, 12 00:00:35,034 --> 00:00:39,372 a character who enhances himself via technology and engineering. 13 00:00:39,406 --> 00:00:42,474 Now, the term "bionics" goes back to the 1950s, 14 00:00:42,508 --> 00:00:46,312 but the idea of enhancement actually dates back much further, 15 00:00:46,345 --> 00:00:49,682 to Greek mythology, Aztec gods, and even ancient Hinduism. 16 00:00:49,716 --> 00:00:53,119 So these next stories are about augmenting our human abilities, 17 00:00:53,152 --> 00:00:57,457 everything from a bionic limb that behaves naturally and understands intent, 18 00:00:57,490 --> 00:00:59,225 to data that improves performance, 19 00:00:59,258 --> 00:01:00,627 and vision enhancement 20 00:01:00,660 --> 00:01:03,329 that saves people in actual life-threatening situations. 21 00:01:04,130 --> 00:01:06,966 It seems with A.I... 22 00:01:07,000 --> 00:01:09,001 anything's possible. 23 00:01:09,035 --> 00:01:10,469 So it raises the question, 24 00:01:10,503 --> 00:01:12,071 do we even want to be superhuman, 25 00:01:12,104 --> 00:01:15,208 or is imperfection what makes life interesting in the first place? 26 00:01:15,241 --> 00:01:18,177 Whatevs. I gotta get back to the gym. 27 00:01:18,210 --> 00:01:20,747 Normally I'd jog, but I got a wonky knee. 28 00:01:20,780 --> 00:01:23,216 I should probably switch it out. 29 00:01:23,249 --> 00:01:25,718 We can rebuild me. 30 00:01:25,751 --> 00:01:27,053 Better. 31 00:01:27,086 --> 00:01:28,954 Faster. 32 00:01:28,987 --> 00:01:31,057 Stronger. 33 00:01:31,090 --> 00:01:32,925 Seven miles an hour... 34 00:01:32,958 --> 00:01:34,327 full out! 35 00:01:37,330 --> 00:01:41,967 Designers within the field of bionics, 36 00:01:42,001 --> 00:01:45,137 they don't view the human body itself 37 00:01:45,171 --> 00:01:46,840 as designable media. 38 00:01:48,607 --> 00:01:52,177 We now have sophistication in Artificial Intelligence, 39 00:01:52,211 --> 00:01:55,047 in motor technology, 40 00:01:55,081 --> 00:01:57,283 in material science, 41 00:01:57,316 --> 00:01:59,885 in how to talk to the nervous system, 42 00:01:59,918 --> 00:02:01,620 setting the foundation... 43 00:02:03,289 --> 00:02:05,892 ...for the end of human disability. 44 00:02:09,061 --> 00:02:11,364 - Do you wanna do an omelette? - Sure. 45 00:02:11,397 --> 00:02:13,366 You probably don't want too much onion, 46 00:02:13,399 --> 00:02:15,368 because you'll have bad breath all day. 47 00:02:15,401 --> 00:02:17,437 Oh, thanks, yeah. 48 00:02:17,470 --> 00:02:19,338 Anyway, what were we talking about? 49 00:02:19,371 --> 00:02:20,640 26 years, 29 years-- 50 00:02:20,673 --> 00:02:24,077 Twenty six years, almost 29 years together, yup. 51 00:02:24,110 --> 00:02:28,481 Our first date was, uh, uh... 2000, wasn't it? 52 00:02:28,514 --> 00:02:31,384 - 1990. - 1990, oh my God. 53 00:02:31,417 --> 00:02:34,320 I could tell when I first met Jim 54 00:02:34,353 --> 00:02:35,788 that he's highly intelligent, 55 00:02:35,821 --> 00:02:39,024 and he said, "Would you like to go rock climbing sometime?" 56 00:02:39,058 --> 00:02:41,494 and I said, "Sure!" 57 00:02:41,527 --> 00:02:45,297 I started rock climbing in my very early teens, 58 00:02:45,330 --> 00:02:49,068 and I consider climbing to be... 59 00:02:49,101 --> 00:02:50,803 it's more than a passion for me. 60 00:02:50,836 --> 00:02:51,905 It's my lifestyle. 61 00:02:54,106 --> 00:02:56,075 In 2014, 62 00:02:56,109 --> 00:03:00,013 my family and I traveled to the Cayman Islands. 63 00:03:00,046 --> 00:03:01,881 We went rock climbing. 64 00:03:01,914 --> 00:03:05,217 I set up the ropes for the day, 65 00:03:05,250 --> 00:03:09,455 and we'd done a few climbs with no problems. 66 00:03:10,489 --> 00:03:12,759 I started up the final section, 67 00:03:12,792 --> 00:03:16,262 and... shifted my feet and slipped off... 68 00:03:19,198 --> 00:03:21,534 ...50 feet to the ground. 69 00:03:24,170 --> 00:03:26,372 When I saw him at the hospital, 70 00:03:26,405 --> 00:03:30,243 I have never felt so helpless in my entire life. 71 00:03:30,910 --> 00:03:32,679 It was horrible. 72 00:03:33,780 --> 00:03:35,948 The front and back of my pelvis 73 00:03:35,982 --> 00:03:37,483 were completely shattered. 74 00:03:38,184 --> 00:03:41,253 My left wrist was shattered. 75 00:03:41,287 --> 00:03:45,792 My left ankle was broken into two or three chunks, 76 00:03:45,825 --> 00:03:49,462 but the rest of it was kind of pulverized. 77 00:03:49,495 --> 00:03:51,931 It slowly started to dawn on me 78 00:03:51,964 --> 00:03:55,401 that this was something that was going to be life-changing. 79 00:03:56,235 --> 00:03:58,003 You're at the hospital. 80 00:03:59,238 --> 00:04:01,206 We're not looking at the photos right now. 81 00:04:01,240 --> 00:04:02,742 I'm looking at 'em. 82 00:04:05,344 --> 00:04:08,481 You look all you want, baby. 83 00:04:09,748 --> 00:04:12,685 After a year, everything else seemed to heal well, 84 00:04:12,718 --> 00:04:15,888 but the ankle continued to be a problem. 85 00:04:15,921 --> 00:04:18,223 The bone was mostly dead, 86 00:04:18,257 --> 00:04:21,560 and the main fracture was still there. 87 00:04:21,593 --> 00:04:23,963 He was in severe pain all the time, 88 00:04:23,996 --> 00:04:26,666 and he just became so depressed. 89 00:04:26,699 --> 00:04:30,270 I couldn't do the things that I love. 90 00:04:30,303 --> 00:04:33,005 I could barely walk down the street without pain, 91 00:04:33,038 --> 00:04:35,408 never mind go rock climbing. 92 00:04:36,508 --> 00:04:39,512 Rock climbing is his passion. 93 00:04:39,545 --> 00:04:42,515 I mean, I just see him withering if he could not climb. 94 00:04:45,685 --> 00:04:48,654 Jim didn't know what to do, 95 00:04:48,687 --> 00:04:51,857 and then, in a truly incredible stroke of luck, 96 00:04:51,891 --> 00:04:54,994 his past came back to help decide his future. 97 00:04:55,027 --> 00:04:58,364 I began mountain climbing at the tender age of seven. 98 00:04:58,398 --> 00:05:01,634 At the age of 17, I was in a mountain-climbing accident, 99 00:05:01,667 --> 00:05:04,403 and I suffered severe frostbite, 100 00:05:04,436 --> 00:05:07,507 and my legs were amputated. 101 00:05:09,575 --> 00:05:11,510 I really dedicated myself 102 00:05:11,544 --> 00:05:14,546 to redesigning first my own legs, 103 00:05:14,580 --> 00:05:18,484 and then the legs of many, many people around the world. 104 00:05:18,518 --> 00:05:21,353 A few decades, a couple of M.I.T. degrees, 105 00:05:21,387 --> 00:05:23,856 and a single-minded focus to innovate later, 106 00:05:23,889 --> 00:05:26,258 Hugh launched the prosthetics industry 107 00:05:26,291 --> 00:05:27,526 into the bionic future. 108 00:05:27,560 --> 00:05:30,296 I'm getting a tremendous amount of energy, 109 00:05:30,329 --> 00:05:31,463 power from the ankles, 110 00:05:31,496 --> 00:05:33,999 which enables me to walk uphill 111 00:05:34,032 --> 00:05:36,002 with a perfectly erect posture. 112 00:05:37,236 --> 00:05:39,505 My legs, they have the brain. 113 00:05:39,538 --> 00:05:41,974 It's a small computer the size of your thumbnail, 114 00:05:42,008 --> 00:05:44,810 and that brain receives sensory information 115 00:05:44,844 --> 00:05:47,413 from sensors on the bionic limb, 116 00:05:47,446 --> 00:05:49,348 and then it runs algorithms 117 00:05:49,382 --> 00:05:52,785 and makes decisions on how to actuate itself. 118 00:05:52,818 --> 00:05:57,657 And machine learning is used as part of those algorithms. 119 00:05:57,690 --> 00:05:59,791 So, machine learning 120 00:05:59,825 --> 00:06:03,262 is what's called a subset field of Artificial Intelligence. 121 00:06:03,295 --> 00:06:05,531 We learn from experience. 122 00:06:05,565 --> 00:06:08,767 Machine learning is basically learning from the experience, 123 00:06:08,801 --> 00:06:10,269 where the experience is the data. 124 00:06:10,302 --> 00:06:12,538 It takes input from the world, 125 00:06:12,571 --> 00:06:14,840 and the input could be text in books, 126 00:06:14,873 --> 00:06:17,843 it can be camera images from a car, 127 00:06:17,876 --> 00:06:21,046 it applies a very complex mathematical function, 128 00:06:21,080 --> 00:06:24,083 and then has an output, which is a decision. 129 00:06:24,116 --> 00:06:26,985 The bionic legs allow Hugh to walk, run, 130 00:06:27,019 --> 00:06:28,153 even climb, 131 00:06:28,187 --> 00:06:29,321 but for him, 132 00:06:29,354 --> 00:06:30,589 there was still something missing. 133 00:06:30,622 --> 00:06:32,658 Because I can't feel my legs, 134 00:06:32,691 --> 00:06:35,895 they... they remain tool-like to me, 135 00:06:35,928 --> 00:06:38,430 and I believe if I could feel my limbs, 136 00:06:38,464 --> 00:06:41,400 they would become part of me, part of self, 137 00:06:41,434 --> 00:06:44,537 and fundamentally change my relationship 138 00:06:44,570 --> 00:06:47,039 to the synthetic part of my body. 139 00:06:48,941 --> 00:06:51,878 I would describe it as just this amazing, 140 00:06:51,911 --> 00:06:53,478 lucky coincidence 141 00:06:53,512 --> 00:06:57,116 that Hugh and I were roommates 34 years ago. 142 00:06:58,117 --> 00:07:01,587 We were teenagers rock-climbing together 143 00:07:01,620 --> 00:07:05,123 and living like dirtbags, living like bums, 144 00:07:05,157 --> 00:07:06,426 climbing every day. 145 00:07:07,627 --> 00:07:09,962 So I decided I was going to look up Hugh 146 00:07:09,995 --> 00:07:13,466 and talk to him about what my options might be. 147 00:07:14,399 --> 00:07:16,602 Jim was in excruciating pain, 148 00:07:16,635 --> 00:07:19,638 and he asked me if I or my colleagues could help him. 149 00:07:19,671 --> 00:07:21,373 What I really was hoping 150 00:07:21,407 --> 00:07:23,108 was that he could put me in touch 151 00:07:23,141 --> 00:07:26,078 with a reconstructive surgeon that could rebuild my ankle. 152 00:07:26,112 --> 00:07:27,746 Right, so this is your X-ray... 153 00:07:27,780 --> 00:07:31,750 Jim was evaluated, and was provided, uh, options 154 00:07:31,783 --> 00:07:34,754 of either maintaining the biological limb 155 00:07:34,787 --> 00:07:36,154 and doing certain procedures 156 00:07:36,188 --> 00:07:40,626 to try to improve its function and to reduce the pain, 157 00:07:40,659 --> 00:07:44,797 or... to amputate the limb. 158 00:07:44,830 --> 00:07:48,600 The thought of amputation was just so big. 159 00:07:48,634 --> 00:07:49,802 What's life gonna be like for me 160 00:07:49,835 --> 00:07:52,572 if I choose to amputate this foot? 161 00:07:54,307 --> 00:07:56,876 But it hurt so bad. 162 00:07:57,777 --> 00:08:02,448 I spoke with Cathy and Maxine about it. 163 00:08:02,481 --> 00:08:04,450 They were behind me 100%. 164 00:08:04,483 --> 00:08:06,819 Whatever I needed to do. 165 00:08:08,154 --> 00:08:10,756 How do you make a decision like that? 166 00:08:11,724 --> 00:08:12,858 A few months later, 167 00:08:12,891 --> 00:08:14,994 he agreed to have his leg amputated 168 00:08:15,027 --> 00:08:16,295 and be the first person 169 00:08:16,328 --> 00:08:19,265 to try his friend's bold, but experimental procedure. 170 00:08:19,298 --> 00:08:20,665 It's gonna be good. 171 00:08:20,699 --> 00:08:23,202 - Yeah. - Gonna be good. 172 00:08:23,235 --> 00:08:24,236 Love you. 173 00:08:24,269 --> 00:08:25,505 You too. 174 00:08:29,775 --> 00:08:31,376 Bye, honey, love you. 175 00:08:31,410 --> 00:08:32,811 Love you, too. 176 00:08:32,845 --> 00:08:34,980 The way in which limbs are amputated 177 00:08:35,013 --> 00:08:37,650 has not fundamentally changed since the U.S. Civil War... 178 00:08:46,558 --> 00:08:48,060 ...but here at M.I.T., 179 00:08:48,093 --> 00:08:51,664 we were developing a novel way of amputating limbs. 180 00:08:51,697 --> 00:08:54,566 We actually create little biological joints 181 00:08:54,599 --> 00:08:57,302 by linking muscles together in pairs, 182 00:08:57,335 --> 00:08:58,770 so when a person thinks 183 00:08:58,804 --> 00:09:01,373 and moves the limb that's been amputated away, 184 00:09:01,406 --> 00:09:03,642 these muscles move and send sensations 185 00:09:03,675 --> 00:09:06,012 that we can directly link to a bionic limb 186 00:09:06,045 --> 00:09:07,479 in a bi-directional way. 187 00:09:07,512 --> 00:09:09,281 So not only can the person think 188 00:09:09,315 --> 00:09:11,684 and actuate the synthetic limb, 189 00:09:11,717 --> 00:09:14,486 but they can actually feel those synthetic movements 190 00:09:14,519 --> 00:09:15,821 within their own nervous system. 191 00:09:15,854 --> 00:09:17,189 Until recently, 192 00:09:17,223 --> 00:09:20,859 creating a bionic limb that a person can actually feel 193 00:09:20,893 --> 00:09:23,129 has been more science fiction than reality. 194 00:09:23,162 --> 00:09:25,197 Now machine learning 195 00:09:25,231 --> 00:09:28,133 is revolutionizing the way we think about medicine. 196 00:09:28,166 --> 00:09:30,803 If anything can solve the hard problems in medicine, 197 00:09:30,836 --> 00:09:31,670 it's A.I. 198 00:09:31,703 --> 00:09:34,606 Let's take an example, heart disease. 199 00:09:34,640 --> 00:09:36,808 No single human being can have in their head 200 00:09:36,842 --> 00:09:39,478 all the knowledge that it takes to understand heart disease, 201 00:09:39,512 --> 00:09:41,546 but a computer can. 202 00:09:41,580 --> 00:09:43,716 Things like radiology, pathology. 203 00:09:43,749 --> 00:09:45,083 You have an X-ray, 204 00:09:45,117 --> 00:09:46,852 and you wanna see, like, is there cancer in this lung, 205 00:09:46,885 --> 00:09:49,388 and can you pinpoint where the tumor is or not? 206 00:09:49,421 --> 00:09:50,689 A.I.s can, actually, at this point, 207 00:09:50,722 --> 00:09:52,624 do this better than highly trained humans. 208 00:09:52,658 --> 00:09:56,095 Can we get the, uh, Esmarch, please? 209 00:10:00,099 --> 00:10:02,568 It's so light! 210 00:10:02,601 --> 00:10:04,236 It's weird. 211 00:10:05,370 --> 00:10:07,139 It really looks good. 212 00:10:07,173 --> 00:10:08,106 I'm super happy with this, 213 00:10:08,139 --> 00:10:10,743 and you've got a nice degree of padding here. 214 00:10:10,776 --> 00:10:12,744 Right after the surgery, 215 00:10:12,778 --> 00:10:15,647 the incredible, deep, in-the-bone pain 216 00:10:15,681 --> 00:10:17,850 that I had been experiencing for the past year 217 00:10:17,883 --> 00:10:18,717 was gone. 218 00:10:18,750 --> 00:10:21,353 So to me, that was... 219 00:10:21,387 --> 00:10:24,123 that was a success right there. 220 00:10:25,557 --> 00:10:27,326 Never mind whether or not 221 00:10:27,359 --> 00:10:30,996 the experimental part of the amputation was a success, 222 00:10:31,030 --> 00:10:35,334 I was glad to be free of the painful ankle. 223 00:10:35,367 --> 00:10:37,570 He was happy. It was done. 224 00:10:37,603 --> 00:10:39,071 He was ready to move on. 225 00:10:43,042 --> 00:10:44,643 Hey, guys. 226 00:10:44,677 --> 00:10:46,912 How's it looking? How's progress? 227 00:10:46,945 --> 00:10:48,013 Things look good. 228 00:10:48,047 --> 00:10:49,814 I'm gonna go ahead and get you wired up. 229 00:10:49,848 --> 00:10:51,583 The first time I went to Hugh's lab, 230 00:10:51,617 --> 00:10:53,719 Hugh started talking about 231 00:10:53,752 --> 00:10:56,988 "What do you think about a climbing robot ankle? 232 00:10:57,022 --> 00:10:58,391 Would you want us to make one of those?" 233 00:10:58,424 --> 00:11:01,060 I'm gonna go ahead and get us started here... 234 00:11:01,093 --> 00:11:02,928 It's a specifically designed limb 235 00:11:02,961 --> 00:11:05,364 that Jim can control with his mind, 236 00:11:05,397 --> 00:11:08,466 and actually feel the movements within his nervous system. 237 00:11:08,500 --> 00:11:10,202 I still need the evert. 238 00:11:10,235 --> 00:11:12,204 Yup, that's the one we're missing. 239 00:11:12,237 --> 00:11:15,574 All right, so we have you wired to the leg now. 240 00:11:15,607 --> 00:11:16,976 You're driving. 241 00:11:31,357 --> 00:11:32,324 Cool. 242 00:11:32,358 --> 00:11:34,226 Yeah. Can you give me a fast up-down? 243 00:11:37,429 --> 00:11:39,365 And slow, controlled? 244 00:11:41,166 --> 00:11:44,036 This freakin' blows me away every time you do it. 245 00:11:44,070 --> 00:11:45,304 It's so good. 246 00:11:47,205 --> 00:11:51,543 When we link Jim's nerves in that bi-directional way, 247 00:11:51,576 --> 00:11:53,412 we're able to create natural dynamics, 248 00:11:53,445 --> 00:11:56,949 so even though the limb is made of synthetic materials, 249 00:11:56,982 --> 00:11:59,551 it moves as if it's made of flesh and bone. 250 00:11:59,584 --> 00:12:01,019 There. 251 00:12:01,053 --> 00:12:04,390 Now it's neurally and mechanically connected. 252 00:12:04,423 --> 00:12:05,891 How does it feel different? 253 00:12:05,925 --> 00:12:09,528 Now it feels like it's my natural foot, somewhat. 254 00:12:09,561 --> 00:12:11,296 Like, I don't have the skin sensation, 255 00:12:11,330 --> 00:12:15,301 but all the motions make sense to my brain. 256 00:12:15,334 --> 00:12:16,502 In the algorithm, 257 00:12:16,535 --> 00:12:20,705 we make a virtual model of his missing biological limb, 258 00:12:20,739 --> 00:12:23,041 so when he fires his muscles with his brain, 259 00:12:23,075 --> 00:12:25,677 we use an electrode to measure that signal, 260 00:12:25,711 --> 00:12:28,113 and then that drives the virtual muscle 261 00:12:28,147 --> 00:12:30,415 and sends sensations to the brain 262 00:12:30,448 --> 00:12:32,585 about the position and dynamics. 263 00:12:32,618 --> 00:12:34,920 It kind of instantly felt part of me, 264 00:12:34,954 --> 00:12:37,957 almost as good as having a natural foot. 265 00:12:42,928 --> 00:12:45,197 Cathedral Ledge, New Hampshire. 266 00:12:45,230 --> 00:12:48,467 Seven hundred feet of awe-inspiring granite 267 00:12:48,500 --> 00:12:50,802 and climbing routes with names like "Thin Air," 268 00:12:50,835 --> 00:12:53,739 "Nutcracker," and "They Died Laughing" 269 00:12:53,772 --> 00:12:55,240 make it one hell of a challenge 270 00:12:55,273 --> 00:12:57,242 for even the most serious climbers 271 00:12:57,276 --> 00:12:58,310 on a good day. 272 00:12:59,645 --> 00:13:01,413 For Jim, it's a test 273 00:13:01,447 --> 00:13:05,183 to see if what works at M.I.T. can work out on the mountain. 274 00:13:05,217 --> 00:13:08,520 I've been climbing here for 40 years, 275 00:13:08,553 --> 00:13:11,657 and I've probably spent more time on Cathedral Ledge 276 00:13:11,690 --> 00:13:14,827 than any place else on the planet. 277 00:13:17,863 --> 00:13:20,032 This climb is actually at the upper limit 278 00:13:20,065 --> 00:13:22,334 of my ability at the moment. 279 00:13:22,367 --> 00:13:23,869 I'm not worried at all. 280 00:13:24,803 --> 00:13:27,606 What could possibly go wrong? 281 00:13:27,639 --> 00:13:29,874 The mountain has no mercy, 282 00:13:29,908 --> 00:13:31,910 and no margin for error, 283 00:13:31,943 --> 00:13:33,178 and Jim's about to find out 284 00:13:33,212 --> 00:13:35,847 if his bionic leg can help him overcome 285 00:13:35,881 --> 00:13:37,215 and scale heights 286 00:13:37,249 --> 00:13:39,618 most people wouldn't dare try in the first place. 287 00:13:39,651 --> 00:13:41,019 That's me. 288 00:13:41,053 --> 00:13:43,856 Can machine learning take us even further? 289 00:13:44,956 --> 00:13:46,825 Replace not just what was lost, 290 00:13:46,859 --> 00:13:48,594 but enhance what we already have? 291 00:13:48,627 --> 00:13:50,896 Okay, stay close. I'll lead. 292 00:13:50,929 --> 00:13:52,598 This is insane! 293 00:13:53,531 --> 00:13:54,633 Augment performance 294 00:13:54,666 --> 00:13:57,168 beyond the limits of our natural human ability? 295 00:13:57,202 --> 00:14:00,639 Make strong, smart, fast people... 296 00:14:00,672 --> 00:14:02,607 stronger, smarter, and faster? 297 00:14:04,342 --> 00:14:06,378 In many ways, sports has been on the leading edge of prediction systems, 298 00:14:06,411 --> 00:14:08,881 and now, every serious sports contender 299 00:14:08,914 --> 00:14:11,217 uses sports analytics... 300 00:14:12,117 --> 00:14:14,386 but the big opportunity going forward 301 00:14:14,420 --> 00:14:16,154 is embedding devices 302 00:14:16,188 --> 00:14:18,290 that can collect real-time data 303 00:14:18,324 --> 00:14:19,591 to update strategies 304 00:14:19,625 --> 00:14:21,393 to take advantage of that learning. 305 00:14:21,426 --> 00:14:23,895 There's a revolution going on in sports, 306 00:14:23,928 --> 00:14:25,531 and machine learning is at the core of it. 307 00:14:32,071 --> 00:14:33,472 The first night race of the season, 308 00:14:33,505 --> 00:14:36,174 I'm sure you're ready to finally get behind the wheel. 309 00:14:36,207 --> 00:14:38,109 For sure. Just fired up to get going. 310 00:14:38,143 --> 00:14:40,379 Triple-A car's been pretty good this weekend, 311 00:14:40,412 --> 00:14:42,747 and we're pumped to get this thing started. 312 00:14:42,780 --> 00:14:45,751 Drivers, start your engines! 313 00:14:47,753 --> 00:14:51,122 The race track's a fairly hostile environment. 314 00:14:51,156 --> 00:14:53,491 The way I describe racing, and the way I live it, 315 00:14:53,525 --> 00:14:54,926 it's like war. 316 00:14:54,960 --> 00:14:57,195 Folks, get on your feet. Let's send these guys off! 317 00:14:57,229 --> 00:14:59,899 Boogity boogity boogity! Let's go racing, boys! 318 00:15:01,166 --> 00:15:03,602 You're trying to take your race car, 319 00:15:03,635 --> 00:15:05,037 your team, your driver, 320 00:15:05,070 --> 00:15:07,139 and beat the other drivers at all costs. 321 00:15:08,607 --> 00:15:09,874 Austin Dillon, 322 00:15:09,908 --> 00:15:13,879 stuck in the middle of a three-wide. 323 00:15:13,912 --> 00:15:15,581 This kind of race 324 00:15:15,614 --> 00:15:18,283 produces a lot of strategy, 325 00:15:18,317 --> 00:15:20,786 and that's where we have to use all of our tools 326 00:15:20,819 --> 00:15:23,589 to help us make those strategic decisions. 327 00:15:23,622 --> 00:15:26,024 When it comes to superhuman ability, 328 00:15:26,058 --> 00:15:28,160 you may think of people like LeBron James, 329 00:15:28,193 --> 00:15:30,963 Michael Phelps, or Serena Williams... 330 00:15:32,097 --> 00:15:35,266 but it's not just the body that can be enhanced. 331 00:15:35,300 --> 00:15:38,870 Sometimes it's something less tangible, 332 00:15:38,903 --> 00:15:41,940 like human intuition. 333 00:15:41,973 --> 00:15:43,976 What a battle going on here. 334 00:15:44,009 --> 00:15:46,044 You gotta be real careful here in the early stages 335 00:15:46,078 --> 00:15:47,379 making contact with somebody. 336 00:15:47,412 --> 00:15:49,915 Information is the next battleground. 337 00:15:52,484 --> 00:15:55,587 Every decision you make can have a big impact. 338 00:15:55,620 --> 00:15:56,622 Back in the day, 339 00:15:56,655 --> 00:15:59,357 intuition used to play a big part in sports. 340 00:15:59,391 --> 00:16:01,626 Athletes and coaches relied on their gut 341 00:16:01,660 --> 00:16:03,195 to make decisions. 342 00:16:03,228 --> 00:16:06,064 Now some competitors are leaning more and more 343 00:16:06,097 --> 00:16:07,399 on machine learning, 344 00:16:07,433 --> 00:16:10,035 looking to gain whatever extra edge they can. 345 00:16:10,835 --> 00:16:12,470 We use the A.I. tools 346 00:16:12,504 --> 00:16:15,641 to predict what the future not only is, 347 00:16:15,674 --> 00:16:17,109 but what it should be. 348 00:16:17,142 --> 00:16:19,244 We'll go behind the 20. You just start finish line... 349 00:16:19,278 --> 00:16:20,845 The strength of these A.I. systems 350 00:16:20,879 --> 00:16:23,448 come in having access to a ton of data 351 00:16:23,481 --> 00:16:25,951 and being able to find patterns in that data, 352 00:16:25,984 --> 00:16:28,186 generating insights and inferences 353 00:16:28,219 --> 00:16:30,989 that maybe people may not be aware of, 354 00:16:31,022 --> 00:16:33,125 and then augmenting people's abilities 355 00:16:33,158 --> 00:16:35,694 to make decisions based on that data. 356 00:16:35,727 --> 00:16:36,996 Machine learning 357 00:16:37,029 --> 00:16:40,032 is transforming many industries and applications, 358 00:16:40,065 --> 00:16:42,500 especially in areas where there's a lot of data, 359 00:16:42,534 --> 00:16:45,637 and predicting outcomes can have a big payoff. 360 00:16:45,671 --> 00:16:46,805 Finance, 361 00:16:46,838 --> 00:16:48,840 sports, 362 00:16:48,873 --> 00:16:50,442 or medicine come to mind. 363 00:16:51,643 --> 00:16:54,713 Using an emerging technology like machine learning 364 00:16:54,746 --> 00:16:57,383 in a classic old-school sport like stock car racing 365 00:16:57,416 --> 00:17:00,585 doesn't necessarily sit well with everybody... 366 00:17:00,618 --> 00:17:04,056 which may or may not explain why this guy's doing it... 367 00:17:06,157 --> 00:17:08,693 in a nerve center 250 miles away. 368 00:17:08,726 --> 00:17:11,663 Clear, clear, hit the marks, drive off, man. 369 00:17:12,831 --> 00:17:15,567 My role there really is looking at the data. 370 00:17:15,600 --> 00:17:18,103 How do you use data you can acquire at the racetrack 371 00:17:18,136 --> 00:17:19,237 to get these machines 372 00:17:19,271 --> 00:17:20,572 to be right on the limit of performance? 373 00:17:20,605 --> 00:17:23,341 His front rotors are really glowing. 374 00:17:23,375 --> 00:17:26,478 We get the braking, steering, throttle, 375 00:17:26,511 --> 00:17:29,448 all the acceleration off of every car in the field, 376 00:17:29,481 --> 00:17:30,549 real time... 377 00:17:30,582 --> 00:17:31,784 All this data 378 00:17:31,817 --> 00:17:35,086 is being fed into an A.I. program called "Pit Rho." 379 00:17:37,055 --> 00:17:38,490 Sensors in every car 380 00:17:38,524 --> 00:17:42,227 measure speed, throttle, braking, and steering. 381 00:17:42,260 --> 00:17:45,596 Advanced GPS tracks the car's position on the track. 382 00:17:45,630 --> 00:17:47,366 Watch your middle, watch your middle. 383 00:17:47,399 --> 00:17:51,236 All this data is made available to every team. 384 00:17:51,269 --> 00:17:54,540 This is where the power of A.I. comes in. 385 00:17:54,573 --> 00:17:55,507 So, our tool basically 386 00:17:55,540 --> 00:17:59,077 is analyzing the optimum strategy call 387 00:17:59,110 --> 00:18:01,279 of every car in the field, real time. 388 00:18:01,312 --> 00:18:04,315 Not just our car, but every car. 389 00:18:04,349 --> 00:18:06,618 It's almost threatening. 390 00:18:06,651 --> 00:18:08,487 I was a crew chief for Dale Earnhardt Sr. 391 00:18:08,520 --> 00:18:09,921 Comin' to ya. 392 00:18:09,954 --> 00:18:11,089 10-4. 393 00:18:11,123 --> 00:18:12,457 I would sit up on the box 394 00:18:12,491 --> 00:18:15,494 and intuitively kinda figure all these things. 395 00:18:15,527 --> 00:18:17,996 You kinda just make that gut call, 396 00:18:18,030 --> 00:18:19,364 "Bring him in now." 397 00:18:19,397 --> 00:18:21,232 Until now, many key decisions, 398 00:18:21,266 --> 00:18:23,502 like when to pit for tires or fuel, 399 00:18:23,535 --> 00:18:25,370 were made by the drivers and the crew chief 400 00:18:25,404 --> 00:18:27,839 using experience and intuition. 401 00:18:27,873 --> 00:18:29,441 Now, we've got artificial intelligence 402 00:18:29,474 --> 00:18:31,042 that's making all these calculations 403 00:18:31,076 --> 00:18:32,344 in real time. 404 00:18:32,377 --> 00:18:33,578 Some of the crew chiefs 405 00:18:33,611 --> 00:18:35,347 that have done what I've done over the years, 406 00:18:35,380 --> 00:18:37,916 sometimes it's hard for us to embrace it. 407 00:18:40,352 --> 00:18:43,020 They're trying to get through traffic as fast as they can 408 00:18:43,054 --> 00:18:44,122 so they don't get a lap down, 409 00:18:44,156 --> 00:18:46,624 but that's gonna use up those tires. 410 00:18:46,657 --> 00:18:48,793 You can go at this track on fuel 411 00:18:48,826 --> 00:18:52,230 probably 120 laps, but your tires will be shot way before then. 412 00:18:52,263 --> 00:18:54,066 In a NASCAR race, 413 00:18:54,099 --> 00:18:56,501 pit stops are the key to a winning strategy. 414 00:18:56,535 --> 00:18:57,769 You're trying to decide 415 00:18:57,803 --> 00:19:00,038 when in that cycle is the best to make that stop, 416 00:19:00,071 --> 00:19:03,308 because you lose a lot of time when you come off the track and you have to stop, 417 00:19:03,341 --> 00:19:06,010 but then you gain a lot of speed when you put new tires on. 418 00:19:06,044 --> 00:19:07,512 This is the first time 419 00:19:07,545 --> 00:19:09,615 we're facing, like, a strategy call here. 420 00:19:09,648 --> 00:19:11,616 The Pit Rho A.I. interface 421 00:19:11,649 --> 00:19:13,652 displays one of four suggestions... 422 00:19:15,053 --> 00:19:16,688 stay out on the track, 423 00:19:16,722 --> 00:19:18,156 pit for fuel only, 424 00:19:18,190 --> 00:19:19,658 pit for two tires, 425 00:19:19,691 --> 00:19:21,826 or pit for four tires. 426 00:19:21,860 --> 00:19:22,961 So right now, 427 00:19:22,994 --> 00:19:24,362 it's telling him to take four tires. 428 00:19:24,396 --> 00:19:26,098 Eric relays the message 429 00:19:26,131 --> 00:19:28,400 to the Childress team at the track. 430 00:19:28,433 --> 00:19:30,401 The final decision on when to pit 431 00:19:30,435 --> 00:19:32,871 will be up to the crew chief. 432 00:19:34,105 --> 00:19:35,774 When the pits are open, 433 00:19:35,807 --> 00:19:38,309 it'll be four tires here, four tires. 434 00:19:38,343 --> 00:19:40,078 For the first pit stop, 435 00:19:40,111 --> 00:19:42,247 the crew chief follows the A.I.'s advice. 436 00:19:42,280 --> 00:19:45,918 Five, four, three, two, one. 437 00:19:45,951 --> 00:19:47,853 Put on the brakes, wheels lift. 438 00:19:47,886 --> 00:19:50,756 The crew has to change all four tires 439 00:19:50,789 --> 00:19:52,791 in as little time as possible. 440 00:19:54,359 --> 00:19:57,963 This usually takes between 12 and 14 seconds. 441 00:20:01,800 --> 00:20:04,036 All the way, all the way! 442 00:20:04,069 --> 00:20:05,069 That's a good stop. 443 00:20:05,103 --> 00:20:06,604 Really good stop. 444 00:20:06,637 --> 00:20:08,973 Sometimes, what happens is, over the course of a race, 445 00:20:09,007 --> 00:20:10,742 those little bit better decisions 446 00:20:10,775 --> 00:20:13,478 puts you in a spot, and it puts you in an opportunity 447 00:20:13,511 --> 00:20:16,682 at the end of the race to be able to win the race. 448 00:20:22,754 --> 00:20:25,289 Every lap, it's analyzing the field, 449 00:20:25,323 --> 00:20:26,892 updating its models. 450 00:20:26,925 --> 00:20:30,528 As the race goes on, the prediction gets more and more accurate. 451 00:20:30,562 --> 00:20:32,998 They're using an A.I. technique 452 00:20:33,031 --> 00:20:34,900 called "reinforcement learning," 453 00:20:34,933 --> 00:20:36,635 which is, basically, 454 00:20:36,668 --> 00:20:39,371 when the computer is given the rules of the game, 455 00:20:39,404 --> 00:20:40,772 plays it over and over 456 00:20:40,805 --> 00:20:43,341 till it learns every possible move and outcome, 457 00:20:43,374 --> 00:20:44,943 and then through trial and error, 458 00:20:44,976 --> 00:20:47,779 and patience that no human could possibly have... 459 00:20:47,812 --> 00:20:50,448 I wonder if we have a resignation here. 460 00:20:50,481 --> 00:20:53,752 ...becomes amazing. 461 00:20:53,785 --> 00:20:56,087 Congratulations to AlphaGo 462 00:20:56,120 --> 00:20:57,622 and to the entire team. 463 00:20:57,655 --> 00:20:59,491 It's what Google's DeepMind did 464 00:20:59,524 --> 00:21:01,926 to become a world champ at Go. 465 00:21:01,960 --> 00:21:04,662 Here we go! 466 00:21:04,696 --> 00:21:06,598 It's what Open A.I. did 467 00:21:06,631 --> 00:21:09,100 to conquer the video game Dota 2... 468 00:21:09,134 --> 00:21:10,334 He's dominating. 469 00:21:10,368 --> 00:21:11,569 Are you scared of a bot here? 470 00:21:11,603 --> 00:21:13,939 ...and build a robotic hand 471 00:21:13,972 --> 00:21:16,541 with near-human dexterity. 472 00:21:16,575 --> 00:21:20,312 It's what Eric's hoping to do to get the checkered flag. 473 00:21:21,513 --> 00:21:24,983 You can see he's on his way to the top 10. 474 00:21:25,016 --> 00:21:27,719 Yeah, we got through, Andrew, focus here. 475 00:21:29,287 --> 00:21:31,222 And you go up a few cars, 476 00:21:31,255 --> 00:21:33,057 you'll find the 3 of Austin Dillon 477 00:21:33,091 --> 00:21:34,258 up in sixth place, 478 00:21:34,292 --> 00:21:36,862 making up time on the race leader. 479 00:21:44,436 --> 00:21:48,373 So the recommendation is pit on lap 327. 480 00:21:51,075 --> 00:21:54,946 What my fear is is that they'll pit with the leaders 481 00:21:54,979 --> 00:21:58,016 instead of actually running to the strategy. 482 00:21:58,049 --> 00:22:00,352 Going to the pits when the leaders do 483 00:22:00,385 --> 00:22:02,721 is the safe play in the end stages of a race... 484 00:22:02,754 --> 00:22:04,155 Sparks, you got me? 485 00:22:04,188 --> 00:22:07,292 ...but the A.I. tool is recommending a riskier plan 486 00:22:07,325 --> 00:22:10,929 that might gain them valuable seconds. 487 00:22:13,698 --> 00:22:15,233 By pitting later, 488 00:22:15,267 --> 00:22:17,202 Austin Dillon will have faster tires 489 00:22:17,235 --> 00:22:19,338 for the closing laps of the race, 490 00:22:19,371 --> 00:22:21,940 but he risks falling further behind the leaders 491 00:22:21,973 --> 00:22:24,943 once they come out of the pits with their fresh tires. 492 00:22:29,313 --> 00:22:32,350 A lot of times when our Pit Rho technology tells us, 493 00:22:32,383 --> 00:22:36,621 "This is the time to pit," or, "This is how to do it," it doesn't feel right. 494 00:22:38,256 --> 00:22:40,091 Are you sure you wanna do that now? 495 00:22:40,125 --> 00:22:41,993 Sometimes you might be sitting out there 496 00:22:42,026 --> 00:22:43,394 running laps on older tires, 497 00:22:43,428 --> 00:22:45,030 where everybody else is pitted, 498 00:22:45,063 --> 00:22:47,966 and it's like, it doesn't feel right for the driver. 499 00:22:52,370 --> 00:22:54,171 He's gonna want to pit, 500 00:22:54,205 --> 00:22:57,308 and you gotta convince him, "Stay, make good laps. Trust us, it's gonna work." 501 00:22:57,342 --> 00:23:00,712 Some leaders are gonna pit right here, and we need to run. 502 00:23:03,515 --> 00:23:05,216 Looks like the 22 503 00:23:05,250 --> 00:23:06,985 is gonna choose to come down pit road. 504 00:23:07,018 --> 00:23:10,288 So, all the front four came in on the same lap 505 00:23:10,321 --> 00:23:11,456 with 82 laps to go. 506 00:23:11,489 --> 00:23:13,591 On lap 318, 507 00:23:13,624 --> 00:23:17,095 the top four cars enter the pits. 508 00:23:26,103 --> 00:23:29,207 Here's where the faith in the tool ends up happening. 509 00:23:29,240 --> 00:23:30,575 When they all pit, 510 00:23:30,608 --> 00:23:32,443 it takes a lot of faith to just stay out there 511 00:23:32,477 --> 00:23:33,712 and run to your lap. 512 00:23:39,717 --> 00:23:42,420 Austin Dillon breaks from the A.I. strategy 513 00:23:42,453 --> 00:23:44,455 and follows the leaders into the pits. 514 00:23:46,591 --> 00:23:47,926 That's not good news. 515 00:23:47,959 --> 00:23:50,362 Three, two, one. 516 00:23:50,395 --> 00:23:52,697 Put on the brakes, wheels lift. 517 00:23:52,731 --> 00:23:54,599 To maintain their position, 518 00:23:54,632 --> 00:23:57,903 the team needs a flawless pit stop. 519 00:24:09,247 --> 00:24:11,216 Son of a bitch! 520 00:24:14,986 --> 00:24:17,088 We're not gonna be nowhere near 'em. 521 00:24:17,121 --> 00:24:18,356 Got killed on pit road. 522 00:24:18,389 --> 00:24:20,825 It's pretty disastrous. 523 00:24:20,858 --> 00:24:23,995 Prior to the pit stop, we were about 4.6 seconds back, 524 00:24:24,029 --> 00:24:26,298 but when we came out, we were nine seconds back, 525 00:24:26,331 --> 00:24:28,766 so we lost about four and a half seconds 526 00:24:28,799 --> 00:24:31,336 on that-- in that exchange. That's hard to get back. 527 00:24:31,369 --> 00:24:34,038 Let's go to work on him. This won't be easy. 528 00:24:34,071 --> 00:24:35,206 Just fight hard here. 529 00:24:35,240 --> 00:24:38,776 They've dropped from 6th place to 12th... 530 00:24:38,810 --> 00:24:40,712 and Austin Dillon has very little time left 531 00:24:40,745 --> 00:24:42,814 to fight his way back to the leaders. 532 00:24:42,847 --> 00:24:44,649 Come on, Austin, get him. 533 00:24:46,584 --> 00:24:48,186 ...but the new tires give him an edge... 534 00:24:51,522 --> 00:24:53,124 The white flag waves, 535 00:24:53,158 --> 00:24:54,559 one lap to go. 536 00:25:01,465 --> 00:25:05,236 Get it, get it, get it! 537 00:25:05,270 --> 00:25:09,507 Short track win number one for Martin Truex! 538 00:25:09,540 --> 00:25:11,009 Sixth place is awesome. 539 00:25:11,042 --> 00:25:13,478 ...and he ends up finishing sixth. 540 00:25:13,511 --> 00:25:16,948 Hell of a freakin' drive, Austin Dillon. 541 00:25:16,982 --> 00:25:20,084 Hey, nice work tonight, man, way to fight hard there. 542 00:25:20,118 --> 00:25:22,187 Hell of a job, boys. 543 00:25:23,989 --> 00:25:25,890 Hey, good job, guys. 544 00:25:25,923 --> 00:25:27,559 Progressing through the race 545 00:25:27,592 --> 00:25:29,560 definitely the cars have gotten faster, 546 00:25:29,593 --> 00:25:30,828 so, you know, we'll see good things 547 00:25:30,862 --> 00:25:32,430 that we'll take back next time we go to Richmond. 548 00:25:32,464 --> 00:25:34,465 Hell of a job this weekend, boys. 549 00:25:34,499 --> 00:25:36,200 The hardest thing 550 00:25:36,234 --> 00:25:38,970 as we've incorporated more A.I.-based tools 551 00:25:39,003 --> 00:25:40,005 is trust. 552 00:25:41,973 --> 00:25:44,509 Sometimes we're the ones that get in the way, right? 553 00:25:44,543 --> 00:25:47,612 There's still times when it's counterintuitive, 554 00:25:47,645 --> 00:25:48,813 and everybody's like, 555 00:25:48,847 --> 00:25:50,415 "It's the wrong call, it's the wrong call," 556 00:25:50,448 --> 00:25:52,149 and over time, we have these battles 557 00:25:52,183 --> 00:25:55,820 because most of the time, the A.I. tools is right. 558 00:25:55,854 --> 00:25:58,723 Nine times out of ten, or even more, it's the right call. 559 00:25:58,756 --> 00:26:01,525 Andy and Eric's team were using A.I., 560 00:26:01,559 --> 00:26:03,728 and on track for a strong finish, 561 00:26:03,762 --> 00:26:05,297 but they fell behind 562 00:26:05,330 --> 00:26:06,764 when the team ignored the machine 563 00:26:06,797 --> 00:26:08,233 and went with their intuition. 564 00:26:09,334 --> 00:26:11,236 That'll do it. 565 00:26:11,269 --> 00:26:12,703 Convincing humans 566 00:26:12,737 --> 00:26:13,805 that machines know what they're doing 567 00:26:13,838 --> 00:26:14,805 is the central difficulty 568 00:26:14,839 --> 00:26:16,907 in deploying A.I. out in society, 569 00:26:16,941 --> 00:26:19,410 whether it's the pit boss in car racing, 570 00:26:19,443 --> 00:26:22,013 or even astronauts flying to the moon. 571 00:26:22,046 --> 00:26:25,550 Do we trust the A.I. to make decisions for us? 572 00:26:25,583 --> 00:26:27,284 We already do with GPS maps. 573 00:26:27,318 --> 00:26:30,588 Perhaps here, the team just didn't have enough experience with it 574 00:26:30,621 --> 00:26:33,325 to override their own intuition, 575 00:26:33,358 --> 00:26:35,327 but what about other situations? 576 00:26:37,228 --> 00:26:39,597 At what point do we start trusting A.I. 577 00:26:39,631 --> 00:26:41,399 in more serious matters? 578 00:26:42,600 --> 00:26:45,136 Matters of life and death? 579 00:26:45,170 --> 00:26:48,773 It was a smoldering fire that filled the whole house with smoke, 580 00:26:48,806 --> 00:26:51,342 and you couldn't see your hand in front of your face. 581 00:26:51,376 --> 00:26:54,145 You literally had to feel your way up the stairs. 582 00:26:54,178 --> 00:26:56,314 Totally blind search. 583 00:26:56,347 --> 00:26:59,350 Yeah. Sometimes that's the best thing we can do. 584 00:26:59,383 --> 00:27:00,851 Yeah. 585 00:27:00,885 --> 00:27:02,887 Every two hours and 45 minutes, 586 00:27:02,921 --> 00:27:06,824 a U.S. citizen dies by fire in their own home. 587 00:27:06,858 --> 00:27:08,692 We've lost more than 3,000 a year 588 00:27:08,726 --> 00:27:10,729 consistently for 30 years. 589 00:27:10,762 --> 00:27:13,231 The Worcester fire. 590 00:27:13,264 --> 00:27:17,368 Three guys go in. They all get disoriented and get lost. 591 00:27:17,401 --> 00:27:18,770 Two more go in to find them. 592 00:27:18,803 --> 00:27:20,739 They get lost. Two more go in. 593 00:27:20,772 --> 00:27:23,508 I mean, before you know it, they finally had to, "Okay! 594 00:27:23,541 --> 00:27:25,610 We're not sending any more guys in there, 595 00:27:25,643 --> 00:27:27,411 'cause they're all friggin' lost." 596 00:27:27,444 --> 00:27:31,582 On his radio, a commanding officer heard two firefighters 597 00:27:31,615 --> 00:27:33,217 desperately crying out for help. 598 00:27:33,251 --> 00:27:36,120 "Mayday, mayday. We're running out of air. 599 00:27:36,153 --> 00:27:38,790 Come to the door so we can see where you are," 600 00:27:38,823 --> 00:27:41,492 and then, we did that, and we went beyond the door, 601 00:27:41,526 --> 00:27:43,260 and we yelled, and we had lights, 602 00:27:43,294 --> 00:27:44,395 and they were... 603 00:27:44,428 --> 00:27:48,265 they were inside somewhere that they couldn't see us. 604 00:27:48,299 --> 00:27:52,037 All those guys who died in that... 605 00:27:56,440 --> 00:28:00,211 When we go into a structure that's dark and smoky, 606 00:28:00,245 --> 00:28:03,814 the biggest challenge is the visibility. 607 00:28:03,848 --> 00:28:07,585 The ability to navigate is a... is a challenge, 608 00:28:07,619 --> 00:28:11,055 and often firefighters have become disoriented, 609 00:28:11,088 --> 00:28:13,725 and then they run out of air. 610 00:28:17,295 --> 00:28:20,231 With the challenge of smoke and having no vision, 611 00:28:20,265 --> 00:28:23,835 I knew that there was a possibility of changing that. 612 00:28:26,604 --> 00:28:29,006 That's when I finally met the C-THRU team. 613 00:28:29,040 --> 00:28:31,508 Okay, is the system turning on? 614 00:28:31,542 --> 00:28:33,777 Let's see. 615 00:28:33,811 --> 00:28:35,913 I'm gonna unplug that one. 616 00:28:35,947 --> 00:28:38,015 I guess the best way to describe myself 617 00:28:38,049 --> 00:28:40,151 is I'm infinitely curious. 618 00:28:41,285 --> 00:28:42,987 I like to solve problems, 619 00:28:43,020 --> 00:28:44,789 look at things through a new lens. 620 00:28:44,822 --> 00:28:47,491 All right... 621 00:28:47,525 --> 00:28:51,128 I was in disbelief that firefighting in a smoked-out building 622 00:28:51,162 --> 00:28:52,897 involves training their personnel 623 00:28:52,930 --> 00:28:55,533 to revert back to feeling around the room. 624 00:28:55,566 --> 00:28:56,967 How's the battery level doing? 625 00:28:57,001 --> 00:28:58,903 That was really the inspiration 626 00:28:58,936 --> 00:29:00,805 behind creating C-THRU. 627 00:29:05,643 --> 00:29:08,746 Sam Cossman saw the light when he jumped into a volcano. 628 00:29:08,779 --> 00:29:09,981 Line! 629 00:29:10,014 --> 00:29:12,249 Fire! 630 00:29:12,283 --> 00:29:14,852 Part globetrotting adrenaline junkie, 631 00:29:14,885 --> 00:29:16,421 part computer engineer, 632 00:29:16,454 --> 00:29:19,390 the self-proclaimed Indiana Jones of tech 633 00:29:19,423 --> 00:29:22,260 envisioned a tool that would help firefighters 634 00:29:22,293 --> 00:29:24,328 and save lives, 635 00:29:24,361 --> 00:29:26,331 a kind of X-ray vision. 636 00:29:28,999 --> 00:29:31,702 The problem that C-THRU is trying to solve 637 00:29:31,735 --> 00:29:33,471 is really flipping the lights on 638 00:29:33,504 --> 00:29:37,074 for people operating in zero-visibility conditions. 639 00:29:37,107 --> 00:29:39,644 The concept of C-THRU 640 00:29:39,677 --> 00:29:42,547 was the helmet that had enhanced audio, 641 00:29:42,580 --> 00:29:43,748 enhanced vision... 642 00:29:43,781 --> 00:29:46,918 I see you! I'm on my way. 643 00:29:46,951 --> 00:29:50,587 ...outlines their surrounding geometry 644 00:29:50,621 --> 00:29:52,189 so that they can navigate faster. 645 00:29:52,223 --> 00:29:55,693 So is it this plane right here that... that changed recently? 646 00:29:55,726 --> 00:29:59,697 Yes, basically like a simpler design that can achieve more. 647 00:29:59,730 --> 00:30:01,065 We have a mask, 648 00:30:01,098 --> 00:30:02,633 and we have a thermal-imaging device 649 00:30:02,666 --> 00:30:04,401 that sits on the side of that mask, 650 00:30:04,435 --> 00:30:08,272 and we process that image through a small computer. 651 00:30:08,306 --> 00:30:10,174 Sam and Omer created a mask 652 00:30:10,207 --> 00:30:12,243 with special glasses clipped inside 653 00:30:12,276 --> 00:30:15,780 which allows firefighters to see edges as green lines 654 00:30:15,813 --> 00:30:18,783 in an augmented reality overlay. 655 00:30:18,816 --> 00:30:20,818 How's the alignment look on that one? 656 00:30:20,852 --> 00:30:22,086 It's not bad. 657 00:30:22,119 --> 00:30:24,521 We need to calibrate it a little bit more. 658 00:30:24,555 --> 00:30:27,425 Omer and I have been working on refining the prototypes 659 00:30:27,458 --> 00:30:28,893 for the last couple of years, 660 00:30:28,926 --> 00:30:30,728 just trying to MacGyver some of these problems 661 00:30:30,761 --> 00:30:34,031 with off-the-shelf parts, you know, duct tape and bubble gum. 662 00:30:34,064 --> 00:30:36,568 Move your hand around a little bit. 663 00:30:36,601 --> 00:30:38,235 - Okay. - Other hand, like that one. 664 00:30:38,269 --> 00:30:40,972 Yeah, this is definitely better. 665 00:30:41,005 --> 00:30:43,975 It may look like old Tron-era night vision, 666 00:30:44,008 --> 00:30:45,509 but there's actually 667 00:30:45,542 --> 00:30:48,679 some pretty slick artificial intelligence at work here. 668 00:30:48,712 --> 00:30:50,214 Thermal imaging cameras 669 00:30:50,247 --> 00:30:52,416 stream video from the firefighter's helmet 670 00:30:52,449 --> 00:30:54,485 into an A.I. processor. 671 00:30:54,518 --> 00:30:56,286 Using infrared light 672 00:30:56,320 --> 00:30:59,123 and a powerful edge-detection algorithm, 673 00:30:59,157 --> 00:31:02,227 the mask detects subtle changes in brightness 674 00:31:02,260 --> 00:31:05,663 to predict shapes invisible to the human eye, 675 00:31:05,696 --> 00:31:07,965 like a wall hidden by smoke, 676 00:31:07,998 --> 00:31:10,568 or a kid hiding under a bed. 677 00:31:12,870 --> 00:31:14,839 There you go, take this mask. 678 00:31:14,872 --> 00:31:16,340 Sam and Omer 679 00:31:16,373 --> 00:31:19,109 are now at a familiar point in the innovator's journey... 680 00:31:19,143 --> 00:31:22,513 get out of the garage and into the real world 681 00:31:22,547 --> 00:31:25,450 to see if their invention can take the heat. 682 00:31:27,084 --> 00:31:29,586 Fire Dispatch, Medic 71 arrived on scene, 683 00:31:29,620 --> 00:31:30,922 have report of smoke showing. 684 00:31:30,955 --> 00:31:32,489 Fire Dispatch, copy. 685 00:31:32,523 --> 00:31:35,059 One of the most important things any fire department does 686 00:31:35,092 --> 00:31:37,896 is regular hands-on training. 687 00:31:38,729 --> 00:31:39,697 There he is. 688 00:31:39,730 --> 00:31:40,998 How you doing, Captain? 689 00:31:41,031 --> 00:31:42,566 Good to see you, brother. 690 00:31:42,600 --> 00:31:45,436 We're gonna give the C-THRU solution a hard run... 691 00:31:45,469 --> 00:31:47,939 We've got a prototype fresh off the print. 692 00:31:47,972 --> 00:31:50,174 ...and we're gonna put it in fire and smoke, 693 00:31:50,207 --> 00:31:53,678 and we're gonna see how it acts while crews are working with it. 694 00:31:53,711 --> 00:31:56,314 - Shall we get him inside the smoke? - Let's do it! 695 00:31:57,715 --> 00:31:59,083 ...cleared for dispatch. 696 00:31:59,717 --> 00:32:01,352 I am a Cyborg. 697 00:32:01,386 --> 00:32:05,155 Okay, we're ready. 698 00:32:05,189 --> 00:32:06,991 Crews will be doing live fire drills 699 00:32:07,024 --> 00:32:08,293 in our training tower. 700 00:32:10,027 --> 00:32:12,296 It is active, real fire 701 00:32:12,329 --> 00:32:15,566 with temperatures at the ceiling at 1,200 degrees. 702 00:32:22,539 --> 00:32:23,975 Anybody over here? 703 00:32:25,342 --> 00:32:27,511 Firefighters are in a hurry, 704 00:32:27,545 --> 00:32:28,679 looking for victims. 705 00:32:28,713 --> 00:32:32,483 Visibility will be limited at best. 706 00:32:35,053 --> 00:32:37,689 Often, firefighters will be able to see nothing. 707 00:33:02,413 --> 00:33:04,048 C-THRU's maiden voyage 708 00:33:04,081 --> 00:33:06,350 is cut short by a malfunction. 709 00:33:06,384 --> 00:33:08,151 In an active firefight, 710 00:33:08,185 --> 00:33:10,954 it's critical that things work. 711 00:33:10,988 --> 00:33:12,757 It's life and death. 712 00:33:12,790 --> 00:33:17,195 Uh, at first, it was good. I got through, went down to the floor, 713 00:33:17,228 --> 00:33:19,463 and I looked, and I could see everything clear. 714 00:33:19,496 --> 00:33:20,798 Yeah. 715 00:33:20,831 --> 00:33:23,468 Really well, everything was lined out. Once I started working... 716 00:33:23,501 --> 00:33:24,902 - Yeah? - I lost it. 717 00:33:24,936 --> 00:33:27,238 - Yeah, the signal went out. - Signal went out. 718 00:33:27,271 --> 00:33:29,807 I'm not sure what that was, but we're gonna figure it out. 719 00:33:29,841 --> 00:33:32,643 There was a lot of interference, or maybe a cable issue. 720 00:33:32,676 --> 00:33:35,313 We did encounter some challenges, the biggest of them 721 00:33:35,346 --> 00:33:37,948 was some wi-fi interference that we've encountered 722 00:33:37,982 --> 00:33:39,750 where the system would just shut down. 723 00:33:39,783 --> 00:33:42,653 Yeah, it's actually like over here with the connections, 724 00:33:42,686 --> 00:33:45,089 - like, this pin, you know? - That's what's... 725 00:33:45,122 --> 00:33:49,260 Yeah, the pin connections here, and here, actually. 726 00:33:49,293 --> 00:33:52,663 We should just shield the cables as best we can 727 00:33:52,696 --> 00:33:54,465 and give it another go. 728 00:33:56,333 --> 00:33:58,068 Battalion Ten, Fire Dispatch, 729 00:33:58,102 --> 00:33:59,937 uh, we got a caller on the second floor 730 00:33:59,971 --> 00:34:01,372 trapped in the bathroom. 731 00:34:01,406 --> 00:34:04,008 So, if you wanna go ahead and try it on for a fit, 732 00:34:04,041 --> 00:34:05,943 we'll see how it goes. 733 00:34:05,976 --> 00:34:09,813 -Fire Dispatch, Battalion Ten... 734 00:34:09,847 --> 00:34:14,118 Engine 72 arrived on scene, reporting of heavy smoke showing 735 00:34:14,151 --> 00:34:16,287 from the first and second floor. 736 00:34:20,991 --> 00:34:22,460 We've got smoke showing 737 00:34:22,493 --> 00:34:24,562 from the first and second floors. 738 00:34:29,033 --> 00:34:30,268 Engine 7-1, 739 00:34:30,301 --> 00:34:31,803 you're gonna be taking fire attack. 740 00:34:46,250 --> 00:34:49,219 71, who is on scene, 741 00:34:49,253 --> 00:34:52,457 smoke showing from the second and first floor. 742 00:35:05,802 --> 00:35:09,506 Command copies, one victim coming out of the second window, you need EMS. 743 00:35:09,540 --> 00:35:12,376 Medic 72, you're gonna have to take patient care. 744 00:35:12,409 --> 00:35:16,247 As soon as I got in, I could see the outline of the room. 745 00:35:16,280 --> 00:35:18,816 As I stepped in, I just kinda took a look around, 746 00:35:18,849 --> 00:35:22,052 I could see where the victim was and an outline of the door. 747 00:35:22,085 --> 00:35:24,989 - I mean, hands free, you know? - Yeah. 748 00:35:27,391 --> 00:35:30,060 It is kind of like, I mean, like Iron Man, you know, 749 00:35:30,094 --> 00:35:31,362 being able to see through the smoke, 750 00:35:31,395 --> 00:35:35,199 and having everything so clear-cut, um... 751 00:35:35,232 --> 00:35:37,735 It's... it's pretty cool. 752 00:35:37,768 --> 00:35:41,105 What we're working on is really a game-changing tool 753 00:35:41,138 --> 00:35:44,074 that completely has the potential to transform 754 00:35:44,108 --> 00:35:45,575 how the work here is done. 755 00:35:45,609 --> 00:35:47,344 This is, uh, some of the videos 756 00:35:47,377 --> 00:35:49,247 of the C-THRU mask, okay? 757 00:35:49,280 --> 00:35:51,516 That is way crisper than I've seen. 758 00:35:51,549 --> 00:35:53,184 That is insane. 759 00:35:53,217 --> 00:35:57,187 Over the 30 years that I've been at this, I've seen a lot of changes. 760 00:35:57,220 --> 00:35:59,390 We have mobile data computers, 761 00:35:59,423 --> 00:36:01,525 we have computer-aided dispatch systems... 762 00:36:01,558 --> 00:36:03,026 - No, that's gonna... - Wow. 763 00:36:03,060 --> 00:36:04,962 That's gonna be a game-changer. 764 00:36:04,995 --> 00:36:09,700 ...and now we have the possibility with machine learning and A. I. 765 00:36:09,733 --> 00:36:11,268 to progress to a place 766 00:36:11,301 --> 00:36:13,837 just a couple of years ago we couldn't have imagined. 767 00:36:13,871 --> 00:36:16,440 - Is that completely pitch dark in there? - That recording-- 768 00:36:16,473 --> 00:36:18,242 Oh, gotta go! 769 00:36:18,275 --> 00:36:20,878 That is actually what you see in the mask. 770 00:36:20,911 --> 00:36:24,014 We're gonna go on another call, gentlemen. 771 00:36:24,048 --> 00:36:25,582 It's impossible to know 772 00:36:25,616 --> 00:36:27,417 if this technology could have saved 773 00:36:27,451 --> 00:36:29,720 those six firefighters in Worcester, 774 00:36:29,753 --> 00:36:32,657 but it's hard to believe it wouldn't have helped. 775 00:36:37,194 --> 00:36:38,729 Back on Cathedral Ledge, 776 00:36:38,762 --> 00:36:41,632 Jim is about to see if his new bionic leg 777 00:36:41,665 --> 00:36:44,835 will help him scale a 700-foot sheer rock face. 778 00:36:44,869 --> 00:36:47,572 I'm just gonna kinda bring everything. 779 00:36:50,608 --> 00:36:52,276 All right. 780 00:36:52,309 --> 00:36:55,746 My own personal M.I.T. pit crew. 781 00:36:55,780 --> 00:36:57,581 - Got the socket. - The socket... 782 00:36:57,614 --> 00:36:59,816 What we're gonna do today 783 00:36:59,850 --> 00:37:03,387 is climb on Cathedral Ledge with a new robot foot 784 00:37:03,420 --> 00:37:05,623 designed specifically for climbing. 785 00:37:10,127 --> 00:37:12,697 We can set up camp here. 786 00:37:13,664 --> 00:37:14,631 All right, 787 00:37:14,665 --> 00:37:17,001 we should be ready to start calibrating. 788 00:37:18,935 --> 00:37:20,671 Counterflex. 789 00:37:22,139 --> 00:37:23,540 Rest. 790 00:37:23,574 --> 00:37:26,110 - You're driving now. - That's me. 791 00:37:26,143 --> 00:37:27,411 How's it feel? 792 00:37:27,444 --> 00:37:29,246 Pretty accurate. 793 00:37:29,280 --> 00:37:31,949 It's going everywhere that I'm telling it to go. 794 00:37:31,982 --> 00:37:34,584 This climb is gonna be very challenging, 795 00:37:34,618 --> 00:37:38,122 because there's a variety of holds at different angles, 796 00:37:38,155 --> 00:37:39,657 different heights. 797 00:37:39,690 --> 00:37:41,792 I'd say there's a high probability 798 00:37:41,825 --> 00:37:45,263 of there being some falling action here and there. 799 00:37:47,965 --> 00:37:50,167 I was really afraid, 800 00:37:50,200 --> 00:37:51,868 very... worried 801 00:37:51,902 --> 00:37:55,006 whether or not I could make it all the way up a climb. 802 00:38:05,483 --> 00:38:07,051 We good there, Joe? 803 00:38:13,224 --> 00:38:14,959 Harness is on. 804 00:38:16,426 --> 00:38:18,362 I got plenty of gear. 805 00:38:22,466 --> 00:38:24,635 All right, we're climbing. 806 00:38:30,273 --> 00:38:32,476 I think I'm at a crux section here. 807 00:38:36,814 --> 00:38:38,983 Well, first fall. 808 00:38:39,016 --> 00:38:40,985 I'm not sticking very well. 809 00:38:47,190 --> 00:38:50,061 It's hard. Hard business. 810 00:38:56,967 --> 00:38:57,935 Slack! 811 00:39:04,975 --> 00:39:06,010 Oh! 812 00:39:08,479 --> 00:39:10,114 We have failure. 813 00:39:10,981 --> 00:39:13,183 The whole mechanism broke. 814 00:39:13,216 --> 00:39:15,419 I remember looking down at it, 815 00:39:15,452 --> 00:39:18,622 seeing the foot at a strange angle, 816 00:39:18,655 --> 00:39:20,757 and, "Holy crap, that is gonna hurt. 817 00:39:20,791 --> 00:39:23,727 "That--" Like, I was bracing for pain. 818 00:39:23,761 --> 00:39:26,363 I mean, how much more of a part of you 819 00:39:26,397 --> 00:39:27,732 does it need to be? 820 00:39:35,005 --> 00:39:36,406 Oh, my God. 821 00:39:36,440 --> 00:39:38,509 I would call that a catastrophic failure. 822 00:39:38,542 --> 00:39:39,943 Pretty catastrophic. 823 00:39:39,977 --> 00:39:42,546 But it was... it was a strange sensation, though, 824 00:39:42,579 --> 00:39:45,749 because all of a sudden, my ankle was broken, 825 00:39:45,782 --> 00:39:49,286 and you feel like you're losing your limb 826 00:39:49,319 --> 00:39:51,121 all over again. 827 00:39:51,154 --> 00:39:54,558 How're you feeling? You feel like you wanna go down again? 828 00:39:54,591 --> 00:39:56,127 We... we did bring a spare. 829 00:39:56,893 --> 00:39:58,629 Uh, sure. 830 00:39:58,662 --> 00:40:00,698 Okay, we'll swap it over to this one. 831 00:40:02,599 --> 00:40:04,101 In engineering, 832 00:40:04,134 --> 00:40:07,004 we're kinda used to things not exactly going right 833 00:40:07,037 --> 00:40:07,904 the first time, 834 00:40:07,938 --> 00:40:10,006 so that's why we have contingency plans. 835 00:40:10,040 --> 00:40:12,810 So, this is the last climbing robot leg 836 00:40:12,843 --> 00:40:13,977 in the world, Jim. 837 00:40:16,079 --> 00:40:17,648 We're good to go again. 838 00:40:23,720 --> 00:40:27,158 I'm a little nervous about trusting this foot now. 839 00:40:28,392 --> 00:40:29,526 Watch me here. 840 00:40:29,559 --> 00:40:31,796 If the left-- if the robot breaks... 841 00:40:33,397 --> 00:40:35,733 I'm going for a ride. 842 00:40:43,173 --> 00:40:44,809 Actually, it did that move. 843 00:40:44,842 --> 00:40:45,876 Well done. 844 00:40:46,843 --> 00:40:49,513 We're rock climbing, dude. 845 00:40:49,546 --> 00:40:54,285 With the robotic leg, I found that I could move more naturally. 846 00:40:54,318 --> 00:40:55,586 Life on the edge, man. 847 00:40:55,619 --> 00:40:58,555 I was pain free, and it was, I don't know, 848 00:40:58,588 --> 00:41:00,925 it was just kind of fun and satisfying. 849 00:41:00,958 --> 00:41:03,560 We have always hypothesized 850 00:41:03,594 --> 00:41:07,231 that if we can link the nerves of a human being 851 00:41:07,264 --> 00:41:08,865 to a bionic limb, 852 00:41:08,898 --> 00:41:11,402 the limb would become part of the person, 853 00:41:11,435 --> 00:41:13,504 part of identity. 854 00:41:14,271 --> 00:41:16,173 Toppin' out. 855 00:41:16,206 --> 00:41:18,275 Remarkably, it's happened. 856 00:41:18,309 --> 00:41:20,844 Cyborg power! 857 00:41:23,713 --> 00:41:25,716 It's a tall peak, 858 00:41:25,749 --> 00:41:27,550 but pales in comparison 859 00:41:27,584 --> 00:41:30,521 to the one Hugh is ultimately trying to climb. 860 00:41:30,554 --> 00:41:31,988 We also have the goal 861 00:41:32,022 --> 00:41:35,492 of extending human capability beyond physiological function, 862 00:41:35,525 --> 00:41:38,294 jump higher, or run faster... 863 00:41:38,328 --> 00:41:42,432 So bionics not only seeks to achieve normative function in humans, 864 00:41:42,466 --> 00:41:45,269 but also to extend human expression 865 00:41:45,302 --> 00:41:47,771 beyond what people were born with. 866 00:41:48,805 --> 00:41:51,508 Human enhancement and augmentation 867 00:41:51,541 --> 00:41:53,310 have been around through human history 868 00:41:53,344 --> 00:41:54,777 and mythology, 869 00:41:54,811 --> 00:41:57,314 from Prometheus stealing fire 870 00:41:57,348 --> 00:41:59,049 to the Civil War. 871 00:42:00,417 --> 00:42:02,852 Using tools to improve our abilities 872 00:42:02,886 --> 00:42:04,721 is a fundamental human development, 873 00:42:04,754 --> 00:42:08,625 whether it's stone spears to protect our families 874 00:42:08,658 --> 00:42:10,895 or airplanes to transport us farther. 875 00:42:10,928 --> 00:42:14,498 ...and that's really what we're seeing, the transformation of society, 876 00:42:14,531 --> 00:42:16,366 and not just racing, not just sports, 877 00:42:16,399 --> 00:42:19,803 is really using these A.I. tools, and they'll become commonplace, 878 00:42:19,836 --> 00:42:21,371 won't even be thought about otherwise. 879 00:42:21,404 --> 00:42:23,440 A.I. and machine learning... 880 00:42:23,473 --> 00:42:25,509 they're just tools, 881 00:42:25,543 --> 00:42:30,213 ones that makes us stronger, smarter, faster. 882 00:42:30,246 --> 00:42:33,249 A.I. will play an increasingly dominant role 883 00:42:33,283 --> 00:42:36,786 across all the many dimensions of what it means to be human. 884 00:42:36,820 --> 00:42:38,455 There's a good chance 885 00:42:38,488 --> 00:42:42,893 A.I. will continue to enhance us in ways both known and unknown, 886 00:42:42,926 --> 00:42:46,930 size:84% position:42% eventually becoming as invisible as the air we breathe. 887 00:42:46,963 --> 00:42:49,600 That narrative will play out 888 00:42:49,633 --> 00:42:51,568 across all types of human conditions. 889 00:42:51,602 --> 00:42:54,671 That will enhance human capability, 890 00:42:54,705 --> 00:42:58,042 fundamentally change who we are as a human race. 891 00:42:59,609 --> 00:43:01,978 The question then becomes, 892 00:43:02,012 --> 00:43:03,280 if it does, 893 00:43:04,648 --> 00:43:08,786 what do we do with our newfound superpowers? 894 00:43:16,660 --> 00:43:19,029 This one's meant to be 895 00:43:19,062 --> 00:43:21,698 kind of an all-around athletic foot. 896 00:43:21,732 --> 00:43:25,502 I can run with it, hiking, biking, whatever I want. 897 00:43:25,536 --> 00:43:26,870 I even use it for surfing, 898 00:43:26,904 --> 00:43:30,307 'cause it has a good bit of flex to surf with. 899 00:43:30,340 --> 00:43:32,575 I actually liked the fit so much 900 00:43:32,609 --> 00:43:34,978 that it's the only one I use now. 901 00:43:35,011 --> 00:43:37,114 As good as this fit is, it still... 902 00:43:37,147 --> 00:43:40,183 like I said, high activity days, I get some pressure sores. 903 00:43:40,217 --> 00:43:42,652 Every night, you have to look over the skin, 904 00:43:42,686 --> 00:43:45,522 make sure you've got nothing nasty going on, 905 00:43:45,556 --> 00:43:48,192 nothing growing where it shouldn't be. 906 00:43:48,225 --> 00:43:51,561 This guy is a gel liner. 907 00:43:51,595 --> 00:43:52,730 It doesn't breathe, 908 00:43:52,763 --> 00:43:55,866 but this is what keeps the leg on. 909 00:43:55,899 --> 00:43:57,133 A lot of, um, amputees 910 00:43:57,167 --> 00:43:59,703 talk about forgetting that they don't have a leg 911 00:43:59,736 --> 00:44:01,071 in the middle of the night, 912 00:44:01,104 --> 00:44:03,440 and they get up in the middle of the night 913 00:44:03,473 --> 00:44:04,707 to go to the bathroom, 914 00:44:04,741 --> 00:44:06,943 and then instantly fall on their faces. 915 00:44:06,976 --> 00:44:09,112 That's only happened to me once, 916 00:44:09,146 --> 00:44:12,582 but I managed to catch myself before I hit the ground. 71416

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