All language subtitles for NOVA.S51E17.Building.Stuff.Change.It.1080p.WEB.h264-BAE_track3_[eng]

af Afrikaans
ak Akan
sq Albanian
am Amharic
ar Arabic Download
hy Armenian
az Azerbaijani
eu Basque
be Belarusian
bem Bemba
bn Bengali
bh Bihari
bs Bosnian
br Breton
bg Bulgarian
km Cambodian
ca Catalan
ceb Cebuano
chr Cherokee
ny Chichewa
zh-CN Chinese (Simplified)
zh-TW Chinese (Traditional)
co Corsican
hr Croatian
cs Czech
da Danish
nl Dutch
en English
eo Esperanto
et Estonian
ee Ewe
fo Faroese
tl Filipino
fi Finnish
fr French
fy Frisian
gaa Ga
gl Galician
ka Georgian
de German
gn Guarani
gu Gujarati
ht Haitian Creole
ha Hausa
haw Hawaiian
iw Hebrew
hi Hindi
hmn Hmong
hu Hungarian
is Icelandic
ig Igbo
id Indonesian
ia Interlingua
ga Irish
it Italian
ja Japanese
jw Javanese
kn Kannada
kk Kazakh
rw Kinyarwanda
rn Kirundi
kg Kongo
ko Korean
kri Krio (Sierra Leone)
ku Kurdish
ckb Kurdish (Soranî)
ky Kyrgyz
lo Laothian
la Latin
lv Latvian
ln Lingala
lt Lithuanian
loz Lozi
lg Luganda
ach Luo
lb Luxembourgish
mk Macedonian
mg Malagasy
ms Malay
ml Malayalam
mt Maltese
mi Maori
mr Marathi
mfe Mauritian Creole
mo Moldavian
mn Mongolian
my Myanmar (Burmese)
sr-ME Montenegrin
ne Nepali
pcm Nigerian Pidgin
nso Northern Sotho
no Norwegian
nn Norwegian (Nynorsk)
oc Occitan
or Oriya
om Oromo
ps Pashto
fa Persian
pl Polish
pt-BR Portuguese (Brazil)
pt Portuguese (Portugal)
pa Punjabi
qu Quechua
ro Romanian
rm Romansh
nyn Runyakitara
ru Russian
sm Samoan
gd Scots Gaelic
sr Serbian
sh Serbo-Croatian
st Sesotho
tn Setswana
crs Seychellois Creole
sn Shona
sd Sindhi
si Sinhalese
sk Slovak
sl Slovenian
so Somali
es Spanish
es-419 Spanish (Latin American)
su Sundanese
sw Swahili
sv Swedish
tg Tajik
ta Tamil
tt Tatar
te Telugu
th Thai
ti Tigrinya
to Tonga
lua Tshiluba
tum Tumbuka
tr Turkish
tk Turkmen
tw Twi
ug Uighur
uk Ukrainian
ur Urdu
uz Uzbek
vi Vietnamese
cy Welsh
wo Wolof
xh Xhosa
yi Yiddish
yo Yoruba
zu Zulu
Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:05,666 --> 00:00:09,600 NARRATOR: We live in a built world. 2 00:00:09,600 --> 00:00:11,833 Engineering and technology, 3 00:00:11,833 --> 00:00:14,900 built upon innovations and inventions, 4 00:00:14,900 --> 00:00:17,433 stretching back thousands of years. 5 00:00:17,433 --> 00:00:20,133 Some of our creations, like machines, 6 00:00:20,133 --> 00:00:22,033 boost our bodies' abilities. 7 00:00:22,033 --> 00:00:25,566 Others help us reach outside our comfort zones. 8 00:00:25,566 --> 00:00:29,200 We have left an indelible mark on the planet. 9 00:00:29,200 --> 00:00:33,433 And now the time has come to use our skills 10 00:00:33,433 --> 00:00:35,433 to make a better world. 11 00:00:35,433 --> 00:00:38,400 WORKER: ...two, three, lower. 12 00:00:38,400 --> 00:00:41,066 NARRATOR: Like inventing a new way to fly, 13 00:00:41,066 --> 00:00:42,900 electrically. 14 00:00:44,333 --> 00:00:46,233 Or a device that can smell... 15 00:00:46,233 --> 00:00:48,966 ANN PERSON: I get very excited when technology works. 16 00:00:48,966 --> 00:00:51,400 NARRATOR: ...to save food from going to waste. 17 00:00:51,400 --> 00:00:54,366 THARINDU MADDUMA: Food waste is enormous global problem. 18 00:00:55,700 --> 00:00:57,400 NARRATOR: Creating a machine... RESEARCHER: Rob, I'm going in. 19 00:00:57,400 --> 00:00:59,466 NARRATOR: ...to heal coral reefs. 20 00:00:59,466 --> 00:01:02,033 ARAN MOONEY: How do we fix the environment that's 21 00:01:02,033 --> 00:01:03,133 sort of dying in front of us? 22 00:01:04,466 --> 00:01:07,633 NARRATOR: Or even combining a traditional work of art... 23 00:01:07,633 --> 00:01:10,833 LEWIS STETSON ROWLES: We see this amazing opportunity to use pottery. 24 00:01:10,833 --> 00:01:13,033 NARRATOR: ...with modern chemistry... 25 00:01:13,033 --> 00:01:14,500 NAVID SALEH: Could you actually make something like that? 26 00:01:14,500 --> 00:01:15,900 Do you have something similar? 27 00:01:15,900 --> 00:01:19,233 NARRATOR: ...to provide clean drinking water. 28 00:01:19,233 --> 00:01:21,200 I made a shape similar to that. 29 00:01:21,200 --> 00:01:24,733 NARRATOR: "Building Stuff: Change It!" 30 00:01:24,733 --> 00:01:27,266 Right now, on "NOVA." 31 00:01:27,266 --> 00:01:32,300 ♪ ♪ 32 00:02:00,300 --> 00:02:01,833 NARRATOR: Human beings have been 33 00:02:01,833 --> 00:02:06,366 changing our surroundings for thousands of years. 34 00:02:06,366 --> 00:02:11,200 The signs are written on the land itself. 35 00:02:11,200 --> 00:02:12,866 We're builders and makers. 36 00:02:12,866 --> 00:02:15,900 And the evidence is plain to see. 37 00:02:15,900 --> 00:02:19,066 ADAM STELTZNER: Our whole lives are constructed. 38 00:02:19,066 --> 00:02:23,600 We live in the modern world in a very altered environment. 39 00:02:23,600 --> 00:02:25,600 And all of that alteration 40 00:02:25,600 --> 00:02:28,000 starts and finishes with engineering. 41 00:02:28,000 --> 00:02:32,066 ANDREA ARMANI: Engineering can transform a community by 42 00:02:32,066 --> 00:02:35,500 bringing power, bringing water, growing food. 43 00:02:36,500 --> 00:02:40,500 DEB CHACHRA: Taking sewage away, the power grid, telecommunications, 44 00:02:40,500 --> 00:02:42,500 these are all engineering systems 45 00:02:42,500 --> 00:02:45,533 that are not about making any one of us 46 00:02:45,533 --> 00:02:49,100 smarter or stronger or faster, but making us, collectively, 47 00:02:49,100 --> 00:02:51,366 have more agency and more capacity. 48 00:02:51,366 --> 00:02:52,933 NARRATOR: But building the modern world 49 00:02:52,933 --> 00:02:55,266 has come with steep costs 50 00:02:55,266 --> 00:02:58,233 and changes to more than just the land, 51 00:02:58,233 --> 00:03:02,200 like altering the chemical composition of our atmosphere. 52 00:03:02,200 --> 00:03:04,966 But now there's a new generation 53 00:03:04,966 --> 00:03:08,733 that wants to engineer a cleaner planet. 54 00:03:08,733 --> 00:03:10,200 So, as an engineer, 55 00:03:10,200 --> 00:03:13,933 when you see the world as it is, you begin to think, 56 00:03:13,933 --> 00:03:15,633 "How could we make it better?" 57 00:03:16,633 --> 00:03:18,766 So that's our job, to take the world as it is 58 00:03:18,766 --> 00:03:20,266 and make it better. 59 00:03:20,266 --> 00:03:21,800 Everyone's engineering background, 60 00:03:21,800 --> 00:03:23,833 it comes from that purpose of saying, 61 00:03:23,833 --> 00:03:26,600 "I want to solve a problem that just changes the world." 62 00:03:34,633 --> 00:03:37,300 NARRATOR: One daunting challenge we face today 63 00:03:37,300 --> 00:03:39,000 is to reduce the carbon emissions 64 00:03:39,000 --> 00:03:41,466 caused by burning fossil fuels. 65 00:03:41,466 --> 00:03:45,200 Electrifying transportation offers some hope. 66 00:03:45,200 --> 00:03:47,600 On the ground, cars, buses, trucks 67 00:03:47,600 --> 00:03:50,300 and trains are gradually making the switch. 68 00:03:50,300 --> 00:03:54,000 But what about in the air? 69 00:03:54,000 --> 00:03:56,800 Is there a way to go green in flight? 70 00:04:00,433 --> 00:04:03,666 At Joby Aviation in Marina, California, 71 00:04:03,666 --> 00:04:05,366 engineers think so. 72 00:04:06,700 --> 00:04:10,133 They're testing a new kind of aircraft. 73 00:04:12,000 --> 00:04:13,000 WILSON: So, today, 74 00:04:13,000 --> 00:04:15,200 uh, Joby's flight test team 75 00:04:15,200 --> 00:04:16,666 is putting the aircraft through its paces, 76 00:04:16,666 --> 00:04:19,200 flying range and endurance missions. 77 00:04:19,200 --> 00:04:21,700   NARRATOR: The aircraft is a hybrid-- 78 00:04:21,700 --> 00:04:25,466 like a helicopter, able to take off vertically, 79 00:04:25,466 --> 00:04:27,133 but also, like an airplane, 80 00:04:27,133 --> 00:04:30,433 able to fly horizontally at high speeds. 81 00:04:30,433 --> 00:04:33,866 And it's completely electric. 82 00:04:33,866 --> 00:04:35,366 ARMANI: The challenge is, you know, 83 00:04:35,366 --> 00:04:37,100 how do we make a personal helicopter? 84 00:04:37,100 --> 00:04:38,800 How do we make them sustainable? 85 00:04:38,800 --> 00:04:42,500 Right, we don't want to bring more jet fuel into the world. 86 00:04:43,600 --> 00:04:45,733 WILSON: It is routine for us to fly 87 00:04:45,733 --> 00:04:50,566 three times a day, cruising around at about 100 knots. 88 00:04:50,566 --> 00:04:52,466 NARRATOR: Joby's ultimate dream 89 00:04:52,466 --> 00:04:55,600 is to deploy the aircraft in cities around the world 90 00:04:55,600 --> 00:04:57,600 as flying taxis, 91 00:04:57,600 --> 00:05:00,200 reducing congestion on the ground. 92 00:05:00,200 --> 00:05:03,266 Today they're in the final testing stages 93 00:05:03,266 --> 00:05:05,033 of their latest prototype. 94 00:05:05,033 --> 00:05:07,333 But despite promising results, 95 00:05:07,333 --> 00:05:11,400 they're not taking chances with humans on this round. 96 00:05:11,400 --> 00:05:13,033 WILSON: There's actually nobody on board 97 00:05:13,033 --> 00:05:14,966 the aircraft while it's in flight. 98 00:05:14,966 --> 00:05:17,133 The pilots are simply sat on the ground 99 00:05:17,133 --> 00:05:20,233 in the ground control station, flying the aircraft remotely. 100 00:05:21,233 --> 00:05:24,166 NARRATOR: Technically, it's known as an EVTOL, 101 00:05:24,166 --> 00:05:27,533 Electric Vertical Take Off and Landing. 102 00:05:27,533 --> 00:05:31,566 But it's also capable of level, forward flight. 103 00:05:31,566 --> 00:05:34,266 As we're going through our airspeed expansion, 104 00:05:34,266 --> 00:05:36,333 we are testing a, a certain airspeed, 105 00:05:36,333 --> 00:05:37,833 performing a bunch of tests 106 00:05:37,833 --> 00:05:39,400 to make sure our aircraft is stable, 107 00:05:39,400 --> 00:05:42,633 and then expanding into different airspeed regimes 108 00:05:42,633 --> 00:05:44,533 all the way to fully wing-borne flights. 109 00:05:44,533 --> 00:05:48,100 NARRATOR: This day's testing is winding down. 110 00:05:48,100 --> 00:05:51,333 A sudden tilt on touchdown is quickly corrected 111 00:05:51,333 --> 00:05:53,166 by the remote pilot. 112 00:05:53,166 --> 00:05:55,166 Something to tweak for future flights. 113 00:05:55,166 --> 00:05:58,000 WILSON: Our analysts look at the data after the flight 114 00:05:58,000 --> 00:05:59,900 to make sure that the aircraft is performing 115 00:05:59,900 --> 00:06:02,233 exactly as we expect it to. 116 00:06:02,233 --> 00:06:05,000 NARRATOR: As Joby engineers work to realize their dream, 117 00:06:05,000 --> 00:06:07,700 significant engineering challenges remain 118 00:06:07,700 --> 00:06:10,833 before regular passenger flights become a reality. 119 00:06:11,833 --> 00:06:14,466 DARAIO: As you're trying to develop transportation devices, 120 00:06:14,466 --> 00:06:16,600 you really need to understand the environment 121 00:06:16,600 --> 00:06:19,300 in which these systems need to operate 122 00:06:19,300 --> 00:06:22,666 and iterate the engineering design, 123 00:06:22,666 --> 00:06:25,966   the components, the testing specifically to those needs. 124 00:06:27,033 --> 00:06:31,433 NARRATOR: Today, it's not uncommon to see helicopters in city skies. 125 00:06:31,433 --> 00:06:33,433 But they have drawbacks. 126 00:06:33,433 --> 00:06:37,333 They're noisy, the learning curve to fly them is steep, 127 00:06:37,333 --> 00:06:42,166 they have limited forward speed, and they burn fossil fuels. 128 00:06:43,166 --> 00:06:47,533 Joby's design is an attempt to address all of those problems. 129 00:06:48,833 --> 00:06:50,166 VALERO-CUEVAS: You have identified a problem. 130 00:06:50,166 --> 00:06:52,300 Can you make an airplane 131 00:06:52,300 --> 00:06:55,533 that uses propellers like a helicopter 132 00:06:55,533 --> 00:06:57,066 but doesn't have that noise? 133 00:06:57,066 --> 00:06:58,700 Well, you've dreamt it up. 134 00:06:58,700 --> 00:07:00,333 The question is, 135 00:07:00,333 --> 00:07:03,500 how do you actually bring it into existence? 136 00:07:06,266 --> 00:07:07,366 WORKER: All right, 137 00:07:07,366 --> 00:07:10,200 one, two, three, lower. 138 00:07:10,200 --> 00:07:12,200 NARRATOR: One of the biggest challenges 139 00:07:12,200 --> 00:07:15,700 has been to invent a new propulsion system. 140 00:07:15,700 --> 00:07:20,566 The idea was to design a vehicle for four passengers and a pilot 141 00:07:20,566 --> 00:07:23,066 that can rise straight off the ground 142 00:07:23,066 --> 00:07:27,233 and then somehow transition to fly like an airplane. 143 00:07:27,233 --> 00:07:30,166 Joby's solution-- six electric motors 144 00:07:30,166 --> 00:07:32,566 that can individually pivot, 145 00:07:32,566 --> 00:07:35,633 propelling the vehicle up to 200 miles per hour, 146 00:07:35,633 --> 00:07:40,333 eliminating fossil fuels and reducing noise, 147 00:07:40,333 --> 00:07:42,133 a critical improvement 148 00:07:42,133 --> 00:07:46,200 if they have any hope of widespread adoption. 149 00:07:46,200 --> 00:07:50,666 That's what gives the aircraft its unusual profile. 150 00:07:50,666 --> 00:07:52,766 Six smaller propellers 151 00:07:52,766 --> 00:07:56,066 that are quieter than a single helicopter blade. 152 00:07:56,066 --> 00:07:57,700 But because they're small, 153 00:07:57,700 --> 00:08:01,300 everything depended on finding the right propeller shape, 154 00:08:01,300 --> 00:08:04,000 a surprisingly complicated problem, 155 00:08:04,000 --> 00:08:06,000 part art and part science, 156 00:08:06,000 --> 00:08:08,000 with much of the know-how 157 00:08:08,000 --> 00:08:12,766 handed down since the early pioneers of powered flight. 158 00:08:12,766 --> 00:08:16,300 These propellers may seem wholly modern. 159 00:08:16,300 --> 00:08:18,666 But if we trace their evolution, 160 00:08:18,666 --> 00:08:23,766 we can see clear connections to the past. 161 00:08:24,766 --> 00:08:26,500 Leonardo da Vinci's notebooks 162 00:08:26,500 --> 00:08:29,733 contain one of the most famous early conceptualizations 163 00:08:29,733 --> 00:08:33,333 of a device resembling the modern propeller. 164 00:08:33,333 --> 00:08:36,233 Da Vinci, in turn, may have been inspired by 165 00:08:36,233 --> 00:08:40,600 the Greek philosopher Archimedes and his screw-shaped water pump, 166 00:08:40,600 --> 00:08:42,533 or even by nature. 167 00:08:42,533 --> 00:08:46,233 Certain plants and seeds, like the maple and sycamore, 168 00:08:46,233 --> 00:08:48,133 have evolved similar shapes. 169 00:08:48,133 --> 00:08:51,733 When they fall from trees, they look and work 170 00:08:51,733 --> 00:08:55,166 remarkably like helicopter blades. 171 00:08:55,166 --> 00:08:59,033 At Joby, the design team is looking for the best shape 172 00:08:59,033 --> 00:09:03,100 to balance power and noise. 173 00:09:03,100 --> 00:09:04,700 We went through a lot of experimentation 174 00:09:04,700 --> 00:09:07,433 with actual propeller, uh, prototypes. 175 00:09:07,433 --> 00:09:10,800   We needed to put real work in, in terms of experiments, 176 00:09:10,800 --> 00:09:12,100 to really understand this phenomenon. 177 00:09:14,033 --> 00:09:18,833 NARRATOR: To reduce noise, it helps to understand what causes it. 178 00:09:18,833 --> 00:09:21,866 As each propeller blade slices through the air, 179 00:09:21,866 --> 00:09:25,266 it creates pressure vibrations. 180 00:09:25,266 --> 00:09:27,333 The strength of those vibrations 181 00:09:27,333 --> 00:09:30,500 depends in turn on a propeller's shape, 182 00:09:30,500 --> 00:09:32,933 how fast it spins 183 00:09:32,933 --> 00:09:35,900 and the number of blades. 184 00:09:35,900 --> 00:09:38,366 MIKIC: So we iterated with a number of designs. 185 00:09:38,366 --> 00:09:41,833 We took blades with a lot of blade area 186 00:09:41,833 --> 00:09:44,666 and then much thinner blades and, uh, trying to see 187 00:09:44,666 --> 00:09:46,966 how that results in acoustic generation. 188 00:09:46,966 --> 00:09:49,733 These propellers are turning much slower 189 00:09:49,733 --> 00:09:51,700 than traditional helicopter blades. 190 00:09:51,700 --> 00:09:55,200 We varied the shape, a lot of experimentation. 191 00:09:56,900 --> 00:09:58,666 I think this trial and error system 192 00:09:58,666 --> 00:10:02,533 is something that allows us to ever more refine design, 193 00:10:02,533 --> 00:10:07,666 produce and, uh, test, which, in multiple iterations, 194 00:10:07,666 --> 00:10:11,000 allows us to arrive to, uh, to optimal solutions. 195 00:10:12,866 --> 00:10:15,733 NARRATOR: The company has tested several blade shapes, 196 00:10:15,733 --> 00:10:17,900 hoping to find the best combination 197 00:10:17,900 --> 00:10:21,600 of efficiency, lightness and durability. 198 00:10:21,600 --> 00:10:24,600 To test each new propeller design, 199 00:10:24,600 --> 00:10:26,666 the company has built a large circular track 200 00:10:26,666 --> 00:10:29,466 in an old quarry near Santa Cruz. 201 00:10:29,466 --> 00:10:31,700 MIKIC: In quarry, we have what we call "The Whirlybird," 202 00:10:31,700 --> 00:10:34,933 which is a track kind of like a roller coaster track 203 00:10:34,933 --> 00:10:36,266 that goes around in circles. 204 00:10:36,266 --> 00:10:38,800 And we have to test this propeller 205 00:10:38,800 --> 00:10:42,300 not only in hover conditions, but through all the conditions 206 00:10:42,300 --> 00:10:44,000 that it experienced through transition 207 00:10:44,000 --> 00:10:45,233 as well as forward flight. 208 00:10:45,233 --> 00:10:46,766 NARRATOR: On the track, 209 00:10:46,766 --> 00:10:49,000 they test each iteration of the propeller 210 00:10:49,000 --> 00:10:51,500 for durability and blade design, 211 00:10:51,500 --> 00:10:53,000 as well as for noise. 212 00:10:53,000 --> 00:10:55,600 MIKIC: And then we adjust the angle of the propeller, 213 00:10:55,600 --> 00:10:56,766 the speed of the propeller, 214 00:10:56,766 --> 00:10:58,500 the variable pitch on it 215 00:10:58,500 --> 00:11:00,833 to see how it operates in different regimes of flight 216 00:11:00,833 --> 00:11:03,233 that the real airplane would experience. 217 00:11:03,233 --> 00:11:04,966 And we can do this for hours on end, 218 00:11:04,966 --> 00:11:08,600 days on end, uh, to see how the system performs. 219 00:11:09,800 --> 00:11:12,000 ARMANI: The design of a propeller 220 00:11:12,000 --> 00:11:15,733 is a very theoretically heavy lift. 221 00:11:15,733 --> 00:11:20,733 However, at the end of the day, experimental results rule. 222 00:11:20,733 --> 00:11:24,966 And their ability to build that huge test ring 223 00:11:24,966 --> 00:11:28,500 to really, you know, compare their experimental results 224 00:11:28,500 --> 00:11:30,800 with the, the theoretical predictions 225 00:11:30,800 --> 00:11:33,100 are really what allowed them to advance 226 00:11:33,100 --> 00:11:35,500 and push their entire plane forward. 227 00:11:35,500 --> 00:11:39,233 NARRATOR: Ultimately, they discovered that their original design, 228 00:11:39,233 --> 00:11:40,766 which was wider, 229 00:11:40,766 --> 00:11:44,900 actually performed better than subsequent slimmer designs. 230 00:11:44,900 --> 00:11:47,666 The greater surface area allowed them to slow down 231 00:11:47,666 --> 00:11:49,466 the propeller's rotation speed, 232 00:11:49,466 --> 00:11:52,600 reducing noise while meeting power requirements. 233 00:11:52,600 --> 00:11:53,833 MIKIC: When you do the experiments, 234 00:11:53,833 --> 00:11:55,100 you realize you're going down 235 00:11:55,100 --> 00:11:56,766 the wrong path, then you start to go back and see, 236 00:11:56,766 --> 00:11:59,466 like, well, why is the thing that I tried to do 237 00:11:59,466 --> 00:12:01,066 that makes things better actually worse? 238 00:12:01,066 --> 00:12:02,666 So you challenge your own assumptions. 239 00:12:04,333 --> 00:12:06,666 DARAIO: Challenging assumption is something that 240 00:12:06,666 --> 00:12:08,900 is an essential component in engineering. 241 00:12:08,900 --> 00:12:12,666 Being able to harvest the advances of divergent thinking 242 00:12:12,666 --> 00:12:14,233 and creative thinking 243 00:12:14,233 --> 00:12:16,500 is something that, in the end, 244 00:12:16,500 --> 00:12:19,233 promotes innovation and allows us 245 00:12:19,233 --> 00:12:21,266 to advance technology much faster. 246 00:12:21,266 --> 00:12:24,100 NARRATOR: A change to the shape of the propeller 247 00:12:24,100 --> 00:12:25,966 helps with the nature of turbulence 248 00:12:25,966 --> 00:12:28,433 generated by the blade. 249 00:12:28,433 --> 00:12:29,766 Exactly how they did it, 250 00:12:29,766 --> 00:12:34,433 a Joby representative said, is a trade secret. 251 00:12:34,433 --> 00:12:38,400 But the result is a vehicle that the company says 252 00:12:38,400 --> 00:12:44,566 produces 100 times less acoustic power than a helicopter. 253 00:12:47,133 --> 00:12:50,066 Eventually, they're hoping to expand their test program 254 00:12:50,066 --> 00:12:52,100 to include passengers 255 00:12:52,100 --> 00:12:54,533 and move toward full certification 256 00:12:54,533 --> 00:12:58,633 from the Federal Aviation Administration. 257 00:12:58,633 --> 00:13:01,566 DIDIER PAPADOPOULOS: Safety is non-negotiable. 258 00:13:01,566 --> 00:13:04,633 Look, I'm gonna put my kids on these airplanes, 259 00:13:04,633 --> 00:13:06,566 and so this is, this is close to me, 260 00:13:06,566 --> 00:13:08,200 just as it is close to everybody else. 261 00:13:11,900 --> 00:13:13,133   WILSON: Now being able to travel 262 00:13:13,133 --> 00:13:16,333 routinely with an aircraft like this, 263 00:13:16,333 --> 00:13:18,733 and be able to do it relatively low cost 264 00:13:18,733 --> 00:13:23,133 and super available to the masses, is so exciting. 265 00:13:24,166 --> 00:13:27,833 NARRATOR: Today, air travel accounts for an estimated 10% 266 00:13:27,833 --> 00:13:31,733 of the carbon produced by all transportation. 267 00:13:31,733 --> 00:13:33,500 It's this kind of experimentation 268 00:13:33,500 --> 00:13:38,100 that could lead to bigger changes in air travel. 269 00:13:38,100 --> 00:13:40,533 Electrifying aviation is one of the hardest 270 00:13:40,533 --> 00:13:42,866 engineering challenges we face. 271 00:13:42,866 --> 00:13:47,533 But not every problem requires such a difficult solution. 272 00:13:47,533 --> 00:13:51,200 When it comes to finding ways to reduce carbon emissions, 273 00:13:51,200 --> 00:13:54,833 there is some lower-hanging fruit. 274 00:13:56,433 --> 00:13:58,000 Over thousands of years, 275 00:13:58,000 --> 00:13:59,600 we've gotten more and more efficient 276 00:13:59,600 --> 00:14:02,800 at growing food for an ever-growing population. 277 00:14:04,466 --> 00:14:08,333 But the road from farm to table can be long and wasteful. 278 00:14:09,333 --> 00:14:11,766 Globally, a third of all crops go bad 279 00:14:11,766 --> 00:14:14,066 before they reach the table. 280 00:14:14,066 --> 00:14:16,600 And with food production accounting for about 30% 281 00:14:16,600 --> 00:14:18,866 of global greenhouse gas emissions, 282 00:14:18,866 --> 00:14:21,800 reducing food waste could be one solution 283 00:14:21,800 --> 00:14:23,533 to our climate problem. 284 00:14:23,533 --> 00:14:28,566 At least, that's the idea behind a Norwegian rot-sniffing robot. 285 00:14:31,366 --> 00:14:33,500 The BAMA food warehouse in Oslo, Norway. 286 00:14:36,300 --> 00:14:41,066 NARRATOR: Anne Person is the director of quality assurance. 287 00:14:41,066 --> 00:14:43,100 We get about 2,000 pallets 288 00:14:43,100 --> 00:14:44,400 in here every night. 289 00:14:45,566 --> 00:14:47,600 NARRATOR: The produce comes in from 80 countries. 290 00:14:48,533 --> 00:14:50,233 They're being scanned here. 291 00:14:50,233 --> 00:14:53,766 And then they go straight to the quality control tower. 292 00:14:53,766 --> 00:14:56,600 This is the first control that is being done 293 00:14:56,600 --> 00:14:59,066 when it comes to Norway. 294 00:14:59,066 --> 00:15:01,166 NARRATOR: Inspectors screen the produce 295 00:15:01,166 --> 00:15:04,733 for spoilage, as best they can, before sending it 296 00:15:04,733 --> 00:15:05,933 to the supermarket. 297 00:15:07,300 --> 00:15:08,666 The problem is we don't have very much time 298 00:15:08,666 --> 00:15:09,766 to inspect the pallets. 299 00:15:09,766 --> 00:15:11,400 It's maximum 60 seconds. 300 00:15:13,500 --> 00:15:16,900   And also, due to the setup of the quality stations, 301 00:15:16,900 --> 00:15:20,700 we are only able to control the two upper layers, maximum. 302 00:15:22,133 --> 00:15:24,700 NARRATOR: That means, even with experience, 303 00:15:24,700 --> 00:15:26,900 visual inspection only goes so far. 304 00:15:26,900 --> 00:15:31,066 Inevitably, some spoiled produce goes undetected 305 00:15:31,066 --> 00:15:34,466 and gets shipped along with the rest of the produce 306 00:15:34,466 --> 00:15:37,166 all over Norway to local supermarkets. 307 00:15:38,266 --> 00:15:39,566 PERSON: So our question was, 308 00:15:39,566 --> 00:15:42,400 how can we check the whole pallets? 309 00:15:42,400 --> 00:15:45,366 So that's when we started to look at the new technology. 310 00:15:46,833 --> 00:15:51,133 The goal is increased freshness and reduced food waste. 311 00:15:51,133 --> 00:15:54,200 If you can detect spoilage earlier in the value chain, 312 00:15:54,200 --> 00:15:55,533 we are also able to do more 313 00:15:55,533 --> 00:15:57,266 with the products that we might reject. 314 00:15:57,266 --> 00:16:01,100 We can sort them, we can give them to food banks. 315 00:16:02,266 --> 00:16:07,266 NARRATOR: BAMA connected with Tunable, a small tech company in Oslo, 316 00:16:07,266 --> 00:16:09,200 inventors of an artificial nose, 317 00:16:09,200 --> 00:16:11,833 or machine olfaction device, 318 00:16:11,833 --> 00:16:14,266 that is already in use monitoring 319 00:16:14,266 --> 00:16:16,300 the amount of greenhouse gasses 320 00:16:16,300 --> 00:16:18,233 emitted by container ships. 321 00:16:18,233 --> 00:16:23,166 Tharindu Madduma is Tunable's business development manager. 322 00:16:23,166 --> 00:16:25,000 MADDUMA: BAMA came to us. 323 00:16:25,000 --> 00:16:26,800 They explained that they had this problem 324 00:16:26,800 --> 00:16:29,966 of determining the quality of the fruits and vegetables, 325 00:16:29,966 --> 00:16:33,300 being able to do it at a large scale 326 00:16:33,300 --> 00:16:34,566 and being accurate. 327 00:16:34,566 --> 00:16:36,600 VALERO-CUEVAS: There's a long history 328 00:16:36,600 --> 00:16:38,433 of inventions 329 00:16:38,433 --> 00:16:41,000 that allow us to extend our senses. 330 00:16:41,000 --> 00:16:43,433 So we've done that for sight. 331 00:16:43,433 --> 00:16:45,166 We've done that for hearing. 332 00:16:46,166 --> 00:16:48,800 MADDUMA: So, we have microscopes, we have hearing aid, 333 00:16:48,800 --> 00:16:51,533 but smell is still a sense 334 00:16:51,533 --> 00:16:53,133 that we haven't digitalized. 335 00:16:53,133 --> 00:16:55,000 And that's what we're doing. 336 00:16:55,000 --> 00:16:58,300 NARRATOR: Kristian Hovet is Tunable's C.E.O. 337 00:16:58,300 --> 00:16:59,533 HOVET: When you take a breath, 338 00:16:59,533 --> 00:17:01,766 you're doing a multi-gas analysis. 339 00:17:01,766 --> 00:17:03,100 You're pulling in molecules, 340 00:17:03,100 --> 00:17:06,033 and those molecules are detected by your nose, 341 00:17:06,033 --> 00:17:08,266   and then it's detected by your brain 342 00:17:08,266 --> 00:17:09,633 to tell you what you're smelling. 343 00:17:09,633 --> 00:17:11,733 NARRATOR: The challenge for Tunable 344 00:17:11,733 --> 00:17:15,500 was to take their existing analyzer for emission analysis 345 00:17:15,500 --> 00:17:17,400 and increase its sensitivity 346 00:17:17,400 --> 00:17:20,300 without making the device too big and cumbersome 347 00:17:20,300 --> 00:17:23,366 to be useful on a warehouse floor. 348 00:17:23,366 --> 00:17:25,966 So why use smell? 349 00:17:27,100 --> 00:17:29,366 Our noses are sensitive detectors, 350 00:17:29,366 --> 00:17:31,733 able to identify a wide variety 351 00:17:31,733 --> 00:17:35,766 of chemicals in the air, even at low concentrations. 352 00:17:35,766 --> 00:17:39,266 Airborne molecules can also potentially reveal 353 00:17:39,266 --> 00:17:41,333 what's hidden in the pallets. 354 00:17:41,333 --> 00:17:44,800 These molecules tell a chemical story 355 00:17:44,800 --> 00:17:48,100 of fruits and vegetables as they rot. 356 00:17:48,100 --> 00:17:51,166 But the device would have to be far more sensitive 357 00:17:51,166 --> 00:17:53,866 than a human nose, and able to detect spoilage 358 00:17:53,866 --> 00:17:58,300 more reliably than a human eye. 359 00:17:58,300 --> 00:18:01,200 Produce, like all living things, 360 00:18:01,200 --> 00:18:02,833 decays after death 361 00:18:02,833 --> 00:18:05,666 as microbes consume dead cells, 362 00:18:05,666 --> 00:18:08,966 releasing volatile organic compounds. 363 00:18:08,966 --> 00:18:12,466 In theory, the team should be able to tune their machine 364 00:18:12,466 --> 00:18:15,600 to recognize those molecules. 365 00:18:15,600 --> 00:18:18,600 We knew that we could look at complex gasses. 366 00:18:20,466 --> 00:18:25,400 We redesigned emission analyzer, and then we started testing. 367 00:18:26,933 --> 00:18:29,600 NARRATOR: Eivind Jülke Røer 368 00:18:29,600 --> 00:18:32,933 is the lead engineer on the Tunable e-nose project. 369 00:18:32,933 --> 00:18:35,500 RØER: So now I'm going to measure fresh grapes 370 00:18:35,500 --> 00:18:37,033 and then some spoiled grapes. 371 00:18:37,033 --> 00:18:38,766 See our e-nose can smell the difference. 372 00:18:38,766 --> 00:18:39,866 I'll start with collecting 373 00:18:39,866 --> 00:18:41,900 a sample from the ambient air 374 00:18:41,900 --> 00:18:43,466 as a baseline for the measurement. 375 00:18:43,466 --> 00:18:45,233   (machine whirring) 376 00:18:46,766 --> 00:18:49,466 And the noise you can hear now is actually the compressor pump 377 00:18:49,466 --> 00:18:52,833 pulling air, uh, into the analyzer. 378 00:18:54,766 --> 00:18:57,666 So now I'm going to take a sample from the fresh grapes 379 00:18:57,666 --> 00:19:00,900 to see if there is anything present there. 380 00:19:00,900 --> 00:19:02,233 NARRATOR: The probe pulls in air 381 00:19:02,233 --> 00:19:03,733 and then compresses it 382 00:19:03,733 --> 00:19:05,400 by a factor of five, 383 00:19:05,400 --> 00:19:07,733 which increases the density of the sample 384 00:19:07,733 --> 00:19:11,233 and makes molecules easier to detect. 385 00:19:11,233 --> 00:19:14,633 Next, infrared light shines through the sample. 386 00:19:14,633 --> 00:19:16,800 The light then passes through a chip 387 00:19:16,800 --> 00:19:19,166 that sorts different types of molecules 388 00:19:19,166 --> 00:19:21,000 based on the specific wavelengths 389 00:19:21,000 --> 00:19:23,133 of light they absorb, 390 00:19:23,133 --> 00:19:25,266 which ultimately allows the analyzer 391 00:19:25,266 --> 00:19:28,100 and accompanying software to reliably detect 392 00:19:28,100 --> 00:19:29,633 the presence and concentration 393 00:19:29,633 --> 00:19:31,733 of molecules that signal spoilage 394 00:19:31,733 --> 00:19:34,200 with extreme sensitivity. 395 00:19:34,200 --> 00:19:35,966 RØER: The reading I got now 396 00:19:35,966 --> 00:19:38,433 doesn't really show any molecules present at all 397 00:19:38,433 --> 00:19:40,433 compared to ambient air, 398 00:19:40,433 --> 00:19:43,466 which is more or less what I would expect from fresh fruit. 399 00:19:43,466 --> 00:19:45,266 (machine whirring) 400 00:19:45,266 --> 00:19:47,566 So now I'm going to take a sample 401 00:19:47,566 --> 00:19:49,566 for the, um, spoiled grapes. 402 00:19:49,566 --> 00:19:50,866 We see a clear difference. 403 00:19:50,866 --> 00:19:53,200 We see up to 12% absorption 404 00:19:53,200 --> 00:19:56,066 at ethanol wavelength, which is a good indication 405 00:19:56,066 --> 00:19:58,000 that we actually smell the rotten grapes. 406 00:19:58,000 --> 00:20:00,633 So, uh, this looks really promising. 407 00:20:01,633 --> 00:20:03,266 HOVET: The fumes we were able to collect, 408 00:20:03,266 --> 00:20:05,666 we were able to see the, the kind of the signatures. 409 00:20:05,666 --> 00:20:07,633 NARRATOR: The engineers then tested 410 00:20:07,633 --> 00:20:10,733 different kinds of fruits and vegetables 411 00:20:10,733 --> 00:20:15,633 as they decayed, building up a database of chemical profiles. 412 00:20:15,633 --> 00:20:20,533 HOVET: We saw a tomato was different, somewhat, from a banana. 413 00:20:21,666 --> 00:20:26,033 Grapes were different from avocado, for example. 414 00:20:27,066 --> 00:20:30,500 And we thought, well, this must be interesting. 415 00:20:30,500 --> 00:20:32,133 (laughs) 416 00:20:32,133 --> 00:20:33,400 (compressed air can sprays) 417 00:20:33,400 --> 00:20:34,800 NARRATOR: Thor Bakke 418 00:20:34,800 --> 00:20:38,000 is the founder and Chief Technology Officer of Tunable. 419 00:20:38,000 --> 00:20:41,566 He's been working with microelectromechanical systems 420 00:20:41,566 --> 00:20:43,433 for over 30 years. 421 00:20:43,433 --> 00:20:47,166 BAKKE: Tunable is a component, uh, inside our analyzers. 422 00:20:47,166 --> 00:20:50,133 That's the Tunable filter. 423 00:20:50,133 --> 00:20:51,966 It's used to change the wavelength of light 424 00:20:51,966 --> 00:20:54,166 so we can scan the wavelength and do spectroscopy. 425 00:20:54,166 --> 00:20:56,833   (radio playing static between stations) 426 00:20:56,833 --> 00:20:58,733 Spectroscopy is very much like, uh, 427 00:20:58,733 --> 00:21:01,766 tuning a radio to find a particular station. 428 00:21:01,766 --> 00:21:04,866 The gasses are separated in the infrared spectrum, 429 00:21:04,866 --> 00:21:06,433 just like radio stations. 430 00:21:06,433 --> 00:21:08,766 And then you can basically detect each one of them. 431 00:21:08,766 --> 00:21:11,500 So that's where the word Tunable comes from. 432 00:21:12,566 --> 00:21:15,400 NARRATOR: After extensive fine tuning in the lab, 433 00:21:15,400 --> 00:21:19,133 it's time for the very first field test in the warehouse. 434 00:21:20,166 --> 00:21:21,433 STELTZNER: Sometimes you can't learn 435 00:21:21,433 --> 00:21:23,333 about all of the variables 436 00:21:23,333 --> 00:21:25,333 that will be involved in an engineered system 437 00:21:25,333 --> 00:21:27,533 sitting on a desk 438 00:21:27,533 --> 00:21:30,033 with a pen and paper or at a computer screen. 439 00:21:30,033 --> 00:21:31,333 You need to go out into the field. 440 00:21:31,333 --> 00:21:33,533   You need to put it in the actual environment 441 00:21:33,533 --> 00:21:36,766 and see how it interacts, learn from that, make changes, 442 00:21:36,766 --> 00:21:38,300 and move forward. 443 00:21:38,300 --> 00:21:41,600 RØER: Now I'm capturing; I'm in there. 444 00:21:41,600 --> 00:21:44,966 Now I'm ready to do the measurement on the grapes. 445 00:21:47,300 --> 00:21:49,500 NARRATOR: Eivind watches the screen, 446 00:21:49,500 --> 00:21:53,400 waiting to see the telltale grape waveform. 447 00:21:53,400 --> 00:21:56,566 But the pump just whirrs away. 448 00:21:56,566 --> 00:21:59,600 And eventually he gives up. 449 00:22:01,933 --> 00:22:04,866 Uh, I don't really know what happened here. 450 00:22:04,866 --> 00:22:07,400 Uh... 451 00:22:07,400 --> 00:22:11,366 For some reason, um, the results wasn't as expected. 452 00:22:11,366 --> 00:22:14,466 NARRATOR: The first time definitely wasn't the charm. 453 00:22:14,466 --> 00:22:15,766 Murphy's law. 454 00:22:15,766 --> 00:22:17,066 Yeah. 455 00:22:18,133 --> 00:22:20,700 HOVET: We know that it works in a laboratory environment. 456 00:22:20,700 --> 00:22:22,633 So the big thing now 457 00:22:22,633 --> 00:22:24,700 is showing that it actually works... 458 00:22:24,700 --> 00:22:29,166 (chuckling): ...in real life, and as you see, there's been some challenges. 459 00:22:30,166 --> 00:22:32,100 CHACHRA: We tend to think of failure as a bad thing, right? 460 00:22:32,100 --> 00:22:34,800 That something that is not supposed to happen, happens. 461 00:22:34,800 --> 00:22:37,333 But if you're doing anything new, 462 00:22:37,333 --> 00:22:39,700 failure is an integral part of the process. 463 00:22:39,700 --> 00:22:42,200 And the reason for that is because we can't 464 00:22:42,200 --> 00:22:44,633   perfectly predict or understand how things are gonna work 465 00:22:44,633 --> 00:22:46,266 in the real world until we try them. 466 00:22:47,566 --> 00:22:50,233 NARRATOR: Turns out the warehouse temperature, 467 00:22:50,233 --> 00:22:52,600 a chilly 41 degrees Fahrenheit, 468 00:22:52,600 --> 00:22:55,866 affected the test result. 469 00:22:55,866 --> 00:22:57,533 HOVET: The cold part. 470 00:22:57,533 --> 00:23:01,033 We did know that it was cold in that area, 471 00:23:01,033 --> 00:23:03,833 but did we take it on account enough? 472 00:23:03,833 --> 00:23:05,166 No, we didn't. 473 00:23:05,166 --> 00:23:07,400 We should, of course, have thought about that. 474 00:23:07,400 --> 00:23:09,766 But, uh, but that's the kind of the learning, 475 00:23:09,766 --> 00:23:10,800 that's the process. 476 00:23:12,766 --> 00:23:13,933 NARRATOR: Back in the lab, 477 00:23:13,933 --> 00:23:17,000 the Tunable team recalibrated their chip 478 00:23:17,000 --> 00:23:20,066 to account for the BAMA warehouse temperature. 479 00:23:20,066 --> 00:23:22,433 They also adjusted the design 480 00:23:22,433 --> 00:23:25,833 to include the pumps that compress the sample, 481 00:23:25,833 --> 00:23:27,566 increasing the density of the gas 482 00:23:27,566 --> 00:23:29,933 to compensate for the lower metabolic rate 483 00:23:29,933 --> 00:23:33,000 of the food in the refrigerated environment. 484 00:23:34,200 --> 00:23:36,300 RØER: It will be really interesting to see 485 00:23:36,300 --> 00:23:38,900 if the alterations we have, uh, made, 486 00:23:38,900 --> 00:23:40,266 will actually do the difference in the field. 487 00:23:41,633 --> 00:23:45,733 NARRATOR: Eivind is back with the latest iteration of the e-nose. 488 00:23:45,733 --> 00:23:47,733 Further testing in the lab 489 00:23:47,733 --> 00:23:49,500 showed that, even with the changes, 490 00:23:49,500 --> 00:23:53,733 the machine needs time to adjust to the conditions 491 00:23:53,733 --> 00:23:55,266 in the warehouse. 492 00:23:59,366 --> 00:24:01,900 RØER: Now, I'll let the instrument stay here for the night 493 00:24:01,900 --> 00:24:03,600 to reach a steady temperature, 494 00:24:03,600 --> 00:24:05,200 and then we'll do measurements tomorrow. 495 00:24:08,666 --> 00:24:13,400 ♪ ♪ 496 00:24:13,400 --> 00:24:15,933 Well, after a long cold night, 497 00:24:15,933 --> 00:24:17,566 the system should be ready to go. 498 00:24:17,566 --> 00:24:22,066 (machine whirring) 499 00:24:27,100 --> 00:24:28,733 Now we see absorption of light 500 00:24:28,733 --> 00:24:31,700 at more or less 9.5, ten microns, 501 00:24:31,700 --> 00:24:34,633 which, um, indicate ethanol being present. 502 00:24:34,633 --> 00:24:37,966 This really shows that our new chip is working 503 00:24:37,966 --> 00:24:41,133 in this real environment. 504 00:24:41,133 --> 00:24:43,233 NARRATOR: Eivind uses the e-nose 505 00:24:43,233 --> 00:24:45,566 to sample the air from various locations 506 00:24:45,566 --> 00:24:47,766 on the entire pallet stack. 507 00:24:47,766 --> 00:24:52,166 RØER: Actually, we see a spike at the ethanol absorption wavelength, 508 00:24:52,166 --> 00:24:53,733 so that might be something. 509 00:24:53,733 --> 00:24:56,366 NARRATOR: They've taken an important step. 510 00:24:56,366 --> 00:24:58,200 A successful real-world test 511 00:24:58,200 --> 00:25:02,733 of the newest version of the Tunable e-nose. 512 00:25:03,700 --> 00:25:05,700 I'm not the most excited guy, but, um... 513 00:25:05,700 --> 00:25:07,666 (giggles) 514 00:25:07,666 --> 00:25:09,233 ...this is, uh, this is exciting. 515 00:25:09,233 --> 00:25:11,500 (e-nose humming) 516 00:25:11,500 --> 00:25:14,733 I expected it, although you never know. 517 00:25:14,733 --> 00:25:15,933 It's a big win. 518 00:25:18,133 --> 00:25:20,300 I get very excited when technology works. 519 00:25:21,900 --> 00:25:25,666 NARRATOR: Still, there is work ahead to make the technology viable 520 00:25:25,666 --> 00:25:29,000 and, most importantly, scalable. 521 00:25:29,000 --> 00:25:32,300 MADUMMA: We hope that we can make them more efficient. 522 00:25:32,300 --> 00:25:35,600 Food waste is enormous global problem. 523 00:25:35,600 --> 00:25:40,300   8% of all greenhouse gasses comes from food waste. 524 00:25:40,300 --> 00:25:44,600 So if we can be a part of the solution, it's huge. 525 00:25:46,466 --> 00:25:49,200 NARRATOR: Reducing food waste is one of many ways 526 00:25:49,200 --> 00:25:52,533 engineers are trying to slow climate change. 527 00:25:54,033 --> 00:25:56,100 But the negative changes we've made to our climate 528 00:25:56,100 --> 00:25:59,200 are already damaging some environments 529 00:25:59,200 --> 00:26:01,500 like coral reefs. 530 00:26:01,500 --> 00:26:03,200 MOONEY: Coral reefs are in decline. 531 00:26:03,200 --> 00:26:04,933 So one of the things that I really think about 532 00:26:04,933 --> 00:26:06,200 is how do we fix the environment 533 00:26:06,200 --> 00:26:07,833 that's sort of dying in front of us? 534 00:26:09,600 --> 00:26:13,033 NARRATOR: Healthy coral reefs can be stunningly beautiful 535 00:26:13,033 --> 00:26:16,233 and play a critical role in coastal ecosystems. 536 00:26:16,233 --> 00:26:20,700 They harbor a tremendous diversity of marine life 537 00:26:20,700 --> 00:26:22,100 and contribute to the overall health 538 00:26:22,100 --> 00:26:25,633 of the world's oceans and their coastlines. 539 00:26:25,633 --> 00:26:29,233 A quarter of all marine species depend on them for survival. 540 00:26:29,233 --> 00:26:32,566 They're also important to humans. 541 00:26:32,566 --> 00:26:34,600 Often located in shallow water, 542 00:26:34,600 --> 00:26:36,900 they can protect coastal communities 543 00:26:36,900 --> 00:26:39,633 from damaging storm surges. 544 00:26:39,633 --> 00:26:42,400 And the reefs host a primary, sustainable food source 545 00:26:42,400 --> 00:26:46,733 for hundreds of millions of people around the world. 546 00:26:46,733 --> 00:26:49,666 But as the oceans warm, 547 00:26:49,666 --> 00:26:53,033 corals are struggling to survive. 548 00:26:53,033 --> 00:26:56,466 Excessive heat drives away the microscopic algae 549 00:26:56,466 --> 00:26:59,200 the coral depend on. 550 00:26:59,200 --> 00:27:01,066 That leads to a dramatic loss of color, 551 00:27:01,066 --> 00:27:03,300 known as coral bleaching-- 552 00:27:03,300 --> 00:27:07,266 a powerful visual indicator of an unhealthy reef. 553 00:27:07,266 --> 00:27:12,133 But bleaching isn't the only indicator of a reef in peril... 554 00:27:12,133 --> 00:27:13,500 MOONEY: Not only it looks brown 555 00:27:13,500 --> 00:27:15,200 and is lacking these beautiful, vibrant colors, 556 00:27:15,200 --> 00:27:16,700 but it just sounds dead. 557 00:27:16,700 --> 00:27:18,466 (underwater ambient noise) 558 00:27:18,466 --> 00:27:23,333 NARRATOR: That's where sensory biologist Aran Mooney comes in. 559 00:27:23,333 --> 00:27:26,366 MOONEY: My background is in hearing and in bioacoustics. 560 00:27:26,366 --> 00:27:29,166 And I study how animals perceive the world around them. 561 00:27:29,166 --> 00:27:30,866 (wildlife chittering) 562 00:27:30,866 --> 00:27:32,966 Coral reefs are kind of rainforest of the sea, 563 00:27:32,966 --> 00:27:34,666 and just like a really rich forest 564 00:27:34,666 --> 00:27:36,166 might have a lot of birds calling, 565 00:27:36,166 --> 00:27:38,433 and you might hear the monkeys calling in the background, 566 00:27:38,433 --> 00:27:41,066 coral reefs are really the same. 567 00:27:41,066 --> 00:27:42,666 So basically a healthy coral reef 568 00:27:42,666 --> 00:27:44,366 has a really healthy rich soundscape. 569 00:27:44,366 --> 00:27:46,633 (crackling, snapping) 570 00:27:46,633 --> 00:27:49,100 NARRATOR: Snapping shrimp, lobster, and fish 571 00:27:49,100 --> 00:27:53,000 create a symphony indicative of a biodiverse community. 572 00:27:54,766 --> 00:27:58,333 MOONEY: And a degraded coral reef is just an impoverished soundscape. 573 00:27:58,333 --> 00:28:01,466 It sounds quiet, kind of desolate. 574 00:28:01,466 --> 00:28:03,166 So, by listening to the soundscape, 575 00:28:03,166 --> 00:28:05,033 we can kind of track that biodiversity 576 00:28:05,033 --> 00:28:07,200 and understand when that change is happening. 577 00:28:07,200 --> 00:28:08,800 ♪ ♪ 578 00:28:08,800 --> 00:28:11,733 NARRATOR: Off the coast of St. John in the Caribbean, 579 00:28:11,733 --> 00:28:14,633 a team from the Woods Hole Oceanographic Institution 580 00:28:14,633 --> 00:28:16,066 in Massachusetts 581 00:28:16,066 --> 00:28:18,100 conducts bleaching surveys, 582 00:28:18,100 --> 00:28:21,366 finding evidence of degraded reefs. 583 00:28:21,366 --> 00:28:23,233 (water splashing) 584 00:28:23,233 --> 00:28:25,033 To your right, there's some bleached coral. 585 00:28:28,200 --> 00:28:30,666 You knew there's going to be bleaching here, right? 586 00:28:30,666 --> 00:28:33,600 But then it's freaking everywhere, right? 587 00:28:33,600 --> 00:28:34,733 YOGI GIRDHAR: I've been coming here 588 00:28:34,733 --> 00:28:35,933 five or six years now, 589 00:28:35,933 --> 00:28:38,833 this was the first time 590 00:28:38,833 --> 00:28:40,533 I have seen such bleaching. 591 00:28:40,533 --> 00:28:44,000 NARRATOR: Yogi Girdhar is a roboticist 592 00:28:44,000 --> 00:28:46,300 and computer scientist at Woods Hole. 593 00:28:46,300 --> 00:28:47,300 GIRDHAR: I am working on 594 00:28:47,300 --> 00:28:48,866 robots and A.I. 595 00:28:48,866 --> 00:28:50,133 and machine learning-based techniques 596 00:28:50,133 --> 00:28:54,233 to understand complex ecosystems in the ocean, 597 00:28:54,233 --> 00:28:55,366 such as coral reefs. 598 00:28:58,800 --> 00:29:00,533 NARRATOR: A question they pose: 599 00:29:00,533 --> 00:29:03,700 is it possible to build a robot 600 00:29:03,700 --> 00:29:05,566 that can seek out and find healthy reefs 601 00:29:05,566 --> 00:29:06,900 on its own? (electronic beeping) 602 00:29:06,900 --> 00:29:09,166 If they succeed, 603 00:29:09,166 --> 00:29:12,533 the robot could provide an efficient and cost-effective way 604 00:29:12,533 --> 00:29:16,066 to find healthy coral reefs, map them, 605 00:29:16,066 --> 00:29:18,700 and monitor their health. 606 00:29:18,700 --> 00:29:21,366 (electronic crackling) 607 00:29:21,366 --> 00:29:23,466 The soundscapes recorded by the robot 608 00:29:23,466 --> 00:29:26,833 could be a vital tool in diagnosing reef health 609 00:29:26,833 --> 00:29:30,366 and tracking decline or improvement. 610 00:29:30,366 --> 00:29:31,966 ♪ ♪ 611 00:29:31,966 --> 00:29:34,433 MOONEY: Good job, team! 612 00:29:34,433 --> 00:29:36,900 ♪ ♪ 613 00:29:36,900 --> 00:29:40,166 NARRATOR: The team has been collecting data on reefs 614 00:29:40,166 --> 00:29:42,266 for over a decade. 615 00:29:42,266 --> 00:29:43,400 You're going through this. Yeah. 616 00:29:43,400 --> 00:29:45,500 I might be able to thread it through here. 617 00:29:45,500 --> 00:29:47,633 NARRATOR: They have mountains of information; 618 00:29:47,633 --> 00:29:50,566 including audio and video. 619 00:29:50,566 --> 00:29:53,500 They've even created 3D models of the reefs 620 00:29:53,500 --> 00:29:55,200 for further study. 621 00:29:55,200 --> 00:29:56,400 Helping them gather this data 622 00:29:56,400 --> 00:30:00,200 is this third-generation robot. 623 00:30:00,200 --> 00:30:01,833 GIRDHAR: We call it CUREE-- 624 00:30:01,833 --> 00:30:04,533 C-U-R-E-E. 625 00:30:04,533 --> 00:30:08,833 It stands for Curious Underwater Robot for Ecosystem Exploration. 626 00:30:08,833 --> 00:30:12,900 NARRATOR: It's equipped with sensors, microphones, and cameras 627 00:30:12,900 --> 00:30:15,266 and is still very much under development. 628 00:30:15,266 --> 00:30:17,300 GIRDHAR: The design of a robot 629 00:30:17,300 --> 00:30:18,566 is always evolving. 630 00:30:18,566 --> 00:30:20,166 Our robot is never finished. 631 00:30:20,166 --> 00:30:22,666 NARRATOR: It's an engineering challenge 632 00:30:22,666 --> 00:30:24,733 with a lot of moving parts. 633 00:30:24,733 --> 00:30:28,366 So they've broken it down into many small steps. 634 00:30:28,366 --> 00:30:29,733 MARIA YANG: There are many, many problems 635 00:30:29,733 --> 00:30:32,466 that you can solve with an engineering solution. 636 00:30:32,466 --> 00:30:33,666 But I think you have to 637 00:30:33,666 --> 00:30:34,900 really understand what the problem is 638 00:30:34,900 --> 00:30:36,500 and sort of pick the two or three 639 00:30:36,500 --> 00:30:38,533 that really you want to address. 640 00:30:38,533 --> 00:30:40,533   Otherwise, you kind of fall into this trap of 641 00:30:40,533 --> 00:30:41,866 trying to solve all the problems all at once 642 00:30:41,866 --> 00:30:43,166 and you run out of resources. 643 00:30:43,166 --> 00:30:44,866 ♪ ♪ 644 00:30:44,866 --> 00:30:48,000 NARRATOR: This morning, the team is prepping for its latest test 645 00:30:48,000 --> 00:30:50,300 right off the dock. 646 00:30:50,300 --> 00:30:51,300 MOONEY: All right, Dr. Girdhar. 647 00:30:51,300 --> 00:30:53,900 Are you ready? 648 00:30:53,900 --> 00:30:54,933 Always. 649 00:30:59,966 --> 00:31:02,500 GIRDHAR: I'll manage the tether. Got it? 650 00:31:02,500 --> 00:31:06,133 NARRATOR: To start, they'll place a speaker on the ocean floor, 651 00:31:06,133 --> 00:31:10,000 playing a recording of a healthy coral reef. 652 00:31:10,000 --> 00:31:11,600 A sound file they captured 653 00:31:11,600 --> 00:31:12,833 from a previous trip. 654 00:31:13,833 --> 00:31:14,866 SETH McCAMMON: It should be on. 655 00:31:14,866 --> 00:31:15,866 GIRDHAR: Yeah. All right. 656 00:31:15,866 --> 00:31:16,866 We hear it. 657 00:31:16,866 --> 00:31:18,600 (electronic crackling) 658 00:31:18,600 --> 00:31:19,766 NARRATOR: They're hoping the robot 659 00:31:19,766 --> 00:31:22,266 will recognize the sound through the water 660 00:31:22,266 --> 00:31:24,066 and be able to record it. 661 00:31:27,300 --> 00:31:28,566 In this outing, 662 00:31:28,566 --> 00:31:31,300 the robot is not moving autonomously. 663 00:31:31,300 --> 00:31:33,366 Researcher Seth McCammon 664 00:31:33,366 --> 00:31:34,900 is operating the robot remotely 665 00:31:34,900 --> 00:31:39,166 to steer and position it for the test. 666 00:31:39,166 --> 00:31:41,033 I'm getting it in line with the thing 667 00:31:41,033 --> 00:31:43,066 so we can start to look at the data. 668 00:31:45,166 --> 00:31:47,000 GIRDHAR: If the robot doesn't work with this sound, 669 00:31:47,000 --> 00:31:50,000 it's probably not going to work on the real coral reef, 670 00:31:50,000 --> 00:31:52,433 so it's a good, good test. 671 00:31:52,433 --> 00:31:55,566 NARRATOR: Experimenting with sound underwater 672 00:31:55,566 --> 00:31:58,700 is not a new idea. 673 00:31:58,700 --> 00:32:00,566 In the 1800s, 674 00:32:00,566 --> 00:32:03,366 a Swiss physicist and a French mathematician, 675 00:32:03,366 --> 00:32:05,666 armed with a bell and stopwatch, 676 00:32:05,666 --> 00:32:09,600 measured the speed at which sound traveled underwater. 677 00:32:09,600 --> 00:32:11,633 On one side of Lake Geneva, 678 00:32:11,633 --> 00:32:14,866 Charles François Sturm rang a submerged bell, 679 00:32:14,866 --> 00:32:17,400 (bell ringing) while Jean-Daniel Colladon 680 00:32:17,400 --> 00:32:21,300 used a long tube to listen underwater across the lake... 681 00:32:21,300 --> 00:32:22,933 (watch clicks) ...pressing his stopwatch 682 00:32:22,933 --> 00:32:24,466 to keep track of how long it took 683 00:32:24,466 --> 00:32:27,166 the sound to travel across. 684 00:32:27,166 --> 00:32:29,433 Surprisingly, they found that water 685 00:32:29,433 --> 00:32:32,433 is a better conduit for sound than air. 686 00:32:32,433 --> 00:32:34,666 Sound travels through water 687 00:32:34,666 --> 00:32:36,433 roughly five times faster. 688 00:32:37,600 --> 00:32:39,333 Today, the Woods Hole team 689 00:32:39,333 --> 00:32:41,833 will be using the speed of sound underwater 690 00:32:41,833 --> 00:32:44,300 as part of their calculations. 691 00:32:44,300 --> 00:32:45,933 The robot is equipped 692 00:32:45,933 --> 00:32:49,533 with four microphones designed for underwater use 693 00:32:49,533 --> 00:32:50,933 called hydrophones. 694 00:32:50,933 --> 00:32:54,066 As the sound from the speaker speeds through the water 695 00:32:54,066 --> 00:32:55,633 in all directions, 696 00:32:55,633 --> 00:32:59,766 it reaches the hydrophones at slightly different times-- 697 00:32:59,766 --> 00:33:02,633 just milliseconds apart. 698 00:33:02,633 --> 00:33:05,066 The researchers look at a computer display 699 00:33:05,066 --> 00:33:06,600 that shows the signals recorded... 700 00:33:06,600 --> 00:33:09,433 (electronic chirping) ...on each hydrophone. 701 00:33:09,433 --> 00:33:11,733 McCAMMON: And so it will hit one hydrophone before the others 702 00:33:11,733 --> 00:33:13,900 and by looking at the relative time of arrival 703 00:33:13,900 --> 00:33:15,366 at those different hydrophones, 704 00:33:15,366 --> 00:33:17,566 we can figure out which direction it came from first 705 00:33:17,566 --> 00:33:19,733 and then steer the robot in that direction. 706 00:33:19,733 --> 00:33:23,400 ♪ ♪ 707 00:33:23,400 --> 00:33:26,600 NARRATOR: The robot correctly identifies 708 00:33:26,600 --> 00:33:28,200 the direction of the sound-- 709 00:33:28,200 --> 00:33:32,533 an important first step toward autonomous navigation. 710 00:33:32,533 --> 00:33:34,533 ♪ ♪ 711 00:33:34,533 --> 00:33:37,833 A small but important victory. 712 00:33:37,833 --> 00:33:41,266 ♪ ♪ 713 00:33:41,266 --> 00:33:42,400 McCAMMON: It's like you're building out of LEGOS 714 00:33:42,400 --> 00:33:43,533 and you're building up a house, 715 00:33:43,533 --> 00:33:44,966 brick by brick by brick. 716 00:33:44,966 --> 00:33:46,100 And it only works 717 00:33:46,100 --> 00:33:47,500 when the house is fully done. 718 00:33:47,500 --> 00:33:48,666 But you need to know 719 00:33:48,666 --> 00:33:50,300 that each single brick in that 720 00:33:50,300 --> 00:33:51,766 works on its own in isolation 721 00:33:51,766 --> 00:33:53,466   before you're willing to add it to the larger picture. 722 00:33:53,466 --> 00:33:55,833 MABRY: And so, you have this massive goal 723 00:33:55,833 --> 00:33:57,200 that you're trying to achieve, 724 00:33:57,200 --> 00:33:59,500 but there needs to be attainable goals along the way 725 00:33:59,500 --> 00:34:01,266 because ultimately, 726 00:34:01,266 --> 00:34:03,566 you're dealing with a system of components, 727 00:34:03,566 --> 00:34:05,700 a system of elements 728 00:34:05,700 --> 00:34:06,900 that need to work together 729 00:34:06,900 --> 00:34:08,166 in order for this 730 00:34:08,166 --> 00:34:09,233 to be successful. 731 00:34:09,233 --> 00:34:12,200 NARRATOR: CUREE is ready to step up 732 00:34:12,200 --> 00:34:13,866 to a bigger challenge. 733 00:34:13,866 --> 00:34:16,900 Locating an actual healthy reef by sound-- 734 00:34:16,900 --> 00:34:20,000 something less predictable than what the speaker provided. 735 00:34:20,000 --> 00:34:23,000 One of the healthier reefs in St. John 736 00:34:23,000 --> 00:34:24,800 is in nearby Joel's Shoal. 737 00:34:24,800 --> 00:34:26,700 GIRDHAR: I propose we drop the robot 738 00:34:26,700 --> 00:34:28,033 like 20 meters... 739 00:34:28,033 --> 00:34:29,133 MOONEY: We're like ten meters 740 00:34:29,133 --> 00:34:30,600 off the reef right now. 741 00:34:30,600 --> 00:34:32,533 NARRATOR: They'll place CUREE 742 00:34:32,533 --> 00:34:34,433 approximately 20 meters from the reef. 743 00:34:34,433 --> 00:34:35,900 (electronic chirping) To succeed, 744 00:34:35,900 --> 00:34:37,833 it just needs to orient itself 745 00:34:37,833 --> 00:34:39,700 toward the sound. 746 00:34:39,700 --> 00:34:41,566 Robot going in. 747 00:34:44,100 --> 00:34:47,600 All right, cast away! (electronic melody) 748 00:34:47,600 --> 00:34:48,833 McCAMMON: So the test today 749 00:34:48,833 --> 00:34:50,933 is mostly just trying to figure out 750 00:34:50,933 --> 00:34:53,200 if the robot can accurately determine 751 00:34:53,200 --> 00:34:55,500 which direction the reef sound is in. 752 00:34:55,500 --> 00:34:58,466 NARRATOR: It's a more complex test. 753 00:34:58,466 --> 00:35:00,900 This time CUREE is untethered 754 00:35:00,900 --> 00:35:03,233 and the boat is drifting with the ocean current. 755 00:35:03,233 --> 00:35:07,300 NARRATOR: If they lose contact, 756 00:35:07,300 --> 00:35:09,500 they could easily lose the robot entirely, 757 00:35:09,500 --> 00:35:12,833 and all of the engineering that went into it. 758 00:35:12,833 --> 00:35:14,766 ♪ ♪ 759 00:35:14,766 --> 00:35:17,600 MABRY: When they began to design this autonomous robot 760 00:35:17,600 --> 00:35:18,900 that would go underwater, 761 00:35:18,900 --> 00:35:21,500 there is a need to make sure that 762 00:35:21,500 --> 00:35:23,666 this thing is able to behave 763 00:35:23,666 --> 00:35:25,566 in an environment where, if it doesn't, 764 00:35:25,566 --> 00:35:26,600 we can retrieve it... 765 00:35:28,200 --> 00:35:30,600 NARRATOR: CUREE locates the direction of the healthy reef. 766 00:35:30,600 --> 00:35:32,900 Which is encouraging. 767 00:35:32,900 --> 00:35:34,933 NARRATOR: It's another successful test. 768 00:35:34,933 --> 00:35:36,066 (electronic crackling) 769 00:35:36,066 --> 00:35:38,300 The next big hurdle, 770 00:35:38,300 --> 00:35:39,700 can CUREE not only locate, 771 00:35:39,700 --> 00:35:43,400 but then move towards a healthy reef autonomously. 772 00:35:43,400 --> 00:35:47,366 This will be a crucial milestone in the mission, 773 00:35:47,366 --> 00:35:49,966 which is to ultimately build a fleet of robots 774 00:35:49,966 --> 00:35:51,733 to map, monitor, and record 775 00:35:51,733 --> 00:35:56,500 the health of corals around the globe. 776 00:35:56,500 --> 00:35:58,533 While reefs are under serious threat all over, 777 00:35:58,533 --> 00:36:01,166 there are some signs of hope, 778 00:36:01,166 --> 00:36:04,500 and some surprising ideas for ways to protect them; 779 00:36:04,500 --> 00:36:08,000 including one that came from this team's research. 780 00:36:08,000 --> 00:36:09,533 ♪ ♪ 781 00:36:09,533 --> 00:36:12,600 In their work, they discovered that the sound of a healthy reef 782 00:36:12,600 --> 00:36:15,400 might actually have an indirect healing effect 783 00:36:15,400 --> 00:36:16,800 on a stressed reef. 784 00:36:16,800 --> 00:36:20,833 It has to do with the coral animal's life cycle. 785 00:36:20,833 --> 00:36:23,100 Newly born baby corals-- 786 00:36:23,100 --> 00:36:25,300 tiny larvae-- drift in the ocean, 787 00:36:25,300 --> 00:36:27,500 searching for somewhere to settle. 788 00:36:27,500 --> 00:36:30,400 It turns out the sound of a thriving coral reef 789 00:36:30,400 --> 00:36:33,233 signals them to settle into place. 790 00:36:33,233 --> 00:36:34,833 Once they find a spot, 791 00:36:34,833 --> 00:36:36,466 they can be very resilient 792 00:36:36,466 --> 00:36:38,700 and grow for centuries. 793 00:36:38,700 --> 00:36:40,633 So the more larvae a reef can attract, 794 00:36:40,633 --> 00:36:43,233 the healthier it will be. 795 00:36:43,233 --> 00:36:46,666 And that gave the team an idea. 796 00:36:46,666 --> 00:36:47,766 We know these reefs are degraded 797 00:36:47,766 --> 00:36:49,100 and we want to rebuild them 798 00:36:49,100 --> 00:36:51,200 by attracting the larvae, the baby coral. 799 00:36:51,200 --> 00:36:53,833   NARRATOR: In a past experiment, 800 00:36:53,833 --> 00:36:55,366 the team found that larvae 801 00:36:55,366 --> 00:36:59,166 could be drawn to recordings of healthy reefs. 802 00:36:59,166 --> 00:37:02,200 So by placing speakers in strategic locations, 803 00:37:02,200 --> 00:37:05,533 they could give a boost where it's needed most. 804 00:37:05,533 --> 00:37:06,700 MOONEY: And that system actually 805 00:37:06,700 --> 00:37:08,466 leverages the healthy landscape 806 00:37:08,466 --> 00:37:10,200 and plays it back into the environment 807 00:37:10,200 --> 00:37:12,600 and the idea is that it induces coral larvae 808 00:37:12,600 --> 00:37:14,066 to kind of choose that environment and settle. 809 00:37:15,600 --> 00:37:17,300 NARRATOR: The result? 810 00:37:17,300 --> 00:37:19,466 Up to seven times more larvae settlement 811 00:37:19,466 --> 00:37:21,300 compared to a degraded reef 812 00:37:21,300 --> 00:37:23,566 without the audio boost. 813 00:37:23,566 --> 00:37:26,533 A very encouraging sign. 814 00:37:26,533 --> 00:37:28,666 ♪ ♪ 815 00:37:28,666 --> 00:37:31,066 But back to St. John and CUREE. 816 00:37:31,066 --> 00:37:34,433 The team is ready for the final test of the day. 817 00:37:34,433 --> 00:37:36,466 McCAMMON: The robot is going to use the direction 818 00:37:36,466 --> 00:37:38,100 that it's finding from its hydrophones 819 00:37:38,100 --> 00:37:39,400 and then drive itself 820 00:37:39,400 --> 00:37:42,200 to whatever the nearest acoustic source is, 821 00:37:42,200 --> 00:37:44,500 which we're hoping is going to be Joel's Shoal Reef. 822 00:37:44,500 --> 00:37:47,833 NARRATOR: This time, since CUREE will pilot itself, 823 00:37:47,833 --> 00:37:50,200 it's tethered for safety. 824 00:37:50,200 --> 00:37:53,000 They put CUREE in the water and give it the green light. 825 00:37:53,000 --> 00:37:54,133 NATE FORMEL: Are we expecting it 826 00:37:54,133 --> 00:37:55,800 to be moving or not? McCAMMON: We are. 827 00:37:55,800 --> 00:37:57,800 NARRATOR: It looks at first as though it's orienting 828 00:37:57,800 --> 00:37:58,866 toward the sound of the reef. 829 00:37:58,866 --> 00:38:01,033 It thinks it's moving. 830 00:38:01,033 --> 00:38:03,266 NARRATOR: But after a few minutes it's clear 831 00:38:03,266 --> 00:38:05,466 that CUREE isn't making much headway. 832 00:38:05,466 --> 00:38:07,333 It's just dumb stuff in the way that I wrote. 833 00:38:07,333 --> 00:38:11,166 NARRATOR: It seems there's an issue with the software. 834 00:38:11,166 --> 00:38:12,266 ♪ ♪ 835 00:38:12,266 --> 00:38:14,966 All right, bring it back. 836 00:38:14,966 --> 00:38:16,600 (ratcheting) 837 00:38:18,866 --> 00:38:21,300 It's coming up. 838 00:38:21,300 --> 00:38:22,933 FORMEL: I can now see it. 839 00:38:22,933 --> 00:38:25,033 NARRATOR: They're starting to lose the light. 840 00:38:25,033 --> 00:38:27,466 It's getting dark. (indistinct chatter) 841 00:38:27,466 --> 00:38:30,100 NARRATOR: They weren't able to check off everything 842 00:38:30,100 --> 00:38:31,866 on the day's to-do list, 843 00:38:31,866 --> 00:38:34,333 yet they remain upbeat. 844 00:38:34,333 --> 00:38:36,533 GIRDHAR: Overall, I am happy right now because... 845 00:38:36,533 --> 00:38:37,600 McCAMMON: We ended the day with as many robots 846 00:38:37,600 --> 00:38:39,033 as we started the day with. 847 00:38:39,033 --> 00:38:41,266 NARRATOR: It's frustrating in the moment, 848 00:38:41,266 --> 00:38:43,266 but they're making progress. 849 00:38:45,266 --> 00:38:48,433 STELTZNER: The creative act of engineering 850 00:38:48,433 --> 00:38:51,333 has got disappointment, 851 00:38:51,333 --> 00:38:53,733 has got failure, 852 00:38:53,733 --> 00:38:55,933 and that's how we learn. 853 00:38:55,933 --> 00:39:00,200 (chuckling): So, it is a big ball of, of... 854 00:39:00,200 --> 00:39:02,166 ...two steps forward and one step back. 855 00:39:02,166 --> 00:39:04,800 When you have a very massive "Why" 856 00:39:04,800 --> 00:39:07,533 and a very massive purpose for what you're trying to do, 857 00:39:07,533 --> 00:39:09,966 such as save the coral reefs, 858 00:39:09,966 --> 00:39:12,800 it allows you to experience the disappointment 859 00:39:12,800 --> 00:39:14,433 but not be defeated by it, 860 00:39:14,433 --> 00:39:16,666 and continue to try the process of moving it forward. 861 00:39:16,666 --> 00:39:18,166 ♪ ♪ 862 00:39:18,166 --> 00:39:20,400 If you're not failing you're not trying hard enough. 863 00:39:20,400 --> 00:39:21,400 (voiceover): Yeah, it's very frustrating 864 00:39:21,400 --> 00:39:22,833 but when it works, 865 00:39:22,833 --> 00:39:24,466 it's very satisfying. 866 00:39:24,466 --> 00:39:27,933   NARRATOR: Engineering solutions to the climate crisis 867 00:39:27,933 --> 00:39:31,200 will require creativity, innovation, 868 00:39:31,200 --> 00:39:34,000 and a global commitment to making smart choices. 869 00:39:34,000 --> 00:39:37,333 But we face many other challenges as well; 870 00:39:37,333 --> 00:39:40,000 like restoring balance to the land 871 00:39:40,000 --> 00:39:43,133 after decades of industrial pollution. 872 00:39:43,133 --> 00:39:44,533 ♪ ♪ 873 00:39:44,533 --> 00:39:46,866 On Navajo land in Arizona, 874 00:39:46,866 --> 00:39:50,700 an Indigenous artist and engineers are collaborating 875 00:39:50,700 --> 00:39:53,500 on a unique, local approach 876 00:39:53,500 --> 00:39:57,100 to purifying contaminated drinking water. 877 00:39:57,100 --> 00:40:00,300 (birds chirping) This pristine-seeming landscape 878 00:40:00,300 --> 00:40:02,900 conceals a serious problem. 879 00:40:02,900 --> 00:40:06,800 30% of the population in the Navajo Nation 880 00:40:06,800 --> 00:40:10,266 lacks access to clean drinking water. 881 00:40:10,266 --> 00:40:11,500 Decades of uranium mining 882 00:40:11,500 --> 00:40:14,000 has polluted the land. 883 00:40:14,000 --> 00:40:15,466 The United States government 884 00:40:15,466 --> 00:40:16,900 used the heavy metal 885 00:40:16,900 --> 00:40:18,566 to develop the atomic bomb 886 00:40:18,566 --> 00:40:21,700 and power its nuclear weapons program 887 00:40:21,700 --> 00:40:24,733 after World War II. 888 00:40:24,733 --> 00:40:25,900 CHACHRA: When we think of engineering, 889 00:40:25,900 --> 00:40:29,733 people are suspicious of it because, 890 00:40:29,733 --> 00:40:32,166 for a good part of the 20th century, 891 00:40:32,166 --> 00:40:34,033   one of the stories of engineering 892 00:40:34,033 --> 00:40:37,266 was engineers making decisions about systems 893 00:40:37,266 --> 00:40:40,033 that affected a lot of other people. 894 00:40:40,033 --> 00:40:42,466 And often those effects were not positive. 895 00:40:42,466 --> 00:40:45,066 NARRATOR: Byproducts of uranium mining, 896 00:40:45,066 --> 00:40:46,700 such as strontium, 897 00:40:46,700 --> 00:40:48,766 can mimic calcium in the body, 898 00:40:48,766 --> 00:40:51,466 causing it to be absorbed by bones. 899 00:40:51,466 --> 00:40:55,133 The E.P.A. has awarded $3.8 million 900 00:40:55,133 --> 00:40:57,500 to support three drinking water projects 901 00:40:57,500 --> 00:40:59,366 to benefit the Navajo Nation. 902 00:41:01,466 --> 00:41:05,433 Some are proposing other, more homegrown solutions, as well. 903 00:41:05,433 --> 00:41:07,500 (stone grinding) 904 00:41:07,500 --> 00:41:10,233 Deanna Tso is a third-generation 905 00:41:10,233 --> 00:41:12,966 Navajo artist who works in clay. 906 00:41:12,966 --> 00:41:15,000 TSO: People always ask me, 907 00:41:15,000 --> 00:41:17,200 "When'd you learn how to do pottery?" 908 00:41:17,200 --> 00:41:18,466 I always say, 909 00:41:18,466 --> 00:41:19,800 "I was born making it." 910 00:41:19,800 --> 00:41:22,900 Both my parents, my mother and my father, 911 00:41:22,900 --> 00:41:25,366 both did Navajo pottery. 912 00:41:25,366 --> 00:41:29,100 (car doors closing) 913 00:41:29,100 --> 00:41:30,700 NARRATOR: She has been collaborating with scientists 914 00:41:30,700 --> 00:41:33,366 Navid Saleh and Stetson Rowles... 915 00:41:33,366 --> 00:41:34,400 (knocks on door) 916 00:41:35,600 --> 00:41:36,600 Hey! Good morning. 917 00:41:36,600 --> 00:41:37,733 Hey, Deanna. 918 00:41:37,733 --> 00:41:38,833 NARRATOR: ...on a project meant to address 919 00:41:38,833 --> 00:41:40,766 the water contamination problem 920 00:41:40,766 --> 00:41:44,133 on a very human scale. 921 00:41:44,133 --> 00:41:47,133 SALEH (voiceover): I believe that engineering without people 922 00:41:47,133 --> 00:41:48,766 is destined to fail. 923 00:41:48,766 --> 00:41:50,700   Good. Good. Long drive. 924 00:41:50,700 --> 00:41:53,166 SALEH (voiceover): There is this experiential knowledge, 925 00:41:53,166 --> 00:41:55,833 knowledge that is housed within people's lives, 926 00:41:55,833 --> 00:41:56,900 yet to be unlocked. 927 00:41:56,900 --> 00:41:59,766 NARRATOR: Not all people here use 928 00:41:59,766 --> 00:42:02,466 or have access to municipal water, 929 00:42:02,466 --> 00:42:05,400 so the goal is to call upon local knowledge 930 00:42:05,400 --> 00:42:07,700 to find a sustainable way to purify water 931 00:42:07,700 --> 00:42:08,833 closer to the home. 932 00:42:08,833 --> 00:42:10,000 YANG: We often think 933 00:42:10,000 --> 00:42:12,433 of engineering as only being 934 00:42:12,433 --> 00:42:15,233 the latest and greatest technology. 935 00:42:15,233 --> 00:42:18,700   But, people have practices that are very effective now 936 00:42:18,700 --> 00:42:20,733 and, and have been for, 937 00:42:20,733 --> 00:42:23,300 you know, decades, centuries longer. 938 00:42:23,300 --> 00:42:27,133 And so what can we learn from those, existing approaches 939 00:42:27,133 --> 00:42:28,766 that are already effective? 940 00:42:28,766 --> 00:42:31,700 So Deanna, this was something that... 941 00:42:31,700 --> 00:42:34,866 NARRATOR: On this trip, the scientists want to build 942 00:42:34,866 --> 00:42:36,566 a new prototype clay filter 943 00:42:36,566 --> 00:42:39,766 for use in household water containers. 944 00:42:39,766 --> 00:42:44,300 The hope is to integrate locally sourced minerals 945 00:42:44,300 --> 00:42:47,500 so that the finished filter will remove uranium byproducts, 946 00:42:47,500 --> 00:42:50,600 like strontium, from the water. 947 00:42:50,600 --> 00:42:52,466 SALEH: Could you actually make something like that? 948 00:42:52,466 --> 00:42:53,533 Do you have something similar? 949 00:42:53,533 --> 00:42:55,566 I have one that I make 950 00:42:55,566 --> 00:42:57,366 with the cone shape. 951 00:42:57,366 --> 00:42:59,000 NARRATOR: Navajo potters like Deanna 952 00:42:59,000 --> 00:43:01,733 use a local tree sap as a glaze. 953 00:43:01,733 --> 00:43:04,733 Navid and his team wondered if the sap could be used 954 00:43:04,733 --> 00:43:06,700 as part of a decontamination filter. 955 00:43:07,900 --> 00:43:10,466 SALEH (voiceover): What we found was how much knowledge 956 00:43:10,466 --> 00:43:14,133 the Navajos already had about the sap. 957 00:43:14,133 --> 00:43:18,266 They already knew it has health benefits. 958 00:43:18,266 --> 00:43:20,000 So this is a printout of the... 959 00:43:20,000 --> 00:43:22,200 NARRATOR: Navid and his team recently conducted tests 960 00:43:22,200 --> 00:43:24,533 that translated Indigenous knowledge 961 00:43:24,533 --> 00:43:27,233 into the language of biochemistry; 962 00:43:27,233 --> 00:43:32,000 quantifying the extent of the sap's antimicrobial properties. 963 00:43:32,000 --> 00:43:35,500 Now, they hope to expand the filter's capabilities 964 00:43:35,500 --> 00:43:38,333 to radioactive contaminants. 965 00:43:38,333 --> 00:43:40,600 YANG: They worked together, collaboratively, 966 00:43:40,600 --> 00:43:42,300 to make something new and better 967 00:43:42,300 --> 00:43:45,833 that serves her community in a really, powerful 968 00:43:45,833 --> 00:43:48,800 and very collaborative way. 969 00:43:48,800 --> 00:43:51,166 We can engineer a shape or a design 970 00:43:51,166 --> 00:43:53,300 that's going to work well, not only to filter water, 971 00:43:53,300 --> 00:43:56,066 but people will want to use. 972 00:43:56,066 --> 00:43:58,133 (voiceover): We see this amazing opportunity 973 00:43:58,133 --> 00:43:59,766 to be able to use pottery, 974 00:43:59,766 --> 00:44:02,400 or ceramics, as filters, 975 00:44:02,400 --> 00:44:04,033 because it's so a part 976 00:44:04,033 --> 00:44:05,133 of people's everyday life. 977 00:44:06,233 --> 00:44:08,400 Particularly in places like the Navajo Nation 978 00:44:08,400 --> 00:44:11,200 where traditional practices are so important. 979 00:44:11,200 --> 00:44:13,100 TSO: Okay. 980 00:44:13,100 --> 00:44:14,966 ROWLES: Which way? 981 00:44:14,966 --> 00:44:17,700 NARRATOR: Navid and Stetson want to learn the process of making pottery 982 00:44:17,700 --> 00:44:20,700 the way Deanna's mother taught her-- 983 00:44:20,700 --> 00:44:22,766 because collaboration is strongest 984 00:44:22,766 --> 00:44:24,933 when it is truly interdisciplinary. 985 00:44:24,933 --> 00:44:26,133 TSO: Yes. 986 00:44:26,133 --> 00:44:28,266 You see that gray spot? 987 00:44:28,266 --> 00:44:29,666 NARRATOR: Deanna starts from scratch, 988 00:44:29,666 --> 00:44:32,000 harvesting clay from a rocky outcropping 989 00:44:32,000 --> 00:44:33,233 on Navajo land. 990 00:44:33,233 --> 00:44:34,733 Okay, so this portion is what? 991 00:44:34,733 --> 00:44:37,466 That portion is clay. Okay. 992 00:44:37,466 --> 00:44:40,100 SALEH (voiceover): We often as scientists believe 993 00:44:40,100 --> 00:44:42,333 that we know a lot. 994 00:44:42,333 --> 00:44:43,366 But we forget, 995 00:44:43,366 --> 00:44:45,133 science as a discipline 996 00:44:45,133 --> 00:44:47,400 has only been around for 500 years. 997 00:44:47,400 --> 00:44:50,433 NARRATOR: There are many ways of generating knowledge 998 00:44:50,433 --> 00:44:52,166 besides the modern scientific process. 999 00:44:53,466 --> 00:44:54,700 CHACHA: These are all different ways 1000 00:44:54,700 --> 00:44:56,300 in which we interact with the physical world. 1001 00:44:56,300 --> 00:44:58,433 That diversity gives you new ideas. 1002 00:44:58,433 --> 00:45:00,166 And thinking about how to put together 1003 00:45:00,166 --> 00:45:01,766 old technologies and new technologies 1004 00:45:01,766 --> 00:45:03,500 might lead to entirely new paths. 1005 00:45:03,500 --> 00:45:05,933 It creates a symbiotic effect, 1006 00:45:05,933 --> 00:45:07,933 because the more people feel included 1007 00:45:07,933 --> 00:45:10,033 in what is being produced by something, 1008 00:45:10,033 --> 00:45:12,600   the more people see themselves being a part of 1009 00:45:12,600 --> 00:45:14,066 the producing of that thing. 1010 00:45:14,066 --> 00:45:17,666 NARRATOR: Next-- they source sap from pinyon trees. 1011 00:45:17,666 --> 00:45:20,500 There's one right here, let's check this one. 1012 00:45:20,500 --> 00:45:24,066 ♪ ♪ 1013 00:45:24,066 --> 00:45:25,366 (crunches) ROWLES: Whoo! 1014 00:45:25,366 --> 00:45:28,166 We hit the jackpot with this tree. 1015 00:45:28,166 --> 00:45:30,466 TSO: We were blessed for the day. 1016 00:45:31,466 --> 00:45:33,433 Come on in. (keys jangling) 1017 00:45:33,433 --> 00:45:36,533 I usually just take this much out. 1018 00:45:36,533 --> 00:45:38,333 NARRATOR: Deanna demonstrates how to grind minerals 1019 00:45:38,333 --> 00:45:41,466 into the fine grains that make up her clay. 1020 00:45:41,466 --> 00:45:44,333 One of you want to go ahead and give it a try? 1021 00:45:44,333 --> 00:45:46,600 ROWLES: I think there's a lot of engineering 1022 00:45:46,600 --> 00:45:49,100 that goes into creating pottery. 1023 00:45:49,100 --> 00:45:52,600 The freedom that it allows to make any shape. 1024 00:45:52,600 --> 00:45:54,066 (squeaking) 1025 00:45:54,066 --> 00:45:55,900 STELTZNER: The fusion of art and engineering. 1026 00:45:55,900 --> 00:46:01,966 Or maybe even the boundaries between art and engineering... 1027 00:46:01,966 --> 00:46:03,700 ...perhaps they don't exist. 1028 00:46:03,700 --> 00:46:05,366 Perhaps they're really the same thing, 1029 00:46:05,366 --> 00:46:08,200 painted with a different palette. 1030 00:46:08,200 --> 00:46:11,300 NARRATOR: Stetson and Navid are working with Deanna 1031 00:46:11,300 --> 00:46:15,633 to prototype a shape for the clay filter. 1032 00:46:15,633 --> 00:46:17,066 I don't know if you know Deanna, 1033 00:46:17,066 --> 00:46:19,533 but I've been making some pottery since high school, 1034 00:46:19,533 --> 00:46:21,900 and I made this shape to try and see 1035 00:46:21,900 --> 00:46:24,400 if maybe we can explore making some shapes together. 1036 00:46:24,400 --> 00:46:28,666 I made a shape similar to that... 1037 00:46:30,233 --> 00:46:32,233 ...and it looks like this. 1038 00:46:32,233 --> 00:46:36,166 And we do make these traditional Navajo pipes. 1039 00:46:36,166 --> 00:46:37,433 Do you think you can make some grooves 1040 00:46:37,433 --> 00:46:40,166 similar to something like this? 1041 00:46:40,166 --> 00:46:41,966 Kind of like an accordion basically, 1042 00:46:41,966 --> 00:46:45,233 so it has the same surface area but in a smaller size. 1043 00:46:45,233 --> 00:46:47,300 ♪ ♪ 1044 00:46:47,300 --> 00:46:52,633 NARRATOR: Adding grooves increases the total surface area of the shape. 1045 00:46:52,633 --> 00:46:55,766 More surface area will mean more contact 1046 00:46:55,766 --> 00:46:58,500 with the water inside. 1047 00:46:58,500 --> 00:46:59,566 TSO: I'm going to show you 1048 00:46:59,566 --> 00:47:03,333 an option we have that we can try: 1049 00:47:03,333 --> 00:47:04,800 Coil. Yeah. Making a coil. 1050 00:47:04,800 --> 00:47:05,966 Making a coil. 1051 00:47:05,966 --> 00:47:07,500 ♪ ♪ 1052 00:47:07,500 --> 00:47:10,133 NARRATOR: Next, the new prototypes 1053 00:47:10,133 --> 00:47:11,500 need to be fired. 1054 00:47:13,500 --> 00:47:14,500 SALEH: We have been working with Deanna 1055 00:47:14,500 --> 00:47:16,600 for almost nine years now. 1056 00:47:16,600 --> 00:47:19,700 TSO: Make sure we have it covered nice and good. 1057 00:47:21,333 --> 00:47:23,333 SALEH: Working with her side-by-side 1058 00:47:23,333 --> 00:47:25,433 as an equal partner intellectually, 1059 00:47:25,433 --> 00:47:30,000 only opens opportunities that are more meaningful 1060 00:47:30,000 --> 00:47:32,933 than we scientists would ever find 1061 00:47:32,933 --> 00:47:34,366 sitting at our desks. 1062 00:47:36,733 --> 00:47:38,633 NARRATOR: The last step: 1063 00:47:38,633 --> 00:47:42,900 heat and strain the pinyon sap, 1064 00:47:42,900 --> 00:47:44,900 creating the microbe-resistant resin, 1065 00:47:44,900 --> 00:47:48,500 which acts as a glaze to coat the pottery. 1066 00:47:48,500 --> 00:47:51,500 And now, a new addition to the filter. 1067 00:47:51,500 --> 00:47:52,966 ROWLES: Can you grab the zeolite? 1068 00:47:52,966 --> 00:47:55,400 NARRATOR: The scientists are using powdered chabazite, 1069 00:47:55,400 --> 00:47:57,666 a type of naturally occurring zeolite, 1070 00:47:57,666 --> 00:48:01,533 found abundantly on Navajo land. 1071 00:48:01,533 --> 00:48:02,700 ♪ ♪ 1072 00:48:02,700 --> 00:48:04,900 Chabazite is a porous crystal 1073 00:48:04,900 --> 00:48:08,533 made of sodium, calcium, and aluminum silicates 1074 00:48:08,533 --> 00:48:10,166 that has the ability to trap 1075 00:48:10,166 --> 00:48:12,433 and absorb contaminants. 1076 00:48:12,433 --> 00:48:14,233 ♪ ♪ 1077 00:48:14,233 --> 00:48:17,200 Finally, Deanna applies the resin. 1078 00:48:17,200 --> 00:48:20,500 TSO: The pottery itself has to be hot. 1079 00:48:20,500 --> 00:48:22,333 The sap has to be hot. 1080 00:48:22,333 --> 00:48:24,533 NARRATOR: The team hopes the chabazite 1081 00:48:24,533 --> 00:48:27,566 will add function to the resin, 1082 00:48:27,566 --> 00:48:30,000 removing uranium byproducts, like strontium, 1083 00:48:30,000 --> 00:48:32,800 from any water that comes into contact with it. 1084 00:48:32,800 --> 00:48:34,766 ROWLES: Wow, the colors are beautiful. 1085 00:48:34,766 --> 00:48:36,733 ♪ ♪ 1086 00:48:36,733 --> 00:48:39,633 NARRATOR: Back at the University of Texas at Austin, 1087 00:48:39,633 --> 00:48:43,266 it's time to test their water filter prototypes in the lab. 1088 00:48:43,266 --> 00:48:44,300 We've got some of the clay. 1089 00:48:44,300 --> 00:48:45,300 NARRATOR: Using the materials 1090 00:48:45,300 --> 00:48:46,600 they sourced with Deanna, 1091 00:48:46,600 --> 00:48:50,133 the scientists create small clay discs... 1092 00:48:50,133 --> 00:48:51,800 ROWLES: ...try and just punch out, 1093 00:48:51,800 --> 00:48:53,966 a little disc like that... 1094 00:48:53,966 --> 00:48:56,766 NARRATOR: And coat them with the same chabazite-enriched resin. 1095 00:48:56,766 --> 00:49:01,500 These are tiny lab versions of Deanna's pottery. 1096 00:49:01,500 --> 00:49:03,766 To test the discs, 1097 00:49:03,766 --> 00:49:05,566 the researchers expose them 1098 00:49:05,566 --> 00:49:08,200 to strontium-contaminated water 1099 00:49:08,200 --> 00:49:11,200 to see if the resin will successfully absorb 1100 00:49:11,200 --> 00:49:12,366 the uranium byproduct. 1101 00:49:12,366 --> 00:49:14,233 ♪ ♪ 1102 00:49:14,233 --> 00:49:16,966 If the filter works as expected, 1103 00:49:16,966 --> 00:49:20,000 the chabazite will capture strontium from the water 1104 00:49:20,000 --> 00:49:24,000 through ion exchange as the water passes through. 1105 00:49:24,000 --> 00:49:25,633 ♪ ♪ 1106 00:49:25,633 --> 00:49:26,633 ROWLES: Hey, Andrei. ANDREI DOLOCAN: What's up, bud? 1107 00:49:26,633 --> 00:49:28,300 Here's the sample. 1108 00:49:28,300 --> 00:49:29,333 Yeah, thank you. 1109 00:49:29,333 --> 00:49:31,666 NARRATOR: Senior research scientist 1110 00:49:31,666 --> 00:49:33,400 Andrei Dolocan 1111 00:49:33,400 --> 00:49:35,733 loads a sample into an ion mass spectrometer. 1112 00:49:36,800 --> 00:49:37,800 It scans the sample 1113 00:49:37,800 --> 00:49:39,200 on the molecular level, 1114 00:49:39,200 --> 00:49:42,933 layer by layer, over several hours. 1115 00:49:42,933 --> 00:49:44,566 When it's done, 1116 00:49:44,566 --> 00:49:46,233 the result is a map of the elements 1117 00:49:46,233 --> 00:49:48,233 within the scanned sample surface. 1118 00:49:48,233 --> 00:49:51,200 When the clay disc is completely scanned, 1119 00:49:51,200 --> 00:49:53,100 it's time to check the results. 1120 00:49:53,100 --> 00:49:55,700 This is the strontium signal. 1121 00:49:55,700 --> 00:49:58,300 NARRATOR: The data show that the strontium is found 1122 00:49:58,300 --> 00:50:01,366 in the same places as chabazite in the resin... 1123 00:50:01,366 --> 00:50:04,000 DOLOCAN: We have the zeolite, obviously sodium, 1124 00:50:04,000 --> 00:50:05,500 aluminum-silicon. 1125 00:50:05,500 --> 00:50:07,500 Uh-huh. DOLOCAN: Okay. 1126 00:50:07,500 --> 00:50:10,266 And now the, strontium is increasing exactly like... 1127 00:50:10,266 --> 00:50:12,066 NARRATOR: It's an encouraging sign 1128 00:50:12,066 --> 00:50:14,233 that the chabazite is working as expected 1129 00:50:14,233 --> 00:50:17,933 when used with Deanna's pottery technique. 1130 00:50:17,933 --> 00:50:18,966 SALEH: So I guess it was a really, 1131 00:50:18,966 --> 00:50:20,700 successful run, Andrei. 1132 00:50:20,700 --> 00:50:22,333 Yeah. We can see association 1133 00:50:22,333 --> 00:50:23,466 of strontium with the zeolite. 1134 00:50:23,466 --> 00:50:25,966 DOLOCAN: I agree, this is a good start. 1135 00:50:25,966 --> 00:50:27,266 ZILEVU: One thing that I've learned 1136 00:50:27,266 --> 00:50:28,400 from the research and design process 1137 00:50:28,400 --> 00:50:29,666 is that kind of doing 1138 00:50:29,666 --> 00:50:31,233 co-creation activities with the end user, 1139 00:50:31,233 --> 00:50:33,200 it's really a way to kind of bridge and create 1140 00:50:33,200 --> 00:50:34,366 new, innovative process, 1141 00:50:34,366 --> 00:50:35,400 because you're bringing the people 1142 00:50:35,400 --> 00:50:36,900 who are using the technology 1143 00:50:36,900 --> 00:50:37,933 throughout the whole journey. 1144 00:50:38,933 --> 00:50:40,700 So this is the one that Deanna made... 1145 00:50:40,700 --> 00:50:43,700 NARRATOR: Now a few steps closer to their goal, 1146 00:50:43,700 --> 00:50:44,833 the researchers will work 1147 00:50:44,833 --> 00:50:47,166 to incorporate Deanna's spiral 1148 00:50:47,166 --> 00:50:49,433 and the chabazite's filtering power 1149 00:50:49,433 --> 00:50:53,133 into their final design. 1150 00:50:53,133 --> 00:50:54,500 So moving forward, 1151 00:50:54,500 --> 00:50:56,133 I think the most difficult engineering challenge 1152 00:50:56,133 --> 00:50:57,133 is yet to come. 1153 00:50:57,133 --> 00:50:58,700 And I think it's going to be 1154 00:50:58,700 --> 00:51:01,366 translating our results from, 1155 00:51:01,366 --> 00:51:03,800 you know, a lab scale experiment 1156 00:51:03,800 --> 00:51:05,866 to something that's going to be usable 1157 00:51:05,866 --> 00:51:07,833 in households throughout the Navajo Nation. 1158 00:51:07,833 --> 00:51:08,866 ♪ ♪ 1159 00:51:08,866 --> 00:51:11,333 (birds chirping) 1160 00:51:11,333 --> 00:51:12,466 MABRY: At the end of the day, 1161 00:51:12,466 --> 00:51:14,500 we want to unlock human potential. 1162 00:51:14,500 --> 00:51:17,333 And in order to unlock human potential, 1163 00:51:17,333 --> 00:51:19,700 we are not doing ourselves a justice 1164 00:51:19,700 --> 00:51:22,800 if we continue to only demand certain solutions 1165 00:51:22,800 --> 00:51:25,733 from a subset of our populations, 1166 00:51:25,733 --> 00:51:27,766 the more we can get more people included, 1167 00:51:27,766 --> 00:51:29,466 the more we can unlock 1168 00:51:29,466 --> 00:51:32,266 not just solutions to problems that we now see, 1169 00:51:32,266 --> 00:51:35,266 but things that are yet to come. 1170 00:51:35,266 --> 00:51:37,233 ♪ ♪ 1171 00:51:37,233 --> 00:51:39,466 NARRATOR: As we change our world through engineering, 1172 00:51:39,466 --> 00:51:41,566 it's up to us to make changes; 1173 00:51:41,566 --> 00:51:45,700 for all of us, by all of us. 1174 00:51:45,700 --> 00:51:47,966 ARMANI: I think we're all engineers. 1175 00:51:47,966 --> 00:51:49,933 We all build things, 1176 00:51:49,933 --> 00:51:52,633 we all design things. 1177 00:51:52,633 --> 00:51:53,766 (chuckling): We all break things 1178 00:51:53,766 --> 00:51:54,966 and then have to fix them 1179 00:51:54,966 --> 00:51:56,000 and put them back together. 1180 00:51:56,000 --> 00:51:57,600 NARRATOR: And we get to decide 1181 00:51:57,600 --> 00:52:00,300 what comes next. 1182 00:52:00,300 --> 00:52:01,466 What if we were to design this? 1183 00:52:01,466 --> 00:52:02,900 What if the world was to look like this 1184 00:52:02,900 --> 00:52:04,933 in 50, 100 years? What could that look like? 1185 00:52:04,933 --> 00:52:06,933 ♪ ♪ 1186 00:52:06,933 --> 00:52:08,866 ALI HAJIMIRI: The engineer's work is never done... 1187 00:52:08,866 --> 00:52:10,933 If you're not failing, you're not trying hard enough. 1188 00:52:10,933 --> 00:52:13,466 You can always create something new. 1189 00:52:13,466 --> 00:52:15,133 ♪ ♪ 1190 00:52:15,133 --> 00:52:16,733 NARRATOR: Building stuff 1191 00:52:16,733 --> 00:52:18,833 to change the world. 1192 00:52:18,833 --> 00:52:20,966 ♪ ♪ 1193 00:52:41,166 --> 00:52:44,033 ♪ ♪ 1194 00:52:44,966 --> 00:52:52,500 ♪ ♪ 1195 00:52:56,333 --> 00:53:03,933 ♪ ♪ 1196 00:53:07,766 --> 00:53:15,300 ♪ ♪ 1197 00:53:16,933 --> 00:53:24,466 ♪ ♪ 1198 00:53:26,100 --> 00:53:33,633 ♪ ♪ 92180

Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.