All language subtitles for Smart Underwear Tracks Farts & AI Produces Brad Pitt⧸Tom Cruise Fight Over Epstein | The Daily Show [SodT-Gmc_qs].en

af Afrikaans
ak Akan
sq Albanian
am Amharic
ar Arabic
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 Download
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
el Greek
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 00:00:00,719 --> 00:00:03,830 What's up, techno sapiens? I'm Grace 2 00:00:03,830 --> 00:00:03,840 What's up, techno sapiens? I'm Grace 3 00:00:03,840 --> 00:00:06,309 What's up, techno sapiens? I'm Grace Coolish, but you can call me by my first 4 00:00:06,309 --> 00:00:06,319 Coolish, but you can call me by my first 5 00:00:06,319 --> 00:00:09,509 Coolish, but you can call me by my first screen name, The Color Purple 29, 6 00:00:09,509 --> 00:00:09,519 screen name, The Color Purple 29, 7 00:00:09,519 --> 00:00:12,070 screen name, The Color Purple 29, inspired by my favorite color unrelated 8 00:00:12,070 --> 00:00:12,080 inspired by my favorite color unrelated 9 00:00:12,080 --> 00:00:15,190 inspired by my favorite color unrelated to the film about domestic abuse. Sorry, 10 00:00:15,190 --> 00:00:15,200 to the film about domestic abuse. Sorry, 11 00:00:15,200 --> 00:00:18,470 to the film about domestic abuse. Sorry, Oprah. Anyway, this is Tech Yeah, where 12 00:00:18,470 --> 00:00:18,480 Oprah. Anyway, this is Tech Yeah, where 13 00:00:18,480 --> 00:00:21,670 Oprah. Anyway, this is Tech Yeah, where I tell CPU all about the biggest stories 14 00:00:21,670 --> 00:00:21,680 I tell CPU all about the biggest stories 15 00:00:21,680 --> 00:00:24,470 I tell CPU all about the biggest stories in tech. Let's begin with some news from 16 00:00:24,470 --> 00:00:24,480 in tech. Let's begin with some news from 17 00:00:24,480 --> 00:00:26,710 in tech. Let's begin with some news from a town I've been told I'll never work in 18 00:00:26,710 --> 00:00:26,720 a town I've been told I'll never work in 19 00:00:26,720 --> 00:00:29,589 a town I've been told I'll never work in again. Hollywood, 20 00:00:29,589 --> 00:00:29,599 again. Hollywood, 21 00:00:29,599 --> 00:00:32,310 again. Hollywood, where the city of angels just might be 22 00:00:32,310 --> 00:00:32,320 where the city of angels just might be 23 00:00:32,320 --> 00:00:35,270 where the city of angels just might be turning into the city of a angels. Two 24 00:00:35,270 --> 00:00:35,280 turning into the city of a angels. Two 25 00:00:35,280 --> 00:00:37,270 turning into the city of a angels. Two of the biggest stars in the world, Brad 26 00:00:37,270 --> 00:00:37,280 of the biggest stars in the world, Brad 27 00:00:37,280 --> 00:00:39,590 of the biggest stars in the world, Brad Pitt and Tom Cruz, apparently duking it 28 00:00:39,590 --> 00:00:39,600 Pitt and Tom Cruz, apparently duking it 29 00:00:39,600 --> 00:00:42,069 Pitt and Tom Cruz, apparently duking it out on the roof of a skyscraper. But 30 00:00:42,069 --> 00:00:42,079 out on the roof of a skyscraper. But 31 00:00:42,079 --> 00:00:44,950 out on the roof of a skyscraper. But it's completely fake, digitally created 32 00:00:44,950 --> 00:00:44,960 it's completely fake, digitally created 33 00:00:44,960 --> 00:00:47,510 it's completely fake, digitally created with AI technology. It's the video that 34 00:00:47,510 --> 00:00:47,520 with AI technology. It's the video that 35 00:00:47,520 --> 00:00:49,670 with AI technology. It's the video that has Hollywood freaking out. 36 00:00:49,670 --> 00:00:49,680 has Hollywood freaking out. 37 00:00:49,680 --> 00:00:54,069 has Hollywood freaking out. >> YOU KILLED JEFFREY EPSTEIN, YOU ANIMAL. 38 00:00:54,069 --> 00:00:54,079 >> YOU KILLED JEFFREY EPSTEIN, YOU ANIMAL. 39 00:00:54,079 --> 00:00:57,990 >> YOU KILLED JEFFREY EPSTEIN, YOU ANIMAL. >> HE WAS A GOOD MAN. 40 00:00:57,990 --> 00:00:58,000 41 00:00:58,000 --> 00:01:01,110 OKAY, THIS is crazy. I literally wrote a 42 00:01:01,110 --> 00:01:01,120 OKAY, THIS is crazy. I literally wrote a 43 00:01:01,120 --> 00:01:03,910 OKAY, THIS is crazy. I literally wrote a movie with that exact scene in it. Great 44 00:01:03,910 --> 00:01:03,920 movie with that exact scene in it. Great 45 00:01:03,920 --> 00:01:06,550 movie with that exact scene in it. Great minds, but I get why everyone in 46 00:01:06,550 --> 00:01:06,560 minds, but I get why everyone in 47 00:01:06,560 --> 00:01:09,429 minds, but I get why everyone in Hollywood is freaking out. This looked 48 00:01:09,429 --> 00:01:09,439 Hollywood is freaking out. This looked 49 00:01:09,439 --> 00:01:12,070 Hollywood is freaking out. This looked so real, right down to the glimmer of 50 00:01:12,070 --> 00:01:12,080 so real, right down to the glimmer of 51 00:01:12,080 --> 00:01:14,550 so real, right down to the glimmer of hope in Tom Cruz's eyes that this stunt 52 00:01:14,550 --> 00:01:14,560 hope in Tom Cruz's eyes that this stunt 53 00:01:14,560 --> 00:01:17,590 hope in Tom Cruz's eyes that this stunt would finally be the one that kills him. 54 00:01:17,590 --> 00:01:17,600 would finally be the one that kills him. 55 00:01:17,600 --> 00:01:20,710 would finally be the one that kills him. Of course, some haters are worried that 56 00:01:20,710 --> 00:01:20,720 Of course, some haters are worried that 57 00:01:20,720 --> 00:01:23,109 Of course, some haters are worried that it's going to destroy the entire film 58 00:01:23,109 --> 00:01:23,119 it's going to destroy the entire film 59 00:01:23,119 --> 00:01:25,670 it's going to destroy the entire film industry and ruin countless lives along 60 00:01:25,670 --> 00:01:25,680 industry and ruin countless lives along 61 00:01:25,680 --> 00:01:28,149 industry and ruin countless lives along the way. Well, you know what? That's 62 00:01:28,149 --> 00:01:28,159 the way. Well, you know what? That's 63 00:01:28,159 --> 00:01:30,230 the way. Well, you know what? That's what Hollywood gets for not casting me 64 00:01:30,230 --> 00:01:30,240 what Hollywood gets for not casting me 65 00:01:30,240 --> 00:01:32,390 what Hollywood gets for not casting me in sinners. 66 00:01:32,390 --> 00:01:32,400 in sinners. 67 00:01:32,400 --> 00:01:35,030 in sinners. Michael B. Jordan gets to be two people 68 00:01:35,030 --> 00:01:35,040 Michael B. Jordan gets to be two people 69 00:01:35,040 --> 00:01:37,670 Michael B. Jordan gets to be two people and I can't even be in one of the many 70 00:01:37,670 --> 00:01:37,680 and I can't even be in one of the many 71 00:01:37,680 --> 00:01:40,630 and I can't even be in one of the many Connolingus scenes. 72 00:01:40,630 --> 00:01:40,640 Connolingus scenes. 73 00:01:40,640 --> 00:01:43,830 Connolingus scenes. MAKE THAT MAKE SENSE. 74 00:01:43,830 --> 00:01:43,840 MAKE THAT MAKE SENSE. 75 00:01:43,840 --> 00:01:46,310 MAKE THAT MAKE SENSE. BUT IF you can't make it in Hollywood, 76 00:01:46,310 --> 00:01:46,320 BUT IF you can't make it in Hollywood, 77 00:01:46,320 --> 00:01:48,950 BUT IF you can't make it in Hollywood, you can always fall back on your second 78 00:01:48,950 --> 00:01:48,960 you can always fall back on your second 79 00:01:48,960 --> 00:01:50,230 you can always fall back on your second dream job. 80 00:01:50,230 --> 00:01:50,240 dream job. 81 00:01:50,240 --> 00:01:52,069 dream job. >> Whimo has come up with a low tech 82 00:01:52,069 --> 00:01:52,079 >> Whimo has come up with a low tech 83 00:01:52,079 --> 00:01:54,230 >> Whimo has come up with a low tech solution for a high-tech problem. 84 00:01:54,230 --> 00:01:54,240 solution for a high-tech problem. 85 00:01:54,240 --> 00:01:56,230 solution for a high-tech problem. Customers are leaving doors open after 86 00:01:56,230 --> 00:01:56,240 Customers are leaving doors open after 87 00:01:56,240 --> 00:01:58,069 Customers are leaving doors open after exiting the self-driving vehicles, 88 00:01:58,069 --> 00:01:58,079 exiting the self-driving vehicles, 89 00:01:58,079 --> 00:02:00,469 exiting the self-driving vehicles, making them inoperable. Whimo is now 90 00:02:00,469 --> 00:02:00,479 making them inoperable. Whimo is now 91 00:02:00,479 --> 00:02:02,870 making them inoperable. Whimo is now paying nearby door dashers to shut the 92 00:02:02,870 --> 00:02:02,880 paying nearby door dashers to shut the 93 00:02:02,880 --> 00:02:05,030 paying nearby door dashers to shut the open doors so the driverless cars can 94 00:02:05,030 --> 00:02:05,040 open doors so the driverless cars can 95 00:02:05,040 --> 00:02:07,990 open doors so the driverless cars can pick up their next passenger. 96 00:02:07,990 --> 00:02:08,000 pick up their next passenger. 97 00:02:08,000 --> 00:02:11,589 pick up their next passenger. >> Yes. It's just like they say when God 98 00:02:11,589 --> 00:02:11,599 >> Yes. It's just like they say when God 99 00:02:11,599 --> 00:02:14,949 >> Yes. It's just like they say when God closes a door, he can make six bucks on 100 00:02:14,949 --> 00:02:14,959 closes a door, he can make six bucks on 101 00:02:14,959 --> 00:02:17,190 closes a door, he can make six bucks on Door Dash. 102 00:02:17,190 --> 00:02:17,200 Door Dash. 103 00:02:17,200 --> 00:02:19,589 Door Dash. Look how awesome this is. Most people 104 00:02:19,589 --> 00:02:19,599 Look how awesome this is. Most people 105 00:02:19,599 --> 00:02:22,390 Look how awesome this is. Most people are stuck in an office all day, but you 106 00:02:22,390 --> 00:02:22,400 are stuck in an office all day, but you 107 00:02:22,400 --> 00:02:25,030 are stuck in an office all day, but you you get to drive around hunting for open 108 00:02:25,030 --> 00:02:25,040 you get to drive around hunting for open 109 00:02:25,040 --> 00:02:28,070 you get to drive around hunting for open car doors. And if you close enough of 110 00:02:28,070 --> 00:02:28,080 car doors. And if you close enough of 111 00:02:28,080 --> 00:02:30,309 car doors. And if you close enough of them, you get to eat dinner. The future 112 00:02:30,309 --> 00:02:30,319 them, you get to eat dinner. The future 113 00:02:30,319 --> 00:02:33,030 them, you get to eat dinner. The future is amazing. 114 00:02:33,030 --> 00:02:33,040 is amazing. 115 00:02:33,040 --> 00:02:35,430 is amazing. I'd be great at this job. I love closing 116 00:02:35,430 --> 00:02:35,440 I'd be great at this job. I love closing 117 00:02:35,440 --> 00:02:38,550 I'd be great at this job. I love closing things. Gates, trunks, coffins. I don't 118 00:02:38,550 --> 00:02:38,560 things. Gates, trunks, coffins. I don't 119 00:02:38,560 --> 00:02:41,270 things. Gates, trunks, coffins. I don't care if the funeral is open casket, your 120 00:02:41,270 --> 00:02:41,280 care if the funeral is open casket, your 121 00:02:41,280 --> 00:02:44,630 care if the funeral is open casket, your grandma is busted. 122 00:02:44,630 --> 00:02:44,640 grandma is busted. 123 00:02:44,640 --> 00:02:46,470 grandma is busted. Moving on because it's time for 124 00:02:46,470 --> 00:02:46,480 Moving on because it's time for 125 00:02:46,480 --> 00:02:51,030 Moving on because it's time for tonight's study that makes you go h 126 00:02:51,030 --> 00:02:51,040 tonight's study that makes you go h 127 00:02:51,040 --> 00:02:58,550 tonight's study that makes you go h tonight's study is all about this 128 00:02:58,550 --> 00:02:58,560 129 00:02:58,560 --> 00:03:00,150 dancing. If 130 00:03:00,150 --> 00:03:00,160 dancing. If 131 00:03:00,160 --> 00:03:01,589 dancing. If >> you wonder why some guys light up the 132 00:03:01,589 --> 00:03:01,599 >> you wonder why some guys light up the 133 00:03:01,599 --> 00:03:02,790 >> you wonder why some guys light up the dance floor while others look like 134 00:03:02,790 --> 00:03:02,800 dance floor while others look like 135 00:03:02,800 --> 00:03:04,470 dance floor while others look like they're swatting invisible flies, 136 00:03:04,470 --> 00:03:04,480 they're swatting invisible flies, 137 00:03:04,480 --> 00:03:06,470 they're swatting invisible flies, science may have the answer. In a study 138 00:03:06,470 --> 00:03:06,480 science may have the answer. In a study 139 00:03:06,480 --> 00:03:08,790 science may have the answer. In a study by North Umbrea University, volunteers 140 00:03:08,790 --> 00:03:08,800 by North Umbrea University, volunteers 141 00:03:08,800 --> 00:03:10,390 by North Umbrea University, volunteers danced for 30 seconds while motion 142 00:03:10,390 --> 00:03:10,400 danced for 30 seconds while motion 143 00:03:10,400 --> 00:03:12,149 danced for 30 seconds while motion sensors tracked every move. They were 144 00:03:12,149 --> 00:03:12,159 sensors tracked every move. They were 145 00:03:12,159 --> 00:03:14,309 sensors tracked every move. They were later turned into faceless 3D avatars to 146 00:03:14,309 --> 00:03:14,319 later turned into faceless 3D avatars to 147 00:03:14,319 --> 00:03:15,910 later turned into faceless 3D avatars to allow women to rank the routines. Men 148 00:03:15,910 --> 00:03:15,920 allow women to rank the routines. Men 149 00:03:15,920 --> 00:03:17,270 allow women to rank the routines. Men rated as good dancers showed more 150 00:03:17,270 --> 00:03:17,280 rated as good dancers showed more 151 00:03:17,280 --> 00:03:19,110 rated as good dancers showed more variety in upper body movement while 152 00:03:19,110 --> 00:03:19,120 variety in upper body movement while 153 00:03:19,120 --> 00:03:21,430 variety in upper body movement while repetitive moves and sip twisting scored 154 00:03:21,430 --> 00:03:21,440 repetitive moves and sip twisting scored 155 00:03:21,440 --> 00:03:25,830 repetitive moves and sip twisting scored near the bottom. 156 00:03:25,830 --> 00:03:25,840 157 00:03:25,840 --> 00:03:27,670 >> Thanks to this cuttingedge tech, 158 00:03:27,670 --> 00:03:27,680 >> Thanks to this cuttingedge tech, 159 00:03:27,680 --> 00:03:29,750 >> Thanks to this cuttingedge tech, scientists have confirmed what we 160 00:03:29,750 --> 00:03:29,760 scientists have confirmed what we 161 00:03:29,760 --> 00:03:32,789 scientists have confirmed what we already suspected. Women love men having 162 00:03:32,789 --> 00:03:32,799 already suspected. Women love men having 163 00:03:32,799 --> 00:03:34,949 already suspected. Women love men having seizures. 164 00:03:34,949 --> 00:03:34,959 seizures. 165 00:03:34,959 --> 00:03:37,589 seizures. My cousin has epilepsy and he is 166 00:03:37,589 --> 00:03:37,599 My cousin has epilepsy and he is 167 00:03:37,599 --> 00:03:40,789 My cousin has epilepsy and he is drowning in [\h__\h] 168 00:03:40,789 --> 00:03:40,799 drowning in [\h__\h] 169 00:03:40,799 --> 00:03:43,589 drowning in [\h__\h] And this data is very useful because now 170 00:03:43,589 --> 00:03:43,599 And this data is very useful because now 171 00:03:43,599 --> 00:03:45,830 And this data is very useful because now we know if you're a good dancer and 172 00:03:45,830 --> 00:03:45,840 we know if you're a good dancer and 173 00:03:45,840 --> 00:03:50,070 we know if you're a good dancer and can't get a date, you ugly brother. 174 00:03:50,070 --> 00:03:50,080 can't get a date, you ugly brother. 175 00:03:50,080 --> 00:03:52,309 can't get a date, you ugly brother. Of course, you can't just dance to 176 00:03:52,309 --> 00:03:52,319 Of course, you can't just dance to 177 00:03:52,319 --> 00:03:54,710 Of course, you can't just dance to attract women. You need to have the 178 00:03:54,710 --> 00:03:54,720 attract women. You need to have the 179 00:03:54,720 --> 00:03:57,670 attract women. You need to have the perfect pickup line as well. Luckily, I 180 00:03:57,670 --> 00:03:57,680 perfect pickup line as well. Luckily, I 181 00:03:57,680 --> 00:03:59,030 perfect pickup line as well. Luckily, I know just the one. 182 00:03:59,030 --> 00:03:59,040 know just the one. 183 00:03:59,040 --> 00:04:03,429 know just the one. >> YOU KILLED JEFFREY EPSTEIN, YOU ANIMAL. 184 00:04:03,429 --> 00:04:03,439 >> YOU KILLED JEFFREY EPSTEIN, YOU ANIMAL. 185 00:04:03,439 --> 00:04:06,550 >> YOU KILLED JEFFREY EPSTEIN, YOU ANIMAL. He was a good man. 186 00:04:06,550 --> 00:04:06,560 He was a good man. 187 00:04:06,560 --> 00:04:09,830 He was a good man. >> Yeah, that's a panty dropper. And 188 00:04:09,830 --> 00:04:09,840 >> Yeah, that's a panty dropper. And 189 00:04:09,840 --> 00:04:12,789 >> Yeah, that's a panty dropper. And speaking of underwear, if you like me 190 00:04:12,789 --> 00:04:12,799 speaking of underwear, if you like me 191 00:04:12,799 --> 00:04:15,350 speaking of underwear, if you like me love putting gadgets in all the wrong 192 00:04:15,350 --> 00:04:15,360 love putting gadgets in all the wrong 193 00:04:15,360 --> 00:04:18,469 love putting gadgets in all the wrong places, have I got a Tekken product for 194 00:04:18,469 --> 00:04:18,479 places, have I got a Tekken product for 195 00:04:18,479 --> 00:04:19,270 places, have I got a Tekken product for you. 196 00:04:19,270 --> 00:04:19,280 you. 197 00:04:19,280 --> 00:04:21,270 you. >> Researchers are looking for volunteers 198 00:04:21,270 --> 00:04:21,280 >> Researchers are looking for volunteers 199 00:04:21,280 --> 00:04:24,070 >> Researchers are looking for volunteers to put on a new type of smart underwear 200 00:04:24,070 --> 00:04:24,080 to put on a new type of smart underwear 201 00:04:24,080 --> 00:04:26,550 to put on a new type of smart underwear designed to track human flatulence and 202 00:04:26,550 --> 00:04:26,560 designed to track human flatulence and 203 00:04:26,560 --> 00:04:29,030 designed to track human flatulence and also stress test them. University of 204 00:04:29,030 --> 00:04:29,040 also stress test them. University of 205 00:04:29,040 --> 00:04:30,790 also stress test them. University of Maryland scientists say that the garment 206 00:04:30,790 --> 00:04:30,800 Maryland scientists say that the garment 207 00:04:30,800 --> 00:04:33,030 Maryland scientists say that the garment has a snap-in device which acts like a 208 00:04:33,030 --> 00:04:33,040 has a snap-in device which acts like a 209 00:04:33,040 --> 00:04:35,670 has a snap-in device which acts like a glucose monitor but for intestinal gas 210 00:04:35,670 --> 00:04:35,680 glucose monitor but for intestinal gas 211 00:04:35,680 --> 00:04:37,350 glucose monitor but for intestinal gas and it says that it could open the door 212 00:04:37,350 --> 00:04:37,360 and it says that it could open the door 213 00:04:37,360 --> 00:04:43,590 and it says that it could open the door to measuring gut microbiome metabolism. 214 00:04:43,590 --> 00:04:43,600 215 00:04:43,600 --> 00:04:45,990 Smell that? That is the smell of 216 00:04:45,990 --> 00:04:46,000 Smell that? That is the smell of 217 00:04:46,000 --> 00:04:48,070 Smell that? That is the smell of innovation 218 00:04:48,070 --> 00:04:48,080 innovation 219 00:04:48,080 --> 00:04:50,950 innovation and falafel. Sorry, 220 00:04:50,950 --> 00:04:50,960 and falafel. Sorry, 221 00:04:50,960 --> 00:04:53,990 and falafel. Sorry, but I love this product. Finally a way 222 00:04:53,990 --> 00:04:54,000 but I love this product. Finally a way 223 00:04:54,000 --> 00:04:56,390 but I love this product. Finally a way for health nuts to get in there 10,000 224 00:04:56,390 --> 00:04:56,400 for health nuts to get in there 10,000 225 00:04:56,400 --> 00:04:58,070 for health nuts to get in there 10,000 farts a day. 226 00:04:58,070 --> 00:04:58,080 farts a day. 227 00:04:58,080 --> 00:05:01,510 farts a day. Heck, I think I'll get two. One for the 228 00:05:01,510 --> 00:05:01,520 Heck, I think I'll get two. One for the 229 00:05:01,520 --> 00:05:06,230 Heck, I think I'll get two. One for the back and one to track my cooter tutors. 230 00:05:06,230 --> 00:05:06,240 back and one to track my cooter tutors. 231 00:05:06,240 --> 00:05:08,870 back and one to track my cooter tutors. I swear when those two get to yapping, 232 00:05:08,870 --> 00:05:08,880 I swear when those two get to yapping, 233 00:05:08,880 --> 00:05:10,550 I swear when those two get to yapping, I'M LIKE, "YOU GIRLS SHOULD START A 234 00:05:10,550 --> 00:05:10,560 I'M LIKE, "YOU GIRLS SHOULD START A 235 00:05:10,560 --> 00:05:18,870 I'M LIKE, "YOU GIRLS SHOULD START A PODCAST." 236 00:05:18,870 --> 00:05:18,880 237 00:05:18,880 --> 00:05:22,150 >> SO, what have we learned from this data 238 00:05:22,150 --> 00:05:22,160 >> SO, what have we learned from this data 239 00:05:22,160 --> 00:05:25,110 >> SO, what have we learned from this data so far? Previous studies asked people to 240 00:05:25,110 --> 00:05:25,120 so far? Previous studies asked people to 241 00:05:25,120 --> 00:05:27,350 so far? Previous studies asked people to estimate and they guessed between 10 to 242 00:05:27,350 --> 00:05:27,360 estimate and they guessed between 10 to 243 00:05:27,360 --> 00:05:29,189 estimate and they guessed between 10 to 15 times a day. Early testing has 244 00:05:29,189 --> 00:05:29,199 15 times a day. Early testing has 245 00:05:29,199 --> 00:05:31,029 15 times a day. Early testing has revealed something interesting. We found 246 00:05:31,029 --> 00:05:31,039 revealed something interesting. We found 247 00:05:31,039 --> 00:05:32,870 revealed something interesting. We found that people farted an average of 32 248 00:05:32,870 --> 00:05:32,880 that people farted an average of 32 249 00:05:32,880 --> 00:05:35,029 that people farted an average of 32 times per day. 250 00:05:35,029 --> 00:05:35,039 times per day. 251 00:05:35,039 --> 00:05:39,830 times per day. The average person farts 32 times a day. 252 00:05:39,830 --> 00:05:39,840 The average person farts 32 times a day. 253 00:05:39,840 --> 00:05:41,909 The average person farts 32 times a day. Imagine if you're an above average 254 00:05:41,909 --> 00:05:41,919 Imagine if you're an above average 255 00:05:41,919 --> 00:05:45,430 Imagine if you're an above average person. Like Beyonce must be farting 256 00:05:45,430 --> 00:05:45,440 person. Like Beyonce must be farting 257 00:05:45,440 --> 00:05:49,590 person. Like Beyonce must be farting 6,000 TIMES A DAY. WHAT a lucky horse. 258 00:05:49,590 --> 00:05:49,600 6,000 TIMES A DAY. WHAT a lucky horse. 259 00:05:49,600 --> 00:05:52,390 6,000 TIMES A DAY. WHAT a lucky horse. Now I know what you're thinking. I must 260 00:05:52,390 --> 00:05:52,400 Now I know what you're thinking. I must 261 00:05:52,400 --> 00:05:55,350 Now I know what you're thinking. I must be part of this study. Well, bad news 262 00:05:55,350 --> 00:05:55,360 be part of this study. Well, bad news 263 00:05:55,360 --> 00:05:57,830 be part of this study. Well, bad news because due to overwhelming demand, 264 00:05:57,830 --> 00:05:57,840 because due to overwhelming demand, 265 00:05:57,840 --> 00:06:00,070 because due to overwhelming demand, enrollment in the fart tracking study is 266 00:06:00,070 --> 00:06:00,080 enrollment in the fart tracking study is 267 00:06:00,080 --> 00:06:02,310 enrollment in the fart tracking study is currently paused. 268 00:06:02,310 --> 00:06:02,320 currently paused. 269 00:06:02,320 --> 00:06:04,950 currently paused. But lucky for you, I'm conducting my own 270 00:06:04,950 --> 00:06:04,960 But lucky for you, I'm conducting my own 271 00:06:04,960 --> 00:06:07,430 But lucky for you, I'm conducting my own study. All you have to do is email me 272 00:06:07,430 --> 00:06:07,440 study. All you have to do is email me 273 00:06:07,440 --> 00:06:08,950 study. All you have to do is email me every time you fart at the 274 00:06:08,950 --> 00:06:08,960 every time you fart at the 275 00:06:08,960 --> 00:06:14,390 every time you fart at the colorpurps.com. 276 00:06:14,390 --> 00:06:14,400 277 00:06:14,400 --> 00:06:16,309 Well, that's all for this edition of 278 00:06:16,309 --> 00:06:16,319 Well, that's all for this edition of 279 00:06:16,319 --> 00:06:18,390 Well, that's all for this edition of Tech Yeah. Tune in next time when we 280 00:06:18,390 --> 00:06:18,400 Tech Yeah. Tune in next time when we 281 00:06:18,400 --> 00:06:20,870 Tech Yeah. Tune in next time when we debate which one of these pictures has a 282 00:06:20,870 --> 00:06:20,880 debate which one of these pictures has a 283 00:06:20,880 --> 00:06:23,270 debate which one of these pictures has a motorcycle in it. If I get this wrong, 284 00:06:23,270 --> 00:06:23,280 motorcycle in it. If I get this wrong, 285 00:06:23,280 --> 00:06:27,160 motorcycle in it. If I get this wrong, it's over for me. 24928

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