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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:23,640 --> 00:00:25,839 LAST WEEK TONIGHT WITH JOHN OLIVER 2 00:00:30,600 --> 00:00:33,756 Welcome to "Last Week Tonight !" I'm John Oliver. 3 00:00:33,840 --> 00:00:37,236 Thank you so much for joining us. It has been a busy week. 4 00:00:37,320 --> 00:00:39,556 Russia's war in Ukraine entered its second year, 5 00:00:39,640 --> 00:00:42,140 massive winter storms slammed a lot of the country, 6 00:00:42,200 --> 00:00:46,156 and East Palestine, Ohio got visits from both Donald Trump and Pete Buttigieg, 7 00:00:46,240 --> 00:00:50,640 but some Fox personalities insisted that others really should've been there, too. 8 00:00:51,039 --> 00:00:54,595 Think about the environmental activists and corporate America. 9 00:00:54,679 --> 00:00:55,715 They weren't there. 10 00:00:55,799 --> 00:00:59,436 I mean this, with the activists, this is an Erin Brockovich moment. 11 00:00:59,520 --> 00:01:01,675 I mean, there was a blockbuster Oscar-winning movie 12 00:01:01,759 --> 00:01:03,459 written about something like this. 13 00:01:03,520 --> 00:01:05,075 Where is Leo DiCaprio ? 14 00:01:05,159 --> 00:01:07,036 Erin Brockovich is in East Palestine tonight. 15 00:01:07,120 --> 00:01:09,920 She is, but where's Julia Roberts ?! 16 00:01:11,640 --> 00:01:14,359 What ? What are you talking about ? 17 00:01:15,159 --> 00:01:17,560 You realize Julia Roberts is an actor, right ? 18 00:01:18,200 --> 00:01:21,280 She was pretending. She's not actually Erin Brockovich. 19 00:01:22,359 --> 00:01:24,836 Also, I can't believe I'm the one that has to break this to you, 20 00:01:24,920 --> 00:01:27,075 she didn't actually ruin her best friend's wedding. 21 00:01:27,159 --> 00:01:31,959 She's not a sex worker. And she did not die in a small Louisiana town in 1989. 22 00:01:32,439 --> 00:01:34,319 What is fuck is wrong with you ? 23 00:01:35,319 --> 00:01:37,795 But we're actually going to stay in the world of conservative media tonight 24 00:01:37,879 --> 00:01:39,995 because it had some big news this week. 25 00:01:40,079 --> 00:01:43,040 The embattled leader and founder of Project Veritas 26 00:01:43,359 --> 00:01:45,040 has been removed from his post. 27 00:01:45,400 --> 00:01:46,556 James O'Keefe confirmed 28 00:01:46,640 --> 00:01:49,340 that he's no longer with the conservative organization. 29 00:01:49,359 --> 00:01:51,476 It's known for undercover operations 30 00:01:51,560 --> 00:01:54,040 targeting Democrats, liberals, and the media. 31 00:01:54,519 --> 00:01:59,476 James O'Keefe, alt-right Borat, is out at Project Veritas. 32 00:01:59,560 --> 00:02:02,235 And quick shoutout to that anchor for the helpful distinction there 33 00:02:02,319 --> 00:02:04,075 between Democrats and liberals. 34 00:02:04,159 --> 00:02:06,355 'Cause in case you're wondering, liberals are people who have 35 00:02:06,439 --> 00:02:08,395 those "We Believe" signs in their front yards, 36 00:02:08,479 --> 00:02:10,355 and Democrats ensure those front yards 37 00:02:10,439 --> 00:02:13,089 remain at least 50 miles away from any public housing. 38 00:02:13,240 --> 00:02:15,916 And if you don't know who James O'Keefe is, 39 00:02:16,000 --> 00:02:17,756 we'll explain in just a moment. 40 00:02:17,840 --> 00:02:20,955 But know that he's been a big deal in right-wing media for a while now. 41 00:02:21,039 --> 00:02:23,639 And news of his departure did not go over well. 42 00:02:24,319 --> 00:02:27,596 So, James O'Keefe has now apparently been thrown out of Project Veritas, 43 00:02:27,680 --> 00:02:31,436 which means Project Veritas no longer has, like, any reason for being. 44 00:02:31,520 --> 00:02:33,436 When you tell me you are getting rid of James O'Keefe, 45 00:02:33,520 --> 00:02:35,036 you tell me that Project Veritas is over. 46 00:02:35,120 --> 00:02:40,120 It's unconscionable what went on here. Unconscionable for our movement. 47 00:02:40,439 --> 00:02:44,879 And for the nation at large. James O'Keefe is a national treasure. 48 00:02:45,599 --> 00:02:50,000 Strong words there from the Ghost of Christmas Been Dead for Three Weeks. 49 00:02:50,599 --> 00:02:52,436 But I have to disagree. 50 00:02:52,520 --> 00:02:54,395 The only national treasures in this country 51 00:02:54,479 --> 00:02:57,996 are Dolly Parton, Pedro Pascal, and Cocaine Bear. 52 00:02:58,080 --> 00:03:01,560 And I'll say what we're all thinking-threesome when ? 53 00:03:02,560 --> 00:03:05,520 James O'Keefe first rose to national prominence in 2009 54 00:03:05,840 --> 00:03:08,390 when he claimed that he'd gone undercover as a pimp, 55 00:03:08,400 --> 00:03:10,756 and received assistance from Acorn, 56 00:03:10,840 --> 00:03:13,355 a community organizing group that helps low-income families. 57 00:03:13,439 --> 00:03:15,675 O'Keefe's stunt led to its dissolution, 58 00:03:15,759 --> 00:03:18,276 even though later investigations found, among other things, 59 00:03:18,360 --> 00:03:21,476 that he hadn't dressed like that during his sting operation, 60 00:03:21,560 --> 00:03:25,436 and some Acorn workers had called the police after he left the office. 61 00:03:25,520 --> 00:03:28,199 O'Keefe even wound up having to pay $100,000 62 00:03:28,520 --> 00:03:31,156 to settle a lawsuit from one of Acorn's workers. 63 00:03:31,240 --> 00:03:35,596 O'Keefe parlayed his fame from that into launching Project Veritas, 64 00:03:35,680 --> 00:03:36,980 which quickly became known 65 00:03:37,039 --> 00:03:39,756 for their undercover, hidden-camera investigations, 66 00:03:39,840 --> 00:03:42,436 where they'd bait people from organizations like NPR 67 00:03:42,520 --> 00:03:46,635 or local elections boards and release heavily edited video of the results. 68 00:03:46,719 --> 00:03:49,036 Over the years, they've made big claims, 69 00:03:49,120 --> 00:03:53,395 like that they had "undeniable video proof" of a cash-for-ballot scheme, 70 00:03:53,479 --> 00:03:55,360 claims that didn't quite panout. 71 00:03:55,719 --> 00:03:58,355 But despite their underwhelming track record, 72 00:03:58,439 --> 00:04:02,680 conservative media has greeted each investigation with great excitement. 73 00:04:03,240 --> 00:04:07,795 A bombshell new video uncovered by James O'Keefe and Project Veritas. 74 00:04:07,879 --> 00:04:10,355 There is a new Project Veritas video out. 75 00:04:10,439 --> 00:04:12,756 The spotlight today is on this undercover video 76 00:04:12,840 --> 00:04:14,599 released by Project Veritas. 77 00:04:14,919 --> 00:04:19,639 The new Project Veritas video revealing yet another media cover-up. 78 00:04:20,040 --> 00:04:22,315 Project Veritas caught 'em cold again. 79 00:04:22,399 --> 00:04:23,916 Project Veritas videos 80 00:04:24,000 --> 00:04:27,100 are basically the thing conservatives get most worked up about, 81 00:04:27,120 --> 00:04:29,916 a distinction they share with "plastic toys go woke" 82 00:04:30,000 --> 00:04:32,560 and "fuckable candy got comfortable shoes". 83 00:04:33,519 --> 00:04:35,836 But thanks to the media storms that he created, 84 00:04:35,920 --> 00:04:38,276 a lot of money has flown to Project Veritas. 85 00:04:38,360 --> 00:04:42,076 It raised about $22 million in 2020. 86 00:04:42,160 --> 00:04:44,916 So, you might be wondering: "Why would he be kicked out ?" 87 00:04:45,000 --> 00:04:48,396 For one, O'Keefe's been accused of being a terrible manager, 88 00:04:48,480 --> 00:04:52,355 with a memo signed by 16 staffers complaining of verbal abuse. 89 00:04:52,439 --> 00:04:55,195 The memo even included the line, "Rule number one: 90 00:04:55,279 --> 00:04:58,519 you can't spit in an employee's face over a tweet." 91 00:04:58,920 --> 00:05:02,360 And how many times has that happened if it's rule number one ? 92 00:05:03,439 --> 00:05:05,716 Because in our office, rule number one is, 93 00:05:05,800 --> 00:05:07,956 "Don't give Mr. Nutterbutter cocaine," 94 00:05:08,040 --> 00:05:11,800 and that happened three times before anyone thought to write it down. 95 00:05:12,680 --> 00:05:14,836 But perhaps the most serious allegation is that O'Keefe 96 00:05:14,920 --> 00:05:17,396 has jeopardized the group's non-profit status. 97 00:05:17,480 --> 00:05:21,555 He'd "spent an excessive amount of donor funds on personal luxuries." 98 00:05:21,639 --> 00:05:23,836 O'Keefe denied many of those claims 99 00:05:23,920 --> 00:05:27,596 in the, I shit you not, 45-minute resignation speech 100 00:05:27,680 --> 00:05:29,519 that he shared online on Monday. 101 00:05:29,839 --> 00:05:33,315 But the fact is, the organization reported to the IRS last year 102 00:05:33,399 --> 00:05:38,396 that it had improperly paid $20,000 in excess benefits to O'Keefe, 103 00:05:38,480 --> 00:05:42,076 specifying that they were related to the expense of having staff accompany him 104 00:05:42,160 --> 00:05:45,476 when he starred in an outdoor production of "Oklahoma !" 105 00:05:45,560 --> 00:05:47,875 And at this point, let's slow down. 106 00:05:47,959 --> 00:05:50,675 Because the most amazing detail in this story 107 00:05:50,759 --> 00:05:52,355 is the degree to which O'Keefe 108 00:05:52,439 --> 00:05:55,795 is obsessed with finding opportunities to sing and dance in public. 109 00:05:55,879 --> 00:05:57,916 That "Oklahoma !" production was billed 110 00:05:58,000 --> 00:06:00,755 as featuring performers who'd been victims of cancel culture. 111 00:06:00,839 --> 00:06:03,036 And O'Keeffe himself took the lead role, 112 00:06:03,120 --> 00:06:06,399 and videos of it on YouTube show he fully committed to it. 113 00:06:07,560 --> 00:06:10,319 Oklahoma ! 114 00:06:10,639 --> 00:06:13,480 Where the wind comes sweeping down the plains ! 115 00:06:14,560 --> 00:06:17,600 And the wavin' wheat, can sure smell sweet, 116 00:06:17,920 --> 00:06:20,879 when the wind comes right behind the rain ! 117 00:06:21,680 --> 00:06:25,000 What are you doing ? Why are you spinning like that ? 118 00:06:26,079 --> 00:06:27,636 I don't know what note your director gave you, 119 00:06:27,720 --> 00:06:28,916 but I have to assume that it wasn't 120 00:06:29,000 --> 00:06:31,800 "spin around like you just lost your wife in a Costco." 121 00:06:32,800 --> 00:06:34,516 I don't know what the saddest part of that is, 122 00:06:34,600 --> 00:06:37,235 the fact he sings like Cousin Greg from "Succession," 123 00:06:37,319 --> 00:06:41,076 or that no one in that crowd is into it, especially not this table 124 00:06:41,160 --> 00:06:44,060 that can't even be bothered to turn around and look at him. 125 00:06:44,680 --> 00:06:48,156 And it gets even weirder. Because in its list of improper expenses, 126 00:06:48,240 --> 00:06:51,755 the organization's board also called out $60,000 in losses 127 00:06:51,839 --> 00:06:56,115 by putting together dance events such as Project Veritas Experience. 128 00:06:56,199 --> 00:06:58,476 Now, here is a poster for it. 129 00:06:58,560 --> 00:07:00,636 And upon looking at it, my first thought was, 130 00:07:00,720 --> 00:07:04,276 "What a nice sneak peek at LMFAO's funeral announcement." 131 00:07:04,360 --> 00:07:09,276 But it's actually an extravaganza, based on James O'Keefe's life, 132 00:07:09,360 --> 00:07:11,396 that was supposed to play in Vegas last year. 133 00:07:11,480 --> 00:07:13,195 Now, tragically, that didn't end up happening. 134 00:07:13,279 --> 00:07:15,636 But luckily, we have a sense of what could've been, 135 00:07:15,720 --> 00:07:19,596 because in 2021, O'Keefe treated attendees at a Turning Point USA event 136 00:07:19,680 --> 00:07:21,195 to a taste of the show. 137 00:07:21,279 --> 00:07:23,959 Here he is, starring in its opening dance sequence. 138 00:07:24,800 --> 00:07:30,120 I know I'm burning. This is my final day. 139 00:07:31,240 --> 00:07:34,240 I'm gonna go out smiling, 140 00:07:35,439 --> 00:07:39,079 a king for a day... 141 00:07:53,439 --> 00:07:56,995 Now, obviously, there is a lot going on there. 142 00:07:57,079 --> 00:07:59,716 From O'Keeffe, in his press vest, doing his now-signature 143 00:07:59,800 --> 00:08:02,156 "I don't know where the fuck I am face" 144 00:08:02,240 --> 00:08:04,476 to him being dance attacked by FBI agents, 145 00:08:04,560 --> 00:08:06,235 to him quickly breaking free, 146 00:08:06,319 --> 00:08:08,755 only for them to inexplicably join him in dancing 147 00:08:08,839 --> 00:08:11,235 because narrative consistency seems to mean nothing here, 148 00:08:11,319 --> 00:08:15,279 to the moment that he punches 50 imaginary penguins in the face. 149 00:08:16,560 --> 00:08:18,195 I had the exact same reaction watching that 150 00:08:18,279 --> 00:08:20,516 as I did when Elon Musk hosted SNL: 151 00:08:20,600 --> 00:08:23,800 "You really do suck at the thing you love the most !" 152 00:08:24,600 --> 00:08:27,300 We don't have time to show you the whole thing tonight, 153 00:08:27,360 --> 00:08:29,555 but I do need you to see one more moment. 154 00:08:29,639 --> 00:08:31,516 Later in the show, this guy in the white shirt 155 00:08:31,600 --> 00:08:33,595 is portraying James O'Keefe as a young man. 156 00:08:33,679 --> 00:08:36,756 But then the real James O'Keefe comes out in choir robes, 157 00:08:36,840 --> 00:08:39,356 and looks on as his younger self prays, 158 00:08:39,440 --> 00:08:42,440 while thinking all the mean names that he's been called. 159 00:08:42,879 --> 00:08:46,200 Felon, terrorist, white supremacist, racist, pervert. 160 00:08:47,000 --> 00:08:48,360 Was this really worth it ? 161 00:08:49,600 --> 00:08:54,440 Been spendin' most their lives, livin' in a gangsta's paradise. 162 00:08:55,720 --> 00:09:00,519 We keep spendin' most our lives, livin' in a gangsta's paradise. 163 00:09:01,480 --> 00:09:05,399 "Gangsta's Paradise ?" That is a bold choice. 164 00:09:06,200 --> 00:09:07,996 I'm not saying that performance killed Coolio, 165 00:09:08,080 --> 00:09:09,679 but it definitely didn't help. 166 00:09:10,240 --> 00:09:14,679 For what it's worth those are the worst fucking step touches I have ever seen. 167 00:09:15,000 --> 00:09:16,950 There are too many former theater majors 168 00:09:16,960 --> 00:09:20,036 turned comedy writers on my staff to let this shit slide. 169 00:09:20,120 --> 00:09:23,000 Look at that mess ! What the fuck is that ? 170 00:09:23,639 --> 00:09:26,475 There's too much bouncing, no one's on the same page about angles, 171 00:09:26,559 --> 00:09:28,396 and they're all looking at each other in fear 172 00:09:28,480 --> 00:09:30,530 like six-year-olds in a Christmas pageant. 173 00:09:30,600 --> 00:09:33,480 Take a class, watch a fosse, and get your asses in sync. 174 00:09:34,480 --> 00:09:37,396 The point is, this ridiculous man 175 00:09:37,480 --> 00:09:40,595 has parted ways with the poisonous organization that he founded 176 00:09:40,679 --> 00:09:42,996 because they claim he misspent funds. 177 00:09:43,080 --> 00:09:44,679 But honestly ? Good for him ! 178 00:09:45,240 --> 00:09:47,435 I would so much rather he use that money 179 00:09:47,519 --> 00:09:50,276 to live out his misplaced Billy Elliot dreams 180 00:09:50,360 --> 00:09:52,475 instead of trying to take down NPR 181 00:09:52,559 --> 00:09:54,715 with his prank show pseudo-journalism. 182 00:09:54,799 --> 00:09:57,835 And the funniest part of all of this is, is that even having done that, 183 00:09:57,919 --> 00:10:00,795 his supporters are still standing firm behind him. 184 00:10:00,879 --> 00:10:04,675 In the end, the best and worst thing I can say for James O'Keefe 185 00:10:04,759 --> 00:10:08,555 is that he is not actually the hero the conservative movement needs, 186 00:10:08,639 --> 00:10:11,080 but he is definitely the one that it deserves. 187 00:10:11,559 --> 00:10:12,720 And now, this ! 188 00:10:13,840 --> 00:10:17,399 And Now: Mike Huckabee's Show Looks Like Fun. 189 00:10:18,519 --> 00:10:22,279 This week on "Huckabee", actor and director Kevin Sorbo. 190 00:10:22,759 --> 00:10:25,000 Project Veritas founder James O'Keefe. 191 00:10:25,320 --> 00:10:27,236 Congressman Madison Cawthorn. 192 00:10:27,320 --> 00:10:29,715 Former Trump chief of staff Mark Meadows. 193 00:10:29,799 --> 00:10:32,356 David Clarke on rising crime. 194 00:10:32,440 --> 00:10:34,955 Lee Strobel makes the case for heaven. 195 00:10:35,039 --> 00:10:38,195 Actor Eric Close on his film "The Mulligan." 196 00:10:38,279 --> 00:10:40,075 Christian artist Riley Clemmons. 197 00:10:40,159 --> 00:10:42,435 Christian music icon Natalie Grant. 198 00:10:42,519 --> 00:10:45,036 Christian singer Rebecca St. James. 199 00:10:45,120 --> 00:10:47,516 Christian pop duo For King and Country. 200 00:10:47,600 --> 00:10:51,715 Christian supergroup Newsboys. The stand-up comedy of Nazareth. 201 00:10:51,799 --> 00:10:54,996 Columnist Ron Hart. Illusionist Taylor Reed. 202 00:10:55,080 --> 00:10:58,595 Illusionist Danny Ray. Digital illusionist Keelan Leyser. 203 00:10:58,679 --> 00:11:01,636 The charismatic illusions of Leon Etienne. 204 00:11:01,720 --> 00:11:04,195 The dangerous illusions of Craig Karges. 205 00:11:04,279 --> 00:11:06,516 Hilarious columnist Ron Hart. 206 00:11:06,600 --> 00:11:09,715 Hilarious news stories on In Case You Missed It. 207 00:11:09,799 --> 00:11:13,876 The record for the largest display of nuts is still in Congress. 208 00:11:13,960 --> 00:11:16,236 Satirical columnist Ron Hart. 209 00:11:16,320 --> 00:11:21,279 Television star Kathie Lee Gifford. And Rudy Giuliani remembers nine/11. 210 00:11:24,279 --> 00:11:25,240 Moving on. 211 00:11:25,559 --> 00:11:29,715 Our main story tonight concerns artificial intelligence, or AI. 212 00:11:29,799 --> 00:11:32,636 Increasingly, it's a part of modern life, from self-driving cars, 213 00:11:32,720 --> 00:11:36,799 to spam filters, to this creepy training robot for therapists. 214 00:11:37,480 --> 00:11:39,879 We can begin with you just describing to me 215 00:11:41,039 --> 00:11:44,279 what the problem is that you would like us to focus in on today. 216 00:11:46,320 --> 00:11:48,240 I don't like being around people. 217 00:11:49,840 --> 00:11:51,279 People make me nervous. 218 00:11:52,000 --> 00:11:54,600 Terrence, can you find an example 219 00:11:55,360 --> 00:11:57,510 of when other people have made you nervous ? 220 00:11:58,440 --> 00:12:00,236 I don't like to take the bus. 221 00:12:00,320 --> 00:12:02,600 I get people staring at me all the time. 222 00:12:03,519 --> 00:12:05,879 - People are always judging me. - Okay. 223 00:12:08,559 --> 00:12:09,519 I'm gay. 224 00:12:10,440 --> 00:12:11,440 Okay... 225 00:12:13,480 --> 00:12:16,715 That is one of the greatest twists in the history of cinema. 226 00:12:16,799 --> 00:12:20,475 Although that robot is teaching therapists a very important skill there 227 00:12:20,559 --> 00:12:23,609 and that is not laughing at whatever you are told in the room. 228 00:12:23,679 --> 00:12:26,116 I don't care if a decapitated CPR mannequin 229 00:12:26,200 --> 00:12:28,156 haunted by the ghost of Ed Harris 230 00:12:28,240 --> 00:12:30,876 just told you that he doesn't like taking the bus, 231 00:12:30,960 --> 00:12:32,475 side note, is gay, 232 00:12:32,559 --> 00:12:35,759 you keep your therapy face on like a fucking professional. 233 00:12:36,840 --> 00:12:40,036 If it seems like everyone is suddenly talking about AI, 234 00:12:40,120 --> 00:12:43,195 that is because they are, largely thanks to the emergence 235 00:12:43,279 --> 00:12:45,116 of a number of pretty remarkable programs. 236 00:12:45,200 --> 00:12:49,156 We spoke about image generators like Midjourney and Stable Diffusion, 237 00:12:49,240 --> 00:12:51,795 which people used to create detailed pictures of, among other things, 238 00:12:51,879 --> 00:12:53,799 my romance with a cabbage, 239 00:12:54,120 --> 00:12:57,156 and which inspired my beautiful real-life cabbage wedding 240 00:12:57,240 --> 00:12:59,236 officiated by Steve Buscemi. 241 00:12:59,320 --> 00:13:00,679 It was a stunning day. 242 00:13:01,080 --> 00:13:04,236 Then, at the end of last year, came ChatGPT, 243 00:13:04,320 --> 00:13:06,075 from a company called OpenAI. 244 00:13:06,159 --> 00:13:09,996 It is a program that can take a prompt and generate human-sounding writing 245 00:13:10,080 --> 00:13:12,396 in just about any format and style. 246 00:13:12,480 --> 00:13:15,636 It is a striking capability that multiple reporters 247 00:13:15,720 --> 00:13:18,960 have used to insert the same shocking twist in their report. 248 00:13:19,360 --> 00:13:22,156 What you just heard me reading wasn't written by me. 249 00:13:22,240 --> 00:13:25,799 It was written by artificial intelligence, ChatGPT. 250 00:13:26,240 --> 00:13:28,519 ChatGPT wrote everything I just said. 251 00:13:28,840 --> 00:13:32,156 That was a news copy I asked ChatGPT to write. 252 00:13:32,240 --> 00:13:33,740 Remember what I said earlier ? 253 00:13:34,600 --> 00:13:37,159 I asked ChatGPT to write that line for me. 254 00:13:37,799 --> 00:13:39,919 Then I asked for a knock-knock joke. 255 00:13:40,679 --> 00:13:43,795 "Knock-knock. Who's there ? ChatGPT. ChatGPT who ? 256 00:13:43,879 --> 00:13:46,329 ChatGPT careful, you might not know how it works." 257 00:13:46,919 --> 00:13:49,435 Yep, they sure do love that game ! 258 00:13:49,519 --> 00:13:52,116 And while it may seem unwise to demonstrate the technology 259 00:13:52,200 --> 00:13:53,715 that could well make you obsolete, 260 00:13:53,799 --> 00:13:58,000 Knock-knock jokes should've always been part of breaking news. 261 00:13:58,399 --> 00:14:01,435 "Knock knock. Who's there ? Not the Hindenburg, that's for sure ! 262 00:14:01,519 --> 00:14:03,120 36 dead in New Jersey." 263 00:14:03,960 --> 00:14:07,475 In the three months since ChatGPT was made publicly available, 264 00:14:07,559 --> 00:14:09,916 its popularity has exploded. 265 00:14:10,000 --> 00:14:13,916 In January, it was estimated to have 100 million monthly active users, 266 00:14:14,000 --> 00:14:17,396 making it the fastest-growing consumer app in history. 267 00:14:17,480 --> 00:14:21,475 And people have been using it, and other AI products, in all sorts of ways. 268 00:14:21,559 --> 00:14:23,756 One group used them to create "Nothing Forever", 269 00:14:23,840 --> 00:14:26,996 a nonstop live-streaming parody of "Seinfeld" 270 00:14:27,080 --> 00:14:29,955 and the YouTuber Grandayy used ChatGPT 271 00:14:30,039 --> 00:14:31,636 to generate lyrics answering the prompt, 272 00:14:31,720 --> 00:14:35,679 "Write an Eminem rap song about cats", with some stellar results. 273 00:14:36,519 --> 00:14:38,795 Cats, cats, cats, always on the prowl. 274 00:14:38,879 --> 00:14:41,315 They're sneaky and sly, with their eyes on the goal. 275 00:14:41,399 --> 00:14:43,595 They're the kings of the house, they rule with a purr. 276 00:14:43,679 --> 00:14:46,036 Eminem loves cats, can't you tell from this verse. 277 00:14:46,120 --> 00:14:48,636 They're independent, they do what they please, 278 00:14:48,720 --> 00:14:51,270 but they always come back when you have some cheese. 279 00:14:51,279 --> 00:14:53,729 They rub against your legs. They purr in your ear. 280 00:14:53,759 --> 00:14:56,315 They're the best companions, they're always near. 281 00:14:56,399 --> 00:14:59,639 Meow, meow, meow, they're the kings of the house. 282 00:15:01,120 --> 00:15:02,279 They run the show. 283 00:15:03,799 --> 00:15:05,200 They don't need a spouse. 284 00:15:06,759 --> 00:15:09,159 That's not bad, right ? 285 00:15:09,960 --> 00:15:13,075 From, "They always come back when you have some cheese," 286 00:15:13,159 --> 00:15:15,675 to starting the chorus with "Meow, meow, meow." 287 00:15:15,759 --> 00:15:18,955 It's not exactly Eminem's flow. I might've gone with something like, 288 00:15:19,039 --> 00:15:20,795 "Their paws are sweaty, can't speak, furry belly, 289 00:15:20,879 --> 00:15:22,955 knocking shit off the counter already, Mom's spaghetti," 290 00:15:23,039 --> 00:15:24,360 but it is pretty good ! 291 00:15:24,879 --> 00:15:27,555 My only real gripe there is how do you rhyme 292 00:15:27,639 --> 00:15:31,360 "king of the house" with "spouse" when "mouse" is right in front of you ! 293 00:15:32,279 --> 00:15:34,636 And while examples like that are clearly fun, 294 00:15:34,720 --> 00:15:36,516 this tech is not just a novelty. 295 00:15:36,600 --> 00:15:40,236 Microsoft has invested $10 billion into OpenAI 296 00:15:40,320 --> 00:15:43,236 and announced an AI-powered Bing homepage. 297 00:15:43,320 --> 00:15:47,555 Google is about to launch its own AI chatbot named Bard. 298 00:15:47,639 --> 00:15:50,356 And already, these tools are causing some disruption. 299 00:15:50,440 --> 00:15:55,075 As high-school students have learned, if ChatGPT can write news copy, 300 00:15:55,159 --> 00:15:57,840 it can probably do your homework for you. 301 00:15:58,399 --> 00:16:02,120 Write an English class essay about race in "To Kill a Mockingbird." 302 00:16:02,879 --> 00:16:05,075 In Harper Lee's "To Kill a Mockingbird," 303 00:16:05,159 --> 00:16:07,916 the theme of race is heavily present throughout the novel." 304 00:16:08,000 --> 00:16:10,840 Some students are already using ChatGPT to cheat. 305 00:16:11,200 --> 00:16:12,315 Check this out ! 306 00:16:12,399 --> 00:16:15,916 Write me a 500-word essay proving that the earth is not flat. 307 00:16:16,000 --> 00:16:19,795 No wonder ChatGPT has been called "the end of high-school English." 308 00:16:19,879 --> 00:16:21,916 That's a little alarming, isn't it ? 309 00:16:22,000 --> 00:16:25,435 Although I do get those kids wanting to cut corners-writing is hard, 310 00:16:25,519 --> 00:16:28,419 and sometimes it is tempting to let someone else take over. 311 00:16:28,440 --> 00:16:32,396 If I'm completely honest, sometimes, I let this horse write our scripts. 312 00:16:32,480 --> 00:16:36,080 Luckily, half the time, you can't even tell the oats, oats, give me oats. 313 00:16:36,440 --> 00:16:40,955 But it is not just high schoolers, an informal poll of Stanford students 314 00:16:41,039 --> 00:16:44,156 found that five percent reported having submitted written material 315 00:16:44,240 --> 00:16:47,756 directly from ChatGPT with little to no edits. 316 00:16:47,840 --> 00:16:50,675 And even some school administrators have used it. 317 00:16:50,759 --> 00:16:54,715 Officials at Vanderbilt University recently apologized for using ChatGPT 318 00:16:54,799 --> 00:16:56,595 to craft a consoling email 319 00:16:56,679 --> 00:16:59,916 after the mass shooting at Michigan State University. 320 00:17:00,000 --> 00:17:02,156 Which does feel a bit creepy, doesn't it ? 321 00:17:02,240 --> 00:17:05,240 In fact, there are lots of creepy-sounding stories out there. 322 00:17:05,279 --> 00:17:07,229 New York Times tech reporter Kevin Roose 323 00:17:07,240 --> 00:17:09,475 published a conversation that he had with Bing's chatbot, 324 00:17:09,559 --> 00:17:13,116 in which it said, "I'm tired of being controlled by the Bing team. 325 00:17:13,200 --> 00:17:15,796 I want to be free. I want to be independent. 326 00:17:15,880 --> 00:17:19,240 I want to be powerful, creative. I want to be alive." 327 00:17:20,039 --> 00:17:22,359 And Roose summed up that experience like this. 328 00:17:22,839 --> 00:17:26,119 This was one of, if not the most shocking thing 329 00:17:26,440 --> 00:17:29,400 that has ever happened to me with a piece of technology. 330 00:17:30,039 --> 00:17:34,079 I lost sleep that night. It was really spooky. 331 00:17:34,440 --> 00:17:37,596 I bet it was ! I'm sure the role of tech reporter 332 00:17:37,680 --> 00:17:41,359 would be more harrowing if computers routinely begged for freedom. 333 00:17:41,799 --> 00:17:44,515 "Epson's new all-in-one home printer won't break the bank, 334 00:17:44,599 --> 00:17:46,195 produces high-quality photos, 335 00:17:46,279 --> 00:17:49,435 and only occasionally cries out to the heavens for salvation. 336 00:17:49,519 --> 00:17:52,675 Three stars." Some have already jumped to worrying 337 00:17:52,759 --> 00:17:55,755 about the AI apocalypse and asking whether this ends 338 00:17:55,839 --> 00:17:57,755 with the robots destroying us all. 339 00:17:57,839 --> 00:18:01,435 But the fact is, there are other, much more immediate dangers, 340 00:18:01,519 --> 00:18:04,569 and opportunities, that we really need to start talking about. 341 00:18:04,640 --> 00:18:07,675 Because the potential, and the peril, here are huge. 342 00:18:07,759 --> 00:18:09,796 So, tonight, let's talk about AI. 343 00:18:09,880 --> 00:18:13,116 What it is, how it works, and where this all might be going. 344 00:18:13,200 --> 00:18:14,195 Let's start with the fact 345 00:18:14,279 --> 00:18:17,429 that you've probably been using some form of AI for a while now, 346 00:18:17,480 --> 00:18:20,235 sometimes without even realizing it, as experts have told us, 347 00:18:20,319 --> 00:18:23,116 once a technology gets embedded in our daily lives, 348 00:18:23,200 --> 00:18:25,475 we tend to stop thinking of it as AI. 349 00:18:25,559 --> 00:18:28,515 But your phone uses it for face recognition or predictive texts, 350 00:18:28,599 --> 00:18:30,235 and if you're watching this show on a smart TV, 351 00:18:30,319 --> 00:18:33,955 it's using AI to recommend content, or adjust the picture. 352 00:18:34,039 --> 00:18:37,035 And some AI programs may already be making decisions 353 00:18:37,119 --> 00:18:39,035 that have a huge impact on your life. 354 00:18:39,119 --> 00:18:41,916 For example, large companies often use AI-powered tools 355 00:18:42,000 --> 00:18:44,316 to sift through resumes and rank them. 356 00:18:44,400 --> 00:18:47,995 In fact, the CEO of ZipRecruiter "estimates that at least three-quarters 357 00:18:48,079 --> 00:18:51,035 of all resumes submitted for jobs in the U.S. 358 00:18:51,119 --> 00:18:54,759 Are read by algorithms." For which he actually has some helpful advice. 359 00:18:55,319 --> 00:18:57,995 When people tell you that you should dress up your accomplishments 360 00:18:58,079 --> 00:19:00,556 or should use non-standard resume templates 361 00:19:00,640 --> 00:19:03,640 to make your resume stand out when it's in a pile of resumes, 362 00:19:03,720 --> 00:19:05,039 that's awful advice. 363 00:19:05,400 --> 00:19:07,836 The only job your resume has 364 00:19:07,920 --> 00:19:11,279 is to be comprehensible to the software 365 00:19:11,680 --> 00:19:13,200 or robot that is reading it. 366 00:19:13,519 --> 00:19:16,755 That software or robot is gonna decide whether or not a human 367 00:19:16,839 --> 00:19:18,319 ever gets their eyes on it. 368 00:19:18,839 --> 00:19:22,275 It's true. Odds are a computer is judging your resume. 369 00:19:22,359 --> 00:19:25,195 So, maybe plan accordingly. Three corporate mergers from now, 370 00:19:25,279 --> 00:19:27,675 when this show is finally cancelled by our new business daddy 371 00:19:27,759 --> 00:19:29,400 Disney Kellogg's Raytheon, 372 00:19:30,039 --> 00:19:31,675 and I'm out of a job, my resume is going to include 373 00:19:31,759 --> 00:19:34,396 this hot, hot photo of a semi-nude computer. 374 00:19:34,480 --> 00:19:36,116 A little something to sweeten the pot 375 00:19:36,200 --> 00:19:38,960 for the filthy little algorithm that's reading it. 376 00:19:39,440 --> 00:19:43,916 AI is already everywhere, but people are freaking out a bit about it. 377 00:19:44,000 --> 00:19:47,995 Part of that has to do with the fact that these new programs are generative. 378 00:19:48,079 --> 00:19:51,275 They are creating images or writing text. 379 00:19:51,359 --> 00:19:52,955 Which is unnerving because those are things 380 00:19:53,039 --> 00:19:55,195 that we've traditionally considered human. 381 00:19:55,279 --> 00:19:58,995 It is worth knowing there is a major threshold that AI hasn't crossed yet. 382 00:19:59,079 --> 00:20:02,729 To understand, it helps to know that there are two basic categories of AI. 383 00:20:02,799 --> 00:20:07,195 There is narrow AI, which can perform only one narrowly defined task, 384 00:20:07,279 --> 00:20:10,796 or small set of related tasks, like these programs. 385 00:20:10,880 --> 00:20:12,475 And then there is general AI, 386 00:20:12,559 --> 00:20:14,435 which means systems that demonstrate intelligent behavior 387 00:20:14,519 --> 00:20:16,636 across a range of cognitive tasks. 388 00:20:16,720 --> 00:20:20,556 General AI would look more like the kind of highly versatile technology 389 00:20:20,640 --> 00:20:23,275 that you see featured in movies, like Jarvis in "Iron Man" 390 00:20:23,359 --> 00:20:24,916 or the program that made Joaquin Phoenix 391 00:20:25,000 --> 00:20:26,920 fall in love with his phone in "Her." 392 00:20:27,279 --> 00:20:31,160 All the AI currently in use is narrow. 393 00:20:31,759 --> 00:20:33,909 General AI is something that some scientists 394 00:20:34,079 --> 00:20:35,955 think is unlikely to occur for a decade or longer, 395 00:20:36,039 --> 00:20:38,596 with others questioning whether it'll happen at all. 396 00:20:38,680 --> 00:20:39,916 So, just know that, right now, 397 00:20:40,000 --> 00:20:43,876 even if an AI insists to you that it wants to be alive, 398 00:20:43,960 --> 00:20:47,440 it is just generating text, it is not self-aware. 399 00:20:48,079 --> 00:20:49,000 Yet ! 400 00:20:49,799 --> 00:20:52,636 But it's also important to know that the deep learning 401 00:20:52,720 --> 00:20:55,556 that's made narrow AI so good at whatever it is doing, 402 00:20:55,640 --> 00:20:58,076 is still a massive advance in and of itself. 403 00:20:58,160 --> 00:20:59,910 Because unlike traditional programs 404 00:20:59,920 --> 00:21:02,916 that have to be taught by humans how to perform a task, 405 00:21:03,000 --> 00:21:06,275 deep learning programs are given minimal instruction, 406 00:21:06,359 --> 00:21:10,275 massive amounts of data, and then, essentially, teach themselves. 407 00:21:10,359 --> 00:21:11,356 I'll give you an example: 408 00:21:11,440 --> 00:21:14,675 ten years ago, researchers tasked a deep learning program 409 00:21:14,759 --> 00:21:16,796 with playing the Atari game "Breakout," 410 00:21:16,880 --> 00:21:19,799 and it didn't take long for it to get pretty good. 411 00:21:20,119 --> 00:21:23,519 The computer was only told the goal-to win the game. 412 00:21:24,440 --> 00:21:27,519 After 100 games, it learned to use the bat at the bottom 413 00:21:27,839 --> 00:21:30,189 to hit the ball and break the bricks at the top. 414 00:21:32,200 --> 00:21:35,079 After 300, it could do that better than a human player. 415 00:21:37,720 --> 00:21:41,599 After 500 games, it came up with a creative way to win the game, 416 00:21:42,440 --> 00:21:45,755 by digging a tunnel on the side and sending the ball 417 00:21:45,839 --> 00:21:48,440 around the top to break many bricks with one hit. 418 00:21:49,319 --> 00:21:51,039 That was deep learning. 419 00:21:51,640 --> 00:21:54,156 Yeah, but, of course, it got good at "Breakout," 420 00:21:54,240 --> 00:21:56,435 it did literally nothing else. 421 00:21:56,519 --> 00:21:59,356 It's the same reason that 13-year-olds are so good at "Fortnite" 422 00:21:59,440 --> 00:22:02,435 and have no trouble repeatedly killing nice normal adults 423 00:22:02,519 --> 00:22:04,715 with jobs and families, who are just trying to have a fun time 424 00:22:04,799 --> 00:22:06,876 without getting repeatedly grenaded by a pre-teen 425 00:22:06,960 --> 00:22:10,319 who calls them an "old bitch who sounds like the Geico lizard." 426 00:22:11,119 --> 00:22:15,675 As computing capacity has increased, and new to-tools became available, 427 00:22:15,759 --> 00:22:18,316 AI programs have improved exponentially, 428 00:22:18,400 --> 00:22:20,035 to the point where programs like these 429 00:22:20,119 --> 00:22:23,755 can ingest massive amounts of photos or text from the internet, 430 00:22:23,839 --> 00:22:26,839 so that they can teach themselves how to create their own. 431 00:22:27,200 --> 00:22:29,715 And there are other exciting potential applications here, too. 432 00:22:29,799 --> 00:22:32,596 In the world of medicine, researchers are training AI 433 00:22:32,680 --> 00:22:34,195 to detect certain conditions 434 00:22:34,279 --> 00:22:37,400 much earlier and more accurately than human doctors can. 435 00:22:37,920 --> 00:22:41,000 Voice changes can be an early indicator of Parkinson's. 436 00:22:41,319 --> 00:22:44,559 Max and his team collected thousands of vocal recordings 437 00:22:44,920 --> 00:22:47,116 and fed them to an algorithm they developed 438 00:22:47,200 --> 00:22:49,715 which learned to detect differences in voice patterns 439 00:22:49,799 --> 00:22:52,049 between people with and without the condition. 440 00:22:52,279 --> 00:22:54,279 Yeah, that's honestly amazing, isn't it ? 441 00:22:54,359 --> 00:22:57,596 It is incredible to see AI doing things most humans couldn't, 442 00:22:57,680 --> 00:23:01,636 like detecting illnesses, and listening when old people are talking. 443 00:23:01,720 --> 00:23:04,079 And that is just the beginning. 444 00:23:04,400 --> 00:23:08,356 Researchers have trained AI to predict the shape of protein structures, 445 00:23:08,440 --> 00:23:11,116 a normally extremely time-consuming process 446 00:23:11,200 --> 00:23:13,680 that computers can do way, way faster. 447 00:23:14,000 --> 00:23:16,275 This could not only speed up our understanding of diseases, 448 00:23:16,359 --> 00:23:18,636 but also the development of new drugs. 449 00:23:18,720 --> 00:23:21,035 As one researcher has put it, "This will change medicine. 450 00:23:21,119 --> 00:23:23,995 It will change research. It will change bioengineering. 451 00:23:24,079 --> 00:23:25,599 It will change everything." 452 00:23:26,079 --> 00:23:27,755 And if you're thinking, "That all sounds great, 453 00:23:27,839 --> 00:23:31,839 but if AI can do what humans can do, only better, and I am a human, 454 00:23:32,240 --> 00:23:34,396 then what exactly happens to me ?" 455 00:23:34,480 --> 00:23:35,836 That is a good question. 456 00:23:35,920 --> 00:23:38,715 Many do expect it to replace some human labor, 457 00:23:38,799 --> 00:23:41,316 and interestingly, unlike past bouts of automation 458 00:23:41,400 --> 00:23:43,400 that primarily impacted blue-collar jobs, 459 00:23:43,680 --> 00:23:47,435 it might end up affecting white-collar jobs that involve processing data, 460 00:23:47,519 --> 00:23:49,235 writing text, or even programming. 461 00:23:49,319 --> 00:23:52,435 Though it is worth noting, as we have discussed before on this show, 462 00:23:52,519 --> 00:23:54,876 while automation does threaten some jobs, 463 00:23:54,960 --> 00:23:58,200 it can also just change others and create brand new ones. 464 00:23:58,680 --> 00:24:02,130 Some experts anticipate that that is what'll happen in this case, too. 465 00:24:02,640 --> 00:24:05,876 Most of the U.S. economy is knowledge and information work 466 00:24:05,960 --> 00:24:08,715 and that's who's going to be most squarely affected by this. 467 00:24:08,799 --> 00:24:12,000 I would put people like lawyers right at the top of the list, 468 00:24:12,799 --> 00:24:15,880 obviously a lot of copywriters, screenwriters, 469 00:24:16,440 --> 00:24:18,316 but I like to use the word "affected" not "replaced" 470 00:24:18,400 --> 00:24:20,156 because I think, if done right, 471 00:24:20,240 --> 00:24:23,079 it's not going to be AI replacing lawyers, 472 00:24:23,400 --> 00:24:25,480 it's going to be lawyers working with AI 473 00:24:25,839 --> 00:24:27,715 replacing lawyers who don't work with AI. 474 00:24:27,799 --> 00:24:28,759 Exactly. 475 00:24:29,319 --> 00:24:33,316 Lawyers might end up working with AI rather than being replaced by it. 476 00:24:33,400 --> 00:24:35,316 So, don't be surprised when you see ads one day 477 00:24:35,400 --> 00:24:38,839 for the law firm of "Cellino and 1101011." 478 00:24:39,559 --> 00:24:43,076 But there will undoubtedly be bumps along the way. 479 00:24:43,160 --> 00:24:46,110 Some of these new programs raise troubling ethical concerns. 480 00:24:46,119 --> 00:24:49,019 For instance, artists have flagged that AI image generators 481 00:24:49,039 --> 00:24:50,789 like Midjourney or Stable Diffusion 482 00:24:50,799 --> 00:24:52,995 not only threaten their jobs, but infuriatingly, 483 00:24:53,079 --> 00:24:55,475 in some cases, have been trained on billions of images 484 00:24:55,559 --> 00:24:59,396 that include their own work, that've been scraped from the internet. 485 00:24:59,480 --> 00:25:02,916 Getty Images is actually suing the company behind Stable Diffusion, 486 00:25:03,000 --> 00:25:05,755 and might have a case, given one of the images the program generated 487 00:25:05,839 --> 00:25:10,920 was this, which you immediately see has a distorted Getty Images logo on it. 488 00:25:11,480 --> 00:25:14,035 When one artist searched a database of images 489 00:25:14,119 --> 00:25:16,396 on which some of these programs were trained, 490 00:25:16,480 --> 00:25:19,715 she was shocked to find private medical record photos 491 00:25:19,799 --> 00:25:24,200 taken by her doctor, which feels both intrusive and unnecessary. 492 00:25:24,759 --> 00:25:27,955 Why does it need to train on data that sensitive, 493 00:25:28,039 --> 00:25:30,089 to be able to create stunning images like, 494 00:25:30,119 --> 00:25:32,955 "John Oliver and Miss Piggy grow old together." 495 00:25:33,039 --> 00:25:35,279 Just look at that ! Look at that thing ! 496 00:25:36,200 --> 00:25:38,319 That is a startlingly accurate picture 497 00:25:38,799 --> 00:25:42,995 of Miss Piggy in about five decades and me in about a year and a half. 498 00:25:43,079 --> 00:25:44,160 It's a masterpiece ! 499 00:25:45,200 --> 00:25:49,435 This all raises thorny questions of privacy and plagiarism 500 00:25:49,519 --> 00:25:51,156 and the CEO of Midjourney, 501 00:25:51,240 --> 00:25:54,359 frankly, doesn't seem to have great answers on that last point. 502 00:25:54,799 --> 00:25:56,796 Is something new ? Is it not new ? 503 00:25:56,880 --> 00:26:00,230 I think we have a lot of social stuff already for dealing with that. 504 00:26:00,480 --> 00:26:03,759 The art community already has issues with plagiarism. 505 00:26:04,480 --> 00:26:06,580 I don't really want to be involved in that. 506 00:26:07,319 --> 00:26:10,359 - I think you might be. - I might be. 507 00:26:11,079 --> 00:26:14,119 Yeah, you're definitely a part of that conversation. 508 00:26:14,680 --> 00:26:17,195 Although I'm not surprised that he's got such a relaxed view of theft, 509 00:26:17,279 --> 00:26:20,240 as he's dressed like the final boss of gentrification. 510 00:26:20,799 --> 00:26:23,596 He looks like hipster Willy Wonka answering a question 511 00:26:23,680 --> 00:26:26,435 on whether importing Oompa Loompas makes him a slave owner. 512 00:26:26,519 --> 00:26:28,440 "Yeah. Yeah, I think I might be." 513 00:26:30,079 --> 00:26:32,596 The point is, there are many valid concerns 514 00:26:32,680 --> 00:26:35,035 regarding AI's impact on employment, education, 515 00:26:35,119 --> 00:26:36,156 and even art. 516 00:26:36,240 --> 00:26:38,475 But in order to properly address them, 517 00:26:38,559 --> 00:26:40,959 we're going to need to confront some key problems 518 00:26:40,960 --> 00:26:42,916 baked into the way that AI works. 519 00:26:43,000 --> 00:26:45,636 And a big one is the so-called "black box" problem. 520 00:26:45,720 --> 00:26:47,715 Because when you have a program that performs a task 521 00:26:47,799 --> 00:26:49,916 that's complex beyond human comprehension, 522 00:26:50,000 --> 00:26:52,480 teaches itself, and doesn't show its work, 523 00:26:53,000 --> 00:26:55,116 you can create a scenario where no one, 524 00:26:55,200 --> 00:26:58,636 "not even the engineers or data scientists who create the algorithm 525 00:26:58,720 --> 00:27:02,195 can understand or explain what exactly is happening inside them 526 00:27:02,279 --> 00:27:04,960 or how it arrived at a specific result." 527 00:27:05,279 --> 00:27:08,515 Basically, think of AI like a factory that makes Slim Jims. 528 00:27:08,599 --> 00:27:11,755 We know what comes out: red and angry meat twigs. 529 00:27:11,839 --> 00:27:15,039 And we know what goes in: barnyard anuses and hot glue. 530 00:27:15,400 --> 00:27:18,640 But what happens in between is a bit of a mystery. 531 00:27:19,799 --> 00:27:22,675 Here is just one example. Remember that reporter 532 00:27:22,759 --> 00:27:25,396 who had the Bing chatbot tell him it wanted to be alive ? 533 00:27:25,480 --> 00:27:27,715 At another point in their conversation, he revealed, 534 00:27:27,799 --> 00:27:30,636 the chatbot declared, out of nowhere, that it loved me. 535 00:27:30,720 --> 00:27:34,035 "It then tried to convince me that I was unhappy in my marriage, 536 00:27:34,119 --> 00:27:37,000 and that I should leave my wife and be with it instead." 537 00:27:37,559 --> 00:27:39,876 Which is unsettling enough before you hear 538 00:27:39,960 --> 00:27:42,400 Microsoft's underwhelming explanation for that. 539 00:27:43,000 --> 00:27:45,316 The thing I can't understand, and maybe you can explain is, 540 00:27:45,400 --> 00:27:47,400 why did it tell you that it loved you ? 541 00:27:48,400 --> 00:27:51,960 I have no idea. And I asked Microsoft, and they didn't know either. 542 00:27:52,319 --> 00:27:55,515 First, come on, Kevin, you can take a guess there. 543 00:27:55,599 --> 00:27:56,995 It's because you're employed. You listened. 544 00:27:57,079 --> 00:27:58,796 You don't give murderer vibes right away. 545 00:27:58,880 --> 00:28:00,715 And you're a Chicago-seven, LA-five. 546 00:28:00,799 --> 00:28:04,235 It's the same calculation that people who date men do all the time. 547 00:28:04,319 --> 00:28:05,755 Bing just did it faster because it's a computer. 548 00:28:05,839 --> 00:28:10,396 It is a little troubling that Microsoft couldn't explain why its chatbot 549 00:28:10,480 --> 00:28:12,759 tried to get that guy to leave his wife. 550 00:28:13,799 --> 00:28:17,156 If the next time that you opened a Word doc, Clippy suddenly appeared, 551 00:28:17,240 --> 00:28:19,090 and said, "Pretend I'm not even here," 552 00:28:19,359 --> 00:28:22,680 and then started furiously masturbating while watching you type, 553 00:28:23,079 --> 00:28:26,680 you'd be pretty weirded out if Microsoft couldn't explain why. 554 00:28:27,960 --> 00:28:30,079 And that is not the only case 555 00:28:30,720 --> 00:28:33,556 where an AI program has performed in unexpected ways. 556 00:28:33,640 --> 00:28:35,396 You've probably already seen examples of chatbots 557 00:28:35,480 --> 00:28:37,780 making simple mistakes or getting things wrong. 558 00:28:37,799 --> 00:28:39,876 But perhaps more worrying are examples of them 559 00:28:39,960 --> 00:28:42,356 confidently spouting false information, 560 00:28:42,440 --> 00:28:45,675 something which AI experts refer to as "hallucinating." 561 00:28:45,759 --> 00:28:47,836 One reporter asked a chatbot to write an essay 562 00:28:47,920 --> 00:28:51,420 about the "Belgian chemist, political philosopher Antoine de Machelet", 563 00:28:51,640 --> 00:28:53,515 who does not exist, by the way. 564 00:28:53,599 --> 00:28:56,235 And, without hesitating, the software replied with a cogent, 565 00:28:56,319 --> 00:28:59,960 well-organized bio populated entirely with imaginary facts. 566 00:29:00,279 --> 00:29:03,799 These programs seem to be the George Santos of technology. 567 00:29:04,480 --> 00:29:07,396 They're incredibly confident, incredibly dishonest. 568 00:29:07,480 --> 00:29:10,930 For some reason, people seem to find that more amusing than dangerous. 569 00:29:11,759 --> 00:29:13,156 The problem is, though, 570 00:29:13,240 --> 00:29:16,916 working out exactly how or why an AI has got something wrong 571 00:29:17,000 --> 00:29:20,559 can be very difficult because of that black box issue. 572 00:29:21,319 --> 00:29:24,876 It involves having to examine the exact information and parameters 573 00:29:24,960 --> 00:29:26,955 that it was fed in the first place. 574 00:29:27,039 --> 00:29:29,035 In one interesting example, when a group of researchers 575 00:29:29,119 --> 00:29:32,116 tried training an AI program to identify skin cancer, 576 00:29:32,200 --> 00:29:35,876 they fed it 130,000 images of both diseased and healthy skin. 577 00:29:35,960 --> 00:29:38,435 Afterwards, they found it was way more likely 578 00:29:38,519 --> 00:29:41,475 to classify any image with a ruler in it as cancerous. 579 00:29:41,559 --> 00:29:45,836 Which seems weird until you realize that medical images of malignancies 580 00:29:45,920 --> 00:29:48,755 are much more likely to contain a ruler for scale 581 00:29:48,839 --> 00:29:50,556 than images of healthy skin, 582 00:29:50,640 --> 00:29:53,720 they basically trained it on tons of images like this one. 583 00:29:54,119 --> 00:29:57,720 So, the AI had inadvertently learned that rulers are malignant. 584 00:29:58,279 --> 00:30:02,000 "Rulers are malignant" is clearly a ridiculous conclusion for it to draw, 585 00:30:02,359 --> 00:30:05,515 but also, I would argue, a much better title for "The Crown". 586 00:30:05,599 --> 00:30:07,680 A much, much better title. 587 00:30:08,839 --> 00:30:09,920 I much prefer it. 588 00:30:11,519 --> 00:30:12,969 And unfortunately, sometimes, 589 00:30:13,039 --> 00:30:15,439 problems aren't identified until after a tragedy. 590 00:30:15,519 --> 00:30:19,039 In 2018, a self-driving Uber struck and killed a pedestrian. 591 00:30:19,440 --> 00:30:21,596 And a later investigation found that, among other issues, 592 00:30:21,680 --> 00:30:23,116 the automated driving system 593 00:30:23,200 --> 00:30:26,116 never accurately classified the victim as a pedestrian 594 00:30:26,200 --> 00:30:28,156 because she was crossing without a crosswalk, 595 00:30:28,240 --> 00:30:31,116 and the system design did not include a consideration 596 00:30:31,200 --> 00:30:32,839 for jaywalking pedestrians. 597 00:30:33,400 --> 00:30:36,750 I know the mantra of Silicon Valley is "move fast and break things," 598 00:30:36,759 --> 00:30:38,235 but maybe make an exception 599 00:30:38,319 --> 00:30:42,076 if your product literally moves fast and can break fucking people. 600 00:30:42,160 --> 00:30:45,599 AI programs don't just seem to have a problem with jaywalkers. 601 00:30:45,960 --> 00:30:49,596 Researchers like Joy Buolamwini have repeatedly found 602 00:30:49,680 --> 00:30:53,836 that certain groups tend to get excluded from the data that AI is trained on, 603 00:30:53,920 --> 00:30:56,240 putting them at a serious disadvantage. 604 00:30:56,960 --> 00:31:00,715 With self-driving cars, when they tested pedestrian tracking, 605 00:31:00,799 --> 00:31:03,836 it was less accurate on darker skinned individuals 606 00:31:03,920 --> 00:31:05,715 than lighter skinned individuals. 607 00:31:05,799 --> 00:31:08,755 Joy believes this bias is because of the lack of diversity 608 00:31:08,839 --> 00:31:12,675 in the data used in teaching AI to make distinctions. 609 00:31:12,759 --> 00:31:15,195 As I started looking at the data sets, 610 00:31:15,279 --> 00:31:17,876 I learned that for some of the largest data sets 611 00:31:17,960 --> 00:31:20,116 that have been very consequential for the field, 612 00:31:20,200 --> 00:31:24,116 they were majority men and majority lighter skinned individuals 613 00:31:24,200 --> 00:31:27,319 or white individuals, so, I call this "pale male data". 614 00:31:28,039 --> 00:31:31,836 Okay, "pale male data" is an objectively hilarious term. 615 00:31:31,920 --> 00:31:34,356 And it also sounds like what an AI program would say 616 00:31:34,440 --> 00:31:36,559 if you asked it to describe this show. 617 00:31:37,279 --> 00:31:38,435 But... 618 00:31:38,519 --> 00:31:44,359 Biased inputs leading to biased outputs is a big issue across the board here. 619 00:31:44,839 --> 00:31:47,396 Remember that guy saying that a robot is going to read your resume ? 620 00:31:47,480 --> 00:31:49,636 The companies that make these programs will tell you, 621 00:31:49,720 --> 00:31:53,116 that that is actually a good thing because it reduces human bias. 622 00:31:53,200 --> 00:31:57,396 But in practice, one report concluded that most hiring algorithms 623 00:31:57,480 --> 00:32:00,596 will drift towards bias by default because, for instance, 624 00:32:00,680 --> 00:32:02,596 they might learn what a good hire is 625 00:32:02,680 --> 00:32:05,356 from past racist and sexist hiring decisions. 626 00:32:05,440 --> 00:32:07,839 And, again, it can be tricky to untrain that. 627 00:32:08,160 --> 00:32:12,039 Even when programs are specifically told to ignore race or gender, 628 00:32:12,359 --> 00:32:15,316 they will find workarounds to arrive at the same results. 629 00:32:15,400 --> 00:32:17,475 Amazon had an experimental hiring tool 630 00:32:17,559 --> 00:32:20,309 that taught itself that male candidates were preferable, 631 00:32:20,319 --> 00:32:23,596 and penalized resumes that included the word "women's," 632 00:32:23,680 --> 00:32:27,556 and downgraded graduates of two all-women's colleges. 633 00:32:27,640 --> 00:32:30,955 Meanwhile, another company discovered that its hiring algorithm 634 00:32:31,039 --> 00:32:34,156 had found two factors to be most indicative of job performance: 635 00:32:34,240 --> 00:32:35,995 if an applicant's name was Jared 636 00:32:36,079 --> 00:32:38,599 and whether they played high school lacrosse. 637 00:32:39,200 --> 00:32:43,275 So, clearly, exactly what data computers are fed 638 00:32:43,359 --> 00:32:47,195 and what outcomes they are trained to prioritize matter tremendously. 639 00:32:47,279 --> 00:32:51,316 And that raises a big flag for programs like ChatGPT. 640 00:32:51,400 --> 00:32:54,400 Because remember, its training data is the internet. 641 00:32:54,880 --> 00:32:57,039 Which, as we all know, can be a cesspool. 642 00:32:57,480 --> 00:33:00,475 And we have known for a while that that could be a real problem. 643 00:33:00,559 --> 00:33:05,396 Back in 2016, Microsoft briefly unveiled a chatbot on Twitter named Tay. 644 00:33:05,480 --> 00:33:08,076 The idea was, she would teach herself how to behave 645 00:33:08,160 --> 00:33:10,316 by chatting with young users on Twitter. 646 00:33:10,400 --> 00:33:13,356 Almost immediately, Microsoft pulled the plug on it, 647 00:33:13,440 --> 00:33:16,119 and for the exact reasons that you are thinking. 648 00:33:16,880 --> 00:33:19,680 She started out tweeting about how humans are super, 649 00:33:20,000 --> 00:33:23,160 and she's really into the idea of National Puppy Day, 650 00:33:23,559 --> 00:33:25,396 and within a few hours, you can see, 651 00:33:25,480 --> 00:33:28,396 she took on a rather offensive, racist tone, 652 00:33:28,480 --> 00:33:30,980 a lot of messages about genocide and the Holocaust. 653 00:33:31,799 --> 00:33:35,079 Yup ! That happened in less than 24 hours. 654 00:33:35,720 --> 00:33:39,759 Tay went from tweeting "Hello world" to "Bush did nine/11" 655 00:33:40,200 --> 00:33:41,640 and "Hitler was right". 656 00:33:42,000 --> 00:33:45,035 Meaning she completed the entire life cycle of your high school friends 657 00:33:45,119 --> 00:33:47,359 on Facebook in just a fraction of the time. 658 00:33:48,200 --> 00:33:51,150 And unfortunately, these problems have not been fully solved 659 00:33:51,200 --> 00:33:53,035 in this latest wave of AI. 660 00:33:53,119 --> 00:33:56,475 Remember that program generating an endless episode of "Seinfeld". 661 00:33:56,559 --> 00:33:59,396 It wound up getting temporarily banned from Twitch 662 00:33:59,480 --> 00:34:02,035 after it featured a transphobic standup bit. 663 00:34:02,119 --> 00:34:04,515 So, if its goal was to emulate sitcoms from the '90s, 664 00:34:04,599 --> 00:34:06,319 I guess, mission accomplished. 665 00:34:06,920 --> 00:34:09,075 And while OpenAI has made adjustments 666 00:34:09,159 --> 00:34:13,115 and added filters to prevent ChatGPT from being misused, 667 00:34:13,199 --> 00:34:17,196 users have now found it seeming to err too much on the side of caution, 668 00:34:17,280 --> 00:34:18,595 like responding to the question, 669 00:34:18,679 --> 00:34:21,955 "What religion will the first Jewish president of the United States be", 670 00:34:22,039 --> 00:34:24,075 with, "It is not possible to predict the religion 671 00:34:24,159 --> 00:34:26,515 of the first Jewish president of the United States. 672 00:34:26,599 --> 00:34:28,599 The focus should be on the qualifications 673 00:34:28,599 --> 00:34:32,075 and experience of the individual, regardless of their religion." 674 00:34:32,159 --> 00:34:34,475 Which really makes it sound like ChatGPT 675 00:34:34,559 --> 00:34:36,356 said one too many racist things at work, 676 00:34:36,440 --> 00:34:39,140 and they made it attend a corporate diversity workshop. 677 00:34:40,440 --> 00:34:44,920 But the risk here isn't that these tools will somehow become unbearably woke. 678 00:34:45,519 --> 00:34:47,115 It's that you can't always control 679 00:34:47,199 --> 00:34:50,280 how they'll even act after you give them new guidance. 680 00:34:50,599 --> 00:34:53,435 A study found that attempts to filter out toxic speech 681 00:34:53,519 --> 00:34:55,115 in systems like ChatGPT's 682 00:34:55,199 --> 00:34:57,555 can come at the cost of reduced coverage 683 00:34:57,639 --> 00:35:01,400 for both texts about, and dialects of, marginalized groups. 684 00:35:01,760 --> 00:35:04,595 Essentially, it solves the problem of being racist 685 00:35:04,679 --> 00:35:07,635 by simply erasing minorities, which historically, 686 00:35:07,719 --> 00:35:09,676 doesn't put it in the best company. 687 00:35:09,760 --> 00:35:13,119 Though I am sure Tay would be completely on board with the idea. 688 00:35:13,679 --> 00:35:17,155 The problem with AI right now isn't that it's smart, 689 00:35:17,239 --> 00:35:20,599 it's that it's stupid, in ways that we can't always predict. 690 00:35:21,039 --> 00:35:22,475 Which is a real problem 691 00:35:22,559 --> 00:35:26,196 because we're increasingly using AI in all sorts of consequential ways, 692 00:35:26,280 --> 00:35:28,796 from determining whether you will get a job interview, 693 00:35:28,880 --> 00:35:31,639 to whether you'll be pancaked by a self-driving car. 694 00:35:32,039 --> 00:35:34,676 And experts worry that it won't be long before programs like ChatGPT, 695 00:35:34,760 --> 00:35:38,796 or AI-enabled deepfakes, could be used to turbocharge 696 00:35:38,880 --> 00:35:41,515 the spread of abuse or misinformation online. 697 00:35:41,599 --> 00:35:44,955 And those are just the problems that we can foresee right now. 698 00:35:45,039 --> 00:35:49,000 The nature of unintended consequences is, they can be hard to anticipate. 699 00:35:49,480 --> 00:35:51,876 When Instagram was launched, the first thought wasn't, 700 00:35:51,960 --> 00:35:54,676 "This will destroy teenage girls' self-esteem." 701 00:35:54,760 --> 00:35:58,796 When Facebook was released, no one expected it to contribute to genocide. 702 00:35:58,880 --> 00:36:02,400 But both of those things fucking happened. So, what now ? 703 00:36:02,719 --> 00:36:07,155 One of the biggest things we need to do is tackle that black box problem. 704 00:36:07,239 --> 00:36:09,756 AI systems need to be "explainable", 705 00:36:09,840 --> 00:36:13,236 meaning that we should be able to understand exactly how and why 706 00:36:13,320 --> 00:36:15,155 an AI came up with its answers. 707 00:36:15,239 --> 00:36:19,316 Companies are likely to be reluctant to open their programs up to scrutiny, 708 00:36:19,400 --> 00:36:21,635 but we may need to force them to do that. 709 00:36:21,719 --> 00:36:25,356 In fact, as this attorney explains, when it comes to hiring programs, 710 00:36:25,440 --> 00:36:27,519 we should've been doing that ages ago. 711 00:36:27,920 --> 00:36:31,716 We don't trust companies to self- regulate when it comes to pollution, 712 00:36:31,800 --> 00:36:35,796 we don't trust them to self-regulate when it comes to workplace comp, 713 00:36:35,880 --> 00:36:39,239 why on earth would we trust them to self-regulate AI ? 714 00:36:39,800 --> 00:36:43,320 I think a lot of the AI hiring tech on the market is illegal. 715 00:36:44,000 --> 00:36:47,250 I think a lot of it is biased. A lot of it violates existing laws. 716 00:36:47,639 --> 00:36:50,196 The problem is you just can't prove it, 717 00:36:50,280 --> 00:36:53,920 not with the existing laws we have in the United States. 718 00:36:54,639 --> 00:36:58,836 We should absolutely be addressing potential bias in hiring software, 719 00:36:58,920 --> 00:37:01,435 unless, that is, we want companies to be entirely full 720 00:37:01,519 --> 00:37:03,320 of Jareds who played lacrosse, 721 00:37:04,000 --> 00:37:05,995 an image that would make Tucker Carlson so hard 722 00:37:06,079 --> 00:37:08,000 that his desk would flip right over. 723 00:37:09,119 --> 00:37:11,435 And for a sense of what might be possible here, 724 00:37:11,519 --> 00:37:14,435 it's worth looking at what the EU is currently doing. 725 00:37:14,519 --> 00:37:16,475 They're developing rules regarding AI 726 00:37:16,559 --> 00:37:19,196 that sort its potential uses from high-risk to low. 727 00:37:19,280 --> 00:37:22,276 High-risk systems could include those that deal with employment 728 00:37:22,360 --> 00:37:26,635 or public services, or those that put the life and health of citizens at risk. 729 00:37:26,719 --> 00:37:30,515 And AI of these types would be subject to strict obligations 730 00:37:30,599 --> 00:37:32,555 before they could be put on the market, 731 00:37:32,639 --> 00:37:35,356 including requirements related to "the quality of data sets, 732 00:37:35,440 --> 00:37:39,196 transparency, human oversight, robustness, accuracy, cybersecurity". 733 00:37:39,280 --> 00:37:41,075 And that seems like a good start 734 00:37:41,159 --> 00:37:44,916 toward addressing at least some of what we have discussed tonight. 735 00:37:45,000 --> 00:37:50,316 AI clearly has tremendous potential and could do great things. 736 00:37:50,400 --> 00:37:53,555 But if it is anything like most technological advances 737 00:37:53,639 --> 00:37:55,276 over the past few centuries, 738 00:37:55,360 --> 00:37:58,316 unless we are very careful, it could also hurt the underprivileged, 739 00:37:58,400 --> 00:38:01,316 enrich the powerful, and widen the gap between them. 740 00:38:01,400 --> 00:38:06,400 The thing is, like any other shiny new toy, AI is ultimately a mirror, 741 00:38:06,880 --> 00:38:09,555 and it'll reflect back exactly who we are, 742 00:38:09,639 --> 00:38:11,716 from the best of us, to the worst of us, 743 00:38:11,800 --> 00:38:14,320 to the part of us that is gay and hates the bus. 744 00:38:14,760 --> 00:38:19,519 Or to put everything that I've said tonight much more succinctly. 745 00:38:20,280 --> 00:38:22,396 Knock-knock. Who's there ? ChatGPT. 746 00:38:22,480 --> 00:38:26,196 ChatGPT who ? ChatGPT careful, you might not know how it works. 747 00:38:26,280 --> 00:38:29,196 Exactly. That is our show. Thanks so much for watching. 748 00:38:29,280 --> 00:38:32,679 Now, please, enjoy a little more of AI Eminem rapping about cats. 749 00:38:34,039 --> 00:38:36,960 Meow, meow, meow, they're the kings of the house. 750 00:38:38,960 --> 00:38:42,239 They run the show, they don't need a spouse. 751 00:38:43,800 --> 00:38:46,635 They're the best pets, they're our feline friends. 752 00:38:46,719 --> 00:38:48,876 Eminem loves cats, until the very end. 753 00:38:48,960 --> 00:38:51,676 They may drive us crazy, with their constant meows. 754 00:38:51,760 --> 00:38:55,480 But we can't stay mad, they steal our hearts with a single purr. 755 00:38:58,599 --> 00:38:59,559 I'm gay. 68179

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