1 00:00:10,080 --> 00:00:13,185 Mike Rugnetta: Friends, hello, and welcome to Never Post, a 2 00:00:13,185 --> 00:00:15,265 podcast for and about the Internet. I'm your host, Mike 3 00:00:15,265 --> 00:00:20,065 Rugnetta. This intro was written on Tuesday, 09/23/2025 at 4 00:00:20,065 --> 00:00:25,950 11:56AM eastern, and we have a slightly late show for you this 5 00:00:25,950 --> 00:00:28,910 week. Sorry about that. I got a toddler cold, and we're in the 6 00:00:28,910 --> 00:00:32,190 middle of renegotiating AI Mike's contract, but we're here. 7 00:00:32,590 --> 00:00:37,150 We made it. We did it. In our third ever show length segment, 8 00:00:37,150 --> 00:00:41,285 contributing producer Tori Dominguez Peak returns to look 9 00:00:41,285 --> 00:00:44,885 at artificial intelligence in the classroom, including one 10 00:00:44,885 --> 00:00:50,485 instructor who has said no more and has banned its use entirely. 11 00:00:50,725 --> 00:00:54,245 Tori tells Jason what that entails and tackles the old 12 00:00:54,245 --> 00:01:02,100 Kennard is writing, thinking, and also bop spotter. But right 13 00:01:02,100 --> 00:01:03,540 now, we're gonna take a quick break. 14 00:01:03,540 --> 00:01:05,780 You're gonna listen to some ads unless you're on the member 15 00:01:05,780 --> 00:01:08,500 feed. And when we return, we're gonna talk about a few of the 16 00:01:08,500 --> 00:01:11,855 things that have happened since the last time you heard from us. 17 00:01:13,055 --> 00:01:16,895 Hello? Is it five stories for you this week you're looking 18 00:01:16,895 --> 00:01:22,015 for? YouTube is reinstating the channels of creators previously 19 00:01:22,015 --> 00:01:25,535 suspended for violating COVID nineteen and election 20 00:01:24,830 --> 00:01:28,190 disinformation guidelines an alphabet lawyer says are no 21 00:01:28,190 --> 00:01:29,630 longer in force. 22 00:01:29,710 --> 00:01:32,670 This according to a document obtained by Fox News and 23 00:01:32,670 --> 00:01:35,870 prepared for the US house judiciary committee. Reflecting 24 00:01:35,870 --> 00:01:38,990 on the company's commitment to free expression, Daniel f 25 00:01:38,990 --> 00:01:42,375 Donovan, counsel for alphabet rights, YouTube will provide an 26 00:01:42,375 --> 00:01:45,655 opportunity for all creators to rejoin the platform if the 27 00:01:45,655 --> 00:01:48,455 company terminated their channels for repeated violations 28 00:01:48,455 --> 00:01:52,535 of COVID nineteen and elections integrity policies that are no 29 00:01:52,535 --> 00:01:55,920 longer in effect. YouTube takes seriously the importance of 30 00:01:55,920 --> 00:01:59,360 protecting free expression, the document states elsewhere, and 31 00:01:59,360 --> 00:02:02,880 access to a range of viewpoints. The document also explicitly 32 00:02:02,880 --> 00:02:06,335 points out that YouTube has not, does not, and will not employ 33 00:02:06,335 --> 00:02:09,295 any kind of fact checking or labeling mechanism in its 34 00:02:09,295 --> 00:02:12,735 software. At time of writing, no list of the channels to be 35 00:02:12,735 --> 00:02:16,095 potentially reinstated has been published, but I bet it's not 36 00:02:16,095 --> 00:02:18,255 that hard to figure out who might be on it. 37 00:02:18,255 --> 00:02:24,080 In completely unrelated news, Alex Jones recently appeared on 38 00:02:24,080 --> 00:02:29,120 his show sporting a Hitler mustache about which he said, I 39 00:02:29,120 --> 00:02:31,440 could tell you it had a wild effect on women. 40 00:02:31,440 --> 00:02:32,240 Jason Oberholtzer: Ew. 41 00:02:33,360 --> 00:02:36,325 Mike Rugnetta: The US Secret Service shut down a high powered 42 00:02:36,325 --> 00:02:39,045 cellular network that they claimed posed a threat to tri 43 00:02:39,045 --> 00:02:43,125 state area mobile communications this week. CBS News reports 44 00:02:43,125 --> 00:02:46,405 that, quote, law enforcement discovered 300 SIM servers over 45 00:02:46,405 --> 00:02:49,850 a 100,000 SIM cards, enabling encrypted anonymous 46 00:02:49,850 --> 00:02:53,450 communication and capable of sending 30,000,000 text messages 47 00:02:53,450 --> 00:02:56,890 per minute that could have, again, allegedly disabled cell 48 00:02:56,890 --> 00:02:59,450 phone towers and launched a distributed denial of service 49 00:02:59,450 --> 00:03:02,490 attack with the ability to block emergency communications like 50 00:03:02,490 --> 00:03:06,335 EMS and police dispatch, end quote. Secret service claims the 51 00:03:06,335 --> 00:03:09,375 operation was well funded and possibly under control of state 52 00:03:09,375 --> 00:03:12,975 actors looking to cause trouble for UN week in New York. 53 00:03:13,055 --> 00:03:16,140 Independent tech auditors and security analysts are not so 54 00:03:16,140 --> 00:03:19,500 convinced. Well funded, yes, but capable of causing such 55 00:03:19,500 --> 00:03:23,420 widespread havoc in New York City of all places, not so much. 56 00:03:23,500 --> 00:03:25,660 There is nothing about this infrastructure that would be 57 00:03:25,660 --> 00:03:29,580 hugely disruptive or damaging to mobile phone networks, writes t 58 00:03:29,580 --> 00:03:32,995 profit, the self described telecom informer for Hacker 59 00:03:32,995 --> 00:03:41,555 Magazine 2,600 on Blue Sky. BookTok has managed to shoot 60 00:03:41,555 --> 00:03:46,110 Timothy Snyder's lean but weighty 2017 book on tyranny to 61 00:03:46,110 --> 00:03:49,230 the top of indie bookshop sales lists over the last few months. 62 00:03:49,230 --> 00:03:52,750 On Tyranny's bullet point style format and short chapters, 63 00:03:52,750 --> 00:03:55,870 writes Laura Miller for Slate, make it easy to break into 64 00:03:55,870 --> 00:03:58,830 nuggets of exhortation. A particular favorite is lesson 65 00:03:58,830 --> 00:04:03,175 number one, do not obey in advance, urging individuals and 66 00:04:03,175 --> 00:04:07,095 institutions not to appease authoritarian governments before 67 00:04:07,095 --> 00:04:10,775 they are even asked to. Some fans on TikTok temporarily turn 68 00:04:10,775 --> 00:04:14,135 over their accounts to On Tyranny, reading one chapter 69 00:04:14,135 --> 00:04:17,390 aloud per video until they've narrated the whole thing. 70 00:04:18,910 --> 00:04:22,670 Speaking of TikTok, an alleged so called framework deal has 71 00:04:22,670 --> 00:04:25,790 been penned regarding the sale of the Chinese owned platform to 72 00:04:25,790 --> 00:04:29,470 domestic concerns. The US government and ByteDance have 73 00:04:29,470 --> 00:04:32,315 brokered a forthcoming deal whereby Oracle, Silver Lake 74 00:04:32,315 --> 00:04:35,435 Technology Management, and Andreessen Horowitz would 75 00:04:35,435 --> 00:04:39,595 oversee TikTok's US operations. This group would have an 80% 76 00:04:39,595 --> 00:04:42,715 ownership share, and a member of the board would be appointed by 77 00:04:42,715 --> 00:04:45,630 the US government. President Trump has also suggested Fox 78 00:04:45,630 --> 00:04:49,550 News Baron Rupert Murdoch will likely be involved somehow. The 79 00:04:49,550 --> 00:04:53,310 US based owners would lease TikTok's infamous algorithm, 80 00:04:53,390 --> 00:04:56,845 which Oracle would oversee and, quote, retrain. 81 00:04:57,085 --> 00:05:01,165 Larry Ellison, CTO and founder of Oracle, has also recently 82 00:05:01,165 --> 00:05:04,285 financed a number of large scale media mergers with his son, 83 00:05:04,285 --> 00:05:08,205 David Ellison. Paramount Skydance controls CBS, Paramount 84 00:05:08,205 --> 00:05:11,480 Pictures, and the streamer Paramount Plus. The Ellisons are 85 00:05:11,480 --> 00:05:14,760 also allegedly eyeing a takeover of Warner Brothers Discovery, 86 00:05:14,840 --> 00:05:28,425 which owns, among other things, CNN. And finally, get ready for 87 00:05:28,425 --> 00:05:29,705 a really good sentence. You ready? 88 00:05:29,705 --> 00:05:34,425 Ready for this good sentence? Here we go. Limewire, relaunched 89 00:05:34,425 --> 00:05:37,865 as an NFT marketplace, has purchased the rights to the 90 00:05:37,865 --> 00:05:43,510 infamous fire festival brand. The New York Times reports that 91 00:05:43,510 --> 00:05:47,670 the purchase was made for 245,000 US dollars in an eBay 92 00:05:47,670 --> 00:05:50,950 auction. It is unclear what Limewire fire will become. 93 00:05:51,185 --> 00:05:54,465 The music downloader turned NFT peddler is apparently aiming for 94 00:05:54,465 --> 00:05:57,105 something that, quote, expands beyond the digital realm and 95 00:05:57,105 --> 00:06:00,625 taps into real world experiences, community, and 96 00:06:00,625 --> 00:06:04,385 surprise, a thing which no doubt aligns well with the fire 97 00:06:04,385 --> 00:06:11,070 festival brand. Ew. In show news this week, if you ordered a t 98 00:06:11,070 --> 00:06:14,030 shirt, they are being printed next week. Once the print is 99 00:06:14,030 --> 00:06:16,510 done, they will head to Neverpost HQ where they will be 100 00:06:16,510 --> 00:06:22,155 packed and shipped one by one by hand with love. We will also 101 00:06:22,155 --> 00:06:25,995 have very few stock designs available at the end of that 102 00:06:25,995 --> 00:06:26,635 process. 103 00:06:26,635 --> 00:06:29,595 I'm gonna let you know in the show news portion of future 104 00:06:29,595 --> 00:06:32,715 episodes when and where you can snag those if you missed out. 105 00:06:32,715 --> 00:06:35,115 But when I say very few, I really mean it. We're gonna 106 00:06:35,115 --> 00:06:40,630 have, like, fewer than 10 stock shirts. And finally, holy cow, 107 00:06:40,950 --> 00:06:45,910 we are a signal podcast award finalist in the technology 108 00:06:45,910 --> 00:06:47,270 category. That is fun. 109 00:06:47,270 --> 00:06:51,155 Heck yeah. If you could please go vote for us, we would love 110 00:06:51,155 --> 00:06:53,795 that. We'll put a link in the show notes. We are up against 111 00:06:53,795 --> 00:06:57,315 some really rad folks, including close all tabs, who you may 112 00:06:57,315 --> 00:07:00,995 remember from our hentai segment and kill switch of whom we're 113 00:07:00,995 --> 00:07:03,630 just generally fans. But please go vote for us. 114 00:07:03,630 --> 00:07:06,910 We will love you forever. Signal awards, technology category, 115 00:07:06,910 --> 00:07:09,790 there's a link in the show notes. Okay. That's the news I 116 00:07:09,790 --> 00:07:12,830 have for you this week. In this episode, Tory talks with Jason 117 00:07:12,830 --> 00:07:14,430 about AI in the classroom. 118 00:07:14,430 --> 00:07:19,855 But first, BopSpotter is a project by Riley Walls, and it's 119 00:07:19,855 --> 00:07:25,295 described this way. Somewhere in the Mission District of San 120 00:07:25,295 --> 00:07:29,455 Francisco is a microphone pointed down at the street 121 00:07:29,455 --> 00:07:48,125 below. It is using a Shazam manner. So in our interstitials 122 00:07:48,125 --> 00:07:54,285 this week, Hans took it upon himself to recreate what he 123 00:07:54,285 --> 00:08:00,450 imagined to be the sonic environment at the time of some 124 00:08:00,450 --> 00:08:04,850 spotting. So what you are about to hear are not field 125 00:08:04,850 --> 00:08:09,970 recordings, but carefully crafted audio collages 126 00:09:09,235 --> 00:10:32,410 Jason Oberholtzer: Midnight, 12AM. 03:30AM. So I'm sitting 127 00:10:32,410 --> 00:10:36,090 here at my desk today, watching the leaves slowly change color 128 00:10:36,090 --> 00:10:39,115 when an email comes in, From friend of the show, Tori 129 00:10:39,115 --> 00:10:42,635 Dominguez Peek, who submitted to us a year ago a piece you might 130 00:10:42,635 --> 00:10:46,875 remember, wherein AI chatbot companies reached out to her 131 00:10:47,035 --> 00:10:50,955 with the proposition of turning her deceased mother into an AI 132 00:10:50,955 --> 00:10:56,790 chatbot. Well, Tori is back with another piece. I asked, what's 133 00:10:56,790 --> 00:10:57,430 it about? 134 00:10:57,430 --> 00:11:00,950 No one would let me know. Tori wanted to tell me herself. So 135 00:11:00,950 --> 00:11:04,390 please, welcome back to Neverpost, Tori. 136 00:11:04,565 --> 00:11:06,485 Tori Dominguez-Peak: Hey, Jason. Thanks so much for bringing me 137 00:11:06,485 --> 00:11:06,885 on. 138 00:11:06,885 --> 00:11:09,205 Jason Oberholtzer: I'm excited to learn what I'm about to 139 00:11:09,205 --> 00:11:09,685 learn. 140 00:11:10,165 --> 00:11:15,045 Tori Dominguez-Peak: So today, I have a tale for you about AI on 141 00:11:15,045 --> 00:11:21,410 college campuses and Brazilian Portuguese and solving crimes. 142 00:11:24,930 --> 00:11:29,010 Jason Oberholtzer: All three of my biggest interests. Let's get 143 00:11:29,010 --> 00:11:29,410 started. 144 00:11:29,410 --> 00:11:32,215 Tori Dominguez-Peak: Story in three acts, the Aspheric life. 145 00:11:33,735 --> 00:11:37,175 So Jason, like you said, fall is in full swing. 146 00:11:37,175 --> 00:11:37,895 Jason Oberholtzer: Absolutely. 147 00:11:37,895 --> 00:11:39,735 Tori Dominguez-Peak: I have I have purchased pumpkin spice 148 00:11:39,735 --> 00:11:43,495 lattes. The leaves are crunching. Sure. Hans has been 149 00:11:43,495 --> 00:11:44,215 cooking beans. 150 00:11:44,580 --> 00:11:46,900 Jason Oberholtzer: Hans has been cooking beans. I'm up to five or 151 00:11:46,900 --> 00:11:48,820 six layers every time I leave the house. 152 00:11:48,820 --> 00:11:51,860 Tori Dominguez-Peak: And with fall happening comes a new 153 00:11:51,860 --> 00:11:56,020 semester on college campuses everywhere across The US. And so 154 00:11:56,020 --> 00:12:00,165 kind of with that comes the renewed conversation that people 155 00:12:00,165 --> 00:12:05,045 have been having about AI in education, and is it cheating, 156 00:12:05,285 --> 00:12:10,245 and like all of the things. Sure. So earlier this year, the 157 00:12:10,245 --> 00:12:14,540 New Yorker ran this piece with the title, everyone is cheating 158 00:12:14,540 --> 00:12:18,700 through college. And the whole crux of it was just talking 159 00:12:18,700 --> 00:12:22,540 about like how commonplace it is for students to use ChatGPT or 160 00:12:22,540 --> 00:12:26,220 to use it to like help with assignments or even going as a 161 00:12:26,220 --> 00:12:28,845 part of like, write my term paper for me. 162 00:12:28,925 --> 00:12:29,325 Jason Oberholtzer: Mhmm. 163 00:12:29,325 --> 00:12:31,085 Tori Dominguez-Peak: Blurring the line between getting it to 164 00:12:31,085 --> 00:12:35,965 help you and then like, what has become plagiarism. And like, the 165 00:12:35,965 --> 00:12:39,165 most stunning part about that article to me that I still think 166 00:12:39,165 --> 00:12:42,040 about is they did a very small survey. It was like a thousand 167 00:12:42,040 --> 00:12:46,040 college students. But 90% of them had said they had used 168 00:12:46,040 --> 00:12:49,400 ChatGPT to help with homework assignments. 169 00:12:49,720 --> 00:12:51,160 Jason Oberholtzer: And you found this surprising? 170 00:12:51,640 --> 00:12:54,085 Tori Dominguez-Peak: Yeah. Mean, just the the number was wild. I 171 00:12:54,085 --> 00:12:57,525 knew it would be over 50. When I saw 90, I was like, oh, we're 172 00:12:57,525 --> 00:12:58,885 cooked. Okay. 173 00:13:00,165 --> 00:13:04,165 But I just couldn't stop thinking about like, what 174 00:13:04,165 --> 00:13:08,670 happens when we are letting a piece of technology kind of do 175 00:13:08,670 --> 00:13:11,390 the thinking for us or do the talking for us 176 00:13:11,710 --> 00:13:12,190 Jason Oberholtzer: Sure. 177 00:13:12,190 --> 00:13:16,590 Tori Dominguez-Peak: On that scale. And so I decided to talk 178 00:13:16,590 --> 00:13:17,390 to someone. 179 00:13:17,550 --> 00:13:20,430 Megan Fritts: When I'm working on a new paper, I'll be, you 180 00:13:20,430 --> 00:13:23,605 know, typing up some section and realize I don't know how to 181 00:13:23,605 --> 00:13:27,285 phrase it. And that tells me, oh, okay, I need to go figure 182 00:13:27,285 --> 00:13:30,005 out what I actually think here. Because if I can't write about 183 00:13:30,005 --> 00:13:33,205 it, that indicates a lack of understanding there. So I think 184 00:13:33,205 --> 00:13:36,930 what we're missing when we stop writing ourselves is the ability 185 00:13:36,930 --> 00:13:39,330 to check ourselves for misunderstandings. 186 00:13:39,650 --> 00:13:41,810 Tori Dominguez-Peak: So that's a professor I interviewed. She's 187 00:13:41,810 --> 00:13:45,250 professor Megan Fritz. She teaches philosophy at University 188 00:13:45,250 --> 00:13:47,490 of Arkansas at Little Rock. 189 00:13:47,730 --> 00:13:48,930 Jason Oberholtzer: I find it really interesting here that 190 00:13:48,930 --> 00:13:52,185 she's using misunderstandings as a framing. As if like we are 191 00:13:52,185 --> 00:13:56,505 interrogating our own brain when we set down to write. Yeah. That 192 00:13:56,505 --> 00:14:00,345 feels pretty right to me. Tori, can I ask you a question 193 00:14:00,345 --> 00:14:00,985 quickly? 194 00:14:01,145 --> 00:14:01,865 Tori Dominguez-Peak: Sure. 195 00:14:02,425 --> 00:14:03,865 Jason Oberholtzer: Did you cheat in college? 196 00:14:05,070 --> 00:14:06,910 Tori Dominguez-Peak: No. I cheated in high school though. 197 00:14:06,910 --> 00:14:09,310 Jason Oberholtzer: Did you cheat in ways that you think 198 00:14:09,470 --> 00:14:13,070 fundamentally changed your understanding or inhibited your 199 00:14:13,070 --> 00:14:15,630 understanding of what you were doing? Or did they just help you 200 00:14:15,630 --> 00:14:16,430 get a better grade? 201 00:14:19,015 --> 00:14:20,855 Tori Dominguez-Peak: Okay. Let me just lay out the one scenario 202 00:14:20,855 --> 00:14:23,335 that I cheated and I could help you could help me here. 203 00:14:23,335 --> 00:14:23,735 Jason Oberholtzer: Mhmm. 204 00:14:23,735 --> 00:14:27,095 Tori Dominguez-Peak: So I was failing chemistry. And so one 205 00:14:27,095 --> 00:14:29,975 thing I noticed was that kids who took a long time to take the 206 00:14:29,975 --> 00:14:34,410 test, it was like third period. And then you had fourth period 207 00:14:34,410 --> 00:14:37,450 and then there was lunch. And so I was like, oh, if I just take 208 00:14:37,450 --> 00:14:41,850 forever on this test, I can finish it later and study for it 209 00:14:41,850 --> 00:14:43,770 during fourth period. 210 00:14:43,770 --> 00:14:44,250 Jason Oberholtzer: Yeah. 211 00:14:44,250 --> 00:14:46,330 Tori Dominguez-Peak: And so I was just like, oh, it's just 212 00:14:46,330 --> 00:14:49,585 taking me forever. I have a headache. Like, I guess I have 213 00:14:49,585 --> 00:14:51,905 to come back for lunch. And like, come back during lunch and 214 00:14:51,905 --> 00:14:55,585 finish up this test. Bell rings, I go to fourth period. 215 00:14:56,065 --> 00:14:59,825 It's like my study hall period. I'm like study I'm like going 216 00:14:59,825 --> 00:15:03,905 over the stoichiometry formulas. I'm hitting the books. And then 217 00:15:04,290 --> 00:15:07,010 I lunch happens. I go back to the chem room. 218 00:15:07,250 --> 00:15:10,530 I actually reanswered some stuff because I was like, I have it in 219 00:15:10,530 --> 00:15:11,570 my brain fresh now. 220 00:15:11,570 --> 00:15:12,210 Jason Oberholtzer: Beautiful. 221 00:15:12,210 --> 00:15:14,610 Tori Dominguez-Peak: And I took it. Is that cheating? It kind of 222 00:15:14,610 --> 00:15:15,090 is. 223 00:15:15,090 --> 00:15:16,690 Jason Oberholtzer: I well, yeah. I mean, by the letter of the 224 00:15:16,690 --> 00:15:19,625 law, but it's gamesmanship is what I think it is. Like, you 225 00:15:19,625 --> 00:15:20,105 still 226 00:15:20,265 --> 00:15:20,985 Tori Dominguez-Peak: the player. 227 00:15:20,985 --> 00:15:23,065 Jason Oberholtzer: Like Yeah. You still walked in there with 228 00:15:23,065 --> 00:15:26,505 the requisite knowledge or the understanding of how to find the 229 00:15:26,505 --> 00:15:29,465 knowledge, and you applied your brain to the problems at hand 230 00:15:29,465 --> 00:15:33,260 and Yeah. Got a better grade to them. Under the framework that 231 00:15:33,260 --> 00:15:37,580 professor Fritz is setting out here, that seems to be a 232 00:15:37,580 --> 00:15:41,740 different kind of malfeasance in the classroom. And one that I 233 00:15:41,740 --> 00:15:43,260 perhaps look more fondly on. 234 00:15:43,260 --> 00:15:44,300 I cheated constantly. 235 00:15:44,535 --> 00:15:44,935 Tori Dominguez-Peak: Okay. 236 00:15:44,935 --> 00:15:47,575 Jason Oberholtzer: Either buy more time because I had not 237 00:15:47,575 --> 00:15:52,055 prepared myself sufficiently on time, or route myself around 238 00:15:52,055 --> 00:15:55,015 rote memorization which I considered to be an impediment 239 00:15:55,015 --> 00:15:57,815 to learning and not a benchmark by which you measured learning. 240 00:15:58,340 --> 00:16:01,300 And honestly, resented having to regurgitate things that one 241 00:16:01,300 --> 00:16:04,180 could find in a book onto a page later. So I count neither of 242 00:16:04,180 --> 00:16:07,140 those things as cheating. But like the thinking that I had to 243 00:16:07,140 --> 00:16:09,780 do with that information still happened in my head and hit the 244 00:16:09,780 --> 00:16:09,940 page. 245 00:16:10,975 --> 00:16:13,135 Tori Dominguez-Peak: That's the thing. Like, I was still 246 00:16:13,135 --> 00:16:13,935 studying. 247 00:16:14,095 --> 00:16:14,335 Jason Oberholtzer: Yeah. 248 00:16:14,335 --> 00:16:16,575 Tori Dominguez-Peak: I just convinced my chem teacher that I 249 00:16:16,575 --> 00:16:20,975 had a headache when I didn't. Perfect. Right? Yeah. But I was 250 00:16:20,975 --> 00:16:23,855 not plugging formulas in the chattypety and being like, what 251 00:16:23,855 --> 00:16:25,455 are the answers to these questions? 252 00:16:26,170 --> 00:16:29,290 Which I think is kind of different. 253 00:16:29,450 --> 00:16:30,010 Jason Oberholtzer: Sure. 254 00:16:30,090 --> 00:16:31,450 Tori Dominguez-Peak: And professor Fritz wrote this 255 00:16:31,450 --> 00:16:35,130 article for the Chronicle of Higher Education, and I will 256 00:16:35,130 --> 00:16:38,010 read you out loud an excerpt because I think it says 257 00:16:38,010 --> 00:16:41,185 something like really poignant. It says, we're not simply 258 00:16:41,185 --> 00:16:44,625 frustrated by just trying to police AI use or the labor of 259 00:16:44,625 --> 00:16:48,065 having to write up students for academic dishonesty or the way 260 00:16:48,065 --> 00:16:51,345 that reading student work has become a rather nihilistic task. 261 00:16:51,950 --> 00:16:55,630 Our frustration is not merely that we don't care what AI has 262 00:16:55,630 --> 00:16:59,950 to say and therefore get bored grading papers. It is that we 263 00:16:59,950 --> 00:17:03,710 actively miss reading the thoughts of our human students. 264 00:17:05,105 --> 00:17:10,545 Jason Oberholtzer: That is so dispiriting. Wow. Famously easy 265 00:17:10,545 --> 00:17:11,665 job gets easier. 266 00:17:11,665 --> 00:17:15,185 Tori Dominguez-Peak: It's kind of a bummer. And she is like 267 00:17:15,185 --> 00:17:18,225 hitting something here. Like, when you write down something on 268 00:17:18,225 --> 00:17:21,970 a page, you are transmuting your thoughts onto a page. And when 269 00:17:21,970 --> 00:17:24,930 you turn it in, your instructor is reading your thoughts. Like, 270 00:17:24,930 --> 00:17:28,930 we know there is a relationship between writing and thinking. 271 00:17:28,930 --> 00:17:31,010 Jason Oberholtzer: Right. Exactly. And I like that she's 272 00:17:31,010 --> 00:17:33,405 extending it to like a relationship between people on 273 00:17:33,405 --> 00:17:35,725 either side of that activity. You will have a relationship 274 00:17:35,725 --> 00:17:37,725 with your thoughts to the writing and the people reading 275 00:17:37,725 --> 00:17:40,045 your writing have a relationship to those thoughts and therefore 276 00:17:40,045 --> 00:17:43,885 you. And I think that is probably one of the great joys 277 00:17:43,885 --> 00:17:46,445 of teaching is to be in relationship with those people 278 00:17:46,445 --> 00:17:47,245 via their thoughts. 279 00:17:48,210 --> 00:17:51,170 Tori Dominguez-Peak: Yeah. And I I kind of got to this sort of 280 00:17:51,170 --> 00:17:54,450 thing like writing and thinking and like how they're related. 281 00:17:54,530 --> 00:17:57,970 And I asked her, hey, is writing basically the same thing as 282 00:17:57,970 --> 00:18:02,035 thinking or are they kind of intertwined in some way? And she 283 00:18:02,035 --> 00:18:05,635 was like, yeah, they definitely are related. And that we write 284 00:18:05,635 --> 00:18:08,915 it to document our thoughts, but we also write to come up with 285 00:18:08,915 --> 00:18:09,555 thoughts. 286 00:18:09,555 --> 00:18:13,475 It's kind of this really unique like both and relationship. 287 00:18:13,555 --> 00:18:15,670 Jason Oberholtzer: Yeah. You definitely you write to find 288 00:18:15,670 --> 00:18:18,310 when you have to stop writing because you don't know what's 289 00:18:18,310 --> 00:18:20,870 there, and then you have to go think about it. 290 00:18:21,110 --> 00:18:23,750 Tori Dominguez-Peak: Yeah. I mean, whenever I write a script 291 00:18:23,750 --> 00:18:27,510 for Neverpost, I will write like a paragraph and then walk away, 292 00:18:27,510 --> 00:18:29,990 and then come back like two days later. And then, you know, it's 293 00:18:29,990 --> 00:18:33,645 just it's a slow process. But it's because I'm in relation 294 00:18:33,645 --> 00:18:38,685 with my brain and trying to figure it out. And then I'm also 295 00:18:38,685 --> 00:18:42,365 doing something that is also very thinking heavy, which is 296 00:18:42,365 --> 00:18:43,725 I'm learning a second language. 297 00:18:44,300 --> 00:18:49,020 I spoke more Spanish as a kid growing up in a Latin American 298 00:18:49,020 --> 00:18:53,100 household. And then I lost it as it became a teenager. And then 299 00:18:53,100 --> 00:18:56,620 I'm trying to get back into it as an adult. Mhmm. And so like, 300 00:18:56,935 --> 00:19:00,215 when I speak Spanish in my adult learner's Spanish class, I'm 301 00:19:00,215 --> 00:19:04,375 like having a thought in English and then translating it in my 302 00:19:04,375 --> 00:19:07,975 head and then saying it out loud to them in Spanish, right, to my 303 00:19:07,975 --> 00:19:08,535 instructor. 304 00:19:08,535 --> 00:19:11,580 Sure. And then she says something back to me in Spanish, 305 00:19:11,580 --> 00:19:14,140 and then I'm trying to translate it into English in my head. And 306 00:19:14,140 --> 00:19:18,780 it's just this very like mechanical relationship, and 307 00:19:18,780 --> 00:19:21,980 it's not easy. And I feel a little bit like a baby alerting 308 00:19:21,980 --> 00:19:23,820 to speak for the first time. 309 00:19:23,820 --> 00:19:26,395 Jason Oberholtzer: Yeah. Okay. So if I'm if I'm hearing this 310 00:19:26,395 --> 00:19:30,395 right then, it's like you're sort of seeing the the the gap 311 00:19:30,395 --> 00:19:33,035 between the thought that you're having and your ability to 312 00:19:33,035 --> 00:19:38,555 articulate it in this seconds now. I guess, re seconds, third, 313 00:19:38,555 --> 00:19:42,050 second, again, language. Probably especially because at 314 00:19:42,050 --> 00:19:43,730 one point, it was not there. 315 00:19:43,730 --> 00:19:46,450 And you're feeling this, like, this break in the chain between 316 00:19:46,450 --> 00:19:49,410 you having a thought and being able to articulate that. Yeah. 317 00:19:49,410 --> 00:19:53,665 And that feels sort of similar to what you think is happening 318 00:19:53,665 --> 00:19:58,065 with the insertion of these AI tools into the way people are 319 00:19:58,065 --> 00:19:59,265 writing these days. 320 00:19:59,425 --> 00:20:01,105 Tori Dominguez-Peak: Yeah. And professor Fritz kind of brought 321 00:20:01,105 --> 00:20:04,865 this up that AI is kind of this middleman between thought and 322 00:20:04,865 --> 00:20:06,305 language that's never been there before. 323 00:20:06,760 --> 00:20:08,920 Megan Fritts: The difference between being a native speaker 324 00:20:08,920 --> 00:20:11,560 of a language and being someone who's learned a language, I 325 00:20:11,560 --> 00:20:15,720 think is is the perfect example of what we are risking, when we 326 00:20:15,720 --> 00:20:21,465 use generative AI for our writing and, speaking, that we 327 00:20:21,465 --> 00:20:24,905 risk going from this kind of native speaker status to a a a 328 00:20:24,905 --> 00:20:27,465 situation where we have to if we want to have these skills at 329 00:20:27,465 --> 00:20:30,265 all, we have to reteach it to ourselves in a in a really 330 00:20:30,265 --> 00:20:31,225 artificial way. 331 00:20:31,465 --> 00:20:34,690 Jason Oberholtzer: So is the concern there almost like what 332 00:20:34,690 --> 00:20:37,570 happens in its absence? Or like it's like what happens if you're 333 00:20:37,570 --> 00:20:41,170 without your Spanish English dictionary as it were Yeah. That 334 00:20:41,170 --> 00:20:43,330 you'd actually don't have control over the language. 335 00:20:43,730 --> 00:20:47,970 Tori Dominguez-Peak: Yeah. It it feels like if we outsource for 336 00:20:47,970 --> 00:20:52,095 example, you didn't do the reading for your college class, 337 00:20:52,095 --> 00:20:53,855 but you have to write paper about it and you're just like, 338 00:20:53,935 --> 00:20:57,935 shitty, this paper about this thing. You didn't engage with 339 00:20:57,935 --> 00:21:01,135 the text. Yeah. You didn't transmit the text into writing. 340 00:21:01,375 --> 00:21:03,855 And so like, you're probably not even remember what that class 341 00:21:03,855 --> 00:21:07,310 was about. Yeah. You're losing some type of critical like brain 342 00:21:07,310 --> 00:21:11,790 step that helps you metabolize information. Does that make 343 00:21:11,790 --> 00:21:14,510 sense? Like, I think writing helps you metabolize 344 00:21:14,510 --> 00:21:15,390 information. 345 00:21:15,550 --> 00:21:20,105 And like, if I could just be candid, I think writing is kind 346 00:21:20,105 --> 00:21:21,705 of mentally painful for me. 347 00:21:21,705 --> 00:21:23,305 Jason Oberholtzer: Oh, yeah. That's the whole thing about 348 00:21:23,305 --> 00:21:23,705 writing. 349 00:21:23,705 --> 00:21:24,185 Tori Dominguez-Peak: Like It 350 00:21:24,185 --> 00:21:25,305 Jason Oberholtzer: hurts and it's bad. 351 00:21:25,305 --> 00:21:27,305 Tori Dominguez-Peak: It hurts and it's bad, and that's the 352 00:21:27,305 --> 00:21:30,425 whole point. And I will write something and I'll walk away and 353 00:21:30,425 --> 00:21:33,490 I come I will come back three days later, And I'm just like, 354 00:21:33,490 --> 00:21:35,970 oh, this psychologically hurts. But then you get into a groove 355 00:21:35,970 --> 00:21:39,170 and then it feels good and you've kind of metabolized your 356 00:21:39,170 --> 00:21:42,450 thoughts and it comes out and it feels great. And there's just 357 00:21:42,450 --> 00:21:45,970 such an emotional experience in that. And so when you're just 358 00:21:45,970 --> 00:21:51,525 like, hey chat, GBT, write this podcast episode, summarize the 359 00:21:51,525 --> 00:21:53,925 notes from this interview I have with the source, or write the 360 00:21:53,925 --> 00:21:55,125 interview questions. 361 00:21:55,285 --> 00:21:55,605 Jason Oberholtzer: Yeah. 362 00:21:55,605 --> 00:21:58,965 Tori Dominguez-Peak: You're kind of losing some threads. 363 00:21:59,525 --> 00:22:01,605 Jason Oberholtzer: Yeah. You know, not to be the person who's 364 00:22:01,605 --> 00:22:04,980 constantly defending cheating here. But when you brought up 365 00:22:05,140 --> 00:22:08,740 writing the report in the book you have not read, that is 366 00:22:08,740 --> 00:22:11,380 something I believe a It's lot of us have a classic move. You 367 00:22:11,380 --> 00:22:14,420 have to try it at some point. And when you do that, you 368 00:22:14,420 --> 00:22:17,845 marshal enough information about the book, you skim it, you look 369 00:22:17,845 --> 00:22:20,325 up some notes, you try to find as much as you can to walk in 370 00:22:20,325 --> 00:22:20,885 there. 371 00:22:21,125 --> 00:22:26,325 But the thing you're doing when you walk in there is like using 372 00:22:26,325 --> 00:22:30,005 your brain. It is learning. It is performing. It is something 373 00:22:30,005 --> 00:22:33,670 that requires you to undergo a process that will help you be a 374 00:22:33,670 --> 00:22:37,190 better thinker and communicator in the future because you are 375 00:22:37,190 --> 00:22:42,230 actually doing a task. And to me, what feels scary about this 376 00:22:42,310 --> 00:22:45,910 is that it is removing the mental load of doing the task. 377 00:22:46,325 --> 00:22:49,685 Mhmm. Not so much cheating like the information, which is like 378 00:22:49,685 --> 00:22:52,565 the veneer around which we all do the process of learning, but 379 00:22:52,565 --> 00:22:54,885 it's removing the actual mental task, which is the point of 380 00:22:54,885 --> 00:22:58,485 sitting down and being a part of a university or a class or 381 00:22:58,485 --> 00:23:02,500 whatever the case may be. So I feel like this has to feel 382 00:23:02,500 --> 00:23:04,900 different for teachers. Like, they've walked into campuses 383 00:23:04,900 --> 00:23:07,460 every fall for millennia and been like, alright, everyone's 384 00:23:07,460 --> 00:23:09,940 cheating. How do I make sure that I know that they are smart 385 00:23:09,940 --> 00:23:12,900 enough to continue down the road after they continue cheating? 386 00:23:12,900 --> 00:23:15,620 Like, I I that's probably unlikely that people believe 387 00:23:15,620 --> 00:23:19,035 they have a complete fail proof method to stop all cheating 388 00:23:19,035 --> 00:23:21,595 forever. Do you think that professor Fritz or other 389 00:23:21,595 --> 00:23:25,515 professors are feeling like this is a different kind of stop 390 00:23:25,515 --> 00:23:28,155 cheating move they need to make? 391 00:23:28,635 --> 00:23:34,070 Tori Dominguez-Peak: Yeah. So professor Fritz has kind of gone 392 00:23:34,630 --> 00:23:40,070 nuclear. Okay. She instated a policy in her classroom that is 393 00:23:40,070 --> 00:23:43,750 just like, I am banning all AI from my classroom. Okay. 394 00:23:43,750 --> 00:23:47,645 It includes ChatGPT. It even includes like Grammarly, which I 395 00:23:47,645 --> 00:23:50,925 use Grammarly to like make sure that my emails aren't misspelled 396 00:23:50,925 --> 00:23:51,485 or whatever. 397 00:23:51,485 --> 00:23:52,045 Mike Rugnetta: Oh, interesting. Not even 398 00:23:52,045 --> 00:23:53,645 Tori Dominguez-Peak: too many exclamation points. She's like, 399 00:23:53,645 --> 00:23:57,325 nope, not even that. Because Grammarly can suggest rewrites. 400 00:23:57,325 --> 00:23:57,645 Jason Oberholtzer: Yeah. 401 00:23:57,645 --> 00:23:59,885 Tori Dominguez-Peak: And that in itself is kind of generative AI. 402 00:23:59,965 --> 00:24:00,125 Jason Oberholtzer: Yeah. 403 00:24:00,860 --> 00:24:03,100 Tori Dominguez-Peak: And so I asked her like, okay, so how are 404 00:24:03,100 --> 00:24:04,060 you enforcing this? 405 00:24:04,060 --> 00:24:04,460 Jason Oberholtzer: Sure. 406 00:24:04,460 --> 00:24:06,620 Tori Dominguez-Peak: It just seems like a lot of work to 407 00:24:06,620 --> 00:24:11,100 enforce this. And she has a very interesting way of going about 408 00:24:11,100 --> 00:24:16,205 this. So at some point earlier in the semester, she has them 409 00:24:16,205 --> 00:24:19,885 write these short essay type assignments in class. They are 410 00:24:19,885 --> 00:24:24,205 handwritten, and they turn it into her right there, hard copy. 411 00:24:24,445 --> 00:24:27,245 And so it's zero chance of AI use. 412 00:24:27,245 --> 00:24:30,125 She had you write with a pen and paper. Yeah. And she keeps these 413 00:24:30,125 --> 00:24:33,740 essays as kind of evidence of like, this is how these people 414 00:24:33,740 --> 00:24:37,500 write. Oh. This is what your voice, your narrative voice is 415 00:24:37,500 --> 00:24:38,060 like. 416 00:24:38,780 --> 00:24:41,100 And so then later on in the semester, you turn in something 417 00:24:41,100 --> 00:24:45,795 electronically and it's got AI written stuff, she's gonna be 418 00:24:45,795 --> 00:24:47,395 like, this doesn't sound like you. 419 00:24:47,395 --> 00:24:51,075 Jason Oberholtzer: Okay. Does she do the comparison process 420 00:24:51,075 --> 00:24:54,435 herself or does she let AI do the comparisons? 421 00:24:54,755 --> 00:24:57,120 Tori Dominguez-Peak: So she kinda does both. Interesting. 422 00:24:57,200 --> 00:25:00,560 She does use eight different AI detection programs that she runs 423 00:25:00,560 --> 00:25:02,320 it through, which is Wow. It's a lot. 424 00:25:02,320 --> 00:25:03,680 Jason Oberholtzer: Does the school pay for these? 425 00:25:04,000 --> 00:25:06,000 Tori Dominguez-Peak: I don't know. That's great question. 426 00:25:06,800 --> 00:25:08,000 Don't seem cheap, do they? 427 00:25:08,000 --> 00:25:09,360 Jason Oberholtzer: No. I would imagine not. 428 00:25:11,285 --> 00:25:12,805 Tori Dominguez-Peak: And so that she runs it through all of 429 00:25:12,805 --> 00:25:18,005 those. She also just looks at it herself and is like, yeah, I 430 00:25:18,005 --> 00:25:18,725 could tell. 431 00:25:18,885 --> 00:25:20,805 Jason Oberholtzer: Yeah. Of course. Right? Like, teachers 432 00:25:20,805 --> 00:25:22,565 have been seeing this, like, forever. 433 00:25:22,805 --> 00:25:25,525 Tori Dominguez-Peak: But I did ask, like, can you just tell off 434 00:25:25,525 --> 00:25:28,820 the bat of a student's writing as AI generated just by looking 435 00:25:28,820 --> 00:25:34,340 at their paper? And she said, yes. And that there's usually a 436 00:25:34,340 --> 00:25:35,300 couple of clues. 437 00:25:35,300 --> 00:25:37,220 Jason Oberholtzer: Okay. Is this where you're coming from my Em 438 00:25:37,220 --> 00:25:41,285 dashes? Yeah. Alright. I'll listen to it at least. 439 00:25:41,525 --> 00:25:44,565 Tori Dominguez-Peak: The first clue is she calls them 50¢ 440 00:25:44,565 --> 00:25:49,125 words. Weird, like formal words that the typical 18 year old 441 00:25:49,125 --> 00:25:52,085 undergrad would just not be using. Okay. 442 00:25:52,840 --> 00:25:56,680 Megan Fritts: An example of that is like the word want, w o n t, 443 00:25:56,680 --> 00:26:01,160 where so you might use it in a sentence like, I want to take a 444 00:26:01,160 --> 00:26:03,640 walk in the morning. So it's like talking like an 445 00:26:03,640 --> 00:26:09,545 inclination. That's a 50¢ word that I I I would say most of my 446 00:26:09,545 --> 00:26:12,585 students probably aren't just casually using in their 447 00:26:12,585 --> 00:26:13,785 reflection writings. 448 00:26:14,025 --> 00:26:16,105 Jason Oberholtzer: Now you're coming from my vocabulary. 449 00:26:16,265 --> 00:26:17,705 First, my em dashes. 450 00:26:17,785 --> 00:26:19,385 Tori Dominguez-Peak: Are you want to use wants? 451 00:26:19,385 --> 00:26:22,970 Jason Oberholtzer: I mean, of course, I am. But like, where in 452 00:26:22,970 --> 00:26:26,410 an academic setting would I ever run across the need to express 453 00:26:26,410 --> 00:26:29,050 my feelings through the word want? 454 00:26:29,130 --> 00:26:31,370 Tori Dominguez-Peak: Yeah. Exactly. Clue number two, and 455 00:26:31,370 --> 00:26:34,810 you were pretty right about this, was Em dashes. Yeah. It's 456 00:26:34,810 --> 00:26:38,765 not just any Em dash because I I I feel you, like, it sucks that 457 00:26:38,765 --> 00:26:41,325 em dashes have become like this weird red flag and it's like, I 458 00:26:41,325 --> 00:26:42,285 like a good em dash. 459 00:26:42,285 --> 00:26:42,925 Jason Oberholtzer: Sure. 460 00:26:43,005 --> 00:26:46,285 Tori Dominguez-Peak: She said that there's a type of em dash 461 00:26:46,285 --> 00:26:48,045 that is a red flag to her. 462 00:26:48,540 --> 00:26:50,700 Megan Fritts: An acquaintance of mine, another philosophy 463 00:26:50,700 --> 00:26:54,460 professor, he calls those epiphany dashes, where you go 464 00:26:54,460 --> 00:26:58,380 from an ordinary, you know, thought like it's not just a 465 00:26:58,380 --> 00:27:02,380 walk em dash, it's a brainstorming session. This is, 466 00:27:02,380 --> 00:27:03,500 you know, you're having an epiphany. 467 00:27:04,055 --> 00:27:06,695 Jason Oberholtzer: Interesting. Yeah. So there's like an emotive 468 00:27:06,695 --> 00:27:09,175 component to the Emdash when it's used this way. 469 00:27:09,255 --> 00:27:11,095 Tori Dominguez-Peak: Some some sense it sounds like a LinkedIn 470 00:27:11,095 --> 00:27:14,215 post. You know what I mean? Like, has that type type of 471 00:27:14,215 --> 00:27:15,175 cadence for it. 472 00:27:15,175 --> 00:27:16,855 Jason Oberholtzer: Right. It's like It's sort of a it's like 473 00:27:16,855 --> 00:27:21,200 copywriting usage not Yeah. Yeah. Yeah. Oh, that's super 474 00:27:21,200 --> 00:27:21,760 interesting. 475 00:27:21,760 --> 00:27:23,680 Tori Dominguez-Peak: And then obviously, the third main clue 476 00:27:23,680 --> 00:27:26,720 is like she runs it through one of her software programs and 477 00:27:26,720 --> 00:27:30,880 it's like, bing, AI generated. But yeah, mean, it's kind of an 478 00:27:30,880 --> 00:27:31,600 intensive process. 479 00:27:32,175 --> 00:27:34,255 Jason Oberholtzer: Yeah. It sounds exhausting. I guess for 480 00:27:34,255 --> 00:27:36,895 everyone, I suppose. Like, are the students having a good time 481 00:27:36,895 --> 00:27:37,935 while this is happening? 482 00:27:38,415 --> 00:27:40,415 Tori Dominguez-Peak: They don't seem to be big fans. 483 00:27:40,415 --> 00:27:40,975 Jason Oberholtzer: Sure. 484 00:27:41,055 --> 00:27:43,775 Tori Dominguez-Peak: As you can imagine, a lot of them have 485 00:27:43,775 --> 00:27:47,500 reacted by saying, well, I don't get why you've banned it because 486 00:27:47,500 --> 00:27:50,860 my other professors and other classes don't care. So like, why 487 00:27:50,860 --> 00:27:51,740 should it matter? 488 00:27:51,740 --> 00:27:53,980 Jason Oberholtzer: Oh, jeez. Alright. Well, at least they're 489 00:27:53,980 --> 00:27:55,900 still learning something and that something is emotional 490 00:27:55,900 --> 00:27:56,780 manipulation. 491 00:27:57,260 --> 00:27:58,860 Tori Dominguez-Peak: Yeah. I guess they gotta learn to read 492 00:27:58,860 --> 00:27:59,660 the syllabus. 493 00:27:59,980 --> 00:28:00,380 Jason Oberholtzer: Yeah. 494 00:28:00,380 --> 00:28:04,355 Tori Dominguez-Peak: But it does kind of bring up something which 495 00:28:04,355 --> 00:28:09,315 is that, okay, some professors don't care. Professor Fritz very 496 00:28:09,315 --> 00:28:12,595 much does. Yeah. And so college students are kind of navigating 497 00:28:12,595 --> 00:28:15,235 this landscape where it's like in the same semester, they might 498 00:28:15,235 --> 00:28:18,080 have someone who's a real stickler about this stuff. And 499 00:28:18,080 --> 00:28:21,200 they may also have a different teacher who doesn't care. 500 00:28:21,280 --> 00:28:26,160 And it seems like they have to navigate all these individual AI 501 00:28:26,160 --> 00:28:27,120 policies. 502 00:28:27,440 --> 00:28:30,455 Jason Oberholtzer: Or just not use AI? That's one way to 503 00:28:30,455 --> 00:28:31,735 navigate all of them. 504 00:28:31,815 --> 00:28:35,815 Tori Dominguez-Peak: Or just not use it at all. But I asked Fritz 505 00:28:35,815 --> 00:28:38,535 about this like, oh, do you talk with other professors about 506 00:28:38,535 --> 00:28:41,815 handling this? And she said that she actually sits on a couple of 507 00:28:41,815 --> 00:28:43,895 AI related committees at her university. 508 00:28:44,190 --> 00:28:48,990 Megan Fritts: I think instructor uniformity and solidarity on 509 00:28:48,990 --> 00:28:52,990 this issue is pretty important for our students. I thought it 510 00:28:52,990 --> 00:28:56,670 was a good idea, I was excited to to to try to make this 511 00:28:56,670 --> 00:28:59,565 policy. But what ended up happening is that people just 512 00:28:59,565 --> 00:29:06,925 had such different views on AI use in higher education that it 513 00:29:06,925 --> 00:29:15,080 kind of just turned into debate every every meeting, and we we 514 00:29:15,080 --> 00:29:18,120 have not yet made any kind of a policy. 515 00:29:18,600 --> 00:29:20,920 Jason Oberholtzer: Okay. Well, I suppose that's predictable. It's 516 00:29:20,920 --> 00:29:24,360 academia. It's meetings. It's consensus. 517 00:29:24,360 --> 00:29:27,755 That is not necessarily easy to do, but, like, you know, I'm not 518 00:29:27,755 --> 00:29:30,475 gonna tip my hand on where I stand on this. I'm sure everyone 519 00:29:30,475 --> 00:29:33,515 is in deep mystery here. But like, if it's working for 520 00:29:33,915 --> 00:29:37,995 professor Fritz, like, just let her do her thing. It seems like 521 00:29:37,995 --> 00:29:38,475 it works. 522 00:29:38,475 --> 00:29:40,555 Tori Dominguez-Peak: Yeah. I mean, she thinks that she feels 523 00:29:40,555 --> 00:29:45,400 that her system works well for her. Yeah. But her way of doing 524 00:29:45,400 --> 00:29:49,800 things relies on knowing what these students write like, and 525 00:29:49,800 --> 00:29:52,360 knowing what they don't write like, and they're writing as a 526 00:29:52,360 --> 00:29:56,645 sort of fingerprint. But what happens when you can't really 527 00:29:56,645 --> 00:29:59,285 tell and you have to do some detective work? 528 00:29:59,445 --> 00:30:00,005 Jason Oberholtzer: Mhmm. 529 00:30:00,165 --> 00:30:02,405 Tori Dominguez-Peak: So I found a detective. 530 00:30:02,725 --> 00:30:04,325 Jason Oberholtzer: Alright, listeners. This is the most 531 00:30:04,325 --> 00:30:09,180 podcast break we will ever do after these messages, a 532 00:30:09,180 --> 00:30:09,740 detective. 533 00:30:26,435 --> 00:30:29,395 Rui Sousa-Silva: Even though we learn the same languages from 534 00:30:29,395 --> 00:30:33,235 the same books and we learn we can find the same words in the 535 00:30:33,235 --> 00:30:37,235 same dictionaries, the way each one of us uses language is 536 00:30:37,235 --> 00:30:42,040 different. So we have a let's call it a different style of 537 00:30:42,040 --> 00:30:43,160 using language. 538 00:30:45,320 --> 00:30:49,880 Tori Dominguez-Peak: So this is doctor Rui Sosasilva. He's from 539 00:30:49,880 --> 00:30:54,555 Portugal. His whole training and job is as a forensic linguist. 540 00:30:54,555 --> 00:30:58,155 Oh. So like, literally, his field is all about confirming 541 00:30:58,315 --> 00:31:00,555 the identity of who wrote what. 542 00:31:00,635 --> 00:31:03,035 Jason Oberholtzer: Woah. That's a rad job. 543 00:31:03,275 --> 00:31:04,955 Tori Dominguez-Peak: I know. It's such a cool job. And I 544 00:31:04,955 --> 00:31:07,890 literally didn't even know this job existed until I started 545 00:31:07,890 --> 00:31:10,850 writing this episode. And I was like, this is amazing. Wow. 546 00:31:11,810 --> 00:31:17,170 And being able to identify the nuances of what someone sounds 547 00:31:17,170 --> 00:31:20,530 like, that identity is called an idiolect. 548 00:31:21,345 --> 00:31:27,105 Rui Sousa-Silva: So ideolect is your own way of speaking or 549 00:31:27,105 --> 00:31:31,985 writing the language. So it's as if your DNA was related to the 550 00:31:31,985 --> 00:31:32,945 way you use language. 551 00:31:33,420 --> 00:31:34,460 Jason Oberholtzer: I believe it. 552 00:31:34,540 --> 00:31:38,380 Tori Dominguez-Peak: So I mean, like, legally, criminally, 553 00:31:38,860 --> 00:31:42,540 historically, a forensic linguist is the person you call 554 00:31:42,540 --> 00:31:46,300 to match the fingerprints of someone's writing. Right? 555 00:31:46,300 --> 00:31:46,620 Jason Oberholtzer: Woah. 556 00:31:47,155 --> 00:31:49,715 Tori Dominguez-Peak: And so at the same time, he's also a 557 00:31:49,715 --> 00:31:53,795 lecturer. He also teaches, which means that he also has to deal 558 00:31:53,795 --> 00:31:59,235 with the issue of students using AI in his class. Really also has 559 00:31:59,235 --> 00:32:03,600 students write in his class sit down and write. I know you're 560 00:32:03,600 --> 00:32:05,000 not using AI because I can look at you writing. Right? 561 00:32:06,000 --> 00:32:10,320 But when they do that, he's noticing something different 562 00:32:10,560 --> 00:32:12,240 than Professor Fritz. 563 00:32:12,480 --> 00:32:14,880 Rui Sousa-Silva: What we see nowadays, people interact with 564 00:32:14,880 --> 00:32:20,005 generative AI so much that people are starting to write 565 00:32:20,005 --> 00:32:25,445 like machines. With some of my students, I know that they are 566 00:32:25,525 --> 00:32:29,525 sitting an exam and I know they were the ones who wrote the text 567 00:32:29,800 --> 00:32:33,480 and still when I read the text it sounds as if it was generated 568 00:32:33,480 --> 00:32:38,440 by a machine. And that's because we accommodate with other people 569 00:32:38,520 --> 00:32:41,240 and we accommodate in the same way with the machines we 570 00:32:41,240 --> 00:32:45,595 interact with. So we tend to accommodate so much to the 571 00:32:45,595 --> 00:32:48,795 machine that we learn so much from the machine that we start 572 00:32:48,795 --> 00:32:51,355 start writing like machines. So this is a challenge at the 573 00:32:51,355 --> 00:32:51,915 moment. 574 00:32:52,155 --> 00:32:54,555 Jason Oberholtzer: So is he saying that because we're 575 00:32:54,555 --> 00:32:58,070 ingesting so much writing that has been made in this process 576 00:32:58,070 --> 00:33:02,950 that we are starting to regurgitate Yeah. That Yeah. 577 00:33:02,950 --> 00:33:03,910 That checks out. 578 00:33:04,150 --> 00:33:06,630 Tori Dominguez-Peak: That's wild though, isn't it? Yeah. Because 579 00:33:06,630 --> 00:33:09,590 then it makes me think about professor Fritz's class and her 580 00:33:09,590 --> 00:33:13,175 methods, like, what if people just start writing like 581 00:33:13,175 --> 00:33:14,295 ChatchyPT? 582 00:33:14,295 --> 00:33:15,255 Jason Oberholtzer: Oh, boy. 583 00:33:15,255 --> 00:33:17,335 Tori Dominguez-Peak: Then at some point, it's just gonna get 584 00:33:17,575 --> 00:33:18,695 harder to tell. 585 00:33:19,015 --> 00:33:21,575 Jason Oberholtzer: Alright. You've made an un virtuous cycle 586 00:33:21,575 --> 00:33:23,895 here. I see what has happened. 587 00:33:24,135 --> 00:33:26,780 Tori Dominguez-Peak: You see what has happened. Yeah. And so 588 00:33:26,780 --> 00:33:29,900 because Rui is a forensic linguist, like he is the person 589 00:33:29,900 --> 00:33:33,500 you can tell who wrote what, I was like, can you please give me 590 00:33:33,500 --> 00:33:37,420 an example of influencing the way of like how machines 591 00:33:37,420 --> 00:33:41,405 influence the way people write. And he said that when you speak 592 00:33:41,405 --> 00:33:45,805 to ChatGPT in Portuguese, because he lives in Portugal, it 593 00:33:45,805 --> 00:33:51,645 will sometimes reply using a Brazilian Portuguese dialect, 594 00:33:51,965 --> 00:33:53,645 and that has consequences. 595 00:33:53,805 --> 00:33:56,750 Rui Sousa-Silva: Yeah. One example is the way when when 596 00:33:56,750 --> 00:33:59,790 you're writing in English, you usually say, if you want to to 597 00:33:59,790 --> 00:34:03,470 list a set of points, you'll say, firstly, such and such. 598 00:34:03,470 --> 00:34:09,925 Secondly, such and such. And in Portuguese, usually you wouldn't 599 00:34:09,925 --> 00:34:14,405 use the literal pronunciation of the adverb. But people are now 600 00:34:14,405 --> 00:34:17,685 doing that and that's because interestingly Brazilian 601 00:34:17,685 --> 00:34:22,940 Portuguese does that and because when you look at language 602 00:34:22,940 --> 00:34:27,100 variants, I mean in Portugal you've got about 10,000,000 603 00:34:27,260 --> 00:34:31,740 speakers, if you go to Brazil there are 200,000,000, so for 604 00:34:31,740 --> 00:34:35,845 generative AI engines they feed on languages. 605 00:34:35,845 --> 00:34:39,205 So they they are more likely to feed on Brazilian Portuguese 606 00:34:39,205 --> 00:34:40,805 Tori Dominguez-Peak: than There's just more Brazilian 607 00:34:40,805 --> 00:34:44,565 Portuguese language data out And so when Chateapiti speaks 608 00:34:44,565 --> 00:34:46,645 Portuguese, it sounds Brazilian. 609 00:34:46,645 --> 00:34:48,165 Rui Sousa-Silva: It's usually Brazilian Portuguese, even 610 00:34:48,165 --> 00:34:51,300 though nowadays you can ask to write in European Portuguese but 611 00:34:51,300 --> 00:34:54,820 every now and then there is a word in Brazilian Portuguese for 612 00:34:54,820 --> 00:34:59,140 example. So the fact that it was based on Brazilian Portuguese 613 00:34:59,140 --> 00:35:02,340 and the fact that Brazilian Portuguese uses that literal 614 00:35:02,340 --> 00:35:06,265 translation of firstly, secondly, thirdly, now people 615 00:35:06,505 --> 00:35:08,505 are writing like that. 616 00:35:08,745 --> 00:35:10,345 Tori Dominguez-Peak: Oh, that's so interesting. 617 00:35:10,665 --> 00:35:13,145 Rui Sousa-Silva: Even native speakers of European Portuguese 618 00:35:13,145 --> 00:35:14,665 are writing like that at the moment. 619 00:35:14,665 --> 00:35:17,145 Jason Oberholtzer: Wow. As I'm hearing that, I'm just thinking 620 00:35:17,145 --> 00:35:20,550 that that is not necessarily the canary in the coal mine, but 621 00:35:20,550 --> 00:35:23,270 there's probably some better metaphor for it just being the 622 00:35:23,270 --> 00:35:26,950 visible object of something that is also happening under the 623 00:35:26,950 --> 00:35:30,230 layer of language and thinking. If we are regurgitating 624 00:35:30,230 --> 00:35:33,350 different cultural grammar rules, we're probably also 625 00:35:33,350 --> 00:35:37,715 surfacing other imports that we don't know are imported from 626 00:35:37,715 --> 00:35:38,595 different places. 627 00:35:39,075 --> 00:35:41,715 Tori Dominguez-Peak: Yeah. I mean, Brazilian adverbs are 628 00:35:41,715 --> 00:35:45,235 pretty low stakes. I mean, I think it's kind of amusing. But 629 00:35:45,235 --> 00:35:49,070 remember when I said that forensic linguists identify the 630 00:35:49,070 --> 00:35:50,430 DNA of language. 631 00:35:50,430 --> 00:35:50,750 Jason Oberholtzer: Yeah. 632 00:35:50,750 --> 00:35:54,830 Tori Dominguez-Peak: And they do that in criminal settings. Can I 633 00:35:54,830 --> 00:35:58,110 outline a scary scenario for you, Jason? 634 00:35:59,390 --> 00:36:00,750 Jason Oberholtzer: I'm braced. I'm ready. 635 00:36:00,830 --> 00:36:04,895 Tori Dominguez-Peak: Okay. So doctor Sosasilva told me that 636 00:36:04,895 --> 00:36:07,535 one of the things that his colleagues talk about all the 637 00:36:07,535 --> 00:36:12,335 time is the use of AI in criminal activity. So like using 638 00:36:12,335 --> 00:36:16,300 generative AI to impersonate someone's writing style to write 639 00:36:16,300 --> 00:36:18,140 something incriminating. 640 00:36:18,140 --> 00:36:18,540 Jason Oberholtzer: Sure. 641 00:36:18,540 --> 00:36:21,100 Tori Dominguez-Peak: So for example, somebody does not like 642 00:36:21,100 --> 00:36:26,220 you. And so they write a strongly worded threat to a 643 00:36:26,220 --> 00:36:30,825 politician, but they write it Jason Operholzer style. And they 644 00:36:30,825 --> 00:36:33,465 like, maybe they make a sock puppet social media account and 645 00:36:33,465 --> 00:36:36,905 they impersonate you, and they post it on there. And you get a 646 00:36:36,905 --> 00:36:39,785 visit from the police, and they're like, you wrote this. 647 00:36:39,785 --> 00:36:40,265 Jason Oberholtzer: Yeah. 648 00:36:40,265 --> 00:36:41,865 Tori Dominguez-Peak: How do you prove that you didn't? 649 00:36:42,345 --> 00:36:45,850 Jason Oberholtzer: That's a good question. I would probably at 650 00:36:45,850 --> 00:36:52,250 this point, try to point to like the corpus of available writing 651 00:36:52,250 --> 00:36:54,650 that I would have on hand. Like, I'm trying to really take a 652 00:36:54,650 --> 00:36:57,055 cynical view of this and just assume that the the 653 00:36:57,055 --> 00:36:59,455 infrastructure is weighted against me on this one, and 654 00:36:59,455 --> 00:37:02,895 there's now a letter out there signed by me that says, hey, 655 00:37:02,895 --> 00:37:09,135 buddy, I'm gonna kill you. And to disprove this, I don't think 656 00:37:09,135 --> 00:37:11,950 I would have successful time attacking the language on a word 657 00:37:11,950 --> 00:37:15,470 by word basis. I would probably have to compile my own corpus of 658 00:37:15,470 --> 00:37:20,350 writing, and I'd probably have to divulge repositories of data 659 00:37:20,350 --> 00:37:23,710 that I would otherwise want to keep secret, like private 660 00:37:23,710 --> 00:37:28,225 messages and be like, I will take all of my eye messages and 661 00:37:28,225 --> 00:37:29,265 put them in a model. 662 00:37:29,265 --> 00:37:32,145 And you can see the way that I communicate and you can see like 663 00:37:32,145 --> 00:37:35,985 all the communication I have had, all the cynicism I have had 664 00:37:35,985 --> 00:37:39,025 around politicians and the government. And you tell me 665 00:37:39,250 --> 00:37:42,450 where in this trajectory is there the leap to a murderer. 666 00:37:43,010 --> 00:37:47,090 And it is less about word choice and more about state of mental 667 00:37:47,090 --> 00:37:50,450 well-being. Like, is this the and then I'm going to write a 668 00:37:50,450 --> 00:37:52,930 politician and murder them trajectory? And here's every 669 00:37:52,930 --> 00:37:55,405 piece of written correspondence I have available to you. 670 00:37:55,405 --> 00:37:59,885 And just hope that I've got a doctor like the good Portuguese 671 00:37:59,885 --> 00:38:02,685 doctor doctor Sosa Silva on my side who can help me make a 672 00:38:02,685 --> 00:38:05,245 better argument about that material than whoever's on the 673 00:38:05,245 --> 00:38:05,885 other side. 674 00:38:06,365 --> 00:38:08,580 Tori Dominguez-Peak: Yeah. I mean, definitely, you would like 675 00:38:08,580 --> 00:38:10,900 to call doctor Sosa Silva. Right? 676 00:38:10,900 --> 00:38:11,220 Jason Oberholtzer: Yeah. 677 00:38:11,220 --> 00:38:12,900 Tori Dominguez-Peak: They can analyze the text. They're like, 678 00:38:12,900 --> 00:38:17,140 this is this isn't quite reek of Jason. There's something just a 679 00:38:17,140 --> 00:38:18,180 little bit off of this. 680 00:38:18,180 --> 00:38:19,540 Jason Oberholtzer: Speaking of word choice, can we do better 681 00:38:19,540 --> 00:38:20,580 than reek of Jason? 682 00:38:20,580 --> 00:38:21,380 Tori Dominguez-Peak: Reek of Jason. 683 00:38:23,135 --> 00:38:25,215 Jason Oberholtzer: For want of a better term, I suppose you can 684 00:38:25,215 --> 00:38:26,015 keep reek. 685 00:38:26,095 --> 00:38:28,495 Tori Dominguez-Peak: So, yeah, this is where forensic linguist 686 00:38:28,495 --> 00:38:32,015 come in and then you don't get arrested, hopefully. Right? 687 00:38:32,415 --> 00:38:37,695 Okay. But as generative AI gets more sophisticated, Rui thinks 688 00:38:37,695 --> 00:38:39,920 that his work is going to get harder. 689 00:38:40,000 --> 00:38:42,960 Rui Sousa-Silva: The developments in generative AI 690 00:38:42,960 --> 00:38:47,520 will make it more complicated for forensic linguists to 691 00:38:47,520 --> 00:38:52,000 attribute texts, which in turn will mean that forensic 692 00:38:52,000 --> 00:38:55,255 linguists will need to do more research and to further their 693 00:38:55,255 --> 00:38:59,175 research and to have more fine grained methods of attributing 694 00:38:59,175 --> 00:39:04,135 authorship. But there will always be a distinction between 695 00:39:04,455 --> 00:39:08,840 the way humans produce text and the way machines generate texts. 696 00:39:08,840 --> 00:39:13,480 So things generative AI will evolve, forensic linguistics 697 00:39:13,480 --> 00:39:19,400 will evolve, but eventually we will always be able to pinpoint 698 00:39:19,480 --> 00:39:21,320 differences between the texts. 699 00:39:21,905 --> 00:39:25,105 Jason Oberholtzer: So very similarly to the classroom here, 700 00:39:25,105 --> 00:39:27,825 this seems like it is just creating piles of work for 701 00:39:27,825 --> 00:39:28,305 everybody. 702 00:39:28,305 --> 00:39:30,225 Tori Dominguez-Peak: Yeah. It seems like things are just gonna 703 00:39:30,225 --> 00:39:34,225 get harder for everyone, which is kind of a bummer. And I know, 704 00:39:34,225 --> 00:39:36,850 like, the threatening the politician, like, that's a very 705 00:39:36,850 --> 00:39:40,130 dramatic example, obviously. But like you said, with the 706 00:39:40,130 --> 00:39:45,010 classroom, the idea I keep coming back to is cognitive 707 00:39:45,010 --> 00:39:50,275 offloading and like, not doing all these mental processes and 708 00:39:50,275 --> 00:39:53,395 offloading it to AI, which then I guess makes it harder for 709 00:39:53,395 --> 00:39:58,835 forensically. Like, there's been studies from Microsoft, from the 710 00:39:58,835 --> 00:40:04,355 SBS, Swiss Business School about how people who use generative AI 711 00:40:04,480 --> 00:40:08,880 regularly tend to score lower on markers of critical thinking. 712 00:40:08,880 --> 00:40:12,880 Like, there's actual data we have now. Mhmm. Again, I'm 713 00:40:12,880 --> 00:40:16,320 painting kind of a scary picture to you. It's like, okay, so our 714 00:40:16,320 --> 00:40:20,965 critical thinking may be getting compromised by a technology that 715 00:40:20,965 --> 00:40:24,165 is also getting more sophisticated at pretending to 716 00:40:24,165 --> 00:40:29,605 be us. And we're also starting to become influenced by the way 717 00:40:29,605 --> 00:40:30,485 it writes. 718 00:40:30,565 --> 00:40:33,780 Mhmm. Like, that's just such a weird trifecta. 719 00:40:34,100 --> 00:40:35,860 Jason Oberholtzer: Right. But doesn't the cycle also work in 720 00:40:35,860 --> 00:40:39,140 the other direction? Like, we are the corpus of information 721 00:40:39,140 --> 00:40:43,380 that the generative models need to continue their work. And as 722 00:40:43,380 --> 00:40:46,515 we lose cognitive function because of offloading, the 723 00:40:46,515 --> 00:40:50,755 material that we are able to feed depreciates in value as 724 00:40:50,755 --> 00:40:55,075 well, which one imagines leads to worse outputs from machines, 725 00:40:55,075 --> 00:40:58,115 which we are synthesizing and further inhibits our ability to 726 00:40:58,115 --> 00:41:01,730 think and provide a reasonable corpus of information updated to 727 00:41:01,730 --> 00:41:04,370 the moment from which Yeah. The models can select. 728 00:41:04,370 --> 00:41:06,290 Tori Dominguez-Peak: It's kind of like this feedback loop. 729 00:41:06,450 --> 00:41:11,890 Yeah. Us feeding it and then it influencing us, and then all of 730 00:41:11,890 --> 00:41:14,210 a sudden, we're just all speaking Brazilian Portuguese, 731 00:41:14,725 --> 00:41:15,365 right? 732 00:41:15,525 --> 00:41:16,085 Mike Rugnetta: Yeah. 733 00:41:16,405 --> 00:41:18,085 Tori Dominguez-Peak: And getting accused of crimes we didn't 734 00:41:18,085 --> 00:41:23,205 commit. And professor Fritz's concern about the feedback loop 735 00:41:23,205 --> 00:41:26,325 is that it affects everyone differently. 736 00:41:26,980 --> 00:41:31,540 Megan Fritts: A lot of people defend, letting students use AI 737 00:41:31,540 --> 00:41:35,940 in their work, by saying that they see it as a tool for 738 00:41:35,940 --> 00:41:41,115 equity. Maybe for students who, had a less privileged primary 739 00:41:41,115 --> 00:41:44,715 education or students for whom English is a second language, I 740 00:41:44,715 --> 00:41:48,235 would contend the exact opposite is true. That what this is doing 741 00:41:48,235 --> 00:41:51,755 is setting the stage for genuine reading and writing skills 742 00:41:51,755 --> 00:41:56,090 becoming something that is really only accessible to the 743 00:41:56,090 --> 00:42:00,650 elite class, those with a lot of money and leisure time to 744 00:42:00,650 --> 00:42:03,610 cultivate them intentionally. And so that's really something 745 00:42:03,610 --> 00:42:05,130 that concerns me quite a bit. 746 00:42:05,130 --> 00:42:06,890 Jason Oberholtzer: Okay. I'm beginning to see why there's 747 00:42:06,890 --> 00:42:09,865 such difficulty forming consensus around this. I mean, 748 00:42:09,865 --> 00:42:13,385 initially, I'll be honest, my reaction was yeah. It's obvious 749 00:42:13,385 --> 00:42:15,305 that in a space where you're supposed to be practicing 750 00:42:15,305 --> 00:42:20,185 thinking and metastasizing your own thoughts that AI is just not 751 00:42:20,420 --> 00:42:23,540 helpful. It's not there for any reason except for you to finish 752 00:42:23,540 --> 00:42:26,020 the paper, which is a representation of the thoughts 753 00:42:26,020 --> 00:42:28,340 you were supposed to be having, and it is like the wrong 754 00:42:28,340 --> 00:42:30,260 takeaway from what you're doing in the classroom. 755 00:42:31,060 --> 00:42:34,515 But now that they're applying these frameworks around 756 00:42:34,515 --> 00:42:38,435 accessibility, I can see it becoming a little more 757 00:42:38,435 --> 00:42:41,155 complicated. I guess a lot of it boils down to how much you think 758 00:42:41,155 --> 00:42:44,915 the role of the academy is to prepare you for work, 759 00:42:45,390 --> 00:42:49,710 employment, and how much it is to help you engage with how you 760 00:42:49,710 --> 00:42:55,790 think and learn. But I'm beginning to see why the stakes 761 00:42:55,790 --> 00:42:58,670 are a little more complicated than perhaps they feel at first 762 00:42:58,670 --> 00:42:58,910 blush. 763 00:42:59,405 --> 00:43:01,645 Tori Dominguez-Peak: Yeah. And then when you think about 764 00:43:01,805 --> 00:43:05,565 professor Fritz's role in the committees and how it's just 765 00:43:05,565 --> 00:43:10,045 really hard to come to agreement or make any sort of policy, I 766 00:43:10,045 --> 00:43:12,790 think this is something that they're gonna continue to 767 00:43:12,790 --> 00:43:14,630 wrestle with for a long time. 768 00:43:26,070 --> 00:43:30,085 Megan Fritts: As for myself, my policy won't be changing. And 769 00:43:30,165 --> 00:43:33,205 that's, you know, that's about all I can do about that. 770 00:43:44,290 --> 00:43:48,290 Jason Oberholtzer: Tori, thank you as always for bringing in a 771 00:43:48,290 --> 00:43:50,610 really insightful piece here. 772 00:43:51,170 --> 00:43:52,850 Tori Dominguez-Peak: Yeah. Thanks so much for letting me 773 00:43:52,850 --> 00:43:53,730 talk about it. 774 00:43:53,730 --> 00:43:54,210 Jason Oberholtzer: Yeah. 775 00:43:54,690 --> 00:43:56,850 Tori Dominguez-Peak: And I just wanted to shout out thanks to 776 00:43:56,850 --> 00:44:00,025 professor Fritz and to doctor Sosa Silva. 777 00:44:00,265 --> 00:44:02,025 Jason Oberholtzer: At least one of whom is probably going to be 778 00:44:02,025 --> 00:44:05,545 getting me out of jail over the next couple years. So pre thank 779 00:44:05,545 --> 00:44:06,505 you for that one. 780 00:44:06,665 --> 00:44:08,105 Tori Dominguez-Peak: Pre thank you for that one. 781 00:44:09,465 --> 00:44:12,505 Jason Oberholtzer: Tori, where can folks find you and all of 782 00:44:12,505 --> 00:44:15,760 the writing that definitively comes from your own brain on the 783 00:44:15,760 --> 00:44:16,320 web? 784 00:44:16,400 --> 00:44:18,320 Tori Dominguez-Peak: You can find me at toori d p nine 785 00:44:18,320 --> 00:44:24,800 8.bluesky.social. And you can also find my podcast about video 786 00:44:24,800 --> 00:44:29,965 games that I make of my own brain and play with my own brain 787 00:44:30,365 --> 00:44:34,365 at press-startpod.bluesky.social. 788 00:45:15,020 --> 00:45:18,300 Jason Oberholtzer: Tuesday, September 23, 10:30AM. 789 00:47:35,515 --> 00:47:37,435 Mike Rugnetta: That is the show we have for you this week. We're 790 00:47:37,435 --> 00:47:40,460 gonna be back here in the main feed on Wednesday, October 8. We 791 00:47:40,860 --> 00:47:45,980 are proud and thankful and extremely lucky to have the 792 00:47:45,980 --> 00:47:50,620 member community that we do. Without the support of our 793 00:47:50,620 --> 00:47:56,515 members, this show would not and could not exist. So I just wanna 794 00:47:56,515 --> 00:47:58,275 say thank you. 795 00:47:58,915 --> 00:48:02,515 If you would like to become a NeverPost member and join this 796 00:48:02,515 --> 00:48:06,690 community for as little as $4 a month, you can do that at 797 00:48:06,690 --> 00:48:11,890 neverpo.st. Where also if a membership is a little too big 798 00:48:11,890 --> 00:48:15,970 of a commitment in these strange and trying times, you can also 799 00:48:15,970 --> 00:48:20,475 tip us a one time any dollar amount, and we promise that we 800 00:48:20,475 --> 00:48:26,155 will spend every last red cent on arcade games chewing gum and 801 00:48:26,155 --> 00:48:39,730 baseball cards. Become a member at neverpo.st. Never Post's 802 00:48:39,730 --> 00:48:41,890 producers are Audrey Evans, Georgia Hampton, and the 803 00:48:41,890 --> 00:48:44,615 mysterious, doctor first name, last name. Our senior producer 804 00:48:44,615 --> 00:48:45,575 is Hans Buto. 805 00:48:45,575 --> 00:48:48,615 Our executive producer is Jason Oberholzer, and the show's host, 806 00:48:48,615 --> 00:48:55,495 that's me, is Mike Rugnetta. And then this warning flashes on the 807 00:48:55,495 --> 00:48:59,510 light meter. Inside the house, a pilot light is always burning in 808 00:48:59,510 --> 00:49:04,230 the oven's eyes. The low roof is pulled down over the eyes like a 809 00:49:04,230 --> 00:49:07,350 hat. And underneath the warnings, light motif networks 810 00:49:07,350 --> 00:49:12,625 of subterranean lines run like the nervous system or bloodlines 811 00:49:12,865 --> 00:49:16,145 or fractures spreading from tectonic lines of fault. 812 00:49:16,865 --> 00:49:20,065 In distant coasts, heavy and light petroleum is piped across 813 00:49:20,065 --> 00:49:25,105 state lines and gas, electric, oil, and water lines convey 814 00:49:25,105 --> 00:49:31,270 their vital humors to the house. Excerpt of nervous systems by 815 00:49:31,270 --> 00:49:35,430 Greg Williamson. Never Post is a production of charts and leisure 816 00:49:35,430 --> 00:49:37,990 and is distributed by Radiotopia.