1 00:00:05,800 --> 00:00:07,160 Speaker 1: So then we'll plus holding things up. 2 00:00:07,240 --> 00:00:08,720 Speaker 2: If he's a great man and you want to do 3 00:00:08,760 --> 00:00:10,240 Speaker 2: a deal with Danada, why aren't. 4 00:00:10,039 --> 00:00:12,680 Speaker 3: You Because I want to be a great man too. 5 00:00:16,160 --> 00:00:17,119 Speaker 4: Thank you very much. 6 00:00:30,480 --> 00:00:33,040 Speaker 2: Truck gotten funnier as of late, Like I feel like 7 00:00:33,120 --> 00:00:38,040 Speaker 2: in the midst of all this controversy, he's ramped up 8 00:00:38,120 --> 00:00:39,440 Speaker 2: his sense of humor. 9 00:00:39,560 --> 00:00:41,360 Speaker 4: I don't know. I guess I would be laughing too. 10 00:00:42,840 --> 00:00:45,760 Speaker 2: He won a second term, not consecutively. He's not going 11 00:00:45,840 --> 00:00:50,040 Speaker 2: to run again. He's moving forward with a mandate. Yes 12 00:00:50,080 --> 00:00:54,640 Speaker 2: it is a mandate, and watching the Democrats flip out 13 00:00:54,720 --> 00:00:58,560 Speaker 2: all around him has got to be entertaining. I'm hoping 14 00:00:58,640 --> 00:01:01,320 Speaker 2: that the show will be as entertaining this morning. Here 15 00:01:01,560 --> 00:01:04,600 Speaker 2: from the six five to one carpet Next Day Install Studios, 16 00:01:04,720 --> 00:01:07,840 Speaker 2: my name is John Justice on Twin Cities News Talk 17 00:01:07,880 --> 00:01:11,680 Speaker 2: AM eleven thirty and one oh three five FM. Devin 18 00:01:11,720 --> 00:01:14,760 Speaker 2: is in the master control booth next door this morning. 19 00:01:14,800 --> 00:01:16,880 Speaker 2: Glad to see him here. We got a busy show today. 20 00:01:17,160 --> 00:01:19,000 Speaker 2: I want to start off with some audio to kick 21 00:01:19,040 --> 00:01:22,160 Speaker 2: things off, but in a bit of a preview of 22 00:01:22,160 --> 00:01:24,880 Speaker 2: what to expect. Liz Colin from Alpha News we'll be 23 00:01:24,959 --> 00:01:28,080 Speaker 2: joining us as she does every Wednesday at eight thirty, 24 00:01:28,280 --> 00:01:31,800 Speaker 2: Representative Tom Emmer, we'll talk about the shutdown and President 25 00:01:31,840 --> 00:01:35,160 Speaker 2: Donald Trump. He'll join the show at eight o'clock. We 26 00:01:35,240 --> 00:01:38,920 Speaker 2: have audio from Governor Tim Walls held a very friendly 27 00:01:39,040 --> 00:01:41,640 Speaker 2: well was part of a very friendly forum with JB. 28 00:01:41,760 --> 00:01:45,720 Speaker 2: Pritzker hosted by the Minnesota Star Tribune and shares his 29 00:01:45,880 --> 00:01:50,600 Speaker 2: thoughts on the National Guard potentially coming to Minnesota in Minneapolis. 30 00:01:50,640 --> 00:01:52,120 Speaker 2: So we'll get to that in the eight o'clock hour. 31 00:01:52,200 --> 00:01:56,640 Speaker 2: At six thirty this morning, David Gartenstein Ross, the CEO 32 00:01:57,120 --> 00:01:59,720 Speaker 2: of Expert Theory, we'll talk AI and LAN. 33 00:02:00,040 --> 00:02:02,520 Speaker 4: It's been in Minnesota utilizing AI. 34 00:02:02,680 --> 00:02:03,960 Speaker 2: So we got a lot of ground to cover, a 35 00:02:03,960 --> 00:02:05,720 Speaker 2: lot of stories in between that we'll be getting to 36 00:02:05,760 --> 00:02:08,440 Speaker 2: as well. But I want to start here probably one 37 00:02:08,480 --> 00:02:11,720 Speaker 2: of the most amazing pieces of audio that you will 38 00:02:11,760 --> 00:02:15,919 Speaker 2: hear today. This is Katie Porter. Katie Porter is the 39 00:02:16,040 --> 00:02:19,000 Speaker 2: leading candidate for governor. They have their own race coming up. 40 00:02:19,680 --> 00:02:24,920 Speaker 2: Is facing backlash after footage has been released of this 41 00:02:25,040 --> 00:02:28,280 Speaker 2: Democrat threatening to leave this interview. 42 00:02:28,800 --> 00:02:31,520 Speaker 4: It was held a few weeks ago as she. 43 00:02:31,560 --> 00:02:35,720 Speaker 2: Grew frustrated that the journalist was actually asking her relevant 44 00:02:35,840 --> 00:02:40,680 Speaker 2: questions and wouldn't let the questions go. When this candidate 45 00:02:40,720 --> 00:02:44,880 Speaker 2: in California for governor refused to go and be straight 46 00:02:44,919 --> 00:02:48,040 Speaker 2: with her, Listen to this exchange. 47 00:02:48,360 --> 00:02:50,320 Speaker 5: What do you say to the forty percent of California 48 00:02:50,400 --> 00:02:51,480 Speaker 5: voters who you'll need. 49 00:02:51,320 --> 00:02:53,880 Speaker 4: In order to win, who voted for Trump? 50 00:02:55,520 --> 00:02:57,000 Speaker 6: How would I need them in order to win? 51 00:02:57,040 --> 00:02:59,160 Speaker 5: A man, Well, unless you think you're going to get 52 00:02:59,600 --> 00:03:02,240 Speaker 5: sixty percent of the vote, you think you'll get sixty 53 00:03:02,280 --> 00:03:04,480 Speaker 5: pus all everybody who did not vote for Trump will 54 00:03:04,520 --> 00:03:04,960 Speaker 5: vote for you. 55 00:03:05,080 --> 00:03:08,400 Speaker 6: That's what you're in a general election. Yes, if it 56 00:03:08,480 --> 00:03:11,040 Speaker 6: is me versus a Republican, I think that I will 57 00:03:11,080 --> 00:03:12,920 Speaker 6: win the people who did not vote for Trump. When 58 00:03:12,919 --> 00:03:15,080 Speaker 6: if it's you versus another Democrat, I don't intend that 59 00:03:15,120 --> 00:03:15,720 Speaker 6: to be the case. 60 00:03:16,360 --> 00:03:20,640 Speaker 2: So real quick out of the gate when you watch 61 00:03:20,680 --> 00:03:22,360 Speaker 2: the video, and you can hear it a bit in 62 00:03:22,400 --> 00:03:27,160 Speaker 2: the reporter's voice like she seemed a little right put 63 00:03:27,240 --> 00:03:29,440 Speaker 2: off at the fact that she was like what, why 64 00:03:29,480 --> 00:03:31,200 Speaker 2: would I need to do that? But you can tell 65 00:03:31,240 --> 00:03:34,560 Speaker 2: the reporters laughing like they're not being aggressive by any stretch. 66 00:03:35,120 --> 00:03:37,960 Speaker 2: She was just clarifying, and she's smiling and laughing about 67 00:03:37,960 --> 00:03:39,880 Speaker 2: it as the exchange continues. 68 00:03:40,480 --> 00:03:42,640 Speaker 5: So how do you not intend that to be the case? 69 00:03:42,640 --> 00:03:44,720 Speaker 5: Do you are you going to ask them not to run? 70 00:03:45,080 --> 00:03:45,240 Speaker 7: No? 71 00:03:45,080 --> 00:03:47,080 Speaker 6: No, I'm saying I'm going to build the support. I 72 00:03:47,120 --> 00:03:49,720 Speaker 6: have the support already in terms of name recognition, and 73 00:03:49,760 --> 00:03:51,280 Speaker 6: so I'm going to do the very best I can 74 00:03:51,400 --> 00:03:53,160 Speaker 6: to make sure that we get through this primary in 75 00:03:53,200 --> 00:03:55,160 Speaker 6: a really strong position. But let me be clear with you. 76 00:03:55,560 --> 00:03:58,960 Speaker 6: I represented Orange County. I represented a purple area. I 77 00:03:59,040 --> 00:04:01,800 Speaker 6: have stood on my own two and one Republican votes before. 78 00:04:01,920 --> 00:04:04,480 Speaker 6: That's not something every candidate in this race can say. 79 00:04:04,720 --> 00:04:06,600 Speaker 6: If you're from a deep blue area, if you're from 80 00:04:06,720 --> 00:04:09,640 Speaker 6: LA or you're from Oakland, you don't have an. 81 00:04:09,520 --> 00:04:11,800 Speaker 5: Experience, is that you don't need those Trump voters. 82 00:04:11,800 --> 00:04:13,119 Speaker 6: So you asked me if I need them to win? 83 00:04:13,880 --> 00:04:16,400 Speaker 6: So you don't feel like this is unnecessarily argumentative. 84 00:04:16,480 --> 00:04:17,239 Speaker 1: What is your question? 85 00:04:17,920 --> 00:04:20,680 Speaker 5: The question is the same thing I asked everybody that 86 00:04:20,800 --> 00:04:24,920 Speaker 5: this is being called the empowering voters to stop Trump's 87 00:04:25,000 --> 00:04:27,200 Speaker 5: power grap Every other candidate has answered this question. 88 00:04:27,240 --> 00:04:29,600 Speaker 4: This is not I said, I support it. 89 00:04:30,080 --> 00:04:32,520 Speaker 5: So and the question is what do you say to 90 00:04:32,560 --> 00:04:35,119 Speaker 5: the forty percent of voters who voted for Trump. 91 00:04:35,200 --> 00:04:37,080 Speaker 6: Oh, I'm happy to say that it's the do you 92 00:04:37,240 --> 00:04:39,160 Speaker 6: need them to win? Part that I don't understand. 93 00:04:39,240 --> 00:04:43,400 Speaker 4: I'm does this even the more I listen to it, 94 00:04:43,400 --> 00:04:45,000 Speaker 4: the more ridiculous it gets. 95 00:04:45,440 --> 00:04:50,599 Speaker 2: Doesn't even like a question that Katie Porter, this Democrat 96 00:04:50,680 --> 00:04:53,760 Speaker 2: candidate like doesn't know. Like, it's not even like a 97 00:04:53,960 --> 00:04:59,440 Speaker 2: knowledge question. She's not asking her anything relating to policy 98 00:04:59,600 --> 00:05:03,400 Speaker 2: that she may or may not be familiar with, or 99 00:05:04,200 --> 00:05:08,160 Speaker 2: you know, an exit strategy out of the middle easter, just. 100 00:05:08,279 --> 00:05:09,400 Speaker 4: You you name it. 101 00:05:09,000 --> 00:05:15,640 Speaker 2: It's just a simple question relating to votes in which 102 00:05:15,680 --> 00:05:18,360 Speaker 2: party you need to get them from in order to win, 103 00:05:18,480 --> 00:05:21,080 Speaker 2: and how you're gonna have to have people crossover for 104 00:05:21,200 --> 00:05:25,000 Speaker 2: you to go and be compelled to victory. And she 105 00:05:25,080 --> 00:05:26,080 Speaker 2: can't handle it, ok. 106 00:05:26,040 --> 00:05:27,799 Speaker 6: To answer question's for the question is you haven't written? 107 00:05:27,800 --> 00:05:28,360 Speaker 7: And I'll answer. 108 00:05:28,520 --> 00:05:30,680 Speaker 5: And we've also asked the other candidates do you think 109 00:05:30,680 --> 00:05:33,679 Speaker 5: you need any of those forty percent of California voters 110 00:05:33,680 --> 00:05:35,000 Speaker 5: to win? And you're saying, no, you don't. 111 00:05:35,120 --> 00:05:36,800 Speaker 6: No, I'm saying I'm going to try to win every 112 00:05:36,880 --> 00:05:39,359 Speaker 6: vote I can. And what I'm saying to you is 113 00:05:39,400 --> 00:05:42,960 Speaker 6: that well to those voters. Okay, so you I don't 114 00:05:42,960 --> 00:05:47,440 Speaker 6: want to keep doing this. I'm gonna call it. Thank you. 115 00:05:47,440 --> 00:05:49,440 Speaker 5: You're not gonna do the interview with them? 116 00:05:49,480 --> 00:05:51,320 Speaker 6: Nope, not like this. I'm not not with seven follow 117 00:05:51,400 --> 00:05:52,680 Speaker 6: ups to every single question. 118 00:05:52,440 --> 00:05:54,560 Speaker 5: You asked every other candidate has. 119 00:05:54,760 --> 00:05:55,200 Speaker 6: I don't care. 120 00:05:55,839 --> 00:05:56,320 Speaker 8: I don't care. 121 00:05:56,720 --> 00:05:59,440 Speaker 6: I want to have a pleasant, positive conversation, which you 122 00:05:59,440 --> 00:06:01,159 Speaker 6: asked me about it every issue on this list. 123 00:06:01,520 --> 00:06:05,760 Speaker 4: She was being blessed. The reporter was laughing. She wasn't 124 00:06:05,800 --> 00:06:07,760 Speaker 4: being aggressive by any stretch. 125 00:06:08,520 --> 00:06:10,400 Speaker 6: And if every question you're going to make up a 126 00:06:10,400 --> 00:06:12,479 Speaker 6: follow up question, then we're never going to get there 127 00:06:13,960 --> 00:06:16,640 Speaker 6: and we're just going to circle around. I anator had 128 00:06:16,680 --> 00:06:20,800 Speaker 6: to do this before ever, You've never had had annversation 129 00:06:21,240 --> 00:06:22,080 Speaker 6: to order. 130 00:06:22,920 --> 00:06:24,520 Speaker 5: Okay, but every other candidate has done this. 131 00:06:25,320 --> 00:06:28,720 Speaker 6: What part of I'm me? I'm running for governor because 132 00:06:28,720 --> 00:06:31,160 Speaker 6: I'm a leader, So I am going to make so. 133 00:06:31,120 --> 00:06:33,200 Speaker 5: You're not going to answer questions from reporters. 134 00:06:33,600 --> 00:06:34,799 Speaker 4: Okay, why don't we go through? 135 00:06:34,960 --> 00:06:37,360 Speaker 5: I will continue to ask follow up questions because that's 136 00:06:37,360 --> 00:06:39,400 Speaker 5: my job as a journalist, but I will go through 137 00:06:39,440 --> 00:06:41,240 Speaker 5: and ask these and if you don't want to answer, 138 00:06:41,279 --> 00:06:45,160 Speaker 5: you don't want to answer, so nearly every legislative. 139 00:06:45,240 --> 00:06:47,719 Speaker 6: I don't want to have an unhappy experience with you, 140 00:06:47,960 --> 00:06:49,279 Speaker 6: and I don't want to sell on camera. 141 00:06:49,560 --> 00:06:51,599 Speaker 5: I don't want to have an unhappy experience with you either. 142 00:06:51,880 --> 00:06:53,839 Speaker 5: I would love to continue to ask these questions so 143 00:06:53,839 --> 00:06:57,440 Speaker 5: that we can show our viewers what every candidate feels 144 00:06:57,480 --> 00:06:59,200 Speaker 5: about every one of these issues that they care about, 145 00:07:00,040 --> 00:07:01,919 Speaker 5: and redist you're taking us a massive issue. We're going 146 00:07:01,960 --> 00:07:04,360 Speaker 5: to do an entire story just on the responses to 147 00:07:04,480 --> 00:07:07,080 Speaker 5: that question. And I asked everybody the same follow. 148 00:07:06,880 --> 00:07:09,680 Speaker 4: Up questions, what a world, man? What a world? 149 00:07:09,720 --> 00:07:14,240 Speaker 2: Apparently, Katie Porter has had a sixty five percent staff 150 00:07:14,280 --> 00:07:18,080 Speaker 2: turnover rate that is four times higher than the average 151 00:07:18,120 --> 00:07:23,160 Speaker 2: congressional office. She's been repeatedly accused of abusing and berating 152 00:07:23,240 --> 00:07:28,000 Speaker 2: staffers until they cried. Apparently, she also sculled at her 153 00:07:28,000 --> 00:07:30,520 Speaker 2: ex husband by throwing a pot of mashed potatoes on 154 00:07:30,600 --> 00:07:34,080 Speaker 2: his head. And it's funny because of this interview, now 155 00:07:34,840 --> 00:07:39,360 Speaker 2: there's all these other clips that are coming out exposing 156 00:07:39,440 --> 00:07:44,239 Speaker 2: just what an absolute nutbar that she is. I just oh, 157 00:07:44,320 --> 00:07:47,440 Speaker 2: as Rush said, having more fun than a human being 158 00:07:47,480 --> 00:07:50,240 Speaker 2: should be allowed to have. All right, I want to 159 00:07:50,280 --> 00:07:54,360 Speaker 2: hear from you this morning, Justice at iHeartRadio dot com, 160 00:07:54,400 --> 00:07:57,000 Speaker 2: if you're listening on the iHeartRadio App. Thank you for 161 00:07:57,040 --> 00:07:59,440 Speaker 2: doing so. Leave us a talkback comment. We'll get to 162 00:07:59,480 --> 00:08:01,800 Speaker 2: those coming up. Brought to you by Lyndahl Realty ahead 163 00:08:01,800 --> 00:08:05,480 Speaker 2: of my conversation with Devide Gartenstein Ross President Donald Trump yesterday. 164 00:08:05,480 --> 00:08:08,080 Speaker 2: As you heard at the start of the show, at 165 00:08:08,080 --> 00:08:12,080 Speaker 2: a press conference from the Oval Office talking about Democrats 166 00:08:12,160 --> 00:08:14,920 Speaker 2: being on a political suicide mission with their nothing to 167 00:08:15,000 --> 00:08:18,200 Speaker 2: lose posture. I have some audio from the President I'll 168 00:08:18,200 --> 00:08:20,120 Speaker 2: share with you next and we'll get to your comments 169 00:08:20,360 --> 00:08:22,760 Speaker 2: here on twind City's News Talk at AM eleven thirty 170 00:08:22,800 --> 00:08:24,320 Speaker 2: and one oh three five FM. 171 00:08:24,400 --> 00:08:25,920 Speaker 9: Good morning, and I love your show. 172 00:08:26,200 --> 00:08:29,920 Speaker 10: HI one conference plus Paul the next day, install studios 173 00:08:30,120 --> 00:08:33,040 Speaker 10: on Twin Cities News Talk, Hey Yeah Me eleven thirty 174 00:08:33,240 --> 00:08:35,920 Speaker 10: FM one O three point five and on the free 175 00:08:36,120 --> 00:08:37,280 Speaker 10: iHeartRadio app. 176 00:08:37,720 --> 00:08:45,400 Speaker 2: It's all true, surety, don your ficial story cracks theep Oh. 177 00:08:45,480 --> 00:08:49,440 Speaker 1: Look, yet another Democrat who has power over someone. 178 00:08:49,200 --> 00:08:52,640 Speaker 10: Abuses the people underneath them to their point where they 179 00:08:52,640 --> 00:08:56,480 Speaker 10: cry they can't be questioned because they're too smart. 180 00:08:56,880 --> 00:08:59,640 Speaker 4: How dare they be held accountable for any of their opinions? 181 00:08:59,640 --> 00:09:06,440 Speaker 1: And we're people want an explanation because they get upset. Well, 182 00:09:06,480 --> 00:09:11,360 Speaker 1: that's what happens when you confront delusional people on there. 183 00:09:12,400 --> 00:09:14,920 Speaker 2: Yeah, there's a lot of videos, as I mentioned now 184 00:09:15,080 --> 00:09:18,000 Speaker 2: Katie Porter floating around. I could probably share those with 185 00:09:18,080 --> 00:09:21,560 Speaker 2: you throughout the rest of the morning. But what JD 186 00:09:21,720 --> 00:09:24,600 Speaker 2: that you found that as entertaining as I did here 187 00:09:24,600 --> 00:09:25,640 Speaker 2: on Twinday's Newstalk. 188 00:09:27,559 --> 00:09:30,120 Speaker 9: Good morning to John Mike from Minnetonka. You brought up 189 00:09:30,160 --> 00:09:34,560 Speaker 9: Rush Limbaugh and you're opening monologue and this Porter woman, 190 00:09:35,200 --> 00:09:39,400 Speaker 9: Rush would have described her as, quote, another shining jewel 191 00:09:39,640 --> 00:09:43,560 Speaker 9: of colossal ignorance. He was amazing. You're amazing as well. 192 00:09:43,640 --> 00:09:44,360 Speaker 9: Keep up the great one. 193 00:09:44,360 --> 00:09:45,800 Speaker 4: Oh thank you for that. I appreciate it. 194 00:09:47,280 --> 00:09:50,679 Speaker 2: So Democrats are on a political suicide mission as we 195 00:09:50,720 --> 00:09:52,680 Speaker 2: continue the show from the six to five one carpet, 196 00:09:52,720 --> 00:09:55,959 Speaker 2: Next Day install studios, but there's nothing to lose. A 197 00:09:56,120 --> 00:10:01,360 Speaker 2: posture and foisting a government shutdown, which continue on Americans, 198 00:10:01,800 --> 00:10:06,559 Speaker 2: according to President Donald Trump, likening their strategy to a 199 00:10:06,679 --> 00:10:08,440 Speaker 2: Kama Kazi attack. 200 00:10:08,679 --> 00:10:10,880 Speaker 3: Well, they're the ones that started it, they're the ones 201 00:10:10,880 --> 00:10:14,520 Speaker 3: that have it, and it's almost like a Kama Kazi 202 00:10:14,640 --> 00:10:16,800 Speaker 3: attack by them. You want to know the truth, This 203 00:10:16,840 --> 00:10:22,079 Speaker 3: is like a Kama Kazi attack. They they almost. You know, 204 00:10:22,360 --> 00:10:26,360 Speaker 3: they have nothing to lose. They've lost the elections. They've 205 00:10:26,400 --> 00:10:29,840 Speaker 3: lost the presidential election in a landslide. I saw the 206 00:10:29,880 --> 00:10:33,200 Speaker 3: other day where kamaliss so this was a very close election, 207 00:10:33,400 --> 00:10:36,559 Speaker 3: very This was one of the biggest sweeps that anybody's 208 00:10:36,559 --> 00:10:39,680 Speaker 3: ever had. Won the popular vote by millions, won the 209 00:10:40,640 --> 00:10:44,360 Speaker 3: electoral college by a massive amount. They said, if I 210 00:10:44,400 --> 00:10:47,080 Speaker 3: got two hundred and seventy. 211 00:10:46,800 --> 00:10:47,559 Speaker 4: That would be great. 212 00:10:48,040 --> 00:10:50,240 Speaker 3: But I got I think three hundred and twelve or 213 00:10:50,280 --> 00:10:53,880 Speaker 3: three hundred and fifteen, and they got two twenty. So 214 00:10:54,120 --> 00:10:56,320 Speaker 3: you know, we won that, but we want counties. The 215 00:10:56,320 --> 00:10:59,000 Speaker 3: big thing is counties. So out of all of the 216 00:10:59,040 --> 00:11:03,280 Speaker 3: counties thousands, we got two thousand, five hundred. They got 217 00:11:03,280 --> 00:11:06,800 Speaker 3: five hundred and twenty five. It was a landslide, and 218 00:11:07,040 --> 00:11:10,120 Speaker 3: we listen, Oh yes, it was clus It was one 219 00:11:10,160 --> 00:11:12,720 Speaker 3: of the greatest victories ever and it was a mandate 220 00:11:13,240 --> 00:11:14,200 Speaker 3: to do what we're doing. 221 00:11:15,200 --> 00:11:16,400 Speaker 4: I saw a post yesterday. 222 00:11:16,400 --> 00:11:18,680 Speaker 2: I'm actually looking at it right now on this comes 223 00:11:18,679 --> 00:11:24,360 Speaker 2: from the Conservative Alternative at Old World Order and they 224 00:11:24,360 --> 00:11:27,640 Speaker 2: wrote this, and I completely agree. I think the Democrats 225 00:11:27,679 --> 00:11:31,679 Speaker 2: realize they've lost the people's support and now they're just 226 00:11:31,720 --> 00:11:34,920 Speaker 2: trying to torch the country like a toddler throwing a 227 00:11:35,040 --> 00:11:38,840 Speaker 2: tantrum and flipping over the game board. And I completely 228 00:11:38,880 --> 00:11:41,280 Speaker 2: agree with that, but it's on various levels, and we'll 229 00:11:41,720 --> 00:11:44,320 Speaker 2: discuss that throughout the show. There's the violent end of 230 00:11:44,360 --> 00:11:46,720 Speaker 2: it and what we see taking place on the streets, 231 00:11:47,000 --> 00:11:50,800 Speaker 2: and then you have the political side of things, whether 232 00:11:50,840 --> 00:11:53,680 Speaker 2: it's commentary. We have audio of JB. Pritzker, who was 233 00:11:53,720 --> 00:11:57,239 Speaker 2: with Governor Tim Walls yesterday. Tim Walls and his ridiculous 234 00:11:57,240 --> 00:12:01,439 Speaker 2: commentary over whether or not President Donald Trump will actually 235 00:12:01,480 --> 00:12:05,520 Speaker 2: have National Guard troops come to Minnesota and Minneapolis, trust me, 236 00:12:05,520 --> 00:12:07,320 Speaker 2: that's what he wants. I don't think it's gonna happen, 237 00:12:08,120 --> 00:12:10,480 Speaker 2: and I'll get into this after seven o'clock this morning, 238 00:12:10,760 --> 00:12:13,240 Speaker 2: but that's what Governor Tim Walls wants. The over the 239 00:12:13,240 --> 00:12:16,800 Speaker 2: top rhetoric at all points to this. The Democrats, the 240 00:12:16,840 --> 00:12:19,040 Speaker 2: polling numbers, and I have the audio to share with 241 00:12:19,080 --> 00:12:21,240 Speaker 2: you of the recent polling data as well relating to 242 00:12:21,280 --> 00:12:25,720 Speaker 2: President Donald Trump. It's all bad for the left and 243 00:12:25,720 --> 00:12:29,640 Speaker 2: there's another issue that they're faced with as well. And 244 00:12:29,720 --> 00:12:32,040 Speaker 2: in the middle of commentary that I was doing on 245 00:12:32,080 --> 00:12:36,880 Speaker 2: the show Tuesday, I sort of stumbled across this as 246 00:12:36,880 --> 00:12:40,600 Speaker 2: a reminder that there is no leader for the Democrat 247 00:12:40,679 --> 00:12:44,200 Speaker 2: party right now. They don't have anybody to look at 248 00:12:44,800 --> 00:12:47,120 Speaker 2: to be sort of a guiding voice. And when you 249 00:12:47,160 --> 00:12:50,880 Speaker 2: don't have that and you have all these individuals like 250 00:12:50,960 --> 00:12:55,480 Speaker 2: the people I just mentioned that are jockeying for various 251 00:12:55,559 --> 00:12:59,600 Speaker 2: positions or wanting to move up from wherever they are 252 00:13:00,360 --> 00:13:04,079 Speaker 2: just jocking to be that voice, you end up with 253 00:13:04,120 --> 00:13:07,240 Speaker 2: the scattered commentary. I mean Donald Trump was talking about 254 00:13:07,320 --> 00:13:11,080 Speaker 2: this yesterday, the fact that the Democrats don't have a leader. 255 00:13:11,360 --> 00:13:13,360 Speaker 5: Are you going to start about the delays at airports 256 00:13:13,360 --> 00:13:15,520 Speaker 5: and how do you see the shut any Oh? 257 00:13:15,559 --> 00:13:18,760 Speaker 3: Sure, I mean it's they're all Democrat delays. There are 258 00:13:18,800 --> 00:13:21,600 Speaker 3: odd delays at the airport that standard. And again this 259 00:13:21,679 --> 00:13:25,120 Speaker 3: is something that we've every day. We put forth a bill. 260 00:13:25,840 --> 00:13:28,360 Speaker 3: It's just a continuation. It's a very simple thing to 261 00:13:28,440 --> 00:13:31,640 Speaker 3: sign and very simple to do. And I really think 262 00:13:31,640 --> 00:13:34,280 Speaker 3: that these are people that I think they have nothing 263 00:13:34,320 --> 00:13:36,600 Speaker 3: to lose. They have a party that's out of control, 264 00:13:36,640 --> 00:13:39,040 Speaker 3: they have no leader. Nobody knows who the leader is. 265 00:13:39,440 --> 00:13:42,280 Speaker 3: I look at people with very low IQs like Crockett. 266 00:13:42,280 --> 00:13:44,760 Speaker 3: This woman Crockett, I never met her, but she's a 267 00:13:44,800 --> 00:13:49,240 Speaker 3: low IQ individual. I look at AOC talking about how 268 00:13:49,640 --> 00:13:51,640 Speaker 3: if they want to negotiate, they can come. 269 00:13:51,520 --> 00:13:52,240 Speaker 1: To my office. 270 00:13:52,240 --> 00:13:53,840 Speaker 3: She's done in that position to do that. 271 00:13:54,520 --> 00:13:56,320 Speaker 1: And who the hell is she just say that? 272 00:13:56,760 --> 00:13:59,280 Speaker 3: And then I watched Nancy Pelosi not knowing what to do. 273 00:13:59,360 --> 00:14:03,840 Speaker 3: I watch, I watched their leadership. Look, Schumer is petrified 274 00:14:03,880 --> 00:14:06,600 Speaker 3: of primary because he's not going to win probably against 275 00:14:06,679 --> 00:14:10,080 Speaker 3: anybody in a primary. You know, Schumer did the right thing, 276 00:14:11,240 --> 00:14:12,320 Speaker 3: but he handled it badly. 277 00:14:13,040 --> 00:14:16,360 Speaker 2: And here's one more quick clip of President Donald Trumpy 278 00:14:16,480 --> 00:14:20,640 Speaker 2: yesterday speaking continuing to speak to this very issue and 279 00:14:20,760 --> 00:14:26,160 Speaker 2: his latest go to in terms of anecdotal stories relating 280 00:14:26,240 --> 00:14:28,920 Speaker 2: to leadership and specifically Somalia. 281 00:14:29,160 --> 00:14:31,960 Speaker 3: I'll tell you what. I'm getting calls from Democrats wanting 282 00:14:32,000 --> 00:14:35,280 Speaker 3: to meet I never even heard their names before, and 283 00:14:35,320 --> 00:14:38,080 Speaker 3: they're claiming to be leading. The Democrats have no leader. 284 00:14:39,640 --> 00:14:44,640 Speaker 3: They remind me of Somalia. Okay, you know, and I 285 00:14:44,720 --> 00:14:47,320 Speaker 3: met the president of Somalia. I told him about the problem. 286 00:14:47,360 --> 00:14:50,240 Speaker 3: He scott, I said, you have somebody from Somalia who's 287 00:14:50,280 --> 00:14:53,119 Speaker 3: telling us how to run our country from Somalia. 288 00:14:53,160 --> 00:14:53,920 Speaker 10: He said, would you. 289 00:14:53,920 --> 00:14:56,240 Speaker 3: Like to take her back? He said, no, I don't 290 00:14:56,240 --> 00:14:56,640 Speaker 3: want her. 291 00:14:57,400 --> 00:15:02,400 Speaker 4: Okay, you know who I'm talking about, all right? The 292 00:15:02,480 --> 00:15:04,760 Speaker 4: vid Gartz and stein Ross is going to join us next. 293 00:15:05,160 --> 00:15:09,720 Speaker 2: AI is helping some Minnesota police officers write reports. But 294 00:15:09,920 --> 00:15:14,120 Speaker 2: with this, prosecutors in Minnesota are having to navigate how 295 00:15:14,160 --> 00:15:17,800 Speaker 2: to deal with those AI generated police reports, while the 296 00:15:18,520 --> 00:15:22,600 Speaker 2: anti cop crowd is very concerned apparently about the use 297 00:15:22,680 --> 00:15:25,280 Speaker 2: of AI. We'll get your questions in as well. You 298 00:15:25,280 --> 00:15:28,880 Speaker 2: can leave those via the iHeartRadio app. Talkbacks here on 299 00:15:29,000 --> 00:15:31,040 Speaker 2: Twin Cities News Talk Mam eleven thirty and one oh 300 00:15:31,080 --> 00:15:33,320 Speaker 2: three five FM. 301 00:15:33,600 --> 00:15:37,800 Speaker 11: Quarren, John think Play and Cauldon here back around the 302 00:15:38,240 --> 00:15:40,440 Speaker 11: time of the election that what we're seeing is the 303 00:15:40,560 --> 00:15:44,520 Speaker 11: destruction is fracturing of the Democrat Party. They're just gonna 304 00:15:44,600 --> 00:15:49,240 Speaker 11: keep stooping lower and lower, shedding voter after voter, and 305 00:15:49,600 --> 00:15:52,080 Speaker 11: they're eventually going to fracture into multiple parties. 306 00:15:53,120 --> 00:16:02,640 Speaker 4: I ready to get beat Twistity's news talking. 307 00:16:02,680 --> 00:16:05,960 Speaker 2: I'm eleven thirty one or three five FM from the 308 00:16:06,000 --> 00:16:09,520 Speaker 2: six five to one Carpet Next Day Install Studios. Do 309 00:16:09,600 --> 00:16:12,240 Speaker 2: you have a question for d v to gartenstein Ross, 310 00:16:12,440 --> 00:16:16,000 Speaker 2: the CEO at Expert Theory in our AI Experts and 311 00:16:16,280 --> 00:16:18,880 Speaker 2: analyst you can leave those via the talkback on the 312 00:16:18,920 --> 00:16:22,320 Speaker 2: iHeartRadio app, and he joins us once again, this morning, 313 00:16:22,360 --> 00:16:23,000 Speaker 2: Good morning, Devid. 314 00:16:23,000 --> 00:16:25,880 Speaker 1: How we doing, Good morning, John, We're doing great. 315 00:16:26,240 --> 00:16:29,920 Speaker 2: I'm fascinated by this cycle, at least as I see it. 316 00:16:30,080 --> 00:16:34,800 Speaker 2: Wherein I can usually tell when an app has updated 317 00:16:35,080 --> 00:16:40,120 Speaker 2: its features specific and relative to AI. When suddenly a 318 00:16:40,200 --> 00:16:44,680 Speaker 2: bunch of AI generated parody videos begin appearing on my feed. 319 00:16:45,200 --> 00:16:47,840 Speaker 2: Things have gotten quiet for a little while, but then suddenly, 320 00:16:47,960 --> 00:16:51,240 Speaker 2: now I see these Sora ai videos that have just 321 00:16:51,320 --> 00:16:53,800 Speaker 2: taken the next step in realism and people that have 322 00:16:53,880 --> 00:16:54,880 Speaker 2: been taking advantage of it. 323 00:16:54,920 --> 00:16:56,280 Speaker 4: I find the whole thing fascinating. 324 00:16:56,360 --> 00:17:00,120 Speaker 1: David, I agree. I think we were talking about this 325 00:17:00,400 --> 00:17:01,280 Speaker 1: last week, weren't we. 326 00:17:02,080 --> 00:17:04,320 Speaker 4: I believe so, and it really stepped out the course 327 00:17:04,359 --> 00:17:04,960 Speaker 4: of the past week. 328 00:17:05,680 --> 00:17:09,000 Speaker 8: Yeah, obviously we didn't have this leap forward last week, 329 00:17:09,080 --> 00:17:11,040 Speaker 8: but what we were talking about, if I recall the 330 00:17:11,119 --> 00:17:17,880 Speaker 8: question correctly, you asked me about AI not meeting people's expectations, right, Yeah, 331 00:17:18,119 --> 00:17:21,840 Speaker 8: And like your example was actually Sora videos and the 332 00:17:22,359 --> 00:17:25,440 Speaker 8: moment when there was this leap forward with lots of 333 00:17:25,560 --> 00:17:29,639 Speaker 8: videos out there, and then suddenly it bottomed out, and 334 00:17:29,720 --> 00:17:32,040 Speaker 8: I think I was making the point that AI, like 335 00:17:32,200 --> 00:17:34,240 Speaker 8: to me, really has not disappointed. 336 00:17:34,640 --> 00:17:34,760 Speaker 9: Right. 337 00:17:35,240 --> 00:17:36,440 Speaker 1: It's transforming our world. 338 00:17:36,520 --> 00:17:39,760 Speaker 8: And sometimes you'll have people binging on one particular feature 339 00:17:40,080 --> 00:17:44,040 Speaker 8: and then using it less, but that you know, AI 340 00:17:44,280 --> 00:17:47,240 Speaker 8: has already fundamentally reshaped a lot of the way we 341 00:17:47,320 --> 00:17:49,360 Speaker 8: think about labor, a lot of the way we think 342 00:17:49,359 --> 00:17:51,640 Speaker 8: about what we're able to create. So to me, it's 343 00:17:51,680 --> 00:17:55,000 Speaker 8: a good extension of this observation based on kind of 344 00:17:55,040 --> 00:17:57,240 Speaker 8: the short term of people surging in their use of 345 00:17:57,320 --> 00:17:59,960 Speaker 8: Sora and then tailing off. It'll probably happened again here, 346 00:18:00,600 --> 00:18:03,560 Speaker 8: but we've seen another major leap forward, and yet again 347 00:18:04,000 --> 00:18:06,960 Speaker 8: I think AI, you know, delights us and does stuff 348 00:18:06,960 --> 00:18:08,160 Speaker 8: we didn't realize as possible. 349 00:18:08,440 --> 00:18:08,760 Speaker 9: Well, and. 350 00:18:10,840 --> 00:18:14,760 Speaker 2: Do you remember again talking with David garten Steinrosti, David 351 00:18:14,800 --> 00:18:19,680 Speaker 2: garten Stein Ross, do you remember The Soup, the show 352 00:18:19,720 --> 00:18:22,480 Speaker 2: where they would go back and had various hosts and 353 00:18:22,520 --> 00:18:25,439 Speaker 2: they would cover the talk shows and just play clips 354 00:18:25,480 --> 00:18:28,200 Speaker 2: from specific shows in a half hour format. 355 00:18:28,240 --> 00:18:29,000 Speaker 4: Do you remember that show. 356 00:18:31,119 --> 00:18:33,520 Speaker 8: I'm familiar with the idea, I don't remember the specific show. 357 00:18:33,520 --> 00:18:35,000 Speaker 8: I don't think it's called The Soup. 358 00:18:35,119 --> 00:18:36,000 Speaker 4: Yeah, it was called the Soup. 359 00:18:36,160 --> 00:18:36,320 Speaker 1: Yeah. 360 00:18:36,359 --> 00:18:38,240 Speaker 4: This goes back to early two thousands. 361 00:18:39,000 --> 00:18:41,359 Speaker 8: Okay, I don't remember it, but I know the idea 362 00:18:41,400 --> 00:18:42,760 Speaker 8: of the show, a show like that, so I can 363 00:18:43,080 --> 00:18:43,960 Speaker 8: play along here, John. 364 00:18:44,080 --> 00:18:48,600 Speaker 2: I only bring it up because I would absolutely watch 365 00:18:48,960 --> 00:18:51,800 Speaker 2: that style of show where it was just you know, 366 00:18:51,920 --> 00:18:55,400 Speaker 2: twenty minutes to a half hour devoted to the best 367 00:18:55,640 --> 00:19:00,520 Speaker 2: AI generated videos. Because there's this cycle that pops up 368 00:19:00,560 --> 00:19:04,520 Speaker 2: again kind of keeping with this, wherein these videos emerge 369 00:19:04,880 --> 00:19:08,920 Speaker 2: and they look so real, they're very different in terms 370 00:19:09,000 --> 00:19:12,040 Speaker 2: of the of the content, and it takes me and 371 00:19:12,160 --> 00:19:14,440 Speaker 2: other individuals a minute to realize, Oh, it's AI. 372 00:19:14,600 --> 00:19:15,920 Speaker 4: And the latest trend has. 373 00:19:15,920 --> 00:19:22,200 Speaker 2: Been children on doorsteps with wild animals approaching and like 374 00:19:22,400 --> 00:19:25,240 Speaker 2: parents coming and rescuing them. And I saw one or 375 00:19:25,280 --> 00:19:26,840 Speaker 2: two of these and I'm like, well, these are wild 376 00:19:26,960 --> 00:19:30,440 Speaker 2: videos and then I'm like, oh, duh, John, it's AI related. 377 00:19:33,600 --> 00:19:39,000 Speaker 1: Yeah. I mean, I think this points to a the 378 00:19:39,080 --> 00:19:44,199 Speaker 1: fact that AI has gotten so good that we. 379 00:19:44,440 --> 00:19:47,680 Speaker 8: Don't realize right away whether what we're seeing is really 380 00:19:47,760 --> 00:19:51,560 Speaker 8: or not seeing is no longer believing, and I think 381 00:19:51,680 --> 00:19:54,680 Speaker 8: it's already having an impact on how people understand the world. 382 00:19:54,800 --> 00:19:58,760 Speaker 8: I mean, there's there are already places where there's AI 383 00:19:59,320 --> 00:20:03,600 Speaker 8: video that it has fooled people and ill formed fundamentally 384 00:20:03,640 --> 00:20:06,160 Speaker 8: their impression of something going on in the world. 385 00:20:07,080 --> 00:20:10,440 Speaker 2: Well, let's move over from AI and its realism to 386 00:20:11,119 --> 00:20:16,000 Speaker 2: its accuracy or lack thereof a couple of stories here. First, 387 00:20:16,040 --> 00:20:20,000 Speaker 2: one out of Channel five locally, police officers in Minnesota 388 00:20:20,160 --> 00:20:26,359 Speaker 2: are increasingly using artificial intelligence to write police reports. The 389 00:20:26,440 --> 00:20:31,359 Speaker 2: technology is from Axon Enterprise. This is an Arizona based company, 390 00:20:32,000 --> 00:20:36,440 Speaker 2: the maker of the taser and outfits thousands of police 391 00:20:36,480 --> 00:20:40,200 Speaker 2: departments across the country with body cameras, evidence management systems, 392 00:20:40,200 --> 00:20:42,800 Speaker 2: and other equipment. And essentially this is a for my 393 00:20:42,920 --> 00:20:45,000 Speaker 2: understanding and reading of the article DIVIAT, it looks like 394 00:20:45,040 --> 00:20:49,520 Speaker 2: it's a combination of using the body camera footage and 395 00:20:49,640 --> 00:20:53,560 Speaker 2: then AI to basically write what would typically be, you know, 396 00:20:53,640 --> 00:20:57,120 Speaker 2: a lengthy process in terms of the reporting. 397 00:20:57,160 --> 00:20:58,960 Speaker 4: Am I getting the understanding of this correct? 398 00:20:59,720 --> 00:21:00,400 Speaker 1: Yeah? Absolutely? 399 00:21:01,119 --> 00:21:05,320 Speaker 2: So this is bringing about some concern specifically from the ACLU, 400 00:21:05,520 --> 00:21:08,119 Speaker 2: and we'll get into how prosecutors are looking at this 401 00:21:08,240 --> 00:21:11,720 Speaker 2: as well. But the ACLU authored a white paper back 402 00:21:11,760 --> 00:21:15,679 Speaker 2: in December arguing that because police reports play a crucial 403 00:21:15,800 --> 00:21:20,560 Speaker 2: role in our justice systems, officers shouldn't use AI to 404 00:21:20,760 --> 00:21:24,520 Speaker 2: help right them. I would suspect that the inclusion of 405 00:21:24,560 --> 00:21:29,960 Speaker 2: the body camera footage would substantiate to any legitimacy of 406 00:21:30,080 --> 00:21:31,400 Speaker 2: the AI summary. 407 00:21:31,640 --> 00:21:33,520 Speaker 4: But what do you and what are your thoughts on 408 00:21:33,600 --> 00:21:35,080 Speaker 4: the concern from the ACLU here. 409 00:21:35,960 --> 00:21:37,480 Speaker 1: I disagree with those concerns. 410 00:21:38,920 --> 00:21:42,280 Speaker 8: You know, the number one, as you said, body camera 411 00:21:42,359 --> 00:21:47,800 Speaker 8: footage mitigates the chance of fabrication. Number two, in using 412 00:21:47,920 --> 00:21:50,480 Speaker 8: AI to formulate the report, they're still responsible for the 413 00:21:50,560 --> 00:21:55,320 Speaker 8: report fundamentally, right, like it can draft or report, you 414 00:21:55,440 --> 00:21:57,400 Speaker 8: need to go through it, be accountable for every word. 415 00:21:58,320 --> 00:22:02,280 Speaker 8: And so you know, if the AI hallucinates or lies, 416 00:22:02,720 --> 00:22:06,199 Speaker 8: the person should catch it, and if they don't, they 417 00:22:06,240 --> 00:22:09,240 Speaker 8: should be subject to sanctions or punishment, which is exactly 418 00:22:09,280 --> 00:22:14,640 Speaker 8: what should happen if a police officer lies in a report. Obviously, 419 00:22:15,080 --> 00:22:17,600 Speaker 8: you know, prior to the advent of AI, there have 420 00:22:17,720 --> 00:22:20,959 Speaker 8: been problems with police officers fabricating reports. It's something that happens. 421 00:22:21,040 --> 00:22:24,440 Speaker 8: Lots of people in positions of authority have misused that authority. 422 00:22:25,160 --> 00:22:31,960 Speaker 8: And so to me, the solution is really teaching the 423 00:22:32,000 --> 00:22:35,320 Speaker 8: ethics of AI, as opposed to saying do not use. 424 00:22:36,000 --> 00:22:38,480 Speaker 8: It's going to make police officers more effective. If done, 425 00:22:38,520 --> 00:22:41,719 Speaker 8: writing can make police officer reports more accurate because they 426 00:22:41,760 --> 00:22:43,800 Speaker 8: have more time to think about all of the claims 427 00:22:43,800 --> 00:22:46,240 Speaker 8: that are in there and have to spend less time 428 00:22:46,359 --> 00:22:48,600 Speaker 8: with the mechanics of writing like That's one of the 429 00:22:48,640 --> 00:22:50,560 Speaker 8: things that AI allows us to do, to focus on 430 00:22:50,680 --> 00:22:53,520 Speaker 8: what we are saying and focus less on the mechanics 431 00:22:53,600 --> 00:22:56,920 Speaker 8: of how we put together what is being said. But 432 00:22:57,000 --> 00:22:59,920 Speaker 8: it depends on officers knowing how to use AI. 433 00:23:00,280 --> 00:23:00,480 Speaker 7: Well. 434 00:23:01,520 --> 00:23:06,080 Speaker 8: I think that there are legitimately concerns there. I believe 435 00:23:06,280 --> 00:23:09,480 Speaker 8: that AI should help officers rite reports fundamentally. 436 00:23:09,880 --> 00:23:13,200 Speaker 2: I want to follow up with this subsequent story, also 437 00:23:13,240 --> 00:23:16,400 Speaker 2: from Channel five here in a moment relating to prosecutors 438 00:23:16,720 --> 00:23:19,320 Speaker 2: in Minnesota, and the headline is navigating how to deal 439 00:23:19,400 --> 00:23:22,760 Speaker 2: with AI generated police reports. Before I do that, talking 440 00:23:22,800 --> 00:23:25,480 Speaker 2: with our AI expert, VD Gartzenstein Ross, let's go to 441 00:23:25,520 --> 00:23:29,600 Speaker 2: the iHeartRadio app talkbacks and a comment based on the 442 00:23:29,760 --> 00:23:31,240 Speaker 2: start of our conversation this morning. 443 00:23:31,440 --> 00:23:32,280 Speaker 1: Good morning, John. 444 00:23:33,080 --> 00:23:36,040 Speaker 4: I'm not gonna lie, John, you ruined that for me. 445 00:23:36,160 --> 00:23:39,560 Speaker 8: I saw those videos of animals and like a baby 446 00:23:39,640 --> 00:23:42,760 Speaker 8: sitting in front yard where a cat chased away a 447 00:23:42,840 --> 00:23:44,320 Speaker 8: bear right before the parent came out. 448 00:23:45,080 --> 00:23:47,240 Speaker 1: I honestly thought those were real and now I feel 449 00:23:47,240 --> 00:23:47,720 Speaker 1: like it cool. 450 00:23:49,440 --> 00:23:52,760 Speaker 4: Thanks d V and John, don't see like a fool. 451 00:23:52,880 --> 00:23:53,640 Speaker 4: I did too. 452 00:23:53,880 --> 00:23:55,919 Speaker 2: It wasn't until I saw the follow up where an 453 00:23:55,960 --> 00:24:00,320 Speaker 2: alligator was walking up to the doorstep with the child. Okay, 454 00:24:00,440 --> 00:24:02,680 Speaker 2: this is not happening all the time now, and I 455 00:24:03,400 --> 00:24:05,600 Speaker 2: ended up realizing that it was AI. 456 00:24:05,800 --> 00:24:09,200 Speaker 1: It was AI generated da vide, so like an alligator. 457 00:24:09,240 --> 00:24:10,399 Speaker 1: That was the breaking point for you? 458 00:24:10,600 --> 00:24:12,320 Speaker 2: That was the breaking point. Yeah, it was a child 459 00:24:12,400 --> 00:24:14,200 Speaker 2: trying to give a lollipop to an alligator and the 460 00:24:14,359 --> 00:24:16,800 Speaker 2: and the mom came out and I came out and 461 00:24:16,960 --> 00:24:19,280 Speaker 2: rescued them, and I'm watching the video and I'm thinking, 462 00:24:19,400 --> 00:24:23,080 Speaker 2: I'm shocked the alligator got that close and didn't strike. 463 00:24:23,240 --> 00:24:25,000 Speaker 2: And then it was at that moment that I saw 464 00:24:25,119 --> 00:24:30,000 Speaker 2: the little sorah moving you know, message on the screen. 465 00:24:29,800 --> 00:24:31,760 Speaker 4: Going Okay, I got I got duped again. 466 00:24:33,480 --> 00:24:33,760 Speaker 9: I was. 467 00:24:34,880 --> 00:24:38,520 Speaker 8: I mean, I'm just waiting for the wooly mammoth or velociraptor. 468 00:24:39,440 --> 00:24:39,560 Speaker 5: Right. 469 00:24:40,160 --> 00:24:41,600 Speaker 2: Well, let me let me add something to this and 470 00:24:41,640 --> 00:24:43,200 Speaker 2: then we'll get back to the to the to the 471 00:24:43,240 --> 00:24:47,199 Speaker 2: AI generated police reports. But there is one specific account 472 00:24:47,240 --> 00:24:49,200 Speaker 2: that's out there and I haven't seen it. I don't 473 00:24:49,240 --> 00:24:50,920 Speaker 2: follow it. I haven't seen it pop up on my 474 00:24:51,000 --> 00:24:54,280 Speaker 2: feed for for a while, but they had been constantly 475 00:24:54,440 --> 00:24:58,960 Speaker 2: posting videos of specifically President Donald Trump, and I believe 476 00:24:58,960 --> 00:25:02,240 Speaker 2: they're actually fans of Trump, to be honest, but they 477 00:25:02,280 --> 00:25:05,679 Speaker 2: were AI generated videos where they were changing what President 478 00:25:05,760 --> 00:25:08,320 Speaker 2: Donald Trump was saying. They were, you know, taken from 479 00:25:08,400 --> 00:25:12,080 Speaker 2: an Oval office visit and looks like pre existing video footage. 480 00:25:12,359 --> 00:25:15,679 Speaker 2: There's another one going around of an exchange between Caitlin 481 00:25:15,760 --> 00:25:20,359 Speaker 2: Collins and CNN and il Hannah Omar. That's rather funny 482 00:25:20,359 --> 00:25:22,440 Speaker 2: that I will never play on the air or retweet, 483 00:25:22,520 --> 00:25:28,040 Speaker 2: but it's interesting because there's no disclaimers on them right 484 00:25:28,400 --> 00:25:31,359 Speaker 2: They're leaving them up to the viewer to decide in 485 00:25:31,960 --> 00:25:34,080 Speaker 2: I'm kind of surprised that X hasn't. 486 00:25:33,960 --> 00:25:37,640 Speaker 4: Moved to require that, and I guess the question is, should. 487 00:25:37,640 --> 00:25:41,359 Speaker 2: X move to require that AI generated videos, you know, 488 00:25:41,680 --> 00:25:44,320 Speaker 2: notate that they're AI generated or should it be left 489 00:25:44,400 --> 00:25:45,919 Speaker 2: up to the community notes. 490 00:25:45,720 --> 00:25:46,080 Speaker 5: To do that. 491 00:25:47,040 --> 00:25:50,119 Speaker 8: No, they should require it in my view, because what 492 00:25:50,280 --> 00:25:52,320 Speaker 8: we're seeing right now is sort of you can call 493 00:25:52,359 --> 00:25:55,680 Speaker 8: them parodies. Sure, we're going to have deep fakes that 494 00:25:55,720 --> 00:25:58,840 Speaker 8: are used for disinformation, and so it's going to be 495 00:25:58,960 --> 00:26:03,320 Speaker 8: used by far in the state, and that's an issue. 496 00:26:05,240 --> 00:26:09,359 Speaker 8: You know, we fundamentally, as a platform, you can't become 497 00:26:09,440 --> 00:26:12,840 Speaker 8: the platform that is known where you have no idea 498 00:26:12,920 --> 00:26:17,320 Speaker 8: what's real and what's not, just as a matter of 499 00:26:17,400 --> 00:26:21,399 Speaker 8: kind of preserving the platform from you know, AI gunk 500 00:26:21,800 --> 00:26:24,600 Speaker 8: that distorts our view of reality. I think that their 501 00:26:24,720 --> 00:26:27,280 Speaker 8: terms of that that they should require that so that 502 00:26:27,359 --> 00:26:28,800 Speaker 8: they have the option to take it down. 503 00:26:29,800 --> 00:26:32,520 Speaker 4: Is gunk different than slop in your opinion? 504 00:26:34,880 --> 00:26:36,920 Speaker 1: Actually so slop. 505 00:26:37,040 --> 00:26:39,280 Speaker 8: You know, we talked about AI WORKSLOP a little while 506 00:26:39,400 --> 00:26:42,479 Speaker 8: back at AI workslop is just bad work produced with AI. Right, 507 00:26:43,160 --> 00:26:45,399 Speaker 8: I think I'm using gunk the opposite of slop. Right 508 00:26:45,960 --> 00:26:48,520 Speaker 8: that what I'm describing this as gunk. What I'm saying 509 00:26:48,600 --> 00:26:51,920 Speaker 8: is that it's actually so effective and looks so real 510 00:26:52,720 --> 00:26:56,359 Speaker 8: that it erodes our understanding of what's really happening and 511 00:26:56,440 --> 00:27:01,040 Speaker 8: what's not. So I would describe gunk as really really 512 00:27:01,080 --> 00:27:07,320 Speaker 8: effective forgery is that fundamentally can make your social media 513 00:27:07,400 --> 00:27:11,800 Speaker 8: platform become a rabbit hole where you jumped into things 514 00:27:11,880 --> 00:27:15,320 Speaker 8: that aren't real, aren't happening, and have no idea how 515 00:27:15,359 --> 00:27:16,600 Speaker 8: to separate truth from fiction. 516 00:27:17,280 --> 00:27:22,800 Speaker 2: I'm fascinated by or I'm anticipating and looking forward to 517 00:27:23,000 --> 00:27:25,840 Speaker 2: and interested in just how and I know, really going 518 00:27:25,880 --> 00:27:27,680 Speaker 2: off the rails here, but just popped into my mind 519 00:27:27,720 --> 00:27:30,159 Speaker 2: because because I am I'm interested in this and what 520 00:27:30,359 --> 00:27:32,920 Speaker 2: kind of impact that AI, in this AI generation is 521 00:27:33,000 --> 00:27:35,680 Speaker 2: just going to have on entertainment as a as a 522 00:27:35,800 --> 00:27:39,200 Speaker 2: whole and what we consume and what we are, you know, 523 00:27:39,560 --> 00:27:43,359 Speaker 2: what we laugh at, and the creating aspect of this too, 524 00:27:43,480 --> 00:27:45,600 Speaker 2: and what this does and giving the you know, the 525 00:27:45,720 --> 00:27:49,240 Speaker 2: ability to create realistic images and somebody who might have 526 00:27:49,320 --> 00:27:51,399 Speaker 2: a great sense of humor but now has a tool 527 00:27:51,920 --> 00:27:54,280 Speaker 2: right there on their keyboard for free to go and 528 00:27:54,440 --> 00:27:58,480 Speaker 2: generate content. I'm really curious to see just what kind 529 00:27:58,480 --> 00:28:01,000 Speaker 2: of impact this has as a whole whole on pop 530 00:28:01,040 --> 00:28:03,720 Speaker 2: culture and and entertainment. I don't you know, I don't 531 00:28:03,720 --> 00:28:06,320 Speaker 2: see a lot of discussion going on around that. But 532 00:28:06,400 --> 00:28:08,359 Speaker 2: then again, I also haven't been looking David. 533 00:28:09,680 --> 00:28:12,560 Speaker 1: Yeah, I mean, I think it's because. 534 00:28:14,240 --> 00:28:16,760 Speaker 8: We have trouble getting our minds wrapped around this from 535 00:28:16,800 --> 00:28:21,240 Speaker 8: all different angles, right, you know, from the angle of 536 00:28:21,400 --> 00:28:23,920 Speaker 8: what it does to creativity, from the angle of what 537 00:28:24,040 --> 00:28:29,240 Speaker 8: it does to you know, stalkers or harassers. Yeah, like 538 00:28:29,320 --> 00:28:32,879 Speaker 8: imagine the all the music, social media accounts with like 539 00:28:33,480 --> 00:28:35,679 Speaker 8: fake videos of John Justice that can be out there 540 00:28:35,720 --> 00:28:36,520 Speaker 8: in the future. 541 00:28:36,320 --> 00:28:39,200 Speaker 4: For the oh, don't don't throw that into the holy 542 00:28:40,520 --> 00:28:41,160 Speaker 4: But I mean the. 543 00:28:41,280 --> 00:28:42,360 Speaker 8: Point is, like you and I are both in the 544 00:28:42,400 --> 00:28:46,520 Speaker 8: public sphere, right, we understand kind of the dark corners, 545 00:28:46,720 --> 00:28:49,920 Speaker 8: and like the weirdos who are going to, you know, 546 00:28:50,000 --> 00:28:52,160 Speaker 8: find a way to come at you with whatever technology 547 00:28:52,200 --> 00:28:52,440 Speaker 8: they have. 548 00:28:53,040 --> 00:28:55,040 Speaker 1: So my point is, just think the dark and the 549 00:28:55,160 --> 00:28:56,000 Speaker 1: light coexist. 550 00:28:56,440 --> 00:28:59,400 Speaker 8: There's this amazing creativity that's going to be unleashed, an 551 00:28:59,400 --> 00:29:02,840 Speaker 8: amazing kind of destructive creativity that's going to be unleashed 552 00:29:02,840 --> 00:29:07,120 Speaker 8: at the same time. And this is like years ago, John, 553 00:29:07,480 --> 00:29:09,360 Speaker 8: I actually want to kind of analogize this to something 554 00:29:09,360 --> 00:29:12,520 Speaker 8: because you and I we've been on the air together. 555 00:29:12,320 --> 00:29:14,680 Speaker 1: For a long time. Yeah, yeah, I really want to 556 00:29:15,200 --> 00:29:15,760 Speaker 1: eighteen years. 557 00:29:16,000 --> 00:29:17,000 Speaker 4: Yeah, just about it. 558 00:29:17,000 --> 00:29:19,160 Speaker 8: And so we've we've been together, you know, back when 559 00:29:19,200 --> 00:29:22,400 Speaker 8: I was more of a substantive commentator on geopolitics, and 560 00:29:22,680 --> 00:29:27,320 Speaker 8: there are these issues where you know, I understood the 561 00:29:27,320 --> 00:29:30,400 Speaker 8: world differently than most people, and I think the areas 562 00:29:30,400 --> 00:29:32,840 Speaker 8: where I understood differently have aged well. Like the Air 563 00:29:32,880 --> 00:29:35,840 Speaker 8: of Spring, we had this massive optimism about what social 564 00:29:35,880 --> 00:29:38,440 Speaker 8: media is going to do that. It's democratic movements will 565 00:29:38,480 --> 00:29:42,000 Speaker 8: overthrow authoritarians. And I remember warning at the time about 566 00:29:42,080 --> 00:29:44,320 Speaker 8: all the other uses we'd see of social media, which 567 00:29:44,360 --> 00:29:46,560 Speaker 8: is you know, I think that's a set of views 568 00:29:46,600 --> 00:29:50,080 Speaker 8: that have aged well. And you know, one of the 569 00:29:50,160 --> 00:29:52,440 Speaker 8: interesting questions I think when people look at social media 570 00:29:52,480 --> 00:29:54,640 Speaker 8: this day of age, we see it as necessary. 571 00:29:55,520 --> 00:29:56,800 Speaker 1: But I think a lot of people if. 572 00:29:56,680 --> 00:29:58,800 Speaker 8: They could stamp their fingers and go back to the 573 00:29:58,880 --> 00:30:02,880 Speaker 8: pre social media world world, many would many wouldn't. But 574 00:30:02,960 --> 00:30:07,280 Speaker 8: there's a downside, downside and anxiety and psychological disorder and 575 00:30:07,800 --> 00:30:14,160 Speaker 8: you know, suicides, and you know, and ostracism and uh, 576 00:30:14,760 --> 00:30:19,720 Speaker 8: you know, the mobilization of extraordinarily intolerant movements across multiple issues. 577 00:30:19,760 --> 00:30:22,360 Speaker 1: Sets Like, there's all these downsides that come with the upside. 578 00:30:22,920 --> 00:30:24,680 Speaker 8: And I wonder if we're going to look at AI 579 00:30:24,880 --> 00:30:30,080 Speaker 8: the same way in you know, ten years, people if 580 00:30:30,120 --> 00:30:32,120 Speaker 8: they could, they would snap their fingers and go back 581 00:30:32,160 --> 00:30:34,760 Speaker 8: to the pre AI world, kind of like social media. 582 00:30:35,280 --> 00:30:37,400 Speaker 8: It's just something that you wouldn't be able to do. 583 00:30:38,200 --> 00:30:41,440 Speaker 8: I hope that's not the case. But you know, darkness 584 00:30:41,520 --> 00:30:44,680 Speaker 8: and light coexist. Yeah, we're going to see both at 585 00:30:44,760 --> 00:30:46,640 Speaker 8: scale with artificial intelligence. 586 00:30:46,960 --> 00:30:50,240 Speaker 4: So let's touch upon this the prosecutor aspect. 587 00:30:50,280 --> 00:30:53,000 Speaker 2: They just have one quick comment and you can respond 588 00:30:53,080 --> 00:30:57,040 Speaker 2: talking with David Gartenstein Ross again, the CEO of Expert Theory, 589 00:30:57,080 --> 00:30:58,960 Speaker 2: and then we'll work through some of the talkbacks that 590 00:30:59,080 --> 00:31:02,320 Speaker 2: have rolled in. But in this subsequent article about prosecutors 591 00:31:02,360 --> 00:31:04,040 Speaker 2: dealing with what we were talking about a moment ago, 592 00:31:04,320 --> 00:31:06,280 Speaker 2: and that is the combination of using AI and the 593 00:31:06,320 --> 00:31:09,640 Speaker 2: body cameras to create the police reports that are very 594 00:31:09,680 --> 00:31:13,720 Speaker 2: time consuming. In this they talk about again the concerns 595 00:31:13,760 --> 00:31:18,640 Speaker 2: of AI generated police reports materializing, says one individual, At 596 00:31:18,680 --> 00:31:22,000 Speaker 2: every step of the trial process, an officer is going 597 00:31:22,040 --> 00:31:24,480 Speaker 2: to be cross examined by the defense on work that 598 00:31:24,640 --> 00:31:26,760 Speaker 2: the AI did in the case, and so the officer 599 00:31:26,960 --> 00:31:30,239 Speaker 2: her or himself needs to be able to articulate how 600 00:31:30,280 --> 00:31:32,719 Speaker 2: that AI process worked. I mean, this really goes back 601 00:31:32,760 --> 00:31:35,320 Speaker 2: to what we've talked about all along in our discussions, 602 00:31:35,360 --> 00:31:38,920 Speaker 2: and that is the continued need currently for the human 603 00:31:39,200 --> 00:31:41,720 Speaker 2: element relating to AI advancements. 604 00:31:42,400 --> 00:31:45,760 Speaker 8: Yeah, fully, and I mean the answer there is very obvious. 605 00:31:45,840 --> 00:31:47,520 Speaker 8: It's the one that I gave before, which is that 606 00:31:47,760 --> 00:31:50,200 Speaker 8: the officer is just responsible for it. The AI as 607 00:31:50,240 --> 00:31:52,719 Speaker 8: a tool, but the officer needs to go through, they 608 00:31:52,760 --> 00:31:54,400 Speaker 8: need to edit, they need to make sure that every 609 00:31:54,480 --> 00:31:56,680 Speaker 8: line is true and that they can defend it. So 610 00:31:56,760 --> 00:31:59,120 Speaker 8: it's not really about to me defending the AI process, 611 00:32:00,120 --> 00:32:02,640 Speaker 8: you know, it's about you know, if someone says, did 612 00:32:02,720 --> 00:32:05,520 Speaker 8: you use AI to help to produce your report, you say, yes, 613 00:32:05,560 --> 00:32:07,280 Speaker 8: I used it to draft and that I went through 614 00:32:07,880 --> 00:32:11,560 Speaker 8: and I ensure that every sentence was correct, and so 615 00:32:11,680 --> 00:32:14,400 Speaker 8: that it's not a cross examination about the AI process. 616 00:32:15,000 --> 00:32:18,360 Speaker 8: It's a cross examination about how you know this. You know, 617 00:32:18,520 --> 00:32:20,240 Speaker 8: how you know these facts to be correct when you 618 00:32:20,320 --> 00:32:23,000 Speaker 8: were there at the time, Just like nobody cross examines 619 00:32:23,000 --> 00:32:27,280 Speaker 8: an officer on their use of Microsoft word right, and 620 00:32:27,320 --> 00:32:29,640 Speaker 8: so they would just take for granted. I really think 621 00:32:29,720 --> 00:32:31,760 Speaker 8: this is similar. I think that of all the uses 622 00:32:31,800 --> 00:32:34,000 Speaker 8: of AI, this is one of the least problematic. It's 623 00:32:34,080 --> 00:32:37,080 Speaker 8: using AI to draft something where there's a video and 624 00:32:37,200 --> 00:32:39,400 Speaker 8: the officer was literally there on the scene. 625 00:32:40,040 --> 00:32:41,400 Speaker 2: So I had a question to rolled in for the 626 00:32:41,400 --> 00:32:42,960 Speaker 2: sake of time, Well, just give the gist of it. 627 00:32:43,120 --> 00:32:46,560 Speaker 2: It's dovetailing off of whether or not X should go 628 00:32:46,720 --> 00:32:49,400 Speaker 2: and require a marking for these AI videos. But they're 629 00:32:49,440 --> 00:32:51,360 Speaker 2: asking if you think that there should be some sort 630 00:32:51,360 --> 00:32:56,520 Speaker 2: of law relating to anything created by AI would automatically 631 00:32:56,720 --> 00:33:02,320 Speaker 2: have to have something saying, hey, this is created by AI. 632 00:33:02,440 --> 00:33:05,200 Speaker 2: I mean in terms of the law aspect of it. 633 00:33:05,280 --> 00:33:06,960 Speaker 2: I think some places have already attempted to do this. 634 00:33:07,080 --> 00:33:12,120 Speaker 2: I believe it's a really interesting question. You know, I'd 635 00:33:12,160 --> 00:33:14,560 Speaker 2: have to think about it just in general. 636 00:33:14,760 --> 00:33:16,520 Speaker 8: As I think you know, I'm not one to like 637 00:33:16,640 --> 00:33:19,400 Speaker 8: slap laws onto things just because there's a problem there. Sure, 638 00:33:19,560 --> 00:33:22,320 Speaker 8: it's an interesting question. I wouldn't say no, but I'd 639 00:33:22,360 --> 00:33:22,920 Speaker 8: be skeptical. 640 00:33:23,320 --> 00:33:25,480 Speaker 2: All right, let's throw this one in before we wrap 641 00:33:25,600 --> 00:33:27,480 Speaker 2: up with Davide Gartensteinross John. 642 00:33:27,520 --> 00:33:29,800 Speaker 7: There's kind of a telltale sheet that you can look 643 00:33:29,840 --> 00:33:32,280 Speaker 7: at when discertaining. 644 00:33:32,160 --> 00:33:33,960 Speaker 1: Something as AI. 645 00:33:35,480 --> 00:33:38,720 Speaker 7: It still looks like everything's recorded through a snapchat filter. 646 00:33:40,120 --> 00:33:44,280 Speaker 7: People's skin is way too smooth and perfect. And really, 647 00:33:44,600 --> 00:33:47,920 Speaker 7: you know, if a shot is from far away, it 648 00:33:48,200 --> 00:33:49,880 Speaker 7: can be really hard to tell, but up close you 649 00:33:49,920 --> 00:33:52,240 Speaker 7: can still usually tell. But I'm sure they'll figure that 650 00:33:52,280 --> 00:33:53,280 Speaker 7: out no matter a few months. 651 00:33:53,640 --> 00:33:55,960 Speaker 2: Devian, I would actually argue that some of these programs, 652 00:33:56,040 --> 00:33:58,520 Speaker 2: especially with the Sora update, they've kind of already figured 653 00:33:58,560 --> 00:34:00,640 Speaker 2: that out. I understand what you saying, and I agree 654 00:34:00,680 --> 00:34:03,800 Speaker 2: with it to a certain extent. But if you've seen 655 00:34:03,840 --> 00:34:05,840 Speaker 2: some of the more recent videos, I mean they've they've 656 00:34:05,920 --> 00:34:11,360 Speaker 2: they've moved beyond those telltale signs. As the talkback was mentioning, yeah, I. 657 00:34:11,360 --> 00:34:12,120 Speaker 1: Think I agree with you. 658 00:34:12,320 --> 00:34:14,120 Speaker 8: And it's similar to other things that used to be 659 00:34:14,200 --> 00:34:17,520 Speaker 8: telltale signs that something was ai, like the eyes won't 660 00:34:17,600 --> 00:34:21,120 Speaker 8: blink at this point they do. Yeah, and you're listener 661 00:34:21,200 --> 00:34:24,800 Speaker 8: in the talkback says that that that it will be 662 00:34:25,000 --> 00:34:27,239 Speaker 8: solved even if it's still as a tell, and I 663 00:34:27,320 --> 00:34:27,680 Speaker 8: agree with you. 664 00:34:27,719 --> 00:34:29,920 Speaker 1: I don't see that it is. But even if it is, 665 00:34:30,080 --> 00:34:31,600 Speaker 1: it's not going to be a tell a few months 666 00:34:31,600 --> 00:34:32,080 Speaker 1: down the line. 667 00:34:32,760 --> 00:34:37,239 Speaker 2: David gartan Stein Ross again, CEO at Experts to Theory. 668 00:34:37,320 --> 00:34:39,560 Speaker 2: I hope things are going well for you, and I 669 00:34:39,640 --> 00:34:42,799 Speaker 2: always appreciate the time that you give to us every week. 670 00:34:42,880 --> 00:34:44,040 Speaker 2: It was good talking with you this morning. 671 00:34:44,480 --> 00:34:45,240 Speaker 1: Absolutely a pleasure. 672 00:34:45,320 --> 00:34:45,440 Speaker 11: Job. 673 00:34:45,480 --> 00:34:46,800 Speaker 1: I look forward to next week. 674 00:34:47,080 --> 00:34:47,839 Speaker 4: All right, coming up. 675 00:34:48,480 --> 00:34:52,760 Speaker 2: National Guard deploys two hundred troops to Illinois for federal 676 00:34:52,840 --> 00:34:56,880 Speaker 2: protection protection amid the protests that are ongoing. 677 00:34:57,120 --> 00:34:58,759 Speaker 4: We have some audio of A. JB. 678 00:34:58,880 --> 00:35:01,279 Speaker 2: Pritzker. Certainly I'm not toning down the rhetoric. I don't 679 00:35:01,280 --> 00:35:04,040 Speaker 2: think the Democrats have any desire to go and do so. 680 00:35:04,320 --> 00:35:07,279 Speaker 2: And then Governor Tim Walls says the Twin Cities must 681 00:35:07,360 --> 00:35:11,000 Speaker 2: be ready for Trump to send in the National Guard. 682 00:35:11,680 --> 00:35:14,920 Speaker 2: I don't think that Trump has a reason yet. You 683 00:35:15,000 --> 00:35:17,160 Speaker 2: may disagree with me and feel free to leave a 684 00:35:17,320 --> 00:35:20,480 Speaker 2: comment on the iHeartRadio app. When you compare what's been 685 00:35:20,560 --> 00:35:24,560 Speaker 2: taking place in you know, say Illinois, or what's been 686 00:35:24,600 --> 00:35:27,320 Speaker 2: taking place in Portland, and you compare it to what 687 00:35:27,360 --> 00:35:29,400 Speaker 2: we've been dealing with in Minneapolis, I'm not sure the 688 00:35:29,520 --> 00:35:32,640 Speaker 2: argument's there, but I'm open to a discussion and I'd 689 00:35:32,680 --> 00:35:34,880 Speaker 2: love to hear from you vi the iHeartRadio app Talkbacks. 690 00:35:34,880 --> 00:35:36,560 Speaker 2: We'll get into that, and we have some audio Governor 691 00:35:36,600 --> 00:35:38,640 Speaker 2: Tim Walls to share as well, coming up an hour 692 00:35:38,760 --> 00:35:41,000 Speaker 2: two here on Twin Cities News Talk AM eleven thirty 693 00:35:41,040 --> 00:35:42,080 Speaker 2: and one h three five FM.