1 00:00:00,040 --> 00:00:04,080 Speaker 1: Canopegroup dot com. John, You're a total rock star. Love 2 00:00:04,120 --> 00:00:06,880 Speaker 1: you so much and love the talk radio audience all 3 00:00:06,880 --> 00:00:08,959 Speaker 1: throughout Minnesota. You do such a great job. I'm a 4 00:00:09,000 --> 00:00:09,440 Speaker 1: big fan. 5 00:00:09,680 --> 00:00:19,960 Speaker 2: Oh hell yeah made waves overnight. 6 00:00:21,600 --> 00:00:29,320 Speaker 1: That was unexpected. I heard about that. You never know 7 00:00:29,440 --> 00:00:32,680 Speaker 1: what is going to land like you just don't. I mean, 8 00:00:36,120 --> 00:00:38,000 Speaker 1: if you ever have a boss, if you work in media, 9 00:00:38,200 --> 00:00:40,839 Speaker 1: you have a boss, say make a viral videos. You 10 00:00:41,000 --> 00:00:44,879 Speaker 1: just said you don't. You just don't know. I went 11 00:00:44,880 --> 00:00:49,360 Speaker 1: on Fox News last night Mlkday. Laura Ingram had the 12 00:00:49,479 --> 00:00:53,680 Speaker 1: night off, so Kaylee mccannaney was filling in. So that 13 00:00:53,760 --> 00:00:57,320 Speaker 1: was fun. It is Twin City's news talk Am eleven 14 00:00:57,400 --> 00:00:59,480 Speaker 1: thirty one oh three five FM. My name is John 15 00:00:59,600 --> 00:01:03,760 Speaker 1: Justice Morning. That other voice you heard is Sam He's 16 00:01:03,760 --> 00:01:06,880 Speaker 1: in the master control booth, Morrina John So I was 17 00:01:06,880 --> 00:01:10,240 Speaker 1: on with Pastor Dale. Met him last night. Fantastic fellow. 18 00:01:10,959 --> 00:01:12,959 Speaker 1: They have him on the show in the very near future, 19 00:01:14,120 --> 00:01:18,959 Speaker 1: obviously talking about you know, the controversies here in Minnesota, 20 00:01:19,000 --> 00:01:24,520 Speaker 1: but specifically the city's church incident that took place, and 21 00:01:24,560 --> 00:01:26,520 Speaker 1: as these you know, peel back the curtain a little 22 00:01:26,560 --> 00:01:29,240 Speaker 1: bit on these When I do these hits, I've done 23 00:01:29,400 --> 00:01:33,120 Speaker 1: enough of them that I know that if I'm there 24 00:01:33,160 --> 00:01:37,520 Speaker 1: with somebody else, I'm probably only going to be able 25 00:01:37,560 --> 00:01:43,600 Speaker 1: to get one comment comment in. And depending on who 26 00:01:43,640 --> 00:01:46,119 Speaker 1: I'm with and how long they go and who they 27 00:01:46,160 --> 00:01:49,240 Speaker 1: go to first, I mean that could be even trim 28 00:01:49,280 --> 00:01:51,240 Speaker 1: Like the last time that I did Laura Ingram a 29 00:01:51,240 --> 00:01:54,080 Speaker 1: couple of Fridays back when she was at the Capitol. 30 00:01:54,160 --> 00:01:56,800 Speaker 1: I mean that was like stupid short. I mean I 31 00:01:56,840 --> 00:01:58,960 Speaker 1: think it was like I got like a minute. I 32 00:01:59,280 --> 00:02:01,720 Speaker 1: maybe not maybe ninety seconds. I think last night it 33 00:02:01,760 --> 00:02:03,520 Speaker 1: was about two. We have the clip. I'll share it. Yeah, 34 00:02:03,520 --> 00:02:04,960 Speaker 1: I'm looking at it right now. It's about two minutes. 35 00:02:05,120 --> 00:02:06,640 Speaker 1: We have the clip. I'll share it with you a 36 00:02:06,640 --> 00:02:10,320 Speaker 1: little bit later on this morning. So yeah, I made 37 00:02:10,360 --> 00:02:13,799 Speaker 1: my comments and said the names of the individuals, just 38 00:02:13,840 --> 00:02:15,720 Speaker 1: a few of them that were involved in that protest 39 00:02:15,760 --> 00:02:19,520 Speaker 1: on Sunday, namely the former Black Lives Matter leader, and 40 00:02:19,560 --> 00:02:23,680 Speaker 1: we'll have audio from her on Nakeema Levy Armstrong who's 41 00:02:23,680 --> 00:02:27,920 Speaker 1: doubling down and trying to rationalize what they did on Sunday, 42 00:02:27,960 --> 00:02:32,080 Speaker 1: but also that lunatic, mentally deranged dude William Kelly. Well, 43 00:02:32,320 --> 00:02:39,280 Speaker 1: I guess my saying who they were. Struck people as 44 00:02:39,800 --> 00:02:44,919 Speaker 1: people were surprised by that. It was funny because Kaylie 45 00:02:45,000 --> 00:02:48,480 Speaker 1: mcinnin he even said later on, well, I'll verify who 46 00:02:48,680 --> 00:02:50,360 Speaker 1: you know. I knew, I knew that's I was one 47 00:02:50,440 --> 00:02:52,639 Speaker 1: hundred percent sure when I made mention of the names 48 00:02:53,440 --> 00:02:55,320 Speaker 1: who they were. But even Kaylee was like, and I 49 00:02:55,360 --> 00:02:56,960 Speaker 1: get it, you know, we need to verify that that's 50 00:02:57,000 --> 00:02:59,840 Speaker 1: who those people are. But she asked me, She asked 51 00:02:59,880 --> 00:03:01,600 Speaker 1: me straight up, who were these people? You know what, 52 00:03:01,639 --> 00:03:02,919 Speaker 1: I'll just go ahead and play it now. I guess 53 00:03:02,960 --> 00:03:05,400 Speaker 1: I've teased it enough, right. The point is, I woke 54 00:03:05,480 --> 00:03:08,280 Speaker 1: up this morning got a full night sleep, Thank you 55 00:03:08,320 --> 00:03:12,600 Speaker 1: Minnie Leaf and your night gummies, because it's always difficult 56 00:03:12,600 --> 00:03:14,800 Speaker 1: when I do these hits to go to sleep. But 57 00:03:14,880 --> 00:03:17,920 Speaker 1: I woke up this morning and the way I usually 58 00:03:17,960 --> 00:03:20,280 Speaker 1: compile the audio, of which we have a ton again 59 00:03:20,360 --> 00:03:23,320 Speaker 1: today to share, I just get it from X. I 60 00:03:23,400 --> 00:03:26,200 Speaker 1: just go into my feed and I just scroll through 61 00:03:26,240 --> 00:03:29,480 Speaker 1: and start grabbing clips. And as I'm doing that, I'm like, hey, there, 62 00:03:29,480 --> 00:03:31,000 Speaker 1: I am there, I am Oh, look there, I am 63 00:03:31,240 --> 00:03:32,880 Speaker 1: Oh there. I've just it was all over the place. 64 00:03:33,600 --> 00:03:37,240 Speaker 1: I was seeing my face everywhere, my goofy glasses. 65 00:03:37,840 --> 00:03:39,880 Speaker 3: Yeah, no doubt they handled it the right way. John. 66 00:03:39,920 --> 00:03:42,960 Speaker 3: You're also in Minnesota, and some of the details I 67 00:03:43,000 --> 00:03:46,680 Speaker 3: heard Harmeat Dylan say this earlier, that there was one 68 00:03:46,680 --> 00:03:49,480 Speaker 3: of the people shouting at a family with a child 69 00:03:49,560 --> 00:03:52,760 Speaker 3: was recently arrested under catch and release in Minnesota. She 70 00:03:52,920 --> 00:03:55,200 Speaker 3: believes he was released on Friday, just two days before 71 00:03:55,200 --> 00:03:57,920 Speaker 3: the church protest. What are you hearing about these protesters 72 00:03:57,920 --> 00:04:00,200 Speaker 3: because it's very vague and I don't quite understand who 73 00:04:00,200 --> 00:04:02,000 Speaker 3: they are. We're trying to gather as much information as 74 00:04:02,000 --> 00:04:03,760 Speaker 3: we can see. 75 00:04:03,840 --> 00:04:08,040 Speaker 1: Like I said, she asked me, who are they? So 76 00:04:08,160 --> 00:04:10,720 Speaker 1: I told her, well, you kind of have a combination 77 00:04:10,840 --> 00:04:12,960 Speaker 1: of groups here. I believe the person you were speaking 78 00:04:13,000 --> 00:04:16,280 Speaker 1: about is William Kelly. He's this lunatic that apparently is 79 00:04:16,320 --> 00:04:19,200 Speaker 1: well funded, going state to state and pushing back on Ice. 80 00:04:19,520 --> 00:04:23,000 Speaker 1: The other individuals, and specifically the person who organized the 81 00:04:23,040 --> 00:04:26,640 Speaker 1: protest was Nakima Leevy Armstrong. She used to be the 82 00:04:26,680 --> 00:04:29,680 Speaker 1: head of the Black Lives Matter movement going back to 83 00:04:29,760 --> 00:04:33,080 Speaker 1: the George Floyd riots. And what we saw on Sunday 84 00:04:33,279 --> 00:04:37,279 Speaker 1: was as diabolical as it was cowardly. You know, it's 85 00:04:37,320 --> 00:04:39,920 Speaker 1: cold outside and so they have to find other ways 86 00:04:39,920 --> 00:04:42,240 Speaker 1: to go and protest. But the truth of the matter 87 00:04:42,360 --> 00:04:44,840 Speaker 1: is the reason why they did that is because they're 88 00:04:44,880 --> 00:04:49,000 Speaker 1: losing the argument on the ground against their ridiculous and 89 00:04:49,120 --> 00:04:53,320 Speaker 1: irational pushback on ICE. Thank goodness that Elon Musk now 90 00:04:53,400 --> 00:04:57,360 Speaker 1: runs X because the messaging is not only exclusive to 91 00:04:57,400 --> 00:05:00,360 Speaker 1: the left. Now conservatives are out there to counter all 92 00:05:00,400 --> 00:05:04,400 Speaker 1: the misinformation and lies about what Immigrations and Customs Enforcement 93 00:05:04,480 --> 00:05:07,240 Speaker 1: is doing. What they did on Sunday was a ramping 94 00:05:07,360 --> 00:05:10,359 Speaker 1: up because they're losing the battle on the ground, and 95 00:05:10,400 --> 00:05:13,880 Speaker 1: it really goes to show this is merely an extreme 96 00:05:14,160 --> 00:05:17,720 Speaker 1: case of Trump derangement syndrome. Now there's a little bit 97 00:05:17,720 --> 00:05:19,600 Speaker 1: more to this, and I'll share the rest for later 98 00:05:19,680 --> 00:05:22,360 Speaker 1: on in the show this morning. But what is hysterical 99 00:05:22,400 --> 00:05:25,240 Speaker 1: about that is that the Kima Leivie Armstrong went on 100 00:05:25,360 --> 00:05:30,239 Speaker 1: a bunch of different news outlets yesterday defending her going 101 00:05:30,279 --> 00:05:32,359 Speaker 1: in with this group along with Don Lemon, to the 102 00:05:32,400 --> 00:05:36,320 Speaker 1: church of which the Department of Justice is now investigating. 103 00:05:37,120 --> 00:05:37,160 Speaker 4: Ed. 104 00:05:39,200 --> 00:05:40,240 Speaker 1: Just listen for yourself. 105 00:05:41,279 --> 00:05:45,039 Speaker 5: Listen, listen. I'm not going to go down this line 106 00:05:45,040 --> 00:05:49,520 Speaker 5: of questioning with you. This is gaslighting who has gone 107 00:05:49,520 --> 00:05:54,960 Speaker 5: too far as Donald Trump, Christy Nome, jd Vance, and 108 00:05:55,040 --> 00:05:56,559 Speaker 5: the people who run ICE. 109 00:05:57,040 --> 00:05:59,200 Speaker 1: She tells me that they're not worried about the Department 110 00:05:59,240 --> 00:06:02,720 Speaker 1: of Justice investment dating them because they investigate everyone. 111 00:06:02,760 --> 00:06:05,200 Speaker 6: That's what she said. And she also says that so. 112 00:06:05,080 --> 00:06:08,720 Speaker 1: She pretty much substantiated exactly what I said that this 113 00:06:08,839 --> 00:06:12,440 Speaker 1: was the Trump derangement syndrome issue that they were dealing with. 114 00:06:12,480 --> 00:06:14,120 Speaker 1: So we had a lot of ground to cover with this. 115 00:06:14,320 --> 00:06:17,240 Speaker 1: So that was my night last night. An interesting side 116 00:06:17,320 --> 00:06:20,520 Speaker 1: note to that I'll share with you I received and 117 00:06:20,560 --> 00:06:23,800 Speaker 1: this wasn't like insider information. This was just taking place 118 00:06:24,360 --> 00:06:31,160 Speaker 1: via casual conversation at the studio in downtown Minneapolis last night, 119 00:06:31,200 --> 00:06:36,360 Speaker 1: but I learned something very very interesting regarding Minneapolis man 120 00:06:36,360 --> 00:06:39,200 Speaker 1: baby may or Mom Jean's Jacob Fryes. I'll share that 121 00:06:39,240 --> 00:06:41,680 Speaker 1: with you Coming up in just a moment here on 122 00:06:42,000 --> 00:06:44,920 Speaker 1: Twin Cities News Talk at six point thirty this morning, 123 00:06:45,320 --> 00:06:48,880 Speaker 1: Devine Gartenstein Ross will be joining us our AI analyst 124 00:06:49,040 --> 00:06:52,960 Speaker 1: and expert, and we actually have a few AI related 125 00:06:53,000 --> 00:06:56,560 Speaker 1: issues that relate directly to what we're dealing with here 126 00:06:56,600 --> 00:07:01,320 Speaker 1: in Minnesota and the controversy surrounding the ICE enforcement. Keith 127 00:07:01,360 --> 00:07:05,159 Speaker 1: Ellison doubles down, saying that they were justified in going 128 00:07:05,279 --> 00:07:08,040 Speaker 1: into the church and as a matter of fact, from 129 00:07:08,080 --> 00:07:12,760 Speaker 1: my old stomping grounds, this is absolutely disgusting. Democrat Congresswoman 130 00:07:12,800 --> 00:07:16,440 Speaker 1: Adelie de Grijalva praised the storming of the church last 131 00:07:16,520 --> 00:07:19,120 Speaker 1: night as well. Know this on a CNN. 132 00:07:18,800 --> 00:07:21,080 Speaker 7: Real agent now knows what it's like to have his 133 00:07:21,200 --> 00:07:24,560 Speaker 7: daily life and privacy interrupted. This is a daily occurrence 134 00:07:24,600 --> 00:07:27,960 Speaker 7: in our immigrant communities being followed, being kidnapped us out 135 00:07:27,960 --> 00:07:30,160 Speaker 7: of our schools, churches, and hospitals. 136 00:07:30,360 --> 00:07:31,800 Speaker 5: The difference is this. 137 00:07:31,720 --> 00:07:33,480 Speaker 7: Agent got to go home at the end of the day, 138 00:07:33,840 --> 00:07:37,480 Speaker 7: where many families have no idea where their loved ones are. 139 00:07:38,120 --> 00:07:42,520 Speaker 1: Wendy, this is such an incredibly dangerous precedent to set. 140 00:07:43,320 --> 00:07:46,400 Speaker 1: It really is. So we'll have the audio later on 141 00:07:46,440 --> 00:07:50,040 Speaker 1: in the show this morning from Ellison from Armstrong. As 142 00:07:50,080 --> 00:07:53,760 Speaker 1: they attempt to go and spend and justify, they're breaking 143 00:07:53,800 --> 00:07:57,400 Speaker 1: the law in my opinion, and we'll find out from 144 00:07:57,400 --> 00:08:02,600 Speaker 1: the Department of Justice by going into the church on Sunday. 145 00:08:02,680 --> 00:08:05,080 Speaker 1: You know, for the rational on the left end right, 146 00:08:05,120 --> 00:08:07,360 Speaker 1: and yes there are some individuals that are rational on 147 00:08:07,400 --> 00:08:11,320 Speaker 1: the left, this was a huge mistake and I've seen 148 00:08:11,320 --> 00:08:15,280 Speaker 1: the commentary. People understand that, but the radicals will not relent. 149 00:08:17,200 --> 00:08:19,440 Speaker 1: And I'm fearful of what this means if they continue 150 00:08:19,480 --> 00:08:22,840 Speaker 1: to go down this path and attempting to justify going 151 00:08:22,880 --> 00:08:29,720 Speaker 1: into places of worship because of their subjective view that 152 00:08:29,760 --> 00:08:32,880 Speaker 1: they use to justify the actions, because that's what this is. 153 00:08:34,679 --> 00:08:37,920 Speaker 1: They went in under the justification in their minds because 154 00:08:38,440 --> 00:08:41,400 Speaker 1: a faculty member at the church was in charge of 155 00:08:41,440 --> 00:08:44,960 Speaker 1: some ICE operations. That was just an excuse, by the way, 156 00:08:45,760 --> 00:08:49,720 Speaker 1: that was just an excuse, because this is much more 157 00:08:50,120 --> 00:08:55,199 Speaker 1: about again their Trump Arrangement syndrome. But they're Marxist radical 158 00:08:55,360 --> 00:08:59,400 Speaker 1: beliefs and a desire to tear down Christianity in this 159 00:08:59,520 --> 00:09:03,360 Speaker 1: country as well. We've got a couple of court orders 160 00:09:03,400 --> 00:09:06,800 Speaker 1: we'll get you updated on regarding the DHS policy limiting 161 00:09:06,800 --> 00:09:10,920 Speaker 1: Congress and Congress members to access ICE facilities. Also, the 162 00:09:10,960 --> 00:09:13,760 Speaker 1: feeds did respond to the Minnesota lawsuit seeking to end 163 00:09:13,920 --> 00:09:16,920 Speaker 1: Operation Metro Surge. I want to hear from you this morning. 164 00:09:17,080 --> 00:09:19,280 Speaker 1: You can call brand new phone number eight four four 165 00:09:19,440 --> 00:09:22,000 Speaker 1: nine four six five eight five five eight four four 166 00:09:22,080 --> 00:09:24,400 Speaker 1: nine four six five eight five five. Of course, you 167 00:09:24,400 --> 00:09:27,840 Speaker 1: can email Justice at iHeartRadio dot com and if you're 168 00:09:27,880 --> 00:09:30,760 Speaker 1: listening on the iHeartRadio app, leave us a talk back. 169 00:09:30,760 --> 00:09:33,000 Speaker 1: We'll get to those comments coming up. Those are brought 170 00:09:33,040 --> 00:09:35,320 Speaker 1: to you from the iHeartRadio App by Lyndahl Real team 171 00:09:35,480 --> 00:09:37,960 Speaker 1: here on Twin Cities News Talk Am eleven thirty and 172 00:09:38,000 --> 00:09:40,240 Speaker 1: one oh three five FM. Good morning, and I love 173 00:09:40,320 --> 00:09:52,559 Speaker 1: your show, Twin Cities News Talk John Justice, and I'm 174 00:09:52,559 --> 00:09:54,160 Speaker 1: glad you're with the show this morning. We'll get to 175 00:09:54,200 --> 00:09:57,000 Speaker 1: a few of your comments from the iHeartRadio app coming 176 00:09:57,040 --> 00:10:00,960 Speaker 1: up in just a moment. A federal the judge refused 177 00:10:01,120 --> 00:10:05,439 Speaker 1: yesterday to temporarily block the Trump's administration the Trump administration 178 00:10:05,559 --> 00:10:09,880 Speaker 1: from enforcing the new policy requiring a week's notice before 179 00:10:09,880 --> 00:10:15,440 Speaker 1: members of Congress can visit immigration detention facility oh As Angie. 180 00:10:15,120 --> 00:10:16,960 Speaker 6: Craig said. 181 00:10:17,760 --> 00:10:21,720 Speaker 1: Tiers from Kelly Morrison and Ilan Omar. The US District 182 00:10:21,840 --> 00:10:25,760 Speaker 1: judge concluded the Department of Homeland Security didn't violate an 183 00:10:25,760 --> 00:10:29,120 Speaker 1: earlier court order when it reimposed a seven day notice 184 00:10:29,160 --> 00:10:34,760 Speaker 1: requirement for congressional oversight visits to ICE facilities, So the 185 00:10:34,840 --> 00:10:38,360 Speaker 1: last month the judge had temporarily blocked the administration oversight 186 00:10:38,480 --> 00:10:43,120 Speaker 1: visit policy. She rolled on December seventeenth that it was 187 00:10:43,240 --> 00:10:46,440 Speaker 1: likely illegal for ICE to demand a week's notice from 188 00:10:46,520 --> 00:10:49,080 Speaker 1: members of Congress seeking to visit or observe the condition 189 00:10:49,120 --> 00:10:52,719 Speaker 1: in ICE facilities. A day after Good's death, Department of 190 00:10:52,760 --> 00:10:56,120 Speaker 1: Homeland Security Secretary of Christi nom secretly signed a new 191 00:10:56,120 --> 00:11:01,559 Speaker 1: memorandum secretly. May things she didn't tell anybody didn't put 192 00:11:01,559 --> 00:11:05,400 Speaker 1: out a press release is that I don't know a 193 00:11:05,440 --> 00:11:09,640 Speaker 1: new memorandum reinstating another seven day and notice requirement. The 194 00:11:09,640 --> 00:11:14,599 Speaker 1: plaintiffs from the Democracy Forward Legal Advisory Group said the 195 00:11:14,679 --> 00:11:18,840 Speaker 1: DHS didn't disclose the latest policy until after Omar, Morrison, 196 00:11:18,880 --> 00:11:21,880 Speaker 1: and Craig initially were turned away on Monday. CAP rule 197 00:11:21,880 --> 00:11:25,240 Speaker 1: that the new policy is similar but different to the 198 00:11:25,280 --> 00:11:28,920 Speaker 1: one announced in twenty twenty five. So sorry, Angie, Sorry Morrison, 199 00:11:29,040 --> 00:11:30,040 Speaker 1: Sorry Omar. 200 00:11:30,640 --> 00:11:31,640 Speaker 6: Two bad, so sad. 201 00:11:33,960 --> 00:11:37,840 Speaker 1: A week after Minnesota leaders filed the lawsuit teaking to 202 00:11:37,960 --> 00:11:41,439 Speaker 1: end to Operation Metro Surge, the federal government is calling 203 00:11:42,000 --> 00:11:45,200 Speaker 1: their claims frivolous. They had until yesterday to respond, I 204 00:11:45,200 --> 00:11:48,439 Speaker 1: believe now Attorney General Keith Ellison has until the twenty 205 00:11:48,480 --> 00:11:51,760 Speaker 1: second to reply to that. The defendants in the lawsuit 206 00:11:51,880 --> 00:11:55,160 Speaker 1: include Christy Nome, acting Director of Vice todd Lyons in 207 00:11:55,200 --> 00:12:00,760 Speaker 1: their response to the lawsuit, they call Minnesota's argument to absurd. 208 00:12:01,720 --> 00:12:05,560 Speaker 1: Last week of federal judge did not take immediate action 209 00:12:05,679 --> 00:12:08,400 Speaker 1: on the State of Minnesota's request for a restraining order 210 00:12:08,440 --> 00:12:11,200 Speaker 1: to stop the surge of ICE operations in the state, 211 00:12:12,040 --> 00:12:15,760 Speaker 1: instead sending timelines for state and federal government to respond 212 00:12:16,080 --> 00:12:23,439 Speaker 1: to the case. Top of next hour, we will share 213 00:12:23,480 --> 00:12:25,880 Speaker 1: with you. I will share with you. Christy no made 214 00:12:25,960 --> 00:12:33,280 Speaker 1: a startling admission on just how successful Operation Metro Surge 215 00:12:33,440 --> 00:12:36,880 Speaker 1: is not just the number of illegal criminal aliens that 216 00:12:36,920 --> 00:12:40,320 Speaker 1: have been arrested, but also the amount of fraud that 217 00:12:40,400 --> 00:12:42,800 Speaker 1: they may have uncovered in the wake of this, and 218 00:12:42,840 --> 00:12:47,640 Speaker 1: again it is staggering. And if you want to know 219 00:12:47,679 --> 00:12:52,200 Speaker 1: why there's such staunch pushback by Democrat leadership as to 220 00:12:52,240 --> 00:12:55,760 Speaker 1: what is going on right now, the number of individuals 221 00:12:56,880 --> 00:13:00,320 Speaker 1: that have been arrested by ICE and the amount of fraud. Yeah, 222 00:13:00,320 --> 00:13:04,280 Speaker 1: that's going to have a tangible impact on the upcoming elections. 223 00:13:04,360 --> 00:13:06,400 Speaker 1: So again we'll get into that coming up at seven 224 00:13:06,400 --> 00:13:10,240 Speaker 1: o'clock this morning ahead of my conversation with Devin Gartzenstein Ross, 225 00:13:10,320 --> 00:13:14,400 Speaker 1: Let's go to your talkbacks from the iHeartRadio app this morning. 226 00:13:14,440 --> 00:13:17,280 Speaker 1: Couple rolled in regarding Nakima Levy Armstrong. 227 00:13:18,520 --> 00:13:21,000 Speaker 4: Good morning, John, Well, well, well, well, the left is 228 00:13:21,040 --> 00:13:23,439 Speaker 4: now exposed in the comments that you've made this morning. 229 00:13:23,160 --> 00:13:25,400 Speaker 6: Exactly what they're all about and what they're trying to do. 230 00:13:26,280 --> 00:13:28,520 Speaker 4: They're trying to get rid of all the Americans that 231 00:13:28,559 --> 00:13:32,720 Speaker 4: are here and repopulate this country with illegal aliens, no 232 00:13:32,760 --> 00:13:36,600 Speaker 4: matter what they are, criminal or legit, and they're going 233 00:13:36,720 --> 00:13:42,439 Speaker 4: to take power over them. That's what it's ultimately about, taking. 234 00:13:42,120 --> 00:13:46,680 Speaker 6: Power, having power, and pushing power. That's what they're all about. 235 00:13:49,280 --> 00:13:52,080 Speaker 8: Yeah, I'm actually kind of surprised, probably the first time 236 00:13:52,120 --> 00:13:55,720 Speaker 8: in a long time I've heard a lefty tell the truth. 237 00:13:56,880 --> 00:13:59,520 Speaker 1: That Armstrong chick, which she was doing a little interview there. 238 00:14:00,160 --> 00:14:03,520 Speaker 1: This is gas landing just saying what she's about to 239 00:14:03,559 --> 00:14:05,600 Speaker 1: do gas line right. 240 00:14:08,400 --> 00:14:11,720 Speaker 9: So my question is this, if they would have found 241 00:14:11,760 --> 00:14:13,640 Speaker 9: the person in the church that they were looking for, 242 00:14:14,160 --> 00:14:15,920 Speaker 9: what were their intentions? What were they going to do 243 00:14:16,000 --> 00:14:20,400 Speaker 9: with them? Drag them in the street, beat them, kidnap them. 244 00:14:20,720 --> 00:14:23,360 Speaker 9: This is going way too far, ye is the stuff. 245 00:14:24,000 --> 00:14:29,320 Speaker 1: It's an incredibly dangerous precedent to set. And you've got 246 00:14:29,360 --> 00:14:32,760 Speaker 1: Representative Lee Finkey, he came out yesterday saying this needs 247 00:14:32,760 --> 00:14:35,960 Speaker 1: to happen more. Keith Ellison doubled down, saying that this 248 00:14:36,080 --> 00:14:39,280 Speaker 1: was completely justified. You heard Adelie de Grihavo will play 249 00:14:39,320 --> 00:14:41,880 Speaker 1: for you, Leavy Armstrong coming up, because if you go 250 00:14:42,000 --> 00:14:45,000 Speaker 1: down this path and again it's subjective in their view, 251 00:14:45,480 --> 00:14:49,160 Speaker 1: just because they were of the opinion that an individual 252 00:14:49,320 --> 00:14:53,560 Speaker 1: working in the church was a member of ICE and 253 00:14:53,640 --> 00:14:58,600 Speaker 1: a part of facilitating and organizing the efforts, that was 254 00:14:58,640 --> 00:15:01,760 Speaker 1: all the justification they need to go into a place 255 00:15:01,760 --> 00:15:04,760 Speaker 1: of worship and potentially go and violate the law. But 256 00:15:04,800 --> 00:15:07,760 Speaker 1: that opens the door for anybody to bust into anywhere 257 00:15:08,760 --> 00:15:12,040 Speaker 1: for whatever subjective reason they want and disrupt. 258 00:15:14,240 --> 00:15:17,200 Speaker 6: Good morning, John, and San this is matter mapple Valley. 259 00:15:17,760 --> 00:15:21,320 Speaker 9: I got some information from my sister who disagrees with 260 00:15:21,360 --> 00:15:26,840 Speaker 9: me quite a bit from her church, Westminster Presbyterian Downtown. 261 00:15:27,960 --> 00:15:33,000 Speaker 1: They truly believe it's a sin for conservative. 262 00:15:34,320 --> 00:15:39,040 Speaker 9: Christians, and they call us white Christian Nash and it's 263 00:15:39,040 --> 00:15:41,520 Speaker 9: a sin according to them and their peoples. 264 00:15:41,720 --> 00:15:44,160 Speaker 1: I'm good day, John, So you may have to leave 265 00:15:44,200 --> 00:15:46,200 Speaker 1: them to talk back there because I kind of missed. 266 00:15:46,320 --> 00:15:50,040 Speaker 1: They believe it's a sin for Christian's what I can 267 00:15:50,200 --> 00:15:56,440 Speaker 1: comment though on something here, this whole terminology regarding Christian nationalism, 268 00:15:57,240 --> 00:16:00,040 Speaker 1: that is nothing more than a means for those on 269 00:16:00,120 --> 00:16:03,480 Speaker 1: the left who call themselves Christians to vilify and demonize 270 00:16:03,520 --> 00:16:06,480 Speaker 1: conservatives who call themselves Christians. That's all that that is, 271 00:16:09,200 --> 00:16:11,560 Speaker 1: because you simply, if you're on the left, you cannot 272 00:16:11,600 --> 00:16:14,440 Speaker 1: be aligned in any way, shape or form with anybody 273 00:16:14,440 --> 00:16:19,240 Speaker 1: on the right, even in your own personal belief in 274 00:16:19,320 --> 00:16:23,440 Speaker 1: the Lord Jesus Christ. And truth be told, if you're 275 00:16:23,440 --> 00:16:25,840 Speaker 1: on the left, if you're a Democrat and you call 276 00:16:25,880 --> 00:16:28,240 Speaker 1: yourself a Christian, there should be some soul searching there 277 00:16:28,240 --> 00:16:30,880 Speaker 1: depending on the policies that you adhere to. Now that 278 00:16:30,920 --> 00:16:33,040 Speaker 1: being said, that's not for me to judge. That's for 279 00:16:33,160 --> 00:16:35,800 Speaker 1: the Lord. That's for the Lord God Almighty to go 280 00:16:36,120 --> 00:16:40,120 Speaker 1: and judge, you know, judge an individual based off the 281 00:16:40,160 --> 00:16:42,800 Speaker 1: content of their character, not the color of their skin, 282 00:16:42,880 --> 00:16:46,120 Speaker 1: their race or religion. But that's all that is. When 283 00:16:46,160 --> 00:16:50,200 Speaker 1: people use the white Christian, the Christian nationalists, they're just 284 00:16:50,280 --> 00:16:54,520 Speaker 1: talking about conservative Christians. And it's usually exclusively used by 285 00:16:54,560 --> 00:16:58,480 Speaker 1: individuals on the left like Armstrong, like that lunatic William Kelly, 286 00:16:58,600 --> 00:17:02,520 Speaker 1: who called themselves Christians even though they act in abhorrent, 287 00:17:03,400 --> 00:17:08,960 Speaker 1: sinful ways like they did on Sunday. All right, switching gears. 288 00:17:09,040 --> 00:17:12,240 Speaker 1: What sounds like President Donald Trump narrating a new Fannie 289 00:17:12,280 --> 00:17:15,840 Speaker 1: May ad is actually an AI cloned voice reading text. 290 00:17:15,920 --> 00:17:18,280 Speaker 1: According to a disclaimer in the video, the voice in 291 00:17:18,320 --> 00:17:21,639 Speaker 1: the ad, created with permission from the Trump administration, promises 292 00:17:21,640 --> 00:17:25,480 Speaker 1: an all new Fannie May, calls for the institution, calls 293 00:17:25,520 --> 00:17:28,960 Speaker 1: the institution the protector of the American Dream. The ad 294 00:17:29,000 --> 00:17:31,520 Speaker 1: comes as the administration is making a big push to 295 00:17:31,600 --> 00:17:34,880 Speaker 1: show voters and is responding to their concerns about affordability, 296 00:17:34,920 --> 00:17:37,280 Speaker 1: including in the housing market. I bring it up because 297 00:17:37,280 --> 00:17:39,920 Speaker 1: we're going to talk a little AI. David Gartenstein Ross, 298 00:17:39,920 --> 00:17:43,200 Speaker 1: our AI analyst and expert, will join us next first 299 00:17:43,200 --> 00:17:46,720 Speaker 1: appearance in twenty twenty six. Watch out for AI fakes 300 00:17:46,720 --> 00:17:49,960 Speaker 1: and misinformation in the wake of the Ice shooting and 301 00:17:50,000 --> 00:17:53,520 Speaker 1: the controversies that have ensued, and what Representative Walter Hudson 302 00:17:53,560 --> 00:17:56,920 Speaker 1: believes it's time for Republicans to address to AI. We'll 303 00:17:56,960 --> 00:17:59,359 Speaker 1: get the thoughts from David Gartenstein Ross, and any questions 304 00:17:59,359 --> 00:18:01,440 Speaker 1: that you have for our guests, you can get those 305 00:18:01,480 --> 00:18:03,919 Speaker 1: in on the iHeartRadio app It's all coming up on 306 00:18:03,920 --> 00:18:05,840 Speaker 1: Twin City's News Talk AM eleven thirty and one oh 307 00:18:05,880 --> 00:18:12,320 Speaker 1: three five FM. 308 00:18:12,480 --> 00:18:16,480 Speaker 10: The morning June delies around ice is just mind boggling 309 00:18:16,480 --> 00:18:16,679 Speaker 10: to me. 310 00:18:16,960 --> 00:18:18,400 Speaker 6: Talk to two friends yesterday. 311 00:18:18,840 --> 00:18:22,240 Speaker 10: One said, they're detaining these people at the ice facility 312 00:18:22,880 --> 00:18:25,679 Speaker 10: and they are putting them in cages like dogs and 313 00:18:25,720 --> 00:18:27,639 Speaker 10: feeding them food that's. 314 00:18:27,400 --> 00:18:28,360 Speaker 1: Full of maggots. 315 00:18:29,480 --> 00:18:31,800 Speaker 10: I'm like, where do you get this information? And then 316 00:18:31,800 --> 00:18:35,280 Speaker 10: the second one said, they're walking around in circles outside 317 00:18:35,320 --> 00:18:39,239 Speaker 10: of schools waiting for the kids to get out and 318 00:18:39,280 --> 00:18:42,000 Speaker 10: then arrest them. They're not arresting. 319 00:18:41,720 --> 00:18:47,760 Speaker 1: Kids, No, they're not. They're absolutely not. You have like 320 00:18:47,840 --> 00:18:52,960 Speaker 1: the extreme levels of misinformation that's out there, and then 321 00:18:53,040 --> 00:18:56,800 Speaker 1: you have well, they're all extreme. I don't want to 322 00:18:57,400 --> 00:18:59,200 Speaker 1: I don't want to downplay any of this because they're 323 00:18:59,240 --> 00:19:01,639 Speaker 1: all bad. I mean, that's over the top, you know, 324 00:19:01,800 --> 00:19:04,600 Speaker 1: feeding individuals, putting them in cages and feeding them dog food. 325 00:19:04,640 --> 00:19:07,960 Speaker 1: I mean, come on, I mean, if you I don't 326 00:19:08,000 --> 00:19:10,200 Speaker 1: care what side of the political spectrum you're on, if 327 00:19:10,200 --> 00:19:13,800 Speaker 1: you actually believe that's true for a moment. There's larger 328 00:19:14,040 --> 00:19:16,640 Speaker 1: issues in my opinion, going on there. But for example, 329 00:19:17,000 --> 00:19:19,160 Speaker 1: Twin Cities News Talk, my name is John Justice. We'll 330 00:19:19,160 --> 00:19:21,600 Speaker 1: talk with Davide Gartenstein Ross here in just a minute, 331 00:19:21,600 --> 00:19:24,919 Speaker 1: but it's actually going to relate to the first artificial 332 00:19:24,960 --> 00:19:27,960 Speaker 1: intelligence topic that we have for this morning. And as always, 333 00:19:28,000 --> 00:19:31,399 Speaker 1: if you have any questions artificial intelligence related, you can 334 00:19:31,440 --> 00:19:33,720 Speaker 1: get those in on the iHeart Radio app. But there 335 00:19:33,880 --> 00:19:37,920 Speaker 1: was a there was a report going around on Next yesterday. 336 00:19:37,920 --> 00:19:40,560 Speaker 1: It included a photo and it was being spread by Democrats, 337 00:19:40,720 --> 00:19:43,040 Speaker 1: and it was the photo of an ICE agent standing 338 00:19:43,040 --> 00:19:45,359 Speaker 1: in the doorway with an individual with his head down 339 00:19:45,880 --> 00:19:48,600 Speaker 1: in handcuffs, shirts off, looked like they may have a 340 00:19:48,640 --> 00:19:52,160 Speaker 1: towel around their shoulders. I mean it's freezing outside. And 341 00:19:52,840 --> 00:19:57,520 Speaker 1: the summary of what was happening was ICE detains a 342 00:19:57,560 --> 00:20:00,679 Speaker 1: man in his home and brings him outside and below 343 00:20:00,760 --> 00:20:05,320 Speaker 1: freezing temperatures. And just as totally egregious post about how 344 00:20:05,359 --> 00:20:09,920 Speaker 1: horrible and awful this is. Well, the Department of Homeland Security, 345 00:20:10,000 --> 00:20:14,439 Speaker 1: the Assistant secretary reposted that and added the clarity. The 346 00:20:14,440 --> 00:20:19,399 Speaker 1: clarity was this individual was a child sex offender that 347 00:20:19,520 --> 00:20:25,120 Speaker 1: was arrested. That's where the person was when law enforcement 348 00:20:25,200 --> 00:20:28,840 Speaker 1: went in. He resisted. They had to go and handcuff 349 00:20:28,880 --> 00:20:31,360 Speaker 1: him and take him out. You know. The shirt part 350 00:20:31,400 --> 00:20:32,760 Speaker 1: of it, and they ended up putting a coat on 351 00:20:32,840 --> 00:20:35,840 Speaker 1: him once they got him outside before they went and 352 00:20:35,920 --> 00:20:38,399 Speaker 1: hauled them off, so the context was completely lost. But 353 00:20:38,440 --> 00:20:40,960 Speaker 1: it didn't matter. The information was out there, it was 354 00:20:40,960 --> 00:20:45,080 Speaker 1: spread around and people don't bother one typically going and 355 00:20:45,119 --> 00:20:48,520 Speaker 1: correcting those posts, or two going and looking for the truth. 356 00:20:48,640 --> 00:20:50,360 Speaker 1: And then, of course when you add in the element 357 00:20:50,440 --> 00:20:53,800 Speaker 1: of the element of artificial intelligence in the videos that 358 00:20:53,840 --> 00:20:57,000 Speaker 1: could be created, you have a whole other set of circumstances, 359 00:20:57,000 --> 00:21:00,359 Speaker 1: which brings us to CEO of Expert Theory, Friend of 360 00:21:00,400 --> 00:21:04,040 Speaker 1: the Show, AI analyst and expert himself. Is it too 361 00:21:04,080 --> 00:21:06,880 Speaker 1: late to say Happy New Year? Devin gartan Stein Ross's 362 00:21:07,200 --> 00:21:09,400 Speaker 1: ord January twentieth, when we haven't talked to you yet 363 00:21:09,400 --> 00:21:10,800 Speaker 1: this year, Welcome to the show. 364 00:21:11,480 --> 00:21:13,040 Speaker 6: It's never too late to say Happy New Year. 365 00:21:13,119 --> 00:21:15,159 Speaker 8: John, Happy new Year to you as well, and I 366 00:21:15,240 --> 00:21:19,560 Speaker 8: hope you are having a good year despite all the 367 00:21:19,640 --> 00:21:22,080 Speaker 8: various obvious things that could be knocking it a bit 368 00:21:22,119 --> 00:21:22,520 Speaker 8: off course. 369 00:21:22,600 --> 00:21:25,040 Speaker 1: So it's never a dull moment, that's for sure. So listen, 370 00:21:25,119 --> 00:21:28,239 Speaker 1: let let's dive into it. The Minnesota Star Tribune has 371 00:21:28,240 --> 00:21:31,679 Speaker 1: a post an article out from earlier this week. Actually 372 00:21:31,720 --> 00:21:34,000 Speaker 1: this goes back to a week ago. I'd hung onto 373 00:21:34,000 --> 00:21:37,720 Speaker 1: this in anticipation of our conversation watch out for AI 374 00:21:37,800 --> 00:21:40,960 Speaker 1: fakes and misinformation in the wake of the Ice shooting. 375 00:21:41,240 --> 00:21:43,920 Speaker 1: Now we can go ahead and expand this to beyond 376 00:21:44,359 --> 00:21:47,280 Speaker 1: the Ice shootings and since then with the ICE operations, 377 00:21:47,320 --> 00:21:51,959 Speaker 1: there are dozens of videos floating around of Ice and 378 00:21:52,000 --> 00:21:54,320 Speaker 1: their actions, just like I was talking about. But in 379 00:21:54,400 --> 00:21:57,720 Speaker 1: the piece itself, it says the fatal shooting of the 380 00:21:57,760 --> 00:22:00,840 Speaker 1: thirty seven year old by the federal agents already spurred 381 00:22:00,880 --> 00:22:04,320 Speaker 1: a flood of online misinformation that may be shared unwittingly 382 00:22:04,400 --> 00:22:07,440 Speaker 1: by people with good intentions. They go and they lay 383 00:22:07,480 --> 00:22:12,400 Speaker 1: out three different examples and tips for telling fake videos 384 00:22:12,440 --> 00:22:16,960 Speaker 1: and images from real ones. Now, before we get into 385 00:22:17,560 --> 00:22:22,040 Speaker 1: the actual specifics here of the images the AI fake 386 00:22:22,119 --> 00:22:24,000 Speaker 1: videos that they refer to, I guess I just have 387 00:22:24,040 --> 00:22:27,800 Speaker 1: a question upfront and a bit of an anecdote here 388 00:22:28,800 --> 00:22:31,320 Speaker 1: going through this article de vid in the three examples 389 00:22:31,320 --> 00:22:35,040 Speaker 1: that they gave, I'm on X all the time. I mean, 390 00:22:35,040 --> 00:22:37,760 Speaker 1: that's where I get my audio, primarily for the show, 391 00:22:38,080 --> 00:22:42,200 Speaker 1: and presumably because the algorithm didn't feed me this footage. 392 00:22:42,280 --> 00:22:45,679 Speaker 1: I didn't see any of these clips and as a 393 00:22:45,680 --> 00:22:50,360 Speaker 1: matter of fact, I haven't personally seen any clips that 394 00:22:50,480 --> 00:22:55,000 Speaker 1: have been AI generated with an attempt to skew what's 395 00:22:55,040 --> 00:22:58,159 Speaker 1: taking place on the on the street. And I was 396 00:22:58,200 --> 00:23:01,080 Speaker 1: actually kind of surprised by this, and it maybe a 397 00:23:01,160 --> 00:23:03,880 Speaker 1: little bit more optimistic than I have been in the 398 00:23:03,920 --> 00:23:08,320 Speaker 1: past of the nefarious intent of individuals that will want 399 00:23:08,320 --> 00:23:14,160 Speaker 1: to create these videos to go and confuse the public. 400 00:23:14,280 --> 00:23:17,440 Speaker 1: So I guess with that divide, do you think there's 401 00:23:17,440 --> 00:23:19,320 Speaker 1: going to be a point where we've talked about this 402 00:23:19,400 --> 00:23:22,000 Speaker 1: a lot that will change and we will be unable 403 00:23:22,040 --> 00:23:24,399 Speaker 1: to tell the difference between the two. Or do you 404 00:23:24,400 --> 00:23:27,480 Speaker 1: think there will always be some level in which keen 405 00:23:27,520 --> 00:23:30,560 Speaker 1: observers will be able to tell what video is real 406 00:23:30,600 --> 00:23:32,160 Speaker 1: and what video isn't real. 407 00:23:32,200 --> 00:23:34,800 Speaker 8: And I'm hand it off to you. There's a lot 408 00:23:34,880 --> 00:23:39,080 Speaker 8: to unpack there. I think you're asking a number of 409 00:23:39,320 --> 00:23:43,880 Speaker 8: really great questions, So to get into them. Number one, 410 00:23:44,080 --> 00:23:48,040 Speaker 8: I think that with the naked eye, it's difficult to 411 00:23:48,080 --> 00:23:53,840 Speaker 8: tell the difference today between real and fake AI generated video. 412 00:23:54,480 --> 00:23:55,320 Speaker 6: Not impossible. 413 00:23:55,840 --> 00:24:00,320 Speaker 8: There are certain tells, you know, AI gibberish will often 414 00:24:00,359 --> 00:24:04,280 Speaker 8: show up in lieu of words. If there are signs 415 00:24:04,320 --> 00:24:07,680 Speaker 8: and the like there are other kind of subtle tells. 416 00:24:08,320 --> 00:24:09,720 Speaker 6: Sometimes people will have the wrong. 417 00:24:09,600 --> 00:24:13,640 Speaker 8: Number of fingers, things like that, but it's really difficult 418 00:24:13,680 --> 00:24:18,879 Speaker 8: to tell them apart. So what you're saying is really interesting, John, 419 00:24:19,240 --> 00:24:22,639 Speaker 8: the fact that you expected there to be more AI 420 00:24:22,800 --> 00:24:25,680 Speaker 8: fakes than there have been, and you know, I don't 421 00:24:25,680 --> 00:24:31,199 Speaker 8: spend that much time on X these days. LinkedIn has 422 00:24:31,240 --> 00:24:35,080 Speaker 8: become by medium as I've been leaning into making sure 423 00:24:35,240 --> 00:24:39,040 Speaker 8: I'm spending time promoting my business. But in the time 424 00:24:39,040 --> 00:24:41,199 Speaker 8: that I do spend on X, similarly, I have not 425 00:24:41,359 --> 00:24:46,480 Speaker 8: seen good, you know, very much in the way of 426 00:24:46,800 --> 00:24:52,320 Speaker 8: AI fakes. My guess is that the reason why is 427 00:24:52,440 --> 00:24:56,200 Speaker 8: probably the algorithm is based on X on our networks, 428 00:24:56,280 --> 00:25:01,680 Speaker 8: right right, Like you tend to get footage from people 429 00:25:01,880 --> 00:25:04,320 Speaker 8: who you click on either because you like them or 430 00:25:04,359 --> 00:25:06,720 Speaker 8: because you hate them, but either way, you click on 431 00:25:06,720 --> 00:25:08,560 Speaker 8: their profile and take a look at what they have 432 00:25:08,640 --> 00:25:11,919 Speaker 8: to say, and then the algorithm feeds you more of 433 00:25:11,960 --> 00:25:15,320 Speaker 8: their stuff. And my guess is you've just self selected 434 00:25:15,359 --> 00:25:18,640 Speaker 8: out of a lot of the AI generated disinformation. I'd 435 00:25:18,640 --> 00:25:21,600 Speaker 8: be interested to know how much there is, as a percentage, 436 00:25:21,600 --> 00:25:24,560 Speaker 8: how viral it's been. I haven't seen evidence of that, 437 00:25:24,800 --> 00:25:27,440 Speaker 8: but I think that you and I are both doing intuitively. 438 00:25:28,400 --> 00:25:31,000 Speaker 8: I think one of the best steps is which is 439 00:25:31,720 --> 00:25:36,080 Speaker 8: not just looking for tells that something is AI generated, 440 00:25:36,720 --> 00:25:41,119 Speaker 8: but networks of trust. People who I follow tend to 441 00:25:41,359 --> 00:25:46,440 Speaker 8: do a very diligent job of looking into source material 442 00:25:46,520 --> 00:25:49,440 Speaker 8: to make sure that it's authentic. I don't follow people 443 00:25:49,480 --> 00:25:52,960 Speaker 8: who tend to fall for fakes, and as a result, 444 00:25:53,160 --> 00:25:56,440 Speaker 8: probably that filters out a large number of fake videos 445 00:25:56,720 --> 00:25:58,720 Speaker 8: that otherwise might come across. 446 00:25:58,400 --> 00:26:05,280 Speaker 1: My feed, so many little facets to AI and habits 447 00:26:05,320 --> 00:26:08,359 Speaker 1: that form that I've already began to form. Like to 448 00:26:08,480 --> 00:26:11,840 Speaker 1: your point, this is probably more related to algorithms, but 449 00:26:11,880 --> 00:26:15,399 Speaker 1: these algorithms are largely AI driven now. But I find 450 00:26:15,480 --> 00:26:19,800 Speaker 1: myself going seeing a post. I might be interested in 451 00:26:19,840 --> 00:26:22,320 Speaker 1: clicking it, but at the clicking on it, but at 452 00:26:22,320 --> 00:26:24,320 Speaker 1: the same time, I'm like, you know what, I really 453 00:26:24,400 --> 00:26:25,840 Speaker 1: don't want to spend a lot of time on that 454 00:26:25,880 --> 00:26:28,840 Speaker 1: because I don't want my feed to be filled with 455 00:26:28,920 --> 00:26:30,760 Speaker 1: a bunch of videos like that. And I can give 456 00:26:30,760 --> 00:26:35,600 Speaker 1: you probably half a dozen different different little circumstances in 457 00:26:35,640 --> 00:26:38,760 Speaker 1: different little ways in which I have adjusted my own 458 00:26:38,920 --> 00:26:43,800 Speaker 1: online viewing because I know how the algorithm is going 459 00:26:43,880 --> 00:26:46,600 Speaker 1: to go and treat it and this is one of them. 460 00:26:46,680 --> 00:26:48,560 Speaker 1: This is more of just a comment than it is 461 00:26:48,840 --> 00:26:51,280 Speaker 1: a question to feat. I just find that aspect of 462 00:26:51,320 --> 00:26:53,000 Speaker 1: what we talk about on a daily basis and how 463 00:26:53,040 --> 00:26:56,400 Speaker 1: it's changing our lives to be really interesting as well. 464 00:26:56,560 --> 00:26:59,320 Speaker 8: Yeah, and there we can get into a little bit 465 00:26:59,320 --> 00:27:02,640 Speaker 8: of a technical detail, which is we tend to refer 466 00:27:02,640 --> 00:27:05,919 Speaker 8: to the field as AI mL or AI and machine learning. 467 00:27:06,440 --> 00:27:09,320 Speaker 6: And you know, algorithms are basic machine learning. 468 00:27:09,400 --> 00:27:12,960 Speaker 8: They're not even the kind of sophisticated AI that we use. 469 00:27:13,000 --> 00:27:15,920 Speaker 8: They're very predictable and we understand the way that they 470 00:27:15,960 --> 00:27:19,240 Speaker 8: work that maybe AAI enhanced now, but as you know, 471 00:27:19,480 --> 00:27:23,280 Speaker 8: we've been dealing with algorithms for decades. One of the 472 00:27:23,320 --> 00:27:27,320 Speaker 8: things that made Google the multi billion dollar company that 473 00:27:27,400 --> 00:27:29,920 Speaker 8: it is was its ability to produce a much better 474 00:27:30,000 --> 00:27:34,639 Speaker 8: algorithm for online search than previous search engines had done. 475 00:27:35,160 --> 00:27:38,240 Speaker 1: Let's go here to the iHeartRadio app. Got a question 476 00:27:38,320 --> 00:27:39,720 Speaker 1: for you to v and talkbacks are brought to you 477 00:27:39,760 --> 00:27:40,600 Speaker 1: by Lyndall Realty. 478 00:27:41,480 --> 00:27:46,840 Speaker 11: Question for David. Have you seen the tible videos of 479 00:27:47,320 --> 00:27:50,119 Speaker 11: AI just losing its mind over the fact that it 480 00:27:50,240 --> 00:27:53,400 Speaker 11: can't spell strawberry and it says it only has two 481 00:27:53,520 --> 00:27:56,199 Speaker 11: rs when it actually is three and it can't comprehend 482 00:27:56,240 --> 00:28:00,240 Speaker 11: that there's actually three. These are examples of why I'm 483 00:28:00,240 --> 00:28:02,919 Speaker 11: not worried AI is taking over the world. Like it 484 00:28:02,960 --> 00:28:04,400 Speaker 11: can't even spell strawberry. 485 00:28:04,880 --> 00:28:06,040 Speaker 1: Well, I guess it kind of goes back to a 486 00:28:06,080 --> 00:28:08,359 Speaker 1: similar question I just asked Davide. I mean, will AI 487 00:28:08,440 --> 00:28:12,439 Speaker 1: eventually be able to spell strawberry? I? 488 00:28:12,520 --> 00:28:16,040 Speaker 8: Yes, AI will eventually be able to spell strawberry. I 489 00:28:16,080 --> 00:28:19,000 Speaker 8: love the comment in so many ways, and I think 490 00:28:19,000 --> 00:28:24,719 Speaker 8: it's a deep comment, even though I have some disagreement 491 00:28:24,760 --> 00:28:27,240 Speaker 8: with it. So yeah, I mean all of us have 492 00:28:27,320 --> 00:28:30,639 Speaker 8: seen not just kind of the video, but people have 493 00:28:30,680 --> 00:28:34,280 Speaker 8: interacted with AI. See these areas where it gets basic 494 00:28:34,359 --> 00:28:37,960 Speaker 8: things wrong and isn't able to wrap its head around 495 00:28:37,960 --> 00:28:40,040 Speaker 8: why it got that basic thing wrong. And you might 496 00:28:40,120 --> 00:28:43,800 Speaker 8: go back and forth with it a dozen times if 497 00:28:43,840 --> 00:28:47,080 Speaker 8: you don't lose your patience before then, and it will 498 00:28:47,400 --> 00:28:50,760 Speaker 8: keep missing something that like the dumbest of human beings 499 00:28:50,760 --> 00:28:54,640 Speaker 8: would be able to get right. But like, while AI 500 00:28:55,280 --> 00:29:00,720 Speaker 8: can't spell strawberry, it can create a really believeable video 501 00:29:01,440 --> 00:29:04,880 Speaker 8: that well, let's actually go back for one second. Sure 502 00:29:05,000 --> 00:29:08,880 Speaker 8: it can spell strawberry, it just miscounts the number of ours. Right, 503 00:29:08,920 --> 00:29:11,200 Speaker 8: there's these weird things that it does where it can't 504 00:29:11,200 --> 00:29:15,160 Speaker 8: get its head wrapped around you know, double rs once 505 00:29:15,200 --> 00:29:19,480 Speaker 8: in a while, and that's interesting. But while it may 506 00:29:19,560 --> 00:29:21,360 Speaker 8: not be able to spell strawberry when you ask it 507 00:29:21,400 --> 00:29:24,000 Speaker 8: how many rs there are, it could produce a really 508 00:29:24,600 --> 00:29:28,040 Speaker 8: persuasive deep fake that makes it, you know, look like 509 00:29:28,240 --> 00:29:31,240 Speaker 8: the streets of Minneapolis are being swarmed in a time 510 00:29:31,280 --> 00:29:32,200 Speaker 8: of day that they aren't. 511 00:29:33,520 --> 00:29:35,479 Speaker 1: So that's all I don't get. Why why can't it 512 00:29:35,680 --> 00:29:38,360 Speaker 1: Why can you do chat, GPT and everything that it 513 00:29:38,400 --> 00:29:40,960 Speaker 1: can do relating to the English language, But yet with 514 00:29:41,040 --> 00:29:44,040 Speaker 1: the imagery it seems to have an abundance of difficulty. 515 00:29:45,320 --> 00:29:52,240 Speaker 8: Yeah, well precisely, right there, there's these these niches where 516 00:29:52,560 --> 00:29:56,600 Speaker 8: it just it breaks right, and we've known computers to 517 00:29:56,600 --> 00:29:57,480 Speaker 8: do that for a while. 518 00:29:58,720 --> 00:30:00,840 Speaker 6: And this feeds into a bigger point. 519 00:30:00,840 --> 00:30:01,960 Speaker 8: I know that there's an op ed you want to 520 00:30:01,960 --> 00:30:03,480 Speaker 8: talk about, which I think is a great op ed, 521 00:30:04,160 --> 00:30:07,360 Speaker 8: which is part of what we need to do is 522 00:30:07,400 --> 00:30:10,560 Speaker 8: figure out those things that are uniquely human, where. 523 00:30:10,200 --> 00:30:11,880 Speaker 6: Humans have unique value. 524 00:30:12,920 --> 00:30:17,200 Speaker 8: AI is you know, it's already taking jobs, It's going 525 00:30:17,240 --> 00:30:18,680 Speaker 8: to take more jobs in the future. 526 00:30:19,160 --> 00:30:21,600 Speaker 6: That's not it. That's for the most part, not a 527 00:30:21,640 --> 00:30:22,040 Speaker 6: good thing. 528 00:30:23,040 --> 00:30:26,560 Speaker 8: You know, to some extent, there'll be there'll be good 529 00:30:26,600 --> 00:30:32,240 Speaker 8: that comes out of our relationship with work shifting a bit, 530 00:30:32,960 --> 00:30:34,400 Speaker 8: But I don't think this is a good thing at all, 531 00:30:34,400 --> 00:30:37,000 Speaker 8: and we're not ready for it. So I agree that 532 00:30:37,040 --> 00:30:39,760 Speaker 8: there's there's AI blind spots, and we need to be 533 00:30:39,840 --> 00:30:43,440 Speaker 8: aware of them, but that doesn't stop AI on the 534 00:30:43,480 --> 00:30:48,440 Speaker 8: whole from advancing and day by day making a bigger 535 00:30:48,520 --> 00:30:52,400 Speaker 8: and bigger role in our lives and our future prospects. 536 00:30:52,640 --> 00:30:55,880 Speaker 1: I've already gotten to the point where I am irrationally 537 00:30:56,120 --> 00:31:01,120 Speaker 1: annoyed at the number of AI relate. Did the images 538 00:31:01,240 --> 00:31:07,880 Speaker 1: created by groc or other AI image generators wherein you 539 00:31:08,000 --> 00:31:10,040 Speaker 1: have and I'll just an all poke fun at conservatives 540 00:31:10,040 --> 00:31:11,960 Speaker 1: because it's mostly conservatives doing this, but you know the 541 00:31:12,040 --> 00:31:15,480 Speaker 1: left does it too, where they take a prominent political 542 00:31:15,520 --> 00:31:18,000 Speaker 1: figure and they put them in a specific situation. You know, 543 00:31:18,040 --> 00:31:21,680 Speaker 1: that lends itself to the comment that they're trying to make. 544 00:31:22,120 --> 00:31:23,880 Speaker 1: But the image is really I mean, it doesn't really 545 00:31:23,960 --> 00:31:27,800 Speaker 1: It looks like an approximation of that person, but it 546 00:31:27,880 --> 00:31:31,520 Speaker 1: doesn't really look like like that person, and everything is 547 00:31:31,560 --> 00:31:35,320 Speaker 1: really over exaggerated. I assume that'll get better down the line, 548 00:31:35,360 --> 00:31:38,760 Speaker 1: and we've seen the advancements. But getting back to those little, tiny, 549 00:31:38,840 --> 00:31:41,880 Speaker 1: nuanced things that drive me nuts, it is every time 550 00:31:41,880 --> 00:31:43,520 Speaker 1: I see it now, Devide, I think of our favor 551 00:31:43,560 --> 00:31:45,680 Speaker 1: or at least my favorite term during our discussions, and 552 00:31:45,720 --> 00:31:47,840 Speaker 1: I'm like, oh, look, it's more AI slop. 553 00:31:50,560 --> 00:31:55,560 Speaker 8: I suspect as well that there actually are legal liability reasons. 554 00:31:55,160 --> 00:31:56,240 Speaker 6: Why the pictures come out. 555 00:31:56,520 --> 00:31:57,240 Speaker 1: Oh interesting. 556 00:31:58,520 --> 00:32:01,600 Speaker 8: So there's a lot of concern I think from the 557 00:32:01,600 --> 00:32:05,200 Speaker 8: big AI companies that their pictures will be used for 558 00:32:05,400 --> 00:32:11,840 Speaker 8: defaminatory disinformation against public figures, and so it may try to. 559 00:32:11,720 --> 00:32:14,040 Speaker 6: Make the public figures look a little bit off. 560 00:32:14,760 --> 00:32:17,920 Speaker 8: One thing that's interesting, you can try this take a 561 00:32:17,960 --> 00:32:23,120 Speaker 8: picture of a living person who is famous, like Brad Pitt, 562 00:32:23,400 --> 00:32:25,520 Speaker 8: and say can you tell me who this is? And 563 00:32:25,640 --> 00:32:27,880 Speaker 8: it will not tell you who it is, Like it 564 00:32:27,960 --> 00:32:33,320 Speaker 8: is prohibited from using facial recognition to identify people, even 565 00:32:33,360 --> 00:32:38,920 Speaker 8: if they're famous people, which is interesting. So there's a 566 00:32:38,920 --> 00:32:43,200 Speaker 8: lot of kind of hidden legal liability things within chat 567 00:32:43,240 --> 00:32:47,160 Speaker 8: GPT that are there for copyright purposes or like. Another 568 00:32:47,200 --> 00:32:49,880 Speaker 8: thing it won't do is show you the lyrics to 569 00:32:49,920 --> 00:32:52,960 Speaker 8: a copyrighted song, even though with a single Google search, 570 00:32:53,000 --> 00:32:55,560 Speaker 8: you can find on your Genius or some of our 571 00:32:55,600 --> 00:33:00,320 Speaker 8: other website complete lyrics to a song. An LLL check 572 00:33:00,400 --> 00:33:02,640 Speaker 8: ept will not give you the lyrics to that song, 573 00:33:03,000 --> 00:33:04,120 Speaker 8: even though you can google it. 574 00:33:05,320 --> 00:33:07,520 Speaker 1: Okay, two things here, let's go here really quick. In 575 00:33:07,560 --> 00:33:12,400 Speaker 1: this Star Trebuwing piece about identifying AI fakes misinformation, they do. 576 00:33:12,480 --> 00:33:17,880 Speaker 1: They have three suggestions. Identify the source. AI technology is improving, 577 00:33:17,880 --> 00:33:19,760 Speaker 1: but it makes mistakes. You and I just talked about that. 578 00:33:20,520 --> 00:33:24,400 Speaker 1: Consider their perspective. AI generated videos are often a cinematic 579 00:33:24,520 --> 00:33:27,280 Speaker 1: in appearance, with camera angles that are obviously different from 580 00:33:27,320 --> 00:33:30,040 Speaker 1: videos taken by a person holding a cell phone. You know, 581 00:33:30,040 --> 00:33:32,320 Speaker 1: it's kind of along the same lines as it making mistakes. 582 00:33:32,320 --> 00:33:35,440 Speaker 1: But just consider their perspective. Anything else that you might 583 00:33:35,520 --> 00:33:37,880 Speaker 1: want to, you know, share with the share with the listener, 584 00:33:37,880 --> 00:33:39,960 Speaker 1: that they might consider when they see something that they 585 00:33:40,600 --> 00:33:42,080 Speaker 1: believe is questionable. 586 00:33:43,680 --> 00:33:45,760 Speaker 6: I think those are three good rules. 587 00:33:45,880 --> 00:33:49,080 Speaker 8: I mean, the other one, which I mentioned before, I 588 00:33:49,120 --> 00:33:53,680 Speaker 8: think is just another part of the the identify the 589 00:33:53,760 --> 00:33:58,400 Speaker 8: source advice, which is that increasingly we rely on social networks, 590 00:33:58,880 --> 00:34:02,080 Speaker 8: and this is a good opportun to, you know, make 591 00:34:02,120 --> 00:34:04,720 Speaker 8: sure your social network is comprised of people who don't 592 00:34:04,760 --> 00:34:07,840 Speaker 8: easily fall for AI fakes. 593 00:34:08,320 --> 00:34:11,279 Speaker 1: All right, let's wrap on this state representative friend of 594 00:34:11,280 --> 00:34:14,600 Speaker 1: the show, Walter Hudson has a has an editorial I 595 00:34:14,640 --> 00:34:18,719 Speaker 1: believe it was originally featured in Alpha News talking about 596 00:34:18,840 --> 00:34:22,520 Speaker 1: artificial intelligence robotics. He says are about to wipe out 597 00:34:22,560 --> 00:34:25,640 Speaker 1: tens of millions of jobs, including hundreds of thousands here 598 00:34:25,640 --> 00:34:29,400 Speaker 1: in Minnesota. He gets into conservative talking points, and I 599 00:34:29,440 --> 00:34:30,880 Speaker 1: want to kind of avoid that for the sake of 600 00:34:30,880 --> 00:34:32,880 Speaker 1: the question that I had for you. He does say 601 00:34:32,920 --> 00:34:36,080 Speaker 1: the numbers are brutal projection show AI and automation could 602 00:34:36,200 --> 00:34:40,279 Speaker 1: easily erased nearly one hundred million US jobs in the 603 00:34:40,320 --> 00:34:44,759 Speaker 1: next decade. He gets into some specifics here forty seven 604 00:34:44,840 --> 00:34:49,480 Speaker 1: percent to truck drivers, sixty four percent accountants, ninety percent 605 00:34:49,520 --> 00:34:54,160 Speaker 1: of fast food workers. Says that there's a possibility of 606 00:34:54,239 --> 00:34:57,200 Speaker 1: you know, fifty thousand plus truckers here in Minnesota. I mean, 607 00:34:57,320 --> 00:35:00,640 Speaker 1: Minnesota isn't immune to this. He goes on in the 608 00:35:00,680 --> 00:35:02,600 Speaker 1: piece to say, I don't have a complete answer yet, 609 00:35:02,600 --> 00:35:05,560 Speaker 1: but I know pretending that this isn't happening is not 610 00:35:05,719 --> 00:35:09,640 Speaker 1: a strategy. The first wave of mass AI layoffs, he writes, 611 00:35:10,000 --> 00:35:13,000 Speaker 1: expected to hit in about eighteen months, we better have 612 00:35:13,040 --> 00:35:18,880 Speaker 1: an answer that protects liberty and still keeps people from starving. So, 613 00:35:19,160 --> 00:35:20,879 Speaker 1: first off, and a couple of things here we can 614 00:35:20,960 --> 00:35:22,279 Speaker 1: and again we've got a couple of minutes we can 615 00:35:22,360 --> 00:35:25,920 Speaker 1: work through this. Do you agree that the concerns are 616 00:35:26,160 --> 00:35:30,160 Speaker 1: valid and are there good options to look at right 617 00:35:30,200 --> 00:35:31,640 Speaker 1: now in terms of a solution. 618 00:35:33,080 --> 00:35:35,759 Speaker 8: I think this is a very important op ed. I 619 00:35:35,880 --> 00:35:39,719 Speaker 8: encourage everyone to read it. I think that the concerns 620 00:35:39,760 --> 00:35:43,200 Speaker 8: are valid. I see them a little bit differently, but 621 00:35:43,239 --> 00:35:46,719 Speaker 8: we don't need to spend too much time on the 622 00:35:46,719 --> 00:35:49,520 Speaker 8: differences in terms of how I see them. But I'll 623 00:35:49,520 --> 00:35:52,120 Speaker 8: put the difference up front, which is there are also 624 00:35:52,160 --> 00:35:56,520 Speaker 8: studies which project the opposite, which project AI on net 625 00:35:56,680 --> 00:35:59,960 Speaker 8: creating jobs rather than erasing jobs. 626 00:36:00,560 --> 00:36:02,840 Speaker 6: That being said, it's a difference in terms. 627 00:36:02,680 --> 00:36:06,760 Speaker 8: Of long term outlook rather than degree, because either way, 628 00:36:07,160 --> 00:36:10,160 Speaker 8: it's going to eviscerate jobs within certain industries at the 629 00:36:10,320 --> 00:36:15,960 Speaker 8: very least force reskilling, and that is super painful. You know, 630 00:36:16,000 --> 00:36:22,120 Speaker 8: you you mentioned that he is contemptuous. Representative hunts. It 631 00:36:22,239 --> 00:36:26,080 Speaker 8: is of what he calls, you know, old conservative talking points, 632 00:36:26,600 --> 00:36:30,160 Speaker 8: and you had to avoid that particular angle. But one 633 00:36:30,160 --> 00:36:32,760 Speaker 8: thing I'll say, is you know one thing that I'm 634 00:36:34,160 --> 00:36:38,439 Speaker 8: proud of in my career as a public commentator crew, 635 00:36:38,440 --> 00:36:40,920 Speaker 8: which I think is largely over other than you know, 636 00:36:41,040 --> 00:36:43,640 Speaker 8: talk about AI once in a while on your show, 637 00:36:44,880 --> 00:36:45,600 Speaker 8: is just. 638 00:36:45,560 --> 00:36:46,920 Speaker 6: How little I've had to change. 639 00:36:46,960 --> 00:36:49,279 Speaker 8: And I remember a conversation I had with you and 640 00:36:49,400 --> 00:36:54,440 Speaker 8: Andrew Lee a long time ago where you know, I 641 00:36:54,480 --> 00:36:59,640 Speaker 8: said that that I supported universal basic income and you understand, 642 00:36:59,640 --> 00:37:00,920 Speaker 8: of course reasons. 643 00:37:00,560 --> 00:37:02,479 Speaker 6: Now why this was the case. 644 00:37:02,520 --> 00:37:05,720 Speaker 8: I think what twelve thirteen years ago, it was because 645 00:37:05,760 --> 00:37:08,440 Speaker 8: I saw that AI was going to come along and 646 00:37:08,640 --> 00:37:11,520 Speaker 8: really disrupt the job market. Now, I'm not saying UBI 647 00:37:11,760 --> 00:37:15,759 Speaker 8: is the solution, but I think that Hudson's question is 648 00:37:15,760 --> 00:37:18,880 Speaker 8: the right one. We need to adopt to adapt to 649 00:37:18,920 --> 00:37:22,520 Speaker 8: a world where there's going to be a massive AI disruption, 650 00:37:23,280 --> 00:37:29,360 Speaker 8: And how do we have a property and liberty preserving 651 00:37:29,480 --> 00:37:33,279 Speaker 8: conservative movement that's able to adapt to the reality of 652 00:37:33,320 --> 00:37:36,840 Speaker 8: this massive disruption. We understand kind of what the liberal 653 00:37:36,960 --> 00:37:41,160 Speaker 8: or socialist talking points are, and Hudson is absolutely right 654 00:37:41,680 --> 00:37:46,680 Speaker 8: that conservatives need to have their own set of principles 655 00:37:47,080 --> 00:37:51,759 Speaker 8: to deal with this extraordinarily turbulent world that we are in, 656 00:37:52,080 --> 00:37:53,200 Speaker 8: and increasingly so. 657 00:37:53,719 --> 00:37:57,520 Speaker 1: And Devin Thank you so much for that answer, because 658 00:37:57,520 --> 00:37:59,840 Speaker 1: the only reason why I was avoiding the conservative angle 659 00:38:00,080 --> 00:38:02,680 Speaker 1: it was simply for the sake of time, because that 660 00:38:02,680 --> 00:38:04,920 Speaker 1: would have been a larger discussion. But you really boiled 661 00:38:04,920 --> 00:38:08,840 Speaker 1: that down nicely and included the answer to a question 662 00:38:08,880 --> 00:38:11,799 Speaker 1: that I would have eventually have asked you had we 663 00:38:11,880 --> 00:38:14,000 Speaker 1: had more time. So thank you so much for that, 664 00:38:14,080 --> 00:38:16,280 Speaker 1: and thank you for the time this morning. As always, 665 00:38:16,280 --> 00:38:20,080 Speaker 1: I look forward to continuing our discussions on AI as 666 00:38:20,120 --> 00:38:24,360 Speaker 1: we navigate the controversies here in Minnesota. But as always, 667 00:38:24,360 --> 00:38:26,200 Speaker 1: I thank you for the time, and great happy New 668 00:38:26,239 --> 00:38:27,640 Speaker 1: Year to you and your family, Bud. 669 00:38:28,160 --> 00:38:31,399 Speaker 6: You too, John, have a safe, healthy, happy month. 670 00:38:32,200 --> 00:38:37,480 Speaker 1: All right. Coming up, Christy Nome, DHS arrested how many 671 00:38:38,239 --> 00:38:41,680 Speaker 1: criminal illegal aliens in Minneapolis. I'll give you the number 672 00:38:41,760 --> 00:38:45,840 Speaker 1: next and the amount of fraud that she says the 673 00:38:45,960 --> 00:38:49,400 Speaker 1: DHS has them covered, which can kind of attach to 674 00:38:49,440 --> 00:38:52,680 Speaker 1: the number that we already talk about, and it's a lot. 675 00:38:52,760 --> 00:38:54,360 Speaker 1: We'll get to that next in your comments from the 676 00:38:54,400 --> 00:38:57,759 Speaker 1: iHeartRadio app here on Twinsday's news Talk Am eleven thirty 677 00:38:57,760 --> 00:38:58,839 Speaker 1: and one oh three five FM.