1 00:00:07,880 --> 00:00:14,880 Speaker 1: And it's only the middle of the week, so much 2 00:00:14,960 --> 00:00:15,600 Speaker 1: has happened. 3 00:00:15,680 --> 00:00:17,759 Speaker 2: We have a lot to talk about this morning on 4 00:00:17,800 --> 00:00:21,120 Speaker 2: Twin Cities News Talk Am eleven thirty one oh three 5 00:00:21,239 --> 00:00:25,640 Speaker 2: five FM. My name is John Justice, and I'm glad 6 00:00:25,640 --> 00:00:28,240 Speaker 2: that you're with the show this morning as we embark 7 00:00:29,440 --> 00:00:31,320 Speaker 2: on our broadcast from the six to five to one 8 00:00:31,360 --> 00:00:35,760 Speaker 2: carpet plus Next Day Install studios pushing the buttons in 9 00:00:35,800 --> 00:00:37,400 Speaker 2: the master control booth this morning. 10 00:00:37,479 --> 00:00:39,040 Speaker 1: Is Devin. Nice to see him. 11 00:00:39,640 --> 00:00:41,320 Speaker 2: Got a lot of guests on the show today, But 12 00:00:41,400 --> 00:00:45,120 Speaker 2: let's run down all of the major news items as 13 00:00:45,120 --> 00:00:48,640 Speaker 2: we dive in, dive into everything that needs to be 14 00:00:49,080 --> 00:00:52,239 Speaker 2: covered relating to the fact that Minnesota has become the 15 00:00:52,280 --> 00:00:55,840 Speaker 2: epicenter of the news cycle right now, much to the 16 00:00:55,960 --> 00:01:00,440 Speaker 2: chagrin of Tim Walls. Couldn't have happened to him, dude, 17 00:01:00,480 --> 00:01:03,920 Speaker 2: all right, So on the show today, as I had 18 00:01:03,920 --> 00:01:08,039 Speaker 2: been hearing, it has now become a reality. ICE is 19 00:01:08,160 --> 00:01:13,880 Speaker 2: launching extensive immigration raids here in Minnesota. We have a 20 00:01:13,880 --> 00:01:18,200 Speaker 2: lot of audio to share of local law enforcements, Minneapolis 21 00:01:18,240 --> 00:01:23,080 Speaker 2: man Baby mayors commenting their concern about ICE coming in 22 00:01:23,120 --> 00:01:25,240 Speaker 2: and doing their job. I mean, consider that for just 23 00:01:25,280 --> 00:01:32,880 Speaker 2: a moment, you have law enforcement criticizing that law enforcement 24 00:01:32,959 --> 00:01:34,720 Speaker 2: is going to come in and conduct law enforcement in it. 25 00:01:34,760 --> 00:01:39,800 Speaker 2: Make it make sense small business administration orders an investigation 26 00:01:40,120 --> 00:01:42,600 Speaker 2: into further COVID fraud in Minnesota. 27 00:01:42,600 --> 00:01:44,200 Speaker 1: This is coming from the Trump administration. 28 00:01:44,560 --> 00:01:51,040 Speaker 2: Walls does respond to all the latest of plaguing his administration, 29 00:01:51,360 --> 00:01:54,320 Speaker 2: all of the controversies, and we'll talk about that defeat 30 00:01:54,320 --> 00:01:57,200 Speaker 2: gartzan Stein. Ross will get into some AI conversations at 31 00:01:57,240 --> 00:01:59,920 Speaker 2: six point thirty. You can get your comments in early 32 00:02:00,120 --> 00:02:03,840 Speaker 2: via the iHeartRadio app Talkbacks. Representative Tom Emer will join 33 00:02:03,960 --> 00:02:07,440 Speaker 2: us at seven twenty this morning. Liz Colman with a 34 00:02:07,480 --> 00:02:11,720 Speaker 2: lot to cover relating to news at alphanews dot org. 35 00:02:11,760 --> 00:02:17,920 Speaker 2: That'll be at eight thirty as well. The Department of 36 00:02:18,080 --> 00:02:21,280 Speaker 2: Justice has also officially sued the state of Minnesota to 37 00:02:21,320 --> 00:02:26,000 Speaker 2: obtain the entire statewide voter roles, including all illegal aliens 38 00:02:26,040 --> 00:02:33,160 Speaker 2: living in Minnesota who may have been fraudulently registered to vote. 39 00:02:33,200 --> 00:02:34,480 Speaker 2: This is one of the things we're actually going to 40 00:02:34,480 --> 00:02:37,640 Speaker 2: talk about with David regarding AI because the potential of 41 00:02:37,680 --> 00:02:42,359 Speaker 2: AI actually going and speeding up these investigations. So we 42 00:02:42,480 --> 00:02:44,280 Speaker 2: got a lot of ground to cover today. Let's start 43 00:02:44,280 --> 00:02:48,480 Speaker 2: here though the election overnight that everybody was so worried about. 44 00:02:48,360 --> 00:02:52,440 Speaker 1: Yeah, it wasn't that big of a concern. 45 00:02:53,919 --> 00:02:57,880 Speaker 2: Republican Matt Van Epps fended off a challenge from the 46 00:02:57,919 --> 00:03:02,280 Speaker 2: far left whack a Doodle aften Bean on Tuesday in 47 00:03:02,320 --> 00:03:06,440 Speaker 2: the special election for the Tennessee seventh Congressional District. Ninety 48 00:03:06,440 --> 00:03:09,600 Speaker 2: five percent of the vote in the race was called 49 00:03:09,639 --> 00:03:13,400 Speaker 2: for Epps fifty four to forty five. This was a 50 00:03:13,480 --> 00:03:17,680 Speaker 2: nine point victory. And if you remember the commentary heading 51 00:03:17,720 --> 00:03:21,000 Speaker 2: into it, polls, oh, it's within two points, it's within 52 00:03:21,080 --> 00:03:22,000 Speaker 2: striking distance. 53 00:03:22,680 --> 00:03:23,119 Speaker 3: It wasn't. 54 00:03:23,200 --> 00:03:23,520 Speaker 4: It was not. 55 00:03:23,680 --> 00:03:27,800 Speaker 1: He won by nine points. And this was after the Democrats. 56 00:03:28,760 --> 00:03:32,840 Speaker 2: Poured millions of dollars into this race to try to 57 00:03:32,919 --> 00:03:38,560 Speaker 2: shore up support for this nutbar aften Bean. It was 58 00:03:38,600 --> 00:03:41,960 Speaker 2: forming Fox News this morning was framing this race as 59 00:03:42,000 --> 00:03:42,800 Speaker 2: a nailbier. 60 00:03:43,280 --> 00:03:44,280 Speaker 1: It wasn't even close. 61 00:03:45,080 --> 00:03:48,040 Speaker 2: Can we now have the commentary of the Democrats being 62 00:03:48,080 --> 00:03:51,480 Speaker 2: in disarray and how this election is a harbinger of 63 00:03:51,640 --> 00:03:55,520 Speaker 2: doom and gloom for Democrats in twenty twenty six. I 64 00:03:55,520 --> 00:03:58,080 Speaker 2: haven't seen any of that in the coverage relating to 65 00:03:58,120 --> 00:03:59,800 Speaker 2: this particular race. As a matter of fact, I have 66 00:03:59,840 --> 00:04:02,400 Speaker 2: his I see very little coverage relating to this race, 67 00:04:02,560 --> 00:04:05,520 Speaker 2: certainly when you compare it to the elections that just 68 00:04:05,600 --> 00:04:10,160 Speaker 2: took place last month, and the similarities are right there 69 00:04:10,160 --> 00:04:14,880 Speaker 2: in front of you. This was a safe Republican seat 70 00:04:15,760 --> 00:04:18,920 Speaker 2: that Democrats poured a ton of money into to try 71 00:04:18,960 --> 00:04:25,000 Speaker 2: to win, and they lost by nine points. You go 72 00:04:25,080 --> 00:04:27,360 Speaker 2: back and look at the November elections and you had 73 00:04:27,640 --> 00:04:33,520 Speaker 2: mostly blue races, less money because Republicans typically have less 74 00:04:33,520 --> 00:04:37,800 Speaker 2: money poured into some of those races, and Democrats won. 75 00:04:38,839 --> 00:04:40,720 Speaker 2: But see last month, it was all look at that. 76 00:04:41,600 --> 00:04:47,159 Speaker 2: Republicans better be worried. The Democrats just sweeping all these victories. Yeah, 77 00:04:47,200 --> 00:04:48,799 Speaker 2: they were blue areas. 78 00:04:50,279 --> 00:04:51,119 Speaker 1: In case in point. 79 00:04:51,200 --> 00:04:55,200 Speaker 2: Again, the commentary still this morning after a nine point. 80 00:04:55,120 --> 00:04:57,640 Speaker 1: Victory was it was a nail bier. It wasn't a 81 00:04:57,720 --> 00:04:58,279 Speaker 1: nail bier. 82 00:05:00,920 --> 00:05:05,599 Speaker 2: And this isn't a harbinger of doom and gloom for Democrats. No, 83 00:05:05,800 --> 00:05:10,200 Speaker 2: it was a special election scheduled after former Representative Mark 84 00:05:10,200 --> 00:05:17,000 Speaker 2: Green announced his resignation earlier this year. As the article 85 00:05:17,000 --> 00:05:19,760 Speaker 2: from Daily Wire covering this says, in recent weeks, money 86 00:05:19,760 --> 00:05:24,360 Speaker 2: poured into the race, as often being campaigned with Democrat. 87 00:05:23,880 --> 00:05:25,680 Speaker 1: Favorites Al Gore and AOC. 88 00:05:27,520 --> 00:05:31,360 Speaker 2: But this is no harbinger for Democrats heading in the 89 00:05:31,440 --> 00:05:34,000 Speaker 2: next year anymore than last year was a harbinger of 90 00:05:34,040 --> 00:05:37,760 Speaker 2: doom for Republicans in the other races. It just continues 91 00:05:37,800 --> 00:05:41,120 Speaker 2: to go and demonstrate the bias that we see every 92 00:05:41,360 --> 00:05:45,839 Speaker 2: single day. So it wasn't even I was worried about 93 00:05:45,839 --> 00:05:49,640 Speaker 2: it that Afton Bean was just a crazy person. The 94 00:05:49,640 --> 00:05:51,599 Speaker 2: thing about the crazies on the left, they typically don't 95 00:05:51,640 --> 00:05:55,560 Speaker 2: go away. She's actually raised quite a bit of a 96 00:05:55,640 --> 00:05:59,240 Speaker 2: name id now, which is hugely important when it comes 97 00:05:59,279 --> 00:06:01,080 Speaker 2: to Democrats. I don't think that she's going to be 98 00:06:01,120 --> 00:06:04,080 Speaker 2: going away anytime soon. So got a lot of ground 99 00:06:04,080 --> 00:06:05,400 Speaker 2: to cover. Let me give you one more item and 100 00:06:05,440 --> 00:06:07,279 Speaker 2: then we'll take a quick break here on Twin Cities 101 00:06:07,440 --> 00:06:09,960 Speaker 2: News Talk. As we get into Trump wanting to end 102 00:06:10,920 --> 00:06:14,040 Speaker 2: the federal income tax. Yesterday, Trump did go and make 103 00:06:14,080 --> 00:06:17,760 Speaker 2: it official. He followed through with this pledge announcing that 104 00:06:17,800 --> 00:06:21,920 Speaker 2: he's completely terminated every action signed by former President Joe Biden, 105 00:06:22,320 --> 00:06:26,320 Speaker 2: using the autopen and declaring those measures null and void. 106 00:06:26,400 --> 00:06:31,320 Speaker 2: So legal experts say the auto pen signed presidential actions 107 00:06:31,400 --> 00:06:35,560 Speaker 2: remain valid as long as the president authorized them, regardless 108 00:06:35,560 --> 00:06:38,240 Speaker 2: of who physically operated the device. 109 00:06:39,080 --> 00:06:39,560 Speaker 1: But if the. 110 00:06:39,480 --> 00:06:43,560 Speaker 2: President truly had no knowledge of the device's use or 111 00:06:43,640 --> 00:06:46,960 Speaker 2: did not consent to the signature, some scholars argue those 112 00:06:47,000 --> 00:06:50,719 Speaker 2: actions could be challengeable. The gop led House Oversight Committee 113 00:06:50,760 --> 00:06:54,760 Speaker 2: report questioned the legitimacy of numerous executive actions on the autopen, 114 00:06:54,800 --> 00:07:00,600 Speaker 2: alleging sometimes there was clear proof of Biden's personal approval. 115 00:07:01,040 --> 00:07:03,120 Speaker 2: The committee has urged the US Department of Justice to 116 00:07:03,120 --> 00:07:06,839 Speaker 2: investigate the potential misuse of presidential authority. So my guess 117 00:07:06,880 --> 00:07:10,720 Speaker 2: on what's taking place here, this seems to be more 118 00:07:10,720 --> 00:07:12,600 Speaker 2: of a means to the end, and Trump's been doing 119 00:07:12,640 --> 00:07:17,120 Speaker 2: this a lot lately. Challenges will ensue on the issue 120 00:07:17,200 --> 00:07:21,440 Speaker 2: of the auto pen, but those would likely end up 121 00:07:21,480 --> 00:07:25,200 Speaker 2: exposing the reality of how much, or more importantly, how 122 00:07:25,360 --> 00:07:29,600 Speaker 2: little was Biden involved, not just in the signing off 123 00:07:29,920 --> 00:07:33,640 Speaker 2: on these various issues, but also in his presidency. So again, 124 00:07:33,760 --> 00:07:36,440 Speaker 2: this seems like more of a means to an end 125 00:07:36,600 --> 00:07:40,000 Speaker 2: to bring about clarity on the issue of who was 126 00:07:40,040 --> 00:07:43,160 Speaker 2: really running the Biden administration more so than it is 127 00:07:43,720 --> 00:07:47,200 Speaker 2: nullifying what was signed by the auto pen. It's very 128 00:07:47,240 --> 00:07:52,000 Speaker 2: similar to what Trump did with the temporary protected status, which, 129 00:07:52,040 --> 00:07:54,800 Speaker 2: by the way, though that being said, because I am 130 00:07:54,840 --> 00:07:57,760 Speaker 2: convinced that Trump didn't make that move in order to 131 00:07:57,920 --> 00:08:03,000 Speaker 2: bring national attention to the issue of the fraud here 132 00:08:03,040 --> 00:08:06,520 Speaker 2: in Minnesota. That being said, that TPS will more than 133 00:08:06,640 --> 00:08:09,880 Speaker 2: likely be coming into play as ICE ramps up their 134 00:08:10,000 --> 00:08:13,280 Speaker 2: enforcement here in Minnesota. So we'll be getting into all 135 00:08:13,320 --> 00:08:15,640 Speaker 2: that throughout the show this morning. And of course I 136 00:08:15,640 --> 00:08:17,840 Speaker 2: want to hear from you phone number eight four four 137 00:08:18,160 --> 00:08:20,800 Speaker 2: nine four six five eight five five. You can email 138 00:08:21,080 --> 00:08:24,440 Speaker 2: Justice at iHeartRadio dot com. And of course, if you're 139 00:08:24,440 --> 00:08:27,120 Speaker 2: listening on the iHeartRadio app, your talkbacks are brought to 140 00:08:27,160 --> 00:08:28,240 Speaker 2: you by Lyndahl Realty You. 141 00:08:28,280 --> 00:08:30,280 Speaker 1: We will get to those next here. 142 00:08:30,120 --> 00:08:32,280 Speaker 2: On twin Citay's News Talk Am eleven thirty and one 143 00:08:32,320 --> 00:08:33,480 Speaker 2: oh three five FM. 144 00:08:33,520 --> 00:08:39,600 Speaker 4: Good morning, and I love your show, John. 145 00:08:39,640 --> 00:08:42,200 Speaker 3: I know nine points is a lot. However, when someone 146 00:08:42,320 --> 00:08:46,760 Speaker 3: is that crazy and that extreme, nine points isn't nearly enough. 147 00:08:49,480 --> 00:08:53,800 Speaker 2: Well, when you consider how much attention Democrats gave the 148 00:08:53,840 --> 00:08:55,600 Speaker 2: millions that were poured into that race. 149 00:08:56,080 --> 00:08:57,440 Speaker 1: Yeah, I'll take the nine points. 150 00:08:58,600 --> 00:09:01,520 Speaker 2: And as I mentioned a moment go, the polls were 151 00:09:01,600 --> 00:09:02,480 Speaker 2: all way off. 152 00:09:03,400 --> 00:09:04,480 Speaker 1: The media wanted to. 153 00:09:04,400 --> 00:09:07,559 Speaker 2: Make this look a heck of a lot closer than 154 00:09:07,600 --> 00:09:10,920 Speaker 2: what it really was. You know, I said in the 155 00:09:11,320 --> 00:09:14,880 Speaker 2: earlier segment that the polls had the election down to 156 00:09:14,920 --> 00:09:16,560 Speaker 2: like two points, and. 157 00:09:16,720 --> 00:09:18,520 Speaker 1: It wasn't even It wasn't even close. 158 00:09:20,000 --> 00:09:22,040 Speaker 2: I want to see that commentary of how the Democrats 159 00:09:22,080 --> 00:09:23,480 Speaker 2: are in so much trouble next year. 160 00:09:23,520 --> 00:09:25,160 Speaker 1: I just find the whole thing amusing. 161 00:09:25,520 --> 00:09:26,319 Speaker 4: Morning John. 162 00:09:26,480 --> 00:09:29,920 Speaker 5: I think, honestly, the scariest part about that Nashville election 163 00:09:30,920 --> 00:09:35,720 Speaker 5: is it forty five percent of the people or that 164 00:09:35,840 --> 00:09:40,360 Speaker 5: many people voted for somebody who literally hates their area 165 00:09:40,960 --> 00:09:42,240 Speaker 5: and hates their state. 166 00:09:42,400 --> 00:09:44,960 Speaker 2: It's actually worse than that. So this was an election 167 00:09:45,080 --> 00:09:49,800 Speaker 2: in Tennessee. Nashville was the most prominent area of the 168 00:09:49,840 --> 00:09:52,840 Speaker 2: district as a whole. So Nashville is just a part 169 00:09:53,160 --> 00:09:57,000 Speaker 2: of the district itself, which spanned it, you know, beyond 170 00:09:57,040 --> 00:10:04,160 Speaker 2: the region of Nashville. In Nashville often had seventy seven 171 00:10:04,280 --> 00:10:05,960 Speaker 2: percent of the vote. 172 00:10:08,720 --> 00:10:10,960 Speaker 1: That was over the GOP at like forty four. 173 00:10:11,120 --> 00:10:15,679 Speaker 2: So again it tells you the dynamics in play, and 174 00:10:15,960 --> 00:10:19,240 Speaker 2: a lot like those core blue areas here in Minnesota 175 00:10:19,240 --> 00:10:21,640 Speaker 2: that we talk about so often on Twin Cities News 176 00:10:21,640 --> 00:10:26,000 Speaker 2: talk that drive elections here in the state. No, Nashville 177 00:10:26,080 --> 00:10:29,800 Speaker 2: is much in the same way, and even with winning 178 00:10:29,840 --> 00:10:33,679 Speaker 2: a massive majority in the key area of Nashville, often 179 00:10:33,760 --> 00:10:37,360 Speaker 2: still ended up losing by nine points. Continuing our show 180 00:10:37,400 --> 00:10:40,040 Speaker 2: here from the sixty five to one Carpet plus Next 181 00:10:40,120 --> 00:10:41,319 Speaker 2: Day Install Studios. 182 00:10:42,280 --> 00:10:45,080 Speaker 6: Hey, John, I just wanted to say with that Nashville 183 00:10:45,120 --> 00:10:50,000 Speaker 6: election that we should definitely take the win. Sure, it 184 00:10:50,080 --> 00:10:54,640 Speaker 6: was a blowout, but I like how we kind of 185 00:10:54,679 --> 00:10:58,640 Speaker 6: think of stuff as being super close. It turns out 186 00:10:58,640 --> 00:10:59,560 Speaker 6: the vote a lot more. 187 00:10:59,559 --> 00:11:04,120 Speaker 2: I feel, actually that's not a bad idea, and I 188 00:11:04,120 --> 00:11:06,959 Speaker 2: don't mind it either. I would rather have these And 189 00:11:07,559 --> 00:11:10,120 Speaker 2: here's the thing. On a comment like that, you're going 190 00:11:10,160 --> 00:11:12,920 Speaker 2: to get your wish because this is just what's going 191 00:11:12,960 --> 00:11:14,880 Speaker 2: to happen. Every election is likely you just can't trust 192 00:11:14,920 --> 00:11:20,160 Speaker 2: the polling. Sometimes you'll get more accurate polling, especially on 193 00:11:20,200 --> 00:11:23,520 Speaker 2: the national elections, but in situations like we have in 194 00:11:23,559 --> 00:11:26,000 Speaker 2: this race, where there aren't any other races to focus on, 195 00:11:26,280 --> 00:11:28,600 Speaker 2: the media is looking for things to talk about as 196 00:11:28,600 --> 00:11:31,320 Speaker 2: we head into the holidays, which is part of the 197 00:11:31,400 --> 00:11:34,800 Speaker 2: reason why Minnesota is getting so much attention right now. 198 00:11:35,200 --> 00:11:38,080 Speaker 2: That's not to say if we were outside of the holidays. 199 00:11:38,440 --> 00:11:41,400 Speaker 2: The level of fraud that we've been talking about for 200 00:11:41,559 --> 00:11:46,079 Speaker 2: years on the show would not have received exposure. However, 201 00:11:46,320 --> 00:11:49,280 Speaker 2: right now it is amplified because the media is looking 202 00:11:49,280 --> 00:11:51,160 Speaker 2: for something to talk about. They just ended up with 203 00:11:51,200 --> 00:11:55,360 Speaker 2: something really incredibly juicy to discuss. One thing I failed 204 00:11:55,360 --> 00:11:57,720 Speaker 2: to mention in my rundown of all the different issues 205 00:11:58,360 --> 00:12:04,520 Speaker 2: that Minnesota has faced. Also the Walls administration being in 206 00:12:04,559 --> 00:12:07,880 Speaker 2: the federal spotlight at risk of losing millions of dollars 207 00:12:07,920 --> 00:12:09,360 Speaker 2: in funding. 208 00:12:10,559 --> 00:12:12,760 Speaker 1: For granting illegal. 209 00:12:12,559 --> 00:12:18,040 Speaker 2: Driver's licenses to non domiciled residents those here illegally. 210 00:12:17,559 --> 00:12:18,440 Speaker 1: But authorized to work. 211 00:12:18,440 --> 00:12:22,199 Speaker 2: That's another one of the big issues that Minnesota is 212 00:12:22,200 --> 00:12:24,960 Speaker 2: currently facing right now. I failed to mention that in 213 00:12:24,960 --> 00:12:26,360 Speaker 2: the run down at the start of the show. 214 00:12:27,559 --> 00:12:31,040 Speaker 5: And John, I do have one question for you and 215 00:12:31,120 --> 00:12:34,680 Speaker 5: the Republican Party. Why has it not been all over 216 00:12:34,720 --> 00:12:41,120 Speaker 5: the news Republican people screaming for Walls step down and 217 00:12:41,920 --> 00:12:45,520 Speaker 5: people to be held accountable. All I ever see is 218 00:12:45,640 --> 00:12:48,520 Speaker 5: Walls lying his face off on TV. 219 00:12:49,000 --> 00:12:53,199 Speaker 2: I don't know about individuals calling for him to resign 220 00:12:54,520 --> 00:12:58,000 Speaker 2: elected officials at least, but I knew no. Plenty of 221 00:12:58,080 --> 00:13:00,400 Speaker 2: Republicans have spoken out. We'll talk to represent of Tom 222 00:13:00,400 --> 00:13:03,280 Speaker 2: Emmer in about an hour from now. He's been making 223 00:13:03,320 --> 00:13:05,760 Speaker 2: the news media rounds. He's not going to end up 224 00:13:05,760 --> 00:13:10,120 Speaker 2: on places like MSNBC and CNN, and a lot of 225 00:13:10,120 --> 00:13:13,480 Speaker 2: times local media doesn't go and talk to Republicans. So 226 00:13:13,600 --> 00:13:18,120 Speaker 2: I'm hesitant to go and do too much criticism over 227 00:13:18,200 --> 00:13:22,360 Speaker 2: Republicans apparently not speaking out on any one of these issues, 228 00:13:22,400 --> 00:13:24,880 Speaker 2: because a lot of times it simply is more an 229 00:13:24,920 --> 00:13:28,640 Speaker 2: aspect of the media not giving them proper attention. There 230 00:13:28,640 --> 00:13:32,760 Speaker 2: are plenty of individuals like myself going back to last 231 00:13:32,800 --> 00:13:35,360 Speaker 2: week when this all blew up after that City Journal 232 00:13:35,400 --> 00:13:38,040 Speaker 2: report calling for Walls to resign, and I have seen 233 00:13:38,520 --> 00:13:41,679 Speaker 2: those calls grow, albeit I haven't seen it coming from 234 00:13:41,720 --> 00:13:44,840 Speaker 2: elected officials, even though I have seen many members of 235 00:13:44,840 --> 00:13:48,079 Speaker 2: Congress that have been out speaking regarding the issues that 236 00:13:48,120 --> 00:13:49,720 Speaker 2: Minnesota is facing under Walls. 237 00:13:49,760 --> 00:13:51,800 Speaker 1: And one of those will be Emma joining us next hour. 238 00:13:52,480 --> 00:13:55,959 Speaker 2: So Trump says Americans may soon pay no income tax, 239 00:13:56,000 --> 00:13:59,200 Speaker 2: as White House explores alternative or revenue streams, and there's 240 00:13:59,200 --> 00:14:01,360 Speaker 2: a bit of a slow roll out on this. I've 241 00:14:01,360 --> 00:14:03,800 Speaker 2: been kind of keeping tabs on it. So last week 242 00:14:04,960 --> 00:14:10,680 Speaker 2: Trump made this comment talking with the media. 243 00:14:10,800 --> 00:14:13,120 Speaker 7: Over the next couple of years, I think will substantially 244 00:14:13,160 --> 00:14:16,280 Speaker 7: be cutting and maybe cutting out completely, but we'll be 245 00:14:16,320 --> 00:14:21,080 Speaker 7: cutting income tax, could be almost completely cutting it because 246 00:14:21,080 --> 00:14:22,640 Speaker 7: the money we're taken in and is going to be 247 00:14:23,160 --> 00:14:23,760 Speaker 7: so large. 248 00:14:24,480 --> 00:14:26,200 Speaker 1: And yet other. 249 00:14:26,160 --> 00:14:28,480 Speaker 7: Countries who have been ripping us off for many, many 250 00:14:28,560 --> 00:14:31,520 Speaker 7: years and many years, they've just been ripping us as shreds. 251 00:14:31,920 --> 00:14:35,119 Speaker 2: Now I think I actually reversed these. So that was yesterday. 252 00:14:35,680 --> 00:14:38,160 Speaker 2: That was Trump talking about cutting the income tax during 253 00:14:38,520 --> 00:14:40,920 Speaker 2: that lengthy cabinet meeting that took place off which we 254 00:14:40,920 --> 00:14:43,280 Speaker 2: have a lot of audio to share. I believe this 255 00:14:43,440 --> 00:14:45,640 Speaker 2: is the clip going back to last week. 256 00:14:45,960 --> 00:14:47,520 Speaker 1: And I believe, yeah, Curial. 257 00:14:47,360 --> 00:14:49,560 Speaker 7: That at some point in the not too distant future, 258 00:14:49,600 --> 00:14:53,080 Speaker 7: you won't even have income tax to pay because the 259 00:14:53,120 --> 00:14:55,000 Speaker 7: money would take it in is so great, it's so 260 00:14:55,200 --> 00:14:57,960 Speaker 7: enormous that you're not going to have income tax to pay. 261 00:14:58,840 --> 00:15:00,800 Speaker 7: Whether you get rid of it or just keep it 262 00:15:00,840 --> 00:15:04,080 Speaker 7: around for fun, or have it really low, much lower 263 00:15:04,080 --> 00:15:07,120 Speaker 7: than it is now, but you won't be paying income tax. 264 00:15:07,200 --> 00:15:08,720 Speaker 7: We've slashed one triggua down. 265 00:15:09,120 --> 00:15:11,560 Speaker 2: What this is though for Trump, and he's done this 266 00:15:11,640 --> 00:15:13,000 Speaker 2: a lot when it comes to the things that he 267 00:15:13,040 --> 00:15:15,640 Speaker 2: wants to do, it's been a bit of a slow 268 00:15:15,800 --> 00:15:19,560 Speaker 2: ramp up to the actual action taking place, and I 269 00:15:19,600 --> 00:15:21,400 Speaker 2: had it right the first time. That clip you just 270 00:15:21,440 --> 00:15:24,760 Speaker 2: heard was from yesterday. The one prior was from last week. 271 00:15:25,160 --> 00:15:28,920 Speaker 2: And it's been just a slow rollout preparing everybody for 272 00:15:29,280 --> 00:15:32,960 Speaker 2: the potential of dramatically reducing the federal income tax or 273 00:15:33,000 --> 00:15:37,120 Speaker 2: eliminating it all together. And I'm of the opinion that 274 00:15:37,200 --> 00:15:39,640 Speaker 2: this is one of the keys that the Trump administration 275 00:15:39,880 --> 00:15:45,320 Speaker 2: is looking at utilizing in hopes of one turning the 276 00:15:45,360 --> 00:15:48,960 Speaker 2: economy around, because it's a much different mentality attached to 277 00:15:49,040 --> 00:15:51,440 Speaker 2: the federal income tax going away and how people go 278 00:15:51,480 --> 00:15:53,800 Speaker 2: and spend their money, because that's money that you made 279 00:15:53,800 --> 00:15:55,920 Speaker 2: and earned that the government take is taking from you, 280 00:15:56,400 --> 00:15:59,000 Speaker 2: as opposed to the government going and just arbitrarily cutting 281 00:15:59,040 --> 00:16:01,920 Speaker 2: you a stimulus check, which carries with it a different 282 00:16:02,000 --> 00:16:06,680 Speaker 2: mindset which brings about inflation. Cutting the federal income tax 283 00:16:06,760 --> 00:16:08,840 Speaker 2: is a much better way to drive that money into 284 00:16:08,840 --> 00:16:11,480 Speaker 2: the economy without driving up the cost of inflation. And 285 00:16:11,520 --> 00:16:14,080 Speaker 2: so I'm convinced that one Trump is going to be 286 00:16:14,160 --> 00:16:19,120 Speaker 2: rolling this out after the beginning of the year in 287 00:16:19,160 --> 00:16:22,480 Speaker 2: hopes of shoring up the economy, driving down prices, but 288 00:16:22,560 --> 00:16:25,960 Speaker 2: also helping Republicans in the mid terms later in the 289 00:16:26,040 --> 00:16:28,920 Speaker 2: year and certainly an issue that we'll be talking about 290 00:16:28,920 --> 00:16:32,800 Speaker 2: as we get more details on it in the near future. 291 00:16:32,840 --> 00:16:34,560 Speaker 2: But it is one that I've been watching for a while. 292 00:16:34,560 --> 00:16:39,680 Speaker 2: I'm incredibly excited over. I've heard the arguments from individuals saying, well, 293 00:16:39,920 --> 00:16:42,080 Speaker 2: if it happens and here in Minnesota, you're just going 294 00:16:42,160 --> 00:16:43,960 Speaker 2: to have the government go in taxes more at the 295 00:16:44,000 --> 00:16:50,880 Speaker 2: state level. Listen, it's nothing until it's something. I'm not 296 00:16:50,920 --> 00:16:54,480 Speaker 2: going to downplay my desire to see the federal income 297 00:16:54,520 --> 00:16:58,840 Speaker 2: tax be removed over concerns of what Democrats might attempt 298 00:16:58,880 --> 00:17:01,720 Speaker 2: to do here in Minnesota to offset that or take 299 00:17:01,760 --> 00:17:02,600 Speaker 2: advantage of it. 300 00:17:02,920 --> 00:17:04,639 Speaker 1: We'll deal with that when the time comes. 301 00:17:05,040 --> 00:17:08,800 Speaker 2: All right, coming up, David Gartenstein Ross, our AI expert, 302 00:17:09,160 --> 00:17:10,920 Speaker 2: will be joining us. Got a couple of different issues, 303 00:17:10,920 --> 00:17:13,200 Speaker 2: plus your questions for d V Those are already rolling 304 00:17:13,200 --> 00:17:16,840 Speaker 2: in on the iHeartRadio app. Scammers using AI deep fakes 305 00:17:16,840 --> 00:17:19,800 Speaker 2: to trick holiday shoppers, and almost half of gen Z 306 00:17:20,000 --> 00:17:23,520 Speaker 2: wants AI to run the government. Why you should probably 307 00:17:23,560 --> 00:17:26,160 Speaker 2: be terrified about that. We will talk with the v'd 308 00:17:26,280 --> 00:17:42,560 Speaker 2: next on Twin Cities News Talk Trinsday's News Talk Am 309 00:17:42,560 --> 00:17:45,919 Speaker 2: eleven thirty one three five FM from the sixty five 310 00:17:45,960 --> 00:17:50,800 Speaker 2: to one carpet Next Day Install Studios, John Justice and 311 00:17:50,920 --> 00:17:51,880 Speaker 2: joining me once. 312 00:17:51,720 --> 00:17:54,920 Speaker 1: Again this week CEO. 313 00:17:54,560 --> 00:17:59,040 Speaker 2: At Expert Theory Analysts regarding artificial Intelligence. 314 00:17:59,080 --> 00:18:02,080 Speaker 1: Friend of the Show. So, David Carts and Sein Ross. 315 00:18:02,160 --> 00:18:04,879 Speaker 2: David, good morning, and I hope that you and yours 316 00:18:04,880 --> 00:18:06,639 Speaker 2: had a fantastic Thanksgiving. 317 00:18:06,640 --> 00:18:07,480 Speaker 1: Thanks for joining us. 318 00:18:08,040 --> 00:18:10,919 Speaker 3: We most certainly did, John, and it's great to join you. 319 00:18:10,960 --> 00:18:13,400 Speaker 3: I hope you and yours had a great Thanksgiving too, 320 00:18:13,560 --> 00:18:14,200 Speaker 3: We really did. 321 00:18:14,240 --> 00:18:17,680 Speaker 2: It was a nice little precursor to the holiday break 322 00:18:17,880 --> 00:18:20,800 Speaker 2: coming up. I always I try to use the Thanksgiving 323 00:18:20,800 --> 00:18:22,520 Speaker 2: breaks or to set the stage of how I'm going 324 00:18:22,560 --> 00:18:24,160 Speaker 2: to do with my lengthier. 325 00:18:24,040 --> 00:18:26,800 Speaker 1: Christmas Christmas break, so it's like a trial and error. 326 00:18:26,800 --> 00:18:27,920 Speaker 1: But I'm just weird that way. 327 00:18:27,960 --> 00:18:30,919 Speaker 2: So we have some talkbacks that I've already rolled in, 328 00:18:30,920 --> 00:18:32,480 Speaker 2: also an email that came in. So I want to 329 00:18:32,520 --> 00:18:35,560 Speaker 2: start off with the listener questions for you first divide 330 00:18:35,640 --> 00:18:38,399 Speaker 2: before we dive into some of the articles that we 331 00:18:38,480 --> 00:18:42,000 Speaker 2: pulled for today's show. If that works for you, perfect job, 332 00:18:42,240 --> 00:18:46,160 Speaker 2: all right, So first off, let's go and Jr. Friend 333 00:18:46,200 --> 00:18:49,280 Speaker 2: of the Show. He writes in and says question for 334 00:18:49,520 --> 00:18:54,520 Speaker 2: DGR I tested chat GPT with a mock am track 335 00:18:54,680 --> 00:18:58,280 Speaker 2: trip to Houston and Phoenix. It confused Amtrak codes with 336 00:18:58,560 --> 00:19:03,520 Speaker 2: airport codes, it missed to critical timing and leg requirement details, 337 00:19:03,760 --> 00:19:07,480 Speaker 2: and defaulted to check the website. In addition, despite repeated 338 00:19:07,520 --> 00:19:11,040 Speaker 2: requests for concise answers, when I speak to it, it 339 00:19:11,119 --> 00:19:12,080 Speaker 2: never improves. 340 00:19:12,160 --> 00:19:14,520 Speaker 1: It keeps asking if it. 341 00:19:14,440 --> 00:19:18,040 Speaker 2: Can help, on and on with let me know, if 342 00:19:18,080 --> 00:19:21,200 Speaker 2: I can help, let me know if what we need 343 00:19:21,240 --> 00:19:23,080 Speaker 2: to dig into when we can dig into it. 344 00:19:23,280 --> 00:19:24,879 Speaker 1: So he then goes on to say it raises a 345 00:19:24,960 --> 00:19:25,600 Speaker 1: larger issue. 346 00:19:25,680 --> 00:19:30,560 Speaker 2: If free chat GPT isn't reliable for basic tasks other 347 00:19:30,600 --> 00:19:34,800 Speaker 2: than Google searches, why should anyone trust to llms for 348 00:19:35,000 --> 00:19:37,840 Speaker 2: important questions? Now he has more to this, but I'll 349 00:19:37,880 --> 00:19:40,360 Speaker 2: go ahead and stop there and give you the opportunity 350 00:19:40,400 --> 00:19:41,359 Speaker 2: to respond to vi'd. 351 00:19:42,720 --> 00:19:51,400 Speaker 4: It's a really wonderful question, and the answer is sorry. 352 00:19:51,760 --> 00:19:54,560 Speaker 3: He gets into like this core principle when we get this, 353 00:19:54,920 --> 00:19:56,800 Speaker 3: when if I get this answer right, it will be 354 00:19:56,920 --> 00:19:57,760 Speaker 3: really useful. 355 00:19:57,840 --> 00:20:01,080 Speaker 1: Okay, you better get a people often. 356 00:20:02,400 --> 00:20:03,760 Speaker 4: So yeah, and I apologize. 357 00:20:03,960 --> 00:20:06,160 Speaker 3: I often don't take a second to really formulate how 358 00:20:06,200 --> 00:20:07,000 Speaker 3: to say something. 359 00:20:07,840 --> 00:20:09,959 Speaker 4: But here's the way I would look at it. 360 00:20:11,720 --> 00:20:20,239 Speaker 3: Lms are an assistant, and sometimes they're an amazing assistant, 361 00:20:20,560 --> 00:20:24,920 Speaker 3: and sometimes they're a really subbar assistant. So, as your 362 00:20:24,920 --> 00:20:28,440 Speaker 3: listeners know, I'm a huge fan of using large language models. 363 00:20:29,280 --> 00:20:32,080 Speaker 3: But what the listener described as JR right, is a 364 00:20:32,119 --> 00:20:37,840 Speaker 3: listener what JR describes happens every once in a while 365 00:20:37,880 --> 00:20:42,760 Speaker 3: to me where there's a really basic task and my 366 00:20:42,960 --> 00:20:46,919 Speaker 3: LLLM just fails at it, and I show it specifically 367 00:20:46,960 --> 00:20:51,320 Speaker 3: how to do that task, and even when corrected, it 368 00:20:51,400 --> 00:20:56,439 Speaker 3: will either fail again immediately or fail again after another 369 00:20:56,520 --> 00:21:00,280 Speaker 3: couple of tries, and then a different task which is 370 00:21:00,359 --> 00:21:04,959 Speaker 3: much more complex it will do almost perfectly at So 371 00:21:05,359 --> 00:21:10,200 Speaker 3: I would step back, and here's how I would analogize it. Okay, 372 00:21:11,400 --> 00:21:17,160 Speaker 3: most professionals have worked with employees or assistance of some kind, 373 00:21:18,520 --> 00:21:21,200 Speaker 3: and sometimes there'll be an employee who is very good 374 00:21:22,200 --> 00:21:26,800 Speaker 3: and simply fails at a certain kind of task, and 375 00:21:26,840 --> 00:21:31,600 Speaker 3: you wouldn't fire them, and you wouldn't say that they 376 00:21:31,680 --> 00:21:36,119 Speaker 3: don't have value, but there's just something where you understand 377 00:21:36,160 --> 00:21:38,919 Speaker 3: if you give it to them, they're not going to 378 00:21:39,000 --> 00:21:42,440 Speaker 3: do it well. And they might not do it well. 379 00:21:43,400 --> 00:21:45,280 Speaker 3: They might not do it well at one day and 380 00:21:45,320 --> 00:21:48,520 Speaker 3: they'll do it well on a week later or two 381 00:21:48,520 --> 00:21:52,600 Speaker 3: weeks later, not because they've been improved, but because they've 382 00:21:52,600 --> 00:21:56,960 Speaker 3: had a bad day. So people have to be mindful 383 00:21:57,160 --> 00:22:01,359 Speaker 3: of areas where the LM is just going to fail you. 384 00:22:02,280 --> 00:22:07,040 Speaker 4: And so in areas where you're unfamiliar. 385 00:22:06,400 --> 00:22:10,359 Speaker 3: With a topic, that's the area where you can get 386 00:22:10,560 --> 00:22:13,240 Speaker 3: you know, you can get toasted by over relying on 387 00:22:13,280 --> 00:22:16,200 Speaker 3: an LLM. In Jr's case, he understood that it was 388 00:22:16,240 --> 00:22:21,040 Speaker 3: mixing up Amtrak codes and airplane codes. He was trying 389 00:22:21,040 --> 00:22:23,879 Speaker 3: to correct the LM couldn't get it right and said, okay, 390 00:22:23,960 --> 00:22:26,399 Speaker 3: just go ahead and check the website because it was you, 391 00:22:26,560 --> 00:22:30,280 Speaker 3: quote unquote frustrated. Right, they don't really have personalities or emotions, 392 00:22:30,520 --> 00:22:32,320 Speaker 3: but they kind of approximate them. 393 00:22:32,560 --> 00:22:33,760 Speaker 4: And that's what happened. 394 00:22:33,800 --> 00:22:36,560 Speaker 3: It didn't get it right, it failed, and it just 395 00:22:36,600 --> 00:22:39,600 Speaker 3: defaulted to, yeah, well I'm not getting it right. But 396 00:22:40,080 --> 00:22:41,560 Speaker 3: there's a way you can get it right, which is 397 00:22:41,600 --> 00:22:44,560 Speaker 3: doing the work yourself. And so this is an aspect 398 00:22:44,600 --> 00:22:46,080 Speaker 3: of llm's John and JR. 399 00:22:46,400 --> 00:22:48,800 Speaker 2: This fits in with what he asked at the very 400 00:22:48,960 --> 00:22:52,119 Speaker 2: end of his of his question, he basically says he 401 00:22:52,119 --> 00:22:54,280 Speaker 2: gets into some other technical things of what he does. 402 00:22:54,280 --> 00:22:56,840 Speaker 2: But then he says, for non technical users, what is 403 00:22:56,880 --> 00:23:03,280 Speaker 2: the practical value of llms beyond curiosity or Google search alternatives? 404 00:23:03,280 --> 00:23:08,320 Speaker 2: And why should we contribute our questions to train any 405 00:23:08,400 --> 00:23:09,560 Speaker 2: AI platform? 406 00:23:12,320 --> 00:23:13,440 Speaker 4: The answer is. 407 00:23:15,359 --> 00:23:18,240 Speaker 3: Similar to why you would do Google searches right, and 408 00:23:18,560 --> 00:23:21,439 Speaker 3: Google now uses AI and it search answers as well. 409 00:23:21,840 --> 00:23:26,240 Speaker 3: So llms are almost anywhere that you're searching for information. 410 00:23:27,119 --> 00:23:32,560 Speaker 3: It's that you'll get back information that is useful. Here's 411 00:23:32,600 --> 00:23:38,879 Speaker 3: my practical tip to avoid the problem that JR Is 412 00:23:39,440 --> 00:23:44,480 Speaker 3: putting a finger on. It's asked you, when you formulate 413 00:23:44,520 --> 00:23:49,640 Speaker 3: the question, ask the LM to provide you with links 414 00:23:49,800 --> 00:23:54,800 Speaker 3: substantiating its answer, and I think either on both the 415 00:23:55,520 --> 00:23:59,520 Speaker 3: paid and premium versions of Chat, GPT and others, it 416 00:23:59,560 --> 00:24:04,480 Speaker 3: will you with links. And this allows you to, as 417 00:24:04,520 --> 00:24:08,240 Speaker 3: they say, do your own research. It's especially important with llms, 418 00:24:08,240 --> 00:24:10,160 Speaker 3: which can be black boxes in terms of how they 419 00:24:10,200 --> 00:24:12,919 Speaker 3: think something through. If you make it provide you with 420 00:24:13,040 --> 00:24:17,399 Speaker 3: web with web links to substantiate its answer, it at 421 00:24:17,440 --> 00:24:20,960 Speaker 3: least allows you to go back over it and the 422 00:24:20,960 --> 00:24:23,720 Speaker 3: answer is as to why should we trust it and 423 00:24:23,760 --> 00:24:26,480 Speaker 3: why should we give it? You grace it with giving 424 00:24:26,560 --> 00:24:32,040 Speaker 3: us these training querias. The answer would be, the proof 425 00:24:32,080 --> 00:24:35,840 Speaker 3: is in the value you get back to me. I 426 00:24:35,960 --> 00:24:39,640 Speaker 3: get back got value even understanding that there are sometimes 427 00:24:39,640 --> 00:24:42,640 Speaker 3: that the LM fails me. But there's just an important 428 00:24:42,720 --> 00:24:47,439 Speaker 3: lesson here for everybody that you should be skeptical and 429 00:24:47,480 --> 00:24:49,800 Speaker 3: you should understand that it is going to get some 430 00:24:49,880 --> 00:24:54,840 Speaker 3: things wrong. So unless you're sufficiently specific and double checking, 431 00:24:56,320 --> 00:24:58,360 Speaker 3: it can sometimes lead you astray. 432 00:24:58,840 --> 00:25:00,880 Speaker 2: There's an interesting common thing read with all of this, 433 00:25:00,960 --> 00:25:05,240 Speaker 2: where wherein AI in and of itself is really not 434 00:25:05,359 --> 00:25:09,000 Speaker 2: in every facet. Obviously, it has introduced a lot of 435 00:25:09,160 --> 00:25:14,960 Speaker 2: new aspects in terms of technology beyond that which we're 436 00:25:14,960 --> 00:25:20,639 Speaker 2: familiar with. That being said, AI does do differently a 437 00:25:20,680 --> 00:25:25,440 Speaker 2: lot of things that we were already dealing with. For example, 438 00:25:25,480 --> 00:25:28,359 Speaker 2: you mentioned you know whether or not we should trust AI. 439 00:25:28,800 --> 00:25:30,320 Speaker 2: You know, I think the same thing would ring true 440 00:25:30,320 --> 00:25:32,600 Speaker 2: when it comes to doing a search online prior to 441 00:25:32,640 --> 00:25:36,119 Speaker 2: AI or by example. Let me get into this piece 442 00:25:36,440 --> 00:25:38,560 Speaker 2: really quick, because I think it's along the same lines. 443 00:25:38,800 --> 00:25:40,160 Speaker 1: There's a story out of Fox nine. 444 00:25:40,200 --> 00:25:43,919 Speaker 2: Locally, US consumers lost twelve point five billion to fraud 445 00:25:43,920 --> 00:25:46,800 Speaker 2: in twenty twenty four, with online shopping being the second 446 00:25:46,880 --> 00:25:48,640 Speaker 2: most common scam type. 447 00:25:49,000 --> 00:25:50,480 Speaker 1: AI powered fraud. 448 00:25:50,240 --> 00:25:53,760 Speaker 2: Is becoming increasingly common, with scammers using technology to create 449 00:25:53,840 --> 00:25:57,240 Speaker 2: fake retailer websites, phishing emails, deep fake videos that impersonate 450 00:25:57,440 --> 00:25:59,480 Speaker 2: trusted brands or influencers. 451 00:26:00,000 --> 00:26:01,120 Speaker 1: And again, so many of the. 452 00:26:01,119 --> 00:26:05,359 Speaker 2: Concerns around AI aren't so much new concerns. They're just 453 00:26:05,840 --> 00:26:10,159 Speaker 2: an enhanced method of, in this case, previous scams that 454 00:26:10,160 --> 00:26:12,199 Speaker 2: we were already dealing with, but now we have the 455 00:26:12,240 --> 00:26:15,560 Speaker 2: element of AI being able to assist in this case 456 00:26:15,680 --> 00:26:19,480 Speaker 2: the bad guys with the scams they are trying to perform. 457 00:26:19,680 --> 00:26:21,240 Speaker 4: Yeah, I agree with that. 458 00:26:22,280 --> 00:26:25,439 Speaker 3: It's similar to problems that we faced before, and I 459 00:26:25,440 --> 00:26:33,480 Speaker 3: think the difference is in speed, synthesis, and what we 460 00:26:33,480 --> 00:26:39,960 Speaker 3: could call accuracy. Right, Like, lms are way way faster, 461 00:26:40,320 --> 00:26:43,880 Speaker 3: which allows scams like the one you're talking about, the 462 00:26:43,920 --> 00:26:47,760 Speaker 3: fake holiday shopping websites, which everyone should be able to 463 00:26:47,800 --> 00:26:50,959 Speaker 3: look out for. It allows them to erect it more quickly, 464 00:26:52,400 --> 00:26:55,320 Speaker 3: It allows you know, synthesis. This is the area where 465 00:26:55,320 --> 00:26:59,960 Speaker 3: I think LMS are different than anything we've had before, 466 00:27:00,119 --> 00:27:04,360 Speaker 3: or the ability to synthesize across large amounts of information, 467 00:27:05,680 --> 00:27:08,399 Speaker 3: even understanding that sometimes they're just going to get that 468 00:27:08,520 --> 00:27:13,920 Speaker 3: synthesis wrong, and then the final point, I called it accuracy. 469 00:27:14,920 --> 00:27:18,840 Speaker 3: It's that the Fox Fine article that you're putting your 470 00:27:18,840 --> 00:27:23,000 Speaker 3: figure on talks about it. Quotes Larry Zelvin, who's the 471 00:27:23,200 --> 00:27:27,000 Speaker 3: head of security advisory at BMO at the baker Botreal 472 00:27:27,720 --> 00:27:31,080 Speaker 3: who said that AI has made scams more convincing and 473 00:27:31,160 --> 00:27:34,800 Speaker 3: harder to detect. Fraustters can bimic trusts brands and voices 474 00:27:34,800 --> 00:27:39,919 Speaker 3: with alarming accuracy. And that's that's correct, right for anyone, 475 00:27:39,960 --> 00:27:42,040 Speaker 3: for people doing a scam. I won't get into how 476 00:27:42,800 --> 00:27:45,080 Speaker 3: someone could train an LM to do that, but it's 477 00:27:45,240 --> 00:27:48,399 Speaker 3: not hard at all, and it puts togures that you 478 00:27:48,400 --> 00:27:51,480 Speaker 3: and I have talked about John in other contexts. We've 479 00:27:51,480 --> 00:27:54,159 Speaker 3: talked about how deep fake videos it's. It can be 480 00:27:54,240 --> 00:27:57,840 Speaker 3: hard to determine, like whether the video is actually Trump 481 00:27:59,160 --> 00:28:03,480 Speaker 3: or not until he starts talking about how he's going 482 00:28:03,520 --> 00:28:07,000 Speaker 3: to deploy velociraptors at the border. It's pretty convincing and 483 00:28:07,040 --> 00:28:09,639 Speaker 3: it looks a lot like Donald Trump. And the same 484 00:28:09,680 --> 00:28:13,919 Speaker 3: thing is true of holiday shopping websites. It's easier than 485 00:28:13,960 --> 00:28:20,880 Speaker 3: ever for scammers to make the holiday shopping website look 486 00:28:20,960 --> 00:28:23,040 Speaker 3: a lot more like a legitimate website. 487 00:28:23,119 --> 00:28:25,840 Speaker 4: It erodes the distinction between the two, and. 488 00:28:25,760 --> 00:28:29,000 Speaker 3: There's a lot of danger where now it's not just 489 00:28:29,520 --> 00:28:32,560 Speaker 3: you know, old people or people who aren't Internet savvy 490 00:28:32,880 --> 00:28:35,120 Speaker 3: who could potentially fall for scams. 491 00:28:35,480 --> 00:28:36,280 Speaker 4: They look more real. 492 00:28:36,800 --> 00:28:39,600 Speaker 2: Talking with David garten Stein Ross, a AI analyst, a 493 00:28:39,720 --> 00:28:43,000 Speaker 2: CEO at Expert Theory, these aren't the questions. I just 494 00:28:43,000 --> 00:28:45,040 Speaker 2: want to add to the discussion here a couple of 495 00:28:45,160 --> 00:28:48,400 Speaker 2: a couple of different things. First off, it's really interesting. 496 00:28:48,600 --> 00:28:51,040 Speaker 2: You know, I've got two boys. Kyle's going to be 497 00:28:51,080 --> 00:28:53,600 Speaker 2: nineteen here in about a week and Logan's twenty three, 498 00:28:53,720 --> 00:28:56,320 Speaker 2: and you know, they're on their phones. We all enjoy 499 00:28:56,400 --> 00:28:58,520 Speaker 2: looking at the memes. We've talked about these a lot. 500 00:28:58,880 --> 00:29:03,320 Speaker 2: But it's interesting now because whenever these videos pop up 501 00:29:03,320 --> 00:29:06,280 Speaker 2: and we're sharing them with one another, there is a 502 00:29:06,360 --> 00:29:09,080 Speaker 2: debate that takes place between us if it's not labeled 503 00:29:09,080 --> 00:29:09,520 Speaker 2: as such. 504 00:29:09,560 --> 00:29:11,760 Speaker 1: You know, I wonder if that's AI. I think it's A. 505 00:29:11,960 --> 00:29:12,520 Speaker 1: I don't think it's A. 506 00:29:12,840 --> 00:29:15,920 Speaker 2: It's funny how it's entered into you know that aspect 507 00:29:16,000 --> 00:29:19,960 Speaker 2: of our enjoyment of just entertainment items like that. It's 508 00:29:20,040 --> 00:29:22,760 Speaker 2: just now a part of the of the conversation. I 509 00:29:22,760 --> 00:29:24,000 Speaker 2: have one more thing I want to add, is there 510 00:29:24,040 --> 00:29:25,840 Speaker 2: anything that you would like to mention to or you 511 00:29:25,840 --> 00:29:28,200 Speaker 2: want to say to that to that particular observation. 512 00:29:30,200 --> 00:29:34,959 Speaker 3: The the interesting thing like that observation and the main 513 00:29:35,000 --> 00:29:39,440 Speaker 3: thing I thought about is how the way you deterribine 514 00:29:39,440 --> 00:29:41,720 Speaker 3: if a video or a beam is AI or not 515 00:29:42,480 --> 00:29:44,719 Speaker 3: is similar to what people should do to keep themselves 516 00:29:44,760 --> 00:29:49,720 Speaker 3: safe well, trying to do holiday line and try to 517 00:29:49,800 --> 00:29:53,960 Speaker 3: avoid falling for scams, which is double check, yeah right, 518 00:29:54,280 --> 00:29:57,280 Speaker 3: like just like you could research is a beam legit? 519 00:29:57,720 --> 00:30:02,920 Speaker 3: I always before using a shopping service to haven't used before, 520 00:30:03,360 --> 00:30:06,160 Speaker 3: I'm going to double and triple check to make sure 521 00:30:06,160 --> 00:30:06,800 Speaker 3: it's LEDGECT. 522 00:30:07,440 --> 00:30:09,320 Speaker 2: I'm gonna send you a link to a video once 523 00:30:09,440 --> 00:30:12,400 Speaker 2: I got some time this morning. There's a there's a 524 00:30:12,440 --> 00:30:15,760 Speaker 2: YouTube channel. It's a special effects house run by a 525 00:30:15,800 --> 00:30:19,719 Speaker 2: group of millennials in southern California. 526 00:30:19,800 --> 00:30:21,360 Speaker 1: It's called the Corridor Crew. I don't know if I've 527 00:30:21,360 --> 00:30:24,040 Speaker 1: ever heard of them before, but I happened. 528 00:30:24,320 --> 00:30:28,240 Speaker 2: They analyze movies and CGI in movies and things of 529 00:30:28,240 --> 00:30:30,240 Speaker 2: this nature, and they've been on top of AI. They 530 00:30:30,240 --> 00:30:32,400 Speaker 2: did a video recently and again I'll share with you 531 00:30:32,480 --> 00:30:38,640 Speaker 2: wherein they went online and they ordered products that could 532 00:30:38,640 --> 00:30:41,440 Speaker 2: be purchased at an incredibly cheap price, but they looked 533 00:30:41,480 --> 00:30:45,880 Speaker 2: like high price items. So like a Carhart jacket. We've 534 00:30:45,880 --> 00:30:49,240 Speaker 2: all seen the images of these cable knit sweaters with 535 00:30:49,320 --> 00:30:53,800 Speaker 2: these expansive designs on them like you've never seen before. 536 00:30:53,840 --> 00:30:54,680 Speaker 1: They looked three D. 537 00:30:55,280 --> 00:30:58,120 Speaker 2: So they went and ordered all these products to see 538 00:30:58,360 --> 00:31:02,280 Speaker 2: how closely it matched the obvious AI creation as it 539 00:31:02,320 --> 00:31:05,440 Speaker 2: was being presented online. And of course the result is 540 00:31:05,480 --> 00:31:07,360 Speaker 2: what you would expect the result to be. These were 541 00:31:07,360 --> 00:31:12,480 Speaker 2: incredibly cheap, flimsy items that were just knockoff products that 542 00:31:12,800 --> 00:31:14,520 Speaker 2: ended up ultimately looking. 543 00:31:14,320 --> 00:31:17,160 Speaker 1: Similarly enough online. 544 00:31:16,680 --> 00:31:19,040 Speaker 2: But nothing close to the quality that you would expect 545 00:31:19,120 --> 00:31:22,040 Speaker 2: it was. It's incredibly entertaining and I'll uh, I'll share 546 00:31:22,040 --> 00:31:23,200 Speaker 2: it with you when I get a chance here to v. 547 00:31:24,440 --> 00:31:27,560 Speaker 3: That's so interesting, right, Like it's interesting because they're clearly 548 00:31:27,600 --> 00:31:33,440 Speaker 3: online scams of fake items and the knockoff items. And 549 00:31:33,480 --> 00:31:35,720 Speaker 3: what's interesting is that you know they were able to 550 00:31:35,720 --> 00:31:42,200 Speaker 3: purchase them and they were shipped and received. Yeah, right, Like, 551 00:31:42,440 --> 00:31:44,360 Speaker 3: you know, I typically what I think is that if 552 00:31:44,440 --> 00:31:48,240 Speaker 3: if I'm buying a knockoff, if I'm buying it online, 553 00:31:48,600 --> 00:31:50,240 Speaker 3: I'm not going to expect people to ship it to me, 554 00:31:50,400 --> 00:31:51,959 Speaker 3: it's really interesting that they shipped. 555 00:31:52,040 --> 00:31:54,360 Speaker 2: Yeah, they got the products and they're close proximities, but 556 00:31:54,400 --> 00:31:56,120 Speaker 2: they are not. I mean, essentially, they end up like 557 00:31:56,120 --> 00:31:59,600 Speaker 2: the clothing ends up being of the quality that you 558 00:31:59,640 --> 00:32:02,000 Speaker 2: would get it like a halloween like a cheap Halloween costume. 559 00:32:02,520 --> 00:32:04,080 Speaker 1: That's what the That's what they ended up being. So 560 00:32:04,120 --> 00:32:04,760 Speaker 1: I'll share that with you. 561 00:32:04,840 --> 00:32:06,280 Speaker 2: Let's get back to a couple of the questions that 562 00:32:06,320 --> 00:32:08,480 Speaker 2: the audience has with for you. I actually had one 563 00:32:08,480 --> 00:32:10,280 Speaker 2: that came in from front of the show Rose mount Rick. 564 00:32:10,320 --> 00:32:12,520 Speaker 2: It's a really quick one. He just says, David, do 565 00:32:12,640 --> 00:32:15,920 Speaker 2: you talk to GROC every day? Do you try to 566 00:32:16,000 --> 00:32:16,880 Speaker 2: teach Grock? 567 00:32:18,760 --> 00:32:18,840 Speaker 4: No? 568 00:32:19,160 --> 00:32:24,040 Speaker 3: So I use I use chat GPT, and there's no 569 00:32:24,040 --> 00:32:26,280 Speaker 3: particular reason other than that's the one that I first 570 00:32:26,320 --> 00:32:30,440 Speaker 3: started using. I used CROC once in a while, but 571 00:32:31,640 --> 00:32:34,280 Speaker 3: chat GPT is the system I most hang out in. 572 00:32:34,760 --> 00:32:36,880 Speaker 2: All right, let's go to the iHeart Radio app this 573 00:32:36,920 --> 00:32:39,280 Speaker 2: morning as we continue our discussion with AI analyst de 574 00:32:39,320 --> 00:32:40,600 Speaker 2: Vid Gartenstein Ross. 575 00:32:41,360 --> 00:32:42,160 Speaker 5: Good morning, John. 576 00:32:42,440 --> 00:32:45,120 Speaker 8: Can you please ask d g R about what the 577 00:32:45,160 --> 00:32:50,160 Speaker 8: differences between co pilot or chat GPT and an agent 578 00:32:50,480 --> 00:32:52,560 Speaker 8: created within co pilot or chat GPT. 579 00:32:53,440 --> 00:32:55,160 Speaker 1: Thanks DVD. 580 00:32:56,240 --> 00:33:00,360 Speaker 3: It's a it's a wonderful question. The quick answer is 581 00:33:00,400 --> 00:33:07,360 Speaker 3: that you know Copilot or chat Gypt. The main purpose 582 00:33:07,400 --> 00:33:10,400 Speaker 3: of it is to have a conversation with you. An 583 00:33:10,480 --> 00:33:14,520 Speaker 3: agent will actually perform tasks for you, and for that, 584 00:33:14,680 --> 00:33:18,680 Speaker 3: Copilot is much much better than chat Gypt because it's 585 00:33:18,720 --> 00:33:23,080 Speaker 3: integrated into the Microsoft Office suite, and so if you, 586 00:33:23,120 --> 00:33:26,800 Speaker 3: if you're a Microsoft person, Copilot can integrate any sort 587 00:33:26,840 --> 00:33:32,800 Speaker 3: of agentic function. Well, I don't use agents that often 588 00:33:32,960 --> 00:33:36,760 Speaker 3: in AI. I imagine though, that you know, a year 589 00:33:37,000 --> 00:33:42,280 Speaker 3: or eighteen months from now, I will use agents AI 590 00:33:42,480 --> 00:33:47,640 Speaker 3: agents multiple times a day. They're an important tool that 591 00:33:48,280 --> 00:33:50,640 Speaker 3: most people will be using within the next twelve to 592 00:33:50,680 --> 00:33:51,560 Speaker 3: eighteen months. 593 00:33:51,720 --> 00:33:54,240 Speaker 2: All right, let's go here and talk with friend of 594 00:33:54,240 --> 00:33:56,800 Speaker 2: the show, Raquel, who has a question for David garten 595 00:33:56,800 --> 00:33:57,400 Speaker 2: Stein Ross. 596 00:33:57,440 --> 00:34:00,600 Speaker 9: I have a question for deb Garden Santa Ross Ross. 597 00:34:01,160 --> 00:34:03,280 Speaker 9: A few weeks ago, maybe one or two weeks ago, 598 00:34:04,000 --> 00:34:07,600 Speaker 9: we played a piece of music, and you said that 599 00:34:07,680 --> 00:34:09,400 Speaker 9: you could tell it was written by AI. 600 00:34:09,800 --> 00:34:11,719 Speaker 4: My question is about comic book art. 601 00:34:11,719 --> 00:34:14,680 Speaker 9: My husband is a comic book artist, and comic books 602 00:34:14,680 --> 00:34:18,040 Speaker 9: can take weeks to complete. My husband's fear is that 603 00:34:18,160 --> 00:34:22,800 Speaker 9: AI will completely replace all comic book artists, because you know, 604 00:34:23,080 --> 00:34:25,120 Speaker 9: I could do it in a matter of minutes. I 605 00:34:25,120 --> 00:34:30,160 Speaker 9: would just like your opinion. Thank you Dad, Thanks Keel. 606 00:34:31,600 --> 00:34:32,960 Speaker 9: That's a it's a huge concern. 607 00:34:34,440 --> 00:34:37,960 Speaker 3: AI does comic book art really well, and just most 608 00:34:38,040 --> 00:34:41,480 Speaker 3: kinds of art really well. Right now in certain industries, 609 00:34:42,160 --> 00:34:45,360 Speaker 3: like I work in the games industry as as folks know, 610 00:34:46,320 --> 00:34:51,600 Speaker 3: and for major board games, there's this stigma against using 611 00:34:51,640 --> 00:34:53,720 Speaker 3: AI and a preference for human artists. 612 00:34:54,280 --> 00:34:57,320 Speaker 4: But that stigma is eroding. 613 00:35:00,040 --> 00:35:04,280 Speaker 3: The concern is a real one, and so I'll provide 614 00:35:05,520 --> 00:35:09,360 Speaker 3: what I would do as an artist understanding that threat, 615 00:35:10,280 --> 00:35:15,560 Speaker 3: and that is I would actually it to hedge against that. 616 00:35:16,719 --> 00:35:20,839 Speaker 3: I would start using AI once in a while, not 617 00:35:21,080 --> 00:35:25,040 Speaker 3: in my art for customers, but to play around with 618 00:35:25,120 --> 00:35:29,960 Speaker 3: creating my own comic book art, because yeah, like what 619 00:35:30,120 --> 00:35:33,360 Speaker 3: Raquel is outlining is real, Like it's a huge problem. 620 00:35:33,480 --> 00:35:36,799 Speaker 3: I would definitely feel threat there. But there's going to 621 00:35:36,800 --> 00:35:40,440 Speaker 3: be things that comic book artists just know implicitly that 622 00:35:40,520 --> 00:35:45,480 Speaker 3: they can do more quickly with AI. That's actually, in 623 00:35:45,520 --> 00:35:47,759 Speaker 3: my view where job the security lies. And we can 624 00:35:47,800 --> 00:35:52,280 Speaker 3: talk about that at some length. But like Raquel's husband 625 00:35:52,880 --> 00:35:54,959 Speaker 3: definitely has worried to be concerned there. 626 00:35:55,680 --> 00:35:58,640 Speaker 4: I definitely share the concern that she has Davdi. 627 00:35:58,360 --> 00:35:59,200 Speaker 1: Guards and the Stein Ross. 628 00:35:59,239 --> 00:36:00,880 Speaker 2: I'm going to hang on to the other articles that 629 00:36:00,920 --> 00:36:03,800 Speaker 2: we had planned to talk about today, but I really 630 00:36:03,880 --> 00:36:06,719 Speaker 2: enjoyed the conversation and the fact that many of the 631 00:36:06,840 --> 00:36:09,840 Speaker 2: listeners had a chance to get their questions answered. And always, 632 00:36:10,160 --> 00:36:13,439 Speaker 2: I appreciated the time that you give us every single week. 633 00:36:13,440 --> 00:36:15,400 Speaker 2: If you have further questions for David, you can always 634 00:36:15,440 --> 00:36:18,880 Speaker 2: email me Justice at iHeartRadio dot com. I'm happy to 635 00:36:18,920 --> 00:36:21,600 Speaker 2: forward those over to DVD David. If there's an email 636 00:36:21,600 --> 00:36:22,840 Speaker 2: that you would like to share or any place that 637 00:36:22,880 --> 00:36:24,839 Speaker 2: you would like to go and direct the listener before 638 00:36:24,880 --> 00:36:25,800 Speaker 2: I let you go this morning. 639 00:36:26,360 --> 00:36:28,160 Speaker 4: Absolutely, My email is dav'd. 640 00:36:28,360 --> 00:36:33,800 Speaker 3: That's dav Eed at Expert Theory dot com. 641 00:36:34,000 --> 00:36:36,799 Speaker 2: David Gartinstein Ross. As always, thanks for the time, my friend. 642 00:36:36,880 --> 00:36:38,919 Speaker 2: I look forward to hopefully doing it again next week. 643 00:36:39,680 --> 00:36:42,439 Speaker 4: Likewise, John, so do I all right? 644 00:36:42,880 --> 00:36:46,280 Speaker 2: Cabinet meeting yesterday a lot took place with President Donald Trump. 645 00:36:46,400 --> 00:36:51,640 Speaker 2: Lengthy cabinet meeting, incredibly transparent this administration is. During that 646 00:36:51,719 --> 00:36:53,800 Speaker 2: cabinet meeting, one of the things that was talked about 647 00:36:54,000 --> 00:36:59,240 Speaker 2: was snap Secretary of Agriculture Brook Rawlins deliveredy sweeping update 648 00:36:59,760 --> 00:37:04,560 Speaker 2: on the nation's SNAP program, revealing what she called rampant 649 00:37:04,560 --> 00:37:08,600 Speaker 2: fraud and announcing a dramatic escalation in the administration's effort 650 00:37:08,960 --> 00:37:13,040 Speaker 2: to force state compliance in federal oversight. You may remember 651 00:37:14,040 --> 00:37:16,360 Speaker 2: that Minnesota was one of the states that was pushing 652 00:37:16,400 --> 00:37:19,640 Speaker 2: back on this. Rowland said that under the president's tenure, 653 00:37:19,680 --> 00:37:22,239 Speaker 2: about eight hundred thousand Americans have already moved off of 654 00:37:22,280 --> 00:37:24,520 Speaker 2: SNAP and the program is serving forty two million people. 655 00:37:24,560 --> 00:37:28,080 Speaker 2: She argued the trend underscores the need to restore integrity 656 00:37:28,120 --> 00:37:31,319 Speaker 2: to the system that she's said has been abused for 657 00:37:31,520 --> 00:37:34,640 Speaker 2: far too long. With that, before we head into next hour, 658 00:37:34,719 --> 00:37:38,200 Speaker 2: let me share this with you. This is USDA Secretary 659 00:37:38,280 --> 00:37:41,000 Speaker 2: Rawlins saying that she will be moving to halt federal 660 00:37:41,040 --> 00:37:44,480 Speaker 2: funding two states who refuse to turn over the SNAP 661 00:37:44,560 --> 00:37:47,640 Speaker 2: data to help root out the fraud, like Minnesota. And 662 00:37:47,719 --> 00:37:49,520 Speaker 2: we'll get to more of your talkbacks coming up in 663 00:37:49,560 --> 00:37:51,800 Speaker 2: just a moment here on Twin Cities News Talk. Here 664 00:37:52,000 --> 00:37:54,680 Speaker 2: is the USDA Secretary Ice. 665 00:37:55,000 --> 00:37:57,520 Speaker 10: We had a couple of people receiving benefits in six 666 00:37:57,600 --> 00:38:00,399 Speaker 10: states in February of this year. We asked for all 667 00:38:00,480 --> 00:38:03,480 Speaker 10: the states for the first time to turn over their 668 00:38:03,560 --> 00:38:06,840 Speaker 10: data to the federal government to let the USDA partner 669 00:38:06,880 --> 00:38:09,680 Speaker 10: with them to root out this fraud, to make sure 670 00:38:09,719 --> 00:38:12,000 Speaker 10: that those who really need food stamps are getting them, 671 00:38:12,200 --> 00:38:15,120 Speaker 10: but also to ensure that the American taxpayer is protected. 672 00:38:15,600 --> 00:38:18,960 Speaker 10: Twenty one states said yes. Twenty nine states said yes, 673 00:38:19,000 --> 00:38:21,759 Speaker 10: not surprisingly the Red states, and that's where all of 674 00:38:21,800 --> 00:38:25,000 Speaker 10: that data that fraud comes from. But twenty one states, 675 00:38:25,040 --> 00:38:29,759 Speaker 10: including California, New York, and Minnesota, the Blue states, continue 676 00:38:29,880 --> 00:38:33,920 Speaker 10: to say no. So as of next week, we have 677 00:38:34,120 --> 00:38:38,040 Speaker 10: begun and will begin to stop moving federal funds into 678 00:38:38,080 --> 00:38:41,000 Speaker 10: those states until they comply and they tell us and 679 00:38:41,080 --> 00:38:43,480 Speaker 10: allow us to partner with them to root out this 680 00:38:43,560 --> 00:38:46,360 Speaker 10: fraud and to protect the American taxpayer. 681 00:38:46,520 --> 00:38:47,520 Speaker 4: As Joe Biden was. 682 00:38:47,520 --> 00:38:50,759 Speaker 10: Working to buy an election a year ago, he increased 683 00:38:50,800 --> 00:38:55,440 Speaker 10: food stamp program funding by forty percent. So now as 684 00:38:55,480 --> 00:38:57,640 Speaker 10: we continue to roll that back, so. 685 00:38:58,480 --> 00:39:01,880 Speaker 2: Keep in mind, just really quick, keep in mind that 686 00:39:02,000 --> 00:39:06,640 Speaker 2: not only has billions of your dollars taxpayer money gone 687 00:39:06,719 --> 00:39:10,759 Speaker 2: up and smoke with fraud taking place under the Walls administration, 688 00:39:11,360 --> 00:39:17,640 Speaker 2: Walls now has put the state in a position, whether 689 00:39:17,719 --> 00:39:21,239 Speaker 2: it's because of what was mentioned there by the Agriculture 690 00:39:21,280 --> 00:39:25,719 Speaker 2: Secretary of losing funding for the lack of willingness to 691 00:39:25,800 --> 00:39:26,959 Speaker 2: send over snap data. 692 00:39:27,880 --> 00:39:29,880 Speaker 1: Millions of dollars in funding could be lost. 693 00:39:29,920 --> 00:39:33,799 Speaker 2: There some thirty million dollars in funding over drivers' licenses 694 00:39:34,200 --> 00:39:38,120 Speaker 2: for those who are here illegally. We could also lose 695 00:39:38,120 --> 00:39:41,000 Speaker 2: that funding as well. And it's all happening under Governor 696 00:39:41,040 --> 00:39:42,759 Speaker 2: Tim Walls's watch. 697 00:39:43,000 --> 00:39:43,640 Speaker 1: He needs to. 698 00:39:43,600 --> 00:39:46,759 Speaker 2: Resign, and as I keep saying, I do not expect 699 00:39:47,080 --> 00:39:49,040 Speaker 2: that he is going to be the nominee for governor 700 00:39:49,360 --> 00:39:51,560 Speaker 2: heading into next year. More coming up on Twin City's 701 00:39:51,560 --> 00:39:53,799 Speaker 2: News Talk AM eleven thirty and one to three five FM. 702 00:39:53,960 --> 00:39:54,959 Speaker 4: I'm ready to get beat.