1 00:00:00,120 --> 00:00:02,720 Speaker 1: This is the Bread Show. Let's get you hotel, a 2 00:00:02,800 --> 00:00:06,000 Speaker 1: trip for tunis you Jennifer Lopez her brand new Las 3 00:00:06,080 --> 00:00:09,840 Speaker 1: Vegas residency. Jennifer Lopez Up All Night Live in Las 4 00:00:09,920 --> 00:00:13,320 Speaker 1: Vegas March thirteenth, twenty twenty six at the Coliseum at 5 00:00:13,360 --> 00:00:17,640 Speaker 1: Caesar's Palace, Texas Night to three seven three three seven 6 00:00:17,840 --> 00:00:20,159 Speaker 1: right now for a chance to win two tickets to 7 00:00:20,200 --> 00:00:23,000 Speaker 1: the March thirteenth show at two Night Hotels day March 8 00:00:23,040 --> 00:00:26,200 Speaker 1: twelfth through the fourteenth at Py Flamingo Hotel Casino, Las 9 00:00:26,280 --> 00:00:29,040 Speaker 1: Vegas and the Brown chap Fair Fair. A confirmation text 10 00:00:29,040 --> 00:00:31,720 Speaker 1: will be sent Dennard message and data rates may apply. 11 00:00:31,960 --> 00:00:34,040 Speaker 1: All Thanks to Live Nation. Tickets are on sale now 12 00:00:34,080 --> 00:00:37,400 Speaker 1: at ticketmaster dot com for all shows running December thirtieth 13 00:00:37,479 --> 00:00:40,160 Speaker 1: through January third, and March sixth through the twenty eighth. 14 00:00:40,320 --> 00:00:44,200 Speaker 2: Bread's Show is on Friend's Biggest Stories of the Day. 15 00:00:44,400 --> 00:00:49,160 Speaker 1: So this is a nightmare. Really, Imagine hundreds of oversized 16 00:00:49,200 --> 00:00:52,760 Speaker 1: packages appear on your doorsteps and you have no clue why, 17 00:00:52,840 --> 00:00:55,800 Speaker 1: and I mean one hundreds of big packages just showing up. 18 00:00:57,040 --> 00:01:00,560 Speaker 1: Yet the shopping spree is not a shopping You didn't 19 00:01:00,600 --> 00:01:02,400 Speaker 1: order any of this stuff. It's return. So that's what 20 00:01:02,440 --> 00:01:05,720 Speaker 1: happened to a San Jose, California woman. Her frustrating scenario 21 00:01:06,080 --> 00:01:08,560 Speaker 1: is linked to an overseas online seller who appears to 22 00:01:08,560 --> 00:01:12,160 Speaker 1: be violating Amazon's return policy. So this woman is confused 23 00:01:12,160 --> 00:01:17,160 Speaker 1: as to why palettes of things are showing up at 24 00:01:17,160 --> 00:01:19,960 Speaker 1: her front door NonStop at her house, has no idea 25 00:01:20,000 --> 00:01:21,840 Speaker 1: why and what to do with all this stuff. Inside 26 00:01:21,840 --> 00:01:24,360 Speaker 1: each package is a set of full leather car seat 27 00:01:24,400 --> 00:01:28,160 Speaker 1: covers from a Chinese online seller. The online seller's Amazon 28 00:01:28,160 --> 00:01:33,039 Speaker 1: listing advertises the brand as at Kin selling seat covers 29 00:01:33,080 --> 00:01:36,360 Speaker 1: supposedly made to fit various models and makes of Sedan's 30 00:01:36,360 --> 00:01:38,480 Speaker 1: and SUVs. But as you can see from the front 31 00:01:38,480 --> 00:01:39,880 Speaker 1: of her house if you can see it, and I can, 32 00:01:40,000 --> 00:01:42,720 Speaker 1: but you can't because this is the radio. In many 33 00:01:42,760 --> 00:01:45,720 Speaker 1: of these cases, the covers didn't fit, and consumers said 34 00:01:45,720 --> 00:01:47,800 Speaker 1: that they were forced to pay out of pockets to 35 00:01:47,880 --> 00:01:51,040 Speaker 1: return them to the company's return center. But it was 36 00:01:51,160 --> 00:01:54,680 Speaker 1: this woman's home and they's made up an address, I guess. 37 00:01:55,000 --> 00:01:57,320 Speaker 1: And then people start getting mad on the comments, like 38 00:01:57,320 --> 00:01:59,960 Speaker 1: where's my refund? Well, it's cutting at this woman K's house. 39 00:02:00,760 --> 00:02:02,360 Speaker 1: Little did they know. They're just piling up in her 40 00:02:02,400 --> 00:02:04,800 Speaker 1: garage in part because the seller put her address on 41 00:02:04,840 --> 00:02:09,359 Speaker 1: their return labels, and then Amazon initially says, well, why 42 00:02:09,360 --> 00:02:12,520 Speaker 1: don't you just, I don't know, donate them or something like. 43 00:02:12,600 --> 00:02:14,320 Speaker 1: They weren't they don't want anything to do with this. Well, 44 00:02:14,320 --> 00:02:16,959 Speaker 1: then of course it makes the news. The company came 45 00:02:17,000 --> 00:02:19,200 Speaker 1: and removed all the packages on her property and is 46 00:02:19,280 --> 00:02:21,600 Speaker 1: valuing to crack down on the practices. But I guess 47 00:02:21,639 --> 00:02:23,760 Speaker 1: you can do this. You can. You know it's some 48 00:02:23,800 --> 00:02:27,519 Speaker 1: company somewhere in order to subscribe to the policies to 49 00:02:27,560 --> 00:02:30,120 Speaker 1: sell stuff on Amazon, they have to do the following things. Well, 50 00:02:30,120 --> 00:02:32,120 Speaker 1: they can just put someone else's address there, I guess. 51 00:02:32,639 --> 00:02:34,920 Speaker 1: And then you've got crates of stuff piling up in 52 00:02:34,919 --> 00:02:38,120 Speaker 1: front of your house. I would go crazy, Yes, you would. 53 00:02:38,440 --> 00:02:40,560 Speaker 1: You would call the police. I would call the police. 54 00:02:40,600 --> 00:02:45,639 Speaker 1: I called John. What would you do? The Tampa Airport 55 00:02:45,680 --> 00:02:48,280 Speaker 1: is going viral for a sign about taking your shoes off. 56 00:02:48,400 --> 00:02:51,600 Speaker 1: Tampa International Airports having some fun after the TSA announced 57 00:02:51,600 --> 00:02:54,920 Speaker 1: the passengers can keep their shoes on while passing through security. 58 00:02:55,000 --> 00:02:58,560 Speaker 1: Some airports are now doing this slowly, I guess, reversing 59 00:02:58,600 --> 00:03:01,400 Speaker 1: the whole taking your shoes off thing. TSA took to 60 00:03:01,440 --> 00:03:04,079 Speaker 1: social media on Tuesday to announce the update. A footnote 61 00:03:04,080 --> 00:03:06,400 Speaker 1: at the bottom of the post says, unless you're wearing crocs, 62 00:03:06,919 --> 00:03:09,239 Speaker 1: you should take those off and throw them away anyway. 63 00:03:09,680 --> 00:03:12,359 Speaker 1: Followers were quick to comment, saying things like crocs where 64 00:03:12,360 --> 00:03:16,440 Speaker 1: a person's dignity seeps out through the holes. The airport's 65 00:03:16,480 --> 00:03:19,040 Speaker 1: social media account are known for their humorous and witty content. 66 00:03:19,360 --> 00:03:21,960 Speaker 1: A lot of the TSA counct the main TSA count 67 00:03:22,000 --> 00:03:24,760 Speaker 1: is funny, love it. And remember a few months ago 68 00:03:25,160 --> 00:03:28,160 Speaker 1: or last month, I guess it was the Yosemite one 69 00:03:28,160 --> 00:03:30,959 Speaker 1: of the national parks was getting into it as well. 70 00:03:31,200 --> 00:03:34,560 Speaker 1: So yeah, there are some are right. There are some 71 00:03:34,720 --> 00:03:37,600 Speaker 1: airports though, where you do not have to take your 72 00:03:37,600 --> 00:03:39,880 Speaker 1: shoes off anymore. But I don't know which ones they are, 73 00:03:40,200 --> 00:03:42,680 Speaker 1: and I don't think they've really been formally announced. So 74 00:03:43,360 --> 00:03:45,240 Speaker 1: you get to figure that out when you go and 75 00:03:45,280 --> 00:03:47,480 Speaker 1: get yelled at, or maybe not get yelled at. There's 76 00:03:47,520 --> 00:03:51,040 Speaker 1: a shark attack Alert bill that unanimously passed the Senate. 77 00:03:51,040 --> 00:03:53,119 Speaker 1: I knew nothing about this, but members of the US 78 00:03:53,160 --> 00:03:57,720 Speaker 1: Senate unanimously approve legislation authorizing Lulu's Law. The bill, sponsored 79 00:03:57,720 --> 00:04:01,720 Speaker 1: by an Alabama Republican Senator is named after Lulu Gribbin, 80 00:04:01,760 --> 00:04:04,160 Speaker 1: who was fifteen who suffered a serious shark attack last 81 00:04:04,240 --> 00:04:07,040 Speaker 1: year in the Florida Panhandle. Passing the Senate on Tuesday, 82 00:04:07,240 --> 00:04:10,480 Speaker 1: the new law directs the Federal Communications Commission to issue 83 00:04:10,480 --> 00:04:14,640 Speaker 1: wireless emergency alerts whenever sharks are spotted in coastal waterways. 84 00:04:15,040 --> 00:04:17,559 Speaker 1: The bill will now go to the House for a vote. 85 00:04:17,600 --> 00:04:20,440 Speaker 1: Major League Baseball this is for baseball fans. They plan 86 00:04:20,520 --> 00:04:25,040 Speaker 1: to use robot umpire and technology for ball strike challenges 87 00:04:25,480 --> 00:04:28,599 Speaker 1: in Tuesday's All Star Game in Atlanta. The Commissioner suggested 88 00:04:28,680 --> 00:04:31,960 Speaker 1: last month that the Competition Committee was likely to soon 89 00:04:32,000 --> 00:04:34,000 Speaker 1: take up whether to use it in regular season games 90 00:04:34,000 --> 00:04:36,680 Speaker 1: as well. The system will work as it did during 91 00:04:36,720 --> 00:04:39,440 Speaker 1: its tryout in spring training. A human umpire will make 92 00:04:39,480 --> 00:04:42,400 Speaker 1: the call as usual. Whichever team doesn't like it can 93 00:04:42,440 --> 00:04:45,640 Speaker 1: appeal to the machinery. Each team will have two challenges, 94 00:04:45,760 --> 00:04:48,760 Speaker 1: with the ability to retain them if they're successful. Only 95 00:04:48,800 --> 00:04:51,839 Speaker 1: a pitcher, catcher, or hitter can ask for the challenge, 96 00:04:51,880 --> 00:04:53,960 Speaker 1: which has to be done almost immediately after the pitch. 97 00:04:54,080 --> 00:04:59,719 Speaker 1: AI like this, well you should, because some of these, 98 00:05:00,240 --> 00:05:02,760 Speaker 1: some of these arms, I don't know what happens they're 99 00:05:02,760 --> 00:05:04,480 Speaker 1: having a bad day or what. But like they get 100 00:05:04,520 --> 00:05:06,880 Speaker 1: on a streak of making really bad calls over and 101 00:05:06,880 --> 00:05:09,279 Speaker 1: over again. Well that's what keeps its spicy. Like we 102 00:05:09,320 --> 00:05:11,000 Speaker 1: got to get mad at some people being bad at 103 00:05:11,040 --> 00:05:11,440 Speaker 1: their job. 104 00:05:12,120 --> 00:05:15,520 Speaker 3: You have this robot in here like making real decisions 105 00:05:15,920 --> 00:05:18,440 Speaker 3: somebody would, and. 106 00:05:18,360 --> 00:05:19,880 Speaker 2: It's going to break up like the fights, because you 107 00:05:19,880 --> 00:05:21,200 Speaker 2: know when the fights happen, they have to like they 108 00:05:21,279 --> 00:05:21,800 Speaker 2: kind of get. 109 00:05:21,640 --> 00:05:23,200 Speaker 1: In there right. Well, yeah, there's still going to be 110 00:05:23,200 --> 00:05:24,880 Speaker 1: an up there. But it's just like, I don't know, 111 00:05:24,880 --> 00:05:26,479 Speaker 1: they got to be good at their job because there's 112 00:05:26,480 --> 00:05:29,120 Speaker 1: a computer watching all these jobs saying, oh, yeah, you're 113 00:05:29,160 --> 00:05:30,800 Speaker 1: not going anywhere. We're just going to get you a 114 00:05:30,880 --> 00:05:38,839 Speaker 1: robot you do. Don't worry though, Justice for the Umpires, 115 00:05:39,440 --> 00:05:41,160 Speaker 1: Yeah they're working on a Freda at eye right now. 116 00:05:41,160 --> 00:05:43,479 Speaker 1: But don't worry. There's no way to simulate the mental 117 00:05:43,520 --> 00:05:45,839 Speaker 1: illness that I have. So it's I mean, they'll figure 118 00:05:45,839 --> 00:05:49,200 Speaker 1: it out eventually. But you know, if if you think 119 00:05:49,240 --> 00:05:51,800 Speaker 1: if you think my game is predictable, if you think 120 00:05:51,839 --> 00:05:53,920 Speaker 1: it's that simple, just find someone that can match all 121 00:05:53,920 --> 00:05:59,040 Speaker 1: of our voices, uh uh no. Plus plus, I don't 122 00:05:59,040 --> 00:06:01,000 Speaker 1: know how they're going to simulate screwing with us all 123 00:06:01,040 --> 00:06:03,160 Speaker 1: the time, you know, in order for us to come 124 00:06:03,200 --> 00:06:05,400 Speaker 1: in here disgruntled and messed up, and then, you know, 125 00:06:05,640 --> 00:06:07,719 Speaker 1: and create this incredible content. I don't know how they 126 00:06:07,800 --> 00:06:10,599 Speaker 1: gonna do that. I don't think they'll be able to. 127 00:06:11,680 --> 00:06:15,080 Speaker 1: There's a band making waves on Spotify. You heard of 128 00:06:15,080 --> 00:06:18,359 Speaker 1: the Velvet Sundown. Well, they racked up over a million 129 00:06:18,360 --> 00:06:21,400 Speaker 1: monthly listeners. Kiki has the shirt. But here's the twist. 130 00:06:21,520 --> 00:06:25,359 Speaker 1: It's not even real. It's entirely AI generated from the 131 00:06:25,440 --> 00:06:29,920 Speaker 1: music to the musicians themselves. Their debut album, Floating on Echoes, 132 00:06:30,760 --> 00:06:33,920 Speaker 1: dropped on June fifth, featuring the hit single where is 133 00:06:33,960 --> 00:06:36,440 Speaker 1: this dust on the Wind? Let me find that for 134 00:06:36,520 --> 00:06:40,000 Speaker 1: you guys. I want to hear dust on the wind? 135 00:06:40,160 --> 00:06:44,680 Speaker 1: Go mess with my art? Dust on the wind? Dust 136 00:06:45,080 --> 00:06:47,200 Speaker 1: on the Wind. Let's let's take a listen to this. 137 00:06:47,720 --> 00:06:50,840 Speaker 1: This is all AI and apparently. Well wait is this 138 00:06:50,920 --> 00:06:54,400 Speaker 1: song from Kansas called dust on the Wind? The Velvet Sundown, 139 00:06:54,800 --> 00:06:58,279 Speaker 1: Dust on the Wind? Let's see what this is? This 140 00:06:58,400 --> 00:07:05,760 Speaker 1: is a hit and it's on YouTube called bangers Only. 141 00:07:06,760 --> 00:07:15,920 Speaker 1: Well how about that? Okay, so fans have begun to 142 00:07:16,000 --> 00:07:19,080 Speaker 1: suspect something was off. No life shows no interviews. 143 00:07:19,520 --> 00:07:26,560 Speaker 4: Dust all in the wind, I mean the smoking sounds 144 00:07:26,560 --> 00:07:33,640 Speaker 4: like a public the drums rolls. 145 00:07:35,680 --> 00:07:40,240 Speaker 1: Tell me, brother, if I heard this, I wouldn't necessary 146 00:07:40,280 --> 00:07:43,920 Speaker 1: If I heard this in like Walgreens, I wouldn't necessarily 147 00:07:43,960 --> 00:07:45,480 Speaker 1: think it wasn't a person. 148 00:07:45,720 --> 00:07:47,880 Speaker 2: Right, It sounds like the Kansas song, Like if you're 149 00:07:47,880 --> 00:07:50,680 Speaker 2: gonna be ai, Like, why don't you just make something original? 150 00:07:52,480 --> 00:07:54,960 Speaker 1: The most shocking thing that you've ever said was that 151 00:07:55,000 --> 00:07:59,440 Speaker 1: you know a Kansas song. Yeah, I'm the wind? How 152 00:07:59,440 --> 00:08:02,000 Speaker 1: do you know that? And there's a million how do 153 00:08:02,040 --> 00:08:05,360 Speaker 1: you have it's iconic? I don't even know if I 154 00:08:05,400 --> 00:08:08,720 Speaker 1: know that song. I'm not certain that I'm a music person, 155 00:08:08,840 --> 00:08:11,320 Speaker 1: and especially an old music person. I'm not sure that 156 00:08:11,400 --> 00:08:14,960 Speaker 1: I could identify that. Here. Here is Kansas the why 157 00:08:15,000 --> 00:08:17,360 Speaker 1: is it trying to play Total Africa? Wait? Wait, I'm 158 00:08:17,360 --> 00:08:21,080 Speaker 1: an amazing song. Not upsetting about that. Upset about that, 159 00:08:21,200 --> 00:08:22,920 Speaker 1: But let me hear dust on the Wind. 160 00:08:25,360 --> 00:08:25,880 Speaker 3: The song. 161 00:08:26,280 --> 00:08:29,520 Speaker 1: I couldnot believe that Jason brown cauld identify a Kansas song. 162 00:08:33,480 --> 00:08:35,960 Speaker 1: It's beautiful. I mean, I guess I just the same 163 00:08:36,040 --> 00:08:46,839 Speaker 1: song the wind, smoke in the sky. It doesn't oh, 164 00:08:46,880 --> 00:08:50,679 Speaker 1: I have o yes, but I don't know that I 165 00:08:50,800 --> 00:08:54,480 Speaker 1: know the name or artists. It doesn't, but Jason Brown 166 00:08:54,520 --> 00:08:58,760 Speaker 1: does Jason Kansas. He's never seen back to the future, 167 00:08:58,800 --> 00:09:02,559 Speaker 1: but he can identify Kansas anyway. So fans that were like, 168 00:09:02,600 --> 00:09:05,520 Speaker 1: I guess no one said, which maybe was the point. 169 00:09:05,840 --> 00:09:08,640 Speaker 1: No one said, the Velvet Sundown is not actually a 170 00:09:08,679 --> 00:09:11,880 Speaker 1: real They're not real people. It's not real anything. But 171 00:09:11,920 --> 00:09:14,880 Speaker 1: people were like, wait a minute. You know the band pictures, 172 00:09:14,880 --> 00:09:17,120 Speaker 1: they have banded pictures for these non people, and they 173 00:09:17,120 --> 00:09:20,840 Speaker 1: were like this, why is the guitarist hand fused together? 174 00:09:20,920 --> 00:09:23,760 Speaker 1: Like what's going on? The Velvet Sundown confirmed the truth 175 00:09:23,960 --> 00:09:28,360 Speaker 1: in their Spotify bio. I don't know who they are, right. 176 00:09:28,880 --> 00:09:32,400 Speaker 1: It's anesthetic music project with minimal human oversight, calling it 177 00:09:32,440 --> 00:09:37,160 Speaker 1: an artistic provocation about authorship and identity in the AI era. 178 00:09:37,559 --> 00:09:39,920 Speaker 1: Critics said the music sounds generic and soul list, but 179 00:09:39,960 --> 00:09:42,720 Speaker 1: it fits perfectly as background playlist and relaxing tunes. The 180 00:09:42,720 --> 00:09:45,960 Speaker 1: project raises big questions how many AI generated tracks slipping 181 00:09:46,000 --> 00:09:49,320 Speaker 1: into our daily playlists, platforms label them and what does 182 00:09:49,360 --> 00:09:51,840 Speaker 1: this mean for real artists trying to earn a living. Now. 183 00:09:52,800 --> 00:09:55,040 Speaker 1: I'm not suggesting that I know anything, because I don't, 184 00:09:55,320 --> 00:09:58,480 Speaker 1: but no, I remember well about this at least, But 185 00:09:58,559 --> 00:10:00,480 Speaker 1: imagine if we have a real life Million Vanilli on 186 00:10:00,520 --> 00:10:03,440 Speaker 1: our hands right now and we're a social experiment. Like 187 00:10:03,520 --> 00:10:05,120 Speaker 1: I don't even want to say an artist's name to 188 00:10:05,200 --> 00:10:08,800 Speaker 1: start the rumors, but let's just say that someone who 189 00:10:08,800 --> 00:10:11,880 Speaker 1: we consider to be wildly famous is maybe he's a 190 00:10:11,920 --> 00:10:15,000 Speaker 1: real person, but nothing that we're hearing is real. It's 191 00:10:15,080 --> 00:10:18,520 Speaker 1: all been generated by AI. And you know what I mean, Like, 192 00:10:18,559 --> 00:10:22,400 Speaker 1: what can you imagine? And then are we mad about it? Like? 193 00:10:22,679 --> 00:10:24,959 Speaker 1: Are we mad that we find out that the presenter 194 00:10:25,240 --> 00:10:28,520 Speaker 1: and it doesn't match the creation, but yet they've been 195 00:10:28,520 --> 00:10:31,720 Speaker 1: presenting it in a convincing way and we enjoy the product. 196 00:10:32,160 --> 00:10:33,679 Speaker 1: So are you gonna like? And again, I don't really 197 00:10:33,720 --> 00:10:35,400 Speaker 1: want to make this up, but let's just say Gracie 198 00:10:35,440 --> 00:10:38,560 Speaker 1: Abrams is AI. I'm not saying she rights. See this 199 00:10:38,600 --> 00:10:41,080 Speaker 1: is why, this is why pick somebody. I don't care, 200 00:10:41,240 --> 00:10:46,439 Speaker 1: Sobrina Carmen to pick anybody, anybody want pick anybody. I'm 201 00:10:46,440 --> 00:10:48,880 Speaker 1: not suggesting that they are, but I mean, let's say, 202 00:10:49,080 --> 00:10:52,079 Speaker 1: you know, for for a year or so, everyone thought 203 00:10:52,160 --> 00:10:55,160 Speaker 1: Millie Vanilli was these two brothers, and it turns out 204 00:10:55,240 --> 00:10:57,079 Speaker 1: or whatever they whoever they were. Turns out it was 205 00:10:57,200 --> 00:11:00,320 Speaker 1: it was another guy completely someone else was saying. They 206 00:11:00,360 --> 00:11:04,720 Speaker 1: were performing their image sold the music. So people were 207 00:11:04,720 --> 00:11:06,480 Speaker 1: into the music, they were into the image of the 208 00:11:06,480 --> 00:11:08,120 Speaker 1: two guys. I guess the reason that they picked the 209 00:11:08,160 --> 00:11:10,000 Speaker 1: two guys to be Million Vanilli was because the guy 210 00:11:10,000 --> 00:11:13,120 Speaker 1: who actually sang the song wasn't really that sexy, so 211 00:11:13,160 --> 00:11:15,520 Speaker 1: they picked these two guys they thought were sexy. So 212 00:11:15,840 --> 00:11:18,319 Speaker 1: the all three combined made this thing that people were 213 00:11:18,320 --> 00:11:20,200 Speaker 1: really into until they found out that it was fake, 214 00:11:20,240 --> 00:11:22,080 Speaker 1: and then they got mad about it. But what if 215 00:11:22,120 --> 00:11:23,839 Speaker 1: we were to find out that someone that we really 216 00:11:23,840 --> 00:11:26,240 Speaker 1: love right now is not has nothing to do with 217 00:11:26,320 --> 00:11:29,319 Speaker 1: the production of the music. Are we mad? Are we mad? 218 00:11:29,520 --> 00:11:29,679 Speaker 2: Yes? 219 00:11:29,840 --> 00:11:31,840 Speaker 1: Or are we like? Wait a minute, but I like 220 00:11:31,880 --> 00:11:34,040 Speaker 1: the music and I like the image, so I like 221 00:11:34,120 --> 00:11:37,520 Speaker 1: the package, so I'm fine. Oh yeah, all this just 222 00:11:37,559 --> 00:11:38,720 Speaker 1: seems so unnecessary. 223 00:11:38,760 --> 00:11:40,480 Speaker 3: Like I get it, okay, people are using AI to 224 00:11:40,520 --> 00:11:43,160 Speaker 3: make things more efficient, your life easier, but like, we 225 00:11:43,200 --> 00:11:47,120 Speaker 3: already have musicians, why like, why are we making fake music? 226 00:11:47,120 --> 00:11:48,960 Speaker 1: We have songs that way. But if you found out 227 00:11:49,000 --> 00:11:52,959 Speaker 1: that you liked fake music, then is there anything to 228 00:11:53,000 --> 00:11:55,480 Speaker 1: be upset about? I yeah, I would be upset. 229 00:11:55,600 --> 00:11:57,840 Speaker 3: Because I feel a deep connection to music, like I 230 00:11:57,880 --> 00:11:58,920 Speaker 3: got chills for music. 231 00:11:58,960 --> 00:12:01,640 Speaker 1: So yeah. But you could also make the argument that 232 00:12:01,760 --> 00:12:04,360 Speaker 1: in the last twenty years, a lot of music that 233 00:12:04,400 --> 00:12:07,280 Speaker 1: we listen to is not as it seems. The person 234 00:12:07,320 --> 00:12:09,480 Speaker 1: may have sung it, or someone may have written it, 235 00:12:09,760 --> 00:12:12,120 Speaker 1: but it went through a computer and it was filtered 236 00:12:12,160 --> 00:12:15,959 Speaker 1: and processed and auto tuned. I mean songs that we love, 237 00:12:16,600 --> 00:12:19,960 Speaker 1: like songs that are considered like classic pop songs we've 238 00:12:20,040 --> 00:12:22,920 Speaker 1: later learned that the person sounds like me, but it 239 00:12:22,960 --> 00:12:25,520 Speaker 1: comes out sounding like Britney Spears. That's I was gonna say. 240 00:12:25,600 --> 00:12:27,560 Speaker 2: I'm mad at what they're doing to Britney Spears because 241 00:12:27,679 --> 00:12:29,480 Speaker 2: any music that she has let out in the last 242 00:12:29,559 --> 00:12:31,960 Speaker 2: i'll say ten years is not her and she is 243 00:12:32,000 --> 00:12:34,720 Speaker 2: not aware that it is. Or they are using old stems, 244 00:12:34,720 --> 00:12:37,680 Speaker 2: they're using old vocals and like running them through and 245 00:12:37,720 --> 00:12:38,680 Speaker 2: I'm mad about that. 246 00:12:38,679 --> 00:12:41,320 Speaker 1: That upsets me as a fan. But if you liked 247 00:12:41,360 --> 00:12:44,959 Speaker 1: the song, then I guess I wonder like you if 248 00:12:44,960 --> 00:12:46,520 Speaker 1: you liked the song for a year and then somebody 249 00:12:46,520 --> 00:12:49,480 Speaker 1: comes down and goes, hey, by the way, that was 250 00:12:49,520 --> 00:12:53,720 Speaker 1: all fake. Do you now, how do you take back 251 00:12:53,760 --> 00:12:56,120 Speaker 1: the liking of the song you can't you have to, 252 00:12:56,440 --> 00:12:58,160 Speaker 1: I would stop supporting. Thank you? 253 00:12:58,240 --> 00:13:00,720 Speaker 3: Okay, I say, we turn off all the music tracks 254 00:13:00,720 --> 00:13:06,239 Speaker 3: at jingle Ball and make everybody singing a cappella. 255 00:13:06,360 --> 00:13:07,080 Speaker 1: We want people to have. 256 00:13:10,880 --> 00:13:12,560 Speaker 2: The lineup yet, but I'm not going to have a 257 00:13:12,600 --> 00:13:14,760 Speaker 2: buy your ticket. 258 00:13:16,400 --> 00:13:18,760 Speaker 1: I promise we won't do that. Buy your tickets now? 259 00:13:20,080 --> 00:13:25,720 Speaker 1: Uh so mad? Like if Teddy Swims, I think that man, No, no, 260 00:13:25,760 --> 00:13:27,760 Speaker 1: that dude. That would be really saying because he's so 261 00:13:27,760 --> 00:13:28,200 Speaker 1: so full. 262 00:13:28,320 --> 00:13:29,800 Speaker 3: But I would be so mad if I found out 263 00:13:29,800 --> 00:13:32,400 Speaker 3: it was fake. Of course, so now I needed. Every 264 00:13:32,480 --> 00:13:33,520 Speaker 3: artist needs to be tested. 265 00:13:34,400 --> 00:13:36,000 Speaker 1: When I met Teddy Swims the first time, though, I 266 00:13:36,000 --> 00:13:37,400 Speaker 1: did say that to him, I said, dud, I gotta 267 00:13:37,440 --> 00:13:39,600 Speaker 1: be honest with you, man, I heard your music for 268 00:13:39,720 --> 00:13:42,440 Speaker 1: months before I saw that it was you. And and 269 00:13:42,480 --> 00:13:44,600 Speaker 1: the funny thing is he has a huge security very 270 00:13:44,720 --> 00:13:47,040 Speaker 1: large black man is a security guard. Where your cowboy 271 00:13:47,080 --> 00:13:49,760 Speaker 1: had the two of them sitting next to each other. 272 00:13:49,800 --> 00:13:52,160 Speaker 1: If I didn't know better, I wouldn't know which I mean, 273 00:13:52,240 --> 00:13:54,520 Speaker 1: I would know that Teddy wasn't the security guard, but 274 00:13:54,559 --> 00:13:57,000 Speaker 1: I would also and the security guard laughed because he 275 00:13:57,040 --> 00:13:58,400 Speaker 1: was like, oh yeah, we get this all the time, 276 00:13:58,840 --> 00:14:01,600 Speaker 1: like he's got a very soulful voice. That's not the 277 00:14:01,640 --> 00:14:04,480 Speaker 1: package that I thought, but that happened. You know, from 278 00:14:04,520 --> 00:14:07,040 Speaker 1: time to time things got ugly. On a recent flight 279 00:14:07,080 --> 00:14:09,000 Speaker 1: when a woman I was just talking about this, who 280 00:14:09,040 --> 00:14:11,480 Speaker 1: says she's a lawyer, Not sure why that's important, try 281 00:14:11,520 --> 00:14:13,280 Speaker 1: to skip the line to get off the plane before 282 00:14:13,280 --> 00:14:16,240 Speaker 1: everybody else other passengers without having it, and it became 283 00:14:16,360 --> 00:14:19,440 Speaker 1: a huge shouting match. He told people just deal with it, 284 00:14:19,960 --> 00:14:22,720 Speaker 1: while someone called her a Karen. Then it got even messier. 285 00:14:22,760 --> 00:14:26,240 Speaker 1: John quinnionees, but I didn't. She accused another passenger of 286 00:14:26,240 --> 00:14:28,480 Speaker 1: being racist or making fun of her accent. The drama, 287 00:14:28,480 --> 00:14:30,240 Speaker 1: complete with the crying baby in the background, was all 288 00:14:30,240 --> 00:14:32,920 Speaker 1: caught on video and posted a TikTok. It's now gone viral, 289 00:14:32,920 --> 00:14:35,560 Speaker 1: with thousands weighing in online. Some say that she was rude, 290 00:14:35,600 --> 00:14:38,720 Speaker 1: Others say she held her own again. If you are 291 00:14:38,760 --> 00:14:41,440 Speaker 1: in row thirty, you don't get off before road twenty five. 292 00:14:41,480 --> 00:14:45,680 Speaker 1: You don't unless people are sitting there waiting for another flight. 293 00:14:45,760 --> 00:14:48,320 Speaker 1: If they're like, if you're on another flight, you know, 294 00:14:48,320 --> 00:14:50,960 Speaker 1: if you're on this plane going to Toledo, then I 295 00:14:50,960 --> 00:14:52,440 Speaker 1: don't know why I always pick on Toledo. But if 296 00:14:52,440 --> 00:14:54,680 Speaker 1: you're on this plane going to Wichita. Then you sit 297 00:14:54,760 --> 00:14:57,560 Speaker 1: here and then everyone else gets off. Well that's one thing. 298 00:14:58,200 --> 00:15:02,000 Speaker 1: But or if it's hey, sometimes you'll pull to the 299 00:15:02,000 --> 00:15:03,840 Speaker 1: gate and they'll be like, heyres a lot of tight connections. 300 00:15:04,080 --> 00:15:06,040 Speaker 1: If you have a very tight connection, then you know, 301 00:15:06,160 --> 00:15:08,720 Speaker 1: let everybody on the connection get off first, which half 302 00:15:08,720 --> 00:15:13,080 Speaker 1: the people don't do anyway, But but you don't. I'm 303 00:15:13,080 --> 00:15:15,200 Speaker 1: not going to go on this rant again, but we go. 304 00:15:15,920 --> 00:15:18,720 Speaker 1: We go one, two, three, four in order, and we 305 00:15:18,760 --> 00:15:22,280 Speaker 1: go every other. As Caitlin mentioned yesterday, that's how we 306 00:15:22,280 --> 00:15:24,480 Speaker 1: get off a plane. That's the that should be the 307 00:15:24,560 --> 00:15:27,560 Speaker 1: international standard. Yes, have some manners. Thank you. They seated. 308 00:15:27,640 --> 00:15:29,800 Speaker 3: I'm an aisle, I say seated to set the precedent 309 00:15:29,840 --> 00:15:30,960 Speaker 3: for the other people next to me. 310 00:15:31,120 --> 00:15:34,400 Speaker 1: We're not standing. We're seated and we're waiting our turn. 311 00:15:34,600 --> 00:15:36,760 Speaker 1: There's got to be order. I have to sell. 312 00:15:36,680 --> 00:15:38,920 Speaker 3: Nervous if you're sitting on the aisle and then the 313 00:15:38,920 --> 00:15:41,240 Speaker 3: people inside of your they stand up and I'm like 314 00:15:41,280 --> 00:15:43,960 Speaker 3: waiting for it and I'm not even standing, go for it? 315 00:15:44,080 --> 00:15:48,640 Speaker 1: Who told you? Now? Your booty in our face? There 316 00:15:49,000 --> 00:15:52,320 Speaker 1: is such a we live in a society. It's national 317 00:15:52,360 --> 00:15:55,760 Speaker 1: Pina Kalada Day, National Kitten Day, and Chronic Disease Day 318 00:15:55,800 --> 00:15:58,560 Speaker 1: to educate and advocate for people who live with chronic 319 00:15:58,560 --> 00:16:00,880 Speaker 1: health conditions every day. Be careful as we you know, 320 00:16:01,400 --> 00:16:03,600 Speaker 1: as we share our excitement for each day. Because they don't. 321 00:16:03,640 --> 00:16:06,120 Speaker 1: They don't always go in the right. They don't always 322 00:16:06,160 --> 00:16:08,080 Speaker 1: go from said the happy or happy to said no. 323 00:16:08,160 --> 00:16:08,480 Speaker 1: They don't