1 00:00:00,240 --> 00:00:04,720 Speaker 1: From UFOs to psychic powers and government conspiracies. History is 2 00:00:04,800 --> 00:00:09,119 Speaker 1: riddled with unexplained events. You can turn back now or 3 00:00:09,200 --> 00:00:12,079 Speaker 1: learn the stuff they don't want you to know. A 4 00:00:12,240 --> 00:00:25,599 Speaker 1: production of My Heart Radio. Hello, welcome back to the show. 5 00:00:25,680 --> 00:00:28,320 Speaker 1: My name is Matt, my name is Noel. They called 6 00:00:28,320 --> 00:00:31,280 Speaker 1: me Ben. We're joined as always with our super producer 7 00:00:31,520 --> 00:00:36,159 Speaker 1: Alexis code named Doc Holiday Jackson. Most importantly, you are you. 8 00:00:36,159 --> 00:00:39,400 Speaker 1: You are here, and that makes this the stuff they 9 00:00:39,479 --> 00:00:43,720 Speaker 1: don't want you to know. Uh. If you are, if 10 00:00:43,720 --> 00:00:46,599 Speaker 1: you're like us and you scan the news pretty frequently, 11 00:00:46,920 --> 00:00:51,360 Speaker 1: then we're sure you are already well aware that there 12 00:00:51,479 --> 00:00:55,400 Speaker 1: is a nationwide epidemic apparently of people pretending to play 13 00:00:55,440 --> 00:01:01,600 Speaker 1: the violin is a true story. But I got I've 14 00:01:01,640 --> 00:01:03,720 Speaker 1: got to say, though, when the one thing that bugs 15 00:01:03,720 --> 00:01:06,440 Speaker 1: me about like some films, even if the production value 16 00:01:06,480 --> 00:01:10,440 Speaker 1: is super high, Uh, pantomiming of playing instruments is almost 17 00:01:10,480 --> 00:01:16,000 Speaker 1: always very bad. Guys. Yes, when I grew up playing 18 00:01:16,040 --> 00:01:18,759 Speaker 1: the violin, so it vexes me when I see someone 19 00:01:19,120 --> 00:01:21,679 Speaker 1: shredding on the violin in a movie and just clearly 20 00:01:21,680 --> 00:01:23,520 Speaker 1: not moving their fingers in the right way at all. 21 00:01:23,680 --> 00:01:25,319 Speaker 1: And I imagine this would be the same thing with 22 00:01:25,400 --> 00:01:28,920 Speaker 1: these uh, these fraudulent buskers. It's it's the same thing 23 00:01:28,959 --> 00:01:34,000 Speaker 1: with any almost any specific physical skill. When you see 24 00:01:34,040 --> 00:01:36,880 Speaker 1: it in a film. Often if it's not an action scene, 25 00:01:37,240 --> 00:01:42,240 Speaker 1: then experts will be driven crazy watching watching someone acted 26 00:01:42,280 --> 00:01:44,400 Speaker 1: out right. That's why the good move for a lot 27 00:01:44,440 --> 00:01:48,520 Speaker 1: of films is to teach the actors how how to 28 00:01:48,640 --> 00:01:52,040 Speaker 1: at least play parts of a song or instrument and 29 00:01:52,120 --> 00:01:54,000 Speaker 1: then just do the cutaway to the hands and boom, 30 00:01:54,080 --> 00:01:58,000 Speaker 1: boom boom. But we're not talking to wait, what the 31 00:01:58,080 --> 00:02:02,200 Speaker 1: Jamie Foxx performance? What was what was the movie? Um, 32 00:02:02,520 --> 00:02:07,200 Speaker 1: Charles Ray Charles documentar or uh biopic? He learned all 33 00:02:07,200 --> 00:02:11,120 Speaker 1: that stuff. That's how you do it, filmmakers listening. So 34 00:02:11,320 --> 00:02:14,240 Speaker 1: did the dude that played Eddie Munson and Stranger Things 35 00:02:14,240 --> 00:02:16,919 Speaker 1: when he did the Master of Puppets scene. He actually 36 00:02:17,320 --> 00:02:19,600 Speaker 1: there's video of him learning to play that, so it 37 00:02:19,639 --> 00:02:21,639 Speaker 1: really makes a huge difference, even if it wasn't actually 38 00:02:21,639 --> 00:02:24,080 Speaker 1: his audio. And then there was the other one. There's 39 00:02:24,120 --> 00:02:27,880 Speaker 1: that other uh oh, They're numerous examples, cent your favorite. 40 00:02:28,360 --> 00:02:31,160 Speaker 1: For now, we just want to give a special shout 41 00:02:31,200 --> 00:02:35,520 Speaker 1: out to all the people actually busting by playing a violin. 42 00:02:36,120 --> 00:02:38,440 Speaker 1: We also want to give a shout out to the 43 00:02:38,480 --> 00:02:42,840 Speaker 1: sea floor and everything holy about it. You'll see what 44 00:02:42,840 --> 00:02:46,200 Speaker 1: you mean. Uh, we want to give we want to 45 00:02:46,200 --> 00:02:51,320 Speaker 1: give a shout out to uh the terrifying, the terrifying 46 00:02:51,639 --> 00:02:55,880 Speaker 1: uh rise of pree crime, which is probably gonna happen. Now. 47 00:02:56,040 --> 00:02:59,079 Speaker 1: We predicted it, and as we said earlier, it's sad. 48 00:02:59,120 --> 00:03:02,880 Speaker 1: But usually when we predict a bad thing happening, we 49 00:03:03,400 --> 00:03:06,359 Speaker 1: tend to be correct. Just once we want to predict 50 00:03:06,400 --> 00:03:09,720 Speaker 1: a good thing. But you know, hope springs eternal, especially 51 00:03:09,840 --> 00:03:13,760 Speaker 1: if you are someone playing the lottery in the United 52 00:03:13,840 --> 00:03:19,200 Speaker 1: States today segue, Oh, the luck is in your favor, 53 00:03:19,240 --> 00:03:23,480 Speaker 1: except it's not ever because that's not a thing, well 54 00:03:23,480 --> 00:03:28,040 Speaker 1: maybe a thing. I heard the lottery once described as 55 00:03:28,320 --> 00:03:33,000 Speaker 1: a tax on people who are bad at math kind of. 56 00:03:33,280 --> 00:03:37,000 Speaker 1: But as we're gonna learn in this uh this segment, 57 00:03:37,200 --> 00:03:39,600 Speaker 1: let's call it, we're gonna learn that the lottery can 58 00:03:39,680 --> 00:03:44,200 Speaker 1: be a fun and good thing. Uh. And here's why. 59 00:03:44,320 --> 00:03:46,840 Speaker 1: If you like chicken fingers, you're gonna love this story. 60 00:03:48,520 --> 00:03:50,520 Speaker 1: Shout out to the guys over it. This is important. 61 00:03:50,760 --> 00:03:52,880 Speaker 1: They're the reason I even know about the chain we're 62 00:03:52,880 --> 00:03:57,320 Speaker 1: going to discuss today. Today's story comes out of CNN, 63 00:03:57,720 --> 00:04:01,560 Speaker 1: and it is about a certain franchise owner, a company 64 00:04:01,560 --> 00:04:05,800 Speaker 1: owner that purchased lottery tickets for the Mega Millions lottery 65 00:04:05,880 --> 00:04:12,080 Speaker 1: for all fifty thousand of his employees. Wow, a good, happy, 66 00:04:12,280 --> 00:04:17,839 Speaker 1: nice story. So what really? Yes, let's die the twist 67 00:04:17,839 --> 00:04:20,680 Speaker 1: in the darkness. I'm waiting for the other shooting drop 68 00:04:20,960 --> 00:04:23,480 Speaker 1: the twist in the dark coming. Ben already kind of 69 00:04:23,560 --> 00:04:26,119 Speaker 1: hinted at. But we'll we'll get into it really fast. 70 00:04:26,400 --> 00:04:32,200 Speaker 1: So as of yesterday, Tuesday, July, the lottery, the Mega 71 00:04:32,240 --> 00:04:36,160 Speaker 1: Millions lottery jackpot hit eight hundred and ten million dollars. 72 00:04:36,680 --> 00:04:40,560 Speaker 1: That's a lot of money. So a gentleman named Todd 73 00:04:40,640 --> 00:04:43,080 Speaker 1: Graves who is the founder of a little place called 74 00:04:43,279 --> 00:04:47,600 Speaker 1: Raising Canes. Uh, this is a chicken finger restaurant. If 75 00:04:47,640 --> 00:04:49,880 Speaker 1: you like chicken fingies, this is a place to go. 76 00:04:50,120 --> 00:04:53,599 Speaker 1: If you don't like chicken finghies, stay far away because 77 00:04:53,680 --> 00:04:58,000 Speaker 1: that's all you gott are They tends as well, I 78 00:04:58,040 --> 00:05:06,160 Speaker 1: think tendees fingies, strips. No nuggies, Yeah, no nugs, No nuggs. 79 00:05:06,200 --> 00:05:08,359 Speaker 1: You can just you can cut a fingy into fourth 80 00:05:08,440 --> 00:05:12,200 Speaker 1: and when you get your nuggies, gotta be change d 81 00:05:12,360 --> 00:05:17,360 Speaker 1: I y Seriously, I went on their website. It's literally 82 00:05:17,440 --> 00:05:19,839 Speaker 1: chicken fingers, some fries. I think there might be coastal 83 00:05:20,000 --> 00:05:25,559 Speaker 1: involved if you get one specific thing. There's Texas toasts 84 00:05:26,320 --> 00:05:29,200 Speaker 1: that looked like little hot dog bunts and cane sauces, 85 00:05:29,240 --> 00:05:31,760 Speaker 1: the big But you know what, I respect it, Matt. 86 00:05:31,800 --> 00:05:36,000 Speaker 1: I respect it because at some point they said we're 87 00:05:36,000 --> 00:05:38,440 Speaker 1: going to focus on one thing and we're going to 88 00:05:38,640 --> 00:05:41,160 Speaker 1: nail it and do a good I mean, I appreciate that. 89 00:05:41,240 --> 00:05:45,000 Speaker 1: I respect it. I'm not necessarily given to it. But uh, 90 00:05:45,279 --> 00:05:47,400 Speaker 1: this was just this past Monday, right now, as you 91 00:05:47,440 --> 00:05:50,640 Speaker 1: were saying, till over eight hundred million dollars, and I 92 00:05:50,640 --> 00:05:55,200 Speaker 1: guess eight hundred million was the threshold for the head 93 00:05:55,240 --> 00:05:58,520 Speaker 1: guy of this company to say, all right, I'm gonna 94 00:05:59,200 --> 00:06:03,360 Speaker 1: drop what fifty grand, get everybody a one dollar ticket. 95 00:06:03,800 --> 00:06:05,200 Speaker 1: Oh no, no, no, no, no no, this is a 96 00:06:05,240 --> 00:06:08,640 Speaker 1: hundred grand. So these are mega millions cost two dollars 97 00:06:08,640 --> 00:06:12,080 Speaker 1: to buy. That's along with the old powerball, right, five 98 00:06:12,240 --> 00:06:16,000 Speaker 1: regular old numbers and then one power ball and you 99 00:06:16,040 --> 00:06:18,200 Speaker 1: can you can play whatever numbers you want, or you 100 00:06:18,240 --> 00:06:20,960 Speaker 1: can just get them to randomly generate numbers for you 101 00:06:21,000 --> 00:06:27,320 Speaker 1: as well. Correct yes, yes, yes, yes, yes, yes you can, yeah, 102 00:06:27,560 --> 00:06:29,120 Speaker 1: and you have to use cash. By the way, you 103 00:06:29,160 --> 00:06:31,640 Speaker 1: can use a credit card to buy these tickets. And 104 00:06:32,560 --> 00:06:35,799 Speaker 1: this person, Todd Graves, founder of this place, did indeed 105 00:06:35,839 --> 00:06:38,400 Speaker 1: spend a hundred thousand dollars to buy fifty thousand tickets. 106 00:06:38,880 --> 00:06:42,880 Speaker 1: And the concept here was if anyone in the company 107 00:06:43,120 --> 00:06:45,920 Speaker 1: won this lottery through one of these tickets, then all 108 00:06:45,960 --> 00:06:50,080 Speaker 1: of the fifty thousand employees would share this money, which 109 00:06:50,160 --> 00:06:53,800 Speaker 1: is really really nice a cool idea. Again, I think 110 00:06:53,839 --> 00:06:56,039 Speaker 1: this is like one of the best ideas that I 111 00:06:56,120 --> 00:06:58,919 Speaker 1: that I've heard. Back in the day, we used to 112 00:07:00,040 --> 00:07:02,640 Speaker 1: we used to pool our money at ye old how 113 00:07:02,720 --> 00:07:06,360 Speaker 1: stuff works, and we would buy lottery tickets and if 114 00:07:06,400 --> 00:07:09,440 Speaker 1: any one ticket one within the pool, everybody who played 115 00:07:09,680 --> 00:07:13,360 Speaker 1: gets to share the winnings. Um. But we did that internally, 116 00:07:13,400 --> 00:07:15,200 Speaker 1: we spent our own money. In this case, it's a 117 00:07:15,240 --> 00:07:18,640 Speaker 1: company owner, you know, showing up their own cash as 118 00:07:18,720 --> 00:07:21,920 Speaker 1: a possible benefit for the entirety of the company, which 119 00:07:21,960 --> 00:07:24,360 Speaker 1: is just really cool. And you know, I think is 120 00:07:24,440 --> 00:07:28,040 Speaker 1: interesting about that is that in our situation, which is 121 00:07:28,080 --> 00:07:31,440 Speaker 1: common to many workplaces, at least in the States, in 122 00:07:31,560 --> 00:07:35,600 Speaker 1: our situation, there was some psychology at play because you 123 00:07:35,640 --> 00:07:38,160 Speaker 1: would have you know you've got some change, you got 124 00:07:38,200 --> 00:07:40,800 Speaker 1: a couple of bucks or whatever. You don't want to be, 125 00:07:40,920 --> 00:07:43,400 Speaker 1: even if you never ordinarily gamble, you don't want to 126 00:07:43,440 --> 00:07:46,600 Speaker 1: be the one person who wasn't on the list. And 127 00:07:46,680 --> 00:07:51,200 Speaker 1: now everybody work with except for you is a a millionaire. 128 00:07:51,560 --> 00:07:54,360 Speaker 1: You know what I mean. It's like the risk aversion 129 00:07:54,440 --> 00:07:58,040 Speaker 1: gets switched. So now the founder Graves has done something 130 00:07:58,080 --> 00:08:02,840 Speaker 1: really interesting. He's basically giving everybody two bucks and the 131 00:08:02,960 --> 00:08:05,720 Speaker 1: chance to have much more, so you don't have that 132 00:08:05,840 --> 00:08:08,560 Speaker 1: same fear motivator. I think that's cool. I think it's 133 00:08:08,600 --> 00:08:11,480 Speaker 1: a neat gesture. Unless there's like more to this story, 134 00:08:11,680 --> 00:08:14,000 Speaker 1: it's a bold move on his part two because if 135 00:08:14,160 --> 00:08:16,400 Speaker 1: you know, if everyone, if there is a win, he's 136 00:08:16,400 --> 00:08:19,680 Speaker 1: gonna be out an entire workforce. I mean, that's no, 137 00:08:21,560 --> 00:08:24,840 Speaker 1: there's okay, there's caveats, Okay, Okay, this is why. Okay. 138 00:08:24,840 --> 00:08:28,160 Speaker 1: So when you win a large lottery like this, you 139 00:08:28,200 --> 00:08:32,079 Speaker 1: have two options, especially here in Georgia, and I'm pretty 140 00:08:32,080 --> 00:08:35,240 Speaker 1: sure it's for every place that plays this mega millions lottery. 141 00:08:36,200 --> 00:08:39,560 Speaker 1: You can either take payments that will be sent to 142 00:08:39,600 --> 00:08:43,120 Speaker 1: you like kind of like a paycheck basically, and they 143 00:08:43,160 --> 00:08:47,120 Speaker 1: increase in amount over time to adjust for inflation, but 144 00:08:47,200 --> 00:08:49,640 Speaker 1: it would just be you'd be getting smaller, much much 145 00:08:49,640 --> 00:08:53,920 Speaker 1: smaller payments over time. Or you can cash out and 146 00:08:53,960 --> 00:08:56,320 Speaker 1: you just take a huge cash some and at that 147 00:08:56,400 --> 00:08:59,640 Speaker 1: point the full thing, the full amount is taxed. So 148 00:08:59,760 --> 00:09:02,840 Speaker 1: the estimated cash value of that eight hundred and ten 149 00:09:02,880 --> 00:09:06,240 Speaker 1: million dollar jackpot from yesterday was in fact around four 150 00:09:06,320 --> 00:09:09,800 Speaker 1: hundred and seventy point one million dollars at least according 151 00:09:09,840 --> 00:09:13,560 Speaker 1: to CNN. And that means if you divide that up 152 00:09:13,559 --> 00:09:16,079 Speaker 1: by the fifty thousand employees that would be getting a share, 153 00:09:16,480 --> 00:09:19,760 Speaker 1: you get around nine thousand, four hundred dollars for each 154 00:09:19,840 --> 00:09:27,240 Speaker 1: individual person, Okay, which was that's no, that's no small sum, right, 155 00:09:27,440 --> 00:09:31,760 Speaker 1: my goodness, nine nine point four grand just as an 156 00:09:31,840 --> 00:09:35,680 Speaker 1: extra thing coming to you, that would be amazing, right. Uh, 157 00:09:35,960 --> 00:09:39,120 Speaker 1: But it's not the same as everybody's like, I'm out 158 00:09:39,120 --> 00:09:42,720 Speaker 1: of here, um, no more raising this cane. I'm gonna 159 00:09:43,800 --> 00:09:45,520 Speaker 1: go to Chick fil a. I guess no, I don't know. 160 00:09:45,600 --> 00:09:50,200 Speaker 1: I don't know how you presumably, how dare you go 161 00:09:50,280 --> 00:09:53,560 Speaker 1: to Chick fil a? Judas I say, well, they have 162 00:09:53,640 --> 00:09:56,840 Speaker 1: more than one sauce, I mean they do. I like 163 00:09:56,920 --> 00:09:59,280 Speaker 1: that Polynesian sauce. You can actually buy that bottle that 164 00:09:59,360 --> 00:10:04,400 Speaker 1: the crow around here which you homophobia is what it 165 00:10:04,440 --> 00:10:11,440 Speaker 1: sounds like. Yeah, live for it now, very very streamlined operation. 166 00:10:11,480 --> 00:10:13,640 Speaker 1: They do say, they say my pleasure too much. That 167 00:10:13,679 --> 00:10:16,480 Speaker 1: weird to me out, But no question is the idea 168 00:10:18,240 --> 00:10:19,600 Speaker 1: the idea here? I guess is that you know, with 169 00:10:19,679 --> 00:10:23,360 Speaker 1: fifty thousand uh tickets in the game, you know you 170 00:10:23,360 --> 00:10:26,920 Speaker 1: you you raise your odds. I guess if were they 171 00:10:26,960 --> 00:10:30,000 Speaker 1: all randomly generated? Is that how he played it there? 172 00:10:30,040 --> 00:10:32,080 Speaker 1: What was the idea? And what are the logistics of 173 00:10:32,480 --> 00:10:35,920 Speaker 1: buying fifty thousand lottery tickets with cash as a single 174 00:10:36,080 --> 00:10:39,240 Speaker 1: human being? How do you even get them? Yeah? I 175 00:10:39,800 --> 00:10:41,840 Speaker 1: I don't have all of that information. All we know 176 00:10:41,960 --> 00:10:44,560 Speaker 1: from that CNN article is that it was difficult to 177 00:10:44,600 --> 00:10:50,520 Speaker 1: buy fifty thousand lottery tickets. I tweeted that it's harder 178 00:10:50,520 --> 00:10:55,240 Speaker 1: than you think. Yeah, yeah, exactly. Um, I'm assuming it 179 00:10:55,320 --> 00:10:59,120 Speaker 1: was through some kind of cashier's check. I'm imagining, because 180 00:10:59,160 --> 00:11:01,240 Speaker 1: that's that whole thing is got to be weird. You 181 00:11:01,280 --> 00:11:04,600 Speaker 1: certainly do kind of raise the odds. Except for as 182 00:11:04,640 --> 00:11:07,319 Speaker 1: we discussed before, and Ben, I'm gonna lean on you 183 00:11:07,720 --> 00:11:11,800 Speaker 1: for this just a bit every time you play, especially 184 00:11:11,840 --> 00:11:15,560 Speaker 1: if you're randomly generating numbers, you have the same odds 185 00:11:15,640 --> 00:11:18,640 Speaker 1: every time you play the lottery, essentially, is what I'm saying. 186 00:11:20,280 --> 00:11:25,280 Speaker 1: And it's one in three something million, some ridiculous number. Um, 187 00:11:25,440 --> 00:11:28,719 Speaker 1: a crazy flow chance that you would actually win this 188 00:11:28,800 --> 00:11:32,240 Speaker 1: lottery no matter how many times you play. It's weird, Okay, 189 00:11:32,280 --> 00:11:35,680 Speaker 1: So maybe a way to explain it is, uh, you 190 00:11:35,760 --> 00:11:40,040 Speaker 1: know how gravity is just a little bit different if 191 00:11:40,040 --> 00:11:43,160 Speaker 1: you're in very very very very elevated places. But it's 192 00:11:43,160 --> 00:11:46,360 Speaker 1: a very very very small change. You can use that 193 00:11:46,440 --> 00:11:51,440 Speaker 1: analogy to think about the way the lottery works. So 194 00:11:51,880 --> 00:11:56,679 Speaker 1: if your chance is, for instance, one in just twenty 195 00:11:56,880 --> 00:11:59,760 Speaker 1: nine point two million, that's a that's a number I 196 00:12:00,200 --> 00:12:04,160 Speaker 1: saw in a statistics article on this, then you can 197 00:12:04,360 --> 00:12:07,920 Speaker 1: up your odds by buying ten tickets. You spent way 198 00:12:07,960 --> 00:12:11,920 Speaker 1: more money than you meant to, and in exchange for that, 199 00:12:12,120 --> 00:12:14,720 Speaker 1: your odds go from one in twenty nine point two 200 00:12:14,760 --> 00:12:18,920 Speaker 1: million to ten in twenty nine point two million. It's 201 00:12:18,920 --> 00:12:21,680 Speaker 1: like it does have an effect, but it is so 202 00:12:21,760 --> 00:12:25,160 Speaker 1: small you can only call it technically improving your chances. 203 00:12:25,240 --> 00:12:28,360 Speaker 1: It's kind of like how, um, if you if you're 204 00:12:28,400 --> 00:12:31,319 Speaker 1: running late and you try to speed on the interstate. 205 00:12:32,240 --> 00:12:35,520 Speaker 1: You can shave a couple of minutes off, but you're 206 00:12:35,559 --> 00:12:38,440 Speaker 1: probably still going to be late. If you're already like 207 00:12:38,520 --> 00:12:41,079 Speaker 1: more than fifteen minutes late, it's probably not gonna happen, 208 00:12:41,600 --> 00:12:45,200 Speaker 1: you know what I mean. Yeah, so, statistically, you're not 209 00:12:45,440 --> 00:12:48,480 Speaker 1: really raising your odds, even even though you technically are. 210 00:12:48,800 --> 00:12:51,080 Speaker 1: You are a little bit. Yeah, you are a little bit, 211 00:12:51,120 --> 00:12:55,440 Speaker 1: but it's um not significantly. To do it significantly, you 212 00:12:55,480 --> 00:13:01,880 Speaker 1: would have to, you know, by millions of lottery tickets. 213 00:13:01,920 --> 00:13:05,000 Speaker 1: But to this point, I think we're going well. You also, 214 00:13:05,360 --> 00:13:07,400 Speaker 1: you'd also have to make sure that none of those 215 00:13:07,760 --> 00:13:11,800 Speaker 1: none of those sets of numbers are repeating increase your odds. 216 00:13:12,600 --> 00:13:16,560 Speaker 1: But with that, but if they were randomly generated, right, 217 00:13:16,600 --> 00:13:18,840 Speaker 1: that's what I was going to say. If they're randomly generated, 218 00:13:18,920 --> 00:13:21,840 Speaker 1: they're not the odd like the odds of those same 219 00:13:21,920 --> 00:13:26,360 Speaker 1: numbers being randomly generated in that string that is winning 220 00:13:26,600 --> 00:13:33,120 Speaker 1: just another crappier lottery basically in terms of the odds. Right, So, 221 00:13:33,720 --> 00:13:36,440 Speaker 1: uh so I think it's I mean, I still like 222 00:13:36,520 --> 00:13:40,240 Speaker 1: it's a good question. Fifty fifty thousand tickets, to what 223 00:13:40,360 --> 00:13:42,920 Speaker 1: degree does that increase your odds? Right? If it's one 224 00:13:43,040 --> 00:13:47,040 Speaker 1: iteration or one instance each. Still, I don't think, you know, 225 00:13:47,080 --> 00:13:51,840 Speaker 1: if you're comparing millions to tens of thousands, it's not 226 00:13:51,960 --> 00:13:55,800 Speaker 1: a huge bump. But it is still a really nice gesture, right, 227 00:13:57,920 --> 00:13:59,839 Speaker 1: and not not to be cynical about it, but it's 228 00:13:59,840 --> 00:14:04,440 Speaker 1: a damn fine PR move for a small company that's 229 00:14:04,480 --> 00:14:08,199 Speaker 1: you know, a single owner who presumably treats his employees 230 00:14:08,360 --> 00:14:11,600 Speaker 1: better than you know, larger fast food chains. They're not 231 00:14:11,679 --> 00:14:14,160 Speaker 1: infinite numbers of these. There isn't one in Georgia or 232 00:14:14,160 --> 00:14:17,000 Speaker 1: in Atlanta currently, um there, you know, there's there's a 233 00:14:17,040 --> 00:14:18,719 Speaker 1: limited number of them, and so I think it's more 234 00:14:18,760 --> 00:14:20,960 Speaker 1: of that, like, see, we take care of our people. 235 00:14:21,360 --> 00:14:23,640 Speaker 1: We were all in this together. You know. It's definitely 236 00:14:23,880 --> 00:14:26,280 Speaker 1: I mean obviously got picked up you know, far and 237 00:14:26,320 --> 00:14:29,520 Speaker 1: wide this story. Yeah, it really did. You're correct, it's 238 00:14:29,520 --> 00:14:34,800 Speaker 1: a great PR move. Sadly, guys, it is Wednesday as 239 00:14:34,880 --> 00:14:39,320 Speaker 1: we record this, and uh, nobody won, not a single 240 00:14:39,360 --> 00:14:44,440 Speaker 1: person one the major jackpot all six numbers. Uh. Oh 241 00:14:44,520 --> 00:14:47,080 Speaker 1: and and by the way, according to AP, the chances 242 00:14:47,120 --> 00:14:49,920 Speaker 1: of winning all six or getting all six numbers correct 243 00:14:50,000 --> 00:14:52,400 Speaker 1: is one in three d and two point five million, 244 00:14:53,240 --> 00:14:59,440 Speaker 1: which seems like, uh okay, thinking it's thinking it's a 245 00:14:59,480 --> 00:15:04,840 Speaker 1: love but that means you, guys, in between the time 246 00:15:04,880 --> 00:15:07,600 Speaker 1: that we are recording this right now and the time 247 00:15:08,040 --> 00:15:12,120 Speaker 1: that this comes out on Monday next Monday, which is 248 00:15:12,160 --> 00:15:16,280 Speaker 1: today when you're hearing this, somebody may have won that lottery. 249 00:15:16,840 --> 00:15:20,240 Speaker 1: And the founder of Raising Kanes has said he's going 250 00:15:20,280 --> 00:15:23,280 Speaker 1: to play again, and he's going to keep playing until 251 00:15:23,320 --> 00:15:29,320 Speaker 1: he wins, or until someone wins, until someone wins, because 252 00:15:29,360 --> 00:15:32,920 Speaker 1: imagine like dropping a hundred thousand dollars twice a week. 253 00:15:33,600 --> 00:15:39,560 Speaker 1: Chicken fingers. It's so many chicken fingers. Al Right, guys, 254 00:15:39,600 --> 00:15:41,400 Speaker 1: we don't have to talk about this too much more, 255 00:15:41,840 --> 00:15:43,760 Speaker 1: but there are a couple of places you can go 256 00:15:43,840 --> 00:15:45,880 Speaker 1: to to learn more about the lottery. Not long ago 257 00:15:45,920 --> 00:15:48,440 Speaker 1: we talked about I think it was a woman who 258 00:15:48,680 --> 00:15:51,800 Speaker 1: dreamed about numbers and then play the lottery in one 259 00:15:52,200 --> 00:15:55,040 Speaker 1: was a while ago, um, but we we brought up 260 00:15:55,040 --> 00:15:56,680 Speaker 1: some of the same things I wanted to bring up today, 261 00:15:56,720 --> 00:15:59,360 Speaker 1: but I don't think it's necessary. You can go to 262 00:16:00,040 --> 00:16:04,720 Speaker 1: every state's lottery website and check out. Usually they have 263 00:16:04,760 --> 00:16:08,320 Speaker 1: a section called where the money goes. So if you 264 00:16:08,360 --> 00:16:11,680 Speaker 1: go to g a lottery dot com, you can navigate 265 00:16:11,720 --> 00:16:14,040 Speaker 1: to their where the money goes section and you can 266 00:16:14,120 --> 00:16:18,880 Speaker 1: actually see how much money is spent on uh. You know, 267 00:16:18,960 --> 00:16:22,880 Speaker 1: employees who work for Georgia Lottery on their overhead, basically 268 00:16:22,880 --> 00:16:26,280 Speaker 1: how much they they spend on their staff with the 269 00:16:26,320 --> 00:16:29,120 Speaker 1: money that's brought in by these lottery tickets. It shows 270 00:16:29,160 --> 00:16:31,720 Speaker 1: you how much money is paid out to players, and 271 00:16:31,760 --> 00:16:34,520 Speaker 1: how much money is paid to retailers so people actually 272 00:16:34,560 --> 00:16:37,600 Speaker 1: sell the lottery tickets, and how much money actually goes 273 00:16:37,640 --> 00:16:40,680 Speaker 1: to education. And in the state we live in in Georgia, 274 00:16:41,360 --> 00:16:45,640 Speaker 1: in fiscal year one, Georgia Lottery gave one point five 275 00:16:45,720 --> 00:16:49,520 Speaker 1: four billion dollars in funds to education, which includes things 276 00:16:49,520 --> 00:16:53,720 Speaker 1: like the Hope Scholarship that allows children to go to 277 00:16:53,760 --> 00:16:58,880 Speaker 1: college here in Georgia and in every state has things 278 00:16:58,960 --> 00:17:01,840 Speaker 1: like the Hope scholar Ship, not necessarily the exact same thing, 279 00:17:01,880 --> 00:17:04,840 Speaker 1: but things like that when it comes to funding education 280 00:17:04,880 --> 00:17:07,720 Speaker 1: in their state. Uh. Texas is the only place that 281 00:17:07,800 --> 00:17:10,199 Speaker 1: was really weird. I couldn't find exact numbers on the 282 00:17:10,200 --> 00:17:13,320 Speaker 1: Texas Lottery stuff they want, you know, Matt, Matt, what 283 00:17:13,480 --> 00:17:15,320 Speaker 1: is it that causes the pot to get so much 284 00:17:15,359 --> 00:17:20,080 Speaker 1: bigger when we have these historically large lottery pots, more 285 00:17:20,119 --> 00:17:23,919 Speaker 1: people play. Yeah, right, that's it. I mean, because as 286 00:17:23,960 --> 00:17:25,959 Speaker 1: that number gets bigger, more people are like, all right, 287 00:17:26,000 --> 00:17:30,080 Speaker 1: I've reached my threshold of going mine as well. Waste 288 00:17:30,240 --> 00:17:33,440 Speaker 1: five dollars, ten dollars, whatever it is, two dollars, feedback 289 00:17:33,440 --> 00:17:36,480 Speaker 1: loop and the and the cost that makes senst risk 290 00:17:36,640 --> 00:17:41,320 Speaker 1: analysis or the cost benefit analysis changes because the the 291 00:17:41,359 --> 00:17:47,080 Speaker 1: infinitesimal chance of winning is more attractive as there's more 292 00:17:47,080 --> 00:17:49,080 Speaker 1: and more stuff. And if you're listening, and if you 293 00:17:49,200 --> 00:17:53,439 Speaker 1: won by the time this comes out, uh, congratulations. Just 294 00:17:53,480 --> 00:17:56,240 Speaker 1: give you a couple of quick tips. Don't tell anyone, 295 00:17:56,520 --> 00:18:00,440 Speaker 1: don't tell us, don't tell your family. Honestly, I'm gonna 296 00:18:00,480 --> 00:18:02,520 Speaker 1: say it, this might be a hot take. I would 297 00:18:02,520 --> 00:18:05,920 Speaker 1: think very carefully about telling your romantic partner. Also, don't 298 00:18:05,920 --> 00:18:09,879 Speaker 1: tell your children. Literally omerta for for the rest of 299 00:18:09,920 --> 00:18:12,439 Speaker 1: your life. That's the only way to do it. Make 300 00:18:12,480 --> 00:18:15,119 Speaker 1: a trust fund for each kid, put most of it 301 00:18:15,119 --> 00:18:21,560 Speaker 1: in there, and then enjoy what you got lord like 302 00:18:21,720 --> 00:18:24,000 Speaker 1: like gust spring or something. You know, you just gotta 303 00:18:24,640 --> 00:18:27,520 Speaker 1: really Uh yeah, it's such a weird curse to all 304 00:18:27,560 --> 00:18:32,199 Speaker 1: of a sudden that much. Don't take your job, you 305 00:18:32,240 --> 00:18:34,359 Speaker 1: know what I mean? Keep showing up to work for 306 00:18:34,400 --> 00:18:37,679 Speaker 1: at least like a year. Seriously, you know, it seems 307 00:18:37,680 --> 00:18:39,680 Speaker 1: like a long time because you're a billionaire now or 308 00:18:39,720 --> 00:18:42,760 Speaker 1: you will be. And then also obviously don't take the 309 00:18:42,840 --> 00:18:47,120 Speaker 1: lumps some at all. Don't do it unless you have to. Yeah, 310 00:18:47,359 --> 00:18:51,440 Speaker 1: speaking a billionaire. The Friday drawing that's occurring, you know 311 00:18:51,560 --> 00:18:54,600 Speaker 1: again that just occurred when you're hearing this. It is 312 00:18:54,720 --> 00:18:57,880 Speaker 1: over one billion dollars, were right about one one point 313 00:18:57,960 --> 00:19:02,000 Speaker 1: zero two billion, which is nuts. And and this has 314 00:19:02,040 --> 00:19:06,080 Speaker 1: been our giving advice for things that will never happen segments, Well, statistically, 315 00:19:06,160 --> 00:19:09,520 Speaker 1: it will happen to someone because someone will win. I 316 00:19:09,080 --> 00:19:11,920 Speaker 1: guess what was the most recent payout. It wasn't It 317 00:19:12,040 --> 00:19:15,040 Speaker 1: wasn't insane, It was just a couple of mill Like, wait, 318 00:19:15,119 --> 00:19:20,159 Speaker 1: what's the largest historical lottery payout that's ever happened? I 319 00:19:20,200 --> 00:19:24,760 Speaker 1: think it was one point five billion that was but 320 00:19:24,960 --> 00:19:28,480 Speaker 1: individual it can be that can be split sometimes too. Yeah, 321 00:19:28,560 --> 00:19:31,800 Speaker 1: but that's the that's the amount, right, that's the prize 322 00:19:31,840 --> 00:19:34,479 Speaker 1: amount and then the cash out amount, like like for 323 00:19:34,520 --> 00:19:37,960 Speaker 1: this one that's happening, it's it's valued at one point 324 00:19:38,000 --> 00:19:40,800 Speaker 1: oh two billion dollars, but the actual cash out number 325 00:19:40,840 --> 00:19:45,080 Speaker 1: is six hundred and two point five million. Got it? Yeah, 326 00:19:45,480 --> 00:19:51,679 Speaker 1: so made fortune favor the bold, right, Uh, and you know, 327 00:19:51,760 --> 00:19:55,240 Speaker 1: insert different Hunger Games quotes here. But don't let the 328 00:19:55,240 --> 00:19:58,800 Speaker 1: money change you. Listen, folks, a great amount of fame 329 00:19:59,000 --> 00:20:03,680 Speaker 1: or success, financial, social, whatever you wanna call it it. Uh. 330 00:20:03,760 --> 00:20:07,520 Speaker 1: It doesn't necessarily make people bad. It just allows them 331 00:20:07,560 --> 00:20:10,760 Speaker 1: to be more concentrated, transparent versions of who they were 332 00:20:10,760 --> 00:20:15,359 Speaker 1: in the beginning. So don't let it touch you, you know. 333 00:20:15,680 --> 00:20:18,280 Speaker 1: And at the towns people start approaching you with rocks 334 00:20:18,320 --> 00:20:23,120 Speaker 1: and their clenched fists. Runaway. There you go, guys, next time, 335 00:20:23,160 --> 00:20:26,800 Speaker 1: maybe even a full episode. There's a great story out 336 00:20:26,800 --> 00:20:30,200 Speaker 1: of the Grillo and it's discussing how much the state 337 00:20:30,240 --> 00:20:34,679 Speaker 1: lottery board makes for every one dollar they spend on ads. 338 00:20:35,600 --> 00:20:39,200 Speaker 1: So like the lottery board takes in around a hundred 339 00:20:39,280 --> 00:20:42,840 Speaker 1: and twenty eight dollars for every one dollar they spend 340 00:20:42,880 --> 00:20:46,520 Speaker 1: on ads, which think about that the amount of gains 341 00:20:46,520 --> 00:20:50,600 Speaker 1: you get for putting out just um promotion for your lottery. Hey, 342 00:20:50,600 --> 00:20:54,080 Speaker 1: come on, play the lottery, you might win. You ever 343 00:20:54,160 --> 00:20:59,000 Speaker 1: roll the dice, you might win, and there bucks back 344 00:20:59,040 --> 00:21:02,200 Speaker 1: for everyone. They it's just a lottery episode. Let's do it. 345 00:21:02,280 --> 00:21:04,879 Speaker 1: The time has come. I'll gamble on that. I bet 346 00:21:04,960 --> 00:21:08,560 Speaker 1: it'll be interesting. That's what I'm betting to. All Right, 347 00:21:08,920 --> 00:21:11,080 Speaker 1: we're gonna leave for now, hear words from our sponsor, 348 00:21:11,200 --> 00:21:13,520 Speaker 1: maybe the lottery, and we'll be right back with more 349 00:21:13,560 --> 00:21:24,320 Speaker 1: strange news. And we've returned with some strange news from 350 00:21:24,880 --> 00:21:27,600 Speaker 1: the bottom of the sea. To take a quick diversion, 351 00:21:27,720 --> 00:21:31,280 Speaker 1: have you guys seen the movie Sing We Sing Too, 352 00:21:31,280 --> 00:21:37,919 Speaker 1: perhaps with c g I animals that sing like popular songs. Um. 353 00:21:37,960 --> 00:21:39,960 Speaker 1: I only bring this up because I recently watched Sing 354 00:21:40,080 --> 00:21:43,440 Speaker 1: Too and was very pleasantly surprised to hear a song 355 00:21:43,560 --> 00:21:48,520 Speaker 1: pretty prominently featured by a little remembered band called Mercury 356 00:21:48,560 --> 00:21:52,840 Speaker 1: rev Um, who, in my opinion, sort of had that 357 00:21:52,920 --> 00:21:57,160 Speaker 1: flaming lips sound that like post you know um clouds 358 00:21:57,160 --> 00:21:59,800 Speaker 1: taste metallic flaming lips sound like the soft bullets and era. 359 00:22:00,080 --> 00:22:03,199 Speaker 1: They had that sound on the lock before the flaming 360 00:22:03,240 --> 00:22:05,359 Speaker 1: Lips ever even you know touched it and they have 361 00:22:05,440 --> 00:22:07,959 Speaker 1: the same exact producer, a guy named Dave Fridman. So 362 00:22:08,119 --> 00:22:10,200 Speaker 1: I I like the flaming lips, I like that era, 363 00:22:10,280 --> 00:22:13,400 Speaker 1: but I really feel like they kind of eight mercury revs. Lunch. 364 00:22:13,480 --> 00:22:16,080 Speaker 1: And there's a song in scent too. It's quite beautiful 365 00:22:16,400 --> 00:22:19,800 Speaker 1: called holes. Um. And I always really just tickled when 366 00:22:19,800 --> 00:22:21,720 Speaker 1: it when it came on. UM. So yeah, this this 367 00:22:21,800 --> 00:22:24,240 Speaker 1: is about holes at the bottom of the seed. There's 368 00:22:24,240 --> 00:22:28,000 Speaker 1: an article on Vice that cited a report from the 369 00:22:28,119 --> 00:22:34,960 Speaker 1: National Oceanic Atmospheric Administration, a federal agency that studies weather 370 00:22:35,040 --> 00:22:37,760 Speaker 1: at the sea and things like that. This is specifically 371 00:22:37,960 --> 00:22:42,320 Speaker 1: a wing of Noah that does oceanic exploration. Uh. And 372 00:22:42,359 --> 00:22:47,200 Speaker 1: they had this to say via a tweet on Saturday's dive. 373 00:22:47,440 --> 00:22:51,120 Speaker 1: We saw several sublinear sets of holes in the sea floor. 374 00:22:51,440 --> 00:22:54,919 Speaker 1: The origin of the holes as scientists stumped. The holes 375 00:22:55,000 --> 00:22:58,399 Speaker 1: look human made, but the little piles of sediment around 376 00:22:58,400 --> 00:23:03,240 Speaker 1: them suggest they were ex ai aided by something. What's 377 00:23:03,280 --> 00:23:07,160 Speaker 1: your hypothesis? And it's true there are these neat and tidy, 378 00:23:07,400 --> 00:23:11,800 Speaker 1: little evenly spaced out rows of holes. Um. And I 379 00:23:11,880 --> 00:23:14,640 Speaker 1: learned a new word by the way from this, uh, 380 00:23:14,720 --> 00:23:19,640 Speaker 1: this Noah reporting. Uh. It's called leben spurring or laban spur, 381 00:23:19,760 --> 00:23:22,240 Speaker 1: which sounds vaguely German to me, which is a term 382 00:23:22,320 --> 00:23:27,879 Speaker 1: for biological formed structures things like holes or burrows or 383 00:23:28,040 --> 00:23:31,320 Speaker 1: mounds and then the like. Um. But here's the thing. 384 00:23:32,000 --> 00:23:36,520 Speaker 1: They could have been made perhaps by some sort of 385 00:23:36,720 --> 00:23:39,679 Speaker 1: crab or or or lobster or something with sort of 386 00:23:39,720 --> 00:23:44,600 Speaker 1: like a probing kind of like pointy you know, appendage. Um. 387 00:23:44,720 --> 00:23:48,719 Speaker 1: But upon further exploration and close up photography again, this 388 00:23:48,800 --> 00:23:52,200 Speaker 1: is a mile over a mile under the ocean. Sorry 389 00:23:52,400 --> 00:23:54,480 Speaker 1: not again, I haven't told you. It is in the 390 00:23:54,520 --> 00:23:59,080 Speaker 1: mid Atlantic Ridge area of the ocean, which is deep, deep, 391 00:23:59,119 --> 00:24:02,719 Speaker 1: deep dark shan, which is essentially might as well be space. 392 00:24:02,960 --> 00:24:05,840 Speaker 1: I mean, the reason they're exploring the stuff is because 393 00:24:05,840 --> 00:24:10,240 Speaker 1: it holds all kinds of as of yet undiscovered things, um, 394 00:24:10,359 --> 00:24:13,760 Speaker 1: certain species of sea creature. Ben, you had mentioned that 395 00:24:13,840 --> 00:24:16,840 Speaker 1: you went down a sea creature rabbit hole recently because 396 00:24:16,880 --> 00:24:18,480 Speaker 1: of some of the new ones that have been discovered 397 00:24:18,520 --> 00:24:22,160 Speaker 1: from this very type of exploration, right, yeah, right now. 398 00:24:22,240 --> 00:24:24,359 Speaker 1: The weird thing, and I think I mentioned this before, 399 00:24:25,440 --> 00:24:28,679 Speaker 1: the human species actually knows more about the surface of 400 00:24:28,680 --> 00:24:33,879 Speaker 1: the Moon than it does about the oceotic depths, you know, 401 00:24:33,920 --> 00:24:36,640 Speaker 1: once you get to the very bottom. And so if 402 00:24:36,720 --> 00:24:39,880 Speaker 1: you are a fan of cryptids, if you hold out 403 00:24:39,960 --> 00:24:42,879 Speaker 1: hope that uh, there are more creatures yet to be 404 00:24:42,920 --> 00:24:46,919 Speaker 1: discovered than the deep sea is one of the places 405 00:24:46,920 --> 00:24:50,000 Speaker 1: to start. There's actually, uh, the one that I was 406 00:24:50,480 --> 00:24:54,240 Speaker 1: talking to you about that messed up my research, some 407 00:24:54,320 --> 00:24:56,240 Speaker 1: of the research I was doing this after dude, I 408 00:24:56,320 --> 00:25:02,720 Speaker 1: totally stopped and learned too much about a rare tentacle 409 00:25:02,920 --> 00:25:07,960 Speaker 1: looking creature that scientists just discovered at the bottom of 410 00:25:07,960 --> 00:25:10,320 Speaker 1: the Pacific Ocean. I'll throw it in the chat so 411 00:25:10,320 --> 00:25:13,119 Speaker 1: you guys can get a little grossed out as we're 412 00:25:13,119 --> 00:25:20,879 Speaker 1: talking about this. But but yeah, right, vaguely extraterrestrial appearance. Right, 413 00:25:21,320 --> 00:25:24,720 Speaker 1: It's like someone grabbed the top of an octopus or 414 00:25:24,800 --> 00:25:27,720 Speaker 1: squid and then just sort of taffy pulled it out 415 00:25:27,920 --> 00:25:31,800 Speaker 1: into this long trail and then said, Okay, yeah, that's fine. 416 00:25:31,920 --> 00:25:35,520 Speaker 1: It's a spen. These are real things. Spens are already 417 00:25:35,560 --> 00:25:38,920 Speaker 1: a known thing, but this is like a new possibly 418 00:25:38,920 --> 00:25:42,000 Speaker 1: a new species. But all of this justice saying they 419 00:25:42,000 --> 00:25:48,000 Speaker 1: are continually, uh, strange discoveries being made at the ocean depths. 420 00:25:48,160 --> 00:25:51,679 Speaker 1: And that's why, at least with this story. No, I 421 00:25:51,680 --> 00:25:54,760 Speaker 1: would totally not be surprised if there's a mundane explanation, 422 00:25:54,840 --> 00:25:57,639 Speaker 1: but I would also totally not be surprised if it 423 00:25:57,760 --> 00:26:01,440 Speaker 1: was something brand new, because these do look. Uh, they're 424 00:26:01,560 --> 00:26:06,800 Speaker 1: curring at a regular interval like they look purposefly perforated. 425 00:26:07,000 --> 00:26:12,159 Speaker 1: There we go. Uh, I everything you're saying about this creature, 426 00:26:13,200 --> 00:26:17,280 Speaker 1: it's given me flashbacks. I just watched Nope, no spoilers, 427 00:26:17,280 --> 00:26:22,639 Speaker 1: no spoilers, but I just watched the movie Nope, And uh, 428 00:26:22,840 --> 00:26:24,840 Speaker 1: have you guys seen it yet? I'm very excited to 429 00:26:24,840 --> 00:26:27,960 Speaker 1: see it now. I talked to another friend today who 430 00:26:27,960 --> 00:26:30,240 Speaker 1: saw it and and said they absolutely loved it. So 431 00:26:30,280 --> 00:26:32,159 Speaker 1: I'm looking forward to it. I haven't seen it, but 432 00:26:32,240 --> 00:26:34,520 Speaker 1: you know me, I I read everything about it, so 433 00:26:34,560 --> 00:26:38,400 Speaker 1: I haven't. I like, I'd like Peel enough to not 434 00:26:38,720 --> 00:26:40,560 Speaker 1: spoil his stuff for myself because he's sort of like 435 00:26:40,920 --> 00:26:43,840 Speaker 1: he's like Shamlan but like, you know, good and then 436 00:26:43,920 --> 00:26:46,239 Speaker 1: we has an outstayed as welcome, and Shimlan was good 437 00:26:46,280 --> 00:26:47,560 Speaker 1: when he was good. But I just think he's kind 438 00:26:47,600 --> 00:26:50,400 Speaker 1: of like he's uh, his twists have gotten a little 439 00:26:50,400 --> 00:26:54,000 Speaker 1: bit repetitive and and predictable, whereas I think Peel has 440 00:26:54,119 --> 00:26:58,240 Speaker 1: always managed to surprise me. I really liked get Out obviously, 441 00:26:58,280 --> 00:27:01,320 Speaker 1: and and us, um here's the thing, there was actually 442 00:27:01,320 --> 00:27:03,720 Speaker 1: another Oh sorry, I was I sort of backtracked a 443 00:27:03,760 --> 00:27:07,560 Speaker 1: little bit too to describe where this was. Uh, this 444 00:27:07,640 --> 00:27:11,280 Speaker 1: discovery was found, um, and I didn't mention what was 445 00:27:11,359 --> 00:27:13,919 Speaker 1: said about They could have been made by you know, 446 00:27:14,080 --> 00:27:17,320 Speaker 1: certain types of sea creatures. But there's blind lobsters that 447 00:27:17,359 --> 00:27:20,439 Speaker 1: lived down There's something called squat lobsters, different types of 448 00:27:20,480 --> 00:27:24,159 Speaker 1: you know, subterranean deep deep sea dwelling crustaceans that have 449 00:27:24,359 --> 00:27:28,240 Speaker 1: little pokey diggy things. Um. But upon close ups and 450 00:27:28,240 --> 00:27:33,400 Speaker 1: further inspection, Uh, there weren't any signs of of living organisms, um, 451 00:27:33,440 --> 00:27:37,720 Speaker 1: inhabiting the holes, the researchers said. Um. They go on 452 00:27:37,880 --> 00:27:41,280 Speaker 1: whether the holes were connected beneath the sediment surface was 453 00:27:41,359 --> 00:27:44,879 Speaker 1: not visible. We hope that future studies of the laban 454 00:27:44,960 --> 00:27:48,800 Speaker 1: sporing Uh forgive me if I'm mispronouncing that we report 455 00:27:48,920 --> 00:27:52,800 Speaker 1: here will resolve the mystery of what created them. Um. 456 00:27:52,840 --> 00:27:55,480 Speaker 1: And this has actually happened before. In two thousand three, 457 00:27:55,960 --> 00:28:00,119 Speaker 1: scientists and investigating the same area published a study in 458 00:28:00,160 --> 00:28:04,640 Speaker 1: the journal Frontiers and Marine Scientists, and they found similar 459 00:28:04,920 --> 00:28:07,680 Speaker 1: types of you know, anomalous kind of holes. They were 460 00:28:07,680 --> 00:28:11,480 Speaker 1: described as raised sediment around these holes that indicated they 461 00:28:11,520 --> 00:28:14,080 Speaker 1: could have been dug out by deep sea crustaceans. Like 462 00:28:14,119 --> 00:28:17,840 Speaker 1: I said, um or perhaps excavated by animals living inside 463 00:28:17,880 --> 00:28:22,000 Speaker 1: the sea floor, but the authors were left with without 464 00:28:22,040 --> 00:28:25,359 Speaker 1: a definitive answer um And and we're basically just as 465 00:28:25,400 --> 00:28:28,679 Speaker 1: stumped as the scientists with noah have been about this 466 00:28:28,760 --> 00:28:32,080 Speaker 1: current run um. So I don't know. I like the 467 00:28:32,119 --> 00:28:36,560 Speaker 1: idea that it could be some mysterious creature that's doing it. 468 00:28:36,640 --> 00:28:38,440 Speaker 1: I like the idea that they had. The idea that 469 00:28:38,480 --> 00:28:40,560 Speaker 1: it's man made is interesting to me. And that was 470 00:28:40,600 --> 00:28:43,880 Speaker 1: actually in the in the headline for the Vice article 471 00:28:43,960 --> 00:28:47,520 Speaker 1: by Becky uh Ferrara, which I think is a little 472 00:28:47,520 --> 00:28:49,200 Speaker 1: bit of a bait and switch. But they did say 473 00:28:49,240 --> 00:28:51,720 Speaker 1: it look human made. They didn't really go back to 474 00:28:51,720 --> 00:28:54,320 Speaker 1: to justify that or quantify that at all. Sort Of 475 00:28:54,360 --> 00:28:57,880 Speaker 1: an odd thing to say without any context, but definitely 476 00:28:57,880 --> 00:29:01,120 Speaker 1: got my imagination running wild. We're not talking about somebody 477 00:29:01,200 --> 00:29:09,680 Speaker 1: diving down there, and yeah, why unless you're James Cameron 478 00:29:10,160 --> 00:29:13,160 Speaker 1: or you know, like the government, you can't get down 479 00:29:13,160 --> 00:29:17,040 Speaker 1: here because no, there's no casual exploring the c floor 480 00:29:17,560 --> 00:29:21,280 Speaker 1: um of the mid Atlantic Ridge. I will warn folks, 481 00:29:21,320 --> 00:29:24,080 Speaker 1: mainly because it gives me an excuse to use this term, 482 00:29:24,240 --> 00:29:27,680 Speaker 1: this word I never get to use. If you experience 483 00:29:28,320 --> 00:29:33,840 Speaker 1: uh tripophobia or a fear or discuss the patterns of holes. 484 00:29:33,880 --> 00:29:36,640 Speaker 1: This may not be the article for you. God, I 485 00:29:36,720 --> 00:29:39,280 Speaker 1: love the English language. There's so many words that will 486 00:29:39,280 --> 00:29:43,320 Speaker 1: just will never use, like, yeah, these holes are all 487 00:29:43,880 --> 00:29:46,880 Speaker 1: totally thankfully, these holes as all in a line and 488 00:29:46,880 --> 00:29:49,160 Speaker 1: they're not like clusters. Those are the ones that really 489 00:29:49,200 --> 00:29:52,760 Speaker 1: set off people with that with that condition, there's actually 490 00:29:52,800 --> 00:29:56,960 Speaker 1: an episode or a season of American Horror Story, which 491 00:29:57,040 --> 00:30:00,040 Speaker 1: is not always great, but there's a season where it 492 00:30:00,640 --> 00:30:05,520 Speaker 1: revolves around characters deepest fears and triple phobia is one, 493 00:30:05,640 --> 00:30:09,440 Speaker 1: and they you know, in typical Brad Felt, Chuck and 494 00:30:09,480 --> 00:30:11,520 Speaker 1: whatever that other guy's name is fashion, they go way 495 00:30:11,560 --> 00:30:14,440 Speaker 1: over the top and depicting what the people are seeing 496 00:30:14,480 --> 00:30:17,720 Speaker 1: who are freaked out by holes pretty gross. What if 497 00:30:17,760 --> 00:30:22,280 Speaker 1: you got a little philosophobia, which let's go through more phobias, 498 00:30:22,320 --> 00:30:29,560 Speaker 1: let's do some phobia born I think lorophobias. But if 499 00:30:29,600 --> 00:30:32,800 Speaker 1: you don't have that condition, if you're a deep sea 500 00:30:32,840 --> 00:30:36,000 Speaker 1: investigator and you see this trail of holes and all 501 00:30:36,000 --> 00:30:38,360 Speaker 1: of a sudden you see a clown, you get chlorophobia 502 00:30:38,400 --> 00:30:42,000 Speaker 1: pretty quick just standing there, you know, arms akimbo, and 503 00:30:42,080 --> 00:30:45,560 Speaker 1: it waves to you. I think, right, deep seed clowns. 504 00:30:46,680 --> 00:30:51,680 Speaker 1: Why is that not yet? God deep sea clowns with wings? 505 00:30:52,160 --> 00:30:58,440 Speaker 1: That would freak me out. I have or phobia. I 506 00:30:58,440 --> 00:31:00,600 Speaker 1: don't know why. That feels like a lounge in number 507 00:31:00,640 --> 00:31:02,960 Speaker 1: to me. And maybe in a David Lynch. And also 508 00:31:03,240 --> 00:31:07,320 Speaker 1: that super producer Alexis code enamed Doc Holiday. Jackson pointed 509 00:31:07,360 --> 00:31:09,680 Speaker 1: out that Holes on the Ocean Floor would be a 510 00:31:09,680 --> 00:31:12,000 Speaker 1: really good name for like an emo or post rock 511 00:31:12,040 --> 00:31:16,280 Speaker 1: type band. So yeah, don't steal it. Yeah, I think 512 00:31:16,280 --> 00:31:19,200 Speaker 1: post rock is great. Don't don't steal it. Conspiracy realist. 513 00:31:19,400 --> 00:31:22,520 Speaker 1: Let let Alexis have this named doc is gonna invite 514 00:31:22,560 --> 00:31:25,520 Speaker 1: us to the show next week, she said, So yeah, 515 00:31:25,880 --> 00:31:31,520 Speaker 1: I'm I'm hoping for some very plodding, slow instrumental jams. Okay, 516 00:31:31,840 --> 00:31:34,960 Speaker 1: you just have to wait and see. Yes, Doc, I'm 517 00:31:34,960 --> 00:31:38,880 Speaker 1: gonna buy some shoes specifically for gazing at your show. 518 00:31:40,120 --> 00:31:42,040 Speaker 1: Let's not get it twisted. That would be more of 519 00:31:42,040 --> 00:31:45,280 Speaker 1: a post rock shoegaze kind of situation, and they're not 520 00:31:45,360 --> 00:31:47,560 Speaker 1: mutually exclusive. But they also don't always have to belong 521 00:31:47,600 --> 00:31:50,160 Speaker 1: to you. It's a big diagram that's getting the specific 522 00:31:50,240 --> 00:31:52,720 Speaker 1: shoes you know they need the top of the shoe 523 00:31:52,760 --> 00:31:55,560 Speaker 1: needs to be essing like clown shoes. What about clown shoes? 524 00:31:58,080 --> 00:32:00,920 Speaker 1: My feet are now you're to U. Yeah, my feet 525 00:32:00,960 --> 00:32:03,800 Speaker 1: are basically hands, So I don't want to add more. 526 00:32:03,840 --> 00:32:06,000 Speaker 1: It's more shoe to look at, you know. That's what 527 00:32:06,040 --> 00:32:07,840 Speaker 1: I That's what I say, more shoe to look at. 528 00:32:07,880 --> 00:32:10,160 Speaker 1: But then also you have to stand really far away 529 00:32:10,200 --> 00:32:13,000 Speaker 1: from your massive pedal board, you know, and i'd be 530 00:32:13,000 --> 00:32:15,120 Speaker 1: really hard to hit those buttons with the with the 531 00:32:15,160 --> 00:32:17,840 Speaker 1: tip of that giant shoe, whichould get pretty broad at 532 00:32:17,840 --> 00:32:19,880 Speaker 1: the top. You'd be like slamming all three pedals, which 533 00:32:19,880 --> 00:32:22,400 Speaker 1: actually might be desirable. Uh, you know, if you're one 534 00:32:22,440 --> 00:32:25,680 Speaker 1: of those uh, you know, pedal heads. Anyway, this is 535 00:32:25,720 --> 00:32:28,080 Speaker 1: an interesting one. UM. Thank you all for chatting it 536 00:32:28,160 --> 00:32:32,200 Speaker 1: through and uh and indulging um the speculative kind of 537 00:32:32,240 --> 00:32:34,800 Speaker 1: bent in this story. UM. But I look forward to 538 00:32:34,800 --> 00:32:37,640 Speaker 1: hearing more, um, you know, because these scientists still seem 539 00:32:37,720 --> 00:32:40,040 Speaker 1: to be kind of baffled, and hopefully they'll come to 540 00:32:40,080 --> 00:32:42,760 Speaker 1: some conclusion and share with the class. But in the meantime, 541 00:32:42,800 --> 00:32:44,760 Speaker 1: let's take a quick break here, a word from our sponsor, 542 00:32:44,760 --> 00:32:46,840 Speaker 1: and be back with one more piece of strange news. 543 00:32:53,800 --> 00:32:59,440 Speaker 1: And we've returned, So best of luck to the good 544 00:32:59,640 --> 00:33:04,360 Speaker 1: people at Canes, and best of luck to the scientists 545 00:33:04,520 --> 00:33:09,000 Speaker 1: doing tremendous, mysterious work there at the ocean floor. Our 546 00:33:09,200 --> 00:33:13,920 Speaker 1: last story is a prediction that has come to pass. 547 00:33:14,360 --> 00:33:18,280 Speaker 1: For many years when we talk about big data, big data, 548 00:33:18,640 --> 00:33:23,040 Speaker 1: whatever your preference may be, we've often pointed out the 549 00:33:23,080 --> 00:33:29,720 Speaker 1: imperfections of applying technology to predicting the actions of human beats. 550 00:33:30,320 --> 00:33:35,800 Speaker 1: We've also, not for nothing, sort of internalized the cautionary 551 00:33:35,880 --> 00:33:40,640 Speaker 1: tale of minority reports, the idea that predicting crime, even 552 00:33:40,640 --> 00:33:44,239 Speaker 1: when it seems to be accurate, can be dangerous and 553 00:33:44,280 --> 00:33:49,120 Speaker 1: can have unintended consequences. Well, as we record today and 554 00:33:49,160 --> 00:33:53,280 Speaker 1: as you hear this on Monday, it happened, we're at 555 00:33:53,280 --> 00:33:57,040 Speaker 1: the dawn of pre crime right now. There's a great 556 00:33:57,040 --> 00:34:00,000 Speaker 1: study came about University of Chicago, which is frankly terrified. 557 00:34:01,120 --> 00:34:06,360 Speaker 1: These very clever researchers have created an algorithm that, according 558 00:34:06,440 --> 00:34:14,600 Speaker 1: to them, can predict crime a week in advance with accuracy. 559 00:34:14,840 --> 00:34:18,680 Speaker 1: That's the headline before we go on, Do you guys 560 00:34:18,680 --> 00:34:21,799 Speaker 1: believe it? What data are they using? What are they 561 00:34:21,840 --> 00:34:27,080 Speaker 1: feeding into the thing? Is it? If it's social media? Well, like, really, 562 00:34:27,120 --> 00:34:30,680 Speaker 1: if it's social media posts that are private somehow, or 563 00:34:30,760 --> 00:34:33,560 Speaker 1: really I don't know, maybe their public posts, but they're 564 00:34:33,560 --> 00:34:38,680 Speaker 1: just monitoring specific things. I think you could probably predict 565 00:34:39,160 --> 00:34:43,799 Speaker 1: some mass shooting events if you had the like, the 566 00:34:43,840 --> 00:34:47,680 Speaker 1: most granular data on everybody. But you know, I mean 567 00:34:47,719 --> 00:34:50,640 Speaker 1: on social media, everyone is just portraying the rosiest version 568 00:34:50,680 --> 00:34:53,280 Speaker 1: of their psychopathy, you know. I mean they want everyone 569 00:34:53,320 --> 00:34:56,759 Speaker 1: to think they're the best, sweetest, happiest psycho in the world. 570 00:34:56,760 --> 00:34:58,600 Speaker 1: I mean, nothing on social media has actually should be 571 00:34:58,600 --> 00:35:02,440 Speaker 1: taken a phase value, right, I'm kidding. Obviously people use 572 00:35:02,480 --> 00:35:04,759 Speaker 1: it for all kinds of reasons, but it makes me 573 00:35:04,800 --> 00:35:08,439 Speaker 1: think of there's an Atlanta rapper now I'm forgetting his name, 574 00:35:08,760 --> 00:35:12,680 Speaker 1: but he got wrapped up in some gun charges. I 575 00:35:12,719 --> 00:35:15,040 Speaker 1: don't remember. I'll figure it out a second, But the 576 00:35:15,040 --> 00:35:17,359 Speaker 1: point is, uh, and this might ring a bell for y'all. 577 00:35:17,480 --> 00:35:22,319 Speaker 1: They used his lyrics uh to as evidence against him, 578 00:35:22,640 --> 00:35:25,319 Speaker 1: which obviously isn't it the same as pre crime? But 579 00:35:25,360 --> 00:35:27,600 Speaker 1: I wonder if you could use that, you know, for 580 00:35:27,719 --> 00:35:30,160 Speaker 1: as a form of pre crime. You know, someone is 581 00:35:30,239 --> 00:35:32,920 Speaker 1: rapping about something that maybe as a fantasy, you know, 582 00:35:33,160 --> 00:35:36,520 Speaker 1: or it indicates where their minds, their headspaces. Maybe that's 583 00:35:36,560 --> 00:35:44,120 Speaker 1: something you could use someone's art against them. Well, this 584 00:35:44,280 --> 00:35:48,480 Speaker 1: happened to other performers, to other musicians. There. There's also 585 00:35:48,880 --> 00:35:52,880 Speaker 1: a fantastic key and Peel sketch about this. I will 586 00:35:52,920 --> 00:35:55,440 Speaker 1: send it to you all if you haven't seen remember this. 587 00:35:55,960 --> 00:36:03,360 Speaker 1: I like it's like the five and just yes exactly, 588 00:36:04,640 --> 00:36:07,719 Speaker 1: but it shows that beautifully right. But there's a good 589 00:36:07,800 --> 00:36:10,680 Speaker 1: question where they're getting Where are they getting the information 590 00:36:10,840 --> 00:36:15,840 Speaker 1: that they are feeding the algorithm. They are using publicly 591 00:36:16,000 --> 00:36:20,799 Speaker 1: available data, So this would be reports of crimes. Uh, 592 00:36:20,880 --> 00:36:27,240 Speaker 1: this would be any imaginable demographic metric of a community 593 00:36:27,400 --> 00:36:31,439 Speaker 1: and what they what, what kind of stuff we're talking about. 594 00:36:31,480 --> 00:36:33,920 Speaker 1: Just to give you a brief laundry list. This is 595 00:36:34,719 --> 00:36:38,120 Speaker 1: pulled from the history of the city of Chicago, and 596 00:36:38,200 --> 00:36:42,200 Speaker 1: they had too broad categories that they were testing the 597 00:36:42,280 --> 00:36:45,279 Speaker 1: tool against. Once they fed all this junk into it, 598 00:36:45,320 --> 00:36:48,080 Speaker 1: they wanted to see what they could predict. Their two 599 00:36:48,120 --> 00:36:53,120 Speaker 1: buckets are one violent crimes, homicides, assaults, batteries, you know, 600 00:36:53,320 --> 00:36:58,000 Speaker 1: all of those. And then property crimes burglary, theft, grand theft, auto, 601 00:36:58,120 --> 00:37:03,040 Speaker 1: all the hits and these these particular ones were used 602 00:37:03,400 --> 00:37:07,960 Speaker 1: because they were most likely to be reported to police 603 00:37:08,680 --> 00:37:11,719 Speaker 1: in those areas because as anyone who's lived, you know, 604 00:37:12,040 --> 00:37:17,520 Speaker 1: on the edges of different things, knows, many crimes never 605 00:37:17,600 --> 00:37:20,239 Speaker 1: have the police involved, right, and it's just sort of 606 00:37:20,400 --> 00:37:24,239 Speaker 1: understood between the victim and the perpetrator, even when they 607 00:37:24,239 --> 00:37:29,320 Speaker 1: switch sides and revenge schemes, you know. But they also said, Okay, 608 00:37:29,360 --> 00:37:32,760 Speaker 1: these are going to be reported in urban areas where 609 00:37:32,800 --> 00:37:37,480 Speaker 1: there is historical distrust and lack of cooperation with Johnny law. 610 00:37:37,680 --> 00:37:40,160 Speaker 1: They still they thought, Okay, even if you don't like 611 00:37:40,239 --> 00:37:43,000 Speaker 1: the cops, you're still gonna say, hey, someone stole my car. 612 00:37:43,760 --> 00:37:46,640 Speaker 1: So we know that because of that, there's a high 613 00:37:46,680 --> 00:37:50,000 Speaker 1: likelihood that the number of crimes that have occurred in 614 00:37:50,000 --> 00:37:52,680 Speaker 1: that regard are is very close to the number of 615 00:37:52,719 --> 00:37:57,120 Speaker 1: crimes that are reported, meaning the data is more solid, right, 616 00:37:57,200 --> 00:38:01,319 Speaker 1: especially more solid than something like drug times or some 617 00:38:01,440 --> 00:38:04,680 Speaker 1: misdemeanor in fractions, because those are prone to what's known 618 00:38:04,719 --> 00:38:08,799 Speaker 1: as enforcement bias, which is exactly what it sounds like. 619 00:38:08,840 --> 00:38:12,480 Speaker 1: We don't have to overthink it. So this the PhD 620 00:38:13,200 --> 00:38:17,560 Speaker 1: senior author of the study, uh Ishano Chado Padhai uh 621 00:38:17,600 --> 00:38:20,759 Speaker 1: in part of my mispronunciation of getting this wrong, has 622 00:38:20,800 --> 00:38:23,400 Speaker 1: said that this tool is different because it takes in 623 00:38:23,440 --> 00:38:28,880 Speaker 1: the complex social environment of cities, and it also considers 624 00:38:28,960 --> 00:38:34,560 Speaker 1: the relationship between crime and the effects of police enforcement. 625 00:38:35,280 --> 00:38:38,440 Speaker 1: It's pretty smart. It divides, so picture yourself playing like 626 00:38:39,120 --> 00:38:45,160 Speaker 1: uh SimCity or Civilization or something. It divides the city 627 00:38:45,239 --> 00:38:48,920 Speaker 1: of Chicago into tiles. They're pretty small, about a thousand 628 00:38:48,920 --> 00:38:53,800 Speaker 1: feet across, and it predicts crime within those areas instead 629 00:38:53,800 --> 00:38:58,960 Speaker 1: of relying on traditional boundaries of like neighborhoods or political districts, 630 00:38:59,000 --> 00:39:02,319 Speaker 1: because those are all of subject to bias. It did 631 00:39:02,360 --> 00:39:08,560 Speaker 1: this in Atlanta, in Austin, Detroit, Los Angeles, Philadelphia, Portland, 632 00:39:08,600 --> 00:39:12,040 Speaker 1: and San Francisco in addition to Chicago. So they tested 633 00:39:12,040 --> 00:39:15,760 Speaker 1: this in a couple of different places and what they found, 634 00:39:16,320 --> 00:39:19,160 Speaker 1: and what seems to be true is that they yes, 635 00:39:19,320 --> 00:39:23,600 Speaker 1: they can predict crime about a week in advance, so 636 00:39:24,160 --> 00:39:28,200 Speaker 1: that like about a week in advance with accuracy, they 637 00:39:28,200 --> 00:39:31,280 Speaker 1: can take what happened in the past, and then through 638 00:39:31,360 --> 00:39:34,839 Speaker 1: this algorithm they can they can run it through this 639 00:39:34,880 --> 00:39:38,000 Speaker 1: thing with all these other intervening variables and then it 640 00:39:38,040 --> 00:39:41,359 Speaker 1: will tell them like, hey, the specifics are a little 641 00:39:41,360 --> 00:39:43,320 Speaker 1: tough for me. But then we'll say something along the 642 00:39:43,400 --> 00:39:46,520 Speaker 1: lines of, hey, they're gonna be x number of cars 643 00:39:46,560 --> 00:39:49,920 Speaker 1: stolen next week or something like that. I don't know 644 00:39:49,960 --> 00:39:52,879 Speaker 1: how predictive they can be or how sophisticated. I don't 645 00:39:52,880 --> 00:39:55,880 Speaker 1: know if they could say in two thousand four green 646 00:39:56,000 --> 00:39:59,560 Speaker 1: Honda Odyssey right with a with a crappy transmission or 647 00:39:59,600 --> 00:40:02,799 Speaker 1: anything like that. Do they have to have info on 648 00:40:02,880 --> 00:40:07,480 Speaker 1: the people, like specific to the people, like psychological profiles 649 00:40:07,600 --> 00:40:11,440 Speaker 1: or any information that that that's specific to individuals or 650 00:40:11,480 --> 00:40:14,040 Speaker 1: is it more big picture and like you know, like 651 00:40:14,160 --> 00:40:16,120 Speaker 1: that's a really good point bend civilization or like some 652 00:40:16,280 --> 00:40:18,799 Speaker 1: city like a top down kind of view where the 653 00:40:18,880 --> 00:40:22,320 Speaker 1: individuals are less important than just the way the complex 654 00:40:22,360 --> 00:40:25,759 Speaker 1: system of it all. Kind of yeah, I don't I 655 00:40:25,760 --> 00:40:28,000 Speaker 1: don't think it has a right it's if you're we're 656 00:40:28,080 --> 00:40:31,480 Speaker 1: just talking about the overlay of the city, right, look 657 00:40:31,480 --> 00:40:33,800 Speaker 1: at it like a grid and like here's when things 658 00:40:33,840 --> 00:40:37,680 Speaker 1: happen and exactly where within these little specific parts of 659 00:40:37,719 --> 00:40:40,680 Speaker 1: the city. And I don't know that it's so weird 660 00:40:40,680 --> 00:40:42,960 Speaker 1: to ben. I don't see how you can make it actionable, 661 00:40:43,120 --> 00:40:46,000 Speaker 1: like who can Like who cares if we know that 662 00:40:46,400 --> 00:40:49,880 Speaker 1: five cars are gonna get stolen in the next few days? 663 00:40:49,880 --> 00:40:55,400 Speaker 1: Are we talk about this on The Boys? The Boys 664 00:40:56,200 --> 00:40:58,640 Speaker 1: in that show that like The Seven, they have these 665 00:40:58,680 --> 00:41:01,840 Speaker 1: you know, this department that looking into crime and someone 666 00:41:01,880 --> 00:41:04,040 Speaker 1: makes a comment, they can usually predict a crime like 667 00:41:04,280 --> 00:41:06,840 Speaker 1: a week or so before it happens, So it's you know, 668 00:41:06,920 --> 00:41:09,320 Speaker 1: similar kind of data that they're crunching. It also reminds 669 00:41:09,360 --> 00:41:12,799 Speaker 1: me of this anime called psycho pass Um that is 670 00:41:13,239 --> 00:41:16,040 Speaker 1: more about the individuals where they can actually they're taking 671 00:41:16,400 --> 00:41:20,040 Speaker 1: measurements of the citizens of Japan's you know, biometrics of 672 00:41:20,040 --> 00:41:24,200 Speaker 1: their brains and generating these they call them sematic scans 673 00:41:24,640 --> 00:41:29,040 Speaker 1: that allow them to with a pretty high degree of certainty, 674 00:41:29,080 --> 00:41:33,120 Speaker 1: predict who will be potential criminals, you know, criminal criminality. 675 00:41:33,160 --> 00:41:35,600 Speaker 1: Potential is the term they use, and they get color 676 00:41:35,680 --> 00:41:39,120 Speaker 1: coded on a map. But the dangerous is that this 677 00:41:39,160 --> 00:41:42,000 Speaker 1: is science fic. Well, there's there's something very dangerous with 678 00:41:42,040 --> 00:41:46,480 Speaker 1: all these things and their historical precedents. Right, law enforcement 679 00:41:46,880 --> 00:41:52,120 Speaker 1: throughout all civilizations, various versions of law enforcement were attempting 680 00:41:52,200 --> 00:41:57,239 Speaker 1: to predict crime, and their rubric for this although it 681 00:41:57,400 --> 00:42:00,680 Speaker 1: varied widely, it was hardly scientific and most times it 682 00:42:00,760 --> 00:42:04,480 Speaker 1: was really rooted in xenophobia, in racism, in some other 683 00:42:04,600 --> 00:42:09,839 Speaker 1: form of discrimination. Right. Uh So, the question is how 684 00:42:09,960 --> 00:42:14,000 Speaker 1: much of that has descended to this program. And if 685 00:42:14,000 --> 00:42:17,200 Speaker 1: you look at the research just got published in Nature 686 00:42:17,360 --> 00:42:22,080 Speaker 1: Human Behavior, and what they found was a twist to 687 00:42:22,200 --> 00:42:24,440 Speaker 1: the plot, but Shamalan twists, you could say, and I 688 00:42:24,440 --> 00:42:28,919 Speaker 1: don't think it's that bad. But they found that this 689 00:42:28,960 --> 00:42:32,520 Speaker 1: thing could accurately predict crime, especially in Chicago. But it 690 00:42:32,640 --> 00:42:38,359 Speaker 1: also laid bare some heavy biases in police responses. They 691 00:42:38,400 --> 00:42:43,360 Speaker 1: found stuff like if you stress the system, that's what 692 00:42:43,400 --> 00:42:47,239 Speaker 1: the senior author calls it. It requires more resources to 693 00:42:47,360 --> 00:42:51,240 Speaker 1: arrest more people in response to crime in a wealthy area, 694 00:42:51,480 --> 00:42:56,760 Speaker 1: and that almost always draws police resources away from areas 695 00:42:56,800 --> 00:43:01,759 Speaker 1: with lower socioeconomic status. So police are paying more attention 696 00:43:01,840 --> 00:43:06,160 Speaker 1: to the quote unquote good parts of town. They're also 697 00:43:06,239 --> 00:43:10,719 Speaker 1: working kind of in response to earlier experiments from Chicago 698 00:43:10,840 --> 00:43:14,960 Speaker 1: p D or associated with Chicago Police Department. UH. A 699 00:43:15,000 --> 00:43:19,440 Speaker 1: while back, the Chicago p D tried another algorithm that 700 00:43:19,560 --> 00:43:22,960 Speaker 1: created a list of people deemed most at risk being 701 00:43:23,000 --> 00:43:26,440 Speaker 1: involved in a shooting, and it turned out that over 702 00:43:26,600 --> 00:43:31,120 Speaker 1: half of the black men in Chicago between twenty and 703 00:43:32,400 --> 00:43:37,319 Speaker 1: appeared on it. So this is why the people who 704 00:43:37,320 --> 00:43:41,800 Speaker 1: made this new algorithm, this new AI algorithm, are really 705 00:43:41,840 --> 00:43:45,200 Speaker 1: concerned that the data that they're using in their model 706 00:43:45,440 --> 00:43:48,439 Speaker 1: may be biased, which therefore means to your point, Matt, 707 00:43:48,520 --> 00:43:53,520 Speaker 1: that the conclusions it creates will be biased. And they 708 00:43:53,560 --> 00:43:57,160 Speaker 1: also said, just to answer another couple quick questions about 709 00:43:57,160 --> 00:44:00,279 Speaker 1: how far into the dystopio we are, they also said 710 00:44:00,360 --> 00:44:06,920 Speaker 1: that the algorithm, the AI does not identify specific subjects. Right, 711 00:44:07,040 --> 00:44:11,600 Speaker 1: So it's not it's not saying Paul mission controlled decand 712 00:44:12,080 --> 00:44:16,640 Speaker 1: is out on another loose diamond heist over at Zails 713 00:44:16,960 --> 00:44:19,160 Speaker 1: or whatever is in your neck of the woods. Folks 714 00:44:19,520 --> 00:44:24,200 Speaker 1: there used to be Shane Company here. The commercials were hilarious. Instead, 715 00:44:24,360 --> 00:44:30,160 Speaker 1: instead of identifying specific subjects, identifies potential sites of crime. 716 00:44:30,800 --> 00:44:33,600 Speaker 1: And they've released the data and the algorithm. They didn't 717 00:44:33,640 --> 00:44:36,879 Speaker 1: keep it proprietary. They released in the public sphere because 718 00:44:36,920 --> 00:44:41,160 Speaker 1: they want other researchers to check out its reasoning, its conclusions. 719 00:44:41,200 --> 00:44:44,480 Speaker 1: They want to test it. And then one thing that 720 00:44:44,520 --> 00:44:48,200 Speaker 1: really caught me is I was comparing this to previous 721 00:44:49,080 --> 00:44:53,000 Speaker 1: previous kind of DARPA work or DARPEST sponsored excuse me 722 00:44:53,200 --> 00:44:58,520 Speaker 1: work with creating virtual models things like Afghanistan or other 723 00:44:58,560 --> 00:45:01,960 Speaker 1: communities or other areas of the world, you could say, 724 00:45:02,160 --> 00:45:07,440 Speaker 1: and their idea there which was pretty like frighteningly successful. 725 00:45:08,040 --> 00:45:13,000 Speaker 1: Their idea was that if you can create a simulated 726 00:45:13,120 --> 00:45:17,400 Speaker 1: version of a place with enough sophistication, with enough fidelity, 727 00:45:17,480 --> 00:45:21,280 Speaker 1: if you have enough information about it, then you can 728 00:45:21,320 --> 00:45:27,600 Speaker 1: plug in changes right in your make it up seas universe, 729 00:45:28,080 --> 00:45:30,520 Speaker 1: and then what happens as a result of that in 730 00:45:30,520 --> 00:45:33,200 Speaker 1: this virtual environment will be very close to what happens 731 00:45:34,000 --> 00:45:36,680 Speaker 1: in the real world with the same set of actions. 732 00:45:37,640 --> 00:45:41,480 Speaker 1: So that's what they have done. They said, We've made 733 00:45:41,520 --> 00:45:45,479 Speaker 1: this thing that is close enough to the actual city 734 00:45:45,520 --> 00:45:48,680 Speaker 1: of Chicago or those seven other cities for our purposes 735 00:45:49,080 --> 00:45:53,239 Speaker 1: that when we feed it data from the past, it 736 00:45:53,360 --> 00:45:56,160 Speaker 1: tells us what will happen in the future, because like 737 00:45:56,200 --> 00:45:59,240 Speaker 1: a love Craft in Elder God, it sees beyond time. 738 00:45:59,719 --> 00:46:02,040 Speaker 1: They and say that last part I added that in 739 00:46:02,640 --> 00:46:06,600 Speaker 1: for a little bit of editorial, you know style. But 740 00:46:07,200 --> 00:46:11,240 Speaker 1: he also said it's not magical, it has limitations quote, 741 00:46:11,480 --> 00:46:15,919 Speaker 1: but we validated it and it works very very well. 742 00:46:17,520 --> 00:46:21,080 Speaker 1: So if that's what's happening, if we have a kind 743 00:46:21,120 --> 00:46:23,680 Speaker 1: of a thing that's kind of like a a precog 744 00:46:24,120 --> 00:46:29,840 Speaker 1: precognitive bloodhounds for geography of crime, how far along a 745 00:46:29,920 --> 00:46:32,719 Speaker 1: slippery slope are we Do you guys think this would 746 00:46:32,719 --> 00:46:37,480 Speaker 1: get deployed in uh in other cities or get used 747 00:46:37,520 --> 00:46:41,440 Speaker 1: past the experimental proof of concept phase. I mean, I 748 00:46:41,480 --> 00:46:44,560 Speaker 1: think people in the real estate industry would be very 749 00:46:44,600 --> 00:46:49,400 Speaker 1: interested to have this information. Yeah, oh yeah, I can 750 00:46:49,480 --> 00:46:52,839 Speaker 1: imagine that when it comes to how like how much 751 00:46:52,840 --> 00:46:55,720 Speaker 1: they could charge or how little they should charge for homes. 752 00:46:55,760 --> 00:46:58,240 Speaker 1: Everything part of it for the buyers and the sellers, 753 00:46:58,680 --> 00:47:02,759 Speaker 1: you know, if you know, like because there are crime ratings, right, 754 00:47:03,000 --> 00:47:06,520 Speaker 1: safety ratings, who might be called by zip code? Um, 755 00:47:06,560 --> 00:47:09,560 Speaker 1: maybe that's maybe that's not the best example. I don't know. 756 00:47:09,600 --> 00:47:13,680 Speaker 1: It's just like, if you have that statistic, why what's 757 00:47:13,760 --> 00:47:16,520 Speaker 1: the stop please from just saying, okay, we're going to 758 00:47:16,719 --> 00:47:21,359 Speaker 1: pretty much garrison people in these things for a week. Yeah, 759 00:47:21,480 --> 00:47:23,799 Speaker 1: I don't know if that's smart. Yeah, now I get it, 760 00:47:24,680 --> 00:47:26,839 Speaker 1: or or like set up points of entry, you know, 761 00:47:26,960 --> 00:47:29,640 Speaker 1: like roadblocks kind of like you know, I mean, that's 762 00:47:30,400 --> 00:47:32,280 Speaker 1: it sort of takes the sport out of it, doesn't 763 00:47:32,760 --> 00:47:35,239 Speaker 1: I don't mean to sound flipping like that, just you 764 00:47:35,239 --> 00:47:38,160 Speaker 1: know what I mean. Anyway, I'm just gonna go back 765 00:47:38,160 --> 00:47:40,839 Speaker 1: to what I my thought in the beginning of this then, 766 00:47:41,120 --> 00:47:43,680 Speaker 1: and that is, if you take this the models they're 767 00:47:43,680 --> 00:47:47,080 Speaker 1: creating in the statistical analysis that they're doing, and you 768 00:47:47,160 --> 00:47:50,200 Speaker 1: combine that with something like sesame credit or something that 769 00:47:50,320 --> 00:47:55,160 Speaker 1: is very closely monitoring social media and purchases and interaction 770 00:47:55,719 --> 00:47:59,000 Speaker 1: like in interpersonal interaction on that way and generating that 771 00:47:59,360 --> 00:48:02,839 Speaker 1: data set. If you combine those two things, that's when 772 00:48:02,920 --> 00:48:08,319 Speaker 1: you get actual pre cog pre crime insanity. Right, You 773 00:48:08,440 --> 00:48:11,920 Speaker 1: just have to you know, I was thinking about the 774 00:48:11,920 --> 00:48:16,120 Speaker 1: same thing, man, because if you look at your phone, 775 00:48:16,160 --> 00:48:18,080 Speaker 1: a lot of people are listening to this on their phone, right, 776 00:48:18,120 --> 00:48:21,080 Speaker 1: So you look at your phone right now, and imagine 777 00:48:22,040 --> 00:48:25,520 Speaker 1: it's not just as a single invention, but imagine it 778 00:48:25,560 --> 00:48:28,600 Speaker 1: as a multitude of separative inventions that have been combined 779 00:48:28,800 --> 00:48:33,240 Speaker 1: to create something amazing. That's how these sorts of innovations occur. 780 00:48:33,400 --> 00:48:36,400 Speaker 1: That's what pre cog and pre crime stuff will be. 781 00:48:36,600 --> 00:48:38,879 Speaker 1: It will be like your phone, It'll look like one 782 00:48:38,960 --> 00:48:41,359 Speaker 1: thing in the end, but it's really going to be 783 00:48:41,960 --> 00:48:47,840 Speaker 1: uh mass and aggregation of innovations like this, is this 784 00:48:47,920 --> 00:48:53,600 Speaker 1: good will this maybe save lives? Of course that's the hope, um, 785 00:48:53,640 --> 00:48:57,600 Speaker 1: But that part is up to society at large. And 786 00:48:57,600 --> 00:49:01,319 Speaker 1: I want to shout out the science tis involved who 787 00:49:01,360 --> 00:49:04,680 Speaker 1: did excellent work on this study and on this algorithm, 788 00:49:05,160 --> 00:49:09,120 Speaker 1: because they're incredibly conscientious about this is very much a 789 00:49:09,160 --> 00:49:12,120 Speaker 1: case of them having done everything they can do, you 790 00:49:12,160 --> 00:49:14,520 Speaker 1: know what I mean. They can't start a vigilante squad 791 00:49:15,080 --> 00:49:18,560 Speaker 1: and go try to save a Honda Odyssey for some reason. Also, 792 00:49:18,680 --> 00:49:27,120 Speaker 1: why would you? But yeah, but but but I do 793 00:49:27,200 --> 00:49:29,759 Speaker 1: think it's worth everybody's time if you're interested in this. 794 00:49:29,960 --> 00:49:31,359 Speaker 1: I know we have a lot of thoughts on this, 795 00:49:31,920 --> 00:49:36,160 Speaker 1: folks um, fellow conspiracy realists. I would recommend checking out 796 00:49:36,160 --> 00:49:39,360 Speaker 1: the study firsthand and then reading what people have written 797 00:49:39,360 --> 00:49:42,360 Speaker 1: about it. The title is event level prediction of urban 798 00:49:42,400 --> 00:49:47,319 Speaker 1: crime reveals signature of enforcement bias in US cities, So 799 00:49:47,400 --> 00:49:49,719 Speaker 1: they put it in the title. It came out at 800 00:49:49,719 --> 00:49:54,080 Speaker 1: the very end of June Nature Human Behavior. That's the journal. Uh, 801 00:49:54,360 --> 00:49:56,720 Speaker 1: do check it out. Would love to hear your thoughts. 802 00:49:56,960 --> 00:49:59,920 Speaker 1: I would love to hear your take on Kanes. Would 803 00:50:00,040 --> 00:50:03,720 Speaker 1: love to hear what you think about the lottery. And again, 804 00:50:04,280 --> 00:50:07,880 Speaker 1: you know, we want your opinions on what lurks at 805 00:50:07,880 --> 00:50:12,600 Speaker 1: the ocean's depths. We want you, specifically, you conspiracy realist, no, 806 00:50:12,760 --> 00:50:15,560 Speaker 1: not that person next to you. You to write in 807 00:50:15,760 --> 00:50:18,000 Speaker 1: be part of a show. Join us for our listener 808 00:50:18,040 --> 00:50:20,200 Speaker 1: mail segment. As a matter of fact, the only person 809 00:50:20,360 --> 00:50:23,719 Speaker 1: we don't want to hear from if you recently won 810 00:50:23,880 --> 00:50:27,560 Speaker 1: a lottery, don't tell us, keep it secret, keep it 811 00:50:27,640 --> 00:50:31,000 Speaker 1: safe otherwise for everybody else. We try to be easy 812 00:50:31,040 --> 00:50:33,680 Speaker 1: to find online. Correct. You can find us all over 813 00:50:33,719 --> 00:50:37,719 Speaker 1: the internet. We are conspiracy stuff on Facebook, Twitter, and YouTube, 814 00:50:37,880 --> 00:50:41,160 Speaker 1: Conspiracy Stuff show on Instagram. Hey, and if you did 815 00:50:41,200 --> 00:50:44,520 Speaker 1: win the lottery, why not chip in by about I 816 00:50:44,520 --> 00:50:46,960 Speaker 1: don't know, four hundred thousand copies of our new books 817 00:50:47,000 --> 00:50:49,840 Speaker 1: stuff they don't want you to know. We can distribute 818 00:50:49,880 --> 00:50:55,839 Speaker 1: them across the planet together, diary of hearing about it. 819 00:50:55,840 --> 00:50:58,120 Speaker 1: It's like it's a it's pledge drive rules you know 820 00:50:59,520 --> 00:51:01,400 Speaker 1: were and are. Just get the book and then we 821 00:51:01,560 --> 00:51:07,280 Speaker 1: could stop talking about That's it. I'm sorry. You can 822 00:51:07,320 --> 00:51:09,880 Speaker 1: also reach us my phone. Our number is one h 823 00:51:10,000 --> 00:51:13,400 Speaker 1: three three st d w y t K. Tell them 824 00:51:13,400 --> 00:51:16,280 Speaker 1: about it, Ben, that's right. 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