1 00:00:00,480 --> 00:00:03,239 Speaker 1: You know what I've been thinking about lately. Oh my goodness. 2 00:00:03,720 --> 00:00:07,640 Speaker 1: Whenever he started sounds like that, I know some crazy 3 00:00:07,720 --> 00:00:11,440 Speaker 1: is about to come next. Yes, because that's usually how 4 00:00:11,520 --> 00:00:15,280 Speaker 1: it goes. History has told you what rabbit hole are 5 00:00:15,320 --> 00:00:18,000 Speaker 1: you going down? Now? Well, first of all, too many 6 00:00:18,079 --> 00:00:21,279 Speaker 1: rabbit holes. But I'm just thinking about one, and it's 7 00:00:21,320 --> 00:00:25,439 Speaker 1: that we're at our last lap of this semester. Huh. 8 00:00:25,680 --> 00:00:30,000 Speaker 1: It's been a year that we've been doing this. Yes, yes, yes, 9 00:00:30,720 --> 00:00:33,800 Speaker 1: but the science doesn't stop just because the semester is over. 10 00:00:34,200 --> 00:00:37,040 Speaker 1: So the lab lights are still on. That's what you're saying, 11 00:00:37,479 --> 00:00:43,239 Speaker 1: that's frighting overly on. I'm TT and I'm Zakiah and 12 00:00:43,320 --> 00:00:50,600 Speaker 1: this is Dope Labs. Welcome to Dope Labs, a weekly 13 00:00:50,680 --> 00:00:53,840 Speaker 1: podcast that mixes hardcore science with pop culture and a 14 00:00:53,920 --> 00:01:01,320 Speaker 1: healthy dose of friendship. Over the last year, we've covered 15 00:01:01,400 --> 00:01:04,800 Speaker 1: a lot of ground in the labs that we have done. Yeah. 16 00:01:04,840 --> 00:01:07,440 Speaker 1: Some of them have been heavy topics, yes, and others 17 00:01:07,480 --> 00:01:09,960 Speaker 1: have been really fun and some topics that had us 18 00:01:10,000 --> 00:01:16,080 Speaker 1: texting each other like wait, what is happening? Listen. It's 19 00:01:16,080 --> 00:01:18,039 Speaker 1: not just the topics but the news too. I'm still 20 00:01:18,080 --> 00:01:20,880 Speaker 1: wondering what is happening every day I wake up and 21 00:01:20,920 --> 00:01:23,960 Speaker 1: I say, are we still in twenty twenty six? A 22 00:01:24,640 --> 00:01:27,720 Speaker 1: lot has happened in just a few short months. But 23 00:01:27,800 --> 00:01:30,960 Speaker 1: today we're wrapping up semester five. But before we step 24 00:01:31,000 --> 00:01:32,760 Speaker 1: away for a little bit, we want to talk about 25 00:01:32,800 --> 00:01:40,679 Speaker 1: what we've been discussing and what we're keeping our eyes on. Okay, 26 00:01:40,720 --> 00:01:42,600 Speaker 1: so what do we know over the last year. We've 27 00:01:42,640 --> 00:01:45,679 Speaker 1: talked about a lot. But you know what stands out 28 00:01:45,720 --> 00:01:48,160 Speaker 1: to me the most. A lot of our labs that 29 00:01:48,200 --> 00:01:52,280 Speaker 1: we've done have been about systems. So, okay, what do 30 00:01:52,320 --> 00:01:54,800 Speaker 1: you mean by systems? I think, if I were to 31 00:01:54,960 --> 00:01:57,600 Speaker 1: like sum it up, it's like the invisible stuff, the 32 00:01:57,600 --> 00:02:01,440 Speaker 1: stuff we can't see that's shaping how we live our algorithms. 33 00:02:01,480 --> 00:02:07,440 Speaker 1: That's markets, labor markets, economic markets, environmental exposures, institutions. Right, 34 00:02:07,640 --> 00:02:14,119 Speaker 1: because we talked about everything this semester, AI relationships, sports 35 00:02:14,160 --> 00:02:18,840 Speaker 1: betting and prediction markets, the psychology of Black Friday shopping, 36 00:02:19,680 --> 00:02:22,639 Speaker 1: air pollution, and what's actually in the air we're breathing. 37 00:02:22,800 --> 00:02:24,800 Speaker 1: I know the air in my office right now is 38 00:02:24,880 --> 00:02:29,120 Speaker 1: mostly make up setting spray. We even talked about the 39 00:02:29,160 --> 00:02:33,280 Speaker 1: science behind the Grammys, which I still think people are underestimating. Yeah, 40 00:02:33,320 --> 00:02:35,960 Speaker 1: there's a lot of math in the Grammys, a lot 41 00:02:36,000 --> 00:02:40,120 Speaker 1: of psychology, and yeah it's heavy. It's heavy stuff. So 42 00:02:40,200 --> 00:02:43,160 Speaker 1: if you zoom out, what do you think the big 43 00:02:43,240 --> 00:02:48,000 Speaker 1: theme of our year was, Well, to piggyback off of 44 00:02:48,000 --> 00:02:50,720 Speaker 1: what you're saying, because you put that bug in my ear. 45 00:02:51,360 --> 00:02:56,560 Speaker 1: It's probably how science and systems are quietly shaping our lives. 46 00:02:56,960 --> 00:03:00,239 Speaker 1: And unfortunately, or fortunately I feel it was like the 47 00:03:00,280 --> 00:03:03,320 Speaker 1: quiet party is out loud now, oh my goodness, very loud. 48 00:03:04,440 --> 00:03:06,960 Speaker 1: All right, So we did a little look back at 49 00:03:07,000 --> 00:03:10,320 Speaker 1: what we've talked about. But we're going to be away 50 00:03:10,880 --> 00:03:14,040 Speaker 1: and I know we'll text about what we have our 51 00:03:14,080 --> 00:03:17,120 Speaker 1: eyes on. Yes, I want to know, so you can 52 00:03:17,160 --> 00:03:19,720 Speaker 1: tell everybody else what you have your eyes on for 53 00:03:19,760 --> 00:03:23,799 Speaker 1: twenty twenty six. Well, for twenty twenty six, I am 54 00:03:24,440 --> 00:03:28,400 Speaker 1: keeping my eyes open on the policy stuff. You know, 55 00:03:28,480 --> 00:03:31,320 Speaker 1: I'm into the policy arm now and so now all 56 00:03:31,320 --> 00:03:33,960 Speaker 1: of those things are just in the forefront of my mind. 57 00:03:34,200 --> 00:03:37,720 Speaker 1: I feel like so much is going on behind the scenes, 58 00:03:37,800 --> 00:03:40,720 Speaker 1: like people are doing some very strange things that are 59 00:03:41,000 --> 00:03:46,440 Speaker 1: very anti what we're used to, Okay, and so I'm 60 00:03:46,520 --> 00:03:49,200 Speaker 1: just kind of like holding my breath. But also making 61 00:03:49,240 --> 00:03:51,200 Speaker 1: sure that I'm staying up to date with what's going 62 00:03:51,240 --> 00:03:54,720 Speaker 1: on in politics. It could be draining, but it's important. 63 00:03:54,840 --> 00:03:57,000 Speaker 1: It is so important to stay on top of it. 64 00:03:57,360 --> 00:04:00,080 Speaker 1: I'm also keeping my eye out on ways that I 65 00:03:59,840 --> 00:04:02,560 Speaker 1: can make sure that in the midst of all of 66 00:04:02,600 --> 00:04:05,480 Speaker 1: this turmoil and things that are stressing me out, ways 67 00:04:05,800 --> 00:04:10,520 Speaker 1: to incorporate self care because all of this can be 68 00:04:10,600 --> 00:04:13,160 Speaker 1: heavy on your psyche. All this can be heavy on 69 00:04:13,200 --> 00:04:16,039 Speaker 1: your emotions, and so making sure that I'm not suffering 70 00:04:16,080 --> 00:04:18,880 Speaker 1: from burnout with everything that's going on in the world, 71 00:04:18,960 --> 00:04:27,160 Speaker 1: and injecting more levity, injecting more art, injecting more music, 72 00:04:27,400 --> 00:04:30,599 Speaker 1: injecting all the things that make me smile, because I 73 00:04:30,640 --> 00:04:34,280 Speaker 1: think there has to be a balance there, you know. Yeah, 74 00:04:34,320 --> 00:04:39,159 Speaker 1: for sure, what are you keeping your eye on? I 75 00:04:39,200 --> 00:04:43,880 Speaker 1: think everything everything they're shifting around, looking right, looking weird 76 00:04:43,920 --> 00:04:47,359 Speaker 1: in the corner. Okay, I think there are a couple things. 77 00:04:47,400 --> 00:04:51,320 Speaker 1: So when you said self care, that really rang a 78 00:04:51,360 --> 00:04:53,159 Speaker 1: bell for me. You know, I think I've talked about 79 00:04:53,200 --> 00:04:55,360 Speaker 1: it on the show a lot, about getting my right 80 00:04:55,440 --> 00:05:00,760 Speaker 1: sleeping pattern. I'm back biking, hopefully in the next few 81 00:05:00,800 --> 00:05:03,520 Speaker 1: weeks because it's warming up here enough for that. Yeah, 82 00:05:03,520 --> 00:05:07,400 Speaker 1: and so getting outside, being in nature and also trying 83 00:05:07,440 --> 00:05:10,200 Speaker 1: to avoid all the pollen that will come along with that, 84 00:05:11,360 --> 00:05:15,160 Speaker 1: but also some community care and not just self care. 85 00:05:15,240 --> 00:05:17,280 Speaker 1: So one of the things recently I was like, oh, 86 00:05:17,320 --> 00:05:20,400 Speaker 1: I think I'll volunteer. And sometimes it feels like you 87 00:05:20,440 --> 00:05:22,799 Speaker 1: have to go to a church or a library to volunteer. 88 00:05:22,800 --> 00:05:24,359 Speaker 1: And I was like, let me start with the people 89 00:05:24,360 --> 00:05:27,240 Speaker 1: in my community. And so I just reached out to 90 00:05:27,279 --> 00:05:29,080 Speaker 1: some people I know here in Atlanta that are friends 91 00:05:29,080 --> 00:05:30,279 Speaker 1: and I was like, Hey, do you need any help 92 00:05:30,279 --> 00:05:34,400 Speaker 1: with anything today? I love that or tomorrow whatever. And 93 00:05:34,440 --> 00:05:38,800 Speaker 1: so I think, you know, people are aching for a community, 94 00:05:39,160 --> 00:05:41,680 Speaker 1: but a lot of the conversation I've seen is like, hey, 95 00:05:41,680 --> 00:05:44,640 Speaker 1: you got to be willing to contribute and create that 96 00:05:44,680 --> 00:05:47,320 Speaker 1: community that you want to be there for you. And 97 00:05:47,400 --> 00:05:50,080 Speaker 1: so that's one piece. The other thing is a little 98 00:05:50,120 --> 00:05:54,040 Speaker 1: bit more sociology and behavior. But it's Punched the monkey. 99 00:05:54,080 --> 00:06:00,880 Speaker 1: Okay have you seen them acaque over in Japan? Poor Punch. 100 00:06:00,960 --> 00:06:04,440 Speaker 1: I felt so bad for the baby. Let people know 101 00:06:04,480 --> 00:06:07,800 Speaker 1: who Punch is before you you talk about him. Punch 102 00:06:07,960 --> 00:06:10,760 Speaker 1: is a macaque in the I Chicago City Zoo in Japan, 103 00:06:11,320 --> 00:06:13,680 Speaker 1: and he was rejected at birth by his mother. This 104 00:06:13,720 --> 00:06:17,400 Speaker 1: is a very devastating thing to happen for someone like him. 105 00:06:17,640 --> 00:06:22,640 Speaker 1: I'm saying someone an animal. And he was bottle fed 106 00:06:22,760 --> 00:06:25,160 Speaker 1: by the zoo staff. He had to be reintegrated into 107 00:06:25,160 --> 00:06:27,480 Speaker 1: this new troop and they had to he had to 108 00:06:27,560 --> 00:06:29,359 Speaker 1: kind of fight for his own position. And one of 109 00:06:29,360 --> 00:06:33,360 Speaker 1: the monkeys was in there being a bully. Okay, a bully. 110 00:06:33,680 --> 00:06:35,880 Speaker 1: I said, get him out. Listen we he started throwing 111 00:06:35,960 --> 00:06:37,839 Speaker 1: Punch around. I said, get him out of there. Go 112 00:06:37,920 --> 00:06:42,239 Speaker 1: get our boy. The internet rally behind Punch. Yes, Punch 113 00:06:42,240 --> 00:06:45,520 Speaker 1: has pissed so many people on his side, and I 114 00:06:45,560 --> 00:06:48,720 Speaker 1: think that's interesting because it's animal behavior that we're seeing, 115 00:06:49,080 --> 00:06:50,920 Speaker 1: but we're able to resonate with it. And it made 116 00:06:50,960 --> 00:06:53,880 Speaker 1: me think about our first episode Cuffing Season, where we 117 00:06:54,000 --> 00:06:56,480 Speaker 1: talked about animal behavior around mating and how it looks 118 00:06:56,480 --> 00:06:58,760 Speaker 1: similar in some ways to human behavior. And then once 119 00:06:58,800 --> 00:07:01,640 Speaker 1: again we're seeing those same people are able to identify 120 00:07:01,680 --> 00:07:03,719 Speaker 1: and they're like, oh, this person is Punch his friend 121 00:07:03,920 --> 00:07:06,640 Speaker 1: or you know, we're looking at these social behaviors that 122 00:07:06,680 --> 00:07:09,760 Speaker 1: we also exhibit, and so I think it's really I'm like, hey, 123 00:07:09,800 --> 00:07:13,880 Speaker 1: we're all primeates. Baby, Yeah, these fingernails soft claws, soft clause. 124 00:07:13,920 --> 00:07:19,520 Speaker 1: As my friend would say, Oh my goodness, I'm also 125 00:07:19,600 --> 00:07:22,480 Speaker 1: keeping my eye out on AI. And this kind of 126 00:07:22,480 --> 00:07:26,400 Speaker 1: goes to the policy honestly, because I mean AI is 127 00:07:26,480 --> 00:07:30,560 Speaker 1: changing every five minutes, literally, like every week. There's new 128 00:07:30,600 --> 00:07:33,680 Speaker 1: developments in the AI space, and the technology is advancing. 129 00:07:33,760 --> 00:07:37,520 Speaker 1: It is like when we say breakneck speed. It is 130 00:07:38,040 --> 00:07:41,520 Speaker 1: really really fast, and folks are just trying to keep up. 131 00:07:41,960 --> 00:07:46,320 Speaker 1: No one who works in the AI space anticipated that 132 00:07:46,720 --> 00:07:49,480 Speaker 1: it would be where it is right now in twenty 133 00:07:49,520 --> 00:07:52,200 Speaker 1: twenty six, and so one of the big questions that 134 00:07:52,240 --> 00:07:55,160 Speaker 1: scientists are asking right now is whether AI can actually 135 00:07:55,160 --> 00:07:59,720 Speaker 1: help discover new science. Let me tell you I was 136 00:07:59,760 --> 00:08:01,520 Speaker 1: asked from that question a while back, and I think 137 00:08:01,560 --> 00:08:03,480 Speaker 1: part of that is because people think about the large 138 00:08:03,560 --> 00:08:08,160 Speaker 1: language modelers and the interfaces that are like generic uses 139 00:08:08,200 --> 00:08:13,320 Speaker 1: like chat, GPT. But Anthropic has been growing on the scene. 140 00:08:13,920 --> 00:08:15,880 Speaker 1: You know, I've been following what they're doing with AI 141 00:08:16,000 --> 00:08:20,600 Speaker 1: for science, and I gotta tell you, I am I 142 00:08:20,720 --> 00:08:25,400 Speaker 1: am impressed. Okay, Now, I think they're showing us just 143 00:08:25,440 --> 00:08:28,360 Speaker 1: what's possible, even just for like one example I saw 144 00:08:28,480 --> 00:08:31,800 Speaker 1: was like this plug in and you can have all 145 00:08:31,800 --> 00:08:34,000 Speaker 1: your lab's data So think about a lab. Think about 146 00:08:34,000 --> 00:08:36,160 Speaker 1: the lab that I was in in grad school. You know, 147 00:08:36,240 --> 00:08:38,880 Speaker 1: my advisor ended up retiring, but there were samples in 148 00:08:38,920 --> 00:08:43,719 Speaker 1: those freezers from the eighties and nineties. Okay, so we're 149 00:08:43,760 --> 00:08:45,800 Speaker 1: talking in twenty fourteen. I'm trying to look back through 150 00:08:45,880 --> 00:08:49,320 Speaker 1: books and see writing in people's lab notebooks from the nineties. Now, 151 00:08:49,400 --> 00:08:53,000 Speaker 1: imagine all the results from our lab, negative results that 152 00:08:53,040 --> 00:08:55,559 Speaker 1: don't get published, all of that being catalog. Imagine those 153 00:08:55,600 --> 00:08:57,600 Speaker 1: things being scanned in and we have a database. So 154 00:08:57,800 --> 00:09:00,440 Speaker 1: when a new student comes along, they can say, hey, 155 00:09:00,559 --> 00:09:03,600 Speaker 1: actually it's not published, but I know this this and 156 00:09:03,640 --> 00:09:05,400 Speaker 1: this won't work, or here are the results of this 157 00:09:05,440 --> 00:09:07,800 Speaker 1: other experiment. So we don't waste time, we don't waste resources. 158 00:09:07,920 --> 00:09:09,760 Speaker 1: We could say, are there any patterns that I hadn't 159 00:09:09,800 --> 00:09:13,560 Speaker 1: see over the past twenty years but that AI can 160 00:09:13,640 --> 00:09:17,280 Speaker 1: detect and then I can test and verify, Like, baby, 161 00:09:17,320 --> 00:09:19,120 Speaker 1: what do you know what we could have done with 162 00:09:19,200 --> 00:09:26,480 Speaker 1: that type of technology? PhD in ten days they just 163 00:09:26,480 --> 00:09:31,920 Speaker 1: gonna be giving it out. But that's just one small 164 00:09:32,040 --> 00:09:34,800 Speaker 1: use case, and it's exciting. It is. And I think 165 00:09:34,840 --> 00:09:38,880 Speaker 1: that when people talk about AI, it's always just you know, chat, GPT, 166 00:09:39,600 --> 00:09:42,720 Speaker 1: and we got to expand our understanding of what AI 167 00:09:42,920 --> 00:09:45,520 Speaker 1: is and what it's possible with AI, because there's a 168 00:09:45,559 --> 00:09:48,280 Speaker 1: lot of really cool work being done. I know that 169 00:09:48,320 --> 00:09:50,720 Speaker 1: there are a lot of folks talking about its impact 170 00:09:50,840 --> 00:09:54,000 Speaker 1: on the environment and things like that, and we the 171 00:09:54,040 --> 00:09:58,040 Speaker 1: scientists understand and are working on those things too. As 172 00:09:58,280 --> 00:10:03,040 Speaker 1: AI advances, the the sustainability of it all will also advance, 173 00:10:03,760 --> 00:10:05,840 Speaker 1: so I think that that's something that we have to 174 00:10:06,000 --> 00:10:23,400 Speaker 1: keep in mind as well. Another thing that we talked 175 00:10:23,400 --> 00:10:27,120 Speaker 1: about this year was money, Where is it all going? 176 00:10:28,840 --> 00:10:31,720 Speaker 1: People are always want to know what's going on with 177 00:10:31,760 --> 00:10:34,080 Speaker 1: the economy, and I feel like the economy is such 178 00:10:34,120 --> 00:10:37,240 Speaker 1: a big word, like what does that even mean? And 179 00:10:37,360 --> 00:10:41,079 Speaker 1: everything it seems right? And we talked about the psychology 180 00:10:41,160 --> 00:10:45,200 Speaker 1: of spending, Black Friday, gambling, risk and all of those 181 00:10:45,200 --> 00:10:47,600 Speaker 1: things associated with it, because we really wanted to focus 182 00:10:47,640 --> 00:10:50,080 Speaker 1: on the individual because it feels like that's the only 183 00:10:50,120 --> 00:10:52,959 Speaker 1: thing we can control. All of this is behavioral science, 184 00:10:53,040 --> 00:10:55,640 Speaker 1: and I've been interested in this over the years. This 185 00:10:55,760 --> 00:10:58,880 Speaker 1: is not where my training is, okay, but I do 186 00:10:58,920 --> 00:11:01,800 Speaker 1: think I'm curious with the rise of AI and with 187 00:11:01,960 --> 00:11:05,680 Speaker 1: so much information, what about human processing of that information? 188 00:11:05,760 --> 00:11:09,520 Speaker 1: That's gonna matter even more. And so like understanding the 189 00:11:09,600 --> 00:11:11,680 Speaker 1: drivers what makes you do the things you do. With 190 00:11:11,760 --> 00:11:14,280 Speaker 1: all this information, you now have access to what makes 191 00:11:14,280 --> 00:11:16,640 Speaker 1: you act on a thing or not all of that. 192 00:11:16,679 --> 00:11:19,160 Speaker 1: It feels like we're gonna need to understand that even better. 193 00:11:19,240 --> 00:11:22,480 Speaker 1: And I think in a world where capitalism is in 194 00:11:22,480 --> 00:11:26,880 Speaker 1: the driver's seat, it pays to know these things absolutely. 195 00:11:26,920 --> 00:11:28,839 Speaker 1: And the big question, one of the big questions right 196 00:11:28,840 --> 00:11:32,360 Speaker 1: now is what does AI mean for jobs? And I 197 00:11:32,400 --> 00:11:36,320 Speaker 1: think that folks are really nervous. People are nervous about 198 00:11:36,360 --> 00:11:38,160 Speaker 1: what that means for their jobs in the future. I know, 199 00:11:38,679 --> 00:11:41,600 Speaker 1: like in the law space, lawyers are actually really nervous 200 00:11:41,640 --> 00:11:44,760 Speaker 1: because you know all of that. I don't even know 201 00:11:45,000 --> 00:11:49,040 Speaker 1: what the right words are roxy if you're listening case law, 202 00:11:49,720 --> 00:11:53,319 Speaker 1: all those books and stuff. AI is able to do redlines. 203 00:11:53,400 --> 00:11:55,400 Speaker 1: AI is able to do all of these things. And 204 00:11:55,440 --> 00:11:58,960 Speaker 1: so I know it's not just the folks that are 205 00:11:58,960 --> 00:12:02,040 Speaker 1: doing jobs and a factory. It's like every industry is 206 00:12:02,040 --> 00:12:06,440 Speaker 1: feeling the impacts of AI. And so I think we're 207 00:12:06,480 --> 00:12:09,880 Speaker 1: going through one of those moments where technology changes the 208 00:12:10,000 --> 00:12:14,120 Speaker 1: kinds of work that humans do. I don't think that 209 00:12:14,280 --> 00:12:17,640 Speaker 1: it's going to just get rid of like whole sectors, 210 00:12:17,640 --> 00:12:19,679 Speaker 1: like we always going to need lawyers, you know what 211 00:12:19,760 --> 00:12:22,640 Speaker 1: I mean, And we're always going to need doctors, and 212 00:12:22,679 --> 00:12:24,839 Speaker 1: we're always going to need people that can help with 213 00:12:25,080 --> 00:12:28,120 Speaker 1: building people with trade skills. I think the jobs is 214 00:12:28,160 --> 00:12:30,760 Speaker 1: just gonna look a little different. I think they will 215 00:12:30,800 --> 00:12:34,480 Speaker 1: definitely look different, and how we prepare will look different. Listen, 216 00:12:34,840 --> 00:12:39,640 Speaker 1: these types of changes and shifts in the human workforce 217 00:12:39,640 --> 00:12:43,680 Speaker 1: have happened before the Industrial Revolution. Having computers, having the internet, 218 00:12:43,960 --> 00:12:47,400 Speaker 1: all those things have changed how we train, They've changed 219 00:12:47,440 --> 00:12:50,680 Speaker 1: what the education system looks like. And so I'm thinking 220 00:12:50,760 --> 00:12:53,960 Speaker 1: back to our episode where we talked about the economy 221 00:12:53,960 --> 00:12:55,760 Speaker 1: and the changes in the workforce, and I think what 222 00:12:55,840 --> 00:12:58,920 Speaker 1: we will see is a shift to more intermittent training. 223 00:12:59,160 --> 00:13:01,600 Speaker 1: So that means you don't just go to school one 224 00:13:01,640 --> 00:13:03,360 Speaker 1: time and you stay in that career for thirty years 225 00:13:03,360 --> 00:13:06,360 Speaker 1: and you rely on that knowledge from the institutional knowledge 226 00:13:06,400 --> 00:13:09,320 Speaker 1: from school and then the experiential knowledge from over time. 227 00:13:09,360 --> 00:13:10,880 Speaker 1: I don't think that's going to be the way it is. 228 00:13:11,120 --> 00:13:14,600 Speaker 1: I think you'll have three and four careers, and what 229 00:13:14,640 --> 00:13:17,079 Speaker 1: we need is infrastructure to support that to help people 230 00:13:17,160 --> 00:13:20,400 Speaker 1: shift as the workforce does. That's such a great point. 231 00:13:20,640 --> 00:13:24,120 Speaker 1: And that also makes me think about because we talked 232 00:13:24,120 --> 00:13:26,640 Speaker 1: about the politics more, and we talked about politics a 233 00:13:26,640 --> 00:13:29,600 Speaker 1: lot this season and found its way into almost every 234 00:13:29,600 --> 00:13:34,160 Speaker 1: single episode. And that's because science is never isolated from politics. 235 00:13:34,240 --> 00:13:39,000 Speaker 1: You know, we've talked about immigration, surveillance, who knew Enemy 236 00:13:39,000 --> 00:13:45,520 Speaker 1: of the State was real? Nonfiction? All of these things 237 00:13:45,960 --> 00:13:48,920 Speaker 1: are shaped by what policy allows. Even when we look 238 00:13:48,960 --> 00:13:53,520 Speaker 1: at you know, what medicines are available, treatments are being approved, 239 00:13:53,600 --> 00:13:56,120 Speaker 1: what careers people are able to get loans for so 240 00:13:56,160 --> 00:13:58,600 Speaker 1: that they can continue to go to college. All of 241 00:13:58,600 --> 00:14:02,400 Speaker 1: these things are shaped by policy. And we know science 242 00:14:02,880 --> 00:14:05,000 Speaker 1: is also affected by those things because it depends on 243 00:14:05,080 --> 00:14:07,760 Speaker 1: ideas moving, even when they're not clear cut or they 244 00:14:07,760 --> 00:14:10,120 Speaker 1: feel messy at the beginning. So and it depends on 245 00:14:10,160 --> 00:14:15,200 Speaker 1: knowledge moving. Absolutely, those two things are intertwined, as are 246 00:14:15,320 --> 00:14:18,240 Speaker 1: all things. That's why we say science is in everything, 247 00:14:18,320 --> 00:14:20,480 Speaker 1: you know what I mean? Yeah, And that's like the 248 00:14:21,200 --> 00:14:24,520 Speaker 1: bedrock of Dope Labs is that science is in everything 249 00:14:24,600 --> 00:14:26,920 Speaker 1: and that we need to arm ourselves with resources that 250 00:14:26,960 --> 00:14:30,240 Speaker 1: will help us understand what is going on around us 251 00:14:30,280 --> 00:14:33,560 Speaker 1: in the world day to day. Yes, so we talked 252 00:14:33,560 --> 00:14:35,680 Speaker 1: about rabbit holes at the beginning. I didn't quite let 253 00:14:35,720 --> 00:14:38,080 Speaker 1: you get into it. But what rabbit hole are you 254 00:14:38,200 --> 00:14:43,760 Speaker 1: currently in? Thank you for opening the door. It's just 255 00:14:43,800 --> 00:14:47,560 Speaker 1: a little bit, don't get I put my foot in there. Okay, 256 00:14:47,640 --> 00:14:50,360 Speaker 1: you won't be able to close it back for me. 257 00:14:50,880 --> 00:14:53,800 Speaker 1: I think the there are a couple rabbit holes. So 258 00:14:53,800 --> 00:14:59,040 Speaker 1: I mentioned biking, but also birds. It's time the birds 259 00:14:59,080 --> 00:15:03,720 Speaker 1: are moving fir rating. Oh okay, you should have seen 260 00:15:03,720 --> 00:15:06,160 Speaker 1: the look she gave me. All she was not happy 261 00:15:06,240 --> 00:15:09,760 Speaker 1: with what do you mean time for what? Listen? These 262 00:15:09,760 --> 00:15:13,880 Speaker 1: are a pollinators. Sometimes these are our cues of seasons changing. 263 00:15:14,280 --> 00:15:18,360 Speaker 1: They're so exciting. There's so much to learn, and I 264 00:15:18,400 --> 00:15:19,840 Speaker 1: really would like to do a little bit. But what 265 00:15:19,840 --> 00:15:23,720 Speaker 1: do they call that container? Garden it? The earths are expensive, okay, 266 00:15:23,760 --> 00:15:26,840 Speaker 1: and they're going mushy in the refrigerator fab so I 267 00:15:26,880 --> 00:15:28,920 Speaker 1: need my own supply. I remember when we were in 268 00:15:28,960 --> 00:15:31,080 Speaker 1: grad school, you would open up their back door and 269 00:15:31,200 --> 00:15:34,120 Speaker 1: just cut out cut basal leaves and stuff like that, 270 00:15:34,160 --> 00:15:38,240 Speaker 1: and I was like, what is going on back there, honey? 271 00:15:38,360 --> 00:15:40,480 Speaker 1: Like I had never seen that before in my life. 272 00:15:40,480 --> 00:15:43,600 Speaker 1: Somebody opened their door and cut stuff and then put 273 00:15:43,640 --> 00:15:48,560 Speaker 1: it in the food Like is these lawn clippings no flavor. 274 00:15:49,240 --> 00:15:55,160 Speaker 1: Ho really should have excited me for that. Oh my gosh, 275 00:15:55,400 --> 00:15:58,080 Speaker 1: Now what about you, I'm not letting you get away? 276 00:15:58,280 --> 00:16:00,960 Speaker 1: Uh huh Okay. So for me, the rabbit hole that 277 00:16:01,000 --> 00:16:04,560 Speaker 1: I'm going down is mainly focused on human behavior. I 278 00:16:04,560 --> 00:16:07,720 Speaker 1: think the rise of parasocial relationships, like people feeling like 279 00:16:07,920 --> 00:16:11,800 Speaker 1: they know someone because they follow them on Instagram or 280 00:16:11,840 --> 00:16:15,800 Speaker 1: TikTok or watch their YouTube videos. Yes, that is just 281 00:16:15,840 --> 00:16:18,360 Speaker 1: so interesting to me because and it's not even coming 282 00:16:18,360 --> 00:16:20,480 Speaker 1: from a place of judgment. I be feeling like I 283 00:16:20,560 --> 00:16:23,320 Speaker 1: know these people. I've been following some people for over 284 00:16:23,360 --> 00:16:26,120 Speaker 1: a decade, and when they get married, I'm like, wow, 285 00:16:26,160 --> 00:16:30,400 Speaker 1: I didn't even get invited. It's interesting though, because we 286 00:16:31,160 --> 00:16:35,120 Speaker 1: talk about this t t and I feel like there 287 00:16:35,120 --> 00:16:39,960 Speaker 1: are some og Dope Labs listeners that I'm like, hey, 288 00:16:40,600 --> 00:16:44,360 Speaker 1: I can't believe how much your baby has grown, and 289 00:16:44,360 --> 00:16:46,040 Speaker 1: I'm like telling them I said, hello, look at our 290 00:16:46,040 --> 00:16:49,320 Speaker 1: little tiny shift. Right, That's how I feel. And then 291 00:16:49,440 --> 00:16:51,960 Speaker 1: some people I'm like, who is this? You know? Right, 292 00:16:52,160 --> 00:16:56,520 Speaker 1: there's this woman on TikTok who had a very public 293 00:16:56,960 --> 00:16:59,400 Speaker 1: dating relationship. When she went on a first date with 294 00:16:59,440 --> 00:17:01,840 Speaker 1: this man, knew about it. She talked about him. She 295 00:17:02,120 --> 00:17:08,159 Speaker 1: eventually married this man and UH had like a what 296 00:17:08,280 --> 00:17:10,560 Speaker 1: felt like a TikTok wedding where it was like all 297 00:17:10,560 --> 00:17:14,119 Speaker 1: these brands, like brands bought her dress, brands did the 298 00:17:14,160 --> 00:17:16,720 Speaker 1: makeup brands, like it was just very TikTok because she's 299 00:17:16,760 --> 00:17:20,080 Speaker 1: like one of the most popular TikTokers or whatever. She 300 00:17:20,320 --> 00:17:22,520 Speaker 1: also let us like a little bit too much into 301 00:17:22,600 --> 00:17:25,800 Speaker 1: her life and so we were we observed like her 302 00:17:25,880 --> 00:17:31,360 Speaker 1: husband's drug addiction and like now they're getting a divorce, 303 00:17:31,440 --> 00:17:34,119 Speaker 1: and so people are just like in the comments talking 304 00:17:34,160 --> 00:17:37,680 Speaker 1: crazy and I'm just like, we don't know her, we 305 00:17:37,800 --> 00:17:40,600 Speaker 1: don't know her. I'm like, I have the biggest we 306 00:17:40,920 --> 00:17:45,000 Speaker 1: don't know her. We don't know her. Like she's volunteered 307 00:17:45,000 --> 00:17:47,080 Speaker 1: a lot of information, and so people are like, well, 308 00:17:47,119 --> 00:17:48,879 Speaker 1: if you put that out there, we're allowed to comment, 309 00:17:48,920 --> 00:17:51,520 Speaker 1: and I'm like, but you don't have to. But you 310 00:17:51,520 --> 00:17:53,440 Speaker 1: don't have to. You don't have to, and so it's 311 00:17:53,480 --> 00:17:57,040 Speaker 1: just really interesting how people make those decisions. I'm like, 312 00:17:57,119 --> 00:17:59,240 Speaker 1: is it a generational thing? I don't think so, because 313 00:17:59,240 --> 00:18:01,879 Speaker 1: I see some folks my age and older who are 314 00:18:01,920 --> 00:18:05,200 Speaker 1: in the comments section doing stuff behaving terribly. Yeah, because 315 00:18:05,200 --> 00:18:09,320 Speaker 1: I'm and I'm just like me. I'm not like the 316 00:18:09,359 --> 00:18:11,159 Speaker 1: way that I interacted with the Internet. I feel like 317 00:18:11,200 --> 00:18:13,399 Speaker 1: you're the same way too, Like we remember what it 318 00:18:13,480 --> 00:18:17,360 Speaker 1: was like before there was social media. We know, in 319 00:18:17,440 --> 00:18:21,080 Speaker 1: person beef, you know, yes, all this it don't mean 320 00:18:21,119 --> 00:18:25,560 Speaker 1: nothing to me. All that typing, all that typing, right, oh, 321 00:18:25,800 --> 00:18:28,200 Speaker 1: in person beef and internet beef is making me think 322 00:18:28,200 --> 00:18:31,159 Speaker 1: about fifty cent and TI the South got something to 323 00:18:31,160 --> 00:18:36,399 Speaker 1: say now. I haven't even been able to keep up 324 00:18:36,520 --> 00:18:38,919 Speaker 1: what's going on? Can you please bring me up to 325 00:18:38,920 --> 00:18:42,560 Speaker 1: speed really quick. It's not even important. It's grown people 326 00:18:43,680 --> 00:18:48,200 Speaker 1: being ridiculous. Okay, that's all it is. I don't think 327 00:18:48,200 --> 00:18:49,840 Speaker 1: that beef is worth any of our time. There's so 328 00:18:50,000 --> 00:18:53,000 Speaker 1: much on our plate. Take that little piece of beef off. Okakay, 329 00:18:53,359 --> 00:18:55,800 Speaker 1: don't don't even worry about it. That's the gristle. Throw 330 00:18:55,800 --> 00:19:00,800 Speaker 1: it in the track, yes, the gristle. So well, that's 331 00:19:00,920 --> 00:19:03,320 Speaker 1: mainly what my rabbit hole has been. And just like 332 00:19:03,359 --> 00:19:06,800 Speaker 1: why we make the decisions that we make, and trusting 333 00:19:06,840 --> 00:19:10,240 Speaker 1: some technologies over others, and you know, how we interact 334 00:19:10,240 --> 00:19:13,320 Speaker 1: with technology. Those are the things that I've been kind 335 00:19:13,320 --> 00:19:24,840 Speaker 1: of like deep diving into and trying to understand more. 336 00:19:30,520 --> 00:19:33,480 Speaker 1: If there's something I can say, I want everybody to 337 00:19:33,560 --> 00:19:35,840 Speaker 1: keep their eye on and to keep the pressure on, 338 00:19:36,960 --> 00:19:42,199 Speaker 1: is scientific discovery. Don't stop asking for more things, you know. 339 00:19:42,280 --> 00:19:44,240 Speaker 1: I feel like we're seeing funding cuts. I was on 340 00:19:44,280 --> 00:19:46,480 Speaker 1: the GAO website which I said, I don't think I'm 341 00:19:46,520 --> 00:19:49,560 Speaker 1: even supposed to be over here, okay, and that's the 342 00:19:49,640 --> 00:19:53,600 Speaker 1: Government Accountability Office. I've been looking at research infrastructure, how 343 00:19:53,640 --> 00:19:57,320 Speaker 1: it's slowed since twenty twenty four. What was expected. Projects 344 00:19:57,359 --> 00:20:02,280 Speaker 1: are being scrapped, so scope has decreased, delivery is delayed, 345 00:20:02,720 --> 00:20:05,480 Speaker 1: and so I'm just like I recently went to a 346 00:20:05,520 --> 00:20:07,679 Speaker 1: Rare disease conference put on by the Boston Globe, and 347 00:20:07,680 --> 00:20:11,200 Speaker 1: I was looking at what's happening next, what's in the pipeline, 348 00:20:11,520 --> 00:20:13,520 Speaker 1: and how these companies are pushing for these things, and 349 00:20:13,560 --> 00:20:15,680 Speaker 1: I was like, we need our government to push as well, 350 00:20:15,720 --> 00:20:19,800 Speaker 1: and so like, I am keeping my eyes on scientific discovery, 351 00:20:19,920 --> 00:20:24,399 Speaker 1: innovation and application for people to have better lives across 352 00:20:24,400 --> 00:20:27,760 Speaker 1: the board. Absolutely, I think I'm right there with you 353 00:20:28,040 --> 00:20:32,600 Speaker 1: because it is so so important to stay on top 354 00:20:32,640 --> 00:20:34,439 Speaker 1: of these things. What they're hoping is is that we 355 00:20:34,480 --> 00:20:36,959 Speaker 1: won't and that all these things will be going on 356 00:20:37,000 --> 00:20:40,080 Speaker 1: in the background and we are being distracted by a 357 00:20:40,119 --> 00:20:43,040 Speaker 1: bunch of other stuff. The midterm elections are coming up, 358 00:20:43,080 --> 00:20:45,159 Speaker 1: and we have got to get to the polls because 359 00:20:45,200 --> 00:20:48,600 Speaker 1: when we vote, we win. Okay, that's the theme for 360 00:20:48,640 --> 00:20:50,879 Speaker 1: this next midterm is like, you got to get to 361 00:20:50,920 --> 00:20:53,919 Speaker 1: the polls because if we all show up and vote, 362 00:20:54,359 --> 00:20:59,160 Speaker 1: we will win. Period. I like that. And don't be distracted. 363 00:20:59,200 --> 00:21:01,920 Speaker 1: Like you said, you can always click another link, add 364 00:21:01,960 --> 00:21:04,320 Speaker 1: another tab, come back to it later and read it, 365 00:21:04,440 --> 00:21:09,080 Speaker 1: learn about it, be informed exactly. Science doesn't stop, Curiosity 366 00:21:09,160 --> 00:21:13,680 Speaker 1: doesn't stop, that's right, just curiouser and curiouser. So that's 367 00:21:13,720 --> 00:21:17,320 Speaker 1: a wrap on semester five. This has been so much 368 00:21:17,359 --> 00:21:22,000 Speaker 1: fun bringing you labs every single week for the past year, 369 00:21:22,440 --> 00:21:26,720 Speaker 1: hit after hit after hit after here and I guess 370 00:21:26,760 --> 00:21:28,679 Speaker 1: we'll just see you next time. You know where to 371 00:21:28,720 --> 00:21:36,320 Speaker 1: find us, by y'all. You can find us on X 372 00:21:36,400 --> 00:21:39,919 Speaker 1: and Instagram at Dope Labs podcast. You can find me 373 00:21:40,320 --> 00:21:46,840 Speaker 1: ct on X, threads and Instagram at dr Underscore t Sho, 374 00:21:47,320 --> 00:21:51,160 Speaker 1: and you can find Zakiya at z said So. Dope 375 00:21:51,240 --> 00:21:55,320 Speaker 1: Labs is a production of Lemonada Media. Our supervising producer 376 00:21:55,560 --> 00:21:59,320 Speaker 1: is Keegan Zemma. Dope Labs is sound designed, edited, and 377 00:21:59,440 --> 00:22:04,200 Speaker 1: mixed by James sparber Lemonada's Senior Vice President of Content 378 00:22:04,320 --> 00:22:09,280 Speaker 1: and Production is Jackie Danziger. Executive producer from iHeart Podcast 379 00:22:09,400 --> 00:22:13,680 Speaker 1: is Katrina Norvil. Marketing lead is Alison Kanter. Original music 380 00:22:13,800 --> 00:22:18,280 Speaker 1: composed and produced by Takayatsuzawa and Alex suki Ura, with 381 00:22:18,440 --> 00:22:23,400 Speaker 1: additional music by Elijah Harvey. Dope Labs is executive produced 382 00:22:23,440 --> 00:22:26,560 Speaker 1: by us T T Show Dia and Zakiah Wattley.