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[00:00:00.800 --> 00:00:06.880] 10 years from today, Lisa Schneider will trade in her office job to become the leader of a pack of dogs.
[00:00:06.880 --> 00:00:09.600] As the owner of her own dog rescue, that is.
[00:00:09.600 --> 00:00:17.280] A second act made possible by the reskilling courses Lisa's taking now with AARP to help make sure her income lives as long as she does.
[00:00:17.280 --> 00:00:22.240] And she can finally run with the big dogs and the small dogs, who just think they're big dogs.
[00:00:22.240 --> 00:00:25.920] That's why the younger you are, the more you need AARP.
[00:00:25.920 --> 00:00:29.680] Learn more at aarp.org/slash skills.
[00:00:33.200 --> 00:00:36.480] You're listening to the Skeptic's Guide to the Universe.
[00:00:36.480 --> 00:00:39.360] Your escape to reality.
[00:00:40.000 --> 00:00:42.720] Hello, and welcome to The Skeptic's Guide to the Universe.
[00:00:42.720 --> 00:00:47.760] Today is Wednesday, September 25th, 2024, and this is your host, Stephen Novella.
[00:00:47.920 --> 00:00:50.480] Joining me this week are Kara Santa Maria.
[00:00:50.480 --> 00:00:51.120] Howdy.
[00:00:51.120 --> 00:00:52.080] Jay Novella.
[00:00:52.080 --> 00:00:52.400] Hey, guys.
[00:00:52.560 --> 00:00:53.680] Evan Bernstein.
[00:00:53.680 --> 00:00:54.960] Good evening, everyone.
[00:00:54.960 --> 00:00:58.080] And we have a special guest, Andrea Jones-Roy.
[00:00:58.080 --> 00:00:59.920] Andrea, welcome back to the SGU.
[00:00:59.920 --> 00:01:00.640] Thank you so much.
[00:01:00.640 --> 00:01:01.360] Hi, everyone.
[00:01:01.600 --> 00:01:02.880] Oh, it's great to have you, Andrea.
[00:01:03.360 --> 00:01:04.000] Great to be here.
[00:01:04.000 --> 00:01:05.280] Always love hanging out with you all.
[00:01:05.280 --> 00:01:08.000] Now, Andrea, you are a political scientist, right?
[00:01:08.000 --> 00:01:08.480] It's true.
[00:01:08.480 --> 00:01:08.880] Yes.
[00:01:08.880 --> 00:01:13.040] So this must be a really busy time of year for you.
[00:01:13.040 --> 00:01:14.480] Yeah, you know, I was thinking about it.
[00:01:14.480 --> 00:01:20.000] I was like, this is maybe my, Evan, you can tell me if I'm out of bounds here, but maybe my tax season.
[00:01:20.160 --> 00:01:24.800] It only comes every four years, unlike Evan's, which apparently is every year, according to the IRS.
[00:01:25.200 --> 00:01:26.240] But it is very busy.
[00:01:26.240 --> 00:01:30.720] A lot of people want to know what's going to happen, and unfortunately, I don't know.
[00:01:30.720 --> 00:01:32.000] So I'll just get that out of the gates.
[00:01:32.480 --> 00:01:33.200] You don't know what's going to happen.
[00:01:33.680 --> 00:01:35.120] How do you call yourself a scientist?
[00:01:35.360 --> 00:01:35.760] I know.
[00:01:36.240 --> 00:01:37.120] How is this a science?
[00:01:37.120 --> 00:01:40.880] It's become an absolute certitude that's going to happen.
[00:01:40.880 --> 00:01:42.720] 40%, that's the answer.
[00:01:43.920 --> 00:01:46.880] I won't tell you of what, but that's the definite correct answer.
[00:01:47.200 --> 00:01:48.560] The answer is three.
[00:01:48.560 --> 00:01:49.520] Yep, yep, for sure.
[00:01:50.800 --> 00:01:54.400] It's also a time when everyone claims to be a political scientist.
[00:01:54.400 --> 00:01:57.360] I don't know if there are times, Steve, when people claim to be neurologists.
[00:01:57.360 --> 00:01:58.040] Maybe if they are.
[00:01:58.120 --> 00:01:58.840] Oh, yeah.
[00:01:58.000 --> 00:02:00.200] Everyone're a doctor.
[00:02:00.200 --> 00:02:03.320] Everyone thinks they're an expert on whatever they've experienced in their life.
[00:01:59.920 --> 00:02:04.360] Yeah, yeah.
[00:02:04.600 --> 00:02:09.800] So everyone's actually telling me how the election is going to go when I don't have an answer.
[00:02:10.440 --> 00:02:11.000] Right.
[00:02:11.000 --> 00:02:13.000] And nobody has the answer.
[00:02:13.000 --> 00:02:13.720] Right, right.
[00:02:14.040 --> 00:02:15.720] What irritates you the most?
[00:02:15.720 --> 00:02:19.640] What does the media get wrong the most?
[00:02:19.640 --> 00:02:27.160] Well, I think it's, you know, so I was speaking at a conference a couple days ago, and they referenced politics and said, don't worry, we're not going to argue and scream.
[00:02:27.160 --> 00:02:28.280] This isn't politics.
[00:02:28.280 --> 00:02:34.440] And what gets me angry is that politics doesn't have to be screaming, and political science is certainly not screaming.
[00:02:34.440 --> 00:02:38.680] We're a bunch of nerds looking at spreadsheets about people participating in politics.
[00:02:38.680 --> 00:02:46.280] But generally speaking, this idea that because it's politics, it has to be an angry fight really stresses me out.
[00:02:46.280 --> 00:02:50.280] And a lot of what I love about political science is: yes, there are things to be angry about.
[00:02:50.280 --> 00:02:54.440] There are real-world ramifications that I personally, as a human, am furious about.
[00:02:54.440 --> 00:03:00.520] But there's also a lot that we can do to kind of take a bigger picture and say, well, what's actually working and what isn't working?
[00:03:00.520 --> 00:03:01.640] And how can we improve things?
[00:03:01.640 --> 00:03:06.920] And, you know, we can talk, even if we disagree about our political views, we can actually have conversations.
[00:03:06.920 --> 00:03:10.360] And it really frustrates me that people assume that you can't.
[00:03:10.360 --> 00:03:15.320] And it wasn't that long ago that people who disagreed with one another could, you know, have a conversation.
[00:03:15.320 --> 00:03:15.720] Right.
[00:03:15.720 --> 00:03:17.000] It's easy for me to say that.
[00:03:17.000 --> 00:03:21.080] I haven't spoken to someone I disagree with vehemently in a really long time.
[00:03:21.720 --> 00:03:22.200] I haven't.
[00:03:22.280 --> 00:03:28.440] I've had pleasant conversations with people on the opposite end of the political spectrum from myself.
[00:03:28.680 --> 00:03:30.840] Just agree that that's what you're going to do.
[00:03:30.840 --> 00:03:31.240] Right.
[00:03:31.240 --> 00:03:43.240] I mean, and this is also, again, the hardest part maybe about being a political scientist right now is trying to be as non-trying to separate my partisan identity from my work as a scientist, right?
[00:03:43.960 --> 00:03:44.360] How do you?
[00:03:44.520 --> 00:03:46.320] Yeah, it's difficult.
[00:03:46.320 --> 00:03:46.720] Yeah.
[00:03:47.360 --> 00:03:54.400] One thing that helps is that my particular area of research is about China, and I have less personally at stake.
[00:03:54.720 --> 00:04:07.760] Obviously, we all have personal things personally at stake with what China does and how they behave and the decisions they make, but it's less, it riles me up a little bit or a lot less than, say, you know, a particular governor doing something that I find to be egregious.
[00:04:07.920 --> 00:04:13.440] Andrea, I'm sorry, for clarification, are you talking about Chinese politics or how China influences U.S.
[00:04:13.440 --> 00:04:14.080] politics?
[00:04:14.720 --> 00:04:16.320] I talk about Chinese politics.
[00:04:16.320 --> 00:04:18.400] I study Chinese media specifically.
[00:04:19.360 --> 00:04:21.600] Which the United States media, I haven't studied this.
[00:04:21.600 --> 00:04:22.880] This is not my scientific take.
[00:04:22.880 --> 00:04:25.040] This is my citizen take.
[00:04:25.200 --> 00:04:25.680] The U.S.
[00:04:25.680 --> 00:04:29.520] media increasingly looks like just two Chinese medias fighting each other.
[00:04:29.520 --> 00:04:37.520] But insofar as you have media outlets that roughly say what the government or the party talking points are.
[00:04:37.920 --> 00:04:40.960] Yeah, but it is, this time of year is very hard to separate those two.
[00:04:40.960 --> 00:04:44.400] I mean, in the United States, for many years, it's been hard to separate those two.
[00:04:44.400 --> 00:04:49.120] I have some friends who study professionally things they care about personally.
[00:04:49.120 --> 00:04:55.120] So I do a lot of, I'm very interested in things around, you know, capital punishment and abortion access, but I don't research that.
[00:04:55.120 --> 00:05:00.880] And part of the reason I don't research that is I don't think I could be a particularly good scientist from an ethics perspective.
[00:05:00.880 --> 00:05:05.520] But I have friends who do manage to do that, and it's hats off to them.
[00:05:05.520 --> 00:05:06.160] Yeah, right?
[00:05:06.160 --> 00:05:06.800] Yeah.
[00:05:06.800 --> 00:05:11.600] Andrea, do you know, like, a lot of information comes out of the U.S.
[00:05:11.600 --> 00:05:14.320] government about what the government is doing?
[00:05:14.320 --> 00:05:14.880] Yes.
[00:05:14.880 --> 00:05:19.600] And, you know, there's just endless debates online or whether or not that data is trustworthy.
[00:05:20.160 --> 00:05:21.680] Do you have any opinion on that?
[00:05:21.680 --> 00:05:23.840] Like, is there any insight you have about that?
[00:05:23.840 --> 00:05:27.120] Do you mean like, so like jobs data or crime data that everyone has?
[00:05:27.600 --> 00:05:28.320] All those statistics.
[00:05:28.560 --> 00:05:28.960] Yeah.
[00:05:28.960 --> 00:05:32.360] Well, Jay, I'm so glad you asked because I'm actually launching a new podcast.
[00:05:32.360 --> 00:05:43.160] I did not plan on plugging this, but I had a podcast about data and where it comes from, and a couple of the episodes are specifically about data from, let's say, quote-unquote, the government.
[00:05:43.160 --> 00:06:00.680] And I guess there is no real answer in the sense that each one of these data sets, whether it's jobs or crime or inflation or whatever, comes from, of course, imperfect series of sources, of methods of collection, of ways of reporting, and ways of aggregating it.
[00:06:00.680 --> 00:06:03.480] I mean, crime data is very, very messy and very difficult.
[00:06:03.480 --> 00:06:07.000] Like a crime takes place, that doesn't mean a number shows up in a spreadsheet.
[00:06:07.000 --> 00:06:12.680] It has to go through a lot of people with a lot of different incentives in order to end up in that spreadsheet, for example.
[00:06:12.680 --> 00:06:18.840] But I don't, you know, and the jobs data is revised, and half the country decides that that means you throw away all the data.
[00:06:18.840 --> 00:06:25.800] And I think the whole thing is talked about in a frustrating way because all data is imperfect.
[00:06:26.120 --> 00:06:33.080] And basically, if the data is pointing in a direction that my political side likes, then we say, yes, it's government data.
[00:06:33.080 --> 00:06:33.880] We believe it.
[00:06:33.880 --> 00:06:38.120] And then if it's pointing at a direction that my political side doesn't like, we say, see, there's an error.
[00:06:38.120 --> 00:06:39.320] C, they issued a correction.
[00:06:39.320 --> 00:06:40.520] C, they revised these numbers.
[00:06:40.520 --> 00:06:41.560] C, this was wrong.
[00:06:41.560 --> 00:06:47.160] And the real answer is that, and you all know this, is that like any piece of data, any study, there are going to be errors in it.
[00:06:47.160 --> 00:06:48.760] We're going to update it over time.
[00:06:48.760 --> 00:06:51.000] We're going to learn more and we're going to collect things better.
[00:06:51.000 --> 00:06:54.520] And so the entire conversation, some of it's right and some of it's wrong.
[00:06:54.520 --> 00:07:00.280] Generally, I'm inclined to roughly take seriously the broader trends.
[00:07:00.280 --> 00:07:05.160] I don't think it's useful to argue over whether the increase was 0.01 or 0.02.
[00:07:05.160 --> 00:07:06.360] I don't think that matters.
[00:07:06.360 --> 00:07:08.680] And I think it's way too noisy to say anything specific.
[00:07:08.680 --> 00:07:15.000] But we can say things like broadly, some evidence is coming out that like murder went down in 2023.
[00:07:15.360 --> 00:07:22.960] I don't think we can say much more than that as far as like specific percentages, but I am relatively confident in that particular claim.
[00:07:22.960 --> 00:07:27.280] But I wouldn't say much more than that, and we definitely can't say anything causal about any of this stuff.
[00:07:27.760 --> 00:07:38.880] Yeah, I mean, just in general, when you hear job statistics and you hear, you know, you read about economic information, there's information that comes from the government, but then there's economists that write about it.
[00:07:38.880 --> 00:07:39.440] Right.
[00:07:39.440 --> 00:07:46.480] And, you know, even though they have their internal debates, there is somewhat of a consensus of what's going on and what the truth is.
[00:07:46.480 --> 00:07:46.800] Right.
[00:07:46.800 --> 00:07:54.240] And I just find it very challenging to convince people who don't like the results of that data that this is the data.
[00:07:54.240 --> 00:07:59.360] This is how we judge presidencies and bills and all that stuff.
[00:08:00.400 --> 00:08:01.520] This is how it's done.
[00:08:02.160 --> 00:08:09.120] How else can you do it if you're not looking at data that's coming from the departments that are spending that money and running these projects?
[00:08:09.120 --> 00:08:10.480] You're absolutely right, Jay.
[00:08:10.480 --> 00:08:13.680] And I agree that you can't just ignore data that you don't like.
[00:08:13.680 --> 00:08:29.440] And frankly, President Trump, when he was president, made the point of, oh, if we stop measuring, giving people COVID tests, the COVID numbers will go down, is the perfect illustration of the fact that what's in data and what's in reality do not necessarily track exactly correctly, right?
[00:08:29.440 --> 00:08:32.800] We would have still been having COVID even if the numbers went down because we weren't testing.
[00:08:33.040 --> 00:08:33.840] But you're absolutely right.
[00:08:33.840 --> 00:08:37.200] I mean, there are measures that we all sort of roughly agree on.
[00:08:37.200 --> 00:08:38.960] So take something like GDP, right?
[00:08:38.960 --> 00:08:47.040] It's a measure of productivity in the economy and it measures the US GDP versus Canada's GDP, GDP per capita, all those things.
[00:08:47.040 --> 00:08:56.240] It's a very messy, very imperfect, rough metric, and it goes up as horrible things like terrorism and natural disasters can cause GDP to go up.
[00:08:56.240 --> 00:09:10.440] And so we can make claims that indicate health of the economy that maybe under the hood really aren't, but it's still a useful piece of information, and it's one that has some consistency from quarter to quarter, year to year, decade to decade, roughly.
[00:09:10.440 --> 00:09:11.800] So we can still learn things.
[00:09:11.800 --> 00:09:19.320] My friend Drumel, who is a journalism professor and a researcher on polls at Columbia University, basically put it well to me the other day.
[00:09:19.320 --> 00:09:22.680] He was like, we can learn from data, we just can't worship data.
[00:09:22.680 --> 00:09:25.400] And I think that's a really nice way to summarize it.
[00:09:25.400 --> 00:09:31.320] The problem, yeah, is that people pick on the one problem they have and say, therefore, we throw it all away.
[00:09:31.320 --> 00:09:35.160] Or I agree with it, therefore we say data is capital T truth.
[00:09:35.160 --> 00:09:36.920] And both of those interpretations are incorrect.
[00:09:36.920 --> 00:09:38.600] Yeah, so in other words, like all of science.
[00:09:38.920 --> 00:09:39.720] Yeah, exactly.
[00:09:40.760 --> 00:09:43.400] We talk about all the time.
[00:09:43.400 --> 00:09:51.480] And this makes our messaging as skeptics and science communicators very, very difficult because they say all data is imperfect.
[00:09:51.480 --> 00:09:51.960] Yes.
[00:09:51.960 --> 00:09:54.920] And I just went through this, you know, on just a very simple question.
[00:09:54.920 --> 00:09:56.920] You know, what's the effect of net metering on it?
[00:09:56.920 --> 00:09:58.040] I'm not going to talk about this again.
[00:09:58.040 --> 00:09:58.920] Don't worry about it.
[00:09:59.720 --> 00:10:00.120] Come on here.
[00:10:00.520 --> 00:10:01.640] Our email box is full of it.
[00:10:01.880 --> 00:10:05.560] It ultimately came down to like, do you believe the studies or not?
[00:10:05.560 --> 00:10:06.760] That's what it came down to.
[00:10:06.760 --> 00:10:08.280] And of course they're not perfect.
[00:10:08.280 --> 00:10:12.200] So you can say, yeah, but this is all the data we have and this is basically what it shows.
[00:10:12.200 --> 00:10:17.960] And on the other side, they could say, yeah, but that study was done by a proponent of solar power, so I don't trust it.
[00:10:17.960 --> 00:10:21.320] And this study was done by a utility, so the other side doesn't trust it.
[00:10:21.320 --> 00:10:21.960] Or whatever.
[00:10:21.960 --> 00:10:27.320] Or you could just say that they're, you know, I just don't believe it because it doesn't match what I want to believe.
[00:10:27.320 --> 00:10:29.080] So it's true pretty much of everything.
[00:10:29.480 --> 00:10:29.720] Right.
[00:10:29.720 --> 00:10:34.040] What I love, though, is when people argue against, because data come in different forms, right?
[00:10:34.040 --> 00:10:39.320] Like we were just talking about all the different ways that something can be measured and all the different ways that something can be interpreted.
[00:10:39.320 --> 00:10:40.280] Interpreted?
[00:10:40.280 --> 00:10:41.080] Interpretated.
[00:10:41.880 --> 00:10:48.640] I would say the same thing about demographic data, but I think that demographic data in some ways is like a different animal.
[00:10:48.960 --> 00:10:57.120] And I just, I love it when people just argue against demographic data in such a passionate way.
[00:10:57.120 --> 00:11:01.280] And that's always amazing with me, just like these claims that are data-less.
[00:11:01.920 --> 00:11:06.480] And if I just repeat it enough times, somebody's going to buy into it.
[00:11:06.480 --> 00:11:07.520] No, you can say anything.
[00:11:07.680 --> 00:11:10.240] And this is true with any field as well.
[00:11:10.400 --> 00:11:21.040] Politics gets us really riled up, but it's true anywhere where, you know, the kind of louder and more with certainty you say a number or a result, the more people are inclined to believe you.
[00:11:21.040 --> 00:11:30.000] And unfortunately, most people aren't reading original studies and they aren't looking at data and they haven't been trained in data literacy or critical thinking and they don't all listen to your podcast.
[00:11:30.000 --> 00:11:39.040] And so really we just go off of what political scientists and many people call elite cues, which is this guy is the one I trust, therefore I'll believe the numbers he says.
[00:11:39.040 --> 00:11:45.200] And so if someone in a debate says this is happening and that's happening, then I'm going to believe them without questioning it.
[00:11:45.200 --> 00:11:47.200] And then what was the point of the research?
[00:11:47.200 --> 00:11:49.600] You know, I mean, and this happens outside of politics too.
[00:11:49.600 --> 00:11:52.240] Like I'll do a research project and show the results.
[00:11:52.240 --> 00:11:57.520] And then, yeah, even when there aren't political stakes, people are just like, nah, I don't like it.
[00:11:57.520 --> 00:11:58.720] And so you just dismiss the whole thing.
[00:11:59.920 --> 00:12:00.560] Okay.
[00:12:01.520 --> 00:12:03.440] You know, or there's the motivated reasoning, right?
[00:12:03.600 --> 00:12:13.600] Every study is imperfect, but you find the flaws in the ones whose conclusions you don't like and you gloss over the flaws in the ones whose again, this is exactly what you all are talking about all the time.
[00:12:13.600 --> 00:12:13.840] Yeah.
[00:12:14.080 --> 00:12:14.240] Yeah.
[00:12:15.520 --> 00:12:17.360] Because no scientific study is perfect.
[00:12:17.360 --> 00:12:17.600] Exactly.
[00:12:17.760 --> 00:12:20.160] So if you want to find fault with it, you can.
[00:12:21.040 --> 00:12:24.400] Yeah, so you have to give people an understanding.
[00:12:24.400 --> 00:12:27.440] And this is where you have to get wonky, right?
[00:12:27.440 --> 00:12:28.560] There's just no way around it.
[00:12:28.560 --> 00:12:35.960] You've got to get down into the weeds, or you've got to listen to somebody, again, that you trust who gets wonky for you and sort of summarizes for you.
[00:12:36.040 --> 00:12:46.120] Like, this is why scientists don't really put a lot of weight on this data, or this is why this is a consistent result that keeps coming up over and over again.
[00:12:46.120 --> 00:12:50.360] And we're getting pretty confident that this is telling us something about reality.
[00:12:50.600 --> 00:12:58.840] But yeah, people don't like talking in those squishy terms about it could be true, it probably true, it, you know, whatever.
[00:12:59.320 --> 00:13:07.160] They want more absolutes, and it's just so much simpler just to listen to the guy that agrees with your tribe.
[00:13:08.040 --> 00:13:17.240] And then, and Kara, you tell me I'm probably going to get this wrong, but then you get the dopamine hit or the adrenaline hit when they say something that makes you outraged or makes you scared or makes you whatever.
[00:13:17.240 --> 00:13:22.920] And so you listen to it from like a, I don't know, a salivating dog, Pavlovian kind of way, too.
[00:13:22.920 --> 00:13:23.960] And Steve, you're right.
[00:13:23.960 --> 00:13:31.880] I mean, humans are bad at interpreting and making sense of things like probabilities and confidence intervals and uncertainty under the best of circumstances.
[00:13:31.880 --> 00:13:47.240] And nothing in our media environment right now, between social media and headlines and 24-hour news and sound bites of presidents saying wild things in debates, you know, nothing about that lends itself to thoughtful discussion of these things.
[00:13:47.240 --> 00:13:57.320] You know, and even when I'm teaching it, and maybe this is just a fault on my teaching, you know, I have 14 weeks to help students think probabilistically, and I don't know that I succeed.
[00:13:57.320 --> 00:14:02.200] You know, it's not intuitive per se unless you've really had a lot of exposure to it.
[00:14:02.200 --> 00:14:05.800] And even then, you kind of have to be thoughtful because we just want certainty, I think, as people.
[00:14:06.200 --> 00:14:11.480] Well, yeah, and we only have so much brain power, right, to devote to these kinds of questions in our lives.
[00:14:12.120 --> 00:14:14.440] We're all doing a million other things at once.
[00:14:14.520 --> 00:14:18.000] Really, how much effort and time can a person reasonably put into this?
[00:14:18.000 --> 00:14:19.520] Well, it depends if it's your job or not.
[00:14:20.080 --> 00:14:21.600] It depends on if it's your job, yeah.
[00:14:21.600 --> 00:14:23.600] But for the majority of people, it is not their job.
[00:14:23.760 --> 00:14:24.160] It isn't.
[00:14:24.160 --> 00:14:25.280] Yeah, no, I agree.
[00:14:25.280 --> 00:14:33.280] It's like in medicine, we spend eight to ten years teaching students how to think probabilistically in very complicated and nuanced ways.
[00:14:33.280 --> 00:14:37.040] It's hard, and it's hard to get it right, and you fall for a lot of pitfalls.
[00:14:37.600 --> 00:14:40.000] So you're dealing with kind of several layers of things.
[00:14:40.000 --> 00:14:43.680] On the one layer, we're just our brains are good at some things and not good at other things.
[00:14:43.680 --> 00:14:45.600] Like statistics, we're terrible.
[00:14:45.600 --> 00:14:54.400] And then on the other side, we also have motivation not to be good, you know, not to be good at thinking when it doesn't lead to conclusions that we like.
[00:14:54.400 --> 00:15:02.400] But I do think, you know, I don't want to give the impression that we're in a business as usual kind of period in American politics.
[00:15:03.280 --> 00:15:07.600] I don't think that's what you're saying, but I want to give that impression because there are degrees.
[00:15:07.600 --> 00:15:10.320] You know, if there's motivated reasoning, then there's motivated reasoning.
[00:15:10.880 --> 00:15:23.280] There is what we would call spin, right, where there's a core of facts and both sides try to spin reality and emphasize the facts that they prefer, et cetera.
[00:15:23.280 --> 00:15:24.880] And then there's gaslighting, right?
[00:15:24.880 --> 00:15:33.520] Then there's like an order of magnitude where you just chuck out reality and substitute it with a complete and utter knowing fabrication.
[00:15:33.520 --> 00:15:39.280] These are not equivalent just because they both have a strained relationship with the truth.
[00:15:39.520 --> 00:15:44.720] There are orders of magnitude difference between these kind of levels of behavior.
[00:15:44.720 --> 00:15:53.040] And don't you agree that we're in not saying this has never happened before, but we're in a period of time that is different than it was certainly 20 or 30 years ago.
[00:15:53.040 --> 00:15:53.520] Oh, for sure.
[00:15:53.520 --> 00:15:55.200] And for so many reasons, too, right?
[00:15:55.200 --> 00:16:05.960] I mean, the media landscape and the way we talk to each other and the way we consume and hear information is changing by the second, basically, especially with generative AI.
[00:16:05.960 --> 00:16:08.440] But even in the last 10 years, I think that's changed incredibly.
[00:16:08.440 --> 00:16:21.800] And yes, every other underlying force that's contributed to the various types of disagreements and polarization, and whether it's between leaders and citizens and the way we talk about things and it's over issues or it's more affective polarization where we just simply dislike the other person.
[00:16:22.120 --> 00:16:22.920] It's very different.
[00:16:22.920 --> 00:16:26.760] And I think, you know, Steve, at the beginning, you asked what's the most frustrating part.
[00:16:26.760 --> 00:16:35.160] I think I'll update my answer to say that, you know, a lot of times when I tell people I'm a political scientist, people say, well, politics isn't a science.
[00:16:35.160 --> 00:16:38.680] And it's like, well, politics isn't, but you can study politics in a scientific way.
[00:16:39.400 --> 00:16:39.800] Right.
[00:16:40.120 --> 00:16:52.520] But that means assuming there is some, to your point just now, assuming there is something to consistency in the underlying, say, reality that we're studying or the data generating processes.
[00:16:52.520 --> 00:16:59.480] Like, we are assuming that studies I did 10 years ago, 50 years ago, can help me understand the present moment.
[00:16:59.480 --> 00:17:06.680] And in some cases, it can, sort of the underlying things like what motivates someone to show up to vote, what makes someone have the political views that they do well.
[00:17:06.680 --> 00:17:08.200] It depends a lot on their parents and where they grow up.
[00:17:08.280 --> 00:17:10.120] You know, those sorts of things don't tend to change.
[00:17:10.120 --> 00:17:14.840] But a lot of the way we're doing politics and the environment of politics has changed a lot.
[00:17:14.840 --> 00:17:26.200] And so it's really an open question how much of even research from, say, the Obama or the Bush administration applies to now because it just, and again, here's me as a citizen, it just feels so different and bad.
[00:17:26.920 --> 00:17:30.280] It's like what used to motivate people to vote might be totally different.
[00:17:30.280 --> 00:17:33.800] And yeah, we used to all agree on underlying facts, and we simply don't.
[00:17:33.800 --> 00:17:38.760] And I don't know what that does to things we thought we knew about how humans behave politically.
[00:17:38.760 --> 00:17:41.160] Yeah, it's like a game where the rules can change.
[00:17:41.160 --> 00:17:41.720] Exactly.
[00:17:41.720 --> 00:17:45.360] Yeah, and you can't, if you're playing by the old rules, you're going to get it wrong.
[00:17:45.680 --> 00:17:46.160] Exactly.
[00:17:44.920 --> 00:17:46.480] Exactly.
[00:17:46.720 --> 00:17:52.400] And so you're studying it on two levels: what happens with these rules?
[00:17:52.400 --> 00:17:55.200] And then also, oh, and then how are the rules changing over time?
[00:17:55.760 --> 00:18:06.720] There's a really interesting, just one tiny example of research that I think is really interesting is from a couple of scholars, I believe they're at Syracuse, but don't quote me on that, who study the role of anxiety in politics.
[00:18:06.720 --> 00:18:15.600] And I think Kara and Steve, you'd have a lot to say about this, but basically, they kind of try to hit on this and say, look, anxiety has always played a role in politics.
[00:18:15.600 --> 00:18:25.120] It's one of the things fear and other things related to anxiety do motivate us to behave in certain ways in the political sphere, whether it's voting or campaigning or what have you.
[00:18:25.120 --> 00:18:32.640] And basically, they say, look, the level of anxiety in the United States political discourse right now has never been higher.
[00:18:32.640 --> 00:18:39.360] Is it the case that these experiments we ran even 10 years ago still apply today because you're exposed to just so much more?
[00:18:39.360 --> 00:18:41.200] And we don't know.
[00:18:41.520 --> 00:18:50.080] And unfortunately, the consequences of all these things are going to play out in, you know, a month and a half from now, and we're not going to have the studies about it for four years.
[00:18:50.080 --> 00:18:52.480] So should we be angry with you about this?
[00:18:52.960 --> 00:18:55.920] And I do apologize for everything, I have to say.
[00:18:55.920 --> 00:18:57.040] For all of American politics.
[00:18:57.280 --> 00:18:57.840] I'm so sorry.
[00:18:57.840 --> 00:18:59.920] Yeah, I really botched it.
[00:19:00.560 --> 00:19:05.840] We're going to come back to this when you talk about your new segment, but we're going to go through the regular show now.
[00:19:05.840 --> 00:19:08.640] We're going to start with Kara doing a what's the word?
[00:19:08.640 --> 00:19:09.600] What's the word?
[00:19:09.600 --> 00:19:09.920] What?
[00:19:09.920 --> 00:19:11.040] I just made up that song.
[00:19:11.040 --> 00:19:11.680] I like it.
[00:19:13.600 --> 00:19:22.160] So I'm going to talk about a word that was recommended by William Patterson from Sonoma County.
[00:19:22.160 --> 00:19:26.960] He said, I think a good word to analyze is pitch.
[00:19:26.960 --> 00:19:31.640] It has tons of different meanings, some of which are completely unrelated.
[00:19:31.640 --> 00:19:33.160] And you are right, William.
[00:19:33.160 --> 00:19:39.560] I don't think we will have time to talk about every single definition of the word pitch.
[00:19:39.560 --> 00:19:40.680] But let's start.
[00:19:40.680 --> 00:19:53.160] by kind of digging into the etymology a little bit, because in most of the places that I looked, the word pitch is divided into two separate etymological roots.
[00:19:53.160 --> 00:19:57.960] So it's sort of like a convergent evolution thing happening with this word.
[00:19:57.960 --> 00:20:03.400] The first one is the one that I think many of us are probably the least familiar with.
[00:20:03.400 --> 00:20:05.480] And that is like this substance.
[00:20:05.480 --> 00:20:08.600] It's sap, basically, pitch that comes from trees.
[00:20:08.600 --> 00:20:11.160] It's sticky, you know, and it comes out of trees.
[00:20:11.160 --> 00:20:14.120] And you also, it's core, it gets everywhere.
[00:20:14.360 --> 00:20:23.480] You also have another variant of that, which is this viscous material that is left over after you distill crude oil.
[00:20:23.800 --> 00:20:24.680] Petroleum, right?
[00:20:24.680 --> 00:20:26.440] Yeah, that's also called pitch.
[00:20:26.440 --> 00:20:29.560] And in geology, there's pitch stone.
[00:20:29.560 --> 00:20:41.480] And so these all come from an original root that really kind of going all the way back from like the Middle English to the Old English to the Latin to with the cognate in the ancient Greek.
[00:20:41.480 --> 00:20:47.320] Really across the board, the roots for this just mean the word sap or sap from a pine.
[00:20:47.320 --> 00:20:52.120] You'll often see pine being utilized in the definitions.
[00:20:52.120 --> 00:20:56.760] And that's where the term pitch black or pitch dark comes from.
[00:20:56.760 --> 00:20:57.320] Does that make sense?
[00:20:57.400 --> 00:21:00.520] I was going to ask if that I never questioned it before.
[00:21:00.520 --> 00:21:01.240] But yeah.
[00:21:01.480 --> 00:21:06.120] So that comes from that deep dark color of the sap from that pine.
[00:21:06.120 --> 00:21:16.720] Now there's another etymological route, which I can't, so I'm not going to pronounce Middle English here, but like pitchin' or piken that goes to the Old English, that goes down to the Germanic.
[00:21:17.040 --> 00:21:23.680] And that route has to do with thrusting or fastening.
[00:21:23.680 --> 00:21:24.800] Like pitching a tent?
[00:21:26.000 --> 00:21:26.880] Or picking.
[00:21:26.880 --> 00:21:27.280] Yeah.
[00:21:27.280 --> 00:21:31.680] So you can throw a pitch at a baseball game.
[00:21:32.080 --> 00:21:36.000] You can pitch a tent by driving stakes into the ground.
[00:21:37.360 --> 00:21:42.080] The distance between some objects is considered a pitch.
[00:21:42.320 --> 00:21:46.240] The angle at which an object sits is considered to be a pitch.
[00:21:47.360 --> 00:21:55.520] Very often, like in UK English, the area where a campsite sits or certain types of sports fields are called the pitch.
[00:21:56.400 --> 00:21:58.240] On the pitch, exactly.
[00:21:58.240 --> 00:22:14.560] And that all does seem to come back to that early about, that early definition around thrusting or driving a stake or piercing with a sharp point, you know, those stakes of your tent and throwing things and even pitching in, working really vigorously.
[00:22:15.600 --> 00:22:16.000] What a cool thing.
[00:22:16.160 --> 00:22:18.240] Now the muse, yeah, it's a really cool word.
[00:22:18.240 --> 00:22:20.000] It has so many different definitions.
[00:22:20.000 --> 00:22:22.560] The musical sense came much later.
[00:22:22.560 --> 00:22:26.000] That came around the 1630s.
[00:22:26.000 --> 00:22:31.600] And around the same time was the term that we use for ships, like their motion, right?
[00:22:31.920 --> 00:22:33.520] They pitch on the ocean.
[00:22:33.520 --> 00:22:35.440] They kind of rise and fall.
[00:22:35.440 --> 00:22:40.560] And so those seem to be slightly newer definitions of the term.
[00:22:40.560 --> 00:22:43.520] But it does have scientific definitions as well.
[00:22:43.520 --> 00:22:50.560] We think of pitch as the frequency, right, of a sound wave is going to give us its pitch.
[00:22:50.560 --> 00:22:59.040] And often you'll see pitch used in like mathematics and geometry to describe distances between things and angles.
[00:22:59.040 --> 00:23:06.360] So it's a super interesting word that has these sort of similar but different roots.
[00:23:06.760 --> 00:23:10.440] And I think we use it all the time without recognizing it.
[00:23:10.760 --> 00:23:12.840] So I love that.
[00:23:12.840 --> 00:23:17.160] I love when you have a word that's really, really old and it just evolves and evolves and evolves.
[00:23:17.160 --> 00:23:20.840] And you can sort of trace it back to where it started.
[00:23:20.840 --> 00:23:23.560] And sometimes the modern usage feels wildly different.
[00:23:23.560 --> 00:23:29.320] But if you go through this series of changes, it all kind of follows an understandable pattern.
[00:23:29.640 --> 00:23:36.600] I remember when we were in Atlanta for an event, there's peach tree, you know, the area around Atlanta.
[00:23:36.600 --> 00:23:44.040] And we were told, and this is what I found when I looked into it, that actually derives from pitch tree.
[00:23:44.360 --> 00:23:50.440] And it's sort of a bastardization of that, like the original, it was actually a pitch tree, but people then confused it with peach tree.
[00:23:50.440 --> 00:23:51.800] And that's the name that stuck.
[00:23:51.800 --> 00:23:53.400] But Georgia is the peach state.
[00:23:53.400 --> 00:23:56.120] So you can sort of understand why they would have made that connection.
[00:23:56.440 --> 00:23:57.800] Or is Georgia the pitch state?
[00:23:57.800 --> 00:23:58.760] And we've messed it up all the time.
[00:23:58.840 --> 00:23:59.320] Oh, no.
[00:23:59.560 --> 00:24:00.760] Change the license plates.
[00:24:01.000 --> 00:24:02.360] Oh, the pitch there is delicious.
[00:24:03.000 --> 00:24:03.800] Fresh peach.
[00:24:03.960 --> 00:24:04.760] Pitch pie.
[00:24:05.720 --> 00:24:14.120] When I was digging into this word, you go to some obscure places on the internet because you're trying to learn about, you know, how people are grappling with these things.
[00:24:14.120 --> 00:24:18.920] And I found a forum, like a rock climbing or mountaineering forum.
[00:24:18.920 --> 00:24:26.360] And apparently, the word pitch is often used to describe a segment of a long climb between two, how do you pronounce this word?
[00:24:26.360 --> 00:24:27.160] Belays?
[00:24:27.160 --> 00:24:28.040] Belays?
[00:24:28.040 --> 00:24:28.600] Belays?
[00:24:28.920 --> 00:24:29.240] I think so.
[00:24:29.480 --> 00:24:29.960] Okay.
[00:24:29.960 --> 00:24:31.160] Yeah, because I'm not a rock climber.
[00:24:31.160 --> 00:24:31.960] I have no idea.
[00:24:31.960 --> 00:24:41.800] So on this forum, one person's like, oh, it came from the days before rock climbing when climbers used to get their jollies by climbing large trees, pine and spruce trees for the most part.
[00:24:42.120 --> 00:24:46.320] And so they would accumulate all this sap on their hands.
[00:24:44.440 --> 00:24:50.720] So trees that have a lot of sap would mean that like a pitch was typically short.
[00:24:51.040 --> 00:24:53.680] But now they use ropes and ropes are longer.
[00:24:53.680 --> 00:24:58.960] And then somebody else was like, okay, right track, totally wrong definition.
[00:24:59.600 --> 00:25:11.280] That's actually it does come from tree climbing, but the climbing style back then involved throwing a rope as high as possible over a branch and then going up that way.
[00:25:11.280 --> 00:25:17.440] So they would measure the height of a tree by the number of times they had to throw or pitch the rope to get to the top of the tree.
[00:25:17.440 --> 00:25:19.520] And they're saying that's where the word pitch comes from.
[00:25:19.520 --> 00:25:23.360] And then it devolves into them arguing over which one is an urban legend.
[00:25:24.480 --> 00:25:26.000] Classic internet.
[00:25:26.640 --> 00:25:30.400] But also, like, I guess that's what happens when a word has so many different definitions.
[00:25:30.400 --> 00:25:37.840] It's easy to come up with some sort of lore around why this new usage came to be.
[00:25:37.840 --> 00:25:45.920] And you can kind of, yeah, tell a story based on like, oh, there used to be trees on fields, and now we play sports on fields, therefore it's a pitch, right?
[00:25:45.920 --> 00:25:48.640] Like I could make up a story based on a different definition.
[00:25:49.440 --> 00:25:49.840] Yep.
[00:25:49.840 --> 00:25:52.400] Whose ever story wins had the best sales pitch?
[00:25:53.600 --> 00:25:53.920] See that?
[00:25:54.960 --> 00:26:00.640] All right, Jay, tell us about preserving the human genome forever.
[00:26:00.640 --> 00:26:01.680] Oh, thank goodness.
[00:26:01.680 --> 00:26:02.080] All right.
[00:26:02.080 --> 00:26:05.920] So, I mean, you know, this one's cool, but it's also kind of strange.
[00:26:05.920 --> 00:26:06.960] So scientists.
[00:26:07.200 --> 00:26:08.480] It's kind of our sweet spot, Jay.
[00:26:08.480 --> 00:26:09.440] Cool but strange.
[00:26:12.560 --> 00:26:23.520] So the scientists, some scientists at the University of Southampton developed this very awesome way to store the entire human genome on a 5D, right?
[00:26:23.520 --> 00:26:25.920] It's a five-dimensional memory crystal.
[00:26:25.920 --> 00:26:31.720] You know, you can think of this as creating a backup of humanity in case something horrible happens, right?
[00:26:29.760 --> 00:26:33.160] Zombie apocalypse, whatever.
[00:26:33.240 --> 00:26:37.880] You know, I mean, your imagination could take you to the places that would end humanity.
[00:26:37.880 --> 00:26:42.760] So the crystal is coin-sized and it's made from fused quartz.
[00:26:42.760 --> 00:26:49.320] And the really amazing thing is that the storage crystal is designed to last billions of years.
[00:26:49.320 --> 00:26:50.680] That's a long time, guys.
[00:26:50.680 --> 00:26:53.560] It can withstand pretty extreme conditions, right?
[00:26:53.560 --> 00:26:55.240] It can handle cosmic radiation.
[00:26:55.240 --> 00:27:03.160] It can handle pretty severe impact forces, wild ranges in temperature, like from freezing to over 1800 degrees Fahrenheit.
[00:27:03.160 --> 00:27:04.680] What would that be in Celsius, guys?
[00:27:04.680 --> 00:27:05.240] It's hot.
[00:27:05.240 --> 00:27:06.280] It's very hot.
[00:27:06.280 --> 00:27:07.320] Celsius hot.
[00:27:07.320 --> 00:27:10.040] Their idea is that in the, you know, whatever.
[00:27:11.000 --> 00:27:12.520] Let me throw a scenario at you guys.
[00:27:12.520 --> 00:27:20.040] In the distant future, you know, some advanced species is searching the ruined surface of the Earth.
[00:27:20.040 --> 00:27:21.800] And, you know, I don't know where they put it.
[00:27:21.800 --> 00:27:29.720] I don't, you know, they didn't really talk about like the delivery mechanism, but like, you know, they enter this old underground thing and they find this crystal, right?
[00:27:29.720 --> 00:27:31.640] And that crystal will have the information.
[00:27:31.640 --> 00:27:36.040] So whatever these robots or advanced species are, they can recreate humanity.
[00:27:36.040 --> 00:27:37.080] Pretty weird, right?
[00:27:37.080 --> 00:27:41.640] That's strange if you think about it, you know, from different perspectives.
[00:27:41.640 --> 00:27:44.920] But, you know, the technology is awesome, right?
[00:27:44.920 --> 00:27:47.240] So it's a crystal.
[00:27:47.240 --> 00:27:56.280] It can store up to 360 terabytes of information and it can keep that information stable for up to 300 quintillion years.
[00:27:56.280 --> 00:27:57.000] Yeah, right?
[00:27:57.000 --> 00:27:58.280] So that's a long time.
[00:27:58.680 --> 00:28:03.240] That's longer than the universe has existed, quite obviously.
[00:28:03.560 --> 00:28:04.520] It's absurdly long.
[00:28:04.520 --> 00:28:08.360] It's over 21 billion times the current age of the universe.
[00:28:08.360 --> 00:28:09.560] How about that for a statistics?
[00:28:09.560 --> 00:28:16.240] So researchers use this ultra-fast lasers to etch tiny voids in the crystal silica structure, right?
[00:28:16.320 --> 00:28:17.360] Sounds pretty simple.
[00:28:14.920 --> 00:28:21.440] And these nanostructures can hold data, but they hold it in five dimensions.
[00:28:21.600 --> 00:28:26.160] And this is where it got its name: height, length, width, orientation, and position.
[00:28:26.160 --> 00:28:28.640] I think, you know, that's really complicated, right?
[00:28:28.640 --> 00:28:30.400] Just on its face, right?
[00:28:30.400 --> 00:28:32.800] The height, length, width, orientation, and position.
[00:28:32.800 --> 00:28:39.040] Imagine trying to reverse engineer that with the complexity of the human genome encoded in it.
[00:28:39.040 --> 00:28:40.960] It is very complicated.
[00:28:40.960 --> 00:28:50.800] So to help whoever or whatever, you know, robots, you know, they find the crystal, to help them understand what's inside, the researchers added some visual clues on the crystal surface.
[00:28:50.800 --> 00:28:57.440] So they included images of the four essential chemical elements for life: hydrogen, oxygen, carbon, and nitrogen.
[00:28:57.440 --> 00:28:58.000] That's nice.
[00:28:58.000 --> 00:29:05.920] You know, hopefully, whatever thing would find this, they'd be able to figure out that, yeah, we know what the elements are because we're an advanced species.
[00:29:05.920 --> 00:29:12.160] So these are the four that you need in order to do what the crystal is going to give you the information for.
[00:29:12.160 --> 00:29:15.760] They also give a depiction of the DNA double helix.
[00:29:15.760 --> 00:29:22.720] So basically, you're going to create this and diagrams of human, man, and women, human men, and women.
[00:29:22.720 --> 00:29:23.200] Right.
[00:29:23.200 --> 00:29:32.640] Okay, so make one of these using these four things and follow the instructions that are encoded in these little voids that are inside this crystal.
[00:29:32.640 --> 00:29:41.040] They're going to, you know, hopefully, whatever future technology is out there, their AI can look at that and figure it out within, you know, maybe a few hundred million years.
[00:29:41.040 --> 00:29:46.080] So there's also a molecular breakdown of chromosomes and their structures within this cell, right?
[00:29:46.080 --> 00:29:47.360] Other data that they're going to need.
[00:29:47.600 --> 00:29:54.080] They're meant to guide whoever's reading these in the future to interpret the stored genome.
[00:29:54.080 --> 00:29:55.200] It's complicated.
[00:29:55.200 --> 00:29:57.360] Very, very complicated.
[00:29:57.360 --> 00:30:03.400] Of course, there's a catch because no one knows whether the technology to read this data will exist in the future.
[00:30:03.560 --> 00:30:19.400] Now, that's a funny way to put the idea that it's not like the future beings that would read this and use this thing are going to find our old technology and they'll just insert it and somehow that'll even make sense to them.
[00:30:19.400 --> 00:30:22.760] They would literally have to reverse engineer the whole thing.
[00:30:22.760 --> 00:30:24.120] Now, keep this in mind.
[00:30:24.120 --> 00:30:25.960] I'll give you a little bit of perspective.
[00:30:26.200 --> 00:30:34.120] Andrea, tell me a device that you owned like 15, you know, an electronic device you owned 15 years ago, 10, 15, 20 years ago.
[00:30:34.120 --> 00:30:39.240] More like 20 years ago, I had drives that I could put floppy disks into.
[00:30:39.240 --> 00:30:44.280] Okay, now imagine taking one of those and trying to make it work today.
[00:30:44.280 --> 00:30:48.280] I have one, and I would like to make it work for some, and I can't.
[00:30:48.840 --> 00:30:57.720] But yeah, but you know, but the idea is you own this thing, you know exactly what it is, you have tons of technology all around you.
[00:30:57.720 --> 00:31:00.200] You could read about this thing forever.
[00:31:00.200 --> 00:31:05.080] You could just keep reading about all the different pieces of information that are out there on the web about it.
[00:31:05.080 --> 00:31:06.600] But could you really make it work?
[00:31:06.600 --> 00:31:09.640] And this is all made by humans in your lifespan, right?
[00:31:09.640 --> 00:31:10.440] And we can't.
[00:31:10.440 --> 00:31:12.920] We can't, you know, you have an iPad or an iPod.
[00:31:12.920 --> 00:31:14.280] Remember our iPods?
[00:31:14.280 --> 00:31:14.760] Sure.
[00:31:14.760 --> 00:31:17.800] Dig out your old one if you still have it and try to put a file on it.
[00:31:17.800 --> 00:31:18.520] Good luck.
[00:31:18.520 --> 00:31:19.080] Yeah, but I don't know.
[00:31:19.480 --> 00:31:21.160] Honestly, that's not a good analogy, Jay.
[00:31:21.400 --> 00:31:28.360] The real question is: could engineers figure out a way to access the data on that floppy disk?
[00:31:28.360 --> 00:31:29.320] Probably.
[00:31:29.640 --> 00:31:34.760] Yeah, I would think, oh, wait, wait, you're talking of Andrea's floppy disk?
[00:31:34.760 --> 00:31:35.320] Of course.
[00:31:35.320 --> 00:31:40.280] I'm just saying it's a complicated mess from our own technology from 20 years ago.
[00:31:40.280 --> 00:31:42.720] Like, I'm just giving you like a joke.
[00:31:43.440 --> 00:31:45.840] Yeah, but to put things into perspective, I mean, you're right.
[00:31:46.160 --> 00:31:58.240] It would require like very advanced, intelligent, not only species, but individuals within that species who are in the equivalent of engineers and scientists, whatever, probably working for years to reverse engineer it.
[00:31:58.240 --> 00:32:00.560] But, I mean, you know, what's the other option, right?
[00:32:00.560 --> 00:32:01.360] It's going to involve.
[00:32:01.760 --> 00:32:02.480] Oh, I know that.
[00:32:02.480 --> 00:32:02.720] I know.
[00:32:02.720 --> 00:32:11.440] I'm just trying to, you know, look, Steve, but I can't help but almost joke about the idea that something is going to be able to reverse engineer this in the future.
[00:32:11.440 --> 00:32:13.280] But of course, what else can you do?
[00:32:13.280 --> 00:32:14.880] Like, if they wanted to do it, they did it.
[00:32:14.880 --> 00:32:17.520] And I don't see there really can't be a simpler way.
[00:32:17.520 --> 00:32:19.600] You want the thing to be self-contained.
[00:32:19.600 --> 00:32:21.280] You want it to last.
[00:32:21.280 --> 00:32:25.360] And you want to give them some information on how to kind of figure it out.
[00:32:25.680 --> 00:32:27.280] Yeah, that's the important thing, right?
[00:32:27.280 --> 00:32:28.880] Like, it's like the gold record.
[00:32:28.880 --> 00:32:32.640] Didn't they actually print how to build a record player on it?
[00:32:32.960 --> 00:32:36.640] No, there was a record player built into the future.
[00:32:36.800 --> 00:32:39.840] But I thought there was also instructions for how to use it.
[00:32:39.840 --> 00:32:40.160] Yeah.
[00:32:40.320 --> 00:32:41.280] Oh, how to use it.
[00:32:41.280 --> 00:32:41.520] Okay.
[00:32:41.520 --> 00:32:41.760] Yeah, yeah.
[00:32:42.000 --> 00:32:45.440] Carrie, you realize that aliens did find that record and they sent us a message.
[00:32:45.440 --> 00:32:46.320] You know what it was?
[00:32:46.320 --> 00:32:47.360] What was it?
[00:32:47.360 --> 00:32:48.800] Send more Chuck Berry.
[00:32:49.360 --> 00:32:50.480] Oh, I remember that.
[00:32:50.480 --> 00:32:51.280] Sorry Night Live.
[00:32:51.280 --> 00:32:51.600] That's right.
[00:32:52.240 --> 00:32:52.320] Yeah.
[00:32:52.640 --> 00:32:53.280] 70s.
[00:32:53.280 --> 00:32:54.080] Oh, boy.
[00:32:54.080 --> 00:32:54.560] Wow.
[00:32:54.560 --> 00:32:55.040] Sorry.
[00:32:55.120 --> 00:32:55.680] I'm old.
[00:32:57.760 --> 00:33:03.200] So there is the issue of recreating a human from genetic data, right?
[00:33:03.200 --> 00:33:07.600] This is something that scientists aren't capable of doing today.
[00:33:07.600 --> 00:33:17.120] You know, there has been tons of advancements in biology and our understanding of all of the material that it would take to actually do this.
[00:33:17.120 --> 00:33:19.440] It is likely that we will be able to do it, right?
[00:33:19.440 --> 00:33:23.600] It's not like something that's out of our, like, we're not saying, oh, my god, this is going to be hundreds of years.
[00:33:23.600 --> 00:33:25.440] Like, it is going to happen.
[00:33:25.440 --> 00:33:26.640] But we can't do it yet.
[00:33:26.640 --> 00:33:29.360] So, we don't really know what it's going to fully take to do it.
[00:33:29.360 --> 00:33:32.040] But it, you know, they are storing the human genome.
[00:33:29.920 --> 00:33:39.320] They want to, like, other institutions and companies are like want to want to store frozen animal cells and craters on the moon.
[00:33:39.320 --> 00:33:40.760] We have seed vaults.
[00:33:40.760 --> 00:33:42.600] You know, this is something that humans do.
[00:33:42.600 --> 00:33:49.560] We keep coming back to this like disaster recovery situation, some of which might be done by humans themselves.
[00:33:49.560 --> 00:33:56.280] Like, hey, we had an extinction and we want to bring back the Hoosiwatzer animal, you know, whatever it is, or bring back this plant.
[00:33:56.280 --> 00:33:58.360] That seems very reasonable to me.
[00:33:58.360 --> 00:34:06.760] But, you know, thinking of aliens or machines in the future finding this thing alone, that's a Herculean effort just to find the crystal.
[00:34:06.760 --> 00:34:11.560] So, anyway, so there is a few little doodads I want to tell you about this thing.
[00:34:11.560 --> 00:34:15.640] So, they were inspired by the Voyager Golden Records.
[00:34:15.640 --> 00:34:19.400] That was one of the inspirations for doing this project.
[00:34:19.400 --> 00:34:26.680] And the other cool thing is there are applications for this data storage beyond the human genome, right?
[00:34:26.680 --> 00:34:29.800] You know, the technology has a lot of broader applications.
[00:34:29.800 --> 00:34:34.840] I don't know how fast the read-write is on it, but that amount of data storage is phenomenal.
[00:34:34.840 --> 00:34:37.400] That's a phenomenal amount of storage, guys, right?
[00:34:38.120 --> 00:34:41.480] You could back up incredible amounts of data with that.
[00:34:41.480 --> 00:34:46.680] So, I would love to see this technology make its way out to where we could use it today.
[00:34:46.680 --> 00:34:55.640] Again, I really need to know how fast you could read-write on it, but even still, like if you just wanted to do like a big backup, like let's say, let's say that Google wants to back up YouTube, right?
[00:34:55.640 --> 00:34:58.440] You know, but I think that this thing would handle it nicely.
[00:34:58.680 --> 00:35:00.680] Better than my floppy drive, for sure.
[00:35:00.680 --> 00:35:02.360] Yeah, but we could start with the floppy drive.
[00:35:02.360 --> 00:35:03.960] Yeah, as long as, we'll start there.
[00:35:04.000 --> 00:35:10.120] But you let us know when you get it working, and then we'll start doing anyone listening who's an engineer can who can help me with this.
[00:35:10.120 --> 00:35:12.600] Uh, get in get in touch because this thing isn't working.
[00:35:12.920 --> 00:35:16.560] I got to get YouTube on my floppies, so someone reach out.
[00:35:16.560 --> 00:35:19.840] So you try to visualize this huge amount of data storage, right?
[00:35:14.840 --> 00:35:22.080] So there was a picture that Bill Gates took.
[00:35:22.640 --> 00:35:30.640] I remember him being, I think he was like hoisted up super high in the air, and he had a CD in his hand.
[00:35:30.640 --> 00:35:32.960] And they had stacks of paper that went up.
[00:35:32.960 --> 00:35:33.440] I don't know.
[00:35:33.440 --> 00:35:37.040] I mean, it looked like he was up like 60 feet in the air worth of paper.
[00:35:37.040 --> 00:35:44.320] And the idea was that all of that data can fit on this thin, you know, this wafer-thin piece of plastic, and this was new technology.
[00:35:44.320 --> 00:35:49.280] And I remember seeing that picture and like getting the visual and going, wow, that's a lot of data.
[00:35:49.280 --> 00:35:53.040] You know, that is nothing compared to this thing.
[00:35:53.040 --> 00:35:54.400] Like, it's nothing.
[00:35:54.400 --> 00:35:58.320] You know, the amount of data storage that this thing can handle.
[00:35:58.480 --> 00:36:01.680] I mean, I wish that Bob were here because he's really good at coming up with things like that.
[00:36:01.680 --> 00:36:05.760] But it might be like, you know, the size of the earth of paper.
[00:36:05.760 --> 00:36:06.800] You know, like, I don't know.
[00:36:06.800 --> 00:36:13.120] I'm just making that up, but it's phenomenally bigger than what could fit on a CD, like orders of magnitude more.
[00:36:13.120 --> 00:36:16.880] So, anyway, you know, I hope we never need to use it.
[00:36:17.200 --> 00:36:19.440] I hope it doesn't have to be used.
[00:36:19.440 --> 00:36:23.280] But, you know, the way things are going here with the politics in the U.S., we probably do need this.
[00:36:23.520 --> 00:36:25.600] Check in again in late November and let's see.
[00:36:25.600 --> 00:36:27.200] Yeah, I'll see you in November.
[00:36:27.200 --> 00:36:34.480] Now, you bring up an interesting point about our human obsession with making sure that we'll last beyond our own lifetimes, right?
[00:36:34.480 --> 00:36:39.440] I mean, humans having kids, but it's got to be this like deeper evolutionary thing, right?
[00:36:39.440 --> 00:36:44.000] Like you said, with the putting animal cells on the moon and storing seeds.
[00:36:44.000 --> 00:36:46.320] Like, we really do, we are obsessed with it.
[00:36:46.320 --> 00:36:47.360] Is it just evolution?
[00:36:47.360 --> 00:36:48.240] Is it that simple?
[00:36:48.240 --> 00:36:48.560] No.
[00:36:49.040 --> 00:36:51.600] I'd like to think 100% it's not.
[00:36:51.920 --> 00:36:55.520] I mean, I would think it's probably likely that it's a combination of things.
[00:36:55.920 --> 00:36:59.880] I mean, people want to preserve their cultures and their societies and all this stuff.
[00:36:59.880 --> 00:37:02.920] You know, like it's kind of cooked into human psychology, I think.
[00:37:02.920 --> 00:37:04.600] Yeah, but it's also reinforced.
[00:37:04.600 --> 00:37:04.920] Sure.
[00:36:59.600 --> 00:37:06.440] But again, it's inertia of nature.
[00:37:06.520 --> 00:37:08.040] You know, like it's probably both.
[00:37:08.120 --> 00:37:20.440] I also think it's a little bit of projection because, I mean, imagine how cool would it be if we found a time capsule from an alien civilization that had massive amounts of data on it.
[00:37:20.440 --> 00:37:28.600] You know, would we would, of course, launch a project to try to figure it out, to try to reverse engineer it, and try to see what kind of information is in there.
[00:37:28.680 --> 00:37:29.320] Contact.
[00:37:29.320 --> 00:37:43.160] I think we have a, yeah, yeah, we have a sense of history, you know, and talking to history, you know, in the future, preserving knowledge for later could partly because we wish older civilizations had done it for us, you know?
[00:37:43.160 --> 00:37:43.480] Right.
[00:37:43.480 --> 00:37:43.880] Sure.
[00:37:43.880 --> 00:37:44.280] Yeah.
[00:37:44.280 --> 00:37:45.640] They were so lazy back then.
[00:37:46.360 --> 00:37:46.920] Right?
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[00:39:20.160 --> 00:39:22.640] All right, Andrew, we're coming back to you in political science.
[00:39:22.640 --> 00:39:25.920] Tell us how reliable presidential forecasts are.
[00:39:25.920 --> 00:39:26.400] All right.
[00:39:26.400 --> 00:39:34.480] Well, I would love to tell you this, and it's actually going to connect to Jay's news and we'll see how in just a moment.
[00:39:34.480 --> 00:39:37.840] But so this is a research from a preprint.
[00:39:37.840 --> 00:39:39.040] So it's not peer-reviewed.
[00:39:39.040 --> 00:39:43.760] It's a preprint from some political scientists, one of whom I know personally.
[00:39:43.920 --> 00:39:51.040] So maybe a small conflict of interest, but they are writing about political forecasting and particularly political polling.
[00:39:51.040 --> 00:40:01.520] And I think all you really need to know about their argument, or you can learn a lot about their argument from the file name if you go to download the PDF, the file name is end forecasting.
[00:40:01.520 --> 00:40:03.600] And so that's kind of going to be the whole punchline.
[00:40:03.600 --> 00:40:06.960] I got to start using more fun file names is one takeaway.
[00:40:06.960 --> 00:40:17.600] But so they are taking on the polling industry and a lot of the claims from pollsters and polling aggregators about how accurate they are and how useful they are.
[00:40:17.600 --> 00:40:24.560] And the other conflict of interest to share is that the big antagonist they have in this research paper is Nate Silver.
[00:40:24.560 --> 00:40:26.560] And I also worked for Nate Silver for a year and a half.
[00:40:26.560 --> 00:40:32.040] So this is like watching my two quantitative parents fight, I guess.
[00:40:32.360 --> 00:40:40.360] And so basically, the thrust of this research, and a lot of political scientists are skeptical of political forecasting, including me.
[00:40:40.360 --> 00:40:49.480] But the thrust of this particular paper that I really enjoy is: it says, look, you know, forecasting, in their words, captivates the public.
[00:40:49.480 --> 00:40:51.480] It drives media narratives, right?
[00:40:51.480 --> 00:40:56.760] We cannot get away from hearing about polls and horse race coverage and who's ahead and who's behind.
[00:40:56.760 --> 00:41:02.120] I don't even look and seek out polling information very much, though I do cave as we get closer and closer to the election.
[00:41:02.120 --> 00:41:08.520] And I certainly looked at it when I was making a living working on it, but I try not to, but you can't avoid this stuff.
[00:41:08.520 --> 00:41:18.920] And what they say, and what I think is something that you all talk about very elegantly on this podcast a lot, is what they call, quote, a veneer of scientific legitimacy around polls.
[00:41:18.920 --> 00:41:26.040] And Jay, you kind of hit on this earlier, and we put the word data next to something, and we think therefore it's true and it's correct.
[00:41:26.040 --> 00:41:33.800] And so we say, well, Harris is at 48%, Trump is at 46%, therefore we can say something about what's going to happen a month and a half from now.
[00:41:33.800 --> 00:41:40.600] And there are lots of problems, of course, as far as the scientific legitimacy of polls, and in particular, of polling aggregators, right?
[00:41:40.600 --> 00:41:44.360] So we don't know for sure that we're working with representative samples.
[00:41:44.360 --> 00:41:45.640] There's an effort to do so.
[00:41:45.640 --> 00:41:46.920] We don't know for sure.
[00:41:46.920 --> 00:41:49.640] There's selection bias and non-response.
[00:41:49.640 --> 00:41:55.000] And if you get a random call from a number you don't recognize, what are the odds you're going to pick it up?
[00:41:55.000 --> 00:41:59.400] If you do pick it up, what are the odds you're going to stay to answer their questions when you realize it's just a survey?
[00:41:59.400 --> 00:42:03.240] And then what are the odds you're actually going to tell them what you really think about politics to some random person, right?
[00:42:03.240 --> 00:42:07.240] So, we're not working with randomized samples in the way we'd like to in science.
[00:42:07.240 --> 00:42:12.600] We don't know a lot about how different survey firms or aggregators, we don't know what's under the hood in these models.
[00:42:12.560 --> 00:42:20.240] You know, Nate Silver, the New York Times, Politico, other outfits that run these aggregators make a living from these aggregators.
[00:42:14.600 --> 00:42:28.320] So, if you don't know, you know, the 538, or now it's Nate Silver has his own forecast, they aggregate all the polling data that's out there.
[00:42:28.320 --> 00:42:34.960] They weight it according to how good the pollsters have been in the past based on their own metrics.
[00:42:34.960 --> 00:42:46.240] I worked a little bit on collecting data for pollster evaluations back in the day, and I don't know how Nate ultimately took all that data on accuracy and weighted whether a pollster was an A or an A minus or whatever.
[00:42:46.240 --> 00:42:47.840] And so, so it's proprietary information.
[00:42:47.840 --> 00:42:52.960] So, we don't know what goes into the theory and into these models, so we can't really evaluate them that way.
[00:42:52.960 --> 00:42:57.440] We know that we're not reaching all of the electorate, but we also know that we're not reaching all of them in different ways.
[00:42:57.440 --> 00:43:02.400] Maybe the people we reached in 2016 and left out in 2016 are not the same as 2016.
[00:43:02.560 --> 00:43:11.840] So, there's no shortage of really serious empirical research problems, despite many pollsters and polling aggregators working from the best of intentions.
[00:43:11.840 --> 00:43:14.880] Okay, so there are some problems in polling.
[00:43:14.880 --> 00:43:25.600] We all kind of know, and if you paid attention to polling in 2016, you have some probably built-in skepticism or maybe knee-jerk fear when you hear new polling data come out.
[00:43:25.920 --> 00:43:32.640] But people like Nate Silver argue that this is still better, this is sort of the money ball argument for elections, right?
[00:43:32.640 --> 00:43:42.880] This is still better than punditry, this is still better than commentators and talking heads just assuming and thinking and expounding on what they think is going to happen.
[00:43:42.880 --> 00:43:50.960] Here's where this paper comes in, and where I think we start to have a lot of fun, and where Jay's article from our news piece is also relevant.
[00:43:50.960 --> 00:44:11.160] So, these researchers who study, you know, use machine learning to make predictions in politics say, Look, usually in machine learning, if we want to evaluate how good a model is, we have a ton of training and testing data that we train the model on, and then we compare it to how it whether it predicted reality correctly.
[00:44:11.160 --> 00:44:21.800] So, if you think of market data, stock data, weather data, you can evaluate these forecasts and sort of say roughly how good they were or how not good they were.
[00:44:21.800 --> 00:44:28.120] The problem with elections is at least presidential elections in the United States are only once every four years.
[00:44:28.120 --> 00:44:31.800] And that means we've only had about 60 in the United States.
[00:44:31.800 --> 00:44:38.200] So, that's a lot, that's a very limited quantity of data that we can compare the outcomes of these polls to.
[00:44:38.200 --> 00:44:40.520] We also haven't been doing the polling that long, right?
[00:44:40.840 --> 00:44:45.080] Now, some argue that, well, we have state data and we have local elections, so isn't that a lot more data?
[00:44:45.080 --> 00:44:47.800] The problem is that a lot of state data is correlated.
[00:44:47.800 --> 00:45:11.720] How one state goes tells you a lot about how a lot of other states are gonna go, and the way that the United States polling, the way the United States election works with swing states in the Electoral College mean that even if we can get state polling correct, it's still not necessarily the case that we're gonna be particularly accurate at national polling because, as we all have lived through, teeny tiny, seemingly random changes can totally flip how an entire nation.
[00:45:11.800 --> 00:45:14.280] So, state data doesn't really solve the problem.
[00:45:14.280 --> 00:45:23.400] So, what these researchers did is they said, okay, we are going to, and it's worth taking a look at the paper, it's open source at the moment, open access at the moment.
[00:45:23.720 --> 00:45:34.040] They said, we're going to simulate how long it would take to actually have enough data to evaluate whether, say, Nate Silver's model is accurate.
[00:45:34.040 --> 00:45:36.200] It's not, oh, did they get the last handful right?
[00:45:36.200 --> 00:45:41.640] Because we can't really statistically separate that from we happen to throw heads three times in a row or whatever, right?
[00:45:41.640 --> 00:45:44.760] So, they said, let's simulate and see how long it's going to take.
[00:45:44.880 --> 00:46:03.120] And basically, the punchline, and Jay, this is where we're going to need those crystals, is it will take decades more of elections and research to find out if these forecasts that we're all clicking refresh on right now to find out if these probabilistic forecasts are more accurate than pundits and talking heads.
[00:46:03.120 --> 00:46:15.840] It will take centuries of data to actually statistically evaluate and compare the models to see if these forecasters are actually reliably and helpfully predicting state and national elections.
[00:46:15.840 --> 00:46:25.040] And it will take millennia, they say, to figure out what techniques are actually best, like which of these models that purport to be the best one is actually the best one.
[00:46:25.040 --> 00:46:29.360] And they even say, look, maybe we can do better because we're all still living in the present.
[00:46:29.360 --> 00:46:30.560] We don't have these crystals yet.
[00:46:30.560 --> 00:46:34.720] Like we do all want to know what's going to happen in November and four years from now and so on.
[00:46:34.720 --> 00:46:39.680] So one thing they say is, well, you could say, what if we look at forecasters who make predictions about other things?
[00:46:39.680 --> 00:46:52.080] So maybe congressional races that happen more often, maybe sports outcomes, but even that, who's to say that the forces underlying a presidential model are going to be comparable to, say, someone who's accurately predicting if the bills are going to win or whatever.
[00:46:52.080 --> 00:46:56.640] So even that kind of generous version is not particularly helpful.
[00:46:56.800 --> 00:47:16.640] And I guess the last thing I'll say here, and the reason I really like this, is there's an underlying tension that I should be candid about between kind of generally political scientists and polling, where in political science, most of the time we try to predict elections on what we call fundamentals, so the economy, things like that, and not really what people say they're going to do.
[00:47:16.640 --> 00:47:18.640] It's more like what is happening in the country.
[00:47:18.640 --> 00:47:26.560] And so there's sort of this head-to-head between fundamentals versus polling and kind of more public opinion, real-time data collection.
[00:47:26.560 --> 00:47:32.600] And basically, the punchline of all of this is that we don't know and we don't have enough data to know.
[00:47:32.600 --> 00:47:38.600] And the last thing I'll say is, and this kind of reminds me, Steve, of things you've said where people say, well, why are horoscopes so dangerous?
[00:47:38.600 --> 00:47:42.840] Or why are crystals, not Jay's crystals, but like, you know, salt and stuff that people buy?
[00:47:42.840 --> 00:47:46.200] That's like, what's the harm in spending 50 bucks on a blue rock?
[00:47:46.680 --> 00:47:47.800] Well, there is a lot of harm.
[00:47:47.800 --> 00:48:05.480] And it turns out, and there are experimental studies, and there's some great links in this paper to existing peer-reviewed studies, where we say, look, it turns out that there is a lot of evidence to suggest that polling, as we currently talk about it, makes people feel a false sense of confidence or security about what's going to happen.
[00:48:05.480 --> 00:48:07.320] That can cause depressed turnout.
[00:48:07.320 --> 00:48:11.240] For example, we think that might be some of what's going on in 2016.
[00:48:11.240 --> 00:48:15.960] These studies are more experimental, so we're in a lab and we say, hey, here are the polling outcomes.
[00:48:15.960 --> 00:48:16.600] Are you going to vote?
[00:48:16.600 --> 00:48:18.040] So it's not the same thing.
[00:48:18.040 --> 00:48:21.080] And you can't really, really say for sure.
[00:48:21.080 --> 00:48:24.600] But it really can mess with our understanding of what's going to happen.
[00:48:24.600 --> 00:48:32.760] It can mislead the public about how viable certain candidates are and cause them to abandon a candidate that actually could be relatively viable because we're not interpreting probabilities correctly.
[00:48:32.760 --> 00:48:40.280] And it also encourages this kind of horse race coverage that we're all hearing about, as opposed to talking about issues and policies and other things that might matter.
[00:48:40.280 --> 00:48:41.480] So it's not harmless.
[00:48:41.480 --> 00:48:44.600] So their headline is: look, yeah, file name, right?
[00:48:44.600 --> 00:48:46.040] End forecasting.
[00:48:46.040 --> 00:48:53.480] My view on it, if I may, is I think there's a lot of money being made in the polling industry and I don't see it going away.
[00:48:53.480 --> 00:49:04.200] I would like us to be a lot more thoughtful, and we said this at the beginning of the episode today, a lot more thoughtful about what are these results actually able to tell us and what can't they tell us?
[00:49:04.200 --> 00:49:18.080] And that includes things like thinking about probabilities, understanding that if someone is at 58% approval, that doesn't mean they're going to get 58% of the vote, you know, basic things like that, changing how we report on these things, talking about uncertainty a lot more.
[00:49:18.320 --> 00:49:20.800] And I think, you know, instilling this kind of doubt.
[00:49:20.800 --> 00:49:25.200] But it's not the same thing as saying, oh, polls are wrong or polls are right.
[00:49:25.200 --> 00:49:29.200] The punchline here is we don't know and we don't have enough data to scientifically know.
[00:49:29.200 --> 00:49:34.880] So it's some information, but to this moment, we don't know if it's better or worse than a coin flip or a pundit.
[00:49:34.880 --> 00:49:40.080] And Andrew, are you distinguishing polling data from forecasting?
[00:49:40.080 --> 00:49:45.600] Because polling data, every time I read a poll, they're like, this is a snapshot of what people are saying right now.
[00:49:45.600 --> 00:49:48.000] This does not predict what's going to happen in the future.
[00:49:48.800 --> 00:49:50.240] These are two different things.
[00:49:50.560 --> 00:49:51.360] That's true.
[00:49:51.360 --> 00:49:54.720] So the polls are the data that go into the forecasts.
[00:49:54.960 --> 00:49:56.080] And that's a fair distinction, yes.
[00:49:56.320 --> 00:49:59.840] Yeah, but you could say the poll is the poll.
[00:49:59.840 --> 00:50:08.000] And a poll, yeah, I agree that, you know, again, the more I read about it, it's like, oh, yeah, the answer rate on this poll was 1%.
[00:50:09.440 --> 00:50:11.600] So it's really telling us almost nothing.
[00:50:12.480 --> 00:50:15.600] That's just telling us who's willing to answer the poll.
[00:50:16.400 --> 00:50:19.920] I mean, that's probably overwhelming any other factors in there.
[00:50:19.920 --> 00:50:20.080] Right.
[00:50:20.080 --> 00:50:21.120] But then the forecasts are based on that.
[00:50:21.200 --> 00:50:22.400] But the forecasts are based upon that.
[00:50:23.200 --> 00:50:34.400] The forecasts are based on those, and then they're based on other things that are added in around the forecaster's own assumptions about how different demographics are going to show up and how much it's going to matter.
[00:50:34.640 --> 00:50:35.600] And there's some.
[00:50:35.600 --> 00:50:44.000] And the problem is you're always looking through the rearview mirror because you're saying, oh, gosh, we really underestimated how Trump voters would turn out in 2016.
[00:50:44.000 --> 00:50:50.720] So now let's, I'm oversimplifying and this isn't quite how it works, but like now let's overestimate how likely anyone who said they're going to turn out.
[00:50:50.720 --> 00:50:54.960] But then it's like, well, who's to say that this election is going to operate the way from the past?
[00:50:54.960 --> 00:51:01.320] And so we're kind of this jerking around to read through the, but we also, and this is what this article is saying, is we don't know.
[00:51:01.640 --> 00:51:12.760] 2016, there were polls, you know, in Nate Silver's defense, he was more accurate, closer to, more conservative on Hillary's likelihood of winning than many other outfits.
[00:51:12.760 --> 00:51:17.400] But we don't know what was going on in that model that needed to change, right, or what we were missing.
[00:51:17.400 --> 00:51:18.840] Or if it was absolutely correct.
[00:51:18.840 --> 00:51:20.680] And that was the percentage.
[00:51:20.680 --> 00:51:24.200] And we just saw the outcome that was slightly less likely, which is totally reasonable.
[00:51:24.200 --> 00:51:35.160] So I wonder if this system is inherently unpredictable, or if it's just, you know, an order of magnitude more difficult than the tools we're using right now.
[00:51:35.160 --> 00:51:41.880] And I wonder if, what if, you know, again, we seek AI and supercomputer simulations on this?
[00:51:41.880 --> 00:51:46.440] Could we do a thousand years of simulations and test our models that way?
[00:51:46.440 --> 00:51:52.360] Or is it just only real-world data is really going to make it, is really going to help?
[00:51:52.360 --> 00:51:56.840] I mean, I think there is a version of the world where you could put all of that in.
[00:51:56.840 --> 00:52:05.400] What I genuinely don't know, and again, this kind of goes back to where we started this conversation, is what's going to happen in the underlying data generating processes.
[00:52:05.400 --> 00:52:17.160] Like if politics continues roughly as it is now, or it has been for the past, say, eight years, then we could probably simulate a whole bunch of things and we could maybe make some claims with enough computational power and AI.
[00:52:17.160 --> 00:52:22.760] But, you know, it's sort of the equivalent of someone in the 1960s trying to get right what's going on here.
[00:52:22.760 --> 00:52:32.760] And it's, I don't want to use this term lightly, but it's more chaotic system than anything else where teeny tiny little di differences can lead to wildly different outcomes.
[00:52:33.400 --> 00:52:34.360] So it's hard.
[00:52:34.360 --> 00:52:38.120] To me, it seems hard to imagine, but I'm open to it.
[00:52:38.440 --> 00:52:42.840] It also reminds me of psychohistory from the Foundation series.
[00:52:42.840 --> 00:52:43.160] Yep.
[00:52:43.800 --> 00:52:47.360] And I mean, Donald Trump is the mule, right?
[00:52:44.360 --

Prompt 2: Key Takeaways

Now please extract the key takeaways from the transcript content I provided.

Extract the most important key takeaways from this part of the conversation. Use a single sentence statement (the key takeaway) rather than milquetoast descriptions like "the hosts discuss...". 

Limit the key takeaways to a maximum of 3. The key takeaways should be insightful and knowledge-additive. 

IMPORTANT: Return ONLY valid JSON, no explanations or markdown. Ensure:
- All strings are properly quoted and escaped
- No trailing commas
- All braces and brackets are balanced
Format: {"key_takeaways": ["takeaway 1", "takeaway 2"]}

Prompt 3: Segments

Now identify 2-4 distinct topical segments from this part of the conversation.

For each segment, identify:
- Descriptive title (3-6 words)  
- START timestamp when this topic begins (HH:MM:SS format)
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Most important Key takeaway from that segment. Key takeaway must be specific and knowledge-additive.
- Brief summary of the discussion

IMPORTANT: The timestamp should mark when the topic/segment STARTS, not a range. Look for topic transitions and conversation shifts.

Return ONLY valid JSON. Ensure all strings are properly quoted, no trailing commas:
{
  "segments": [
    {
      "segment_title": "Topic Discussion", 
      "timestamp": "01:15:30",
      "key_takeaway": "main point from this segment",
      "segment_summary": "brief description of what was discussed"
    }
  ]
}

Timestamp format: HH:MM:SS (e.g., 00:05:30, 01:22:45) marking the START of each segment.

Prompt 4: Media Mentions

Now scan the transcript content I provided for ACTUAL mentions of specific media titles:

Find explicit mentions of:
- Books (with specific titles)
- Movies (with specific titles)  
- TV Shows (with specific titles)
- Music/Songs (with specific titles)

DO NOT include:
- Websites, URLs, or web services
- Other podcasts or podcast names

IMPORTANT: 
- Only include items explicitly mentioned by name. Do not invent titles.
- Valid categories are: "Book", "Movie", "TV Show", "Music"
- Include the exact phrase where each item was mentioned
- Find the nearest proximate timestamp where it appears in the conversation
- THE TIMESTAMP OF THE MEDIA MENTION IS IMPORTANT - DO NOT INVENT TIMESTAMPS AND DO NOT MISATTRIBUTE TIMESTAMPS
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Timestamps are given as ranges, e.g. 01:13:42.520 --> 01:13:46.720. Use the EARLIER of the 2 timestamps in the range.

Return ONLY valid JSON. Ensure all strings are properly quoted and escaped, no trailing commas:
{
  "media_mentions": [
    {
      "title": "Exact Title as Mentioned",
      "category": "Book",
      "author_artist": "N/A",
      "context": "Brief context of why it was mentioned",
      "context_phrase": "The exact sentence or phrase where it was mentioned",
      "timestamp": "estimated time like 01:15:30"
    }
  ]
}

If no media is mentioned, return: {"media_mentions": []}

Prompt 5: Context Setup

You are an expert data extractor tasked with analyzing a podcast transcript. 
I will provide you with part 2 of 3 from a podcast transcript. 
I will then ask you to extract different types of information from this content in subsequent messages. Please confirm you have received and understood the transcript content.

Transcript section:
8.840 --> 00:51:20.680] And that was the percentage.
[00:51:20.680 --> 00:51:24.200] And we just saw the outcome that was slightly less likely, which is totally reasonable.
[00:51:24.200 --> 00:51:35.160] So I wonder if this system is inherently unpredictable, or if it's just, you know, an order of magnitude more difficult than the tools we're using right now.
[00:51:35.160 --> 00:51:41.880] And I wonder if, what if, you know, again, we seek AI and supercomputer simulations on this?
[00:51:41.880 --> 00:51:46.440] Could we do a thousand years of simulations and test our models that way?
[00:51:46.440 --> 00:51:52.360] Or is it just only real-world data is really going to make it, is really going to help?
[00:51:52.360 --> 00:51:56.840] I mean, I think there is a version of the world where you could put all of that in.
[00:51:56.840 --> 00:52:05.400] What I genuinely don't know, and again, this kind of goes back to where we started this conversation, is what's going to happen in the underlying data generating processes.
[00:52:05.400 --> 00:52:17.160] Like if politics continues roughly as it is now, or it has been for the past, say, eight years, then we could probably simulate a whole bunch of things and we could maybe make some claims with enough computational power and AI.
[00:52:17.160 --> 00:52:22.760] But, you know, it's sort of the equivalent of someone in the 1960s trying to get right what's going on here.
[00:52:22.760 --> 00:52:32.760] And it's, I don't want to use this term lightly, but it's more chaotic system than anything else where teeny tiny little di differences can lead to wildly different outcomes.
[00:52:33.400 --> 00:52:34.360] So it's hard.
[00:52:34.360 --> 00:52:38.120] To me, it seems hard to imagine, but I'm open to it.
[00:52:38.440 --> 00:52:42.840] It also reminds me of psychohistory from the Foundation series.
[00:52:42.840 --> 00:52:43.160] Yep.
[00:52:43.800 --> 00:52:47.360] And I mean, Donald Trump is the mule, right?
[00:52:44.360 --> 00:52:54.880] I mean, he is, yeah, this random factor that nobody could have predicted that changes the rules.
[00:52:54.880 --> 00:52:56.400] Like, you talk about the fundamentals.
[00:52:56.400 --> 00:53:00.160] The fundamentals are meaningless when you have something like this going on, right?
[00:53:00.480 --> 00:53:01.040] Yes.
[00:53:01.040 --> 00:53:08.480] You don't know that it really tells us anything much, you know, not enough, not enough to predict what's going to happen.
[00:53:08.480 --> 00:53:09.040] Right.
[00:53:09.040 --> 00:53:20.000] I mean, and that goes back to Steve to your distinction there between polls and forecasts, which is like the polls, there's information in the polls, and the best way to look at them is with a lot of skepticism.
[00:53:20.000 --> 00:53:21.920] There's all this selection bias that we just talked about.
[00:53:21.920 --> 00:53:23.360] There's very low response rates.
[00:53:23.600 --> 00:53:24.640] There's all kinds of things.
[00:53:24.640 --> 00:53:26.400] We don't really know the methodology, all this stuff.
[00:53:26.400 --> 00:53:42.400] But if you look at the overall suite of the polls and look at them in aggregate, set aside forecasting, just sort of look at the snapshots that we're getting, the stunning thing is that Trump's approval rating does not seem to budge, or Trump's, you know, I'm planning on voting for Trump, doesn't seem to budge no matter what's happening.
[00:53:42.400 --> 00:53:46.400] And that just would not happen 10 years ago, right?
[00:53:46.640 --> 00:53:49.280] President Howard Dean yelled the wrong way, and we never heard from him again.
[00:53:49.680 --> 00:53:52.080] I know, yeah.
[00:53:52.720 --> 00:53:53.440] Right.
[00:53:53.440 --> 00:53:57.040] Lots of theories about why that's the case, but I can't get into that right now.
[00:53:58.880 --> 00:54:10.160] But I think it's an interesting area of, you know, I think it's worth trying to study politics and these sort of squishier, more complex things through these empirical tools that we have.
[00:54:10.160 --> 00:54:18.320] But we also kind of see these spin-offs like pollster and forecasting generating this sense of false security or false certainty.
[00:54:18.320 --> 00:54:25.680] And I think that that, you know, it's sort of, it's worth doing the science, but not when you're not careful about it, the science becomes pseudoscience.
[00:54:26.640 --> 00:54:27.040] All right.
[00:54:27.040 --> 00:54:28.160] Thanks, Andrea.
[00:54:28.160 --> 00:54:28.720] Sure.
[00:54:28.720 --> 00:54:33.240] So are you guys aware that we're having a myopia epidemic?
[00:54:33.240 --> 00:54:33.720] Ooh.
[00:54:29.840 --> 00:54:35.400] Is it contagious?
[00:54:35.960 --> 00:54:37.800] What could possibly be causing that?
[00:54:37.800 --> 00:54:42.200] So what percentage of the world do you think has myopia?
[00:54:42.200 --> 00:54:43.240] 25.
[00:54:43.240 --> 00:54:44.200] Me.
[00:54:44.520 --> 00:54:46.520] I'm going to say 30.
[00:54:46.520 --> 00:54:47.160] I like Jay's.
[00:54:47.320 --> 00:54:48.280] I think it's higher than that.
[00:54:48.280 --> 00:54:48.840] Let me think.
[00:54:48.840 --> 00:54:53.880] All the people that wear glasses, how many of them are like under 45, 50?
[00:54:53.880 --> 00:54:57.880] The aggregate polling on this, Steve, says about 12.3%.
[00:54:58.200 --> 00:54:59.480] I'm going to say it's higher than that.
[00:54:59.480 --> 00:55:01.880] The turnout of people with myopia in the next election is.
[00:55:02.040 --> 00:55:05.800] It's 35.81% in 2023.
[00:55:05.800 --> 00:55:08.600] It was 24% in 1990.
[00:55:08.600 --> 00:55:13.560] So it's gone up from basically 25% to 35% over the last 30 years.
[00:55:13.560 --> 00:55:15.880] Is it really, though, or are we just diagnosing kids younger?
[00:55:16.360 --> 00:55:18.360] So, yeah, that's always the question, right?
[00:55:18.360 --> 00:55:20.280] Is it an artifact or is it real?
[00:55:20.280 --> 00:55:22.200] We talked about this with ADHD.
[00:55:22.200 --> 00:55:23.640] I think it was just last week.
[00:55:23.640 --> 00:55:24.280] And it's real.
[00:55:24.280 --> 00:55:25.640] It's an absolutely real increase.
[00:55:25.960 --> 00:55:30.600] It's not an artifact of diagnosis or changing the definition or whatever.
[00:55:30.920 --> 00:55:32.520] It's really happening.
[00:55:32.520 --> 00:55:35.800] What's interesting is that it's continuing to increase.
[00:55:35.800 --> 00:55:48.280] And there was a recent paper, which is what prompted this discussion, that estimates that by 2050 we'll be at 40%, although other estimates put it closer to 50%.
[00:55:48.360 --> 00:55:55.960] Imagine half the world being myopic, basically, at some time after 2050, is sort of the consensus of opinion here.
[00:55:55.960 --> 00:55:59.880] Now, you say, wow, but there's already parts of the world where it's higher.
[00:55:59.880 --> 00:56:03.720] In China, it's already like 70, 80%.
[00:56:03.720 --> 00:56:04.200] Wow.
[00:56:04.200 --> 00:56:18.960] So what's funny is that, like, if you go back to the 1940s, 1950s, the typical stereotype, right, of somebody from China was you would draw, you know, it was somebody with those small round glasses.
[00:56:19.280 --> 00:56:28.800] That's because there was so much myopia, and that was a very common style of glasses that they wore.
[00:56:28.800 --> 00:56:30.000] So it was actually real.
[00:56:30.000 --> 00:56:30.560] You know what I mean?
[00:56:30.560 --> 00:56:32.880] It was a stereotype, but it was based upon reality.
[00:56:32.880 --> 00:56:33.920] But what was the difference?
[00:56:33.920 --> 00:56:37.680] Sorry, back then, what was the difference in the number of people in China versus the U.S.?
[00:56:38.000 --> 00:56:43.760] I don't know what, I mean, so in the 1950s, like in the U.S., it was 2%, 3%.
[00:56:44.080 --> 00:56:46.880] And it was already in the double digits in China.
[00:56:46.880 --> 00:56:48.560] It was already 20%.
[00:56:48.560 --> 00:56:51.360] So it went from 20% to like 80%.
[00:56:51.360 --> 00:56:55.680] But we were at like 2%, 3%, and now we're at 24%, 25%.
[00:56:55.920 --> 00:56:56.640] But I don't know if you like.
[00:56:57.120 --> 00:57:01.440] Why do I feel like screens are going to be the factor?
[00:57:01.920 --> 00:57:16.320] Well, and I also think we cannot say that it's a, it's, we can say it's a real phenomenon now, but I don't think we can say that the 2% or 3% of Americans who wore glasses for myopia in 1950 were that we were catching all Americans.
[00:57:16.320 --> 00:57:17.440] Were we screening for vision?
[00:57:17.840 --> 00:57:19.280] Yeah, so that's a good question.
[00:57:20.400 --> 00:57:26.480] Because the thing is, we do screen children in school for myopia, for eye problems.
[00:57:26.480 --> 00:57:27.120] For vision.
[00:57:27.440 --> 00:57:30.800] Yeah, and that goes back at least to the 50s and 60s.
[00:57:30.800 --> 00:57:31.680] I took many eye tests.
[00:57:31.920 --> 00:57:32.800] I was screened.
[00:57:32.800 --> 00:57:35.040] So, yeah, I was screened in the 1960s.
[00:57:35.040 --> 00:57:36.560] I was checked for scoliosis.
[00:57:37.040 --> 00:57:37.360] Oh, yeah.
[00:57:37.600 --> 00:57:38.720] I remember that test, yeah.
[00:57:39.040 --> 00:57:40.560] And they used to do lice checks, too.
[00:57:40.880 --> 00:57:43.520] So, yeah, you know, so it goes back to some time like that.
[00:57:43.520 --> 00:57:48.000] Certainly goes back to the second half of the 20th century.
[00:57:48.640 --> 00:57:51.360] How far does good data go?
[00:57:51.360 --> 00:57:51.840] I don't know.
[00:57:52.080 --> 00:57:53.520] That's a little bit harder to say.
[00:57:53.520 --> 00:57:58.160] But a lot of the studies that I was looking at starts to track it in the 1950s.
[00:57:58.160 --> 00:58:00.920] So I'm assuming that's when they started getting good data.
[00:57:59.680 --> 00:58:05.800] So even certainly in the last 30 years, you know, we have pretty consistent data.
[00:58:06.280 --> 00:58:15.880] And there's a steady trend of increasing prevalence and incidence of nearsightedness of myopia.
[00:58:15.880 --> 00:58:18.360] So this is a physical problem with the eye, right?
[00:58:18.360 --> 00:58:32.120] I mean, in myopia, the eye is too elongated, and so the image gets focused too far forward, like in front of the retina, which makes it hard to see in the distance.
[00:58:32.120 --> 00:58:34.920] So that's they're hence nearsighted.
[00:58:35.240 --> 00:58:38.680] So what do you guys think is causing it?
[00:58:38.680 --> 00:58:40.760] Well, it sounds like screens are a factor.
[00:58:40.760 --> 00:58:42.840] So Evan thinks it's screens.
[00:58:42.840 --> 00:58:46.280] I mean, I would think that that's potentially a huge factor.
[00:58:47.640 --> 00:58:53.800] I mean, I know the reason why screens affect you that way is because you're focusing too close for too long.
[00:58:54.680 --> 00:58:55.240] I don't know.
[00:58:55.240 --> 00:58:57.160] I mean, the other thing would be what, diet?
[00:58:57.480 --> 00:59:01.480] Or neurotoxin, or not neurotoxins, sorry, but yeah, toxins that affect development.
[00:59:01.480 --> 00:59:02.360] I mean, plastics.
[00:59:02.840 --> 00:59:06.200] Yeah, like we live in a pre-1950, there was no plastic.
[00:59:06.200 --> 00:59:09.720] We have like so many things in our blood now that didn't exist, like Teflon and all of that.
[00:59:10.120 --> 00:59:26.040] I'm going to go with, I'm with you all on all of those, and I'm going to say maybe we it's like we stopped thinking of people with glasses as less attractive, and so we're all marrying each other with glasses, and we're reproducing with myopia now.
[00:59:26.040 --> 00:59:28.920] It's no longer like evolutionarily disadvantageous.
[00:59:28.920 --> 00:59:30.280] Yeah, assortative mating.
[00:59:30.280 --> 00:59:30.760] There you go.
[00:59:30.760 --> 00:59:31.480] That's what I want.
[00:59:31.480 --> 00:59:31.960] Yeah.
[00:59:32.200 --> 00:59:33.000] Interesting.
[00:59:33.000 --> 00:59:34.160] Those are some good thoughts.
[00:59:34.160 --> 00:59:35.080] Very creative.
[00:59:35.640 --> 00:59:37.000] However, he's like, no, Ron.
[00:59:37.160 --> 00:59:38.440] Oh, thanks for pandering to us.
[00:59:38.840 --> 00:59:42.920] The short answer is, the short answer is we don't know.
[00:59:43.720 --> 00:59:44.800] And it's all of those things.
[00:59:44.520 --> 00:59:45.680] It's no.
[00:59:45.920 --> 00:59:52.960] But there are two basic theories that are the most plausible.
[00:59:52.960 --> 00:59:54.640] Evan's the closest.
[00:59:54.640 --> 00:59:56.400] It's not as so.
[00:59:56.480 --> 01:00:04.560] I mean, it could be partly screens, but and what Jay said is like you're spending a lot of your day focusing close up.
[01:00:04.560 --> 01:00:08.560] But this phenomenon predates screens, though.
[01:00:08.560 --> 01:00:15.920] And it doesn't really explain why some countries have a much higher rate than other countries because it does not track with screen use.
[01:00:15.920 --> 01:00:17.520] Then it would be genetics?
[01:00:17.520 --> 01:00:18.800] No, it's not genetic.
[01:00:18.800 --> 01:00:21.600] It's too fast for this to really be genetic.
[01:00:22.560 --> 01:00:23.600] It's developmental.
[01:00:23.600 --> 01:00:25.440] It's developmental, but what's the factor?
[01:00:26.000 --> 01:00:33.440] There's no signal, there's no hypothesis or notion or evidence that it's anything environmental like literacy rates?
[01:00:33.440 --> 01:00:34.400] Yeah, I'm with Evan.
[01:00:34.640 --> 01:00:36.640] Literacy studying.
[01:00:36.880 --> 01:00:37.200] No.
[01:00:37.200 --> 01:00:44.320] What it is, is, again, very good, very good hypotheses, but the leading hypothesis is that it's sunlight.
[01:00:44.320 --> 01:00:49.040] It's that kids specifically are spending less time outside.
[01:00:49.600 --> 01:00:50.480] Vitamin D?
[01:00:51.120 --> 01:00:51.600] No.
[01:00:51.760 --> 01:00:54.080] Vitamin D is made from absorption in your skin.
[01:00:54.320 --> 01:00:56.160] Sunset direct sunlight to the eyeball.
[01:00:56.400 --> 01:01:04.000] Sunlight causes dopamine to be released in the eye, which also is important for growth.
[01:01:04.000 --> 01:01:08.640] And so within the absence of that, the eye develops abnormally, right?
[01:01:08.640 --> 01:01:10.240] So it is a developmental problem.
[01:01:10.240 --> 01:01:12.800] As the eye grows, it becomes abnormally shaped.
[01:01:12.800 --> 01:01:27.440] So it could be the absence of sunlight, but the other hypothesis is that it's a relative decrease in being in an environment where you have to focus in the distance, at infinity, right?
[01:01:27.440 --> 01:01:30.000] If you're inside all the time, you're never focusing at infinity.
[01:01:30.920 --> 01:01:37.880] And so just from use over years, your eye develops with a bias towards focusing close up.
[01:01:37.880 --> 01:01:43.640] So it doesn't necessarily require screens, but screens certainly are part of that phenomenon.
[01:01:43.960 --> 01:01:47.320] It's really just that kids are not spending enough time outside.
[01:01:47.320 --> 01:01:48.600] That's the bottom line.
[01:01:48.600 --> 01:01:51.160] But is that, but you keep calling that a hypothesis.
[01:01:51.160 --> 01:01:52.440] Is there any evidence to support that?
[01:01:52.680 --> 01:01:53.480] There's a lot of evidence.
[01:01:53.480 --> 01:01:55.160] There's a lot of evidence to support that.
[01:01:55.160 --> 01:01:59.240] That's why I said these are the leading hypotheses because there's evidence to support that.
[01:01:59.640 --> 01:02:01.560] And it comes both ways.
[01:02:01.560 --> 01:02:04.760] It correlates with urbanization.
[01:02:04.760 --> 01:02:08.200] It correlates with time spent inside versus outside.
[01:02:08.680 --> 01:02:09.720] It correlates.
[01:02:09.720 --> 01:02:18.280] And also, if you, as a matter of policy, give kids more time outside, it reduces their risk of myopia.
[01:02:18.280 --> 01:02:20.120] It reverses the trend.
[01:02:20.120 --> 01:02:21.240] That's critical.
[01:02:21.240 --> 01:02:28.120] Because, you know, in order to prove a cause and effect, you have to show that if you eliminate the cause, the effect goes away, right?
[01:02:28.440 --> 01:02:29.960] So we have that data.
[01:02:30.520 --> 01:02:34.520] But they locked up 100 children outside for a year and studied them and see what they did.
[01:02:35.000 --> 01:02:39.960] At this point, there's pretty much no question that it's outdoors time is the critical element.
[01:02:40.280 --> 01:02:43.800] So like the stereotype that little book nerds all have glasses?
[01:02:43.800 --> 01:02:47.480] Yeah, there's actually what we don't know.
[01:02:47.480 --> 01:02:55.560] Yeah, what we don't know is the relative contribution of the dopamine from sunlight versus the focusing in the distance.
[01:02:55.560 --> 01:02:59.720] But it's being outside is the critical element.
[01:03:00.120 --> 01:03:03.400] Is it only for children, or should I go outside and stare at the distance?
[01:03:03.720 --> 01:03:05.320] So that's a really good question.
[01:03:06.120 --> 01:03:11.320] It's developmental, so it's definitely true through high school.
[01:03:12.360 --> 01:03:15.600] Not really sure if there's any effect once you're already an adult.
[01:03:15.600 --> 01:03:17.200] So probably not adults.
[01:03:17.200 --> 01:03:20.400] But it doesn't mean it's not good to get outside and get some sunshine.
[01:03:14.760 --> 01:03:21.760] Nothing wrong with that.
[01:03:22.000 --> 01:03:24.880] So the good news is the fix is simple.
[01:03:24.960 --> 01:03:27.840] Not going to say easy, but it is simple.
[01:03:27.840 --> 01:03:29.840] It is get kids outside.
[01:03:29.840 --> 01:03:32.960] Get them out in the sun, you know, and away from screens.
[01:03:32.960 --> 01:03:34.160] But off my lawn.
[01:03:34.480 --> 01:03:35.200] Right.
[01:03:37.280 --> 01:03:43.360] But it's not necessarily going to be easy because that cultural changes, public health measures like that are hard.
[01:03:43.360 --> 01:03:44.240] They're hard to get people to see.
[01:03:44.400 --> 01:03:45.520] Do you guys remember?
[01:03:45.520 --> 01:03:49.600] I think we talked about this on the show before, but there's like this viral video.
[01:03:49.600 --> 01:03:53.360] The dad's in the kitchen doing dishes or something and the kids outside the kitchen window.
[01:03:53.360 --> 01:03:54.560] And he's like, can I come in yet?
[01:03:54.560 --> 01:03:56.000] And he's like, no, you're grounded.
[01:03:57.760 --> 01:03:58.800] You just want to come inside.
[01:03:59.280 --> 01:04:00.400] Totally reversed.
[01:04:00.400 --> 01:04:02.160] Yeah, like go, go, go play.
[01:04:02.160 --> 01:04:03.200] What am I supposed to do?
[01:04:03.200 --> 01:04:04.480] I don't care.
[01:04:05.760 --> 01:04:07.600] I don't want to see you till supper.
[01:04:07.600 --> 01:04:09.040] Yeah, that's amazing.
[01:04:09.040 --> 01:04:12.080] I mean, when we were kids, we spent the entire day outside.
[01:04:12.080 --> 01:04:12.880] Oh, that was it.
[01:04:12.880 --> 01:04:14.400] Saturday morning, 7 a.m.
[01:04:14.640 --> 01:04:15.920] See you for lunch.
[01:04:15.920 --> 01:04:16.320] Thanks.
[01:04:17.360 --> 01:04:18.800] We didn't know where we were.
[01:04:18.800 --> 01:04:19.120] Right.
[01:04:19.520 --> 01:04:25.760] I mean, my kids, like recently, like in the last few days after school, they've been asking me to take them to the park.
[01:04:25.760 --> 01:04:27.120] And I'm thrilled.
[01:04:27.120 --> 01:04:29.920] You know, I'm like, you want to be outside?
[01:04:29.920 --> 01:04:30.640] You got it.
[01:04:30.640 --> 01:04:32.080] You can stay there as long as you want.
[01:04:32.800 --> 01:04:39.280] The problem is we have shifted to a screen culture and it is so deeply embedded now.
[01:04:39.280 --> 01:04:45.600] I mean, last year, I decided that I was not going to use my phone as much as possible.
[01:04:45.600 --> 01:04:48.720] And I did it for about five or six months.
[01:04:48.720 --> 01:04:50.720] I was not looking at the news.
[01:04:50.720 --> 01:04:52.880] I was mostly off of my phone.
[01:04:52.880 --> 01:04:57.200] And I creeped all the way back to a massive screen addiction again.
[01:04:57.840 --> 01:04:58.800] It just happens.
[01:04:58.800 --> 01:05:03.640] Like, I don't even know what's happening, and all of a sudden, I'm like, I can't sit without looking at a screen.
[01:04:59.840 --> 01:05:06.120] Like, I have to tell myself, you're okay.
[01:05:06.360 --> 01:05:07.320] You know what I mean?
[01:05:07.320 --> 01:05:15.960] But again, as we've said previously, it's probably a better approach to not try to minimize your screen time, but to maximize your not screen time.
[01:05:15.960 --> 01:05:16.520] Yeah.
[01:05:16.520 --> 01:05:19.880] So, like, say, I'm going to do some activity that gets me outside.
[01:05:19.880 --> 01:05:23.000] You know, take up gardening, you know, or whatever.
[01:05:23.160 --> 01:05:24.760] Go play catch with your kid outside.
[01:05:24.760 --> 01:05:28.680] Just do something that has to be happening outside, especially in the sunlight.
[01:05:28.920 --> 01:05:29.720] Go for a walk.
[01:05:29.720 --> 01:05:30.840] Yeah, go for a walk.
[01:05:30.840 --> 01:05:31.400] Just do something.
[01:05:31.400 --> 01:05:36.440] If you have to exercise outside versus inside, do something where you're getting more sunlight.
[01:05:36.440 --> 01:05:41.240] There's also lots of evidence that, yeah, we Americans generally are undersunned.
[01:05:41.240 --> 01:05:43.800] You know, we do have relative vitamin D deficiency.
[01:05:43.800 --> 01:05:47.880] And also, contact with green spaces is psychologically healthy.
[01:05:48.200 --> 01:05:53.160] And yeah, so that also means that, like, again, this is a big problem of urbanization.
[01:05:53.160 --> 01:06:00.840] And the reason why China was, you know, was probably had such a big problem is because of their rapid urbanization and their population.
[01:06:01.160 --> 01:06:03.880] These are just lots of people crammed into cities.
[01:06:04.120 --> 01:06:04.440] Right?
[01:06:04.440 --> 01:06:08.920] So that's why there's such an epidemic there, especially, but this is happening everywhere.
[01:06:09.240 --> 01:06:18.600] So yeah, you know, having green spaces in cities, more parks, more, you know, and school time should incorporate, you know, outside time as much as possible.
[01:06:18.600 --> 01:06:20.840] Classes outside, recess outside.
[01:06:20.840 --> 01:06:22.440] Got to get those kids outside.
[01:06:22.440 --> 01:06:29.800] Steve, what about the like the Google vision, or what is it, Apple Vision and all the goggles?
[01:06:29.800 --> 01:06:37.400] What if we look at the world through those outside, but project like our text messages and the news really far into the future?
[01:06:37.880 --> 01:06:38.840] Far into the distance.
[01:06:37.440 --> 01:06:41.720] Our phone into the distance in the goggles.
[01:06:41.960 --> 01:06:43.560] Artificial horizon kind of thing.
[01:06:43.560 --> 01:06:52.720] Does that mean I can keep up my phone addiction or my children's phone addiction and future children's phone addiction and let them not get myopia?
[01:06:52.720 --> 01:06:54.080] So that's actually been studied.
[01:06:54.080 --> 01:06:54.720] Oh, perfect.
[01:06:54.720 --> 01:06:56.320] And the answer is mixed.
[01:06:56.320 --> 01:06:57.040] We don't know.
[01:06:57.040 --> 01:06:57.600] Ow.
[01:06:57.600 --> 01:06:58.800] So that's the idea.
[01:06:58.800 --> 01:07:04.720] You could put somebody in VR because, you know, when you, I don't know how much you've used VR goggles.
[01:07:04.880 --> 01:07:05.200] Almost not.
[01:07:05.280 --> 01:07:06.800] I've used them extensively.
[01:07:06.800 --> 01:07:10.880] So one thing that's interesting is that I don't have to wear reading glasses when I use VR, right?
[01:07:11.440 --> 01:07:16.480] But also, yeah, you are, when you're focusing in the distance, you're focusing in the distance.
[01:07:16.480 --> 01:07:17.600] It's a real experience.
[01:07:17.600 --> 01:07:18.720] You know what I mean?
[01:07:19.040 --> 01:07:32.960] At least for that part of it, it seems like it should work, but it wouldn't necessarily replace the natural sunlight part of it unless we simulate that in the VR experience, which I guess is growing a lump or something in the room while you're talking about it.
[01:07:33.200 --> 01:07:36.720] But maybe, maybe we may, so this is just like using VR.
[01:07:36.720 --> 01:07:47.760] I wonder if we have optimized VR programs that we test, right, that to treat myopia, to prevent developmental myopia.
[01:07:47.760 --> 01:07:54.000] And that becomes, you know, just like you take your vitamin, you spend an hour in VR every day, make sure you don't get myopia.
[01:07:54.000 --> 01:07:54.480] You know what I mean?
[01:07:54.480 --> 01:08:00.000] Like that just becomes part of adapting to an urbanized life is you have to get your VR time.
[01:08:00.000 --> 01:08:02.080] You guys, myopia is not that bad.
[01:08:02.080 --> 01:08:02.960] That was the other thought.
[01:08:04.240 --> 01:08:04.880] You just wear glasses.
[01:08:05.120 --> 01:08:05.680] That's horrible.
[01:08:06.640 --> 01:08:07.200] It's horrible.
[01:08:07.520 --> 01:08:08.240] I hate wearing glasses.
[01:08:09.280 --> 01:08:10.160] Isn't that what laser sounds?
[01:08:10.400 --> 01:08:10.640] I know.
[01:08:10.960 --> 01:08:13.280] It's not that bad, but if we could prevent it, prevent it.
[01:08:14.480 --> 01:08:16.160] It's also not bad to prevent it.
[01:08:16.160 --> 01:08:16.720] It's all good.
[01:08:16.720 --> 01:08:18.880] It's like, what are you just telling people to spend more time outside?
[01:08:18.880 --> 01:08:20.520] Outside of the mall for outside.
[01:08:20.880 --> 01:08:21.200] Of course.
[01:08:21.200 --> 01:08:24.560] But I don't think we have to go to like insanely great lengths to, I don't know.
[01:08:24.560 --> 01:08:27.040] No, Kara, we take this all the way.
[01:08:29.200 --> 01:08:35.000] Sure, we can't get rid of diseases that are killing everybody, but myopia will come to an end.
[01:08:35.320 --> 01:08:38.360] You know, that twilight zone where the guy breaks his reading glasses.
[01:08:38.600 --> 01:08:39.800] It's not fair.
[01:08:39.800 --> 01:08:41.880] We can't let that happen.
[01:08:42.040 --> 01:08:43.240] Burgess Meredith thing.
[01:08:43.400 --> 01:08:44.120] Yeah, I saw that.
[01:08:44.120 --> 01:08:44.440] Yeah.
[01:08:44.440 --> 01:08:45.000] Yeah.
[01:08:45.000 --> 01:08:51.960] So, but there is a systematic review that I'm looking at where it does conclude that it can be effective if done properly.
[01:08:51.960 --> 01:08:53.080] You know, the right program.
[01:08:53.080 --> 01:08:55.880] VR can work to prevent myopia.
[01:08:56.200 --> 01:08:58.280] Yeah, very interesting.
[01:08:58.600 --> 01:08:58.920] All right.
[01:09:00.600 --> 01:09:02.360] Some monster.
[01:09:03.000 --> 01:09:05.240] Evan, tell us about the bio-twang.
[01:09:05.240 --> 01:09:06.360] Am I saying that right?
[01:09:06.360 --> 01:09:07.560] Yeah, bio-twang.
[01:09:07.560 --> 01:09:09.000] Isn't that a cool word?
[01:09:09.000 --> 01:09:14.600] All right, but I got to set this up a little bit, and I'm going to start with three questions.
[01:09:14.920 --> 01:09:20.200] All right, here's the first question: What is the collective noun for a group of crows?
[01:09:20.200 --> 01:09:20.760] A murder.
[01:09:21.400 --> 01:09:23.080] A murder of crows, correct.
[01:09:23.080 --> 01:09:27.000] And do you know what the collective noun for a group of lions is?
[01:09:27.000 --> 01:09:27.960] Lions is a pride.
[01:09:28.440 --> 01:09:28.840] Yes.
[01:09:28.840 --> 01:09:29.800] And the last one.
[01:09:29.800 --> 01:09:32.280] What's a group of scientists?
[01:09:32.280 --> 01:09:33.880] Collective noun?
[01:09:33.880 --> 01:09:35.160] A fight.
[01:09:37.560 --> 01:09:38.680] Well, I know.
[01:09:38.680 --> 01:09:39.880] Nobody really knows.
[01:09:40.040 --> 01:09:41.000] I came up with the term.
[01:09:41.000 --> 01:09:42.360] It's called a baffle.
[01:09:42.360 --> 01:09:42.840] A baffle.
[01:09:42.920 --> 01:09:46.360] Because scientists are always baffled, it appears.
[01:09:46.360 --> 01:09:54.600] Try searching a news item using the term scientist baffled, and you will come up with news items every single day.
[01:09:54.600 --> 01:09:57.000] I kid you not, because I've done it.
[01:09:57.000 --> 01:10:03.400] And whenever something is weird or unusual or unexplained in the context of science, that means scientists are baffled.
[01:10:03.400 --> 01:10:07.040] Evan, do you remember when we, like, this is going back 15 years?
[01:10:07.760 --> 01:10:13.480] One of the bits that we did for the show was coming up with a skeptical, I mean, a collective word for skepticism.
[01:10:13.560 --> 01:10:14.440] For skeptics, yes.
[01:10:14.440 --> 01:10:15.680] Yeah, I can't remember what we came up with.
[01:10:14.520 --> 01:10:17.200] Oh, yeah, I can't remember either.
[01:10:17.200 --> 01:10:19.760] It must not have been great, otherwise, it would have stuck.
[01:10:14.920 --> 01:10:20.640] Yeah, it didn't stick.
[01:10:21.440 --> 01:10:22.960] A doubt of skeptics or something.
[01:10:23.120 --> 01:10:25.840] Yeah, that was one of the something like that.
[01:10:25.840 --> 01:10:27.760] You know, very predictable.
[01:10:28.720 --> 01:10:31.040] A superciliousness of skeptics.
[01:10:31.040 --> 01:10:38.400] I don't recall if we tackled Steve and Jay this subject when this news first broke in 2014.
[01:10:38.400 --> 01:10:38.720] Right?
[01:10:38.720 --> 01:10:43.840] Underwater audio recordings captured what was described as, well, a bio-twang.
[01:10:44.160 --> 01:10:52.000] And this was during an acoustic survey in the Mariana Trench, which is the world's deepest oceanic trench.
[01:10:52.000 --> 01:11:02.400] It's located in the western Pacific Ocean, about 200 miles, which is 322 kilometers, southwest of Guam and southeast of the Mariana Islands.
[01:11:02.560 --> 01:11:11.920] Stretches to around 1,500 miles, 2,500 kilometers roughly, and it's about 43 miles or 69 kilometers wide.
[01:11:11.920 --> 01:11:15.120] So I know we've covered the bloop, but we've covered the bloop, right?
[01:11:15.120 --> 01:11:17.680] I wasn't sure about the bio-twang, though, right?
[01:11:17.680 --> 01:11:20.080] This is so let's, we don't want to confuse these things.
[01:11:20.320 --> 01:11:22.480] We're talking strictly of the bio-twang.
[01:11:22.480 --> 01:11:22.800] Yeah.
[01:11:22.800 --> 01:11:24.240] There's news about it this week.
[01:11:24.240 --> 01:11:37.040] But back in 2014, the researchers were using underwater gliders, and they recorded this mysterious sound that had two distinct components to it: a low rumble followed by a high-pitched metallic sound.
[01:11:37.040 --> 01:11:41.120] And you can find this on YouTube and other places online and go listen for yourself.
[01:11:41.120 --> 01:11:48.720] It lasts for about between two and a half, three and a half seconds and includes five separate parts with a dramatic range of frequencies.
[01:11:48.720 --> 01:11:55.280] The low is 38 hertz, and then it has this sort of metallic finale to it, 8,000 hertz.
[01:12:01.080 --> 01:12:04.920] And this at the time had scientists baffled.
[01:12:04.920 --> 01:12:09.720] Yep, what the heck could be causing that kind of sound in that area of the ocean?
[01:12:09.720 --> 01:12:14.280] And apparently, there were no other recordings of this noise to compare it against.
[01:12:14.280 --> 01:12:19.240] Was the noise artificial, you know, human-made noise, or was it something living?
[01:12:19.240 --> 01:12:22.040] A large mammal, whale, or something?
[01:12:22.040 --> 01:12:24.040] Was it a kraken or a leviathan?
[01:12:24.040 --> 01:12:31.000] Maybe it was one of those frozen aliens delivered by Xenu 75 million years ago to Earth on a Boeing DC-8.
[01:12:31.320 --> 01:12:33.800] No, that's Scientology, folks.
[01:12:34.600 --> 01:12:35.720] That is actual.
[01:12:35.720 --> 01:12:41.560] That's, yeah, so, but, you know, so they were baffled for a while.
[01:12:41.560 --> 01:12:47.880] 2016, though, a scientific paper was published in the Journal of the Acoustical Society of America.
[01:12:47.880 --> 01:13:08.680] There's a new one for me, authored by researchers from the Cooperative Institute for Marine Resource Studies at Oregon State University and the National Oceanic and Atmospheric Administration, in which they reported the low-frequency moaning part is typical of baleen whales, and that kind of twangy sound that it makes is really unique.
[01:13:08.680 --> 01:13:12.520] We don't find many new baleen whale calls.
[01:13:12.520 --> 01:13:14.520] Okay, their team analyzed the sound.
[01:13:14.600 --> 01:13:19.160] They found it was similar to what they had called the Star Wars sound.
[01:13:19.720 --> 01:13:31.720] A call produced by the dwarf mink or minky, M-I-N-K-E, dwarf minky whale, a type of baleen whale that lives in the northeast coast of Australia near the Great Barrier Reef.
[01:13:31.720 --> 01:13:34.200] Okay, so geographically, that checks out.
[01:13:34.200 --> 01:13:39.480] They said the species is the smallest of the baleen whales, and it doesn't spend much time at the surface.
[01:13:39.480 --> 01:13:41.800] Okay, so way down below.
[01:13:41.800 --> 01:13:47.600] It has an inconspicuous blow and often lives in areas where high seas make sighting difficult.
[01:13:48.400 --> 01:13:52.480] But they call frequently, making them good candidates for acoustic studies.
[01:13:52.480 --> 01:13:54.880] So that was their suggestion at the time.
[01:13:54.880 --> 01:14:00.960] However, they didn't really have a sighting or something to corroborate this.
[01:14:00.960 --> 01:14:05.040] It's just really a theory that they're putting out there, but it would seem to be reasonable.
[01:14:05.040 --> 01:14:17.280] But they also said there might be some complications with that theory because baleen whale calls are typically heard in conjunction with their winter mating season, but this bio-twang has been recorded throughout the year.
[01:14:17.280 --> 01:14:19.280] So, okay, there's an inconsistency.
[01:14:19.280 --> 01:14:21.680] They needed to study this some more.
[01:14:21.680 --> 01:14:30.000] So, yeah, 2016 scientists were maybe less baffled, but still baffled because they couldn't definitively say that this was a whale.
[01:14:30.000 --> 01:14:31.200] More evidence was needed.
[01:14:31.200 --> 01:14:35.920] So, fast forward to now, this week's news item, update on the bio-twang.
[01:14:35.920 --> 01:14:52.320] And I can think we can now say that scientists are even less baffled than before, because the study has revealed the exact origin of the Pacific Ocean's mysterious biotwang noises emanating from a whale called brides whales.
[01:14:52.320 --> 01:14:56.880] That's B-R-Y-D-E apostrophe S, Brides Whales.
[01:14:57.200 --> 01:15:00.400] And it has a taxonomical name which I can't pronounce.
[01:15:00.400 --> 01:15:04.240] Balaenoptera Edeni.
[01:15:04.880 --> 01:15:05.360] I like it.
[01:15:05.680 --> 01:15:08.960] I will say, Evan, or I might ask the group.
[01:15:08.960 --> 01:15:10.720] Sorry, I've waited so long.
[01:15:10.720 --> 01:15:15.040] Is that a New England pronunciation of baleen?
[01:15:15.040 --> 01:15:16.640] Oh, that's an Evan pronunciation.
[01:15:16.880 --> 01:15:17.360] Oh, okay.
[01:15:17.360 --> 01:15:18.720] I'm just making sure.
[01:15:18.720 --> 01:15:19.920] No, I have my own dialect.
[01:15:20.320 --> 01:15:21.840] Yeah, I was like, maybe it is.
[01:15:21.840 --> 01:15:26.080] I'm just a weird Texan, but I've always heard baleen.
[01:15:26.080 --> 01:15:26.800] Mm-hmm.
[01:15:26.800 --> 01:15:27.440] But maybe some people.
[01:15:27.600 --> 01:15:28.240] Have you heard baleen?
[01:15:28.240 --> 01:15:29.280] I've heard baleen.
[01:15:29.880 --> 01:15:33.000] But I say I've never heard baleen until Evan just said it.
[01:15:33.240 --> 01:15:33.800] But it works.
[01:15:33.800 --> 01:15:34.280] Yeah, it's funny.
[01:15:29.840 --> 01:15:34.840] We've got all three.
[01:15:35.160 --> 01:15:38.440] I didn't even connect it to baleen, even though it's about whales.
[01:15:39.000 --> 01:15:47.400] I'm about, I'm gonna, I'm gonna present a scientific paper to the uh to the whaleologist community and offer it up as a correction.
[01:15:48.680 --> 01:15:54.200] But in any case, these brides whales are using the call to locate one another.
[01:15:54.200 --> 01:15:57.320] What they describe as a giant game of Marco Polo.
[01:15:57.320 --> 01:15:58.520] That's what the researchers said.
[01:15:58.520 --> 01:16:05.480] So basically, the whales are playing games with us, and we primates are scratching our collective heads while the whales are making fun of us everywhere.
[01:16:05.480 --> 01:16:27.720] The new study was published, what, a week ago tonight, and it was in the journal Frontiers in Marine Science, where they proved that the brides whales were making the noise thanks to new artificial intelligence tools, which were able to sift through over 200,000 hours of audio recordings containing various ocean sounds.
[01:16:27.720 --> 01:16:31.240] They suspected that the brides whales were behind the biotwang.
[01:16:31.240 --> 01:16:41.560] When they spotted, here we go, they actually found 10 of them, 10 of them, swimming near the Mariana Islands, and they recorded nine of them making this distinctive noise.
[01:16:41.560 --> 01:16:42.280] So here we go.
[01:16:42.280 --> 01:16:44.520] That's better evidence for us.
[01:16:45.080 --> 01:16:48.920] From, let's see, Ann Allen, the study's lead author.
[01:16:49.160 --> 01:16:56.120] Once as a coincidence, twice as a happenstance, nine times though, definitely a bride's whale.
[01:16:57.160 --> 01:17:00.360] They conclusively proved that they were the ones making the call.
[01:17:00.360 --> 01:17:05.400] The team matched the occurrence of the noises to the migration patterns of the species as well.
[01:17:05.400 --> 01:17:13.000] And that meant sorting through, wow, years of audio recordings captured by monitoring stations all across the region.
[01:17:13.320 --> 01:17:30.480] And the study also found that the biotwang could only be heard in the Northwest Pacific, despite brides whales roaming across a much wider area, suggesting that only a specific population of brides whales are the ones making this specific noise.
[01:17:30.480 --> 01:17:31.440] So there you go.
[01:17:31.440 --> 01:17:39.440] Scientists, mostly, for the most part, are now no longer baffled in regards to the Biotwang.
[01:17:40.080 --> 01:17:43.040] I'm going to notice scientists being baffled every day.
[01:17:43.040 --> 01:17:49.760] I think you're totally-I mean, you obviously looked into it, but I can't believe how I've missed out on years of being baffled.
[01:17:50.800 --> 01:17:52.880] Political scientists are baffled.
[01:17:52.880 --> 01:17:53.680] Oh, we're baffled.
[01:17:53.680 --> 01:17:54.960] I tell you what.
[01:17:56.880 --> 01:17:58.080] All right, thanks, Evan.
[01:17:58.080 --> 01:17:59.200] You're welcome.
[01:17:59.200 --> 01:18:01.680] All right, Jay, it's who's that noisy time?
[01:18:01.680 --> 01:18:02.560] All right, guys.
[01:18:02.560 --> 01:18:05.360] Last week, I played this noisy.
[01:18:35.440 --> 01:18:36.800] What is that?
[01:18:37.760 --> 01:18:39.200] Any ideas, guys?
[01:18:39.200 --> 01:18:43.120] Attack of killer mechanical hornets.
[01:18:44.400 --> 01:18:46.000] I don't think we can read that.
[01:18:46.000 --> 01:18:46.320] Okay.
[01:18:46.640 --> 01:18:50.640] It's a combination dentist, drill, and slot machine.
[01:18:50.960 --> 01:18:53.200] I mean, there's so many things in there.
[01:18:53.200 --> 01:18:53.600] I know.
[01:18:53.600 --> 01:18:54.560] It could be so much.
[01:18:54.560 --> 01:18:56.000] It's so cool.
[01:18:56.000 --> 01:19:04.760] I know, because I know what it is, when I listen to it back now, you know, it feels right, but there are so many other things it sounds like.
[01:19:04.760 --> 01:19:07.800] All right, so a listener named Keely Hill wrote in.
[01:19:07.800 --> 01:19:09.000] Keely said, Hi, Jay.
[01:19:09.000 --> 01:19:11.880] The second part of this reminds me so much of cutting wrapping paper.
[01:19:11.880 --> 01:19:16.280] I'll guess it's some machine advancing a big sheet of paper and then cutting it.
[01:19:16.280 --> 01:19:18.040] I thought that was a really good guess.
[01:19:18.040 --> 01:19:24.760] It's not correct, but you know, there is a paper cutting sound in there that I detect as well.
[01:19:24.840 --> 01:19:28.200] Listener named Tom Heimbrock said, Hey, Jay, long time, first time.
[01:19:28.200 --> 01:19:32.440] I think this week's noisy is an acetylene torch being turned on, then lit.
[01:19:32.440 --> 01:19:34.520] I don't hear that as much.
[01:19:34.840 --> 01:19:41.320] I thought that was interesting, though, that you thought that it was like, you know, that pressure sound of the fire happening.
[01:19:41.720 --> 01:19:42.920] That's interesting.
[01:19:42.920 --> 01:19:45.320] Another listener named Christian Schmidt said, Hi, Jay.
[01:19:45.320 --> 01:19:50.280] I think this week's noisy is a bearing being spun up by an air compressor and then released.
[01:19:50.280 --> 01:19:54.360] I've heard this, and it is an interesting sound to hear.
[01:19:54.360 --> 01:19:57.560] And there is a little bit of that in there, but that is not correct.
[01:19:57.560 --> 01:20:06.760] Ben Long wrote in and said, Vibes, Jay, this week's noisy sounds like a laser cutting tool spinning up and then beginning to cut, possibly through a sheet of metal.
[01:20:06.760 --> 01:20:09.560] Yeah, and there is that white noisy sound in there of that.
[01:20:09.880 --> 01:20:11.960] That is not correct, though.
[01:20:11.960 --> 01:20:14.840] And let me give you one more guess.
[01:20:14.840 --> 01:20:17.160] This comes from Dave Siebler.
[01:20:17.160 --> 01:20:20.680] And Dave said, huge fan and longtime listener, first time guessing.
[01:20:20.680 --> 01:20:27.720] This sound reminds me of something I watched explaining how coins were made at the mint.
[01:20:28.440 --> 01:20:30.040] That's a cool thing to say.
[01:20:30.920 --> 01:20:33.160] I've never heard that sound of coins being made.
[01:20:33.160 --> 01:20:38.520] Maybe you might be talking about when the coins are being the striking of the coin, maybe.
[01:20:38.520 --> 01:20:41.800] I'm not sure exactly what you're talking about, but I'd like to learn more about that.
[01:20:41.800 --> 01:20:43.800] I applied for a job at a mint once.
[01:20:43.800 --> 01:20:44.280] What happened?
[01:20:44.280 --> 01:20:46.080] Yeah, I think I made a good impression.
[01:20:47.600 --> 01:20:48.480] I knew it was a joke.
[01:20:48.480 --> 01:20:50.240] I just didn't know where the hell you were going with it.
[01:20:52.560 --> 01:20:53.520] That was for Ian.
[01:20:53.520 --> 01:20:55.200] This is what I find odd.
[01:20:56.000 --> 01:20:57.120] Nobody got it.
[01:20:57.120 --> 01:20:58.560] Nobody won this week.
[01:20:58.560 --> 01:21:02.160] But many people have sent this noisy into me over the years.
[01:21:02.560 --> 01:21:04.160] And now that nobody won, I feel guilty.
[01:21:04.160 --> 01:21:05.600] I should have given it as a clue.
[01:21:05.600 --> 01:21:13.040] This is something that I think once I tell you, you will be surprised at how seemingly obvious it is.
[01:21:13.040 --> 01:21:14.960] Guys, this is glass cutting.
[01:21:15.760 --> 01:21:16.720] Let me play it again.
[01:21:16.720 --> 01:21:19.760] There's two different sounds in here, and I won't make you listen to the whole thing.
[01:21:19.760 --> 01:21:20.560] I'll fast-forward it.
[01:21:20.560 --> 01:21:23.120] But the first one, let me explain this to you.
[01:21:23.120 --> 01:21:26.720] You have a square piece of mirrored glass.
[01:21:26.720 --> 01:21:34.880] There is a suction cup that's put in the center of this, and then there's an arm coming off of the suction cup that can spin around the suction cup, right?
[01:21:34.880 --> 01:21:43.520] At the end of that arm, they affix like the cutting implement, which I do believe is probably some type of diamond or whatever, right?
[01:21:43.520 --> 01:21:45.760] Some type of thing that can cut into the glass.
[01:21:45.760 --> 01:21:50.000] And the first thing that's happening is a person is spinning that around really fast with their finger, right?
[01:21:50.000 --> 01:21:57.360] So you understand how it's, you know, the cutting implement is on the outside of the arm and it's making a large circle, making a, you know, making a mirror, right?
[01:21:57.680 --> 01:22:03.760] And then there is a second cutting implement that they use, which is the really heavy scratching sound.
[01:22:03.760 --> 01:22:06.160] And that like really digs in deep now, right?
[01:22:06.160 --> 01:22:11.520] After they've made the kind of like the runway for the big cutter to go in, then they bring in the heavy gun cutter.
[01:22:11.520 --> 01:22:13.120] And that was the second sound.
[01:22:13.120 --> 01:22:15.520] So I'll play both of those for you.
[01:22:16.640 --> 01:22:18.000] Spinning it up.
[01:22:19.680 --> 01:22:22.160] He's scoring the glass here.
[01:22:23.760 --> 01:22:25.840] And then this second noise.
[01:22:25.840 --> 01:22:28.080] And then, here, I'll pause it.
[01:22:28.080 --> 01:22:32.680] And then the second noise I'll get to is when he's really cutting into the glass now.
[01:22:37.480 --> 01:22:49.800] And after that process, the person doing it then kind of picks up the whole square piece of glass and just breaks off the outer part to reveal the circular mirror that's been cut.
[01:22:49.800 --> 01:22:50.840] Really interesting.
[01:22:50.840 --> 01:22:59.000] You know, and it's very, it's done by hand, you know, and I wonder today how much of that is still done by hand versus, you know, is it done by machine?
[01:22:59.000 --> 01:23:07.720] I would just imagine that it's been all automated at this point, but it's a really cool thing to watch somebody do because it's a very specific process that they're going through.
[01:23:07.720 --> 01:23:10.120] And I would just imagine that's how they did it.
[01:23:10.120 --> 01:23:18.280] You know, going back probably, you know, very long time into human history, that's very likely how they would make, you know, make the shaped mirrors.
[01:23:18.280 --> 01:23:21.160] All right, guys, I have a new noisy for you this week.
[01:23:21.160 --> 01:23:25.080] This noisy comes from a listener named Dale Thurber.
[01:23:38.120 --> 01:23:38.760] Jay.
[01:23:39.080 --> 01:23:40.600] I know, Evan, I thought of you.
[01:23:41.000 --> 01:23:42.280] I thought of you, Evan.
[01:23:42.600 --> 01:23:43.080] Stop it.
[01:23:43.080 --> 01:23:45.080] That can't be the noisy of the squeeze.
[01:23:45.240 --> 01:23:52.200] I dude, I heard it, and I'm like, Evan and I are not going to be able to not laugh when we hear this.
[01:23:53.480 --> 01:23:55.240] It's such a weird sound.
[01:23:55.240 --> 01:23:56.040] Meh.
[01:23:58.840 --> 01:24:02.920] It's a 12-year-old kid trying to act like an 80-year-old man going, meh.
[01:24:04.360 --> 01:24:08.280] I can't wait for you to hear what it is because I just love it.
[01:24:08.280 --> 01:24:09.320] I love everything about it.
[01:24:09.320 --> 01:24:10.520] It's a great noisy.
[01:24:10.520 --> 01:24:23.680] If you think you know what this noisy is, or and if you know, any of these things, if you found something cool and you want to send it to me, you can email me at WTN at the skepticsguide.org.
[01:24:23.680 --> 01:24:31.440] Steve, so much to talk about now because we can't go long without filling up our calendars with tons of stuff.
[01:24:31.440 --> 01:24:34.720] So you can purchase tickets to both our Washington, D.C.
[01:24:34.720 --> 01:24:35.760] shows right now.
[01:24:35.760 --> 01:24:39.680] So we will be having a private show plus on December 7th.
[01:24:39.680 --> 01:24:40.800] That starts at noon.
[01:24:40.800 --> 01:24:46.960] The private show plus is a live recording of a full episode of the podcast, which you'll be, you know, you'll be able to sit there and watch us do it.
[01:24:46.960 --> 01:24:50.080] And then during the last hour, you get audience-only content.
[01:24:50.080 --> 01:24:52.000] We have a ton of fun doing this.
[01:24:52.000 --> 01:24:53.200] George will be there with us.
[01:24:53.440 --> 01:24:55.680] We really hope that you guys can join us.
[01:24:55.680 --> 01:24:57.440] And then that same day at 8 p.m.
[01:24:57.520 --> 01:25:02.080] that night, we are running our skeptical extravaganza of special significance.
[01:25:02.080 --> 01:25:04.080] This is a stage show.
[01:25:04.240 --> 01:25:08.960] We have been doing this show for about a decade now, and we absolutely love it.
[01:25:08.960 --> 01:25:11.360] We get tons of great feedback about the show.
[01:25:11.360 --> 01:25:14.640] We demonstrate all the ways your brain can fool you.
[01:25:14.640 --> 01:25:18.320] We also have several improv comedy bits, music.
[01:25:18.320 --> 01:25:19.520] There's a knife fight.
[01:25:19.520 --> 01:25:20.240] It's crazy.
[01:25:20.240 --> 01:25:21.440] You guys have to come check us out.
[01:25:21.440 --> 01:25:22.320] You can't miss it.
[01:25:22.320 --> 01:25:24.720] Go to the skepticsguide.org.
[01:25:24.720 --> 01:25:30.560] That's theskepticsguide.org for more information, and you will see buttons there for the tickets.
[01:25:30.560 --> 01:25:38.080] And one week from today, I will announce on the show you will be able to buy tickets to Naticon 2025.
[01:25:38.080 --> 01:25:41.440] This will be the weekend of May 16th.
[01:25:41.760 --> 01:25:44.640] It'll be actually Thursday night, the 15th.
[01:25:44.880 --> 01:25:47.120] We'll be doing the VIP.
[01:25:47.120 --> 01:25:52.720] And then Friday and Saturday, that's the 16th and 17th, we will be doing the conference.
[01:25:52.720 --> 01:25:54.480] We had an amazing time last year.
[01:25:54.480 --> 01:25:56.160] I know I've been talking a lot about this.
[01:25:56.160 --> 01:25:58.560] So tickets will be available next week.
[01:25:58.560 --> 01:25:58.960] All right.
[01:25:58.960 --> 01:26:00.360] Thanks, Jay.
[01:26:00.680 --> 01:26:03.560] All right, guys, well, let's go on with science or fiction.
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[01:26:33.720 --> 01:26:38.280] You probably think it's too soon to join AARP, right?
[01:26:38.280 --> 01:26:40.600] Well, let's take a minute to talk about it.
[01:26:40.600 --> 01:26:43.320] Where do you see yourself in 15 years?
[01:26:43.320 --> 01:26:47.720] More specifically, your career, your health, your social life.
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[01:26:57.880 --> 01:27:01.960] Sign up for AARP reskilling courses to help make it a reality.
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[01:27:19.720 --> 01:27:24.120] So it's safe to say it's never too soon to join AARP.
[01:27:24.120 --> 01:27:28.360] They're here to help your money, health, and happiness live as long as you do.
[01:27:28.360 --> 01:27:32.520] That's why the younger you are, the more you need AARP.
[01:27:32.520 --> 01:27:37.080] Learn more at AARP.org/slash wise friend.
[01:27:39.640 --> 01:27:44.440] It's time for science or fiction.
[01:27:49.360 --> 01:28:00.080] Each week I come up with three science news items or facts, two real and one fake, and then I challenge my panel of skeptics to tell me which one is the fake.
[01:28:00.400 --> 01:28:03.280] You guys ready for this week's three news items?
[01:28:03.280 --> 01:28:04.640] Well, I don't know.
[01:28:04.640 --> 01:28:05.440] All right, here we go.
[01:28:05.440 --> 01:28:18.320] Item number one: a recent analysis finds that mammal-like jaw anatomy evolved first in therapsids at least 20 million years before the first cynodonts, which were mammalian ancestors.
[01:28:18.320 --> 01:28:28.480] Item number two, scientists have recently demonstrated that single bacterial genes can flip a portion of their genetic sequence so that they code for a different protein.
[01:28:28.480 --> 01:28:29.120] Wow.
[01:28:29.120 --> 01:28:40.560] And item number three, a recent clinical trial shows that a single infusion with gene therapy was able to effectively treat hemophilia B for at least 15 months of follow-up.
[01:28:40.560 --> 01:28:41.680] Jay, go first.
[01:28:41.680 --> 01:28:42.160] Okay.
[01:28:42.400 --> 01:28:44.320] I want to go to the last one.
[01:28:44.320 --> 01:28:54.560] This is the one clinical trial shows that a single infusion with gene therapy was able to effectively treat hemophilia B for at least 15 months.
[01:28:54.560 --> 01:28:57.280] So I believe that this was CRISPR.
[01:28:57.280 --> 01:29:00.560] Yeah, I completely think that this one is science.
[01:29:00.880 --> 01:29:06.640] It's an incredible feat of technology and biology in order to pull this thing off.
[01:29:06.640 --> 01:29:10.000] But yeah, I think that that one is definitely science.
[01:29:10.000 --> 01:29:19.360] The second one here, scientists have recently demonstrated that single bacterial genes can flip a portion of their genetic sequence so that they code for a different protein.
[01:29:19.360 --> 01:29:22.400] That is freaking amazing if that's true.
[01:29:22.400 --> 01:29:24.160] I'd like to see that in action.
[01:29:24.160 --> 01:29:32.680] Like, so, Steve, is this like, you know, what do you call the part that is, you know, kind of splitting the DNA and then there's another part that copies it, right?
[01:29:32.680 --> 01:29:36.200] Are you talking about like they're actually like flipping it there mechanically?
[01:29:36.200 --> 01:29:38.040] You're talking about transcription and translation?
[01:29:38.040 --> 01:29:43.320] Yeah, yeah, you know, those little biological machines that manipulate your DNA, right?
[01:29:44.440 --> 01:29:46.440] Are they mechanically flipping it?
[01:29:46.440 --> 01:30:00.760] So, basically, what they're saying, what they discovered is that if you have the genetic sequence of a gene, a portion of that gene flips itself around so it's reading the other way, you know, reinserts itself back into the gene.
[01:30:00.760 --> 01:30:04.840] So, now you have a gene with a different sequence that codes for a different protein.
[01:30:04.840 --> 01:30:05.880] What the hell?
[01:30:06.520 --> 01:30:07.800] That's amazing.
[01:30:07.800 --> 01:30:10.280] I mean, who am I to say that that's not happening?
[01:30:10.280 --> 01:30:10.920] I don't know.
[01:30:10.920 --> 01:30:17.960] I mean, it sounds complicated, but all that stuff is so unbelievably complex.
[01:30:17.960 --> 01:30:20.120] I just don't see why this isn't happening.
[01:30:20.120 --> 01:30:21.400] You know, it's just one of those things.
[01:30:21.400 --> 01:30:23.560] Like, I just don't have enough information to really know.
[01:30:23.560 --> 01:30:27.240] But I would think, okay, that sounds like, yeah, it could be happening.
[01:30:27.240 --> 01:30:28.920] All right, let me just go back to number one here now.
[01:30:28.920 --> 01:30:35.960] A recent analysis finds that mammal-like jaw anatomy evolved first in therapsipsids.
[01:30:35.960 --> 01:30:36.760] Therapsids.
[01:30:37.000 --> 01:30:37.720] Therapsids.
[01:30:37.880 --> 01:30:38.920] Oh, not therapists.
[01:30:39.160 --> 01:30:39.320] No.
[01:30:40.760 --> 01:30:41.960] That word sucks.
[01:30:41.960 --> 01:30:43.560] Or for dinosaurs.
[01:30:43.560 --> 01:30:45.960] You know, whoever came up with that word, what the hell?
[01:30:46.680 --> 01:30:48.360] Seriously, like, therapsapsids.
[01:30:48.600 --> 01:30:49.560] Get the hell out of here.
[01:30:50.200 --> 01:30:51.960] Why can't it just be like a duck?
[01:30:51.960 --> 01:30:53.000] You know what I mean?
[01:30:53.000 --> 01:30:53.800] Yeah, Jay.
[01:30:53.800 --> 01:30:54.440] Right.
[01:30:54.440 --> 01:30:59.320] All right, this was at least 20 million years before the first cyanodonts.
[01:30:59.480 --> 01:31:00.600] Cynodonts.
[01:31:00.600 --> 01:31:01.400] Whatever.
[01:31:01.400 --> 01:31:03.160] Which were mammalian ancestors.
[01:31:03.160 --> 01:31:03.600] You know what?
[01:31:03.640 --> 01:31:07.000] I'm going to say that one is fiction just because the words suck.
[01:31:07.960 --> 01:31:09.240] Okay, Andrea.
[01:31:09.240 --> 01:31:09.720] All right.
[01:31:09.720 --> 01:31:12.040] I'm going to go in the same order that Jay did.
[01:31:12.040 --> 01:31:19.920] So I had the opposite reaction to the single infusion with gene therapy treating hemophilia B.
[01:31:20.240 --> 01:31:24.640] I feel like that one to me reads fiction.
[01:31:24.960 --> 01:31:28.480] Maybe I've read too many pseudoscience Gwyneth Paltrow.
[01:31:28.640 --> 01:31:37.840] A single infusion of this can cure your whatever, but something about infusions and just one of them feels a little bit snake oily to me.
[01:31:37.840 --> 01:31:39.920] So I'm going to say that's the fiction.
[01:31:39.920 --> 01:31:46.080] The bacterial gene flipping a portion of their genetic sequence so they can code for a different protein sounds awesome.
[01:31:46.080 --> 01:31:47.040] I'm with Jay on that.
[01:31:47.040 --> 01:31:48.160] I want it to be true.
[01:31:48.160 --> 01:31:55.280] And I feel like we're constantly being surprised, dare I say baffled, by the surprising things that genes can do.
[01:31:55.280 --> 01:31:58.560] And so that reads true to me.
[01:31:58.560 --> 01:32:04.880] And look, we're also constantly being baffled by our understanding of how we all evolved.
[01:32:04.880 --> 01:32:18.960] And if we're being surprised by the order in which mandible-like jaw anatomy evolved, then I believe it's pretty plausible that we could have dug up some old fossil that shows that the therapsids and the cynodots, I'm with Jay on the wording.
[01:32:20.080 --> 01:32:23.520] I'm inclined to believe that we could have made a discovery like that.
[01:32:23.520 --> 01:32:27.040] So I think the single infusion is the fiction today.
[01:32:27.040 --> 01:32:28.160] Okay, Evan.
[01:32:28.400 --> 01:32:32.400] The mammal-like jaw 20 million years before?
[01:32:32.720 --> 01:32:35.680] That's a lot of time on that scale.
[01:32:36.160 --> 01:32:37.760] That one seems extreme.
[01:32:38.000 --> 01:32:40.320] Jay, that was the one you went with fiction, right?
[01:32:40.640 --> 01:32:41.200] Yes.
[01:32:41.200 --> 01:32:44.560] Yes, because having to do with the words and pronunciation.
[01:32:44.560 --> 01:32:47.760] No, I mean, I legitimately don't think that that one is it.
[01:32:48.720 --> 01:33:00.200] Whereas the next one about the bacterial genes, oh boy, a single bacteria single bacterial genes can flip a portion of their genetic sequence so that they code for a different protein.
[01:33:00.200 --> 01:33:01.400] Yeah, okay.
[01:32:59.520 --> 01:33:03.400] That's remarkable in itself as well.
[01:33:03.640 --> 01:33:08.120] But I think, sure, they were able to make that happen.
[01:33:08.120 --> 01:33:09.880] I don't have a problem with that one.
[01:33:09.880 --> 01:33:20.040] The last one, and Andrea, you're saying the one for this is the fiction, the one with gene therapy and hemophilia B for at least 15.
[01:33:20.200 --> 01:33:22.600] Yeah, because you said it's a single infusion.
[01:33:22.600 --> 01:33:26.120] That does seem unlikely.
[01:33:26.440 --> 01:33:30.280] And then the other part of this is that it lasts for at least 15 months.
[01:33:30.280 --> 01:33:33.960] So there's two components there where it could be wrong.
[01:33:34.840 --> 01:33:38.200] So I'm definitely, oh boy, this is a tough one.
[01:33:38.200 --> 01:33:38.920] Which one?
[01:33:38.920 --> 01:33:43.880] 20 million year jaw or single infusion and 15 month follow-up?
[01:33:45.320 --> 01:33:46.040] Boy.
[01:33:46.280 --> 01:33:48.040] I'll just, it's a flip for me.
[01:33:48.040 --> 01:33:49.000] I'll just go with Jay.
[01:33:49.000 --> 01:33:50.280] I'll say the jaw.
[01:33:50.600 --> 01:33:52.680] I'm personally offended, but fine.
[01:33:52.680 --> 01:33:54.200] No, it was a coin flip.
[01:33:54.680 --> 01:33:56.280] It was random.
[01:33:56.280 --> 01:33:57.640] All right, Kara.
[01:33:57.640 --> 01:33:59.000] Fair, fair.
[01:33:59.880 --> 01:34:17.160] I think I'm, you know, I'm waffling between the same two, so, which is crazy because a single bacterial gene flipping a portion of that sequence like around and basically reading the code backward and a whole new protein coming out sounds amazing.
[01:34:17.240 --> 01:34:26.920] It doesn't sound like the process is amazing because genes swap material, or sorry, bacteria swap material all the time, and I could see them swapping material with themselves.
[01:34:27.320 --> 01:34:48.320] What's kind of amazing is that just a backward code would read out for a functional protein, and also, that it means you have to kind of rethink the way that we talk about DNA to RNA to protein, like, because we usually think like one gene eventually is going to lead to one protein, right?
[01:34:48.320 --> 01:34:58.400] So, like, that idea of one gene having multiple proteins that could come from the same gene is like weird, but I think it could happen.
[01:34:58.960 --> 01:35:03.600] I think the two that bother me, and I don't, I don't know which one I'm going to pick, so I'm going to walk through them right now.
[01:35:03.600 --> 01:35:09.440] A mammal-like jaw in the therapsids, 20 million years before the cynodonts.
[01:35:09.520 --> 01:35:17.440] The cynodonts are mammalian ancestors, but therapsids are, I'm assuming here, based on how it's written, cynodont ancestors.
[01:35:17.440 --> 01:35:17.840] Yes, that's a lot.
[01:35:18.000 --> 01:35:24.960] So, it's basically, yeah, like when did the jaw start to look like a mammal and not like a fish or like a reptile?
[01:35:24.960 --> 01:35:28.560] I don't know, you know, because how do we define mammal?
[01:35:28.560 --> 01:35:31.440] Everything is transitional until it's not anymore.
[01:35:31.440 --> 01:35:33.840] So, is it their jaws?
[01:35:33.840 --> 01:35:35.600] Do jaws make mammals mammals?
[01:35:35.600 --> 01:35:37.120] I guess it's some component.
[01:35:37.120 --> 01:35:44.320] And then the other question is: the clinical trial, a single infusion of the gene therapy, so that's very specific.
[01:35:44.320 --> 01:35:48.960] Able to effectively treat hemophilia B for at least 15 months of follow-up.
[01:35:48.960 --> 01:35:57.040] I think that there have been multiple hemophilia clinical trials, but I don't know about like if there's a single infusion one.
[01:35:57.040 --> 01:36:06.400] I don't know how long it was effective, and what does it mean effectively treat, like just a certain percentage of people had positive outcomes from it?
[01:36:06.400 --> 01:36:07.920] Like they were able to clot.
[01:36:07.920 --> 01:36:08.720] I don't know.
[01:36:08.720 --> 01:36:11.520] I'm gonna say, Andrea, you know what?
[01:36:11.520 --> 01:36:12.320] Solidarity.
[01:36:12.320 --> 01:36:13.120] I'm gonna go.
[01:36:13.120 --> 01:36:15.280] You went with the infusion one?
[01:36:15.520 --> 01:36:16.160] Indeed.
[01:36:16.160 --> 01:36:17.360] I'll go with Andrea on this.
[01:36:17.360 --> 01:36:17.840] All right.
[01:36:17.840 --> 01:36:18.400] Thanks, Kara.
[01:36:18.560 --> 01:36:25.920] All right, so we got an even split, and you all are very confident about the middle one, so we'll start.
[01:36:25.920 --> 01:38:07.520] Scientists have recently demonstrated that single bacterial genes can flip a portion of their genetic sequence so that they code for a different protein you all think this one is science and this one is science but what about the central dogma of genetics that one gene one protein this one is more amazing than i think you guys appreciate you should have thought this one was a fiction no way steve this could be this was this is this is this is pretty stunning so this is the first time this has been uh observed in a single gene that these the gene itself sort of rearranges itself and then changes its function it doesn't always code for a functioning protein sometimes it it would basically turns into a it either down regulates its transcription or upregulates it or it changes the actual sequence of amino acids the way it works is that you have a sequence within the gene that is flanked by a palindromic sequence right and these palindromic so the palindromic sequences can flip without changing their orientation does that make sense so that's sort of the palindromic yeah because they're palindromic so so that's the structure that allows this to happen so what the scientists did was they just went through a bunch of bacterial genes looking for these palindromic you know bookends right and then they looked at the genes that were there and then they had to cross-reference them with all like all other bacteria and figure out if there were if they could find any instances where the sequence is flipped.
[01:38:07.520 --> 01:38:17.920] Now, previously they've they've identified, as kara said, or one of you guys said, that you know these genes exchange information with other genes.
[01:38:18.480 --> 01:38:23.120] Yeah, but this is the first time it was observed within a single gene.
[01:38:23.440 --> 01:38:23.840] That's cool.
[01:38:24.000 --> 01:38:30.240] Right, an intragenic DNA inversion expand the bacterial coding capacity.
[01:38:30.240 --> 01:38:31.920] Now, that's the title of the paper.
[01:38:32.160 --> 01:38:40.720] What this means is, like, we're thinking, oh, this bacteria has whatever, it has a thousand genes, but maybe it can make more than a thousand proteins, right?
[01:38:40.720 --> 01:38:41.280] Wow.
[01:38:41.280 --> 01:38:48.960] Because it could do this intragenic inversions to basically expand its repertoire of things that it could do.
[01:38:49.200 --> 01:38:50.480] Pretty amazing.
[01:38:50.480 --> 01:39:02.320] Now, I love the author's description of the process because when they were interviewed for the press release, they basically said when they first discovered this, they didn't believe it.
[01:39:02.480 --> 01:39:05.360] They said, this can't be true.
[01:39:05.360 --> 01:39:09.440] And then they spent four years trying to prove themselves wrong.
[01:39:09.440 --> 01:39:10.320] Amazing, I love it.
[01:39:10.480 --> 01:39:13.600] Basically, trying to figure out, all right, we clearly made a mistake.
[01:39:13.920 --> 01:39:15.200] This can't be true.
[01:39:15.200 --> 01:39:17.280] So let's figure out where we went wrong.
[01:39:17.280 --> 01:39:23.440] But until eventually they had to come to the conclusion that they didn't go wrong, that this is actually happening.
[01:39:23.440 --> 01:39:27.520] But that's how, you know, against what we thought we knew it was.
[01:39:27.520 --> 01:39:30.560] All right, I guess we'll go back to the first one.
[01:39:30.560 --> 01:39:40.880] A recent analysis finds that mammal-like jaw anatomy evolved first in therapsids, at least 20 million years before the first cynodonts, which were mammalian ancestors.
[01:39:41.120 --> 01:39:43.760] Jay and Evan, you think this one is a fiction.
[01:39:43.760 --> 01:39:47.280] Kara and Andrea, you think this one is science.
[01:39:47.280 --> 01:39:57.200] So just for a little bit of background here before I do the reveal, cynodonts, as Kara said, cynodonts are, you know, the reptiles that were, they were mammal-like reptiles.
[01:39:57.200 --> 01:40:05.640] And one of the big anatomical changes that happened from reptiles to mammals was at the connection of the jaw.
[01:40:05.880 --> 01:40:14.920] So reptiles have multiple connection points, multiple bones in the jaw, whereas mammals have a single connection point, right?
[01:40:14.920 --> 01:40:20.440] And those extra bones that we no longer needed became the three bones of the inner ear.
[01:40:20.440 --> 01:40:24.440] That's partly why the ear is right next to your jaw joint, right?
[01:40:24.440 --> 01:40:26.120] Your temporal mandibular joint.
[01:40:26.120 --> 01:40:35.080] Now what that allowed mammals to do, that single connection point between the jaw and the skull, allows for a lot more mobility, right?
[01:40:35.080 --> 01:40:37.480] You can move your mouth from side to side.
[01:40:37.480 --> 01:40:39.400] Reptiles can't do that.
[01:40:40.520 --> 01:40:53.320] So that change, that's like a huge marker for the evolution of mammals, of reptiles to mammals that happened within the cynodont group, right?
[01:40:53.320 --> 01:40:54.760] It's not obviously one species.
[01:40:54.760 --> 01:40:59.160] This is a group of mammal-like reptiles.
[01:40:59.160 --> 01:41:01.640] So the question is, when did that change occur?
[01:41:01.640 --> 01:41:09.000] And of course, things are only as old as the oldest example that we have, and it's very common that we find still older examples.
[01:41:09.000 --> 01:41:11.160] So we want to know, like, when did this happen?
[01:41:11.160 --> 01:41:17.640] When did this iconic change of mammalian anatomy occur?
[01:41:17.960 --> 01:41:23.160] And so there is a study, and what the study found was not this.
[01:41:23.160 --> 01:41:24.200] So this is fiction.
[01:41:24.520 --> 01:41:26.200] Oh, sorry, Kara.
[01:41:26.520 --> 01:41:26.840] That's okay.
[01:41:27.080 --> 01:41:39.240] What the study found was they found older cynodonts that were not on the path to mammals that had the mammal-like jaw anatomy.
[01:41:39.240 --> 01:41:40.040] But they were already...
[01:41:40.280 --> 01:41:40.760] Gosh, I would have told you.
[01:41:42.280 --> 01:41:54.000] They were already a side branch, and they were older than cynodonts that were on their way to becoming mammals, but their jaw anatomy looked more mammalian, right?
[01:41:54.000 --> 01:41:55.440] So that doesn't make any sense.
[01:41:55.440 --> 01:42:19.280] What that means is, is that the cynodonts were evolving a bunch of different jaw anatomy, that they were basically experimenting, you know, evolutionarily speaking, and that a simplified connection between the jaw, you know, the mandible basically and the skull evolved independently more than once in the cynodont line, right?
[01:42:19.280 --> 01:42:44.720] It's kind of like when we drill down deep on like the evolution of birds from theropod dinosaurs, we realize that, yeah, flight, you know, and different features of flight evolved at different times in different branches, you know, only one branch of which led to modern birds, but you know, sometimes you get the same thing independently evolves over and over again, which partly also means that the potential was there, right?
[01:42:44.720 --> 01:42:48.160] It's like it was right on the verge of this evolutionary thing.
[01:42:48.160 --> 01:42:54.240] It was easy to happen evolutionarily, so it happened multiple times independently in different branches.
[01:42:54.240 --> 01:43:00.160] And the one that led to mammals didn't necessarily develop it first.
[01:43:00.160 --> 01:43:01.520] Does that make sense?
[01:43:01.840 --> 01:43:05.440] Yeah, so also a very interesting finding.
[01:43:05.440 --> 01:43:11.200] When I read the headline of the press release, there was like jaw-dropping finding.
[01:43:11.760 --> 01:43:13.520] Haley Todd was like, oh, I wrote that.
[01:43:13.520 --> 01:43:14.480] This has got to be good.
[01:43:14.480 --> 01:43:16.960] And then I'm like, oh, jaw-dropping, you bastards.
[01:43:16.960 --> 01:43:17.520] Okay.
[01:43:17.840 --> 01:43:18.240] Gotcha.
[01:43:18.400 --> 01:43:18.960] It was still good.
[01:43:18.960 --> 01:43:20.560] It was still good, though.
[01:43:20.880 --> 01:43:31.960] Which means that a recent clinical trial shows that a single infusion with gene therapy was able to effectively treat hemophilia B for at least 15 months of follow-up is science.
[01:43:31.960 --> 01:43:37.240] This is also very surprising and very, very cool.
[01:43:37.560 --> 01:43:48.200] I guess not that surprising if you've been following this, but so this is a drug, a gene therapy that is already FDA approved.
[01:43:48.520 --> 01:43:51.080] This is an open-label trial.
[01:43:51.640 --> 01:43:52.360] They studied it.
[01:43:52.360 --> 01:43:57.160] So hemophilia is a genetic mutation that causes bleeding, right?
[01:43:57.160 --> 01:43:59.480] You have uncontrolled bleeding because you're not making factor 9.
[01:43:59.560 --> 01:44:00.920] You need factor 9 to clot your blood.
[01:44:00.920 --> 01:44:02.280] So their blood doesn't clot.
[01:44:02.280 --> 01:44:04.120] So they bleed all the time.
[01:44:04.120 --> 01:44:07.320] And the treatment for this is infusions of factor 9.
[01:44:07.320 --> 01:44:17.320] And people who have hemophilia B have to get these infusions multiple times a week, maybe once every couple of weeks, or somewhere in that spectrum.
[01:44:17.320 --> 01:44:19.640] They're getting it, you know, very, very frequently.
[01:44:19.640 --> 01:44:21.960] These are IV infusions.
[01:44:21.960 --> 01:44:29.080] And it's massively disruptive to their life, you know, because in order to stay alive, they have to constantly get these infusions of factor 9.
[01:44:29.080 --> 01:44:34.440] With this gene therapy, now the name of the drug is, Jay, you talk about dinosaur names.
[01:44:34.440 --> 01:44:35.000] All right, here we go.
[01:44:35.000 --> 01:44:35.800] Are you ready?
[01:44:35.800 --> 01:44:36.200] Yeah.
[01:44:36.520 --> 01:44:38.040] I'm probably not pronouncing it correctly.
[01:44:38.440 --> 01:44:39.800] A little milab.
[01:44:39.800 --> 01:44:40.680] No, it's not a MAB.
[01:44:40.680 --> 01:44:41.400] It's not a MAB.
[01:44:41.640 --> 01:44:42.200] It's a gene.
[01:44:42.200 --> 01:44:46.600] It's Fidanacogene Leparvovec.
[01:44:46.920 --> 01:44:48.360] Leparvovec.
[01:44:48.360 --> 01:44:49.320] Rolls off the chinese.
[01:44:49.800 --> 01:44:50.120] Yeah.
[01:44:50.120 --> 01:44:50.520] Beautiful.
[01:44:50.680 --> 01:44:51.160] Memorize it.
[01:44:51.320 --> 01:44:57.800] Add a dose of 5 times 10 to the 11 vector genome copies per kilogram of body weight.
[01:44:57.800 --> 01:44:58.360] That's a lot of fun.
[01:44:58.520 --> 01:44:59.160] What a funny dose.
[01:44:59.400 --> 01:45:01.320] Vector genome copies.
[01:45:01.320 --> 01:45:02.440] That's amazing.
[01:45:02.440 --> 01:45:10.920] In men, yeah, 18 to 65 years old with hemophilia B, factor 9, less than 2%, who have been getting infusions for the last six months at least.
[01:45:10.920 --> 01:45:13.400] So they're dependent basically on infusions.
[01:45:13.400 --> 01:45:21.520] They gave them a single infusion of the gene therapy, and then they followed them for 15 months, 12 to 15 months.
[01:45:21.840 --> 01:45:27.520] And I'm sorry, they followed them from week 12 to month 15 after the treatment.
[01:45:27.520 --> 01:45:32.320] And they found that they did better than people who were getting the infusion.
[01:45:32.320 --> 01:45:37.600] So some of them didn't, some of the recipients no longer needed infusions.
[01:45:37.600 --> 01:45:40.480] They went 15 months without needing an infusion.
[01:45:40.480 --> 01:45:44.480] Others had a reduction in their need for infusions, but not to zero.
[01:45:44.800 --> 01:45:47.280] So they just started making the factor themselves.
[01:45:47.440 --> 01:45:48.720] Yeah, that's the idea.
[01:45:48.960 --> 01:45:52.480] It goes into the liver, and the liver starts making factor nine.
[01:45:52.480 --> 01:45:57.120] So they basically were demonstrating stable factor nine expression.
[01:45:57.120 --> 01:46:01.600] Now, I said at least 15 months because they didn't stop working at 15 months.
[01:46:01.600 --> 01:46:03.520] That's just how long they followed them.
[01:46:04.080 --> 01:46:07.280] You know, it's possible that this may work for years to come.
[01:46:07.280 --> 01:46:08.160] That's so cool.
[01:46:08.160 --> 01:46:13.200] It reminds me, I know it's a totally different mechanism, but it reminds me of like CAR T cell therapy.
[01:46:13.840 --> 01:46:14.720] Yeah, right?
[01:46:14.720 --> 01:46:17.760] These things where it's like, this is curative, potentially.
[01:46:17.920 --> 01:46:20.160] They said, we're not going to use the word cure.
[01:46:20.480 --> 01:46:21.200] Of course, not yet.
[01:46:22.960 --> 01:46:25.120] Not going to break out the cure word.
[01:46:25.760 --> 01:46:33.360] But damn, you know, you go from having, you need multiple IV infusions every week or two to none for 15 months.
[01:46:33.360 --> 01:46:35.440] That's a life changer right there.
[01:46:35.440 --> 01:46:36.880] I've never been more glad to be wrong.
[01:46:36.880 --> 01:46:37.440] Oh, this is awesome.
[01:46:37.440 --> 01:46:38.160] Right, me too.
[01:46:38.160 --> 01:46:38.560] This is awesome.
[01:46:38.720 --> 01:46:39.360] Me too.
[01:46:39.360 --> 01:46:40.880] Absolutely.
[01:46:40.880 --> 01:46:41.760] All right.
[01:46:42.000 --> 01:46:43.280] Evan, give us a quote.
[01:46:43.280 --> 01:46:43.680] All right.
[01:46:43.680 --> 01:46:51.280] Well, this quote is a bit infamous because I came across it while I was preparing my haha funny moment during my news item.
[01:46:51.280 --> 01:46:52.400] So here you go.
[01:46:52.400 --> 01:46:54.960] You don't get rich writing science fiction.
[01:46:54.960 --> 01:46:57.280] If you want to get rich, you start a religion.
[01:46:57.280 --> 01:46:58.000] Yes.
[01:46:58.440 --> 01:46:58.760] L.
[01:46:58.760 --> 01:46:59.880] Ron Hubbard.
[01:46:59.880 --> 01:47:00.040] L.
[01:46:58.800 --> 01:47:01.080] Ron Hubbard.
[01:47:01.080 --> 01:47:02.920] Oh, gosh, that guy.
[01:47:02.920 --> 01:47:04.360] I mean, did that not tell you?
[01:46:59.520 --> 01:47:06.440] He announced, I'm going to make up a fake religion.
[01:47:06.760 --> 01:47:07.640] Yeah, he did.
[01:47:07.640 --> 01:47:09.240] He basically announced it.
[01:47:09.480 --> 01:47:12.680] So, does this mean Nauticon is actually a religious gathering now?
[01:47:15.320 --> 01:47:15.880] I don't think it's.
[01:47:16.840 --> 01:47:17.480] Yeah.
[01:47:17.480 --> 01:47:19.000] Here we go.
[01:47:19.320 --> 01:47:20.920] I mean, at least he admitted it.
[01:47:20.920 --> 01:47:24.280] He's not the first person to make up a religion for personal gain, but.
[01:47:24.600 --> 01:47:25.080] Right.
[01:47:25.080 --> 01:47:26.440] Yeah, I suppose so.
[01:47:26.920 --> 01:47:28.040] Transparency is logical.
[01:47:28.440 --> 01:47:29.720] He's

Prompt 6: Key Takeaways

Now please extract the key takeaways from the transcript content I provided.

Extract the most important key takeaways from this part of the conversation. Use a single sentence statement (the key takeaway) rather than milquetoast descriptions like "the hosts discuss...". 

Limit the key takeaways to a maximum of 3. The key takeaways should be insightful and knowledge-additive. 

IMPORTANT: Return ONLY valid JSON, no explanations or markdown. Ensure:
- All strings are properly quoted and escaped
- No trailing commas
- All braces and brackets are balanced
Format: {"key_takeaways": ["takeaway 1", "takeaway 2"]}

Prompt 7: Segments

Now identify 2-4 distinct topical segments from this part of the conversation.

For each segment, identify:
- Descriptive title (3-6 words)  
- START timestamp when this topic begins (HH:MM:SS format)
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Most important Key takeaway from that segment. Key takeaway must be specific and knowledge-additive.
- Brief summary of the discussion

IMPORTANT: The timestamp should mark when the topic/segment STARTS, not a range. Look for topic transitions and conversation shifts.

Return ONLY valid JSON. Ensure all strings are properly quoted, no trailing commas:
{
  "segments": [
    {
      "segment_title": "Topic Discussion", 
      "timestamp": "01:15:30",
      "key_takeaway": "main point from this segment",
      "segment_summary": "brief description of what was discussed"
    }
  ]
}

Timestamp format: HH:MM:SS (e.g., 00:05:30, 01:22:45) marking the START of each segment.

Prompt 8: Media Mentions

Now scan the transcript content I provided for ACTUAL mentions of specific media titles:

Find explicit mentions of:
- Books (with specific titles)
- Movies (with specific titles)  
- TV Shows (with specific titles)
- Music/Songs (with specific titles)

DO NOT include:
- Websites, URLs, or web services
- Other podcasts or podcast names

IMPORTANT: 
- Only include items explicitly mentioned by name. Do not invent titles.
- Valid categories are: "Book", "Movie", "TV Show", "Music"
- Include the exact phrase where each item was mentioned
- Find the nearest proximate timestamp where it appears in the conversation
- THE TIMESTAMP OF THE MEDIA MENTION IS IMPORTANT - DO NOT INVENT TIMESTAMPS AND DO NOT MISATTRIBUTE TIMESTAMPS
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Timestamps are given as ranges, e.g. 01:13:42.520 --> 01:13:46.720. Use the EARLIER of the 2 timestamps in the range.

Return ONLY valid JSON. Ensure all strings are properly quoted and escaped, no trailing commas:
{
  "media_mentions": [
    {
      "title": "Exact Title as Mentioned",
      "category": "Book",
      "author_artist": "N/A",
      "context": "Brief context of why it was mentioned",
      "context_phrase": "The exact sentence or phrase where it was mentioned",
      "timestamp": "estimated time like 01:15:30"
    }
  ]
}

If no media is mentioned, return: {"media_mentions": []}

Prompt 9: Context Setup

You are an expert data extractor tasked with analyzing a podcast transcript. 
I will provide you with part 3 of 3 from a podcast transcript. 
I will then ask you to extract different types of information from this content in subsequent messages. Please confirm you have received and understood the transcript content.

Transcript section:
 every week or two to none for 15 months.
[01:46:33.360 --> 01:46:35.440] That's a life changer right there.
[01:46:35.440 --> 01:46:36.880] I've never been more glad to be wrong.
[01:46:36.880 --> 01:46:37.440] Oh, this is awesome.
[01:46:37.440 --> 01:46:38.160] Right, me too.
[01:46:38.160 --> 01:46:38.560] This is awesome.
[01:46:38.720 --> 01:46:39.360] Me too.
[01:46:39.360 --> 01:46:40.880] Absolutely.
[01:46:40.880 --> 01:46:41.760] All right.
[01:46:42.000 --> 01:46:43.280] Evan, give us a quote.
[01:46:43.280 --> 01:46:43.680] All right.
[01:46:43.680 --> 01:46:51.280] Well, this quote is a bit infamous because I came across it while I was preparing my haha funny moment during my news item.
[01:46:51.280 --> 01:46:52.400] So here you go.
[01:46:52.400 --> 01:46:54.960] You don't get rich writing science fiction.
[01:46:54.960 --> 01:46:57.280] If you want to get rich, you start a religion.
[01:46:57.280 --> 01:46:58.000] Yes.
[01:46:58.440 --> 01:46:58.760] L.
[01:46:58.760 --> 01:46:59.880] Ron Hubbard.
[01:46:59.880 --> 01:47:00.040] L.
[01:46:58.800 --> 01:47:01.080] Ron Hubbard.
[01:47:01.080 --> 01:47:02.920] Oh, gosh, that guy.
[01:47:02.920 --> 01:47:04.360] I mean, did that not tell you?
[01:46:59.520 --> 01:47:06.440] He announced, I'm going to make up a fake religion.
[01:47:06.760 --> 01:47:07.640] Yeah, he did.
[01:47:07.640 --> 01:47:09.240] He basically announced it.
[01:47:09.480 --> 01:47:12.680] So, does this mean Nauticon is actually a religious gathering now?
[01:47:15.320 --> 01:47:15.880] I don't think it's.
[01:47:16.840 --> 01:47:17.480] Yeah.
[01:47:17.480 --> 01:47:19.000] Here we go.
[01:47:19.320 --> 01:47:20.920] I mean, at least he admitted it.
[01:47:20.920 --> 01:47:24.280] He's not the first person to make up a religion for personal gain, but.
[01:47:24.600 --> 01:47:25.080] Right.
[01:47:25.080 --> 01:47:26.440] Yeah, I suppose so.
[01:47:26.920 --> 01:47:28.040] Transparency is logical.
[01:47:28.440 --> 01:47:29.720] He's overlighting.
[01:47:30.040 --> 01:47:30.760] What did he say?
[01:47:30.920 --> 01:47:39.960] He said it during a meeting of the Eastern Science Fiction Association in 1948.
[01:47:40.280 --> 01:47:40.760] Yeah.
[01:47:41.960 --> 01:47:44.680] I can think he was a failed science fiction writer.
[01:47:45.240 --> 01:47:45.800] I can't think of him.
[01:47:46.680 --> 01:47:50.440] I cannot think of him without hearing Randy tell us the story.
[01:47:51.240 --> 01:47:51.880] Because I met L.
[01:47:51.960 --> 01:47:55.240] Ron Hubbard two times, and both times he was drunk.
[01:47:57.160 --> 01:47:58.200] I just love that.
[01:47:59.480 --> 01:48:00.120] Oh, man.
[01:48:01.320 --> 01:48:07.320] I guess it just means that he was towards the cynical rather than true believer end of the spectrum.
[01:48:08.280 --> 01:48:10.520] He just was cynically like, oh, yeah.
[01:48:11.160 --> 01:48:16.520] And specifically because he wanted the tax exempt status, you know?
[01:48:17.080 --> 01:48:19.080] And here they are today.
[01:48:19.080 --> 01:48:20.520] Tax exempt status.
[01:48:21.320 --> 01:48:23.000] Evan, can't you change that somehow?
[01:48:23.000 --> 01:48:24.680] Aren't you in charge of taxes?
[01:48:24.680 --> 01:48:26.040] Oh, that's right.
[01:48:26.040 --> 01:48:26.760] Yes, I am.
[01:48:27.240 --> 01:48:28.040] You're a tax scientist.
[01:48:29.640 --> 01:48:30.200] There you go.
[01:48:30.200 --> 01:48:31.000] I like that, Evan.
[01:48:31.160 --> 01:48:31.960] Hang on one second.
[01:48:31.960 --> 01:48:33.720] Let me press enter, and it's done.
[01:48:33.720 --> 01:48:34.760] Now they are taxable.
[01:48:35.160 --> 01:48:35.560] Great.
[01:48:35.120 --> 01:48:36.360] Right, done.
[01:48:36.680 --> 01:48:37.960] If it were that easy.
[01:48:38.200 --> 01:48:39.080] If only.
[01:48:39.080 --> 01:48:39.720] All right.
[01:48:39.720 --> 01:48:42.120] Well, thank you all for joining me this week.
[01:48:42.120 --> 01:48:42.760] Thanks, Steve.
[01:48:42.920 --> 01:48:43.480] Thank you, Cornelius.
[01:48:43.880 --> 01:48:44.680] And thank you, Andrea.
[01:48:44.840 --> 01:48:46.720] Andrea, thank you for straightening us out.
[01:48:45.000 --> 01:48:50.800] You know, we were all sweating this election and reading the polls every day.
[01:48:51.360 --> 01:48:53.840] I like hearing rationality in the discussion of politics.
[01:48:54.000 --> 01:48:56.400] I'm just going to take it easy and ignore the polls from now on.
[01:48:57.200 --> 01:48:59.840] I got to be honest, I don't feel better after what Andrea said.
[01:49:00.480 --> 01:49:01.120] It's not helpful.
[01:49:01.120 --> 01:49:02.240] It really isn't.
[01:49:02.240 --> 01:49:07.840] But I second Kara's message that there also is a very important place for emotion in all of this.
[01:49:08.240 --> 01:49:09.200] Yes.
[01:49:12.080 --> 01:49:14.240] Be in touch with your emotions.
[01:49:14.240 --> 01:49:15.120] That's right.
[01:49:15.120 --> 01:49:17.600] And express everything on the internet.
[01:49:18.560 --> 01:49:19.680] Don't do that.
[01:49:19.680 --> 01:49:20.480] All right.
[01:49:20.480 --> 01:49:22.160] Well, thanks again, everyone.
[01:49:22.160 --> 01:49:27.600] And until next week, this is your Skeptics Guide to the Universe.
[01:49:30.240 --> 01:49:36.880] Skeptic's Guide to the Universe is produced by SGU Productions, dedicated to promoting science and critical thinking.
[01:49:36.880 --> 01:49:41.520] For more information, visit us at theskepticsguide.org.
[01:49:41.520 --> 01:49:45.440] Send your questions to info at the skepticsguide.org.
[01:49:45.440 --> 01:49:56.160] And if you would like to support the show and all the work that we do, go to patreon.com/slash skepticsguide and consider becoming a patron and becoming part of the SGU community.
[01:49:56.160 --> 01:49:59.760] Our listeners and supporters are what make SGU possible.

Prompt 10: Key Takeaways

Now please extract the key takeaways from the transcript content I provided.

Extract the most important key takeaways from this part of the conversation. Use a single sentence statement (the key takeaway) rather than milquetoast descriptions like "the hosts discuss...". 

Limit the key takeaways to a maximum of 3. The key takeaways should be insightful and knowledge-additive. 

IMPORTANT: Return ONLY valid JSON, no explanations or markdown. Ensure:
- All strings are properly quoted and escaped
- No trailing commas
- All braces and brackets are balanced
Format: {"key_takeaways": ["takeaway 1", "takeaway 2"]}

Prompt 11: Segments

Now identify 2-4 distinct topical segments from this part of the conversation.

For each segment, identify:
- Descriptive title (3-6 words)  
- START timestamp when this topic begins (HH:MM:SS format)
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Most important Key takeaway from that segment. Key takeaway must be specific and knowledge-additive.
- Brief summary of the discussion

IMPORTANT: The timestamp should mark when the topic/segment STARTS, not a range. Look for topic transitions and conversation shifts.

Return ONLY valid JSON. Ensure all strings are properly quoted, no trailing commas:
{
  "segments": [
    {
      "segment_title": "Topic Discussion", 
      "timestamp": "01:15:30",
      "key_takeaway": "main point from this segment",
      "segment_summary": "brief description of what was discussed"
    }
  ]
}

Timestamp format: HH:MM:SS (e.g., 00:05:30, 01:22:45) marking the START of each segment.

Prompt 12: Media Mentions

Now scan the transcript content I provided for ACTUAL mentions of specific media titles:

Find explicit mentions of:
- Books (with specific titles)
- Movies (with specific titles)  
- TV Shows (with specific titles)
- Music/Songs (with specific titles)

DO NOT include:
- Websites, URLs, or web services
- Other podcasts or podcast names

IMPORTANT: 
- Only include items explicitly mentioned by name. Do not invent titles.
- Valid categories are: "Book", "Movie", "TV Show", "Music"
- Include the exact phrase where each item was mentioned
- Find the nearest proximate timestamp where it appears in the conversation
- THE TIMESTAMP OF THE MEDIA MENTION IS IMPORTANT - DO NOT INVENT TIMESTAMPS AND DO NOT MISATTRIBUTE TIMESTAMPS
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Timestamps are given as ranges, e.g. 01:13:42.520 --> 01:13:46.720. Use the EARLIER of the 2 timestamps in the range.

Return ONLY valid JSON. Ensure all strings are properly quoted and escaped, no trailing commas:
{
  "media_mentions": [
    {
      "title": "Exact Title as Mentioned",
      "category": "Book",
      "author_artist": "N/A",
      "context": "Brief context of why it was mentioned",
      "context_phrase": "The exact sentence or phrase where it was mentioned",
      "timestamp": "estimated time like 01:15:30"
    }
  ]
}

If no media is mentioned, return: {"media_mentions": []}

Full Transcript

[00:00:00.800 --> 00:00:06.880] 10 years from today, Lisa Schneider will trade in her office job to become the leader of a pack of dogs.
[00:00:06.880 --> 00:00:09.600] As the owner of her own dog rescue, that is.
[00:00:09.600 --> 00:00:17.280] A second act made possible by the reskilling courses Lisa's taking now with AARP to help make sure her income lives as long as she does.
[00:00:17.280 --> 00:00:22.240] And she can finally run with the big dogs and the small dogs, who just think they're big dogs.
[00:00:22.240 --> 00:00:25.920] That's why the younger you are, the more you need AARP.
[00:00:25.920 --> 00:00:29.680] Learn more at aarp.org/slash skills.
[00:00:33.200 --> 00:00:36.480] You're listening to the Skeptic's Guide to the Universe.
[00:00:36.480 --> 00:00:39.360] Your escape to reality.
[00:00:40.000 --> 00:00:42.720] Hello, and welcome to The Skeptic's Guide to the Universe.
[00:00:42.720 --> 00:00:47.760] Today is Wednesday, September 25th, 2024, and this is your host, Stephen Novella.
[00:00:47.920 --> 00:00:50.480] Joining me this week are Kara Santa Maria.
[00:00:50.480 --> 00:00:51.120] Howdy.
[00:00:51.120 --> 00:00:52.080] Jay Novella.
[00:00:52.080 --> 00:00:52.400] Hey, guys.
[00:00:52.560 --> 00:00:53.680] Evan Bernstein.
[00:00:53.680 --> 00:00:54.960] Good evening, everyone.
[00:00:54.960 --> 00:00:58.080] And we have a special guest, Andrea Jones-Roy.
[00:00:58.080 --> 00:00:59.920] Andrea, welcome back to the SGU.
[00:00:59.920 --> 00:01:00.640] Thank you so much.
[00:01:00.640 --> 00:01:01.360] Hi, everyone.
[00:01:01.600 --> 00:01:02.880] Oh, it's great to have you, Andrea.
[00:01:03.360 --> 00:01:04.000] Great to be here.
[00:01:04.000 --> 00:01:05.280] Always love hanging out with you all.
[00:01:05.280 --> 00:01:08.000] Now, Andrea, you are a political scientist, right?
[00:01:08.000 --> 00:01:08.480] It's true.
[00:01:08.480 --> 00:01:08.880] Yes.
[00:01:08.880 --> 00:01:13.040] So this must be a really busy time of year for you.
[00:01:13.040 --> 00:01:14.480] Yeah, you know, I was thinking about it.
[00:01:14.480 --> 00:01:20.000] I was like, this is maybe my, Evan, you can tell me if I'm out of bounds here, but maybe my tax season.
[00:01:20.160 --> 00:01:24.800] It only comes every four years, unlike Evan's, which apparently is every year, according to the IRS.
[00:01:25.200 --> 00:01:26.240] But it is very busy.
[00:01:26.240 --> 00:01:30.720] A lot of people want to know what's going to happen, and unfortunately, I don't know.
[00:01:30.720 --> 00:01:32.000] So I'll just get that out of the gates.
[00:01:32.480 --> 00:01:33.200] You don't know what's going to happen.
[00:01:33.680 --> 00:01:35.120] How do you call yourself a scientist?
[00:01:35.360 --> 00:01:35.760] I know.
[00:01:36.240 --> 00:01:37.120] How is this a science?
[00:01:37.120 --> 00:01:40.880] It's become an absolute certitude that's going to happen.
[00:01:40.880 --> 00:01:42.720] 40%, that's the answer.
[00:01:43.920 --> 00:01:46.880] I won't tell you of what, but that's the definite correct answer.
[00:01:47.200 --> 00:01:48.560] The answer is three.
[00:01:48.560 --> 00:01:49.520] Yep, yep, for sure.
[00:01:50.800 --> 00:01:54.400] It's also a time when everyone claims to be a political scientist.
[00:01:54.400 --> 00:01:57.360] I don't know if there are times, Steve, when people claim to be neurologists.
[00:01:57.360 --> 00:01:58.040] Maybe if they are.
[00:01:58.120 --> 00:01:58.840] Oh, yeah.
[00:01:58.000 --> 00:02:00.200] Everyone're a doctor.
[00:02:00.200 --> 00:02:03.320] Everyone thinks they're an expert on whatever they've experienced in their life.
[00:01:59.920 --> 00:02:04.360] Yeah, yeah.
[00:02:04.600 --> 00:02:09.800] So everyone's actually telling me how the election is going to go when I don't have an answer.
[00:02:10.440 --> 00:02:11.000] Right.
[00:02:11.000 --> 00:02:13.000] And nobody has the answer.
[00:02:13.000 --> 00:02:13.720] Right, right.
[00:02:14.040 --> 00:02:15.720] What irritates you the most?
[00:02:15.720 --> 00:02:19.640] What does the media get wrong the most?
[00:02:19.640 --> 00:02:27.160] Well, I think it's, you know, so I was speaking at a conference a couple days ago, and they referenced politics and said, don't worry, we're not going to argue and scream.
[00:02:27.160 --> 00:02:28.280] This isn't politics.
[00:02:28.280 --> 00:02:34.440] And what gets me angry is that politics doesn't have to be screaming, and political science is certainly not screaming.
[00:02:34.440 --> 00:02:38.680] We're a bunch of nerds looking at spreadsheets about people participating in politics.
[00:02:38.680 --> 00:02:46.280] But generally speaking, this idea that because it's politics, it has to be an angry fight really stresses me out.
[00:02:46.280 --> 00:02:50.280] And a lot of what I love about political science is: yes, there are things to be angry about.
[00:02:50.280 --> 00:02:54.440] There are real-world ramifications that I personally, as a human, am furious about.
[00:02:54.440 --> 00:03:00.520] But there's also a lot that we can do to kind of take a bigger picture and say, well, what's actually working and what isn't working?
[00:03:00.520 --> 00:03:01.640] And how can we improve things?
[00:03:01.640 --> 00:03:06.920] And, you know, we can talk, even if we disagree about our political views, we can actually have conversations.
[00:03:06.920 --> 00:03:10.360] And it really frustrates me that people assume that you can't.
[00:03:10.360 --> 00:03:15.320] And it wasn't that long ago that people who disagreed with one another could, you know, have a conversation.
[00:03:15.320 --> 00:03:15.720] Right.
[00:03:15.720 --> 00:03:17.000] It's easy for me to say that.
[00:03:17.000 --> 00:03:21.080] I haven't spoken to someone I disagree with vehemently in a really long time.
[00:03:21.720 --> 00:03:22.200] I haven't.
[00:03:22.280 --> 00:03:28.440] I've had pleasant conversations with people on the opposite end of the political spectrum from myself.
[00:03:28.680 --> 00:03:30.840] Just agree that that's what you're going to do.
[00:03:30.840 --> 00:03:31.240] Right.
[00:03:31.240 --> 00:03:43.240] I mean, and this is also, again, the hardest part maybe about being a political scientist right now is trying to be as non-trying to separate my partisan identity from my work as a scientist, right?
[00:03:43.960 --> 00:03:44.360] How do you?
[00:03:44.520 --> 00:03:46.320] Yeah, it's difficult.
[00:03:46.320 --> 00:03:46.720] Yeah.
[00:03:47.360 --> 00:03:54.400] One thing that helps is that my particular area of research is about China, and I have less personally at stake.
[00:03:54.720 --> 00:04:07.760] Obviously, we all have personal things personally at stake with what China does and how they behave and the decisions they make, but it's less, it riles me up a little bit or a lot less than, say, you know, a particular governor doing something that I find to be egregious.
[00:04:07.920 --> 00:04:13.440] Andrea, I'm sorry, for clarification, are you talking about Chinese politics or how China influences U.S.
[00:04:13.440 --> 00:04:14.080] politics?
[00:04:14.720 --> 00:04:16.320] I talk about Chinese politics.
[00:04:16.320 --> 00:04:18.400] I study Chinese media specifically.
[00:04:19.360 --> 00:04:21.600] Which the United States media, I haven't studied this.
[00:04:21.600 --> 00:04:22.880] This is not my scientific take.
[00:04:22.880 --> 00:04:25.040] This is my citizen take.
[00:04:25.200 --> 00:04:25.680] The U.S.
[00:04:25.680 --> 00:04:29.520] media increasingly looks like just two Chinese medias fighting each other.
[00:04:29.520 --> 00:04:37.520] But insofar as you have media outlets that roughly say what the government or the party talking points are.
[00:04:37.920 --> 00:04:40.960] Yeah, but it is, this time of year is very hard to separate those two.
[00:04:40.960 --> 00:04:44.400] I mean, in the United States, for many years, it's been hard to separate those two.
[00:04:44.400 --> 00:04:49.120] I have some friends who study professionally things they care about personally.
[00:04:49.120 --> 00:04:55.120] So I do a lot of, I'm very interested in things around, you know, capital punishment and abortion access, but I don't research that.
[00:04:55.120 --> 00:05:00.880] And part of the reason I don't research that is I don't think I could be a particularly good scientist from an ethics perspective.
[00:05:00.880 --> 00:05:05.520] But I have friends who do manage to do that, and it's hats off to them.
[00:05:05.520 --> 00:05:06.160] Yeah, right?
[00:05:06.160 --> 00:05:06.800] Yeah.
[00:05:06.800 --> 00:05:11.600] Andrea, do you know, like, a lot of information comes out of the U.S.
[00:05:11.600 --> 00:05:14.320] government about what the government is doing?
[00:05:14.320 --> 00:05:14.880] Yes.
[00:05:14.880 --> 00:05:19.600] And, you know, there's just endless debates online or whether or not that data is trustworthy.
[00:05:20.160 --> 00:05:21.680] Do you have any opinion on that?
[00:05:21.680 --> 00:05:23.840] Like, is there any insight you have about that?
[00:05:23.840 --> 00:05:27.120] Do you mean like, so like jobs data or crime data that everyone has?
[00:05:27.600 --> 00:05:28.320] All those statistics.
[00:05:28.560 --> 00:05:28.960] Yeah.
[00:05:28.960 --> 00:05:32.360] Well, Jay, I'm so glad you asked because I'm actually launching a new podcast.
[00:05:32.360 --> 00:05:43.160] I did not plan on plugging this, but I had a podcast about data and where it comes from, and a couple of the episodes are specifically about data from, let's say, quote-unquote, the government.
[00:05:43.160 --> 00:06:00.680] And I guess there is no real answer in the sense that each one of these data sets, whether it's jobs or crime or inflation or whatever, comes from, of course, imperfect series of sources, of methods of collection, of ways of reporting, and ways of aggregating it.
[00:06:00.680 --> 00:06:03.480] I mean, crime data is very, very messy and very difficult.
[00:06:03.480 --> 00:06:07.000] Like a crime takes place, that doesn't mean a number shows up in a spreadsheet.
[00:06:07.000 --> 00:06:12.680] It has to go through a lot of people with a lot of different incentives in order to end up in that spreadsheet, for example.
[00:06:12.680 --> 00:06:18.840] But I don't, you know, and the jobs data is revised, and half the country decides that that means you throw away all the data.
[00:06:18.840 --> 00:06:25.800] And I think the whole thing is talked about in a frustrating way because all data is imperfect.
[00:06:26.120 --> 00:06:33.080] And basically, if the data is pointing in a direction that my political side likes, then we say, yes, it's government data.
[00:06:33.080 --> 00:06:33.880] We believe it.
[00:06:33.880 --> 00:06:38.120] And then if it's pointing at a direction that my political side doesn't like, we say, see, there's an error.
[00:06:38.120 --> 00:06:39.320] C, they issued a correction.
[00:06:39.320 --> 00:06:40.520] C, they revised these numbers.
[00:06:40.520 --> 00:06:41.560] C, this was wrong.
[00:06:41.560 --> 00:06:47.160] And the real answer is that, and you all know this, is that like any piece of data, any study, there are going to be errors in it.
[00:06:47.160 --> 00:06:48.760] We're going to update it over time.
[00:06:48.760 --> 00:06:51.000] We're going to learn more and we're going to collect things better.
[00:06:51.000 --> 00:06:54.520] And so the entire conversation, some of it's right and some of it's wrong.
[00:06:54.520 --> 00:07:00.280] Generally, I'm inclined to roughly take seriously the broader trends.
[00:07:00.280 --> 00:07:05.160] I don't think it's useful to argue over whether the increase was 0.01 or 0.02.
[00:07:05.160 --> 00:07:06.360] I don't think that matters.
[00:07:06.360 --> 00:07:08.680] And I think it's way too noisy to say anything specific.
[00:07:08.680 --> 00:07:15.000] But we can say things like broadly, some evidence is coming out that like murder went down in 2023.
[00:07:15.360 --> 00:07:22.960] I don't think we can say much more than that as far as like specific percentages, but I am relatively confident in that particular claim.
[00:07:22.960 --> 00:07:27.280] But I wouldn't say much more than that, and we definitely can't say anything causal about any of this stuff.
[00:07:27.760 --> 00:07:38.880] Yeah, I mean, just in general, when you hear job statistics and you hear, you know, you read about economic information, there's information that comes from the government, but then there's economists that write about it.
[00:07:38.880 --> 00:07:39.440] Right.
[00:07:39.440 --> 00:07:46.480] And, you know, even though they have their internal debates, there is somewhat of a consensus of what's going on and what the truth is.
[00:07:46.480 --> 00:07:46.800] Right.
[00:07:46.800 --> 00:07:54.240] And I just find it very challenging to convince people who don't like the results of that data that this is the data.
[00:07:54.240 --> 00:07:59.360] This is how we judge presidencies and bills and all that stuff.
[00:08:00.400 --> 00:08:01.520] This is how it's done.
[00:08:02.160 --> 00:08:09.120] How else can you do it if you're not looking at data that's coming from the departments that are spending that money and running these projects?
[00:08:09.120 --> 00:08:10.480] You're absolutely right, Jay.
[00:08:10.480 --> 00:08:13.680] And I agree that you can't just ignore data that you don't like.
[00:08:13.680 --> 00:08:29.440] And frankly, President Trump, when he was president, made the point of, oh, if we stop measuring, giving people COVID tests, the COVID numbers will go down, is the perfect illustration of the fact that what's in data and what's in reality do not necessarily track exactly correctly, right?
[00:08:29.440 --> 00:08:32.800] We would have still been having COVID even if the numbers went down because we weren't testing.
[00:08:33.040 --> 00:08:33.840] But you're absolutely right.
[00:08:33.840 --> 00:08:37.200] I mean, there are measures that we all sort of roughly agree on.
[00:08:37.200 --> 00:08:38.960] So take something like GDP, right?
[00:08:38.960 --> 00:08:47.040] It's a measure of productivity in the economy and it measures the US GDP versus Canada's GDP, GDP per capita, all those things.
[00:08:47.040 --> 00:08:56.240] It's a very messy, very imperfect, rough metric, and it goes up as horrible things like terrorism and natural disasters can cause GDP to go up.
[00:08:56.240 --> 00:09:10.440] And so we can make claims that indicate health of the economy that maybe under the hood really aren't, but it's still a useful piece of information, and it's one that has some consistency from quarter to quarter, year to year, decade to decade, roughly.
[00:09:10.440 --> 00:09:11.800] So we can still learn things.
[00:09:11.800 --> 00:09:19.320] My friend Drumel, who is a journalism professor and a researcher on polls at Columbia University, basically put it well to me the other day.
[00:09:19.320 --> 00:09:22.680] He was like, we can learn from data, we just can't worship data.
[00:09:22.680 --> 00:09:25.400] And I think that's a really nice way to summarize it.
[00:09:25.400 --> 00:09:31.320] The problem, yeah, is that people pick on the one problem they have and say, therefore, we throw it all away.
[00:09:31.320 --> 00:09:35.160] Or I agree with it, therefore we say data is capital T truth.
[00:09:35.160 --> 00:09:36.920] And both of those interpretations are incorrect.
[00:09:36.920 --> 00:09:38.600] Yeah, so in other words, like all of science.
[00:09:38.920 --> 00:09:39.720] Yeah, exactly.
[00:09:40.760 --> 00:09:43.400] We talk about all the time.
[00:09:43.400 --> 00:09:51.480] And this makes our messaging as skeptics and science communicators very, very difficult because they say all data is imperfect.
[00:09:51.480 --> 00:09:51.960] Yes.
[00:09:51.960 --> 00:09:54.920] And I just went through this, you know, on just a very simple question.
[00:09:54.920 --> 00:09:56.920] You know, what's the effect of net metering on it?
[00:09:56.920 --> 00:09:58.040] I'm not going to talk about this again.
[00:09:58.040 --> 00:09:58.920] Don't worry about it.
[00:09:59.720 --> 00:10:00.120] Come on here.
[00:10:00.520 --> 00:10:01.640] Our email box is full of it.
[00:10:01.880 --> 00:10:05.560] It ultimately came down to like, do you believe the studies or not?
[00:10:05.560 --> 00:10:06.760] That's what it came down to.
[00:10:06.760 --> 00:10:08.280] And of course they're not perfect.
[00:10:08.280 --> 00:10:12.200] So you can say, yeah, but this is all the data we have and this is basically what it shows.
[00:10:12.200 --> 00:10:17.960] And on the other side, they could say, yeah, but that study was done by a proponent of solar power, so I don't trust it.
[00:10:17.960 --> 00:10:21.320] And this study was done by a utility, so the other side doesn't trust it.
[00:10:21.320 --> 00:10:21.960] Or whatever.
[00:10:21.960 --> 00:10:27.320] Or you could just say that they're, you know, I just don't believe it because it doesn't match what I want to believe.
[00:10:27.320 --> 00:10:29.080] So it's true pretty much of everything.
[00:10:29.480 --> 00:10:29.720] Right.
[00:10:29.720 --> 00:10:34.040] What I love, though, is when people argue against, because data come in different forms, right?
[00:10:34.040 --> 00:10:39.320] Like we were just talking about all the different ways that something can be measured and all the different ways that something can be interpreted.
[00:10:39.320 --> 00:10:40.280] Interpreted?
[00:10:40.280 --> 00:10:41.080] Interpretated.
[00:10:41.880 --> 00:10:48.640] I would say the same thing about demographic data, but I think that demographic data in some ways is like a different animal.
[00:10:48.960 --> 00:10:57.120] And I just, I love it when people just argue against demographic data in such a passionate way.
[00:10:57.120 --> 00:11:01.280] And that's always amazing with me, just like these claims that are data-less.
[00:11:01.920 --> 00:11:06.480] And if I just repeat it enough times, somebody's going to buy into it.
[00:11:06.480 --> 00:11:07.520] No, you can say anything.
[00:11:07.680 --> 00:11:10.240] And this is true with any field as well.
[00:11:10.400 --> 00:11:21.040] Politics gets us really riled up, but it's true anywhere where, you know, the kind of louder and more with certainty you say a number or a result, the more people are inclined to believe you.
[00:11:21.040 --> 00:11:30.000] And unfortunately, most people aren't reading original studies and they aren't looking at data and they haven't been trained in data literacy or critical thinking and they don't all listen to your podcast.
[00:11:30.000 --> 00:11:39.040] And so really we just go off of what political scientists and many people call elite cues, which is this guy is the one I trust, therefore I'll believe the numbers he says.
[00:11:39.040 --> 00:11:45.200] And so if someone in a debate says this is happening and that's happening, then I'm going to believe them without questioning it.
[00:11:45.200 --> 00:11:47.200] And then what was the point of the research?
[00:11:47.200 --> 00:11:49.600] You know, I mean, and this happens outside of politics too.
[00:11:49.600 --> 00:11:52.240] Like I'll do a research project and show the results.
[00:11:52.240 --> 00:11:57.520] And then, yeah, even when there aren't political stakes, people are just like, nah, I don't like it.
[00:11:57.520 --> 00:11:58.720] And so you just dismiss the whole thing.
[00:11:59.920 --> 00:12:00.560] Okay.
[00:12:01.520 --> 00:12:03.440] You know, or there's the motivated reasoning, right?
[00:12:03.600 --> 00:12:13.600] Every study is imperfect, but you find the flaws in the ones whose conclusions you don't like and you gloss over the flaws in the ones whose again, this is exactly what you all are talking about all the time.
[00:12:13.600 --> 00:12:13.840] Yeah.
[00:12:14.080 --> 00:12:14.240] Yeah.
[00:12:15.520 --> 00:12:17.360] Because no scientific study is perfect.
[00:12:17.360 --> 00:12:17.600] Exactly.
[00:12:17.760 --> 00:12:20.160] So if you want to find fault with it, you can.
[00:12:21.040 --> 00:12:24.400] Yeah, so you have to give people an understanding.
[00:12:24.400 --> 00:12:27.440] And this is where you have to get wonky, right?
[00:12:27.440 --> 00:12:28.560] There's just no way around it.
[00:12:28.560 --> 00:12:35.960] You've got to get down into the weeds, or you've got to listen to somebody, again, that you trust who gets wonky for you and sort of summarizes for you.
[00:12:36.040 --> 00:12:46.120] Like, this is why scientists don't really put a lot of weight on this data, or this is why this is a consistent result that keeps coming up over and over again.
[00:12:46.120 --> 00:12:50.360] And we're getting pretty confident that this is telling us something about reality.
[00:12:50.600 --> 00:12:58.840] But yeah, people don't like talking in those squishy terms about it could be true, it probably true, it, you know, whatever.
[00:12:59.320 --> 00:13:07.160] They want more absolutes, and it's just so much simpler just to listen to the guy that agrees with your tribe.
[00:13:08.040 --> 00:13:17.240] And then, and Kara, you tell me I'm probably going to get this wrong, but then you get the dopamine hit or the adrenaline hit when they say something that makes you outraged or makes you scared or makes you whatever.
[00:13:17.240 --> 00:13:22.920] And so you listen to it from like a, I don't know, a salivating dog, Pavlovian kind of way, too.
[00:13:22.920 --> 00:13:23.960] And Steve, you're right.
[00:13:23.960 --> 00:13:31.880] I mean, humans are bad at interpreting and making sense of things like probabilities and confidence intervals and uncertainty under the best of circumstances.
[00:13:31.880 --> 00:13:47.240] And nothing in our media environment right now, between social media and headlines and 24-hour news and sound bites of presidents saying wild things in debates, you know, nothing about that lends itself to thoughtful discussion of these things.
[00:13:47.240 --> 00:13:57.320] You know, and even when I'm teaching it, and maybe this is just a fault on my teaching, you know, I have 14 weeks to help students think probabilistically, and I don't know that I succeed.
[00:13:57.320 --> 00:14:02.200] You know, it's not intuitive per se unless you've really had a lot of exposure to it.
[00:14:02.200 --> 00:14:05.800] And even then, you kind of have to be thoughtful because we just want certainty, I think, as people.
[00:14:06.200 --> 00:14:11.480] Well, yeah, and we only have so much brain power, right, to devote to these kinds of questions in our lives.
[00:14:12.120 --> 00:14:14.440] We're all doing a million other things at once.
[00:14:14.520 --> 00:14:18.000] Really, how much effort and time can a person reasonably put into this?
[00:14:18.000 --> 00:14:19.520] Well, it depends if it's your job or not.
[00:14:20.080 --> 00:14:21.600] It depends on if it's your job, yeah.
[00:14:21.600 --> 00:14:23.600] But for the majority of people, it is not their job.
[00:14:23.760 --> 00:14:24.160] It isn't.
[00:14:24.160 --> 00:14:25.280] Yeah, no, I agree.
[00:14:25.280 --> 00:14:33.280] It's like in medicine, we spend eight to ten years teaching students how to think probabilistically in very complicated and nuanced ways.
[00:14:33.280 --> 00:14:37.040] It's hard, and it's hard to get it right, and you fall for a lot of pitfalls.
[00:14:37.600 --> 00:14:40.000] So you're dealing with kind of several layers of things.
[00:14:40.000 --> 00:14:43.680] On the one layer, we're just our brains are good at some things and not good at other things.
[00:14:43.680 --> 00:14:45.600] Like statistics, we're terrible.
[00:14:45.600 --> 00:14:54.400] And then on the other side, we also have motivation not to be good, you know, not to be good at thinking when it doesn't lead to conclusions that we like.
[00:14:54.400 --> 00:15:02.400] But I do think, you know, I don't want to give the impression that we're in a business as usual kind of period in American politics.
[00:15:03.280 --> 00:15:07.600] I don't think that's what you're saying, but I want to give that impression because there are degrees.
[00:15:07.600 --> 00:15:10.320] You know, if there's motivated reasoning, then there's motivated reasoning.
[00:15:10.880 --> 00:15:23.280] There is what we would call spin, right, where there's a core of facts and both sides try to spin reality and emphasize the facts that they prefer, et cetera.
[00:15:23.280 --> 00:15:24.880] And then there's gaslighting, right?
[00:15:24.880 --> 00:15:33.520] Then there's like an order of magnitude where you just chuck out reality and substitute it with a complete and utter knowing fabrication.
[00:15:33.520 --> 00:15:39.280] These are not equivalent just because they both have a strained relationship with the truth.
[00:15:39.520 --> 00:15:44.720] There are orders of magnitude difference between these kind of levels of behavior.
[00:15:44.720 --> 00:15:53.040] And don't you agree that we're in not saying this has never happened before, but we're in a period of time that is different than it was certainly 20 or 30 years ago.
[00:15:53.040 --> 00:15:53.520] Oh, for sure.
[00:15:53.520 --> 00:15:55.200] And for so many reasons, too, right?
[00:15:55.200 --> 00:16:05.960] I mean, the media landscape and the way we talk to each other and the way we consume and hear information is changing by the second, basically, especially with generative AI.
[00:16:05.960 --> 00:16:08.440] But even in the last 10 years, I think that's changed incredibly.
[00:16:08.440 --> 00:16:21.800] And yes, every other underlying force that's contributed to the various types of disagreements and polarization, and whether it's between leaders and citizens and the way we talk about things and it's over issues or it's more affective polarization where we just simply dislike the other person.
[00:16:22.120 --> 00:16:22.920] It's very different.
[00:16:22.920 --> 00:16:26.760] And I think, you know, Steve, at the beginning, you asked what's the most frustrating part.
[00:16:26.760 --> 00:16:35.160] I think I'll update my answer to say that, you know, a lot of times when I tell people I'm a political scientist, people say, well, politics isn't a science.
[00:16:35.160 --> 00:16:38.680] And it's like, well, politics isn't, but you can study politics in a scientific way.
[00:16:39.400 --> 00:16:39.800] Right.
[00:16:40.120 --> 00:16:52.520] But that means assuming there is some, to your point just now, assuming there is something to consistency in the underlying, say, reality that we're studying or the data generating processes.
[00:16:52.520 --> 00:16:59.480] Like, we are assuming that studies I did 10 years ago, 50 years ago, can help me understand the present moment.
[00:16:59.480 --> 00:17:06.680] And in some cases, it can, sort of the underlying things like what motivates someone to show up to vote, what makes someone have the political views that they do well.
[00:17:06.680 --> 00:17:08.200] It depends a lot on their parents and where they grow up.
[00:17:08.280 --> 00:17:10.120] You know, those sorts of things don't tend to change.
[00:17:10.120 --> 00:17:14.840] But a lot of the way we're doing politics and the environment of politics has changed a lot.
[00:17:14.840 --> 00:17:26.200] And so it's really an open question how much of even research from, say, the Obama or the Bush administration applies to now because it just, and again, here's me as a citizen, it just feels so different and bad.
[00:17:26.920 --> 00:17:30.280] It's like what used to motivate people to vote might be totally different.
[00:17:30.280 --> 00:17:33.800] And yeah, we used to all agree on underlying facts, and we simply don't.
[00:17:33.800 --> 00:17:38.760] And I don't know what that does to things we thought we knew about how humans behave politically.
[00:17:38.760 --> 00:17:41.160] Yeah, it's like a game where the rules can change.
[00:17:41.160 --> 00:17:41.720] Exactly.
[00:17:41.720 --> 00:17:45.360] Yeah, and you can't, if you're playing by the old rules, you're going to get it wrong.
[00:17:45.680 --> 00:17:46.160] Exactly.
[00:17:44.920 --> 00:17:46.480] Exactly.
[00:17:46.720 --> 00:17:52.400] And so you're studying it on two levels: what happens with these rules?
[00:17:52.400 --> 00:17:55.200] And then also, oh, and then how are the rules changing over time?
[00:17:55.760 --> 00:18:06.720] There's a really interesting, just one tiny example of research that I think is really interesting is from a couple of scholars, I believe they're at Syracuse, but don't quote me on that, who study the role of anxiety in politics.
[00:18:06.720 --> 00:18:15.600] And I think Kara and Steve, you'd have a lot to say about this, but basically, they kind of try to hit on this and say, look, anxiety has always played a role in politics.
[00:18:15.600 --> 00:18:25.120] It's one of the things fear and other things related to anxiety do motivate us to behave in certain ways in the political sphere, whether it's voting or campaigning or what have you.
[00:18:25.120 --> 00:18:32.640] And basically, they say, look, the level of anxiety in the United States political discourse right now has never been higher.
[00:18:32.640 --> 00:18:39.360] Is it the case that these experiments we ran even 10 years ago still apply today because you're exposed to just so much more?
[00:18:39.360 --> 00:18:41.200] And we don't know.
[00:18:41.520 --> 00:18:50.080] And unfortunately, the consequences of all these things are going to play out in, you know, a month and a half from now, and we're not going to have the studies about it for four years.
[00:18:50.080 --> 00:18:52.480] So should we be angry with you about this?
[00:18:52.960 --> 00:18:55.920] And I do apologize for everything, I have to say.
[00:18:55.920 --> 00:18:57.040] For all of American politics.
[00:18:57.280 --> 00:18:57.840] I'm so sorry.
[00:18:57.840 --> 00:18:59.920] Yeah, I really botched it.
[00:19:00.560 --> 00:19:05.840] We're going to come back to this when you talk about your new segment, but we're going to go through the regular show now.
[00:19:05.840 --> 00:19:08.640] We're going to start with Kara doing a what's the word?
[00:19:08.640 --> 00:19:09.600] What's the word?
[00:19:09.600 --> 00:19:09.920] What?
[00:19:09.920 --> 00:19:11.040] I just made up that song.
[00:19:11.040 --> 00:19:11.680] I like it.
[00:19:13.600 --> 00:19:22.160] So I'm going to talk about a word that was recommended by William Patterson from Sonoma County.
[00:19:22.160 --> 00:19:26.960] He said, I think a good word to analyze is pitch.
[00:19:26.960 --> 00:19:31.640] It has tons of different meanings, some of which are completely unrelated.
[00:19:31.640 --> 00:19:33.160] And you are right, William.
[00:19:33.160 --> 00:19:39.560] I don't think we will have time to talk about every single definition of the word pitch.
[00:19:39.560 --> 00:19:40.680] But let's start.
[00:19:40.680 --> 00:19:53.160] by kind of digging into the etymology a little bit, because in most of the places that I looked, the word pitch is divided into two separate etymological roots.
[00:19:53.160 --> 00:19:57.960] So it's sort of like a convergent evolution thing happening with this word.
[00:19:57.960 --> 00:20:03.400] The first one is the one that I think many of us are probably the least familiar with.
[00:20:03.400 --> 00:20:05.480] And that is like this substance.
[00:20:05.480 --> 00:20:08.600] It's sap, basically, pitch that comes from trees.
[00:20:08.600 --> 00:20:11.160] It's sticky, you know, and it comes out of trees.
[00:20:11.160 --> 00:20:14.120] And you also, it's core, it gets everywhere.
[00:20:14.360 --> 00:20:23.480] You also have another variant of that, which is this viscous material that is left over after you distill crude oil.
[00:20:23.800 --> 00:20:24.680] Petroleum, right?
[00:20:24.680 --> 00:20:26.440] Yeah, that's also called pitch.
[00:20:26.440 --> 00:20:29.560] And in geology, there's pitch stone.
[00:20:29.560 --> 00:20:41.480] And so these all come from an original root that really kind of going all the way back from like the Middle English to the Old English to the Latin to with the cognate in the ancient Greek.
[00:20:41.480 --> 00:20:47.320] Really across the board, the roots for this just mean the word sap or sap from a pine.
[00:20:47.320 --> 00:20:52.120] You'll often see pine being utilized in the definitions.
[00:20:52.120 --> 00:20:56.760] And that's where the term pitch black or pitch dark comes from.
[00:20:56.760 --> 00:20:57.320] Does that make sense?
[00:20:57.400 --> 00:21:00.520] I was going to ask if that I never questioned it before.
[00:21:00.520 --> 00:21:01.240] But yeah.
[00:21:01.480 --> 00:21:06.120] So that comes from that deep dark color of the sap from that pine.
[00:21:06.120 --> 00:21:16.720] Now there's another etymological route, which I can't, so I'm not going to pronounce Middle English here, but like pitchin' or piken that goes to the Old English, that goes down to the Germanic.
[00:21:17.040 --> 00:21:23.680] And that route has to do with thrusting or fastening.
[00:21:23.680 --> 00:21:24.800] Like pitching a tent?
[00:21:26.000 --> 00:21:26.880] Or picking.
[00:21:26.880 --> 00:21:27.280] Yeah.
[00:21:27.280 --> 00:21:31.680] So you can throw a pitch at a baseball game.
[00:21:32.080 --> 00:21:36.000] You can pitch a tent by driving stakes into the ground.
[00:21:37.360 --> 00:21:42.080] The distance between some objects is considered a pitch.
[00:21:42.320 --> 00:21:46.240] The angle at which an object sits is considered to be a pitch.
[00:21:47.360 --> 00:21:55.520] Very often, like in UK English, the area where a campsite sits or certain types of sports fields are called the pitch.
[00:21:56.400 --> 00:21:58.240] On the pitch, exactly.
[00:21:58.240 --> 00:22:14.560] And that all does seem to come back to that early about, that early definition around thrusting or driving a stake or piercing with a sharp point, you know, those stakes of your tent and throwing things and even pitching in, working really vigorously.
[00:22:15.600 --> 00:22:16.000] What a cool thing.
[00:22:16.160 --> 00:22:18.240] Now the muse, yeah, it's a really cool word.
[00:22:18.240 --> 00:22:20.000] It has so many different definitions.
[00:22:20.000 --> 00:22:22.560] The musical sense came much later.
[00:22:22.560 --> 00:22:26.000] That came around the 1630s.
[00:22:26.000 --> 00:22:31.600] And around the same time was the term that we use for ships, like their motion, right?
[00:22:31.920 --> 00:22:33.520] They pitch on the ocean.
[00:22:33.520 --> 00:22:35.440] They kind of rise and fall.
[00:22:35.440 --> 00:22:40.560] And so those seem to be slightly newer definitions of the term.
[00:22:40.560 --> 00:22:43.520] But it does have scientific definitions as well.
[00:22:43.520 --> 00:22:50.560] We think of pitch as the frequency, right, of a sound wave is going to give us its pitch.
[00:22:50.560 --> 00:22:59.040] And often you'll see pitch used in like mathematics and geometry to describe distances between things and angles.
[00:22:59.040 --> 00:23:06.360] So it's a super interesting word that has these sort of similar but different roots.
[00:23:06.760 --> 00:23:10.440] And I think we use it all the time without recognizing it.
[00:23:10.760 --> 00:23:12.840] So I love that.
[00:23:12.840 --> 00:23:17.160] I love when you have a word that's really, really old and it just evolves and evolves and evolves.
[00:23:17.160 --> 00:23:20.840] And you can sort of trace it back to where it started.
[00:23:20.840 --> 00:23:23.560] And sometimes the modern usage feels wildly different.
[00:23:23.560 --> 00:23:29.320] But if you go through this series of changes, it all kind of follows an understandable pattern.
[00:23:29.640 --> 00:23:36.600] I remember when we were in Atlanta for an event, there's peach tree, you know, the area around Atlanta.
[00:23:36.600 --> 00:23:44.040] And we were told, and this is what I found when I looked into it, that actually derives from pitch tree.
[00:23:44.360 --> 00:23:50.440] And it's sort of a bastardization of that, like the original, it was actually a pitch tree, but people then confused it with peach tree.
[00:23:50.440 --> 00:23:51.800] And that's the name that stuck.
[00:23:51.800 --> 00:23:53.400] But Georgia is the peach state.
[00:23:53.400 --> 00:23:56.120] So you can sort of understand why they would have made that connection.
[00:23:56.440 --> 00:23:57.800] Or is Georgia the pitch state?
[00:23:57.800 --> 00:23:58.760] And we've messed it up all the time.
[00:23:58.840 --> 00:23:59.320] Oh, no.
[00:23:59.560 --> 00:24:00.760] Change the license plates.
[00:24:01.000 --> 00:24:02.360] Oh, the pitch there is delicious.
[00:24:03.000 --> 00:24:03.800] Fresh peach.
[00:24:03.960 --> 00:24:04.760] Pitch pie.
[00:24:05.720 --> 00:24:14.120] When I was digging into this word, you go to some obscure places on the internet because you're trying to learn about, you know, how people are grappling with these things.
[00:24:14.120 --> 00:24:18.920] And I found a forum, like a rock climbing or mountaineering forum.
[00:24:18.920 --> 00:24:26.360] And apparently, the word pitch is often used to describe a segment of a long climb between two, how do you pronounce this word?
[00:24:26.360 --> 00:24:27.160] Belays?
[00:24:27.160 --> 00:24:28.040] Belays?
[00:24:28.040 --> 00:24:28.600] Belays?
[00:24:28.920 --> 00:24:29.240] I think so.
[00:24:29.480 --> 00:24:29.960] Okay.
[00:24:29.960 --> 00:24:31.160] Yeah, because I'm not a rock climber.
[00:24:31.160 --> 00:24:31.960] I have no idea.
[00:24:31.960 --> 00:24:41.800] So on this forum, one person's like, oh, it came from the days before rock climbing when climbers used to get their jollies by climbing large trees, pine and spruce trees for the most part.
[00:24:42.120 --> 00:24:46.320] And so they would accumulate all this sap on their hands.
[00:24:44.440 --> 00:24:50.720] So trees that have a lot of sap would mean that like a pitch was typically short.
[00:24:51.040 --> 00:24:53.680] But now they use ropes and ropes are longer.
[00:24:53.680 --> 00:24:58.960] And then somebody else was like, okay, right track, totally wrong definition.
[00:24:59.600 --> 00:25:11.280] That's actually it does come from tree climbing, but the climbing style back then involved throwing a rope as high as possible over a branch and then going up that way.
[00:25:11.280 --> 00:25:17.440] So they would measure the height of a tree by the number of times they had to throw or pitch the rope to get to the top of the tree.
[00:25:17.440 --> 00:25:19.520] And they're saying that's where the word pitch comes from.
[00:25:19.520 --> 00:25:23.360] And then it devolves into them arguing over which one is an urban legend.
[00:25:24.480 --> 00:25:26.000] Classic internet.
[00:25:26.640 --> 00:25:30.400] But also, like, I guess that's what happens when a word has so many different definitions.
[00:25:30.400 --> 00:25:37.840] It's easy to come up with some sort of lore around why this new usage came to be.
[00:25:37.840 --> 00:25:45.920] And you can kind of, yeah, tell a story based on like, oh, there used to be trees on fields, and now we play sports on fields, therefore it's a pitch, right?
[00:25:45.920 --> 00:25:48.640] Like I could make up a story based on a different definition.
[00:25:49.440 --> 00:25:49.840] Yep.
[00:25:49.840 --> 00:25:52.400] Whose ever story wins had the best sales pitch?
[00:25:53.600 --> 00:25:53.920] See that?
[00:25:54.960 --> 00:26:00.640] All right, Jay, tell us about preserving the human genome forever.
[00:26:00.640 --> 00:26:01.680] Oh, thank goodness.
[00:26:01.680 --> 00:26:02.080] All right.
[00:26:02.080 --> 00:26:05.920] So, I mean, you know, this one's cool, but it's also kind of strange.
[00:26:05.920 --> 00:26:06.960] So scientists.
[00:26:07.200 --> 00:26:08.480] It's kind of our sweet spot, Jay.
[00:26:08.480 --> 00:26:09.440] Cool but strange.
[00:26:12.560 --> 00:26:23.520] So the scientists, some scientists at the University of Southampton developed this very awesome way to store the entire human genome on a 5D, right?
[00:26:23.520 --> 00:26:25.920] It's a five-dimensional memory crystal.
[00:26:25.920 --> 00:26:31.720] You know, you can think of this as creating a backup of humanity in case something horrible happens, right?
[00:26:29.760 --> 00:26:33.160] Zombie apocalypse, whatever.
[00:26:33.240 --> 00:26:37.880] You know, I mean, your imagination could take you to the places that would end humanity.
[00:26:37.880 --> 00:26:42.760] So the crystal is coin-sized and it's made from fused quartz.
[00:26:42.760 --> 00:26:49.320] And the really amazing thing is that the storage crystal is designed to last billions of years.
[00:26:49.320 --> 00:26:50.680] That's a long time, guys.
[00:26:50.680 --> 00:26:53.560] It can withstand pretty extreme conditions, right?
[00:26:53.560 --> 00:26:55.240] It can handle cosmic radiation.
[00:26:55.240 --> 00:27:03.160] It can handle pretty severe impact forces, wild ranges in temperature, like from freezing to over 1800 degrees Fahrenheit.
[00:27:03.160 --> 00:27:04.680] What would that be in Celsius, guys?
[00:27:04.680 --> 00:27:05.240] It's hot.
[00:27:05.240 --> 00:27:06.280] It's very hot.
[00:27:06.280 --> 00:27:07.320] Celsius hot.
[00:27:07.320 --> 00:27:10.040] Their idea is that in the, you know, whatever.
[00:27:11.000 --> 00:27:12.520] Let me throw a scenario at you guys.
[00:27:12.520 --> 00:27:20.040] In the distant future, you know, some advanced species is searching the ruined surface of the Earth.
[00:27:20.040 --> 00:27:21.800] And, you know, I don't know where they put it.
[00:27:21.800 --> 00:27:29.720] I don't, you know, they didn't really talk about like the delivery mechanism, but like, you know, they enter this old underground thing and they find this crystal, right?
[00:27:29.720 --> 00:27:31.640] And that crystal will have the information.
[00:27:31.640 --> 00:27:36.040] So whatever these robots or advanced species are, they can recreate humanity.
[00:27:36.040 --> 00:27:37.080] Pretty weird, right?
[00:27:37.080 --> 00:27:41.640] That's strange if you think about it, you know, from different perspectives.
[00:27:41.640 --> 00:27:44.920] But, you know, the technology is awesome, right?
[00:27:44.920 --> 00:27:47.240] So it's a crystal.
[00:27:47.240 --> 00:27:56.280] It can store up to 360 terabytes of information and it can keep that information stable for up to 300 quintillion years.
[00:27:56.280 --> 00:27:57.000] Yeah, right?
[00:27:57.000 --> 00:27:58.280] So that's a long time.
[00:27:58.680 --> 00:28:03.240] That's longer than the universe has existed, quite obviously.
[00:28:03.560 --> 00:28:04.520] It's absurdly long.
[00:28:04.520 --> 00:28:08.360] It's over 21 billion times the current age of the universe.
[00:28:08.360 --> 00:28:09.560] How about that for a statistics?
[00:28:09.560 --> 00:28:16.240] So researchers use this ultra-fast lasers to etch tiny voids in the crystal silica structure, right?
[00:28:16.320 --> 00:28:17.360] Sounds pretty simple.
[00:28:14.920 --> 00:28:21.440] And these nanostructures can hold data, but they hold it in five dimensions.
[00:28:21.600 --> 00:28:26.160] And this is where it got its name: height, length, width, orientation, and position.
[00:28:26.160 --> 00:28:28.640] I think, you know, that's really complicated, right?
[00:28:28.640 --> 00:28:30.400] Just on its face, right?
[00:28:30.400 --> 00:28:32.800] The height, length, width, orientation, and position.
[00:28:32.800 --> 00:28:39.040] Imagine trying to reverse engineer that with the complexity of the human genome encoded in it.
[00:28:39.040 --> 00:28:40.960] It is very complicated.
[00:28:40.960 --> 00:28:50.800] So to help whoever or whatever, you know, robots, you know, they find the crystal, to help them understand what's inside, the researchers added some visual clues on the crystal surface.
[00:28:50.800 --> 00:28:57.440] So they included images of the four essential chemical elements for life: hydrogen, oxygen, carbon, and nitrogen.
[00:28:57.440 --> 00:28:58.000] That's nice.
[00:28:58.000 --> 00:29:05.920] You know, hopefully, whatever thing would find this, they'd be able to figure out that, yeah, we know what the elements are because we're an advanced species.
[00:29:05.920 --> 00:29:12.160] So these are the four that you need in order to do what the crystal is going to give you the information for.
[00:29:12.160 --> 00:29:15.760] They also give a depiction of the DNA double helix.
[00:29:15.760 --> 00:29:22.720] So basically, you're going to create this and diagrams of human, man, and women, human men, and women.
[00:29:22.720 --> 00:29:23.200] Right.
[00:29:23.200 --> 00:29:32.640] Okay, so make one of these using these four things and follow the instructions that are encoded in these little voids that are inside this crystal.
[00:29:32.640 --> 00:29:41.040] They're going to, you know, hopefully, whatever future technology is out there, their AI can look at that and figure it out within, you know, maybe a few hundred million years.
[00:29:41.040 --> 00:29:46.080] So there's also a molecular breakdown of chromosomes and their structures within this cell, right?
[00:29:46.080 --> 00:29:47.360] Other data that they're going to need.
[00:29:47.600 --> 00:29:54.080] They're meant to guide whoever's reading these in the future to interpret the stored genome.
[00:29:54.080 --> 00:29:55.200] It's complicated.
[00:29:55.200 --> 00:29:57.360] Very, very complicated.
[00:29:57.360 --> 00:30:03.400] Of course, there's a catch because no one knows whether the technology to read this data will exist in the future.
[00:30:03.560 --> 00:30:19.400] Now, that's a funny way to put the idea that it's not like the future beings that would read this and use this thing are going to find our old technology and they'll just insert it and somehow that'll even make sense to them.
[00:30:19.400 --> 00:30:22.760] They would literally have to reverse engineer the whole thing.
[00:30:22.760 --> 00:30:24.120] Now, keep this in mind.
[00:30:24.120 --> 00:30:25.960] I'll give you a little bit of perspective.
[00:30:26.200 --> 00:30:34.120] Andrea, tell me a device that you owned like 15, you know, an electronic device you owned 15 years ago, 10, 15, 20 years ago.
[00:30:34.120 --> 00:30:39.240] More like 20 years ago, I had drives that I could put floppy disks into.
[00:30:39.240 --> 00:30:44.280] Okay, now imagine taking one of those and trying to make it work today.
[00:30:44.280 --> 00:30:48.280] I have one, and I would like to make it work for some, and I can't.
[00:30:48.840 --> 00:30:57.720] But yeah, but you know, but the idea is you own this thing, you know exactly what it is, you have tons of technology all around you.
[00:30:57.720 --> 00:31:00.200] You could read about this thing forever.
[00:31:00.200 --> 00:31:05.080] You could just keep reading about all the different pieces of information that are out there on the web about it.
[00:31:05.080 --> 00:31:06.600] But could you really make it work?
[00:31:06.600 --> 00:31:09.640] And this is all made by humans in your lifespan, right?
[00:31:09.640 --> 00:31:10.440] And we can't.
[00:31:10.440 --> 00:31:12.920] We can't, you know, you have an iPad or an iPod.
[00:31:12.920 --> 00:31:14.280] Remember our iPods?
[00:31:14.280 --> 00:31:14.760] Sure.
[00:31:14.760 --> 00:31:17.800] Dig out your old one if you still have it and try to put a file on it.
[00:31:17.800 --> 00:31:18.520] Good luck.
[00:31:18.520 --> 00:31:19.080] Yeah, but I don't know.
[00:31:19.480 --> 00:31:21.160] Honestly, that's not a good analogy, Jay.
[00:31:21.400 --> 00:31:28.360] The real question is: could engineers figure out a way to access the data on that floppy disk?
[00:31:28.360 --> 00:31:29.320] Probably.
[00:31:29.640 --> 00:31:34.760] Yeah, I would think, oh, wait, wait, you're talking of Andrea's floppy disk?
[00:31:34.760 --> 00:31:35.320] Of course.
[00:31:35.320 --> 00:31:40.280] I'm just saying it's a complicated mess from our own technology from 20 years ago.
[00:31:40.280 --> 00:31:42.720] Like, I'm just giving you like a joke.
[00:31:43.440 --> 00:31:45.840] Yeah, but to put things into perspective, I mean, you're right.
[00:31:46.160 --> 00:31:58.240] It would require like very advanced, intelligent, not only species, but individuals within that species who are in the equivalent of engineers and scientists, whatever, probably working for years to reverse engineer it.
[00:31:58.240 --> 00:32:00.560] But, I mean, you know, what's the other option, right?
[00:32:00.560 --> 00:32:01.360] It's going to involve.
[00:32:01.760 --> 00:32:02.480] Oh, I know that.
[00:32:02.480 --> 00:32:02.720] I know.
[00:32:02.720 --> 00:32:11.440] I'm just trying to, you know, look, Steve, but I can't help but almost joke about the idea that something is going to be able to reverse engineer this in the future.
[00:32:11.440 --> 00:32:13.280] But of course, what else can you do?
[00:32:13.280 --> 00:32:14.880] Like, if they wanted to do it, they did it.
[00:32:14.880 --> 00:32:17.520] And I don't see there really can't be a simpler way.
[00:32:17.520 --> 00:32:19.600] You want the thing to be self-contained.
[00:32:19.600 --> 00:32:21.280] You want it to last.
[00:32:21.280 --> 00:32:25.360] And you want to give them some information on how to kind of figure it out.
[00:32:25.680 --> 00:32:27.280] Yeah, that's the important thing, right?
[00:32:27.280 --> 00:32:28.880] Like, it's like the gold record.
[00:32:28.880 --> 00:32:32.640] Didn't they actually print how to build a record player on it?
[00:32:32.960 --> 00:32:36.640] No, there was a record player built into the future.
[00:32:36.800 --> 00:32:39.840] But I thought there was also instructions for how to use it.
[00:32:39.840 --> 00:32:40.160] Yeah.
[00:32:40.320 --> 00:32:41.280] Oh, how to use it.
[00:32:41.280 --> 00:32:41.520] Okay.
[00:32:41.520 --> 00:32:41.760] Yeah, yeah.
[00:32:42.000 --> 00:32:45.440] Carrie, you realize that aliens did find that record and they sent us a message.
[00:32:45.440 --> 00:32:46.320] You know what it was?
[00:32:46.320 --> 00:32:47.360] What was it?
[00:32:47.360 --> 00:32:48.800] Send more Chuck Berry.
[00:32:49.360 --> 00:32:50.480] Oh, I remember that.
[00:32:50.480 --> 00:32:51.280] Sorry Night Live.
[00:32:51.280 --> 00:32:51.600] That's right.
[00:32:52.240 --> 00:32:52.320] Yeah.
[00:32:52.640 --> 00:32:53.280] 70s.
[00:32:53.280 --> 00:32:54.080] Oh, boy.
[00:32:54.080 --> 00:32:54.560] Wow.
[00:32:54.560 --> 00:32:55.040] Sorry.
[00:32:55.120 --> 00:32:55.680] I'm old.
[00:32:57.760 --> 00:33:03.200] So there is the issue of recreating a human from genetic data, right?
[00:33:03.200 --> 00:33:07.600] This is something that scientists aren't capable of doing today.
[00:33:07.600 --> 00:33:17.120] You know, there has been tons of advancements in biology and our understanding of all of the material that it would take to actually do this.
[00:33:17.120 --> 00:33:19.440] It is likely that we will be able to do it, right?
[00:33:19.440 --> 00:33:23.600] It's not like something that's out of our, like, we're not saying, oh, my god, this is going to be hundreds of years.
[00:33:23.600 --> 00:33:25.440] Like, it is going to happen.
[00:33:25.440 --> 00:33:26.640] But we can't do it yet.
[00:33:26.640 --> 00:33:29.360] So, we don't really know what it's going to fully take to do it.
[00:33:29.360 --> 00:33:32.040] But it, you know, they are storing the human genome.
[00:33:29.920 --> 00:33:39.320] They want to, like, other institutions and companies are like want to want to store frozen animal cells and craters on the moon.
[00:33:39.320 --> 00:33:40.760] We have seed vaults.
[00:33:40.760 --> 00:33:42.600] You know, this is something that humans do.
[00:33:42.600 --> 00:33:49.560] We keep coming back to this like disaster recovery situation, some of which might be done by humans themselves.
[00:33:49.560 --> 00:33:56.280] Like, hey, we had an extinction and we want to bring back the Hoosiwatzer animal, you know, whatever it is, or bring back this plant.
[00:33:56.280 --> 00:33:58.360] That seems very reasonable to me.
[00:33:58.360 --> 00:34:06.760] But, you know, thinking of aliens or machines in the future finding this thing alone, that's a Herculean effort just to find the crystal.
[00:34:06.760 --> 00:34:11.560] So, anyway, so there is a few little doodads I want to tell you about this thing.
[00:34:11.560 --> 00:34:15.640] So, they were inspired by the Voyager Golden Records.
[00:34:15.640 --> 00:34:19.400] That was one of the inspirations for doing this project.
[00:34:19.400 --> 00:34:26.680] And the other cool thing is there are applications for this data storage beyond the human genome, right?
[00:34:26.680 --> 00:34:29.800] You know, the technology has a lot of broader applications.
[00:34:29.800 --> 00:34:34.840] I don't know how fast the read-write is on it, but that amount of data storage is phenomenal.
[00:34:34.840 --> 00:34:37.400] That's a phenomenal amount of storage, guys, right?
[00:34:38.120 --> 00:34:41.480] You could back up incredible amounts of data with that.
[00:34:41.480 --> 00:34:46.680] So, I would love to see this technology make its way out to where we could use it today.
[00:34:46.680 --> 00:34:55.640] Again, I really need to know how fast you could read-write on it, but even still, like if you just wanted to do like a big backup, like let's say, let's say that Google wants to back up YouTube, right?
[00:34:55.640 --> 00:34:58.440] You know, but I think that this thing would handle it nicely.
[00:34:58.680 --> 00:35:00.680] Better than my floppy drive, for sure.
[00:35:00.680 --> 00:35:02.360] Yeah, but we could start with the floppy drive.
[00:35:02.360 --> 00:35:03.960] Yeah, as long as, we'll start there.
[00:35:04.000 --> 00:35:10.120] But you let us know when you get it working, and then we'll start doing anyone listening who's an engineer can who can help me with this.
[00:35:10.120 --> 00:35:12.600] Uh, get in get in touch because this thing isn't working.
[00:35:12.920 --> 00:35:16.560] I got to get YouTube on my floppies, so someone reach out.
[00:35:16.560 --> 00:35:19.840] So you try to visualize this huge amount of data storage, right?
[00:35:14.840 --> 00:35:22.080] So there was a picture that Bill Gates took.
[00:35:22.640 --> 00:35:30.640] I remember him being, I think he was like hoisted up super high in the air, and he had a CD in his hand.
[00:35:30.640 --> 00:35:32.960] And they had stacks of paper that went up.
[00:35:32.960 --> 00:35:33.440] I don't know.
[00:35:33.440 --> 00:35:37.040] I mean, it looked like he was up like 60 feet in the air worth of paper.
[00:35:37.040 --> 00:35:44.320] And the idea was that all of that data can fit on this thin, you know, this wafer-thin piece of plastic, and this was new technology.
[00:35:44.320 --> 00:35:49.280] And I remember seeing that picture and like getting the visual and going, wow, that's a lot of data.
[00:35:49.280 --> 00:35:53.040] You know, that is nothing compared to this thing.
[00:35:53.040 --> 00:35:54.400] Like, it's nothing.
[00:35:54.400 --> 00:35:58.320] You know, the amount of data storage that this thing can handle.
[00:35:58.480 --> 00:36:01.680] I mean, I wish that Bob were here because he's really good at coming up with things like that.
[00:36:01.680 --> 00:36:05.760] But it might be like, you know, the size of the earth of paper.
[00:36:05.760 --> 00:36:06.800] You know, like, I don't know.
[00:36:06.800 --> 00:36:13.120] I'm just making that up, but it's phenomenally bigger than what could fit on a CD, like orders of magnitude more.
[00:36:13.120 --> 00:36:16.880] So, anyway, you know, I hope we never need to use it.
[00:36:17.200 --> 00:36:19.440] I hope it doesn't have to be used.
[00:36:19.440 --> 00:36:23.280] But, you know, the way things are going here with the politics in the U.S., we probably do need this.
[00:36:23.520 --> 00:36:25.600] Check in again in late November and let's see.
[00:36:25.600 --> 00:36:27.200] Yeah, I'll see you in November.
[00:36:27.200 --> 00:36:34.480] Now, you bring up an interesting point about our human obsession with making sure that we'll last beyond our own lifetimes, right?
[00:36:34.480 --> 00:36:39.440] I mean, humans having kids, but it's got to be this like deeper evolutionary thing, right?
[00:36:39.440 --> 00:36:44.000] Like you said, with the putting animal cells on the moon and storing seeds.
[00:36:44.000 --> 00:36:46.320] Like, we really do, we are obsessed with it.
[00:36:46.320 --> 00:36:47.360] Is it just evolution?
[00:36:47.360 --> 00:36:48.240] Is it that simple?
[00:36:48.240 --> 00:36:48.560] No.
[00:36:49.040 --> 00:36:51.600] I'd like to think 100% it's not.
[00:36:51.920 --> 00:36:55.520] I mean, I would think it's probably likely that it's a combination of things.
[00:36:55.920 --> 00:36:59.880] I mean, people want to preserve their cultures and their societies and all this stuff.
[00:36:59.880 --> 00:37:02.920] You know, like it's kind of cooked into human psychology, I think.
[00:37:02.920 --> 00:37:04.600] Yeah, but it's also reinforced.
[00:37:04.600 --> 00:37:04.920] Sure.
[00:36:59.600 --> 00:37:06.440] But again, it's inertia of nature.
[00:37:06.520 --> 00:37:08.040] You know, like it's probably both.
[00:37:08.120 --> 00:37:20.440] I also think it's a little bit of projection because, I mean, imagine how cool would it be if we found a time capsule from an alien civilization that had massive amounts of data on it.
[00:37:20.440 --> 00:37:28.600] You know, would we would, of course, launch a project to try to figure it out, to try to reverse engineer it, and try to see what kind of information is in there.
[00:37:28.680 --> 00:37:29.320] Contact.
[00:37:29.320 --> 00:37:43.160] I think we have a, yeah, yeah, we have a sense of history, you know, and talking to history, you know, in the future, preserving knowledge for later could partly because we wish older civilizations had done it for us, you know?
[00:37:43.160 --> 00:37:43.480] Right.
[00:37:43.480 --> 00:37:43.880] Sure.
[00:37:43.880 --> 00:37:44.280] Yeah.
[00:37:44.280 --> 00:37:45.640] They were so lazy back then.
[00:37:46.360 --> 00:37:46.920] Right?
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[00:39:20.160 --> 00:39:22.640] All right, Andrew, we're coming back to you in political science.
[00:39:22.640 --> 00:39:25.920] Tell us how reliable presidential forecasts are.
[00:39:25.920 --> 00:39:26.400] All right.
[00:39:26.400 --> 00:39:34.480] Well, I would love to tell you this, and it's actually going to connect to Jay's news and we'll see how in just a moment.
[00:39:34.480 --> 00:39:37.840] But so this is a research from a preprint.
[00:39:37.840 --> 00:39:39.040] So it's not peer-reviewed.
[00:39:39.040 --> 00:39:43.760] It's a preprint from some political scientists, one of whom I know personally.
[00:39:43.920 --> 00:39:51.040] So maybe a small conflict of interest, but they are writing about political forecasting and particularly political polling.
[00:39:51.040 --> 00:40:01.520] And I think all you really need to know about their argument, or you can learn a lot about their argument from the file name if you go to download the PDF, the file name is end forecasting.
[00:40:01.520 --> 00:40:03.600] And so that's kind of going to be the whole punchline.
[00:40:03.600 --> 00:40:06.960] I got to start using more fun file names is one takeaway.
[00:40:06.960 --> 00:40:17.600] But so they are taking on the polling industry and a lot of the claims from pollsters and polling aggregators about how accurate they are and how useful they are.
[00:40:17.600 --> 00:40:24.560] And the other conflict of interest to share is that the big antagonist they have in this research paper is Nate Silver.
[00:40:24.560 --> 00:40:26.560] And I also worked for Nate Silver for a year and a half.
[00:40:26.560 --> 00:40:32.040] So this is like watching my two quantitative parents fight, I guess.
[00:40:32.360 --> 00:40:40.360] And so basically, the thrust of this research, and a lot of political scientists are skeptical of political forecasting, including me.
[00:40:40.360 --> 00:40:49.480] But the thrust of this particular paper that I really enjoy is: it says, look, you know, forecasting, in their words, captivates the public.
[00:40:49.480 --> 00:40:51.480] It drives media narratives, right?
[00:40:51.480 --> 00:40:56.760] We cannot get away from hearing about polls and horse race coverage and who's ahead and who's behind.
[00:40:56.760 --> 00:41:02.120] I don't even look and seek out polling information very much, though I do cave as we get closer and closer to the election.
[00:41:02.120 --> 00:41:08.520] And I certainly looked at it when I was making a living working on it, but I try not to, but you can't avoid this stuff.
[00:41:08.520 --> 00:41:18.920] And what they say, and what I think is something that you all talk about very elegantly on this podcast a lot, is what they call, quote, a veneer of scientific legitimacy around polls.
[00:41:18.920 --> 00:41:26.040] And Jay, you kind of hit on this earlier, and we put the word data next to something, and we think therefore it's true and it's correct.
[00:41:26.040 --> 00:41:33.800] And so we say, well, Harris is at 48%, Trump is at 46%, therefore we can say something about what's going to happen a month and a half from now.
[00:41:33.800 --> 00:41:40.600] And there are lots of problems, of course, as far as the scientific legitimacy of polls, and in particular, of polling aggregators, right?
[00:41:40.600 --> 00:41:44.360] So we don't know for sure that we're working with representative samples.
[00:41:44.360 --> 00:41:45.640] There's an effort to do so.
[00:41:45.640 --> 00:41:46.920] We don't know for sure.
[00:41:46.920 --> 00:41:49.640] There's selection bias and non-response.
[00:41:49.640 --> 00:41:55.000] And if you get a random call from a number you don't recognize, what are the odds you're going to pick it up?
[00:41:55.000 --> 00:41:59.400] If you do pick it up, what are the odds you're going to stay to answer their questions when you realize it's just a survey?
[00:41:59.400 --> 00:42:03.240] And then what are the odds you're actually going to tell them what you really think about politics to some random person, right?
[00:42:03.240 --> 00:42:07.240] So, we're not working with randomized samples in the way we'd like to in science.
[00:42:07.240 --> 00:42:12.600] We don't know a lot about how different survey firms or aggregators, we don't know what's under the hood in these models.
[00:42:12.560 --> 00:42:20.240] You know, Nate Silver, the New York Times, Politico, other outfits that run these aggregators make a living from these aggregators.
[00:42:14.600 --> 00:42:28.320] So, if you don't know, you know, the 538, or now it's Nate Silver has his own forecast, they aggregate all the polling data that's out there.
[00:42:28.320 --> 00:42:34.960] They weight it according to how good the pollsters have been in the past based on their own metrics.
[00:42:34.960 --> 00:42:46.240] I worked a little bit on collecting data for pollster evaluations back in the day, and I don't know how Nate ultimately took all that data on accuracy and weighted whether a pollster was an A or an A minus or whatever.
[00:42:46.240 --> 00:42:47.840] And so, so it's proprietary information.
[00:42:47.840 --> 00:42:52.960] So, we don't know what goes into the theory and into these models, so we can't really evaluate them that way.
[00:42:52.960 --> 00:42:57.440] We know that we're not reaching all of the electorate, but we also know that we're not reaching all of them in different ways.
[00:42:57.440 --> 00:43:02.400] Maybe the people we reached in 2016 and left out in 2016 are not the same as 2016.
[00:43:02.560 --> 00:43:11.840] So, there's no shortage of really serious empirical research problems, despite many pollsters and polling aggregators working from the best of intentions.
[00:43:11.840 --> 00:43:14.880] Okay, so there are some problems in polling.
[00:43:14.880 --> 00:43:25.600] We all kind of know, and if you paid attention to polling in 2016, you have some probably built-in skepticism or maybe knee-jerk fear when you hear new polling data come out.
[00:43:25.920 --> 00:43:32.640] But people like Nate Silver argue that this is still better, this is sort of the money ball argument for elections, right?
[00:43:32.640 --> 00:43:42.880] This is still better than punditry, this is still better than commentators and talking heads just assuming and thinking and expounding on what they think is going to happen.
[00:43:42.880 --> 00:43:50.960] Here's where this paper comes in, and where I think we start to have a lot of fun, and where Jay's article from our news piece is also relevant.
[00:43:50.960 --> 00:44:11.160] So, these researchers who study, you know, use machine learning to make predictions in politics say, Look, usually in machine learning, if we want to evaluate how good a model is, we have a ton of training and testing data that we train the model on, and then we compare it to how it whether it predicted reality correctly.
[00:44:11.160 --> 00:44:21.800] So, if you think of market data, stock data, weather data, you can evaluate these forecasts and sort of say roughly how good they were or how not good they were.
[00:44:21.800 --> 00:44:28.120] The problem with elections is at least presidential elections in the United States are only once every four years.
[00:44:28.120 --> 00:44:31.800] And that means we've only had about 60 in the United States.
[00:44:31.800 --> 00:44:38.200] So, that's a lot, that's a very limited quantity of data that we can compare the outcomes of these polls to.
[00:44:38.200 --> 00:44:40.520] We also haven't been doing the polling that long, right?
[00:44:40.840 --> 00:44:45.080] Now, some argue that, well, we have state data and we have local elections, so isn't that a lot more data?
[00:44:45.080 --> 00:44:47.800] The problem is that a lot of state data is correlated.
[00:44:47.800 --> 00:45:11.720] How one state goes tells you a lot about how a lot of other states are gonna go, and the way that the United States polling, the way the United States election works with swing states in the Electoral College mean that even if we can get state polling correct, it's still not necessarily the case that we're gonna be particularly accurate at national polling because, as we all have lived through, teeny tiny, seemingly random changes can totally flip how an entire nation.
[00:45:11.800 --> 00:45:14.280] So, state data doesn't really solve the problem.
[00:45:14.280 --> 00:45:23.400] So, what these researchers did is they said, okay, we are going to, and it's worth taking a look at the paper, it's open source at the moment, open access at the moment.
[00:45:23.720 --> 00:45:34.040] They said, we're going to simulate how long it would take to actually have enough data to evaluate whether, say, Nate Silver's model is accurate.
[00:45:34.040 --> 00:45:36.200] It's not, oh, did they get the last handful right?
[00:45:36.200 --> 00:45:41.640] Because we can't really statistically separate that from we happen to throw heads three times in a row or whatever, right?
[00:45:41.640 --> 00:45:44.760] So, they said, let's simulate and see how long it's going to take.
[00:45:44.880 --> 00:46:03.120] And basically, the punchline, and Jay, this is where we're going to need those crystals, is it will take decades more of elections and research to find out if these forecasts that we're all clicking refresh on right now to find out if these probabilistic forecasts are more accurate than pundits and talking heads.
[00:46:03.120 --> 00:46:15.840] It will take centuries of data to actually statistically evaluate and compare the models to see if these forecasters are actually reliably and helpfully predicting state and national elections.
[00:46:15.840 --> 00:46:25.040] And it will take millennia, they say, to figure out what techniques are actually best, like which of these models that purport to be the best one is actually the best one.
[00:46:25.040 --> 00:46:29.360] And they even say, look, maybe we can do better because we're all still living in the present.
[00:46:29.360 --> 00:46:30.560] We don't have these crystals yet.
[00:46:30.560 --> 00:46:34.720] Like we do all want to know what's going to happen in November and four years from now and so on.
[00:46:34.720 --> 00:46:39.680] So one thing they say is, well, you could say, what if we look at forecasters who make predictions about other things?
[00:46:39.680 --> 00:46:52.080] So maybe congressional races that happen more often, maybe sports outcomes, but even that, who's to say that the forces underlying a presidential model are going to be comparable to, say, someone who's accurately predicting if the bills are going to win or whatever.
[00:46:52.080 --> 00:46:56.640] So even that kind of generous version is not particularly helpful.
[00:46:56.800 --> 00:47:16.640] And I guess the last thing I'll say here, and the reason I really like this, is there's an underlying tension that I should be candid about between kind of generally political scientists and polling, where in political science, most of the time we try to predict elections on what we call fundamentals, so the economy, things like that, and not really what people say they're going to do.
[00:47:16.640 --> 00:47:18.640] It's more like what is happening in the country.
[00:47:18.640 --> 00:47:26.560] And so there's sort of this head-to-head between fundamentals versus polling and kind of more public opinion, real-time data collection.
[00:47:26.560 --> 00:47:32.600] And basically, the punchline of all of this is that we don't know and we don't have enough data to know.
[00:47:32.600 --> 00:47:38.600] And the last thing I'll say is, and this kind of reminds me, Steve, of things you've said where people say, well, why are horoscopes so dangerous?
[00:47:38.600 --> 00:47:42.840] Or why are crystals, not Jay's crystals, but like, you know, salt and stuff that people buy?
[00:47:42.840 --> 00:47:46.200] That's like, what's the harm in spending 50 bucks on a blue rock?
[00:47:46.680 --> 00:47:47.800] Well, there is a lot of harm.
[00:47:47.800 --> 00:48:05.480] And it turns out, and there are experimental studies, and there's some great links in this paper to existing peer-reviewed studies, where we say, look, it turns out that there is a lot of evidence to suggest that polling, as we currently talk about it, makes people feel a false sense of confidence or security about what's going to happen.
[00:48:05.480 --> 00:48:07.320] That can cause depressed turnout.
[00:48:07.320 --> 00:48:11.240] For example, we think that might be some of what's going on in 2016.
[00:48:11.240 --> 00:48:15.960] These studies are more experimental, so we're in a lab and we say, hey, here are the polling outcomes.
[00:48:15.960 --> 00:48:16.600] Are you going to vote?
[00:48:16.600 --> 00:48:18.040] So it's not the same thing.
[00:48:18.040 --> 00:48:21.080] And you can't really, really say for sure.
[00:48:21.080 --> 00:48:24.600] But it really can mess with our understanding of what's going to happen.
[00:48:24.600 --> 00:48:32.760] It can mislead the public about how viable certain candidates are and cause them to abandon a candidate that actually could be relatively viable because we're not interpreting probabilities correctly.
[00:48:32.760 --> 00:48:40.280] And it also encourages this kind of horse race coverage that we're all hearing about, as opposed to talking about issues and policies and other things that might matter.
[00:48:40.280 --> 00:48:41.480] So it's not harmless.
[00:48:41.480 --> 00:48:44.600] So their headline is: look, yeah, file name, right?
[00:48:44.600 --> 00:48:46.040] End forecasting.
[00:48:46.040 --> 00:48:53.480] My view on it, if I may, is I think there's a lot of money being made in the polling industry and I don't see it going away.
[00:48:53.480 --> 00:49:04.200] I would like us to be a lot more thoughtful, and we said this at the beginning of the episode today, a lot more thoughtful about what are these results actually able to tell us and what can't they tell us?
[00:49:04.200 --> 00:49:18.080] And that includes things like thinking about probabilities, understanding that if someone is at 58% approval, that doesn't mean they're going to get 58% of the vote, you know, basic things like that, changing how we report on these things, talking about uncertainty a lot more.
[00:49:18.320 --> 00:49:20.800] And I think, you know, instilling this kind of doubt.
[00:49:20.800 --> 00:49:25.200] But it's not the same thing as saying, oh, polls are wrong or polls are right.
[00:49:25.200 --> 00:49:29.200] The punchline here is we don't know and we don't have enough data to scientifically know.
[00:49:29.200 --> 00:49:34.880] So it's some information, but to this moment, we don't know if it's better or worse than a coin flip or a pundit.
[00:49:34.880 --> 00:49:40.080] And Andrew, are you distinguishing polling data from forecasting?
[00:49:40.080 --> 00:49:45.600] Because polling data, every time I read a poll, they're like, this is a snapshot of what people are saying right now.
[00:49:45.600 --> 00:49:48.000] This does not predict what's going to happen in the future.
[00:49:48.800 --> 00:49:50.240] These are two different things.
[00:49:50.560 --> 00:49:51.360] That's true.
[00:49:51.360 --> 00:49:54.720] So the polls are the data that go into the forecasts.
[00:49:54.960 --> 00:49:56.080] And that's a fair distinction, yes.
[00:49:56.320 --> 00:49:59.840] Yeah, but you could say the poll is the poll.
[00:49:59.840 --> 00:50:08.000] And a poll, yeah, I agree that, you know, again, the more I read about it, it's like, oh, yeah, the answer rate on this poll was 1%.
[00:50:09.440 --> 00:50:11.600] So it's really telling us almost nothing.
[00:50:12.480 --> 00:50:15.600] That's just telling us who's willing to answer the poll.
[00:50:16.400 --> 00:50:19.920] I mean, that's probably overwhelming any other factors in there.
[00:50:19.920 --> 00:50:20.080] Right.
[00:50:20.080 --> 00:50:21.120] But then the forecasts are based on that.
[00:50:21.200 --> 00:50:22.400] But the forecasts are based upon that.
[00:50:23.200 --> 00:50:34.400] The forecasts are based on those, and then they're based on other things that are added in around the forecaster's own assumptions about how different demographics are going to show up and how much it's going to matter.
[00:50:34.640 --> 00:50:35.600] And there's some.
[00:50:35.600 --> 00:50:44.000] And the problem is you're always looking through the rearview mirror because you're saying, oh, gosh, we really underestimated how Trump voters would turn out in 2016.
[00:50:44.000 --> 00:50:50.720] So now let's, I'm oversimplifying and this isn't quite how it works, but like now let's overestimate how likely anyone who said they're going to turn out.
[00:50:50.720 --> 00:50:54.960] But then it's like, well, who's to say that this election is going to operate the way from the past?
[00:50:54.960 --> 00:51:01.320] And so we're kind of this jerking around to read through the, but we also, and this is what this article is saying, is we don't know.
[00:51:01.640 --> 00:51:12.760] 2016, there were polls, you know, in Nate Silver's defense, he was more accurate, closer to, more conservative on Hillary's likelihood of winning than many other outfits.
[00:51:12.760 --> 00:51:17.400] But we don't know what was going on in that model that needed to change, right, or what we were missing.
[00:51:17.400 --> 00:51:18.840] Or if it was absolutely correct.
[00:51:18.840 --> 00:51:20.680] And that was the percentage.
[00:51:20.680 --> 00:51:24.200] And we just saw the outcome that was slightly less likely, which is totally reasonable.
[00:51:24.200 --> 00:51:35.160] So I wonder if this system is inherently unpredictable, or if it's just, you know, an order of magnitude more difficult than the tools we're using right now.
[00:51:35.160 --> 00:51:41.880] And I wonder if, what if, you know, again, we seek AI and supercomputer simulations on this?
[00:51:41.880 --> 00:51:46.440] Could we do a thousand years of simulations and test our models that way?
[00:51:46.440 --> 00:51:52.360] Or is it just only real-world data is really going to make it, is really going to help?
[00:51:52.360 --> 00:51:56.840] I mean, I think there is a version of the world where you could put all of that in.
[00:51:56.840 --> 00:52:05.400] What I genuinely don't know, and again, this kind of goes back to where we started this conversation, is what's going to happen in the underlying data generating processes.
[00:52:05.400 --> 00:52:17.160] Like if politics continues roughly as it is now, or it has been for the past, say, eight years, then we could probably simulate a whole bunch of things and we could maybe make some claims with enough computational power and AI.
[00:52:17.160 --> 00:52:22.760] But, you know, it's sort of the equivalent of someone in the 1960s trying to get right what's going on here.
[00:52:22.760 --> 00:52:32.760] And it's, I don't want to use this term lightly, but it's more chaotic system than anything else where teeny tiny little di differences can lead to wildly different outcomes.
[00:52:33.400 --> 00:52:34.360] So it's hard.
[00:52:34.360 --> 00:52:38.120] To me, it seems hard to imagine, but I'm open to it.
[00:52:38.440 --> 00:52:42.840] It also reminds me of psychohistory from the Foundation series.
[00:52:42.840 --> 00:52:43.160] Yep.
[00:52:43.800 --> 00:52:47.360] And I mean, Donald Trump is the mule, right?
[00:52:44.360 --> 00:52:54.880] I mean, he is, yeah, this random factor that nobody could have predicted that changes the rules.
[00:52:54.880 --> 00:52:56.400] Like, you talk about the fundamentals.
[00:52:56.400 --> 00:53:00.160] The fundamentals are meaningless when you have something like this going on, right?
[00:53:00.480 --> 00:53:01.040] Yes.
[00:53:01.040 --> 00:53:08.480] You don't know that it really tells us anything much, you know, not enough, not enough to predict what's going to happen.
[00:53:08.480 --> 00:53:09.040] Right.
[00:53:09.040 --> 00:53:20.000] I mean, and that goes back to Steve to your distinction there between polls and forecasts, which is like the polls, there's information in the polls, and the best way to look at them is with a lot of skepticism.
[00:53:20.000 --> 00:53:21.920] There's all this selection bias that we just talked about.
[00:53:21.920 --> 00:53:23.360] There's very low response rates.
[00:53:23.600 --> 00:53:24.640] There's all kinds of things.
[00:53:24.640 --> 00:53:26.400] We don't really know the methodology, all this stuff.
[00:53:26.400 --> 00:53:42.400] But if you look at the overall suite of the polls and look at them in aggregate, set aside forecasting, just sort of look at the snapshots that we're getting, the stunning thing is that Trump's approval rating does not seem to budge, or Trump's, you know, I'm planning on voting for Trump, doesn't seem to budge no matter what's happening.
[00:53:42.400 --> 00:53:46.400] And that just would not happen 10 years ago, right?
[00:53:46.640 --> 00:53:49.280] President Howard Dean yelled the wrong way, and we never heard from him again.
[00:53:49.680 --> 00:53:52.080] I know, yeah.
[00:53:52.720 --> 00:53:53.440] Right.
[00:53:53.440 --> 00:53:57.040] Lots of theories about why that's the case, but I can't get into that right now.
[00:53:58.880 --> 00:54:10.160] But I think it's an interesting area of, you know, I think it's worth trying to study politics and these sort of squishier, more complex things through these empirical tools that we have.
[00:54:10.160 --> 00:54:18.320] But we also kind of see these spin-offs like pollster and forecasting generating this sense of false security or false certainty.
[00:54:18.320 --> 00:54:25.680] And I think that that, you know, it's sort of, it's worth doing the science, but not when you're not careful about it, the science becomes pseudoscience.
[00:54:26.640 --> 00:54:27.040] All right.
[00:54:27.040 --> 00:54:28.160] Thanks, Andrea.
[00:54:28.160 --> 00:54:28.720] Sure.
[00:54:28.720 --> 00:54:33.240] So are you guys aware that we're having a myopia epidemic?
[00:54:33.240 --> 00:54:33.720] Ooh.
[00:54:29.840 --> 00:54:35.400] Is it contagious?
[00:54:35.960 --> 00:54:37.800] What could possibly be causing that?
[00:54:37.800 --> 00:54:42.200] So what percentage of the world do you think has myopia?
[00:54:42.200 --> 00:54:43.240] 25.
[00:54:43.240 --> 00:54:44.200] Me.
[00:54:44.520 --> 00:54:46.520] I'm going to say 30.
[00:54:46.520 --> 00:54:47.160] I like Jay's.
[00:54:47.320 --> 00:54:48.280] I think it's higher than that.
[00:54:48.280 --> 00:54:48.840] Let me think.
[00:54:48.840 --> 00:54:53.880] All the people that wear glasses, how many of them are like under 45, 50?
[00:54:53.880 --> 00:54:57.880] The aggregate polling on this, Steve, says about 12.3%.
[00:54:58.200 --> 00:54:59.480] I'm going to say it's higher than that.
[00:54:59.480 --> 00:55:01.880] The turnout of people with myopia in the next election is.
[00:55:02.040 --> 00:55:05.800] It's 35.81% in 2023.
[00:55:05.800 --> 00:55:08.600] It was 24% in 1990.
[00:55:08.600 --> 00:55:13.560] So it's gone up from basically 25% to 35% over the last 30 years.
[00:55:13.560 --> 00:55:15.880] Is it really, though, or are we just diagnosing kids younger?
[00:55:16.360 --> 00:55:18.360] So, yeah, that's always the question, right?
[00:55:18.360 --> 00:55:20.280] Is it an artifact or is it real?
[00:55:20.280 --> 00:55:22.200] We talked about this with ADHD.
[00:55:22.200 --> 00:55:23.640] I think it was just last week.
[00:55:23.640 --> 00:55:24.280] And it's real.
[00:55:24.280 --> 00:55:25.640] It's an absolutely real increase.
[00:55:25.960 --> 00:55:30.600] It's not an artifact of diagnosis or changing the definition or whatever.
[00:55:30.920 --> 00:55:32.520] It's really happening.
[00:55:32.520 --> 00:55:35.800] What's interesting is that it's continuing to increase.
[00:55:35.800 --> 00:55:48.280] And there was a recent paper, which is what prompted this discussion, that estimates that by 2050 we'll be at 40%, although other estimates put it closer to 50%.
[00:55:48.360 --> 00:55:55.960] Imagine half the world being myopic, basically, at some time after 2050, is sort of the consensus of opinion here.
[00:55:55.960 --> 00:55:59.880] Now, you say, wow, but there's already parts of the world where it's higher.
[00:55:59.880 --> 00:56:03.720] In China, it's already like 70, 80%.
[00:56:03.720 --> 00:56:04.200] Wow.
[00:56:04.200 --> 00:56:18.960] So what's funny is that, like, if you go back to the 1940s, 1950s, the typical stereotype, right, of somebody from China was you would draw, you know, it was somebody with those small round glasses.
[00:56:19.280 --> 00:56:28.800] That's because there was so much myopia, and that was a very common style of glasses that they wore.
[00:56:28.800 --> 00:56:30.000] So it was actually real.
[00:56:30.000 --> 00:56:30.560] You know what I mean?
[00:56:30.560 --> 00:56:32.880] It was a stereotype, but it was based upon reality.
[00:56:32.880 --> 00:56:33.920] But what was the difference?
[00:56:33.920 --> 00:56:37.680] Sorry, back then, what was the difference in the number of people in China versus the U.S.?
[00:56:38.000 --> 00:56:43.760] I don't know what, I mean, so in the 1950s, like in the U.S., it was 2%, 3%.
[00:56:44.080 --> 00:56:46.880] And it was already in the double digits in China.
[00:56:46.880 --> 00:56:48.560] It was already 20%.
[00:56:48.560 --> 00:56:51.360] So it went from 20% to like 80%.
[00:56:51.360 --> 00:56:55.680] But we were at like 2%, 3%, and now we're at 24%, 25%.
[00:56:55.920 --> 00:56:56.640] But I don't know if you like.
[00:56:57.120 --> 00:57:01.440] Why do I feel like screens are going to be the factor?
[00:57:01.920 --> 00:57:16.320] Well, and I also think we cannot say that it's a, it's, we can say it's a real phenomenon now, but I don't think we can say that the 2% or 3% of Americans who wore glasses for myopia in 1950 were that we were catching all Americans.
[00:57:16.320 --> 00:57:17.440] Were we screening for vision?
[00:57:17.840 --> 00:57:19.280] Yeah, so that's a good question.
[00:57:20.400 --> 00:57:26.480] Because the thing is, we do screen children in school for myopia, for eye problems.
[00:57:26.480 --> 00:57:27.120] For vision.
[00:57:27.440 --> 00:57:30.800] Yeah, and that goes back at least to the 50s and 60s.
[00:57:30.800 --> 00:57:31.680] I took many eye tests.
[00:57:31.920 --> 00:57:32.800] I was screened.
[00:57:32.800 --> 00:57:35.040] So, yeah, I was screened in the 1960s.
[00:57:35.040 --> 00:57:36.560] I was checked for scoliosis.
[00:57:37.040 --> 00:57:37.360] Oh, yeah.
[00:57:37.600 --> 00:57:38.720] I remember that test, yeah.
[00:57:39.040 --> 00:57:40.560] And they used to do lice checks, too.
[00:57:40.880 --> 00:57:43.520] So, yeah, you know, so it goes back to some time like that.
[00:57:43.520 --> 00:57:48.000] Certainly goes back to the second half of the 20th century.
[00:57:48.640 --> 00:57:51.360] How far does good data go?
[00:57:51.360 --> 00:57:51.840] I don't know.
[00:57:52.080 --> 00:57:53.520] That's a little bit harder to say.
[00:57:53.520 --> 00:57:58.160] But a lot of the studies that I was looking at starts to track it in the 1950s.
[00:57:58.160 --> 00:58:00.920] So I'm assuming that's when they started getting good data.
[00:57:59.680 --> 00:58:05.800] So even certainly in the last 30 years, you know, we have pretty consistent data.
[00:58:06.280 --> 00:58:15.880] And there's a steady trend of increasing prevalence and incidence of nearsightedness of myopia.
[00:58:15.880 --> 00:58:18.360] So this is a physical problem with the eye, right?
[00:58:18.360 --> 00:58:32.120] I mean, in myopia, the eye is too elongated, and so the image gets focused too far forward, like in front of the retina, which makes it hard to see in the distance.
[00:58:32.120 --> 00:58:34.920] So that's they're hence nearsighted.
[00:58:35.240 --> 00:58:38.680] So what do you guys think is causing it?
[00:58:38.680 --> 00:58:40.760] Well, it sounds like screens are a factor.
[00:58:40.760 --> 00:58:42.840] So Evan thinks it's screens.
[00:58:42.840 --> 00:58:46.280] I mean, I would think that that's potentially a huge factor.
[00:58:47.640 --> 00:58:53.800] I mean, I know the reason why screens affect you that way is because you're focusing too close for too long.
[00:58:54.680 --> 00:58:55.240] I don't know.
[00:58:55.240 --> 00:58:57.160] I mean, the other thing would be what, diet?
[00:58:57.480 --> 00:59:01.480] Or neurotoxin, or not neurotoxins, sorry, but yeah, toxins that affect development.
[00:59:01.480 --> 00:59:02.360] I mean, plastics.
[00:59:02.840 --> 00:59:06.200] Yeah, like we live in a pre-1950, there was no plastic.
[00:59:06.200 --> 00:59:09.720] We have like so many things in our blood now that didn't exist, like Teflon and all of that.
[00:59:10.120 --> 00:59:26.040] I'm going to go with, I'm with you all on all of those, and I'm going to say maybe we it's like we stopped thinking of people with glasses as less attractive, and so we're all marrying each other with glasses, and we're reproducing with myopia now.
[00:59:26.040 --> 00:59:28.920] It's no longer like evolutionarily disadvantageous.
[00:59:28.920 --> 00:59:30.280] Yeah, assortative mating.
[00:59:30.280 --> 00:59:30.760] There you go.
[00:59:30.760 --> 00:59:31.480] That's what I want.
[00:59:31.480 --> 00:59:31.960] Yeah.
[00:59:32.200 --> 00:59:33.000] Interesting.
[00:59:33.000 --> 00:59:34.160] Those are some good thoughts.
[00:59:34.160 --> 00:59:35.080] Very creative.
[00:59:35.640 --> 00:59:37.000] However, he's like, no, Ron.
[00:59:37.160 --> 00:59:38.440] Oh, thanks for pandering to us.
[00:59:38.840 --> 00:59:42.920] The short answer is, the short answer is we don't know.
[00:59:43.720 --> 00:59:44.800] And it's all of those things.
[00:59:44.520 --> 00:59:45.680] It's no.
[00:59:45.920 --> 00:59:52.960] But there are two basic theories that are the most plausible.
[00:59:52.960 --> 00:59:54.640] Evan's the closest.
[00:59:54.640 --> 00:59:56.400] It's not as so.
[00:59:56.480 --> 01:00:04.560] I mean, it could be partly screens, but and what Jay said is like you're spending a lot of your day focusing close up.
[01:00:04.560 --> 01:00:08.560] But this phenomenon predates screens, though.
[01:00:08.560 --> 01:00:15.920] And it doesn't really explain why some countries have a much higher rate than other countries because it does not track with screen use.
[01:00:15.920 --> 01:00:17.520] Then it would be genetics?
[01:00:17.520 --> 01:00:18.800] No, it's not genetic.
[01:00:18.800 --> 01:00:21.600] It's too fast for this to really be genetic.
[01:00:22.560 --> 01:00:23.600] It's developmental.
[01:00:23.600 --> 01:00:25.440] It's developmental, but what's the factor?
[01:00:26.000 --> 01:00:33.440] There's no signal, there's no hypothesis or notion or evidence that it's anything environmental like literacy rates?
[01:00:33.440 --> 01:00:34.400] Yeah, I'm with Evan.
[01:00:34.640 --> 01:00:36.640] Literacy studying.
[01:00:36.880 --> 01:00:37.200] No.
[01:00:37.200 --> 01:00:44.320] What it is, is, again, very good, very good hypotheses, but the leading hypothesis is that it's sunlight.
[01:00:44.320 --> 01:00:49.040] It's that kids specifically are spending less time outside.
[01:00:49.600 --> 01:00:50.480] Vitamin D?
[01:00:51.120 --> 01:00:51.600] No.
[01:00:51.760 --> 01:00:54.080] Vitamin D is made from absorption in your skin.
[01:00:54.320 --> 01:00:56.160] Sunset direct sunlight to the eyeball.
[01:00:56.400 --> 01:01:04.000] Sunlight causes dopamine to be released in the eye, which also is important for growth.
[01:01:04.000 --> 01:01:08.640] And so within the absence of that, the eye develops abnormally, right?
[01:01:08.640 --> 01:01:10.240] So it is a developmental problem.
[01:01:10.240 --> 01:01:12.800] As the eye grows, it becomes abnormally shaped.
[01:01:12.800 --> 01:01:27.440] So it could be the absence of sunlight, but the other hypothesis is that it's a relative decrease in being in an environment where you have to focus in the distance, at infinity, right?
[01:01:27.440 --> 01:01:30.000] If you're inside all the time, you're never focusing at infinity.
[01:01:30.920 --> 01:01:37.880] And so just from use over years, your eye develops with a bias towards focusing close up.
[01:01:37.880 --> 01:01:43.640] So it doesn't necessarily require screens, but screens certainly are part of that phenomenon.
[01:01:43.960 --> 01:01:47.320] It's really just that kids are not spending enough time outside.
[01:01:47.320 --> 01:01:48.600] That's the bottom line.
[01:01:48.600 --> 01:01:51.160] But is that, but you keep calling that a hypothesis.
[01:01:51.160 --> 01:01:52.440] Is there any evidence to support that?
[01:01:52.680 --> 01:01:53.480] There's a lot of evidence.
[01:01:53.480 --> 01:01:55.160] There's a lot of evidence to support that.
[01:01:55.160 --> 01:01:59.240] That's why I said these are the leading hypotheses because there's evidence to support that.
[01:01:59.640 --> 01:02:01.560] And it comes both ways.
[01:02:01.560 --> 01:02:04.760] It correlates with urbanization.
[01:02:04.760 --> 01:02:08.200] It correlates with time spent inside versus outside.
[01:02:08.680 --> 01:02:09.720] It correlates.
[01:02:09.720 --> 01:02:18.280] And also, if you, as a matter of policy, give kids more time outside, it reduces their risk of myopia.
[01:02:18.280 --> 01:02:20.120] It reverses the trend.
[01:02:20.120 --> 01:02:21.240] That's critical.
[01:02:21.240 --> 01:02:28.120] Because, you know, in order to prove a cause and effect, you have to show that if you eliminate the cause, the effect goes away, right?
[01:02:28.440 --> 01:02:29.960] So we have that data.
[01:02:30.520 --> 01:02:34.520] But they locked up 100 children outside for a year and studied them and see what they did.
[01:02:35.000 --> 01:02:39.960] At this point, there's pretty much no question that it's outdoors time is the critical element.
[01:02:40.280 --> 01:02:43.800] So like the stereotype that little book nerds all have glasses?
[01:02:43.800 --> 01:02:47.480] Yeah, there's actually what we don't know.
[01:02:47.480 --> 01:02:55.560] Yeah, what we don't know is the relative contribution of the dopamine from sunlight versus the focusing in the distance.
[01:02:55.560 --> 01:02:59.720] But it's being outside is the critical element.
[01:03:00.120 --> 01:03:03.400] Is it only for children, or should I go outside and stare at the distance?
[01:03:03.720 --> 01:03:05.320] So that's a really good question.
[01:03:06.120 --> 01:03:11.320] It's developmental, so it's definitely true through high school.
[01:03:12.360 --> 01:03:15.600] Not really sure if there's any effect once you're already an adult.
[01:03:15.600 --> 01:03:17.200] So probably not adults.
[01:03:17.200 --> 01:03:20.400] But it doesn't mean it's not good to get outside and get some sunshine.
[01:03:14.760 --> 01:03:21.760] Nothing wrong with that.
[01:03:22.000 --> 01:03:24.880] So the good news is the fix is simple.
[01:03:24.960 --> 01:03:27.840] Not going to say easy, but it is simple.
[01:03:27.840 --> 01:03:29.840] It is get kids outside.
[01:03:29.840 --> 01:03:32.960] Get them out in the sun, you know, and away from screens.
[01:03:32.960 --> 01:03:34.160] But off my lawn.
[01:03:34.480 --> 01:03:35.200] Right.
[01:03:37.280 --> 01:03:43.360] But it's not necessarily going to be easy because that cultural changes, public health measures like that are hard.
[01:03:43.360 --> 01:03:44.240] They're hard to get people to see.
[01:03:44.400 --> 01:03:45.520] Do you guys remember?
[01:03:45.520 --> 01:03:49.600] I think we talked about this on the show before, but there's like this viral video.
[01:03:49.600 --> 01:03:53.360] The dad's in the kitchen doing dishes or something and the kids outside the kitchen window.
[01:03:53.360 --> 01:03:54.560] And he's like, can I come in yet?
[01:03:54.560 --> 01:03:56.000] And he's like, no, you're grounded.
[01:03:57.760 --> 01:03:58.800] You just want to come inside.
[01:03:59.280 --> 01:04:00.400] Totally reversed.
[01:04:00.400 --> 01:04:02.160] Yeah, like go, go, go play.
[01:04:02.160 --> 01:04:03.200] What am I supposed to do?
[01:04:03.200 --> 01:04:04.480] I don't care.
[01:04:05.760 --> 01:04:07.600] I don't want to see you till supper.
[01:04:07.600 --> 01:04:09.040] Yeah, that's amazing.
[01:04:09.040 --> 01:04:12.080] I mean, when we were kids, we spent the entire day outside.
[01:04:12.080 --> 01:04:12.880] Oh, that was it.
[01:04:12.880 --> 01:04:14.400] Saturday morning, 7 a.m.
[01:04:14.640 --> 01:04:15.920] See you for lunch.
[01:04:15.920 --> 01:04:16.320] Thanks.
[01:04:17.360 --> 01:04:18.800] We didn't know where we were.
[01:04:18.800 --> 01:04:19.120] Right.
[01:04:19.520 --> 01:04:25.760] I mean, my kids, like recently, like in the last few days after school, they've been asking me to take them to the park.
[01:04:25.760 --> 01:04:27.120] And I'm thrilled.
[01:04:27.120 --> 01:04:29.920] You know, I'm like, you want to be outside?
[01:04:29.920 --> 01:04:30.640] You got it.
[01:04:30.640 --> 01:04:32.080] You can stay there as long as you want.
[01:04:32.800 --> 01:04:39.280] The problem is we have shifted to a screen culture and it is so deeply embedded now.
[01:04:39.280 --> 01:04:45.600] I mean, last year, I decided that I was not going to use my phone as much as possible.
[01:04:45.600 --> 01:04:48.720] And I did it for about five or six months.
[01:04:48.720 --> 01:04:50.720] I was not looking at the news.
[01:04:50.720 --> 01:04:52.880] I was mostly off of my phone.
[01:04:52.880 --> 01:04:57.200] And I creeped all the way back to a massive screen addiction again.
[01:04:57.840 --> 01:04:58.800] It just happens.
[01:04:58.800 --> 01:05:03.640] Like, I don't even know what's happening, and all of a sudden, I'm like, I can't sit without looking at a screen.
[01:04:59.840 --> 01:05:06.120] Like, I have to tell myself, you're okay.
[01:05:06.360 --> 01:05:07.320] You know what I mean?
[01:05:07.320 --> 01:05:15.960] But again, as we've said previously, it's probably a better approach to not try to minimize your screen time, but to maximize your not screen time.
[01:05:15.960 --> 01:05:16.520] Yeah.
[01:05:16.520 --> 01:05:19.880] So, like, say, I'm going to do some activity that gets me outside.
[01:05:19.880 --> 01:05:23.000] You know, take up gardening, you know, or whatever.
[01:05:23.160 --> 01:05:24.760] Go play catch with your kid outside.
[01:05:24.760 --> 01:05:28.680] Just do something that has to be happening outside, especially in the sunlight.
[01:05:28.920 --> 01:05:29.720] Go for a walk.
[01:05:29.720 --> 01:05:30.840] Yeah, go for a walk.
[01:05:30.840 --> 01:05:31.400] Just do something.
[01:05:31.400 --> 01:05:36.440] If you have to exercise outside versus inside, do something where you're getting more sunlight.
[01:05:36.440 --> 01:05:41.240] There's also lots of evidence that, yeah, we Americans generally are undersunned.
[01:05:41.240 --> 01:05:43.800] You know, we do have relative vitamin D deficiency.
[01:05:43.800 --> 01:05:47.880] And also, contact with green spaces is psychologically healthy.
[01:05:48.200 --> 01:05:53.160] And yeah, so that also means that, like, again, this is a big problem of urbanization.
[01:05:53.160 --> 01:06:00.840] And the reason why China was, you know, was probably had such a big problem is because of their rapid urbanization and their population.
[01:06:01.160 --> 01:06:03.880] These are just lots of people crammed into cities.
[01:06:04.120 --> 01:06:04.440] Right?
[01:06:04.440 --> 01:06:08.920] So that's why there's such an epidemic there, especially, but this is happening everywhere.
[01:06:09.240 --> 01:06:18.600] So yeah, you know, having green spaces in cities, more parks, more, you know, and school time should incorporate, you know, outside time as much as possible.
[01:06:18.600 --> 01:06:20.840] Classes outside, recess outside.
[01:06:20.840 --> 01:06:22.440] Got to get those kids outside.
[01:06:22.440 --> 01:06:29.800] Steve, what about the like the Google vision, or what is it, Apple Vision and all the goggles?
[01:06:29.800 --> 01:06:37.400] What if we look at the world through those outside, but project like our text messages and the news really far into the future?
[01:06:37.880 --> 01:06:38.840] Far into the distance.
[01:06:37.440 --> 01:06:41.720] Our phone into the distance in the goggles.
[01:06:41.960 --> 01:06:43.560] Artificial horizon kind of thing.
[01:06:43.560 --> 01:06:52.720] Does that mean I can keep up my phone addiction or my children's phone addiction and future children's phone addiction and let them not get myopia?
[01:06:52.720 --> 01:06:54.080] So that's actually been studied.
[01:06:54.080 --> 01:06:54.720] Oh, perfect.
[01:06:54.720 --> 01:06:56.320] And the answer is mixed.
[01:06:56.320 --> 01:06:57.040] We don't know.
[01:06:57.040 --> 01:06:57.600] Ow.
[01:06:57.600 --> 01:06:58.800] So that's the idea.
[01:06:58.800 --> 01:07:04.720] You could put somebody in VR because, you know, when you, I don't know how much you've used VR goggles.
[01:07:04.880 --> 01:07:05.200] Almost not.
[01:07:05.280 --> 01:07:06.800] I've used them extensively.
[01:07:06.800 --> 01:07:10.880] So one thing that's interesting is that I don't have to wear reading glasses when I use VR, right?
[01:07:11.440 --> 01:07:16.480] But also, yeah, you are, when you're focusing in the distance, you're focusing in the distance.
[01:07:16.480 --> 01:07:17.600] It's a real experience.
[01:07:17.600 --> 01:07:18.720] You know what I mean?
[01:07:19.040 --> 01:07:32.960] At least for that part of it, it seems like it should work, but it wouldn't necessarily replace the natural sunlight part of it unless we simulate that in the VR experience, which I guess is growing a lump or something in the room while you're talking about it.
[01:07:33.200 --> 01:07:36.720] But maybe, maybe we may, so this is just like using VR.
[01:07:36.720 --> 01:07:47.760] I wonder if we have optimized VR programs that we test, right, that to treat myopia, to prevent developmental myopia.
[01:07:47.760 --> 01:07:54.000] And that becomes, you know, just like you take your vitamin, you spend an hour in VR every day, make sure you don't get myopia.
[01:07:54.000 --> 01:07:54.480] You know what I mean?
[01:07:54.480 --> 01:08:00.000] Like that just becomes part of adapting to an urbanized life is you have to get your VR time.
[01:08:00.000 --> 01:08:02.080] You guys, myopia is not that bad.
[01:08:02.080 --> 01:08:02.960] That was the other thought.
[01:08:04.240 --> 01:08:04.880] You just wear glasses.
[01:08:05.120 --> 01:08:05.680] That's horrible.
[01:08:06.640 --> 01:08:07.200] It's horrible.
[01:08:07.520 --> 01:08:08.240] I hate wearing glasses.
[01:08:09.280 --> 01:08:10.160] Isn't that what laser sounds?
[01:08:10.400 --> 01:08:10.640] I know.
[01:08:10.960 --> 01:08:13.280] It's not that bad, but if we could prevent it, prevent it.
[01:08:14.480 --> 01:08:16.160] It's also not bad to prevent it.
[01:08:16.160 --> 01:08:16.720] It's all good.
[01:08:16.720 --> 01:08:18.880] It's like, what are you just telling people to spend more time outside?
[01:08:18.880 --> 01:08:20.520] Outside of the mall for outside.
[01:08:20.880 --> 01:08:21.200] Of course.
[01:08:21.200 --> 01:08:24.560] But I don't think we have to go to like insanely great lengths to, I don't know.
[01:08:24.560 --> 01:08:27.040] No, Kara, we take this all the way.
[01:08:29.200 --> 01:08:35.000] Sure, we can't get rid of diseases that are killing everybody, but myopia will come to an end.
[01:08:35.320 --> 01:08:38.360] You know, that twilight zone where the guy breaks his reading glasses.
[01:08:38.600 --> 01:08:39.800] It's not fair.
[01:08:39.800 --> 01:08:41.880] We can't let that happen.
[01:08:42.040 --> 01:08:43.240] Burgess Meredith thing.
[01:08:43.400 --> 01:08:44.120] Yeah, I saw that.
[01:08:44.120 --> 01:08:44.440] Yeah.
[01:08:44.440 --> 01:08:45.000] Yeah.
[01:08:45.000 --> 01:08:51.960] So, but there is a systematic review that I'm looking at where it does conclude that it can be effective if done properly.
[01:08:51.960 --> 01:08:53.080] You know, the right program.
[01:08:53.080 --> 01:08:55.880] VR can work to prevent myopia.
[01:08:56.200 --> 01:08:58.280] Yeah, very interesting.
[01:08:58.600 --> 01:08:58.920] All right.
[01:09:00.600 --> 01:09:02.360] Some monster.
[01:09:03.000 --> 01:09:05.240] Evan, tell us about the bio-twang.
[01:09:05.240 --> 01:09:06.360] Am I saying that right?
[01:09:06.360 --> 01:09:07.560] Yeah, bio-twang.
[01:09:07.560 --> 01:09:09.000] Isn't that a cool word?
[01:09:09.000 --> 01:09:14.600] All right, but I got to set this up a little bit, and I'm going to start with three questions.
[01:09:14.920 --> 01:09:20.200] All right, here's the first question: What is the collective noun for a group of crows?
[01:09:20.200 --> 01:09:20.760] A murder.
[01:09:21.400 --> 01:09:23.080] A murder of crows, correct.
[01:09:23.080 --> 01:09:27.000] And do you know what the collective noun for a group of lions is?
[01:09:27.000 --> 01:09:27.960] Lions is a pride.
[01:09:28.440 --> 01:09:28.840] Yes.
[01:09:28.840 --> 01:09:29.800] And the last one.
[01:09:29.800 --> 01:09:32.280] What's a group of scientists?
[01:09:32.280 --> 01:09:33.880] Collective noun?
[01:09:33.880 --> 01:09:35.160] A fight.
[01:09:37.560 --> 01:09:38.680] Well, I know.
[01:09:38.680 --> 01:09:39.880] Nobody really knows.
[01:09:40.040 --> 01:09:41.000] I came up with the term.
[01:09:41.000 --> 01:09:42.360] It's called a baffle.
[01:09:42.360 --> 01:09:42.840] A baffle.
[01:09:42.920 --> 01:09:46.360] Because scientists are always baffled, it appears.
[01:09:46.360 --> 01:09:54.600] Try searching a news item using the term scientist baffled, and you will come up with news items every single day.
[01:09:54.600 --> 01:09:57.000] I kid you not, because I've done it.
[01:09:57.000 --> 01:10:03.400] And whenever something is weird or unusual or unexplained in the context of science, that means scientists are baffled.
[01:10:03.400 --> 01:10:07.040] Evan, do you remember when we, like, this is going back 15 years?
[01:10:07.760 --> 01:10:13.480] One of the bits that we did for the show was coming up with a skeptical, I mean, a collective word for skepticism.
[01:10:13.560 --> 01:10:14.440] For skeptics, yes.
[01:10:14.440 --> 01:10:15.680] Yeah, I can't remember what we came up with.
[01:10:14.520 --> 01:10:17.200] Oh, yeah, I can't remember either.
[01:10:17.200 --> 01:10:19.760] It must not have been great, otherwise, it would have stuck.
[01:10:14.920 --> 01:10:20.640] Yeah, it didn't stick.
[01:10:21.440 --> 01:10:22.960] A doubt of skeptics or something.
[01:10:23.120 --> 01:10:25.840] Yeah, that was one of the something like that.
[01:10:25.840 --> 01:10:27.760] You know, very predictable.
[01:10:28.720 --> 01:10:31.040] A superciliousness of skeptics.
[01:10:31.040 --> 01:10:38.400] I don't recall if we tackled Steve and Jay this subject when this news first broke in 2014.
[01:10:38.400 --> 01:10:38.720] Right?
[01:10:38.720 --> 01:10:43.840] Underwater audio recordings captured what was described as, well, a bio-twang.
[01:10:44.160 --> 01:10:52.000] And this was during an acoustic survey in the Mariana Trench, which is the world's deepest oceanic trench.
[01:10:52.000 --> 01:11:02.400] It's located in the western Pacific Ocean, about 200 miles, which is 322 kilometers, southwest of Guam and southeast of the Mariana Islands.
[01:11:02.560 --> 01:11:11.920] Stretches to around 1,500 miles, 2,500 kilometers roughly, and it's about 43 miles or 69 kilometers wide.
[01:11:11.920 --> 01:11:15.120] So I know we've covered the bloop, but we've covered the bloop, right?
[01:11:15.120 --> 01:11:17.680] I wasn't sure about the bio-twang, though, right?
[01:11:17.680 --> 01:11:20.080] This is so let's, we don't want to confuse these things.
[01:11:20.320 --> 01:11:22.480] We're talking strictly of the bio-twang.
[01:11:22.480 --> 01:11:22.800] Yeah.
[01:11:22.800 --> 01:11:24.240] There's news about it this week.
[01:11:24.240 --> 01:11:37.040] But back in 2014, the researchers were using underwater gliders, and they recorded this mysterious sound that had two distinct components to it: a low rumble followed by a high-pitched metallic sound.
[01:11:37.040 --> 01:11:41.120] And you can find this on YouTube and other places online and go listen for yourself.
[01:11:41.120 --> 01:11:48.720] It lasts for about between two and a half, three and a half seconds and includes five separate parts with a dramatic range of frequencies.
[01:11:48.720 --> 01:11:55.280] The low is 38 hertz, and then it has this sort of metallic finale to it, 8,000 hertz.
[01:12:01.080 --> 01:12:04.920] And this at the time had scientists baffled.
[01:12:04.920 --> 01:12:09.720] Yep, what the heck could be causing that kind of sound in that area of the ocean?
[01:12:09.720 --> 01:12:14.280] And apparently, there were no other recordings of this noise to compare it against.
[01:12:14.280 --> 01:12:19.240] Was the noise artificial, you know, human-made noise, or was it something living?
[01:12:19.240 --> 01:12:22.040] A large mammal, whale, or something?
[01:12:22.040 --> 01:12:24.040] Was it a kraken or a leviathan?
[01:12:24.040 --> 01:12:31.000] Maybe it was one of those frozen aliens delivered by Xenu 75 million years ago to Earth on a Boeing DC-8.
[01:12:31.320 --> 01:12:33.800] No, that's Scientology, folks.
[01:12:34.600 --> 01:12:35.720] That is actual.
[01:12:35.720 --> 01:12:41.560] That's, yeah, so, but, you know, so they were baffled for a while.
[01:12:41.560 --> 01:12:47.880] 2016, though, a scientific paper was published in the Journal of the Acoustical Society of America.
[01:12:47.880 --> 01:13:08.680] There's a new one for me, authored by researchers from the Cooperative Institute for Marine Resource Studies at Oregon State University and the National Oceanic and Atmospheric Administration, in which they reported the low-frequency moaning part is typical of baleen whales, and that kind of twangy sound that it makes is really unique.
[01:13:08.680 --> 01:13:12.520] We don't find many new baleen whale calls.
[01:13:12.520 --> 01:13:14.520] Okay, their team analyzed the sound.
[01:13:14.600 --> 01:13:19.160] They found it was similar to what they had called the Star Wars sound.
[01:13:19.720 --> 01:13:31.720] A call produced by the dwarf mink or minky, M-I-N-K-E, dwarf minky whale, a type of baleen whale that lives in the northeast coast of Australia near the Great Barrier Reef.
[01:13:31.720 --> 01:13:34.200] Okay, so geographically, that checks out.
[01:13:34.200 --> 01:13:39.480] They said the species is the smallest of the baleen whales, and it doesn't spend much time at the surface.
[01:13:39.480 --> 01:13:41.800] Okay, so way down below.
[01:13:41.800 --> 01:13:47.600] It has an inconspicuous blow and often lives in areas where high seas make sighting difficult.
[01:13:48.400 --> 01:13:52.480] But they call frequently, making them good candidates for acoustic studies.
[01:13:52.480 --> 01:13:54.880] So that was their suggestion at the time.
[01:13:54.880 --> 01:14:00.960] However, they didn't really have a sighting or something to corroborate this.
[01:14:00.960 --> 01:14:05.040] It's just really a theory that they're putting out there, but it would seem to be reasonable.
[01:14:05.040 --> 01:14:17.280] But they also said there might be some complications with that theory because baleen whale calls are typically heard in conjunction with their winter mating season, but this bio-twang has been recorded throughout the year.
[01:14:17.280 --> 01:14:19.280] So, okay, there's an inconsistency.
[01:14:19.280 --> 01:14:21.680] They needed to study this some more.
[01:14:21.680 --> 01:14:30.000] So, yeah, 2016 scientists were maybe less baffled, but still baffled because they couldn't definitively say that this was a whale.
[01:14:30.000 --> 01:14:31.200] More evidence was needed.
[01:14:31.200 --> 01:14:35.920] So, fast forward to now, this week's news item, update on the bio-twang.
[01:14:35.920 --> 01:14:52.320] And I can think we can now say that scientists are even less baffled than before, because the study has revealed the exact origin of the Pacific Ocean's mysterious biotwang noises emanating from a whale called brides whales.
[01:14:52.320 --> 01:14:56.880] That's B-R-Y-D-E apostrophe S, Brides Whales.
[01:14:57.200 --> 01:15:00.400] And it has a taxonomical name which I can't pronounce.
[01:15:00.400 --> 01:15:04.240] Balaenoptera Edeni.
[01:15:04.880 --> 01:15:05.360] I like it.
[01:15:05.680 --> 01:15:08.960] I will say, Evan, or I might ask the group.
[01:15:08.960 --> 01:15:10.720] Sorry, I've waited so long.
[01:15:10.720 --> 01:15:15.040] Is that a New England pronunciation of baleen?
[01:15:15.040 --> 01:15:16.640] Oh, that's an Evan pronunciation.
[01:15:16.880 --> 01:15:17.360] Oh, okay.
[01:15:17.360 --> 01:15:18.720] I'm just making sure.
[01:15:18.720 --> 01:15:19.920] No, I have my own dialect.
[01:15:20.320 --> 01:15:21.840] Yeah, I was like, maybe it is.
[01:15:21.840 --> 01:15:26.080] I'm just a weird Texan, but I've always heard baleen.
[01:15:26.080 --> 01:15:26.800] Mm-hmm.
[01:15:26.800 --> 01:15:27.440] But maybe some people.
[01:15:27.600 --> 01:15:28.240] Have you heard baleen?
[01:15:28.240 --> 01:15:29.280] I've heard baleen.
[01:15:29.880 --> 01:15:33.000] But I say I've never heard baleen until Evan just said it.
[01:15:33.240 --> 01:15:33.800] But it works.
[01:15:33.800 --> 01:15:34.280] Yeah, it's funny.
[01:15:29.840 --> 01:15:34.840] We've got all three.
[01:15:35.160 --> 01:15:38.440] I didn't even connect it to baleen, even though it's about whales.
[01:15:39.000 --> 01:15:47.400] I'm about, I'm gonna, I'm gonna present a scientific paper to the uh to the whaleologist community and offer it up as a correction.
[01:15:48.680 --> 01:15:54.200] But in any case, these brides whales are using the call to locate one another.
[01:15:54.200 --> 01:15:57.320] What they describe as a giant game of Marco Polo.
[01:15:57.320 --> 01:15:58.520] That's what the researchers said.
[01:15:58.520 --> 01:16:05.480] So basically, the whales are playing games with us, and we primates are scratching our collective heads while the whales are making fun of us everywhere.
[01:16:05.480 --> 01:16:27.720] The new study was published, what, a week ago tonight, and it was in the journal Frontiers in Marine Science, where they proved that the brides whales were making the noise thanks to new artificial intelligence tools, which were able to sift through over 200,000 hours of audio recordings containing various ocean sounds.
[01:16:27.720 --> 01:16:31.240] They suspected that the brides whales were behind the biotwang.
[01:16:31.240 --> 01:16:41.560] When they spotted, here we go, they actually found 10 of them, 10 of them, swimming near the Mariana Islands, and they recorded nine of them making this distinctive noise.
[01:16:41.560 --> 01:16:42.280] So here we go.
[01:16:42.280 --> 01:16:44.520] That's better evidence for us.
[01:16:45.080 --> 01:16:48.920] From, let's see, Ann Allen, the study's lead author.
[01:16:49.160 --> 01:16:56.120] Once as a coincidence, twice as a happenstance, nine times though, definitely a bride's whale.
[01:16:57.160 --> 01:17:00.360] They conclusively proved that they were the ones making the call.
[01:17:00.360 --> 01:17:05.400] The team matched the occurrence of the noises to the migration patterns of the species as well.
[01:17:05.400 --> 01:17:13.000] And that meant sorting through, wow, years of audio recordings captured by monitoring stations all across the region.
[01:17:13.320 --> 01:17:30.480] And the study also found that the biotwang could only be heard in the Northwest Pacific, despite brides whales roaming across a much wider area, suggesting that only a specific population of brides whales are the ones making this specific noise.
[01:17:30.480 --> 01:17:31.440] So there you go.
[01:17:31.440 --> 01:17:39.440] Scientists, mostly, for the most part, are now no longer baffled in regards to the Biotwang.
[01:17:40.080 --> 01:17:43.040] I'm going to notice scientists being baffled every day.
[01:17:43.040 --> 01:17:49.760] I think you're totally-I mean, you obviously looked into it, but I can't believe how I've missed out on years of being baffled.
[01:17:50.800 --> 01:17:52.880] Political scientists are baffled.
[01:17:52.880 --> 01:17:53.680] Oh, we're baffled.
[01:17:53.680 --> 01:17:54.960] I tell you what.
[01:17:56.880 --> 01:17:58.080] All right, thanks, Evan.
[01:17:58.080 --> 01:17:59.200] You're welcome.
[01:17:59.200 --> 01:18:01.680] All right, Jay, it's who's that noisy time?
[01:18:01.680 --> 01:18:02.560] All right, guys.
[01:18:02.560 --> 01:18:05.360] Last week, I played this noisy.
[01:18:35.440 --> 01:18:36.800] What is that?
[01:18:37.760 --> 01:18:39.200] Any ideas, guys?
[01:18:39.200 --> 01:18:43.120] Attack of killer mechanical hornets.
[01:18:44.400 --> 01:18:46.000] I don't think we can read that.
[01:18:46.000 --> 01:18:46.320] Okay.
[01:18:46.640 --> 01:18:50.640] It's a combination dentist, drill, and slot machine.
[01:18:50.960 --> 01:18:53.200] I mean, there's so many things in there.
[01:18:53.200 --> 01:18:53.600] I know.
[01:18:53.600 --> 01:18:54.560] It could be so much.
[01:18:54.560 --> 01:18:56.000] It's so cool.
[01:18:56.000 --> 01:19:04.760] I know, because I know what it is, when I listen to it back now, you know, it feels right, but there are so many other things it sounds like.
[01:19:04.760 --> 01:19:07.800] All right, so a listener named Keely Hill wrote in.
[01:19:07.800 --> 01:19:09.000] Keely said, Hi, Jay.
[01:19:09.000 --> 01:19:11.880] The second part of this reminds me so much of cutting wrapping paper.
[01:19:11.880 --> 01:19:16.280] I'll guess it's some machine advancing a big sheet of paper and then cutting it.
[01:19:16.280 --> 01:19:18.040] I thought that was a really good guess.
[01:19:18.040 --> 01:19:24.760] It's not correct, but you know, there is a paper cutting sound in there that I detect as well.
[01:19:24.840 --> 01:19:28.200] Listener named Tom Heimbrock said, Hey, Jay, long time, first time.
[01:19:28.200 --> 01:19:32.440] I think this week's noisy is an acetylene torch being turned on, then lit.
[01:19:32.440 --> 01:19:34.520] I don't hear that as much.
[01:19:34.840 --> 01:19:41.320] I thought that was interesting, though, that you thought that it was like, you know, that pressure sound of the fire happening.
[01:19:41.720 --> 01:19:42.920] That's interesting.
[01:19:42.920 --> 01:19:45.320] Another listener named Christian Schmidt said, Hi, Jay.
[01:19:45.320 --> 01:19:50.280] I think this week's noisy is a bearing being spun up by an air compressor and then released.
[01:19:50.280 --> 01:19:54.360] I've heard this, and it is an interesting sound to hear.
[01:19:54.360 --> 01:19:57.560] And there is a little bit of that in there, but that is not correct.
[01:19:57.560 --> 01:20:06.760] Ben Long wrote in and said, Vibes, Jay, this week's noisy sounds like a laser cutting tool spinning up and then beginning to cut, possibly through a sheet of metal.
[01:20:06.760 --> 01:20:09.560] Yeah, and there is that white noisy sound in there of that.
[01:20:09.880 --> 01:20:11.960] That is not correct, though.
[01:20:11.960 --> 01:20:14.840] And let me give you one more guess.
[01:20:14.840 --> 01:20:17.160] This comes from Dave Siebler.
[01:20:17.160 --> 01:20:20.680] And Dave said, huge fan and longtime listener, first time guessing.
[01:20:20.680 --> 01:20:27.720] This sound reminds me of something I watched explaining how coins were made at the mint.
[01:20:28.440 --> 01:20:30.040] That's a cool thing to say.
[01:20:30.920 --> 01:20:33.160] I've never heard that sound of coins being made.
[01:20:33.160 --> 01:20:38.520] Maybe you might be talking about when the coins are being the striking of the coin, maybe.
[01:20:38.520 --> 01:20:41.800] I'm not sure exactly what you're talking about, but I'd like to learn more about that.
[01:20:41.800 --> 01:20:43.800] I applied for a job at a mint once.
[01:20:43.800 --> 01:20:44.280] What happened?
[01:20:44.280 --> 01:20:46.080] Yeah, I think I made a good impression.
[01:20:47.600 --> 01:20:48.480] I knew it was a joke.
[01:20:48.480 --> 01:20:50.240] I just didn't know where the hell you were going with it.
[01:20:52.560 --> 01:20:53.520] That was for Ian.
[01:20:53.520 --> 01:20:55.200] This is what I find odd.
[01:20:56.000 --> 01:20:57.120] Nobody got it.
[01:20:57.120 --> 01:20:58.560] Nobody won this week.
[01:20:58.560 --> 01:21:02.160] But many people have sent this noisy into me over the years.
[01:21:02.560 --> 01:21:04.160] And now that nobody won, I feel guilty.
[01:21:04.160 --> 01:21:05.600] I should have given it as a clue.
[01:21:05.600 --> 01:21:13.040] This is something that I think once I tell you, you will be surprised at how seemingly obvious it is.
[01:21:13.040 --> 01:21:14.960] Guys, this is glass cutting.
[01:21:15.760 --> 01:21:16.720] Let me play it again.
[01:21:16.720 --> 01:21:19.760] There's two different sounds in here, and I won't make you listen to the whole thing.
[01:21:19.760 --> 01:21:20.560] I'll fast-forward it.
[01:21:20.560 --> 01:21:23.120] But the first one, let me explain this to you.
[01:21:23.120 --> 01:21:26.720] You have a square piece of mirrored glass.
[01:21:26.720 --> 01:21:34.880] There is a suction cup that's put in the center of this, and then there's an arm coming off of the suction cup that can spin around the suction cup, right?
[01:21:34.880 --> 01:21:43.520] At the end of that arm, they affix like the cutting implement, which I do believe is probably some type of diamond or whatever, right?
[01:21:43.520 --> 01:21:45.760] Some type of thing that can cut into the glass.
[01:21:45.760 --> 01:21:50.000] And the first thing that's happening is a person is spinning that around really fast with their finger, right?
[01:21:50.000 --> 01:21:57.360] So you understand how it's, you know, the cutting implement is on the outside of the arm and it's making a large circle, making a, you know, making a mirror, right?
[01:21:57.680 --> 01:22:03.760] And then there is a second cutting implement that they use, which is the really heavy scratching sound.
[01:22:03.760 --> 01:22:06.160] And that like really digs in deep now, right?
[01:22:06.160 --> 01:22:11.520] After they've made the kind of like the runway for the big cutter to go in, then they bring in the heavy gun cutter.
[01:22:11.520 --> 01:22:13.120] And that was the second sound.
[01:22:13.120 --> 01:22:15.520] So I'll play both of those for you.
[01:22:16.640 --> 01:22:18.000] Spinning it up.
[01:22:19.680 --> 01:22:22.160] He's scoring the glass here.
[01:22:23.760 --> 01:22:25.840] And then this second noise.
[01:22:25.840 --> 01:22:28.080] And then, here, I'll pause it.
[01:22:28.080 --> 01:22:32.680] And then the second noise I'll get to is when he's really cutting into the glass now.
[01:22:37.480 --> 01:22:49.800] And after that process, the person doing it then kind of picks up the whole square piece of glass and just breaks off the outer part to reveal the circular mirror that's been cut.
[01:22:49.800 --> 01:22:50.840] Really interesting.
[01:22:50.840 --> 01:22:59.000] You know, and it's very, it's done by hand, you know, and I wonder today how much of that is still done by hand versus, you know, is it done by machine?
[01:22:59.000 --> 01:23:07.720] I would just imagine that it's been all automated at this point, but it's a really cool thing to watch somebody do because it's a very specific process that they're going through.
[01:23:07.720 --> 01:23:10.120] And I would just imagine that's how they did it.
[01:23:10.120 --> 01:23:18.280] You know, going back probably, you know, very long time into human history, that's very likely how they would make, you know, make the shaped mirrors.
[01:23:18.280 --> 01:23:21.160] All right, guys, I have a new noisy for you this week.
[01:23:21.160 --> 01:23:25.080] This noisy comes from a listener named Dale Thurber.
[01:23:38.120 --> 01:23:38.760] Jay.
[01:23:39.080 --> 01:23:40.600] I know, Evan, I thought of you.
[01:23:41.000 --> 01:23:42.280] I thought of you, Evan.
[01:23:42.600 --> 01:23:43.080] Stop it.
[01:23:43.080 --> 01:23:45.080] That can't be the noisy of the squeeze.
[01:23:45.240 --> 01:23:52.200] I dude, I heard it, and I'm like, Evan and I are not going to be able to not laugh when we hear this.
[01:23:53.480 --> 01:23:55.240] It's such a weird sound.
[01:23:55.240 --> 01:23:56.040] Meh.
[01:23:58.840 --> 01:24:02.920] It's a 12-year-old kid trying to act like an 80-year-old man going, meh.
[01:24:04.360 --> 01:24:08.280] I can't wait for you to hear what it is because I just love it.
[01:24:08.280 --> 01:24:09.320] I love everything about it.
[01:24:09.320 --> 01:24:10.520] It's a great noisy.
[01:24:10.520 --> 01:24:23.680] If you think you know what this noisy is, or and if you know, any of these things, if you found something cool and you want to send it to me, you can email me at WTN at the skepticsguide.org.
[01:24:23.680 --> 01:24:31.440] Steve, so much to talk about now because we can't go long without filling up our calendars with tons of stuff.
[01:24:31.440 --> 01:24:34.720] So you can purchase tickets to both our Washington, D.C.
[01:24:34.720 --> 01:24:35.760] shows right now.
[01:24:35.760 --> 01:24:39.680] So we will be having a private show plus on December 7th.
[01:24:39.680 --> 01:24:40.800] That starts at noon.
[01:24:40.800 --> 01:24:46.960] The private show plus is a live recording of a full episode of the podcast, which you'll be, you know, you'll be able to sit there and watch us do it.
[01:24:46.960 --> 01:24:50.080] And then during the last hour, you get audience-only content.
[01:24:50.080 --> 01:24:52.000] We have a ton of fun doing this.
[01:24:52.000 --> 01:24:53.200] George will be there with us.
[01:24:53.440 --> 01:24:55.680] We really hope that you guys can join us.
[01:24:55.680 --> 01:24:57.440] And then that same day at 8 p.m.
[01:24:57.520 --> 01:25:02.080] that night, we are running our skeptical extravaganza of special significance.
[01:25:02.080 --> 01:25:04.080] This is a stage show.
[01:25:04.240 --> 01:25:08.960] We have been doing this show for about a decade now, and we absolutely love it.
[01:25:08.960 --> 01:25:11.360] We get tons of great feedback about the show.
[01:25:11.360 --> 01:25:14.640] We demonstrate all the ways your brain can fool you.
[01:25:14.640 --> 01:25:18.320] We also have several improv comedy bits, music.
[01:25:18.320 --> 01:25:19.520] There's a knife fight.
[01:25:19.520 --> 01:25:20.240] It's crazy.
[01:25:20.240 --> 01:25:21.440] You guys have to come check us out.
[01:25:21.440 --> 01:25:22.320] You can't miss it.
[01:25:22.320 --> 01:25:24.720] Go to the skepticsguide.org.
[01:25:24.720 --> 01:25:30.560] That's theskepticsguide.org for more information, and you will see buttons there for the tickets.
[01:25:30.560 --> 01:25:38.080] And one week from today, I will announce on the show you will be able to buy tickets to Naticon 2025.
[01:25:38.080 --> 01:25:41.440] This will be the weekend of May 16th.
[01:25:41.760 --> 01:25:44.640] It'll be actually Thursday night, the 15th.
[01:25:44.880 --> 01:25:47.120] We'll be doing the VIP.
[01:25:47.120 --> 01:25:52.720] And then Friday and Saturday, that's the 16th and 17th, we will be doing the conference.
[01:25:52.720 --> 01:25:54.480] We had an amazing time last year.
[01:25:54.480 --> 01:25:56.160] I know I've been talking a lot about this.
[01:25:56.160 --> 01:25:58.560] So tickets will be available next week.
[01:25:58.560 --> 01:25:58.960] All right.
[01:25:58.960 --> 01:26:00.360] Thanks, Jay.
[01:26:00.680 --> 01:26:03.560] All right, guys, well, let's go on with science or fiction.
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[01:26:33.720 --> 01:26:38.280] You probably think it's too soon to join AARP, right?
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[01:26:43.320 --> 01:26:47.720] More specifically, your career, your health, your social life.
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[01:27:39.640 --> 01:27:44.440] It's time for science or fiction.
[01:27:49.360 --> 01:28:00.080] Each week I come up with three science news items or facts, two real and one fake, and then I challenge my panel of skeptics to tell me which one is the fake.
[01:28:00.400 --> 01:28:03.280] You guys ready for this week's three news items?
[01:28:03.280 --> 01:28:04.640] Well, I don't know.
[01:28:04.640 --> 01:28:05.440] All right, here we go.
[01:28:05.440 --> 01:28:18.320] Item number one: a recent analysis finds that mammal-like jaw anatomy evolved first in therapsids at least 20 million years before the first cynodonts, which were mammalian ancestors.
[01:28:18.320 --> 01:28:28.480] Item number two, scientists have recently demonstrated that single bacterial genes can flip a portion of their genetic sequence so that they code for a different protein.
[01:28:28.480 --> 01:28:29.120] Wow.
[01:28:29.120 --> 01:28:40.560] And item number three, a recent clinical trial shows that a single infusion with gene therapy was able to effectively treat hemophilia B for at least 15 months of follow-up.
[01:28:40.560 --> 01:28:41.680] Jay, go first.
[01:28:41.680 --> 01:28:42.160] Okay.
[01:28:42.400 --> 01:28:44.320] I want to go to the last one.
[01:28:44.320 --> 01:28:54.560] This is the one clinical trial shows that a single infusion with gene therapy was able to effectively treat hemophilia B for at least 15 months.
[01:28:54.560 --> 01:28:57.280] So I believe that this was CRISPR.
[01:28:57.280 --> 01:29:00.560] Yeah, I completely think that this one is science.
[01:29:00.880 --> 01:29:06.640] It's an incredible feat of technology and biology in order to pull this thing off.
[01:29:06.640 --> 01:29:10.000] But yeah, I think that that one is definitely science.
[01:29:10.000 --> 01:29:19.360] The second one here, scientists have recently demonstrated that single bacterial genes can flip a portion of their genetic sequence so that they code for a different protein.
[01:29:19.360 --> 01:29:22.400] That is freaking amazing if that's true.
[01:29:22.400 --> 01:29:24.160] I'd like to see that in action.
[01:29:24.160 --> 01:29:32.680] Like, so, Steve, is this like, you know, what do you call the part that is, you know, kind of splitting the DNA and then there's another part that copies it, right?
[01:29:32.680 --> 01:29:36.200] Are you talking about like they're actually like flipping it there mechanically?
[01:29:36.200 --> 01:29:38.040] You're talking about transcription and translation?
[01:29:38.040 --> 01:29:43.320] Yeah, yeah, you know, those little biological machines that manipulate your DNA, right?
[01:29:44.440 --> 01:29:46.440] Are they mechanically flipping it?
[01:29:46.440 --> 01:30:00.760] So, basically, what they're saying, what they discovered is that if you have the genetic sequence of a gene, a portion of that gene flips itself around so it's reading the other way, you know, reinserts itself back into the gene.
[01:30:00.760 --> 01:30:04.840] So, now you have a gene with a different sequence that codes for a different protein.
[01:30:04.840 --> 01:30:05.880] What the hell?
[01:30:06.520 --> 01:30:07.800] That's amazing.
[01:30:07.800 --> 01:30:10.280] I mean, who am I to say that that's not happening?
[01:30:10.280 --> 01:30:10.920] I don't know.
[01:30:10.920 --> 01:30:17.960] I mean, it sounds complicated, but all that stuff is so unbelievably complex.
[01:30:17.960 --> 01:30:20.120] I just don't see why this isn't happening.
[01:30:20.120 --> 01:30:21.400] You know, it's just one of those things.
[01:30:21.400 --> 01:30:23.560] Like, I just don't have enough information to really know.
[01:30:23.560 --> 01:30:27.240] But I would think, okay, that sounds like, yeah, it could be happening.
[01:30:27.240 --> 01:30:28.920] All right, let me just go back to number one here now.
[01:30:28.920 --> 01:30:35.960] A recent analysis finds that mammal-like jaw anatomy evolved first in therapsipsids.
[01:30:35.960 --> 01:30:36.760] Therapsids.
[01:30:37.000 --> 01:30:37.720] Therapsids.
[01:30:37.880 --> 01:30:38.920] Oh, not therapists.
[01:30:39.160 --> 01:30:39.320] No.
[01:30:40.760 --> 01:30:41.960] That word sucks.
[01:30:41.960 --> 01:30:43.560] Or for dinosaurs.
[01:30:43.560 --> 01:30:45.960] You know, whoever came up with that word, what the hell?
[01:30:46.680 --> 01:30:48.360] Seriously, like, therapsapsids.
[01:30:48.600 --> 01:30:49.560] Get the hell out of here.
[01:30:50.200 --> 01:30:51.960] Why can't it just be like a duck?
[01:30:51.960 --> 01:30:53.000] You know what I mean?
[01:30:53.000 --> 01:30:53.800] Yeah, Jay.
[01:30:53.800 --> 01:30:54.440] Right.
[01:30:54.440 --> 01:30:59.320] All right, this was at least 20 million years before the first cyanodonts.
[01:30:59.480 --> 01:31:00.600] Cynodonts.
[01:31:00.600 --> 01:31:01.400] Whatever.
[01:31:01.400 --> 01:31:03.160] Which were mammalian ancestors.
[01:31:03.160 --> 01:31:03.600] You know what?
[01:31:03.640 --> 01:31:07.000] I'm going to say that one is fiction just because the words suck.
[01:31:07.960 --> 01:31:09.240] Okay, Andrea.
[01:31:09.240 --> 01:31:09.720] All right.
[01:31:09.720 --> 01:31:12.040] I'm going to go in the same order that Jay did.
[01:31:12.040 --> 01:31:19.920] So I had the opposite reaction to the single infusion with gene therapy treating hemophilia B.
[01:31:20.240 --> 01:31:24.640] I feel like that one to me reads fiction.
[01:31:24.960 --> 01:31:28.480] Maybe I've read too many pseudoscience Gwyneth Paltrow.
[01:31:28.640 --> 01:31:37.840] A single infusion of this can cure your whatever, but something about infusions and just one of them feels a little bit snake oily to me.
[01:31:37.840 --> 01:31:39.920] So I'm going to say that's the fiction.
[01:31:39.920 --> 01:31:46.080] The bacterial gene flipping a portion of their genetic sequence so they can code for a different protein sounds awesome.
[01:31:46.080 --> 01:31:47.040] I'm with Jay on that.
[01:31:47.040 --> 01:31:48.160] I want it to be true.
[01:31:48.160 --> 01:31:55.280] And I feel like we're constantly being surprised, dare I say baffled, by the surprising things that genes can do.
[01:31:55.280 --> 01:31:58.560] And so that reads true to me.
[01:31:58.560 --> 01:32:04.880] And look, we're also constantly being baffled by our understanding of how we all evolved.
[01:32:04.880 --> 01:32:18.960] And if we're being surprised by the order in which mandible-like jaw anatomy evolved, then I believe it's pretty plausible that we could have dug up some old fossil that shows that the therapsids and the cynodots, I'm with Jay on the wording.
[01:32:20.080 --> 01:32:23.520] I'm inclined to believe that we could have made a discovery like that.
[01:32:23.520 --> 01:32:27.040] So I think the single infusion is the fiction today.
[01:32:27.040 --> 01:32:28.160] Okay, Evan.
[01:32:28.400 --> 01:32:32.400] The mammal-like jaw 20 million years before?
[01:32:32.720 --> 01:32:35.680] That's a lot of time on that scale.
[01:32:36.160 --> 01:32:37.760] That one seems extreme.
[01:32:38.000 --> 01:32:40.320] Jay, that was the one you went with fiction, right?
[01:32:40.640 --> 01:32:41.200] Yes.
[01:32:41.200 --> 01:32:44.560] Yes, because having to do with the words and pronunciation.
[01:32:44.560 --> 01:32:47.760] No, I mean, I legitimately don't think that that one is it.
[01:32:48.720 --> 01:33:00.200] Whereas the next one about the bacterial genes, oh boy, a single bacteria single bacterial genes can flip a portion of their genetic sequence so that they code for a different protein.
[01:33:00.200 --> 01:33:01.400] Yeah, okay.
[01:32:59.520 --> 01:33:03.400] That's remarkable in itself as well.
[01:33:03.640 --> 01:33:08.120] But I think, sure, they were able to make that happen.
[01:33:08.120 --> 01:33:09.880] I don't have a problem with that one.
[01:33:09.880 --> 01:33:20.040] The last one, and Andrea, you're saying the one for this is the fiction, the one with gene therapy and hemophilia B for at least 15.
[01:33:20.200 --> 01:33:22.600] Yeah, because you said it's a single infusion.
[01:33:22.600 --> 01:33:26.120] That does seem unlikely.
[01:33:26.440 --> 01:33:30.280] And then the other part of this is that it lasts for at least 15 months.
[01:33:30.280 --> 01:33:33.960] So there's two components there where it could be wrong.
[01:33:34.840 --> 01:33:38.200] So I'm definitely, oh boy, this is a tough one.
[01:33:38.200 --> 01:33:38.920] Which one?
[01:33:38.920 --> 01:33:43.880] 20 million year jaw or single infusion and 15 month follow-up?
[01:33:45.320 --> 01:33:46.040] Boy.
[01:33:46.280 --> 01:33:48.040] I'll just, it's a flip for me.
[01:33:48.040 --> 01:33:49.000] I'll just go with Jay.
[01:33:49.000 --> 01:33:50.280] I'll say the jaw.
[01:33:50.600 --> 01:33:52.680] I'm personally offended, but fine.
[01:33:52.680 --> 01:33:54.200] No, it was a coin flip.
[01:33:54.680 --> 01:33:56.280] It was random.
[01:33:56.280 --> 01:33:57.640] All right, Kara.
[01:33:57.640 --> 01:33:59.000] Fair, fair.
[01:33:59.880 --> 01:34:17.160] I think I'm, you know, I'm waffling between the same two, so, which is crazy because a single bacterial gene flipping a portion of that sequence like around and basically reading the code backward and a whole new protein coming out sounds amazing.
[01:34:17.240 --> 01:34:26.920] It doesn't sound like the process is amazing because genes swap material, or sorry, bacteria swap material all the time, and I could see them swapping material with themselves.
[01:34:27.320 --> 01:34:48.320] What's kind of amazing is that just a backward code would read out for a functional protein, and also, that it means you have to kind of rethink the way that we talk about DNA to RNA to protein, like, because we usually think like one gene eventually is going to lead to one protein, right?
[01:34:48.320 --> 01:34:58.400] So, like, that idea of one gene having multiple proteins that could come from the same gene is like weird, but I think it could happen.
[01:34:58.960 --> 01:35:03.600] I think the two that bother me, and I don't, I don't know which one I'm going to pick, so I'm going to walk through them right now.
[01:35:03.600 --> 01:35:09.440] A mammal-like jaw in the therapsids, 20 million years before the cynodonts.
[01:35:09.520 --> 01:35:17.440] The cynodonts are mammalian ancestors, but therapsids are, I'm assuming here, based on how it's written, cynodont ancestors.
[01:35:17.440 --> 01:35:17.840] Yes, that's a lot.
[01:35:18.000 --> 01:35:24.960] So, it's basically, yeah, like when did the jaw start to look like a mammal and not like a fish or like a reptile?
[01:35:24.960 --> 01:35:28.560] I don't know, you know, because how do we define mammal?
[01:35:28.560 --> 01:35:31.440] Everything is transitional until it's not anymore.
[01:35:31.440 --> 01:35:33.840] So, is it their jaws?
[01:35:33.840 --> 01:35:35.600] Do jaws make mammals mammals?
[01:35:35.600 --> 01:35:37.120] I guess it's some component.
[01:35:37.120 --> 01:35:44.320] And then the other question is: the clinical trial, a single infusion of the gene therapy, so that's very specific.
[01:35:44.320 --> 01:35:48.960] Able to effectively treat hemophilia B for at least 15 months of follow-up.
[01:35:48.960 --> 01:35:57.040] I think that there have been multiple hemophilia clinical trials, but I don't know about like if there's a single infusion one.
[01:35:57.040 --> 01:36:06.400] I don't know how long it was effective, and what does it mean effectively treat, like just a certain percentage of people had positive outcomes from it?
[01:36:06.400 --> 01:36:07.920] Like they were able to clot.
[01:36:07.920 --> 01:36:08.720] I don't know.
[01:36:08.720 --> 01:36:11.520] I'm gonna say, Andrea, you know what?
[01:36:11.520 --> 01:36:12.320] Solidarity.
[01:36:12.320 --> 01:36:13.120] I'm gonna go.
[01:36:13.120 --> 01:36:15.280] You went with the infusion one?
[01:36:15.520 --> 01:36:16.160] Indeed.
[01:36:16.160 --> 01:36:17.360] I'll go with Andrea on this.
[01:36:17.360 --> 01:36:17.840] All right.
[01:36:17.840 --> 01:36:18.400] Thanks, Kara.
[01:36:18.560 --> 01:36:25.920] All right, so we got an even split, and you all are very confident about the middle one, so we'll start.
[01:36:25.920 --> 01:38:07.520] Scientists have recently demonstrated that single bacterial genes can flip a portion of their genetic sequence so that they code for a different protein you all think this one is science and this one is science but what about the central dogma of genetics that one gene one protein this one is more amazing than i think you guys appreciate you should have thought this one was a fiction no way steve this could be this was this is this is this is pretty stunning so this is the first time this has been uh observed in a single gene that these the gene itself sort of rearranges itself and then changes its function it doesn't always code for a functioning protein sometimes it it would basically turns into a it either down regulates its transcription or upregulates it or it changes the actual sequence of amino acids the way it works is that you have a sequence within the gene that is flanked by a palindromic sequence right and these palindromic so the palindromic sequences can flip without changing their orientation does that make sense so that's sort of the palindromic yeah because they're palindromic so so that's the structure that allows this to happen so what the scientists did was they just went through a bunch of bacterial genes looking for these palindromic you know bookends right and then they looked at the genes that were there and then they had to cross-reference them with all like all other bacteria and figure out if there were if they could find any instances where the sequence is flipped.
[01:38:07.520 --> 01:38:17.920] Now, previously they've they've identified, as kara said, or one of you guys said, that you know these genes exchange information with other genes.
[01:38:18.480 --> 01:38:23.120] Yeah, but this is the first time it was observed within a single gene.
[01:38:23.440 --> 01:38:23.840] That's cool.
[01:38:24.000 --> 01:38:30.240] Right, an intragenic DNA inversion expand the bacterial coding capacity.
[01:38:30.240 --> 01:38:31.920] Now, that's the title of the paper.
[01:38:32.160 --> 01:38:40.720] What this means is, like, we're thinking, oh, this bacteria has whatever, it has a thousand genes, but maybe it can make more than a thousand proteins, right?
[01:38:40.720 --> 01:38:41.280] Wow.
[01:38:41.280 --> 01:38:48.960] Because it could do this intragenic inversions to basically expand its repertoire of things that it could do.
[01:38:49.200 --> 01:38:50.480] Pretty amazing.
[01:38:50.480 --> 01:39:02.320] Now, I love the author's description of the process because when they were interviewed for the press release, they basically said when they first discovered this, they didn't believe it.
[01:39:02.480 --> 01:39:05.360] They said, this can't be true.
[01:39:05.360 --> 01:39:09.440] And then they spent four years trying to prove themselves wrong.
[01:39:09.440 --> 01:39:10.320] Amazing, I love it.
[01:39:10.480 --> 01:39:13.600] Basically, trying to figure out, all right, we clearly made a mistake.
[01:39:13.920 --> 01:39:15.200] This can't be true.
[01:39:15.200 --> 01:39:17.280] So let's figure out where we went wrong.
[01:39:17.280 --> 01:39:23.440] But until eventually they had to come to the conclusion that they didn't go wrong, that this is actually happening.
[01:39:23.440 --> 01:39:27.520] But that's how, you know, against what we thought we knew it was.
[01:39:27.520 --> 01:39:30.560] All right, I guess we'll go back to the first one.
[01:39:30.560 --> 01:39:40.880] A recent analysis finds that mammal-like jaw anatomy evolved first in therapsids, at least 20 million years before the first cynodonts, which were mammalian ancestors.
[01:39:41.120 --> 01:39:43.760] Jay and Evan, you think this one is a fiction.
[01:39:43.760 --> 01:39:47.280] Kara and Andrea, you think this one is science.
[01:39:47.280 --> 01:39:57.200] So just for a little bit of background here before I do the reveal, cynodonts, as Kara said, cynodonts are, you know, the reptiles that were, they were mammal-like reptiles.
[01:39:57.200 --> 01:40:05.640] And one of the big anatomical changes that happened from reptiles to mammals was at the connection of the jaw.
[01:40:05.880 --> 01:40:14.920] So reptiles have multiple connection points, multiple bones in the jaw, whereas mammals have a single connection point, right?
[01:40:14.920 --> 01:40:20.440] And those extra bones that we no longer needed became the three bones of the inner ear.
[01:40:20.440 --> 01:40:24.440] That's partly why the ear is right next to your jaw joint, right?
[01:40:24.440 --> 01:40:26.120] Your temporal mandibular joint.
[01:40:26.120 --> 01:40:35.080] Now what that allowed mammals to do, that single connection point between the jaw and the skull, allows for a lot more mobility, right?
[01:40:35.080 --> 01:40:37.480] You can move your mouth from side to side.
[01:40:37.480 --> 01:40:39.400] Reptiles can't do that.
[01:40:40.520 --> 01:40:53.320] So that change, that's like a huge marker for the evolution of mammals, of reptiles to mammals that happened within the cynodont group, right?
[01:40:53.320 --> 01:40:54.760] It's not obviously one species.
[01:40:54.760 --> 01:40:59.160] This is a group of mammal-like reptiles.
[01:40:59.160 --> 01:41:01.640] So the question is, when did that change occur?
[01:41:01.640 --> 01:41:09.000] And of course, things are only as old as the oldest example that we have, and it's very common that we find still older examples.
[01:41:09.000 --> 01:41:11.160] So we want to know, like, when did this happen?
[01:41:11.160 --> 01:41:17.640] When did this iconic change of mammalian anatomy occur?
[01:41:17.960 --> 01:41:23.160] And so there is a study, and what the study found was not this.
[01:41:23.160 --> 01:41:24.200] So this is fiction.
[01:41:24.520 --> 01:41:26.200] Oh, sorry, Kara.
[01:41:26.520 --> 01:41:26.840] That's okay.
[01:41:27.080 --> 01:41:39.240] What the study found was they found older cynodonts that were not on the path to mammals that had the mammal-like jaw anatomy.
[01:41:39.240 --> 01:41:40.040] But they were already...
[01:41:40.280 --> 01:41:40.760] Gosh, I would have told you.
[01:41:42.280 --> 01:41:54.000] They were already a side branch, and they were older than cynodonts that were on their way to becoming mammals, but their jaw anatomy looked more mammalian, right?
[01:41:54.000 --> 01:41:55.440] So that doesn't make any sense.
[01:41:55.440 --> 01:42:19.280] What that means is, is that the cynodonts were evolving a bunch of different jaw anatomy, that they were basically experimenting, you know, evolutionarily speaking, and that a simplified connection between the jaw, you know, the mandible basically and the skull evolved independently more than once in the cynodont line, right?
[01:42:19.280 --> 01:42:44.720] It's kind of like when we drill down deep on like the evolution of birds from theropod dinosaurs, we realize that, yeah, flight, you know, and different features of flight evolved at different times in different branches, you know, only one branch of which led to modern birds, but you know, sometimes you get the same thing independently evolves over and over again, which partly also means that the potential was there, right?
[01:42:44.720 --> 01:42:48.160] It's like it was right on the verge of this evolutionary thing.
[01:42:48.160 --> 01:42:54.240] It was easy to happen evolutionarily, so it happened multiple times independently in different branches.
[01:42:54.240 --> 01:43:00.160] And the one that led to mammals didn't necessarily develop it first.
[01:43:00.160 --> 01:43:01.520] Does that make sense?
[01:43:01.840 --> 01:43:05.440] Yeah, so also a very interesting finding.
[01:43:05.440 --> 01:43:11.200] When I read the headline of the press release, there was like jaw-dropping finding.
[01:43:11.760 --> 01:43:13.520] Haley Todd was like, oh, I wrote that.
[01:43:13.520 --> 01:43:14.480] This has got to be good.
[01:43:14.480 --> 01:43:16.960] And then I'm like, oh, jaw-dropping, you bastards.
[01:43:16.960 --> 01:43:17.520] Okay.
[01:43:17.840 --> 01:43:18.240] Gotcha.
[01:43:18.400 --> 01:43:18.960] It was still good.
[01:43:18.960 --> 01:43:20.560] It was still good, though.
[01:43:20.880 --> 01:43:31.960] Which means that a recent clinical trial shows that a single infusion with gene therapy was able to effectively treat hemophilia B for at least 15 months of follow-up is science.
[01:43:31.960 --> 01:43:37.240] This is also very surprising and very, very cool.
[01:43:37.560 --> 01:43:48.200] I guess not that surprising if you've been following this, but so this is a drug, a gene therapy that is already FDA approved.
[01:43:48.520 --> 01:43:51.080] This is an open-label trial.
[01:43:51.640 --> 01:43:52.360] They studied it.
[01:43:52.360 --> 01:43:57.160] So hemophilia is a genetic mutation that causes bleeding, right?
[01:43:57.160 --> 01:43:59.480] You have uncontrolled bleeding because you're not making factor 9.
[01:43:59.560 --> 01:44:00.920] You need factor 9 to clot your blood.
[01:44:00.920 --> 01:44:02.280] So their blood doesn't clot.
[01:44:02.280 --> 01:44:04.120] So they bleed all the time.
[01:44:04.120 --> 01:44:07.320] And the treatment for this is infusions of factor 9.
[01:44:07.320 --> 01:44:17.320] And people who have hemophilia B have to get these infusions multiple times a week, maybe once every couple of weeks, or somewhere in that spectrum.
[01:44:17.320 --> 01:44:19.640] They're getting it, you know, very, very frequently.
[01:44:19.640 --> 01:44:21.960] These are IV infusions.
[01:44:21.960 --> 01:44:29.080] And it's massively disruptive to their life, you know, because in order to stay alive, they have to constantly get these infusions of factor 9.
[01:44:29.080 --> 01:44:34.440] With this gene therapy, now the name of the drug is, Jay, you talk about dinosaur names.
[01:44:34.440 --> 01:44:35.000] All right, here we go.
[01:44:35.000 --> 01:44:35.800] Are you ready?
[01:44:35.800 --> 01:44:36.200] Yeah.
[01:44:36.520 --> 01:44:38.040] I'm probably not pronouncing it correctly.
[01:44:38.440 --> 01:44:39.800] A little milab.
[01:44:39.800 --> 01:44:40.680] No, it's not a MAB.
[01:44:40.680 --> 01:44:41.400] It's not a MAB.
[01:44:41.640 --> 01:44:42.200] It's a gene.
[01:44:42.200 --> 01:44:46.600] It's Fidanacogene Leparvovec.
[01:44:46.920 --> 01:44:48.360] Leparvovec.
[01:44:48.360 --> 01:44:49.320] Rolls off the chinese.
[01:44:49.800 --> 01:44:50.120] Yeah.
[01:44:50.120 --> 01:44:50.520] Beautiful.
[01:44:50.680 --> 01:44:51.160] Memorize it.
[01:44:51.320 --> 01:44:57.800] Add a dose of 5 times 10 to the 11 vector genome copies per kilogram of body weight.
[01:44:57.800 --> 01:44:58.360] That's a lot of fun.
[01:44:58.520 --> 01:44:59.160] What a funny dose.
[01:44:59.400 --> 01:45:01.320] Vector genome copies.
[01:45:01.320 --> 01:45:02.440] That's amazing.
[01:45:02.440 --> 01:45:10.920] In men, yeah, 18 to 65 years old with hemophilia B, factor 9, less than 2%, who have been getting infusions for the last six months at least.
[01:45:10.920 --> 01:45:13.400] So they're dependent basically on infusions.
[01:45:13.400 --> 01:45:21.520] They gave them a single infusion of the gene therapy, and then they followed them for 15 months, 12 to 15 months.
[01:45:21.840 --> 01:45:27.520] And I'm sorry, they followed them from week 12 to month 15 after the treatment.
[01:45:27.520 --> 01:45:32.320] And they found that they did better than people who were getting the infusion.
[01:45:32.320 --> 01:45:37.600] So some of them didn't, some of the recipients no longer needed infusions.
[01:45:37.600 --> 01:45:40.480] They went 15 months without needing an infusion.
[01:45:40.480 --> 01:45:44.480] Others had a reduction in their need for infusions, but not to zero.
[01:45:44.800 --> 01:45:47.280] So they just started making the factor themselves.
[01:45:47.440 --> 01:45:48.720] Yeah, that's the idea.
[01:45:48.960 --> 01:45:52.480] It goes into the liver, and the liver starts making factor nine.
[01:45:52.480 --> 01:45:57.120] So they basically were demonstrating stable factor nine expression.
[01:45:57.120 --> 01:46:01.600] Now, I said at least 15 months because they didn't stop working at 15 months.
[01:46:01.600 --> 01:46:03.520] That's just how long they followed them.
[01:46:04.080 --> 01:46:07.280] You know, it's possible that this may work for years to come.
[01:46:07.280 --> 01:46:08.160] That's so cool.
[01:46:08.160 --> 01:46:13.200] It reminds me, I know it's a totally different mechanism, but it reminds me of like CAR T cell therapy.
[01:46:13.840 --> 01:46:14.720] Yeah, right?
[01:46:14.720 --> 01:46:17.760] These things where it's like, this is curative, potentially.
[01:46:17.920 --> 01:46:20.160] They said, we're not going to use the word cure.
[01:46:20.480 --> 01:46:21.200] Of course, not yet.
[01:46:22.960 --> 01:46:25.120] Not going to break out the cure word.
[01:46:25.760 --> 01:46:33.360] But damn, you know, you go from having, you need multiple IV infusions every week or two to none for 15 months.
[01:46:33.360 --> 01:46:35.440] That's a life changer right there.
[01:46:35.440 --> 01:46:36.880] I've never been more glad to be wrong.
[01:46:36.880 --> 01:46:37.440] Oh, this is awesome.
[01:46:37.440 --> 01:46:38.160] Right, me too.
[01:46:38.160 --> 01:46:38.560] This is awesome.
[01:46:38.720 --> 01:46:39.360] Me too.
[01:46:39.360 --> 01:46:40.880] Absolutely.
[01:46:40.880 --> 01:46:41.760] All right.
[01:46:42.000 --> 01:46:43.280] Evan, give us a quote.
[01:46:43.280 --> 01:46:43.680] All right.
[01:46:43.680 --> 01:46:51.280] Well, this quote is a bit infamous because I came across it while I was preparing my haha funny moment during my news item.
[01:46:51.280 --> 01:46:52.400] So here you go.
[01:46:52.400 --> 01:46:54.960] You don't get rich writing science fiction.
[01:46:54.960 --> 01:46:57.280] If you want to get rich, you start a religion.
[01:46:57.280 --> 01:46:58.000] Yes.
[01:46:58.440 --> 01:46:58.760] L.
[01:46:58.760 --> 01:46:59.880] Ron Hubbard.
[01:46:59.880 --> 01:47:00.040] L.
[01:46:58.800 --> 01:47:01.080] Ron Hubbard.
[01:47:01.080 --> 01:47:02.920] Oh, gosh, that guy.
[01:47:02.920 --> 01:47:04.360] I mean, did that not tell you?
[01:46:59.520 --> 01:47:06.440] He announced, I'm going to make up a fake religion.
[01:47:06.760 --> 01:47:07.640] Yeah, he did.
[01:47:07.640 --> 01:47:09.240] He basically announced it.
[01:47:09.480 --> 01:47:12.680] So, does this mean Nauticon is actually a religious gathering now?
[01:47:15.320 --> 01:47:15.880] I don't think it's.
[01:47:16.840 --> 01:47:17.480] Yeah.
[01:47:17.480 --> 01:47:19.000] Here we go.
[01:47:19.320 --> 01:47:20.920] I mean, at least he admitted it.
[01:47:20.920 --> 01:47:24.280] He's not the first person to make up a religion for personal gain, but.
[01:47:24.600 --> 01:47:25.080] Right.
[01:47:25.080 --> 01:47:26.440] Yeah, I suppose so.
[01:47:26.920 --> 01:47:28.040] Transparency is logical.
[01:47:28.440 --> 01:47:29.720] He's overlighting.
[01:47:30.040 --> 01:47:30.760] What did he say?
[01:47:30.920 --> 01:47:39.960] He said it during a meeting of the Eastern Science Fiction Association in 1948.
[01:47:40.280 --> 01:47:40.760] Yeah.
[01:47:41.960 --> 01:47:44.680] I can think he was a failed science fiction writer.
[01:47:45.240 --> 01:47:45.800] I can't think of him.
[01:47:46.680 --> 01:47:50.440] I cannot think of him without hearing Randy tell us the story.
[01:47:51.240 --> 01:47:51.880] Because I met L.
[01:47:51.960 --> 01:47:55.240] Ron Hubbard two times, and both times he was drunk.
[01:47:57.160 --> 01:47:58.200] I just love that.
[01:47:59.480 --> 01:48:00.120] Oh, man.
[01:48:01.320 --> 01:48:07.320] I guess it just means that he was towards the cynical rather than true believer end of the spectrum.
[01:48:08.280 --> 01:48:10.520] He just was cynically like, oh, yeah.
[01:48:11.160 --> 01:48:16.520] And specifically because he wanted the tax exempt status, you know?
[01:48:17.080 --> 01:48:19.080] And here they are today.
[01:48:19.080 --> 01:48:20.520] Tax exempt status.
[01:48:21.320 --> 01:48:23.000] Evan, can't you change that somehow?
[01:48:23.000 --> 01:48:24.680] Aren't you in charge of taxes?
[01:48:24.680 --> 01:48:26.040] Oh, that's right.
[01:48:26.040 --> 01:48:26.760] Yes, I am.
[01:48:27.240 --> 01:48:28.040] You're a tax scientist.
[01:48:29.640 --> 01:48:30.200] There you go.
[01:48:30.200 --> 01:48:31.000] I like that, Evan.
[01:48:31.160 --> 01:48:31.960] Hang on one second.
[01:48:31.960 --> 01:48:33.720] Let me press enter, and it's done.
[01:48:33.720 --> 01:48:34.760] Now they are taxable.
[01:48:35.160 --> 01:48:35.560] Great.
[01:48:35.120 --> 01:48:36.360] Right, done.
[01:48:36.680 --> 01:48:37.960] If it were that easy.
[01:48:38.200 --> 01:48:39.080] If only.
[01:48:39.080 --> 01:48:39.720] All right.
[01:48:39.720 --> 01:48:42.120] Well, thank you all for joining me this week.
[01:48:42.120 --> 01:48:42.760] Thanks, Steve.
[01:48:42.920 --> 01:48:43.480] Thank you, Cornelius.
[01:48:43.880 --> 01:48:44.680] And thank you, Andrea.
[01:48:44.840 --> 01:48:46.720] Andrea, thank you for straightening us out.
[01:48:45.000 --> 01:48:50.800] You know, we were all sweating this election and reading the polls every day.
[01:48:51.360 --> 01:48:53.840] I like hearing rationality in the discussion of politics.
[01:48:54.000 --> 01:48:56.400] I'm just going to take it easy and ignore the polls from now on.
[01:48:57.200 --> 01:48:59.840] I got to be honest, I don't feel better after what Andrea said.
[01:49:00.480 --> 01:49:01.120] It's not helpful.
[01:49:01.120 --> 01:49:02.240] It really isn't.
[01:49:02.240 --> 01:49:07.840] But I second Kara's message that there also is a very important place for emotion in all of this.
[01:49:08.240 --> 01:49:09.200] Yes.
[01:49:12.080 --> 01:49:14.240] Be in touch with your emotions.
[01:49:14.240 --> 01:49:15.120] That's right.
[01:49:15.120 --> 01:49:17.600] And express everything on the internet.
[01:49:18.560 --> 01:49:19.680] Don't do that.
[01:49:19.680 --> 01:49:20.480] All right.
[01:49:20.480 --> 01:49:22.160] Well, thanks again, everyone.
[01:49:22.160 --> 01:49:27.600] And until next week, this is your Skeptics Guide to the Universe.
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