SGU Episode 882

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SGU Episode 882
June 4th 2022
882 optical illusion 2022.jpg
(brief caption for the episode icon)

SGU 881                      SGU 883

Skeptical Rogues
S: Steven Novella
B: Bob Novella
C: Cara Santa Maria
J: Jay Novella
E: Evan Bernstein
Quote of the Week

When I was a kid, people wanted to be an astronaut. Today, kids want to be famous, and that's totally the wrong approach. You have to have authenticity in what you're doing. You have to really care about the core message of what you're saying, and then everything else will fall into place.

David Copperfield, American magician
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Show Notes
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Introduction, Skipping horrible news[edit]

Voice-over: You're listening to the Skeptics' Guide to the Universe, your escape to reality.

S: Hello and welcome to the Skeptics' Guide to the Universe. Today is Wednesday, June 1st 2022, and this is your host, Steven Novella. Joining me this week are Bob Novella...

B: Hey, everybody!

S: Cara Santa Maria...

C: Howdy.

S: Jay Novella...

J: Hey guys.

S: ...and Evan Bernstein.

E: Good evening folks!

S: So here we are. We're trying to figure out what we're going to chat about at the top of the show. And like there's just there's just like horrible news happening in the world, you know what I mean? Like there's really nothing good that we would that we wanted to talk about or that wouldn't sound tone deaf or insensitive. Like you can't talk about something happy and there's only a bunch of sad stuff to talk about. So we decided just not to talk about anything.

C: Get on with it.

J: Let's talk about science.

C: Oh yeah.

S: Let's talk about science. Let's get right so let's, why don't we just get right to our news items and make people feel better about some awesome cool science.

E: Okay.

S: Not to diminish all the bad stuff but this is what we do. All right here we go.

News Items[edit]

(At some point in this episode, the Rogues mention the word/term consilience[v 1]. This is arguably a good word to have tagged for the vocabulary section.)

Exascale Supercomputer (1:05)[edit]

S: Bob tell us about I know you've been waiting what for years for this Bob, right? An exascale super computer.

B: Okay. So, on SGU Episode 151--

C: --Whoa!

B: --June 9th, 2008--

E: --Oh my gosh!

B: --I announced the first petaflop supercomputer: a quadrillion calculations per second, 1015. I have been waiting 5105 days to announce this. That an exascale supercomputer has finally been create. Meaning it can do at least one quintillion calculations per second.

C: Wait Bob, why petaflop but exascale?

B: It's an exascale meaning that it has breached the realm of the exascale computing of quadrillion or quintillion calculations the second. So that's an exascale is that scale of─

S: Order of magnitude.

B: ─quintillion or more. The flops is just like you have to have essentially at least one exaflop to be in the exascale.

J: Bob can you give us some perspective on like how much more powerful this is? Like give it on the spectrum where is this.

B: I mean it's hard to put numbers like quintillions in context, right?

C: Yeah.

B: I mean what is a quintillion. I am told that not everyone is obsessed with such large numbers as I am and I of course respond I don't believe you. But let me see if I can put a quintillion into context. It's 1018. 18 zeros. It's a billion billion. Or this is cool. It's a million million million. Now, those are multiplied. They're not added. When I say a million million million that's not three million. That's a million times a million, times a minute. Just throwing that out there. Okay here's another one. The Milky Way galaxy is one quintillion kilometers wide. Okay, that helps a little bit. One more. Every human, including Jay, doing calculations for four years - the frontier supercomputer can do all those calculations in one second. So it's huge. It's like four times more calculations than number two on this on this TOP500 list of super computers. So yeah it's. It's a milestone. And it's interesting because often if something is ten times bigger, or better, or whatever, an order of magnitude that's dramatic. But for super computers once you have three of those orders of magnitude a thousand times better the number changes from 500-600 petaflops to one exaflop type of deal. At that thousand. And often with it with that three order of magnitude increase in speed you can do dramatically, dramatic new things. Faster things. Many things that you probably couldn't have even thought of with previous computers because it would just be too hard and take too long.

S: Bob there's going to be a new Netflix series called faster things.

J: (laughs) Oh my god.

B: Is that based on the book?

S: It's going to have a whole 80s vibe to it.

B: Oh okay. (laughs)

J: Bob what did they do with this computer?

B: Jay you're throwing it off my flow totally.

J: All right nevermind. Wow Bob. That's this is really awesome man.

B: Okay.

E: Tell us more what you were gonna say next.

B: Okay but I gotta say but listen to this. China already has probably a couple true exascale systems. Exascale super computers. Already have it probably. But they mysteriously, to me anyway, decided not to publish about it and get all those bragging rights. All of them. I mean that would have been all over the news..

E: State secrets man, state secrets.

B: They did of course-- but it hasn't really been that way in the past. They did of course make sure the news was leaked. So it's just kind of weird and but still I don't wanna detract from this accomplishment. Did I say that it's called frontier created by the United States department of energy's famous Oak Ridge National Laboratory.

E: Oh Oak Ridge.

B: Now Frontier. It's an astounding creation. It took more than 10 years of a collaboration between the United States Department of Energy's exascale computing project. But also with national laboratories, private industry, academia. And I think maybe there was a deal with the devil in there as well. So the key thing here, the meat, the meat of this announcement is this. The HPL score was 1.102 exaflops per second. That's it. That's all you need to know. We're done. No we're not. So flop. When I say exaflops a flop is floating point operation. It's just a calculation.

C: I don't like it. I don't like flop.

B: It's yeah. It just─

C: Makes me uncomfortable.

B: ─like yeah. I kind of like exascale as an alternate way to--but exoflop is very specific. And HPL is also critical that stands for High Performance Linpack. It's a benchmark test. It's very important. But it is not this crazy esoteric test to determine how awesome the super computer is. It's really just a dense system of linear equations. That's it. You go through these linear equations and if you do them at a certain in a certain amount of time, bam! You're doing 1.102 exaflops per second. It's a common benchmark and it's it's widespread. That's why they use it because it's used all over the place. And the vast majority of relevant systems can run this test. So that's important because if you had an obscure test that only a very specific type of computer could run it wouldn't be very helpful and it wouldn't be able to put all these computers in context. Remember this is number one on the TOP500 list. So 500 of these super computers need to be able to run this test, this landmark test. So how does it even do this? What's under the hood? You open the hood, what do you see? You don't see a hemi you see Hewlett Packard Enterprises Cray EX platform.

E: Oh the Cray.

B: Yes. yes. And Evan you remember that because─

E: I do.

B: ─that to me that was very nostalgic for me because I remember─

E: The 80s my gosh.

B: ─oh dude you nailing it. I remember the Cray-2 in 1985. The first supercomputer that reached the milestone of one gigaflop. Cara. 1 billion operations a second. That was the fastest computer on the planet. I remember that day and it seemed fast. Frontier is a billion times faster. A billion times faster. The phone in your pocket right now is faster than that Cray-2 but it's awesome to see Cray still being used. I'm not sure─

E: Yeah the Cray brand has legs.

B: Yeah I remember, do you remember what the Cray used to look like? It was basically like one tower with a like a seating, a circular seating all around it. It was very compact and very small compared to what we got today. Okay enough of memory lane. So the HP's Cray EX platform. There's 74 cabinets. It's in 74 purpose-built cabinets. If you look inside those cabinets you're gonna find 9 400 CPUs, Central Processing Units, and 37 000 GPUs or Graphical Processing Units. The total core count is 8 730 112. That's a lot of cores.

J: Oh my god.

B: Also you got cables.

J: You know just the idea of them being able to chain that many─

B: Oh my god it's nuts.

J: ─close together. Like there has to be specific hardware and software that goes with that.

B: It's amazing. And don't forget Jay there's 90 miles of networking cables. Oh my god. That's 90 miles. That's crazy. All right so how do you think you'd even cool that. One of the things I remember reading about exascale supercomputers years ago it's like it's going to run so hot it's gonna consume so much power. How are you going to cool this thing. It's gonna be crazy. Well it uses four 350 horsepower pumps.

C: Wow.

B: Four 350 horsepower pumps moving six thousand gallons of water through the cabinets every minute. Every 60 seconds oh man.

E: The water it's still what you use?

B: It's great for carrying away heat man. And I remember it's also works against you Evan. Because I remember when I was scuba diving and you're in the water and that water takes away your heat. You hear your heat the heat being sucked away. It just cools you down so fast. It's too efficient. Enough of that. All right (laughter) also there's other milestones. And this is really cool. Frontier is number one on the GREEN500 list which scores commercial supercomputers for energy use and efficiency. Number one on that list which is really cool. And there was a new category this year called mixed precision computing. This is a brand new category. And that rates performances based on formats commonly used for artificial intelligence. And here the score was 6.88 exaflops. So you see this is even faster it seems. 6.88 exaflops. But it also it depends on the test, right? Because there's so many ways that you could, how do you encapsulate performance overall performance. You can't. You can't get one number for overall performance. There's lots of different ways to do it. So this is great because it's AI. It's based on these AI formats which of course are becoming increasingly common and all over the news. So the next inevitable question you should be asking is well what can it do that's so special? How do you even?

E: Solve Worlde. (Bob laughs)

C: I got Wordle in two today.

B: It would solve Wordle in one attosecond.

S: I almost got it in two but it took me three tries to get the last two letters.

E: This computer will solve Wordle─

B: Boring.

E: ─before the word is even chosen.

B: Exactly. All right now of course the specific impact like this system and similar systems will have because there's gonna be a lot more of these exaflop super computers as in over the next five to ten years. So that the impact is you can't really predict it but you can say things that you can say that exascale supercomputers without question they're going to impact your life in potentially major ways. Precision medicine, climate change models, water use, material science, nuclear physics, national security, pornography (Cara laughs) you name it. It's going to have an impact. How about this: forecasting viruses that leap species. Zoonotic, right? To avoid the next pandemic. These computers can help with that. Here's another way to look at it. I like this one. Now imagine you're at work and you're investigating a promising new pharmaceutical compound. As one does at work I suppose. People do that all over the place and typically you're going to wait weeks weeks for the results. With Frontier you'll be able to know the answer in a few hours. So instead of waiting for almost a month you're going to get it in just a few hours. In between eating your breakfast bagel and your cafeteria lunch you're going to get the answer. Even better here's another way to look at it. Instead of waiting a half hour or more for the number crunching that you do on your project you can get the answer in seconds. In seconds. So you could stay at your desk. You don't have to get up, get a coffee, chat with your friends, do some other weird stuff whatever you do at work. Then you go back and get in seconds you could stay at your desk and then you can go to the next task. So I think it could really speed things up and really help with creativity. Because you're getting the answer right then and there. As you know in our society there's nothing like having immediate feedback.

C: This is starting to sound really like dystopian to me Bob.

B: Oh my god.

C: You can be tethered to your desk all day every day and constantly accomplish things. (laughs)

B: Oh my god. Pharmaceutical compounds!

E: Don't tease Bob.

B: Imagine! Oh look I just found a cure for cancer my day is so boring. (laughter)

J: Cara, Cara─

E: Wow this thing cures cancer.

J: Cara is this the perfect example of don't get Bob started?

C: I was gonna say that.

B: It is. It's weird though because it's like. It's just a tool. It's just a tool. You sure you could use this to do really nasty stuff. But hey man if we follow that down to its logical conclusion you'd say let's ban all cars because so many people died. This is just a tool.

C: To who are you straw manning right now? (laughs)

B: I'm totally straw manning. So if this pattern holds. Which it won't. I will announce on May 23rd 2036 on SGU episode 1611 that some country, or company, or artificial intelligence perhaps has created a zeta-scale supercomputer with 1021 flops. That's a sextillion calculations per second. And you know the jokes are going to be flying when we have this zetaflop supercomputer sometime in the 2030s. Hopefully. Just real quick when we reach that milestone I'm going to be on the show freaking out as I am this show. And it could, people predict that it will be able to do things like for example accurately forecast the global weather for two weeks in the future. Pretty good. That's really good actually. Two weeks? That's pretty amazing because you're up to that hard limit of chaos and so I'm not sure how much how much deeper in the future we're ever going to be able to get in predicting something chaotic like weather. But two weeks and having a fairly accurate forecast using a zeta scale super computer. Pretty sweet. One more thing. They predict they predict that zetascale or yattascale systems─

E: Yes here we go.

B: ─might be able to accurately model the whole human brain. At least according to Kay Kirkpatrick who published as much in 2019. I think we're gonna see some amazing stuff with the zetascale and yattascale super computers but we now have exascale supercomputers and look forward to it. It's gonna do some amazing things. Good and bad Cara.

C: Are your-- (laughs) I didn't say it was going to do bad things. I don't want to not be able to get up from my desk. That's all I said.

B: You just harshing oh I get you, all right.

S: You can get up from your desk you just don't have to to waste your time.

C: Exactly. Like those breaks are actually healthy. No what I was wondering Bob based on Steve's news item last week. Your estimation of when we're gonna hit this next milestone. Is that utilizing a linearity bias?

B: Oh yeah absolutely that. And that's specifically why I said─

S: If the trend continues which it probably won't.

B: ─no I didn't say trend. I said if this pattern holds. Which it won't. So I just absolutely this may take there are some predictions that say this could happen you know even before 2030. I doubt that. A lot some people are predicting in the 2030s. They could be, they could hit some fundamental problems that are going to prevent this and delay it for even more. Maybe 2040s. But I think it's you're pretty safe to say sometime in the 2030s. That's a pretty big net. 20-2030s

E: Bob when did China come up with their exascale computer?

B: It's I think it was─

E: Years ago?

B: ─like--oh no no. Earlier this year, late last year.

E: Okay so it's been months.

B: Relatively recently but it's just. And kudos to them if it just I know so little about it because they just didn't publish it. And they didn't submit it for this.

S: They're not transparent so it doesn't count.

E: Well. Yeah, that part of it.

B: Yeah. In a sense. In a real sense it doesn't count. But I mean it if it truly existed─

S: That's the thing.

B: ─and it seemed like it really did. Some reliable sources say that this thing does exist. And it did pass the landmark with over one exaflop. So but if it did exist it does and there's no taking that away from them but I mean sorry. You're not in the limelight because you chose not to be in the limelight and we got to give the Oak Ridge National Laboratory all the kudos it deserves for this.

S: All right thanks Bob.

Dinosaurs Warm or Cold Blooded (16:17)[edit]

S: Jay.

J: Steve.

S: Are we finally going to end this long-standing debate where dinosaurs, were dinosaurs warm-blooded or cold-blooded?

J: Steve. I don't think I could bring as much enthusiasm as Bob. (Cara laughs)

S: Yeah.

J: I'm gonna just say that now to get that weirdness out of the way.

S: To lower that bar for yourself right out of the gate.

E: Let's dial this back.

J: All right so the long term question for scientists is could dinosaurs actually sing? Nope that's not the question. The question is that would be interesting though like I want to hear a real dinosaur vocalization, right? Were dinosaurs warm-blooded or cold-blooded? People always assumed or whatever. Whenever we were kids everybody thought that they were cold-blooded. They're lizards. They were big lizards. They were slow.

C: But they're birds.

E: Birdies.

J: But the truth is Cara scientists were never sure. At least the the smart people in the room were not sure and they weren't just assigning it like we were. Paleontologists have tried to figure this out for a long time. And in fact it's one of the earliest questions that paleontologists ask themselves about dinosaurs. Were these creatures warm or cold-blooded? So warm-blooded creatures are called endothermic animals and these are animals that can regulate their body temperature. Their metabolism creates plenty of heat as most of you know after hearing Bob talk. (Cara laughs) This heat is a byproduct of their metabolism and these types of creatures have developed many different things that can handle retaining that heat. Like fat and shedding the heat using sweat glands and feathers and fur as insulation. All of these things regulate body heat. This is called thermoregulation. Endothermic animals metabolize oxygen efficiently and this means that their bodies can absorb and use oxygen fast. These types of creatures also need a lot of calories because of how hot and fast their metabolism is. And this in turn generates more than enough body heat thank god. And of course mammals are warm-blooded. We are all warm-blooded creatures and we're using a lot of calories and oxygen every single day.

E: 98.6.

J: That's a weird average number I guess but the I'm─

S: That's not the real number.

E: No.

J: I'm a degree cooler than that.

S: Yeah there's a range and we actually use a hundred as like the cutoff for saying you have a "fever". 98.6 is a myth basically but go ahead.

J: Ectothermic animals, Steve Novella, these creatures metabolize oxygen more slowly causing them to generate less heat, right Bob? From their metabolism. Their body temperature is dictated by the environment. This allows them to eat less. They don't have to breathe as much. But in hand of course they're less active. And ectothermic animals don't need to eat as often for example. Some of them they might eat once a month. You ever have a pet snake? You don't feed it every day. You feed it one mouse every once in a while. Every week or so. And let's not forget birds. Birds happen to be more warm blooded than mammals. So they're not just warm blooded, they're very warm-blooded. And this trait goes way back into the bird lineage before they could even fly. And this fact makes the question about dinosaurs being hot or cold-blooded even more interesting. So the question is were dinosaurs warm blooded like birds or cold blooded like lizards.

E: Yes.

J: Researchers at the California Institute of Technology with lead study author Jasmina Wiemann they found a new way to determine if dinosaurs are warm or cold-blooded. They look at waste products that form the amount of these waste molecules found in the bones directly relates to the amount of oxygen taken in by the animal. And this shows that the animal was warm or cold blooded. So these waste molecules are luckily preserved during fossilization and that's key to this whole thing. The researchers studied the femurs of 55 different creatures. This included 30 extinct and 25 modern animals. So they had a collection of bone samples that had dinosaurs, pterosaurus, plesiosaurus, modern lizards, birds and other random mammals. A very large spectrum you know that's what they're trying to do. The researchers used a technology called infrared spectroscopy. And this is a tool that measures the interactions between molecules and light. So they shine a laser light at something and that laser light. Some of it is reflected back and they'll be able to read the colors of those reflected lights back and it'll tell them what material they're actually the lasers hitting which very interesting. Very very important technology that is used in lots of different sciences. Using this process they're able to qualify the number of waste molecules in the fossils. In the bones that they were studying. Then they compared their findings with the metabolic rates of modern animals that exist today. This gave them a clear picture of what the ancient fossils metabolic rates were. This is freaking genius what they figured out here. So guess what they found?

E: Warm-blooded.

J: Dinosaurs could actually sing. (Cara laughs) Just kidding. They found out that they were both. They were both warm-blooded and cold-blooded.

E: Both! Of course.

C: As in different ones were warm blooded?

J: Yes. Yes. Not the same ones.

C: Okay. Not like within the same organism.

J: I'm gonna be warm blooded today. No, no. It was different. Different ones. They were able to look at different dinosaur groups and create a timeline of what animals were warm or cold-blooded. Along this very long timeline. It seems that all dinosaurs were warm-blooded in the beginning when we go far enough back. But then in the Triassic period which was between 251.9 million and 201.3 million years ago dinosaurs split into two major groups. So one of the groups is the cervicians and these are considered lizard-hipped creatures. These include the velociraptor the T-Rex. And these are warm-blooded creatures. Warm-blooded dinosaurs were common and modern birds came from this group. Now we move on to the ornithischians. These are the bird-hipped group. Now Steve right out of the gate. And I'm asking you directly about this. Why did birds come from the lizard hipped group and not the bird hip group?

C: Because these were named in 1888.

S: Yeah.

J: Okay. All right.

S: Ten years old when I first asked that question. Because you would think that intuitively you think birds evolved from the bird hip dinosaurs. But yeah it's more, it's has to do with the way the bones are pointing. And it was in fact I'm gonna be talking about this in my news item in a little bit. They were going by morphology and there's a lot of convergent evolution. And if you just base it on superficial morphology you make incorrect designations and this is an example of that.

J: Okay so but they're still using it. So the ornithischians, which I said are the bird-hip group, this includes the triceratops, the stegosaurus. And these are the animals that ended up being cold-blooded. So another question they were able to answer was that about 65 million years ago the Earth's atmosphere had a higher oxygen content. And they wanted to know if the higher level of oxygen meant that animals had a higher metabolism because of the extra oxygen that was available. And they ended up finding out because of this study that there was no connection between higher oxygen levels and metabolism. Jasmina Weimann said: "Birds inherited their exceptionally high metabolic rates from their dinosaur ancestors which is pretty cool." and she actually said that "which is pretty cool" which I liked. Here now Steve you told me about something that I thought was incredibly interesting. This gigantothermic. Could you? Can you explain that?

S: Yeah so one of the ideas was that so the sauropods, these are the really big long-necked Brontosaurus type dinosaurs. They're in the saurischian groups. And they are also warm-blooded. But the question was perhaps they're cold-blooded. But because they're so big they can retain enough heat to be functionally warm-blooded, right? Even the little heat they're making builds up in their large body and because the bigger you are the greater your volume to surface area ratio. Surface area pretty much determines how quickly you will lose heat and your volume will determine how much heat you generate. That's why creatures become larger to adapt to cold environments. They hold on to their heat more. These things were massive. So the idea was oh man they're just so massive they're even if they're cold-blooded they could still generate their own heat and we'll call that gigantothermia. But this study pierced. It said nope they were actually warm-blooded. They were metabolizing as as warm-blooded creatures not as cold-blooded creatures which is interesting.You think those are the big lumbering dinosaurs that we think of. But they were warm-blooded as well.

J: This is really fascinating to know now the answer is really here. Science basically knocked another pin down. And I think that this will lead to more understanding about each individual type of dinosaurs. Where they lived? We know that. When they lived? We know that. But how they lived? There's a lot to be learned there and this is a huge piece to that puzzle.

S: Yeah this I mean this seems like the most direct evidence we have so far because they're measuring the direct metabolic products of oxygen metabolism in the bone. And across many─

E: And that doesn't get contaminated in any way? That's a pure measurement? It's never a contaminated thing?

S: These are highly stable molecules they're looking for. So what would they have to what are they being contaminated with?

E: I don't know.

S: Yeah they're looking for, it's spectroscopy. So it's looking at specific molecules. And but they did have to select for fossils that have retained a lot of organic material. Some fossils don't have a lot of organic material in them and they wouldn't be useful for this study. But if the conditions under which they fossilized from and were preserved allowed them to retain a lot of organic material these molecules are very stable and would last. So at least you know they can compare apples to apples, right? Like if they say these two dinosaurs are relatively the same preserved. This one has a high content of oxygen waste products the other one has a very low content. So you could directly compare their metabolisms there. But I like the fact Jay. You touched upon this but just to zero in on it a little bit. Before going into this study we obviously know that birds are hot blooded. They're even more warm-blooded than mammals. And so the really the question was, and birds are dinosaurs, right? So we already know that some dinosaurs are warm-blooded. So the real question was when did they evolve warm-bloodedness not if they evolved warm-bloodedness. And the thinking was it was when they evolved flight because flight is very metabolically demanding. So birds evolved flight. Then they as an adaptation to flight they became more and more higher metabolism warm blooded.

J: But the warm blooded came first.

S: But the warm blooded came first. This study shows it was there─

E: Interesting.

S: ─throughout all the not only all the theropod dinosaurs but all the saurissians. And maybe even farther back there with the ornithischians in fact sort of slowing down their metabolism over time becoming more cold-blooded. Which is very very interesting. So the conditions for flight were there before birds evolved. They had feathers and they were warm-blooded. The theropod dinosaurs. And birds evolved out of that group.

C: Yeah it speaks to that sort of evolutionary bias that we often have when we're looking backward when we want to ask why instead of how.

S: Yeah.

C: It's like there's no why in evolution. It just happened because of pressures. But they had the equipment necessary.

S: Yeah. We tend to think of evolution as creating the features necessary to adapt. But actually evolution is much more opportunistic. That strategy evolved because the pieces were already there and they were just doing the just following the low-hanging fruit, you know what I mean?

C: Yeah of course. What we see now as an end point is not a goal. It's not even an end point. It's just where we see it as being, yeah.

S: It's just what happened.

C: Yeah exactly.

J: Another cool thing about this news item. In general when you think about what the scientists did here. This is a moment to really marvel at how unbelievably powerful and awesome science is. This information that they're that they have found a way to interpret has always been there─

S: It's always been there, right.

J: ─every paleontologists, right? Every paleontologist that ever dug up a bone and all the time that they spend removing the rock away from these from the fossils. And the information was there and nobody knew it was there. And scientists, science is relentless. They keep trying to find more information out of the material that we have in front of us.

C: Well and also for a long time it was anathema to destroy bones in order to look for evidence within. It really like is a much more modern use of paleontology to say let's look at this microscopically, let's look at this from a chemical perspective because it really was looked at like oh gosh this is such a precious piece of data and now I have to like grind it down or I have to cut it in half in order to get this information out of it. That was a scary prospect.

S: Yeah and ironically Cara one of our archaeologist friends said in archaeology 50 years ago when you found stuff, you found tools or anything the first thing you did was clean it. And now you would never do that (Cara laughs) because you're destroying the DNA evidence. Now it's like you want all the dirt and grime and everything. It's just the techniques adapt to the technology.

Preventing Violent Crime (30:17)[edit]

S: All right Cara tell us about this kind of unusual way to prevent violent crime. Or we see it as unusual.

C: Yeah I was really excited when I came across this research study that was just recently published. The write-up from Vox was actually just published yesterday as of this recording. It takes an interesting approach. It takes a preventive approach. And it takes a very in my mind a duh kind of evidence-based approach. But I think against a backdrop of what we are all kind of struggling with right now. The vicarious trauma that we're struggling with against the backdrop here in the US at least of these kind of terrible reports of gun violence across the country. It's important to sort of take a new look at this. Basically there was a researcher who is friends with a man who lived in Liberia and this man who is living in this west African country runs a program and had been running it for about 15 years called the Sustainable Transformation of Youth in Liberia. And that program was a violence prevention program. And when this researcher who is friends with the man in Liberia. The man in Liberia is named Borh, Johnson Borh. When this researcher, Blatman from the University of Chicago saw the impact of this man's youth program he was like I think I want to study this. This looks kind of too good to be true and I wanna understand it. So he set out along with some colleagues on a pretty inclusive and long-term experiment. So he looked at at 10 years of data across I think it was 999 people in Liberia. And they specifically wanted to look at who they described as the most dangerous men in a city called Monrovia where the crime rate was very very high. So they look at these 999 dangerous men and they randomized them into four different groups. One group received cognitive behavioral therapy and it was an adapted form of cognitive behavioral therapy that specifically focused on crime and violence. It was given in an eight-week time period and it was given in a group format. So I think it's something like 20 men to every one therapist. Or maybe it's just, I'm actually not sure how many therapists were present but it's 20 men per group. So that was one group. Another group was given cash. A third group was given CBT plus cash and the fourth group was the control so they weren't given any intervention at all. And then they looked at the crimes that were associated with each individual at the one year mark and at the 10-year mark. And if I were to ask you guys to guess, I probably should have done this at the top, if I were to ask you guys to guess per crime how much do you think it costs in this paradigm to prevent crime? What would you?

J: Oh my god.

C: Yeah.

J: That's very hard.

E: Put a cost on it?

C: Per yeah. A dollar cost per crime. Not per person just per crime.

B: 100 bucks.

E: I don't. I have no idea.

C: 100 bucks a crime? Okay. You think that would be a good deal for the government? 100 bucks of investment in preventive measures per crime?

E: Sure that sounds like a good deal.

J: Yeah I can't figure this out.

C: Right so because we often talk about─

B: How many crimes are we talking about?

C: ─we often talk about punitive measures. We talk about punishment. We talk about policing. We talk about sort of after the fact crime. I'm putting interventions in quotes because as we know they don't really work very well. That often results sadly in aggressive policing, incarceration, kind of a recapitulation, that's not the right word I'm looking for but an exacerbation of poverty. And of inequality, income inequality so if we're talking about peer prevention and you think about the amount of money we spend on policing in this country and jailing and incarceration. The justice system as a whole. If we're talking about prevention. Bob you said about a hundred bucks a crime would probably be a good investment.

B: That's my wild ass guess yes.

C: In this paradigm it cost a dollar fifty per crime.

B: Yeah I was thinking of going low.

C: That was the calculation that they were able to work out. So as you may have guessed the group with the both the CBT and the money fared the best. They had a significant difference in outcomes. A significant reduction in crime compared to all the other groups. And so the question here is why not just CBT. Why not just a cash infusion. Why is it CBT plus a cash infusion. So let's look at the actual specifics here. I told you the specifics about the the CBT paradigm. The Cognitive Behavioral Therapy paradigm. Which is of course an evidence-based intervention. The cash infusion was only 200 bucks a person. And so the total cost of the group that got CBT plus cash was I think 500 plus or minus per person. Because that included the cost of delivering the therapy. So just CBT alone did reduce violent crime but it didn't really have the staying power that we would hope for. And this is borne out by other literature. We know that evidence-based therapeutic interventions can have a preventive approach. Or they can have a preventive outcome to crime like if we offer therapy to at-risk youth or at-risk individuals we can see reductions in antisocial behaviors. We also know that in some instances cash can help as well. But the question here is why is cash plus CBT not only so effective but so lasting. Because here's the kicker, after 10 years they found that the outcomes held and nobody expected that. Everybody except─

S: Yeah, it's a long time.

C: ─it's a really long time. Everybody expected that the outcomes would have kind of dissolved or petered out. And that does seem to be the case a little bit with just therapy. That's, so that's not to say that therapy doesn't work, right? Like and I think it's important that we think about this not just from a purely scientific perspective but also from a face validity. Like what's the word I'm looking for? From a purely rational perspective. If you want to prevent hunger you will give somebody food. Now they're gonna be hungry again in a few days you have to keep giving them food. That's how that works. And sadly therapy is often similar. Like we can have targeted interventions at very particular pinch points in somebody's life but one dose of therapy is not always going to be sort of like preventive for their entire lives. But the weird thing is the cash infusion which by the way was two hundred dollars one time. Really did work synergistically with the therapy. And so the researchers are hypothesizing that it sort of was a almost like a behavioral activation. So behavioral activation is an important part of cognitive behavioral therapy. It's one of the things that you're often teaching is how do we get motivated and actually start the process. And then once we start the process we can usually keep going. The 200 cash infusion seems to have sort of aided in that. So the idea here was training individuals through the use of therapy to see themselves in a new light to kind of try on a post criminal identity. And sometimes it would involve things like buying new clothes, changing their appearance, trying out new job skills. And it seems as though they needed the money right at the top to be able to maintain the therapeutic skills that they were learning. The economic assistance really did help them utilize these new skills and actually see what it felt like to make money legitimately and get that positive feedback to be able to continue doing that. And so it's really fascinating to see that in this clearly small example which does have some caveats although to be fair the researchers I think did address a lot of the caveats really well. Like for example they looked at 999 different people. A 160 of them iI think by the end the 10-year period had died. And so the question is this like a survivor bias. Like did the individuals who are most likely to be involved in violent crime die because of violent crime. So it looked like the non-violent individuals did better. But the truth of the matter is they were able to use some sort of forensic capabilities to interview friends and family of those who had died and look at medical paperwork. And it turns out that something like two-thirds of the individuals who died died of disease or other associations─

S: Just medical [inaudible].

C: ─yeah medical things and just associations with poverty. This is a very poor city and so tuberculosis is rampant, malaria is rampant and sadly there's an Ebola outbreak around the time when they were collecting this data. So a fair amount of these people did die from disease and other medical things. And then about 20 something ended up dying of actually violent deaths. But they were pretty evenly spread across all four groups. So there really wasn't a good argument to be made that any particular group reduced impacts of violence on the individual. But there is a very good argument to be made that the individuals were engaging less in criminal behavior which includes violent activity. So the researcher who led this study who's actually in Chicago has decided to implement a similar program in Chicago. It's called READI. R-E-A-D-I. And for 18 months they are identifying men in violent districts of the city and offering therapy sessions and job training. So instead of direct cash they're actually giving them sort of access to the economic market. They believe that obviously we can't always compare apples to apples in when we're looking across cultures and across countries with different economic development. And so they're hoping that in Chicago actually getting access to the job market is going to have that kind of staying power that the cash infusion had in Liberia. But they don't have outcomes from this study yet because it's actively going on. But we have seen some really interesting examples like in Ecuador for example in 2007 and this is described in the Vox article. There was basically a lot of gang violence and so the country did something that seems kind of counterintuitive. It's that they actually legalized gangs. They said okay let's actually legitimize these groups and you can register them with the government, you can call yourself a cultural association which opened them up to qualify for things like grants and different social service benefits. And the rate of violence significantly plummeted after that. And so there's this I think and we see this a lot when it comes to gun violence especially in our country. We have these entrenched ideologies that prevent us from looking at evidence-based practices when those evidence-based practices seem to bump up against our sacred cows. And so I think what's so important about this kind of research and being open-minded to this kind of research is that even if it feels counterintuitive. Even if it bumps up against maybe your personal ideology your personal value systems. Really give it a good look. Look at the outcome measures and see how evidence-based approaches can actually be really really transformative. Doing something the way we always did it just because that's the way we feel we should is not good public policy. Looking at the evidence and taking a science-based approach is one of the best ways to move forward. And I think that we can see a lot of parallels between this great study in Liberia, the study that's going on now in Chicago and some of the direct action that we could be taking across our country.

S: Yeah this is an interesting study obviously and whenever you're dealing with human behavior no one study is going to be definitive. You got to look at it as many different ways as you could think try to─

C: Yeah but the sad thing is we don't have that many studies like this.

S: I know.

C: Because there's so much resistance.

S: We need more. We need more. But my reaction to this was for a couple of things. One is that it really does seem that when it comes to changing behavior our instincts are completely wrong. Over and over again like we think that punishment is going to work. It really is not. It's not really effective. Or scared straight. It really doesn't work.

C: And we've known this since like Pavlov. (laughs) It's amazing.

S: No but we keep going back to these things. It's intuitive for some reason and it feels like it should work but the evidence is pretty clear that our our our instincts are wrong. They just don't work. And doing things that seem very counterintuitive. I think because there's multiple different intuitions going on here. And we have our intuition of justice and when you do things that doesn't feel just then it rubs you the wrong way. You wanna do something that feels like justice and then you argue well and it will have the desired outcome that I want. I'm going to punish this guy and it's going to keep other people from doing this bad thing. It's like okay you really just want the punishment. The other thing is just a justification for it. But when you look at the data. The things that work are the things that aren't emotionally gratifying. Like we're gonna pay people to not be criminals. It just doesn't feel right. But if you just say forget about what it feels like. Just forget about that. Do the money ball, right? It's the other idea. Like the money ball approach is just whatever outcome works. Forget about your intuition or your instincts or whatever what feels right. Let's just look at outcomes. And if you just do like how much money are we spending. How much quiet crime are we preventing. Let's do that and forget about how it feels.

C: We saw this in Utah of all places where there was that interesting kind of social experiment to say how much is it costing the state to use all these social services around homelessness. And they say well how much does it cost to just build these people homes. To just like literally put them in homes and it was significantly cheaper. And all these other variables were affected by this. Crime rates went down. So many positive outcomes came from this. But you're right it's this twisting of motivation. And I think it's also a fundamental misunderstanding of human behavior. This links back to a study we covered years ago on the show about belief in pure evil. And the idea that people commit crimes because they are intrinsically evil versus the truth which is that people commit crimes because they are desperate.

S: Yeah it's a fundamental attribution error.

C: Yeah. And if we can create a system in which people don't have the desperate need to engage in gang activity, they don't have the desperate need to steal at gunpoint, to rob, to whatever because of these entrenched social problems like poverty and racism. If we can start working on correcting these things preventively we don't have to rely on punitive responses which don't work. And actually entrench the problems more.

S: And you don't have to keep too many people out of jail to pay for a lot of prevention because keeping people in jail is expensive.

C: It's very expensive.

S: For the state. It's very expensive.

C: But sadly it also makes a lot of money for privatized─

S: Yeah yeah.

C: ─industry. That's a whole other conversion.

S: Separate issue.

Revising Evolutionary Trees (46:29)[edit]

S: All right guys as promised I'm going to talk a little bit about evolutionary trees. Phylogeny. Phylogeny is basically like a diagram of when different species branched off from which other species when. It's mapping out the evolutionary history of how life evolved and all the interrelatedness. The question is how do we come up with those diagrams? How do we determine the phylogenies or the evolutionary relationship among all of the species? Actually it goes this goes back to before evolutionary theory. Because even though we didn't have an evolutionary understanding of nature back in the day we still had a desire to categorize life. And there was the this idea of a tree of life actually predates evolutionary theory. Pre-evolution this is the mind of god. This is the god's plan for life. Everything's organized. We still decide─

E: Here's the map.

S: ─yeah wanted to map out life in this and that would reveal sort of god's design or whatever. But people also could just see that yeah birds are a thing, right? Birds are an identifiable group with bird-like characteristics that we can put them in into one category. But that was famously a very inexact way to group living creatures because morphology could be very deceptive. And sometimes we did like one of the biggest fails. Like with the linnaean system of categorization of taxonomy was with flowers right. Linnaeus just decided to split up the flowers by how many stamen they had. Which had like nothing to do with their revolutionary relationships with each other. It was just like he picked one superficial characteristic and made that the primary criterion for how to to split them up into groups. This by the way gets is repeated in many different situations where we've categorized things based on very superficial characteristics initially and then as our scientific understanding evolves and progresses we figure out more and more precise and meaningful ways to categorize them. This happens a lot with diseases and medicine for example. Okay so post-evolution there was the idea that we should be categorizing species based upon, grouping them, based upon their actual evolutionary relationships. That idea is called cladistics. And that's why we talk about clades. Clade is an evolutionary group not one that's determined by morphology or anything else but by strictly by evolutionary relationships. So given that goal to develop a strictly cladistic evolutionary map of the relationship of life, how do we do it now? There are two─

B: Genes, duh.

S: ─yeah there are two main. Two main methods. We still use morphology just how they look we still use that.

B: Why?

S: And I'll tell you why. And we use molecular methods. Genes, genetic data. And not just genetics but also proteins and amino acids, whatever. We use that level of data. Now why don't we do that all the time Bob? Because we don't always have that data, right? We don't have a full genome of T-rex for example. So if we don't have the molecular data we don't really have much of a choice but to go on morphology.

B: That's true.

S: But the morphology and we've talked about this concept before. Morphology can be tricky because of convergent evolution. There's, remember homologous and analogous traits? Analogous traits look similar because of similar function. Homologous traits look similar because of evolutionary relatedness. And how do we tell the difference? Well it's tricky. And sometimes we get it wrong. But not only that. So even when we know like these are homologous traits and these creatures are closely related to each other it's still hard to tease apart the branching relationship. So if we're thinking about apes for example it's like okay we know that chimpanzees or humans are closest. But who's next? Is it the orangutans or is it the gorillas? And how do you arrange those. And sometimes even when we're doing, even when we have good molecular data. Even with extant species. We have good molecular data. There's multiple ways that you can fit to the data. You could fit a tree to the data. And so you might have like three or four fits that are all pretty much equal, statistically equal, in terms of fitting the molecular evidence that we have. So then what do you do? So we have a couple of other sort of ancillary methods to help us break the tie as it were. If we have multiple options among, based upon morphological or molecular evidence we might and there are other things we can turn to. What do you think those are? There's basically two there as well. I'm oversimplifying a very complicated field but we'll start with these sort of basic concepts. What do you think?

B: Prayer? (laughter)

S: No.

C: So you said okay so not morphological and not genetic.

S: Yeah those are the two primary things. So there are some two secondary methods that we use to help break the tie. To see what correlates best and then we find─

B: Geographic location?

S: Absolutely. Geographic location. And I'll give you a hint. For fossil species specifically. Things that are in the ground.

C: Rocks? Geology.

E: Depth? Depth.

B: Strata.

S: Stratigraphy. Yeah, strata. Stratigraphic correlation. So where do they, how long ago were they in the past. Obviously things can only evolve from things that are older than they are. You can't evolve from a species that appears later in the fossil record. So yes we use those two methods. Okay now this gets to the study that is triggering this discussion. They wanted to know all things considered. Again not just based on our gut feeling or because it's newer or sexier. From an evidence-based perspective which maps are more accurate. Morphological maps or molecular maps based on genes and proteins and whatnot.

B: Is that a trick question? It's got to be molecular.

E: Yeah I would think molecular.

S: That, so that's your instinct, right? It's got to be molecular. But they didn't want to go by that. They wanted to really prove mathematically which one was better. So what the researchers did this is in Communications Biology, Jack Oyston et. al. And they looked at different phylogenetic maps. Morphological and molecular and they they said which one which ones fit a geological map more consistently. So if you also take a sort of a geographical map of the species it's basically an independent line of evidence that you can use to also map out relatedness. The idea being that creatures that are related to each other are more likely to live close to each other.

C: Right it's like, what do you call it? Convergent? Not convergent evolution but convergent validity. Looking at another type yeah it's like consilience there.

S: Exactly. So like for example with the geographic like there's a lot of marsupials in Australia. The marsupials are not evenly distributed around the world they sort of cluster in the southern continents. That's geographic data. So they did a statistical analysis where they compared various morphological and molecular maps, phylogenys. Said which ones match the geographic and the stratigraphic maps more and the answer was the molecular phylogenys.

C: Of course.

S: Which is not surprising it confirmed what we all would have guessed. But it did put a hard number on it which is good. All right so here are the actual numbers. Not as high as you might think. So they write in the discussion: "The observation that biogeographic congruence is significantly greater than expected by chance alone for most of our clades was 69%." So 69% of the claydes they looked at had bio geographical congruence greater than chance alone. So that's still that's pretty good but it's not as high as you might think. But for the morphological trees it was only 50% congruence with the biogeographic data. The biogeographic congruence. So big picture if you look at biogeographic maps, molecular maps, morphological maps, stratigraphic maps they all agree with each other pretty well. But it depends on what level you are digging down to. I mean there aren't like crazy inconsistencies. Like they all overlap pretty well. But when you when you dig down to like the real fine details. And you're asking questions about the exact order in which different species branched off. That's where you see the differences. At that really high resolution level. And that's where they were asking so when we have multiple potential branching patterns that fit the data should we listen to the phylogen the molecular data or the morphological data when they conflict. And this you know it was making a strong argument that we really should go with the molecular data when it conflicts with the morphological data. Which again I think kind of we all would have guessed. Kind of makes sense.

C: But also to be fair the morphological data in this study is modern morphological data. So that's why the consilience is so high. It's not as good but it's pretty good. That's because we're not talking about like in linnaeus's day, we're talking about modern morphological data.

S: Oh yeah that's so here's the other thing they found. Thank you for bringing that up Cara. The other thing they found was that the morphological and the molecular maps get more and more accurate over time.

C: Right. Yeah I'm not surprised by that.

S: So yeah so if you look at the ones we had 100 years ago, they were they didn't do so well. And then over the longer we've been sort of gathering data. So they got like all science it's imperfect but it makes progress. I see it as like a picture coming into focus. And it's getting more and more into focus over time which tells you that these methods are working. If they weren't working then there would be no relationship with, you know, be random. You wouldn't expect it to progress over time to get more accurate as we gather more data. But because the methods are working we get higher and higher resolution. Basically models phylogenetic trees as we go over time. And again I thought it was a fun sort of way to point out also that this is what's happening. Is that we developed this molecular tool. Being able to to sequence genes even from fossils. Sequence protein structures. Amino acid sequences. And with this data it's we're sort of revolutionizing our phylogenetic trees. And we didn't get rid of the older method like the morphology that going all the way back to even pre-evolutionary days. It's still there. It's still important. It's still data. But it just is very tricky to interpret but sometimes that's all we have. There are sometimes we have a skull and that's it. That's the other thing the morphological data is often incomplete. If we had perfect data, if we had the whole genome sequence and protein sequence and a 100% of the skeleton of every species then the maps would completely overlap each other. There would be no discussion here but it's when we have incomplete data the different methods are gonna have different strengths and weaknesses and they may not exactly agree. It's a good example of how science is messy but it works and it trudges forward. And it's good to have kind of a nuanced understanding of how the evolutionary biologists do this. I also like, it's good for people to understand this. Another reason is that creationists exploit people's misunderstanding of this. They will argue that there's like no correlation among different phylogenetic trees because evolution is not real. And that's of course just bullshit. They're just lying. But if you understand how this works and what the data actually is it's a lot easier to refute misinformed creationists. They play with the word correlation, right? What does that mean? If again you could always zoom in to an arbitrary level of detail. Like what's precise, what's accurate, what's correct. It's always within this certain error bars. And you can if you can always deny a scientific finding by just arbitrarily requiring and a higher level of precision or focus.

E: It's like moving the goalpost.

S: Yeah it's moving the goal post absolutely.

C: It's like when they say well there's no transitional fossil between those transitionals, oh god.

E: Oh we found one! But now you got two transitional fossils.

S: Just keep zooming in, keep zooming in.

J: Steve at some point wouldn't it be great if we could tell the creationists like actually that is as granular as it gets.

C: (laughs) Yeah.

S: I think about what we're doing. We're reconstructing a billion years of evolutionary history involving billions of species based upon fragments of evidence. It's amazing what we can do. As you were saying before Jay. We've always had this data now we know which dinosaurs are warming cold-blooded based upon this trace evidence that we have fantastic tools that can pull out the most amazing. I often think of shifting like to astronomy. When you think about it, right now we are being bathed with a tremendous amount of information about the universe we just don't know how to look at it yet. We're just increasing our ability to detect, to analyze and to infer the universe from the data but this information is there. It's hitting us at all times and if we just had the instruments or had the knowledge or the ability. There's so much more we could learn about the universe. And it's the same way when we're looking into the past. We have the fossils are there. The evidence is here it's all around us. We're just increasing our ability to get at it, to analyze it, to infer what's going on with greater and greater detail.

C: And of course Jay like when you said wouldn't it be great if we could actually say that's as granular as you get. We're doing that now. We can do it with covid. We're like look here's a new variant it directly evolved from the last variant. There is no in between.

[commercial brake]

New Optical Illusion (1:02:39)[edit]

S: All right Evan you're going to finish this up with this is a fun little item about I caught this one too before you send it to me. A new optical illusion. Tell us about it.

E: Yeah, yeah. This this news item you have to see to believe. We're gonna post the link in the show notes of course but when I talk about it you should be able to easily find it on your own. I'll do the best I can to describe it. So we've all seen illusions and perhaps you have a favorite. Some people like the illusion called the dress.

S: Yes.

C: Oh god.

E: Is the dress black and blue or are the colors white and gold. It still boggles my mind today. Oh boy. That was a viral sensation back in 2015 and everyone had an opinion on the dress. That phenomenon revealed differences in human color perception which have been the subject of ongoing scientific investigations into neuroscience and vision science producing a number of papers published in peer-reviewed scientific journals. Now the dress was eventually confirmed to be colored black and blue. I still see white and gold. I mean I don't know about you guys.

C: Yeah I still see white and gold too.

E: It's hard even knowing knowing the fact doesn't break the illusion. That's powerful. That's one type of illusion. What do you got Bob?

B: I saw green. (laughter)

E: That's one type of an option.

B: [inaudible] outlier.

E: What about another illusion, the spinning dancer?

B: Yeah. Good one.

E: The silhouette illusion. We've used it on our live shows and talk about it. It comes up often. Some observers initially see the figure is spinning clockwise but others see it counterclockwise. And it may suddenly start spinning in the opposite direction upon observation. So that's about lack of visual cues for depth. That's a key to the ambiguity of which leg might be spinning. So the latest greatest illusion is what many people are describing as an expanding black hole. Calm down Bob. (Bob chuckles) But technically it's just titled the expanding whole illusion because black hole illusion actually refers to something else. Won't get into that right now. The expanding whole illusion is what we're talking about. So picture this. A static picture, right? Nothing is moving. The picture's laid out in a rectangular landscape shape and contained within the rectangle are a series of equal sized small black ovals. In the center of the picture overlaying the ovals is a black blob the same exact black color as the ovals it's covering. When the vast majority of people look at this image it appears as the black blob is growing, getting larger, almost as if it's creeping forward towards you. Have you, did you guys have a chance to look at it?

C: Oh yeah. It works. It's amazing.

J: It's incredible.

C: Looks like you're falling in.

B: I see this the carpet this black hole carpet image which is really cool but it's not what you're talking about. What's expanding hole illusion?

C: No look up, yeah expanding hole.

E: Expanding hole.

C: It's all over. Just click news when you search for it. It's everywhere right now.

B: Oh it's okay. Yeah here it is. All right.

S: And for me it works but if I look around the image it works even better, you know what I mean?

C: Yeah you have to like─

E: Yes.

S: Provokes even more.

C: Yeah.

J: So how does it work?

E: Yeah, well─

B: Whoa what the hell?

C: (laughs) Bob just found out.

E: I know. Ain't that wild? So the researchers described it as it having giving people a growing sense of darkness as if entering a space voided of light. And that's really what it comes down to is about light. Light versus dark. The research article was published in Frontiers in Human Neuroscience. And the title of the article is and this is the title: "The Eye Pupil Adjusts to Illusory Expanding Holes". Yes the word is loserly. Authors are Bruno Laeng and Shoaib Nabil from the University of Oslo and Akiyoshi Kitaoka Ritsumeikan University in Osaka, Japan. So they said that a large class of optical patterns evoke conscious dynamic sensations of illusory movement. Despite being static. There isn't obviously nothing moving. This isn't a spinning image. It's not a gif or jiff─

C: Thank you Evan. (laughter)

E: ─"These illusory motions can be described as a variety of changes in shape or space, like drifting, rotating, oscillating, waving, fluttering, contracting, or expanding." I'm reading from the introduction of the paper here. I'll get to it. "An example of this type illusion which we call" the papers writers call "expanding whole is illustrated in the figure. Typically, when looking at the pattern, observers’ subjective reports are characterized by the perception of a gradually expanding central region, occurring over a span of several seconds." And then they write "However, classic illusions of size do not evoke dynamic sensations of motion like the “expanding hole” presented here." So that's the key part of the news item I feel. The invocation of dynamic sensations of motion. Like you were getting ready to enter the monolith in 2001 A Space Odyssey where it's kind of sucking you in. You know that scene? Obviously Steve you know that scene where you start to get ready to go for the ride. That it almost feels like that to me. So why does this illusion make us feel like we're being sucked in? The speculations by the researchers is that it may have to do with how our visual systems predict a change from brightness to darkness. Sort of like an early warning system the potential of what we think might be a potentially perilous condition. So the authors wrote "Just as glare can dazzle, being plunged into darkness is likely risky when navigating into the darkened environment". The illusion of forward motion was most effective when the hole was black. They tried it out in various colors when the hole was the shade of black. 86% of participants in the study felt as though the darkness was headed towards them. And the authors say they aren't sure why 14% of the group didn't perceive any illusory expansion when the hole was black. But even among those who did perceive the illusion the strength of the sensation varied. So some people felt it more or felt the intensity of it more than others. They tracked the eye movements of participants and it revealed that their pupils were unconsciously dilating at the sight of the black blob. So the pupils are reacting to how we perceive light even if the light is imaginary. And it's not just the amount of light energy that actually enters the eye. Super cool.

S: Yeah so it basically means that the higher. It's not just the, there is a pupillary light reflex of course but that means it's also modified by higher level visual perception. And then the thinking there is that if we're like heading into a dark space our eyes are getting ready. So they're anticipating the darkness by pre-dilating the pupil. So which of course can't be based just on the current light level. It has to be based on the anticipated light level. So that's what they think is happening here. And the other piece to it as far as I can tell from reading this although they're not really sure but their hypothesis is.

E: Right. That light level perception is not based entirely on the actual light levels. It's also based upon ecological interpretation. Like our brains make sense of our environment determine how bright it should be. And that's partly what we perceive. Which again that's basically how vision works. Vision is sort of creating─

C: Yeah that totally tracks.

S: ─that visual experience. With, in our consciousness. It's not just reading straight data. Visual data. So this all fits. But there's there's definitely some more details to learn about exactly the pathways, etc. that are at work here.

J: We are jam-packed with programming.

S: Yeah. Totally.

C: It's amazing.

E: Oh yeah.

C: And we're so good at inferences. That's the crazy. I think the thing that's almost cooler is how we're not like we're jam-packed with programming but we don't follow a perfect script. Like you were saying we aren't just looking at actual light. Like a light meter would in photography. But we're looking at visual cues of light like shading. The same way that we can we can tell that trees are green even in the dark.

S: Exactly.

C: Even though they're not putting off green wavelengths we perceive them as green.

B: Or Cara─

S: We know that they're green.

B: The same applies to color in your peripheral vision. Where the rods predominate. You're not really seeing much color there at all but you're it's that apple still looks red because your brain knows it's red.

C: And we fill in holes in our vision. We've seen all those beautiful optical illusions─

B: Optic nerve, fovea.

C: ─not even the blind spot like literally the ones where it's like a triangle but the corners are missing. You just see the corners until you're told that they're not there. You just fill them in.

B: It's total constructed reality. It's what it is. You're not seeing reality it's your brain's best guess.

C: Yeah yeah.

Quickie with Bob: Gato AI (1:11:44)[edit]

S: All right Bob you have a Quickie with Bob this week.

B: Thank you Steve. This is your Quickie with Bob. Gird your loins people and yet another recent and interesting AI release. Google's deepmind recently announced Gato which is refreshingly different from other AI systems that are fiendishly clever at one specific thing. Like an expert system if you will, correct? Gato on the other hand is multi-modal program. It's a generalist agent. So it could do many things. Some quite well. Many though are just kind of mediocre. Just kind of meh, you know? And in fact it's claimed it can do 600 things. Play video games, stack blocks with robot arms, chat, write compositions, caption images and on and on. It's a really long list. Now this is achieved partly by training gato on a large number of diverse data sets. So some of these data sets that it trains on are digital. Some are based on real world agent interactions. Some are language based. Other data sets are just images. So if you're familiar with how some of these work then that goes a long way into explaining how it could be acquainted with so many different things. So okay the next question is why make an AI agent that's pretty good on some things but also not very good on a bunch of things? So the authors answered that in a couple ways. In one case they cite AI scholar Richard Sutton when he said: "Historically, generic models that are better at leveraging computation have also tended to overtake more specialized domain-specific approaches eventually." So that means that this general approach, historically anyway, could ultimately perform better than systems that only do one thing and do it incredibly well. So that's one possibility that they have in their mind. They also addressed it by speaking more generically and saying things like specifically the creator said: "we here test the hypothesis that training an agent which is generally capable on a large number of tasks is possible; and that this general agent can be adapted with little extra data to succeed at an even larger number of tasks." So they're just basically generally just trying to prove hey yes we can create this generalized agent that could do lots of different things. Because I guess it hasn't really been done to this extent. So that's the other one. And so that that's Gato in a nutshell. Look it up for more details. And even the controversy surrounding it which is kind of interesting and weird. Okay. Loins ungirded. This has been your quickie with Bob. I hope it was good for you too.

S: Thanks Bob.

Who's That Noisy? (1:14:18)[edit]

S: All right Jay it's Who's That Noisy time.

J: All right last week I played this noisy:

Michael Jackson-esque squeals, screams, and scatting

B: Wow.

J: There's one in there it sounds fake to me.

S: Just one?

B: That's a man walking in a room filled with tacks and mouthtrap.

S: I was gonna say that's somebody reaching into the fire and to ow and then reaching back in ow again.

J: All right so let's cut to it shall we? Mark Leighton wrote in and said: "Hey guys this week's noisy sounds like Mick Jagger's contribution to a background track to Sympathy for the Devil or maybe I've been running on this trail for too long." Yeah so that is most definitely not Mick Jagger but if anybody would be confused with this person it would be Mick Jagger I think.

Listener named Ben Frye wrote in said: "That's definitely Michael Jackson." Okay so we've established that this is Michael Jackson.

B: Yes.

J: I'm giving this to the audience. "And it's rapid fire so I'm going to say that it's that video where someone used MJ sounds for all the sound effects from the first John Wick movie."

C: Oh that's funny.

J: Yes. That is incorrect but you got a portion of the way there.

Another listener wrote in named Joe Havelda and Joe wrote: "Hey guys this week's Noisy is all the non-word sounds in the song Bad by Michael Jackson separated from the rest of the song." Joe you're close but you are not a 100% correct.

We do have a winner and there is a one a very specific correct answer. Remember I told you last week be specific. The answer is; Alexander Evans wrote in: "Good morning, I hope this email finds you all well. I believe this noisy is a clip of all the non-word lyrics from Smooth Criminal sung by Michael Jackson." That is absolutely correct. There is no secret here. I'll play a little bit more for you. Here it is a part of it again:

[plays noisy]

Yes it just goes on. So first of all yeah wow I mean that guy was a sound effect machine. He just did remarkable things with his voice. So good job Alexander and thank you William for sending in that noisy. I laugh every time I hear it.

S: But we but we do have to say because we have so many listeners somebody's going to email us about this that using that sound clip is in no way an endorsement of Michael Jackson as a human being or pedophilia. Just sayin'.

C: (laughs) Yes, true. Very true.

E: Okay.

J: Thank you Steve I don't think I could have said that more concisely as you did. (laughter)

New Noisy (1:17:04)[edit]

J: I have a new Noisy. This Noisy was sent in from a listener named Steven T and please identify:

[high pitched whoops and grunting, squeaking calls]

J: The more information you give me, the better. If you think you have an answer, or you want to send me in something cool, just like that Noisy--somebody just sent that in to me--you can email me at WTN@theskepticsguide.org

Announcements (1:17:52)[edit]

J: Steve.

S: Yeah which of our many properties should we promote this week Jay?

J: People that listen to this show might appreciate it so much that they might be willing to become a patron of ours. Our patrons actually keep this show on its feet keep the wheels turning. If you are interested in supporting the SGU you can go to patreon.com/SkepticsGuide.

S: And we should say you get a lot of stuff for that. If you become a patron at the premium membership level you do get premium content. There's over a hundred pieces of premium content available for you to listen to right now and we add new stuff all the time. Plus you get to join our discord server and lots of other things as well. And we are looking all the time for ways of adding more value to all of the patreon levels for people who support the show.

C: What about an ad free episode? Did they get that too?

S: You get the show ad free. Absolutely.

C: That's a big deal.

J: Yeah you get that at a particular level.

S: At the premium level you get that yeah.

C: Yeah check it out we've got a lot of cool stuff.

Questions/Emails/Corrections/Follow-ups (1:18:59)[edit]

So in episode number 880 you all talked about how the gullible acupuncture article relied on a systematic review that showed significance to certain disorders with acupuncture treatment. You talked very briefly about how that systematic review is not valid as the significance would occur from randomness. Therefore I'm now very much wondering which systematic reviews can I and the public trust? What should we look for to see if a systematic review is rigorous or garbage? Especially because I've always thought that systematic reviews are one of the more rigorous and trustworthy of scientific papers in general. Thank you for your time. – Antony

Email #1: Reading Systematic Reviews[edit]

S: We got one email this week from Anthony and he writes: "So in episode number 880 you all talked about how the gullible acupuncture article relied on a systematic review that showed significance to certain disorders with acupuncture treatment. You talked very briefly about how that systematic review is not valid as the significance would occur from randomness. Therefore I'm now very much wondering which systematic reviews can I and the public trust? What should we look for to see if a systematic review is rigorous or garbage? Especially because I've always thought that systematic reviews are one of the more rigorous and trustworthy of scientific papers in general. Thank you for your time." That's a great question and it's there's a complicated answer to it because this is a complicated situation. So systematic reviews as an approach to the literature is a pretty good one. I don't think it's the best. I prefer best evidence reviews. Ones that are weighted to the quality of the evidence more explicitly. I also prefer reviews that include a bayesian analysis which is looks more statistically at how likely the actual hypothesis is not just how unlikely is the data. And looks for patterns in the research like is there a relationship between the quality of the study and the effect size for example. And my latest addition to necessary criteria for a good systematic review. Did they check, especially if it's a medical article, did they check which studies were pre-registered.

E: Pre-registered, yeah, we've talked about that recently.

S: Did they follow the protocol outlined in their pre-registration or did they change the methods from the time of registration to the time of publication, hmm. How many unpublished pre-registered trials are there? Hmmm. So you need to do all of those things. Not all systematic reviews do that. And if you if you don't understand what I'm talking about you need to read articles that you do understand it or you're not going to really be able to decide what the quality of a systematic review is. The problem is in order to really have a to wrap your head around whether or not you can rely upon a systematic review you need to have a certain level of topic expertise, you know what I'm saying? If you have no idea about the topic you're just trusting the authors. You really have no idea. You can read and understand their conclusion but can you tell is that conclusion justified by the data. How would you know unless you have a certain level of topic expertise. So what can you do? One thing you can do I mean you could look for these red flags that I mentioned and to try to see it does it at least past the smell test. It's also good to look at like how good are the best studies. Again sort of that best evidence approach because if all the if it's just putting together a bunch of crappy studies and it's crap and crap out right. Garbage in garbage out. It doesn't really help that you did a systematic review. The systematic review can't make the data itself better than it is. It just tells you what the data's showing, you know what I'm saying. Systematic reviews are also distinct from a meta-analysis which actually statistically combines the data from multiple studies. And that also is very tricky and it does follow the garbage and garbage out but it can give you more statistical power. If you have a lot of small studies it could treat it as one big study and that can be helpful in certain contexts. But it doesn't help the problem if every individual study is not rigorous and crappy to begin with. So one sort of shorthand you can use is to look at every systematic review. Don't just look at one systematic review of that data because there's likely to be multiple systematic reviews. If you find three or four by different authors and they're all saying the same thing that's more reliable than if you just found a single systematic review that was saying that. So I look for that consilience among multiple different authors who are reviewing the data to see if again there's sort of agreement. It's also good to go back in time look at reviews from in the past and see how the data has evolved over time. Is it spinning its wheels or is it progressing? As we said science progresses are they getting more and more detailed better quality data or are they still wrestling with the same questions they were 50 years ago because they really are making no progress, you know what I mean? That's another way to to get a handle on how reliable is this data or you know are the studies that we're talking about. That's as easy as it gets. It's complicated to do it. And again the for most people for most things what you're really going to do is you're not going to be reading systematic reviews yourself. You're going to be reading experts reading systematic reviews and coming to a consensus opinion about what the data says. Or you could listen to a position paper by the World Health Organization or the CDC or the American Headache Association or whatever. And so you have experts multiple experts looking at the data hashing it out and coming with a consensus position paper on that data. That's the most reliable thing for a lay person. Someone who doesn't have the scientific expertise to make an independent judgment about the scientific research. Because it's hard. There's no it's I struggle to do it even if I go slightly out of my area of expertise. So there's nothing easy about it.

C: Yeah I mean the process of getting an advanced degree in science a huge part of that is learning how to read papers.

S: Totally.

C: Like that's a big part of what you do in your training. So it's not surprising that it's hard to do because even people who have been doing it for a very long time and who are trained in this still get tripped up sometimes.

S: Yeah absolutely and this is a very important, part of a reason why I wanted to answer this question, it's a very important issue for skeptics. Because we talk a lot about the quality of science and distinguishing science from pseudoscience but I want to make sure that doesn't give you the illusion that you're a scientist. Or that you can do like independently interpret the technical literature. Because really you are really trying to make you humble to know that you really can't. Maybe you can sniff out the garbage or like you get a sense that all right one systematic review is not the gold standard. There could still be problems with it. So you always want to hunt out expert opinions and multiple opinions and especially contrary opinions. Is there anybody that disagrees with this. And why do they disagree with it? Are they, do they have the appropriate credentials that I should even bother listening to them? Are they are they on the fringe or are they representing more of the mainstream. And then you can get idea. The good news is for most things there's not a lot of controversy. For most scientific questions there may be some like internal like the scientists are debating about the details but on the big the nuances but unlike the big questions the big scientific questions that aren't politically controversial or religiously controversial or whatever. You don't have to worry about it too much. Any scientist pretty much is going to give you the same answer. But when you get to the controversial things it's you, unless you have topic expertise, I don't know what to tell you. It's really really hard.

C: And I think that really good journalists and of course we talk about good science versus pseudoscience and then we have have to talk about good journalism versus pseudo-journalism. But really good journalists systematically do this when they're covering science. And you'll notice that. They'll say dr so-and-so from this institution who was not involved in the study─

S: Exactly.

C: ─thought it was important to point out that x y and z. Because they do want to see what are some dissenting opinions from other experts in the field.

S: Exactly. But you know there's a lot of crappy journalism out there too.

C: Absolutely. And usually you don't see that in the crappy journalism.

S: Yeah but don't forget humility, you know? Don't just don't think because that you're generally skeptical you could have your own opinion about highly detailed technical scientific topics. That's not being skeptical actually. We have to remind ourselves of that. Because we you know a lot of what we do is talking is where science communicating about science in general including things about which we are not experts but I want to point out what we're doing when we do it correctly is reflecting the best understanding of what the consensus of scientific opinion is. We're not substituting our own opinion. I only do that in areas where I feel I have sufficient expertise because of a physician and an expert in science-based medicine etc. But I wouldn't do that in astronomy. I think I know quite a bit about astronomy that would never substitute my opinion for the consensus of astronomer expert opinion. It'd be insanity to do that. It'd be folly. Right? Okay. Let's move on. Guys it's time for science or fiction.

Science or Fiction (1:28:40)[edit]

Item #1: A new analysis finds that the Cueva de Ardales in Spain, famous for its many cave paintings, was likely occupied by human ancestors for as long as 500,000 years.[6]
Item #2: In a prospective study, dogs were able to detect asymptomatic SARS-CoV-2 infection with higher sensitivity than nasopharyngeal antigen testing, 97% vs 84%.[7]
Item #3: Scientists have developed a molecular drill that is activated by visible light and rotates at 2-3 million times per second, which can drill through bacterial membranes and can be used as a broad-spectrum, rapid-acting antibiotic for skin infections.[8]

Answer Item
Fiction 500,000-year occupation
Science dogs better than antigen test
Science
molecular drill
Host Result
Steve sweep
Rogue Guess
Bob
molecular drill
Cara
molecular drill
Evan
molecular drill
Jay
dogs better than antigen test

Voice-over: It's time for Science or Fiction.

S: Each week I come up with three science news items or facts. Two real one fake and then I challenge my panel of skeptics to tell me which one is the fake. No theme this week. We've had a lot of themes recently. We're just gonna do three regular news items. You guys ready?

C: Yep.

J: Let's do it.

S: Here we go. Item #1: A new analysis finds that the Cueva de Ardales in Spain, famous for its many cave paintings, was likely occupied by human ancestors for as long as 500,000 years. Item #2: In a prospective study, dogs were able to detect asymptomatic SARS-CoV-2 infection with higher sensitivity than nasopharyngeal antigen testing, 97% vs 84%. And item #3: Scientists have developed a molecular drill that is activated by visible light and rotates at 2-3 million times per second, which can drill through bacterial membranes and can be used as a broad-spectrum, rapid-acting antibiotic for skin infections. Bob go first.

Bob's Response[edit]

B: All right. So you got this cave here occupied by human ancestors for a half a million years. Wait.

S: Obviously not the same people. (Cara laughs)

B: They were immortal!

E: Happy birthday.

B: I've heard of like serial occupation of a site. About a half a million man. Oof. I mean these are good because they're all just like on the edge here. The dogs that can detect asymptomatic Sars-CoV-2. Better than the antigen testing. Is that home? Is it the home one?

S: Yeah for the one you shove the stick up your nose.

B: Wait that's only 84%? That's pretty lame. I would think that it would be in the 90s. So that in and of itself is surprising and disappointing. All right let's look at this other one. This one though man. Molecular drill activated by visible light rotates two. I could see this high, the thing's so small that it's not rot- it makes sense that it can go some crazy rotations. Two to three million per seconds. Nuts. What I mean I'm having trouble just visualizing how this is going to work. How are you going to do this? How do you know that this is going through bacterial membranes and that other cellular membranes? How do you target the cells? Couldn't there be like thousands and thousands of these bacteria. So how? You're gonna have to. No. All right I'm saying that's fiction.

S: Okay Cara.

Cara's Response[edit]

C: I don't know. Why do you do this to us every week?

B: Stop it!

C: It's so cruel.

J: Cara. It's torture.

C: it's so cruel.

S: Why do you try to fool us every week?

C: I know.

S That's the game sister.

C: The cave in Spain.

B: The cave!

C: [in Cockney accent, as in The Rain in Spain] "The kive in Spine falls minely..." 500 000 years is a long time.

S: Yes it is.

C: I know that like I have seen when we look at these like Ethiopian remains in these different caves that it is multiple generations and like tens of thousands of years. I wasn't sure if it was half a million years. It's a really long time. I don't like this this dog one at all. This one really bothers me but I feel like there's some red flags in the way you wrote it up. You're talking about sensitivity only like it's a prospective study. So maybe we could have seen these. These numbers make me mad. I don't want dogs to be better at detecting covid than the tests I pay money for but maybe there's like some caveats there. I feel like that the wording of this might might make it hold true. And then okay the molecular drill activated by visible light rotating at two three million times a second. Well if it's molecular probably rotates pretty fast. Can drill through bacterial membranes and can be used as a broad spectrum rapid acting antibiotic for skin. I don't even understand that like how that would happen. I guess we don't get any more information. It's just like such an arbitrary application of this thing. There's so many details in that one that I'm kind of leaning towards going with Bob on this. Half a million years. (laughs) Is that possible? I think I'm gonna go with Bob and say the drill. There's something there that's like too good to be true.

S: Okay Evan.

Evan's Response[edit]

E: No. I knew you were going to pick me next. The cave is 500 000 years. That's big. Where's the Lascaux cave that's in France I think. And my understanding of that cave is that the paintings or the the art in there is like somewhere between 40 and 50 000 years old. But fine Spain which is geographically pretty close and be ten times that that. That throws off my perception of the whole darn thing. The second one about the dogs being able to detect the infection with higher sensitivity. 97% versus 84%. 97% that's high. But we've been fooled by. Well not fooled but led to believe that dogs can sniff all kinds of things. And I don't know that we've ever really done a deep dive into exactly how effective they are. This seems improbable. And then of course the last one which a molecular drill could rotate two point. Two to three million times per second? Really? Wha-huh? How? What? And then. No. That's. All three are fiction. (Jay laughs) That's my answer. They all sound like fiction. Every last one which is what makes it great. Good job.

C: But which one is more fiction?

E: Oh gosh I go with Bob and Cara. I guess I'll agree with them. We'll live together die together.

C: Yay!

S: Okay. Jay.

Jay's Response[edit]

J: You would think at this point I would just simply go yes I will go with the group. But I am specifically not gonna go with the group because I'm going for glory Bob. I think you'll understand this.

E: Glory!

J: If I win it doesn't really matter, right?

C: No if you win it's great if you lose it doesn't really matter.

J: No no. If I win with you guys. I'm sorry─

C: Oh I see.

J: If I win with you guys I'm just one of four people. Yeah yeah we swipe we sweep Steve. But if I win by myself here it's even better. So I'm gonna go with the dog one as the fake. I think the cave painting humans in the cave for five hundred thousand years is a lot. It's a lot but I can see that happening. I'm gonna say that the dogs are not able to do this at 97% accuracy.

E: It is a high percentage.

S: Alrighty. I like it Jay. Going for glory. I like that approach.

C: Real quick Steve I have a question. I know our answers.

E: Are our answers really locked?

C: But you're not saying that they were painting in the caves five hundred thousand years ago, right? You're just saying that the caves happened to have painted.

S: I did not say that they were being painted.

C: Yeah because if that were the news item I would have a very different answer.

S: Yeah.

C: Okay.

S: It doesn't say anything about that.

C: They're just they just happen to be famous for cave paintings.

S: Right. And they also were occupied for a very long time.

C: [inaudible]

E: Someone had to occupy it.

S: All right Jay's going with the dogs everyone else is going with the molecular drills─

J: The molecular man!

S: ─so that means you all agree on number one so we'll start there.

Steve Explains Item #1[edit]

A new analysis finds that the Cueva de Ardales in Spain, famous for its many cave paintings, was likely occupied by human ancestors for as long as 500,000 years. You guys all think that one is science. And that one is the fiction.

[collective disbelief]

E: Oh my gosh of course it is.

B: Bastard.

J: Steve good job. (Cara laughs)

S: Because the real answer is 50 000 years. I did that.

E: I was right dammit.

S: It's 50 000 years and 500 000 years is longer than people have been in Spain. So if you knew that timeline that would have been a big deal.

C: There were no pre-humans in Spain?

S: Well there would have had to have been there weren't any hominids in Spain.

C: Oh I didn't know of that.

S: Yeah.

B: Well you said human ancestors I mean well.

C: He said ancestors, yeah, they still be hominids.

E: So I was thinking right.

J: Steve you tricked us.

S: I did trick you.

C: No no no. There would still be hominids I just didn't know they weren't in Spain. I knew it wouldn't be human beings we're not that old.

S: It wasn't modern humans. Yeah modern but what the study found─

C: They were 250 000 years old was.

S: ─that there were probably modern humans in that cave for 50 000 years─

C: That's cool.

S: ─painting for 50 000 years. So there's like multiple multiple different paintings─

B: That's lot of paint.

S: ─that span 50 000 years of history.

C: That's really cool.

S: You think about all of like our modern histories like about 2000 years old before you get to like ancient times. And this was 50 000 years of occupying this one cave just boggles the mind that much.

B: It does.

J: I it must have been nice in the cave though.

S: It must have been a very big cave.

E: As opposed to going out and freezing to death or whatever else was happening out there, sure.

J: I'm sure water was nearby.

B: It's a lot of sex happening in that cave. (Cara laughs)

C: True, yeah.

Steve Explains Item #2[edit]

S: All right this means that in a prospective study, dogs were able to detect asymptomatic SARS-CoV-2 infection with higher sensitivity than nasopharyngeal antigen testing, 97% vs 84% is science. Cara hit on the the omission. The critical omission. I didn't tell you what the specificity was only the sensitivity. So the sensitivity. So just as an example if I just say everyone has SARS that's a 100% sensitivity but it could be very low specificity. So you always have to give both before you get impressed. But they it wasn't bad but they're sensitive. The specificity was lower. So I'll give you the specificity numbers. It was 90% for the dogs. 97% for the the antigen test. So remember I think you guys don't remember this that the antigen tests are not that sensitive but they're very specific. So if it's negative it doesn't really reassure you that much. It's only 84. But if it's positive then you have it. Then you have covid. Remember that? So that that's the the sensitivity is not that great but if it's positive the specificity is good. This is the opposite. The sensitivity is very high but this the specificity was only 90% which is still good. It's just lower than the 97%.

C: That's blowing my mind right now.

S: It's still pretty good though.

C: For like a whole year of the pandemic we could have been like hey dog am I positive? Because we didn't have tests.

S: Right.

C: Remember?

E: Oh initially, yeah.

B: How do they know if they could actually detect it?

C: Well they train them to detect, that's the thing. It's I've seen I did a piece on dogs detecting tumors and yeah they train them, train them, train them, train them and reward them every time they get it. And then somehow they recognize something that's different from other things.

S: Absolutely. These are called volatile organic compounds. And this is the latest thing not just with dogs but with detecting by multiple methods for disease diagnosis. Diseases give off these volatile organic compounds and if we can detect them and diagnose you with cancer or whatever because you're just sweating out these compounds that are markers for the disease. So they just use that but used just in this study they used dogs to see if they could smell, detect the VOCs of Covid and they were able to do pretty well.

C: Yeah researchers are trying to find like a what do they call like a robotic nose or like a molecular nose so that we can figure out exactly what the dogs are smelling and then make a test for, a reagent test or something.

E: Why not they make this molecular drill?

C: Exactly, apparently that what they did.

S: Well let's go to that one.

Steve Explains Item #3[edit]

S: Scientists have developed a molecular drill that is activated by visible light and rotates at 2-3 million times per second, which can drill through bacterial membranes and can be used as a broad-spectrum, rapid-acting antibiotic for skin infections. That of course is science. So in fact they had a previous version of this which worked just as well but it was activated by ultraviolet light. The problem there is that─

B: Give them tan.

S: ─people can get radiation burst from UV light so they wanted to make one that would be activated by visible light. This one is like just in the visible blue spectrum.

E: Oh like what they did to Spock when the thing latched on.

S: Yeah right you didn't have to throw the whole spectrum at him. Right, exactly.

E: That's right.

S: So it's it and you could stay─

C: Oh optigenetics. That's the word I was looking for.

S: You're looking for optogenetics? So you get, it's a little molecule it has one piece on it that rotates about an axis and it gets activated by a specific frequency of blue light. I think it's at 405 nanometers to be specific and it disrupts the membranes. We call it a drill but it disrupts the membranes of the bacteria. They say it works against gram positive and gram negative. Which is basically both kinds, right? That's like all the that's the two main different kinds of bacteria that infect people. Now of course─

B: The country and western.

S: Yeah exactly. This couldn't be used inside the body. So the the primary application of this would be for like burn wound infections which are a huge problem. Basically lather this thing on there activate it with the blue light and it like within two minutes can kill all the bacteria.

C: That's amazing.

S: You brought up a point how do they keep it from─

B: Yes!

S: ─from killing non-bacterial cells and the answer is they don't. Because it doesn't discriminate and so they just it's all a matter of dosing. They just dose it so that it and it's all again it's topical so you know it's just.

C: Chemo is similar like you can't keep chemo from killing healthy cells. You just have to get the right dose.

S: Right exactly.

B: So there's acceptable losses, is that what you're saying?

S: Acceptable losses. But here's another thing. They also test they tested it, this molecular drill, at sublethal doses but then applied a antibiotic including an antibiotic that the bacteria was resistant to and the and it overcame the resistance.

C: Cool.

S: Because it basically made a hole for the antibiotic to get into.

C: That's great.

S: And so even, yes, even when the molecular drill was sub lethal dosing you combine it with like tetracycline and it worked really well.

C: Sorry did you answer this? What's it made out of? Is it, it's a drug? Like I don't, what is a molecular drill?

B: I would guess a protein.

S: The chemistry is, it's not a protein. You can see the picture it looks like a lot of benzene rings. Rings connected together basically.

C: So it's almost like using an optogenetic type of method but it's not. Because optogenetics is usually cells. You can turn on and off cells expression. And so this is almost like turning on and off just just molecules, inorganic molecules. Maybe organic but not living.

S: I don't know if it's resonating with that axis and just transmitting its energy to that piece zipping it up.

C: Almost like a like razor.

S: So it rotates super super fast. So they're focusing their research now on being able to alter its structure so that it does prefer bacteria over non-bacterial cells giving its specific. They want to get more control over it so it's more specific. Also more lethal. But it's already pretty darn good though. I mean two minutes it could just kill all the bacteria on a wound.

C: Maybe it could work for strep too? Like in your throat if you just shine a light?

S: Yeah, maybe.

C: Oh that's brilliant. Because strep is super resistant to penicillin.

S: Yeah and so there's even other better things about this. So when you do antibiotic studies you always do like serial treatments to see if the bacteria are starting to develop resistance. And they detected no resistance.

B: Of course it's total like physical damage. I mean good luck evolving your way out of that.

C: Exactly.

E: Resistance is futile.

S: That's exactly. That's kind of the point is that this is it would be super hard to evolve resistance to this so it's unlikely to occur.

B: That's one of the cool things about if they ever get off their asses and actually make real like nano machines that can physically disrupt the cell. It's like I'm not using chemicals and causing chemicals reactions.

S: That's what this is Bob.

B: No I'm tear. No but you're like tearing it apart. It's physical damage.

C: The only way to evolve resistance to this is just to evolve a thicker membrane or something like that and then we just make a stronger drill.

S: Yeah this is approximately three megahertz motion. Three megahertz. So pretty cool. Pretty cool. And again it's this will be one more tool in the toolbox as we say. And the more things we have. If it just an, and all of the non-antibiotic options that we have then reduces antibiotic resistance, you know what I mean? Because if we can get away without using an antibiotic that's great that. The more different methods we have of treating bacteria the less likely it will be for resistance to occur because that's the one of those looming things that's going to kill everybody.

B: Yes it is.

E: Gonna catch up to us eventually.

B: Oh look? Look Steve I have a scratch on my finger. I'm probably gonna die.

C: Yeah I mean you people used to die of that and [inaudible] going back there.

E: I remember.

S: All right but we'll we'll science our way out of it, right guys?

E: We better.

S: Evan give us a quote.

Skeptical Quote of the Week (1:46:35)[edit]

When I was a kid, people wanted to be an astronaut. Today, kids want to be famous, and that's totally the wrong approach. You have to have authenticity in what you're doing. You have to really care about the core message of what you're saying, and then everything else will fall into place.
David Copperfield, American magician

E: When I was a kid, people wanted to be an astronaut. Today, kids want to be famous, and that's totally the wrong approach. You have to have authenticity in what you're doing. You have to really care about the core message of what you're saying, and then everything else will fall into place. David Copperfield. The magician.

S: Yeah I could, having interviewed him I could totally hear him saying that. Kind of fits his personality. And I like it that's the basically the approach that I take. Is like just do the work and don't worry about the outcome so much. But I would modify it by just saying and everything else may fall into place. Because unfortunately it's no guarantees in life. David Copperfield is what? The richest entertainer on the planet? You might have a little bit of a distorted view of how things work out.

C: But I appreciate the sentiment but basically he's saying like the way kids are now is not good and we should be the way kids aren't. And it's like wait what. Like I agree kind of. I don't like that this is where things are going but...

E: They're going for the tik-tok fame.

C: Yeah.

S: Yeah but you know there may be you could definitely see the older generation's perspective of that. That everyone wants to be famous on social media and maybe we should be focused more on something else. Just doing good work rather than worrying about the fame right up front.

E: Something more central.

C: Yeah but the sad thing is what is the incentivization? Kids are like but I can get rich on social media.

S: Well but it's only the tip of the tip that gets "gets rich". But when we get asked questions about for example like I want to start a podcast what is, what's your advice? And we give similar advice. Just make sure you like doing this, right?

C: Yeah because it's a lot of work.

S: A lot of work. You just showing up is like 90% of it. And you got to decide like what is go in an acceptable outcome for you? If you're, if it's only going to be worth it to you if you like become famous and make a living off of it, you're probably going to be disappointed. Just statistically speaking. That may happen. That's like the icing on the cake but that shouldn't be your plan. Like your plan should be I'm gonna win the lottery. That's basically what you're saying.

E: Good plan.

J: But if you do take a, if you're good on tik-tok and you don't have any kind of preferences about what kind of videos you like and you just watch like the raw stuff that tik-tok sends you you will realize that most of the people in the world should not be creating content. (laughter)

C: Absolutely true.

J: Just an observation I've made. I know we've been doing this a long time. We've learned quite a bit but we did get lucky.

S: Totally.

J: We came at the right time and everything. But we put our hours.

S: The stars aligned.

C: Jay are you actually on tik-tok?

J: Cara I, believe it or not Cara. There is good content on tik-tok.

C: No I know. I know this to be the case I just don't I'm not on tik-tok. I don't know if you saw that meme that's been floating around but it's like I watch my videos a week later on Instagram like an adult. (laughter)

S: Well it is Jay's job to like try to keep up with all the latest social media stuff. Just in case it's a good venue for the SGU.

J: And in fact Cara so Ian and I have been talking. So what we want to do is there's a lot of bad, bad science and just straight up pseudoscience and complete nonsense on tik-tok of course because─

E: Yeah a lot of it viral too.

J: ─and there's very very very little science on there. And there's very little skepticism on there. So I thought it would be fun and cool if we had Steve do this is bullshit. 30 seconds.

C: Right because it has to be really short.

J: Yeah just keep it short.

E: Make sure you choose the right music.

J: Yeah of course. It has to be so loud that it annoys. No but we we were thinking of having just these short videos that we would put up that just to see if people react to it or relate to it in any way. But the fact is there is interesting and legitimate content on there. It's just a different kind of content and my god.

C: But it's very formulaic.

J: It is 99% garbage.

C: Oh yeah.

S: Which is true of the world, right? I mean just like.

C: Right but that's the difference between social media it's just─

S: It's all there.

C: ─it's not curated at all.

S: It's all there. You're curating it.

E: Quantity is not quality.

C: Exactly.

S: Yeah normally that 99% gets filtered out before it gets to the public. And social media it doesn't. It's all there.

E: Unfiltered.

Signoff/Announcements (1:51:16)[edit]

S: All right guys. Well thank you all for joining me this week.

B: Sure man.

C: Thanks Steve.

E: Thank you Steve.

S: Guys don't forget about the live stream on Fridays starting at five o'clock Eastern Time. We're sort of thinking about what we're going to be doing with that live stream. But for now and maybe only for now if you can join us for a free live stream on Friday at five. So check that out if you've not if you haven't watched it before. And until next week, this is your Skeptics' Guide to the Universe.

S: Skeptics' Guide to the Universe is produced by SGU Productions, dedicated to promoting science and critical thinking. For more information, visit us at theskepticsguide.org. Send your questions to info@theskepticsguide.org. And, if you would like to support the show and all the work that we do, go to patreon.com/SkepticsGuide and consider becoming a patron and becoming part of the SGU community. Our listeners and supporters are what make SGU possible.

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