SGU Episode 780
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|SGU Episode 780|
|June 20th 2020|
|SGU 779||SGU 781|
|S: Steven Novella|
|B: Bob Novella|
|J: Jay Novella|
|E: Evan Bernstein|
|C: Cara Santa Maria|
|Quote of the Week|
|Science is elegant and beautiful, but it requires an effort to understand. This is a golden opportunity to educate people. Any person with a scientific temperament, not necessarily a scientist, cannot support these types of messages.|
|Hasan Al Hariri, CEO of the Dubai Astronomy Group, when asked about the June 21, 2020, doomsday insanity|
- 1 Introduction
- 2 COVID-19 Update (1:44)
- 3 News Items
- 4 Who's That Noisy? (59:34)
- 5 Announcements (1:03:34)
- 6 Questions/Emails/Corrections/Follow-ups
- 7 Science or Fiction (1:26:37)
- 8 Skeptical Quote of the Week ()
- 9 Signoff ()
- 10 Today I Learned
- 11 Notes
- 12 References
Voiceover: 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, Jun 17th, 2020, and this is your host, Steven Novella. Joining me this week are Bob Novella...
B: Hey, everybody!
S: Cara Santa Maria...
S: Jay Novella...
J: Hey guys.
S: And Evan Bernstein.
E: Good evening, ladies and gentlemen!
C: Good evening!
S: So the world continues to simmer along. (Rogues laugh) With all of the things—
C: —Just barely.
S: —that are happening—
E: —A week-to-week assessment.
S: Yeah, we were doing the COVID-19 update because we’re in the middle of a pandemic, but then the Black Lives Matter issue has exploded, for good reason, and that deserves a lot of attention as well. We’ve actually been getting a lot of questions about some of the science and critical thinking issues surrounding the protests that are happening and statistics, etc., and studies that have been coming out. So we’re going to address those in the Email and Questions section because I think that’s a good way to deal with those issues, by answering questions and being a little bit more interactive.
J: And since that happens to be in our sweet spot, we might as well dig in.
S: Yeah, yeah. And we’ve been talking about, like, what should we be talking about when all this big, world-changing stuff is happening around us? We can’t just talk about narrow news stories. We want to do what we do best. We want to contribute what we feel is in our sweet spot, as you say, Jay, which is let’s talk about the science, the evidence, the critical thinking. And maybe, hopefully, raise the level of discourse a little bit if we can do that.
COVID-19 Update (1:44)
S: So the quick COVID-19 update. Again, it seems like it’s accelerating. Like, there’s just so many news items with it. One thing that’s interesting that I wrote about earlier in the week is that—and we’ve had a number of these questions—what was the effect of the lockdown on the flu season?
B: Yeah! Great question!
C: Oh, interesting! I think it would help, right?
B: Yeah! Absolutely.
S: And now we have some data. And, of course, it depends on where you are in the world. The COVID-19 hit the US at the tail end of the flu season. This flu season was a particularly bad one; it was at the upper end of the typical range.
E: Was there an issue with the vaccinations, or—
S: —No. No, there wasn’t.
E: —they just weren’t as effective?
S: No, their vaccine was actually fine this year. It wasn’t the vaccine; it was just a bad flu season.
S: Yeah. There [were] between 20,000 and 60,000 deaths in the US, which—
B: —Well, which was it? 20 or 60—I mean, that’s a huge range, man.
C: (laughs) That’s a wide number, yeah. Epidemiology.
S: 290,000 to 650,000 globally. So the reason for the range is because—
B: —Did it kill the person or not?
S: No, no, no. Most people who get the flu don’t get a flu test, and so, then, you have to infer—
B: —That’s right.
S: —so, clinically, did they have the flu? So if you count just laboratory-confirmed cases, that’s at the low end. And if you count probable clinical cases, that’s at the high end. So—
C: —I don’t think I’ve ever had a flu test. I get a vaccine ever year, but I don’t think I've ever gone to the doctor and [have] them actually test me for the flu. They just say, "Oh, you probably have the flu."
S: Exactly. Exactly.
E: So the high number’s the extrapolation of what is likely?
C: It’s based on [inaudible].
S: Based upon—
E: —Infection rate.
S: —Yeah, if you go to your doctor and say, "I have the flu," and they treat you for the flu, that counts, even if they didn’t do a laboratory test to confirm it was actually the influenza virus.
B, C, E: Right.
S: So that’s why there’s a range.
B: I don’t think should even state the low end, then, 'cause it’s clearly wrong, clearly.
C: (laughs) Yeah.
S: Well, whatever. They could be—
E: —There has to be a floor. They want you know it wasn’t zero.
C: We’re basically just showing the error bars. Like, "It’s somewhere in this range."
S: This is a sideshow, guys. Let me get to what we’re actually talking about. So you’re right—
B: —Sometimes a sideshow’s really fascinating!
C: Story of our lives.
S: —So if you graph out the numbers of infected, of hospitalizations, and of deaths, it typically peaks [in] January, February, and trails off through May. But this year, basically, it ended five weeks early.
C: Oh, wow!
S: The lockdown—
B: —Killed it!
S: —completely shut down the flu season.
B: Cut it off at the knees.
C: But you would hope it would.
J: But, Steve, I don’t want to sound like a jerk right now, but no shit!
C: Yeah. Exactly. (laughs)
S: But, Jay, it isn’t "no shit" because think about it: people are questioning whether or not the lockdown was effective, and this is an independent piece of evidence that, yeah, it’s effective! It actually does keep viruses from spreading around, not just COVID-19, but also the flu season. As you say, it should have worked, but it’s nice to know that it actually did.
B: And Jay, don’t forget, a lot of people were saying, "Well, if this coronavirus is so bad, why don’t we take similar measures with bad flu seasons?" And a lot of people were thinking, "Yeah! Maybe we should take it more seriously because that’s a lot of people that die every year. And here is more proof, then, that if we did take it a little bit more seriously, especially in a bad flu season, and do something akin—not a full on lockdown, but just be more careful with touching your face and maybe even more masks—we could cut those numbers down as well for the annual flu.
S: Exactly, Bob. Exactly.
C: Yeah, Bob, wouldn’t it be great if human behavior changed, [if] the biggest thing that came out of this is that people actually follow their own advice and decide not to go to work when they’re feeling sick or not send their kids to school when they’re feeling sick.
E: I think handshakes are done.
C: Yeah, I think so, too.
E: Or if not, severely curtailed. So there are behaviors that are going to change probably forever as a result of this.
S: I think so.
E: And that’s good.
S: And I hope in a good way. So, Bob, I think you’re absolutely right. And think about it: if 60,000 people died this flu season in the US from the flu, or 400,000 died worldwide, it wouldn’t have made headlines.
S: There would’ve been zero discussion about it because we’ve become complacent because it happens every year.
C: Yeah, we’re used to it.
S: And this it not to say that we needed to respond this way to COVID-19 because if we hadn’t, there would’ve been—
B: —A nightmare. More of a nightmare.
C: Oh gosh.
S: —hundreds of thousands more deaths in the US and millions of deaths in the world. It would’ve been much, much worse. But, at the other end, I do think it absolutely means maybe we shouldn’t be so complacent about the flu. And maybe we could take some of the skills that we’re learning, collectively, as a society, and apply that to the flu season. So, if people were, as you say, Cara, if you’re sick, don’t go to work! Don’t send—
C: —I know! People do it all the time, though!
S: —don’t send your kid to [school]! Wear masks! During the high—if you’re going to be in [tightly-packed] public places during flu season, wear a mask. Wash your hands. Don’t touch your face.
C: Don’t fly sick!
S: Don’t fly—exactly! And maybe we need to think carefully about how we just arrange our big social events so that we’re not creating petri dishes all over the place. And maybe coming out of this, we will be better able to treat those flu season because people are more ready to do it, because basically they’ve just mostly been ignoring the CDC every year when it comes to flattening the curve of the flu. The one other little thing is that when we’re counting—we’re trying to calculate the risk versus benefit of the economic shutdown. There’s a lot of things other than COVID-19 deaths that we have to consider on both ends of the equation. But, at the very least, we have to count tens of thousands of people saved from dying of the flu. That counts as well.
C: Oh, you’re right. That’s an economic benefit.
S: Yeah, totally.
C: Interesting. Yeah, I don’t think a lot people think about that.
S: But, again, there’s things on both sides. So the economic shutdown is not benign either. So, clearly, we can’t do this every year. And that was one thing I [inaudible]. How often can we do this? It might 20 years before we could really weather another shutdown like this.
E: Gosh. Maybe. Maybe.
C: Which sucks because I doubt it’ll be 20 years before another —
S: —I know! So what is—
C: —epidemic or pandemic.
S: —That means we need sustainable strategies for mitigating epidemics and pandemics so that we don’t ever have to shut down our economy like this again.
C: And they to be organized. You know, there’s a great—I sound like a broken record—the newest Frontline, which just came out yesterday or the day before on PBS, is—I think it’s called something like "Coronavirus: What Went Wrong"—and it’s all about countries that handled it well, countries that didn’t, all the missteps that we made with the CDC, which missteps are with the WHO. It’s actually really well done. They’ve researched a lot of, like, "What did we f- up this time so we could do better next time?"
E: Look, if we don’t learn from these mistakes, we are just doing ourselves the greatest disservice [to] humanity. If we—
C: Yeah, and it shows. You see South Korea doing so well because they dealt with SARS and then MERS. And they just had—they were like, "We ain’t gonna let this happen again."
C: And they were—they just had this amazingly rapid response. And we could do that, too. I think you’re so right, Steve, that we’ve been complacent. I also wonder if some of the behavior, like the psychological drive behind these behavioral issues is that the people who are most affected are also the people who are most hidden in society, so, like, the elderly, who are often living in nursing homes or don’t go out in public as often, people who are low-SES, who are struggling—they’re sort of not as elevated or visible in society, And so I think it’s easy to just get complacent and forget about it being a problem because if you ask most kind of middle- to upper-middle class people in America, like, "Hey, isn’t the flu dangerous?" They’re like, "The flu doesn’t kill people."
S: Yeah. It’s true.
C: They’re like, "I’ve never met anybody who the flu as killed." And it’s like, "Well, you’re lucky, then."
S: It’s probably not true, also.
C: Yeah. Probably not.
S: Here are the numbers. So about 8.4 million cases worldwide, 450,000 deaths, over 115,000 deaths in the US. So, still, we’re sort of—overall in the US, the numbers are starting to come down, but it’s very state-by-state. And here’s the thing: we’re starting to get—again, it’s not clear if people should call this a surge, a second wave, a resurgence, or whatever. But there are a number of states that are really peaking now. Arizona, Arkansas, South Carolina, Alabama, Oregon, Florida are all having these peaks. I think Arizona’s the worst right now.
C: Yeah, Texas, too.
E: Mostly the southern states.
S: And it relates to their behavior.
S: It relates to the regulations in those states. I mean, it’s pretty clear.
C: They opened up faster and more vigorously.
C: I think we’re at a point now where our number, our infection rate is just exceeding all of western Europe combined.
S: Yeah. We’re still very much in the middle of the first wave of this pandemic.
E: Hmm. That’s the—
S: —People have this overall sense that, "Ah, it’s over." And they’re trying to go back to business as usual. But, no, you look at the numbers. It’s still—we’re in the middle of this, still.
C: Well, and that’s what shows. We kind of discussed this a little on Friday, but it just goes to show a basic misunderstanding about the mechanics of a virus. We’re nowhere near herd immunity. We don’t have a really good treatment yet, although there is that steroid that people have been talking about that seems to be showing promise. We don’t have a vaccine. What reason do we think that we are now more protected than we were before. We’re just not.
S: Right. Yeah. Right. Exactly.
C: It’s just complacency and laziness.
J: What’s basically changed is that the places where the virus was smacking hard have lessened significantly, and now it’s just moved to other states.
C: No, it’s still in those place. That’s the thing. The places where the virus smacked really hard got their shit together and shut down more severely. And the places that never shut down or that did shut down but go, "Hey, I don’t see a problem. Nothing every happened here." (whispering) "Because you were shut down!"—just loosened everything up and now they’re getting hit hard.
S: They’re getting hit.
C: The virus is everywhere. It’s not going away until we can vaccinate against it or until enough people get it that we’re not going to catch it again.
S: Yeah. And that’s the final thing. I do hope that in addition to wearing masks being more socially acceptable, even required, going forward during flu season or whatever, hopefully support for things like vaccines will be much higher. I’d like to see the compliance rate could be much higher with the flu vaccine. Maybe if and when we do develop a COVID-19 vaccine, I wonder if they can pair it with the flu vaccine. And if they were given at the same time, would the compliance with the flu vaccine go up?
C: Oh, interesting. But it’s also interesting how many people are already writing about and doing studies about—there’s already vaccine resistance to the non-existent COVID vaccine.
S: Oh yeah. Right.
E: Steve, in the news recently, they’re talking about steroids helping reduce the—
S: —Yeah, Cara brought that up.
C: That’s a new one, yeah.
S: There’s dexamethasone, which is a powerful intravenous steroid. There was a study which showed that it might decrease the death rate in the sickest people. So, essentially, some infections during parts of the infection—it’s the inflammatory response that does the damage. And so, especially with viruses, the question is—like with some bacteria, it releases toxins, and the infection itself is doing all the harm—with viral infections, often it’s the body’s immune and inflammatory response that actually does the harm. It’s the inflammation. And so the question is—
C: —It’s like when people refer to cytokine storms, Steve, that’s often what’s happening?
S: Yeah, that’s part of it. The cytokine storm is at the end of the sickest patients when things just shut down. But if you give steroids to shut down the inflammation, what will happen? It will sort of reduce the damage from the inflammation, but it also lets the infection go unchecked. And so there’s a balancing act there. And so you have to study it with every virus, every type of infection, and maybe even different phases of the infection. There’s some types of meningitis, for example, where steroids are helpful; other types of meningitis where steroids will kill you. It totally depends on the specific infection. But it was reasonable to study it in COVID-19 because when people are really sick, the virus and the inflammation are just ravaging their organs, especially their lungs. So it was reasonable idea. So they’re saying, "Okay, if we use steroids when, basically, the game is otherwise over, the inflammation is just destroying their organs, can this save some people?" And the preliminary evidence is positive, but, of course, we need to study it a little bit more carefully.
C: I mean, that’s good news for the sickest people. It’s good to hear because the scary part of this virus is what it does to people when they’re already on the ventilator. Like by the time they get there, it doesn’t look good.
S: Yeah. It’s incremental. It’s not a game-changer, but I think that overall, doctors are just getting incrementally better at treating COVID-19. And I think that’s probably a big reason why the death rate is going down faster than the infection rate is. I think we’re just getting better at jumping on people who are sick, testing them, treating them.
C: Yeah, and probably people are getting treated earlier, too.
J: So the bottom line is: we are still within the pandemic. Tell your friends and family that you can, "Be mindful. Don’t let your guard down. We have to keep the protocols high and active." That’s it.
S: Yeah. That’s it. We need endurance at this point and discipline. Otherwise, we may look back at this and think, "Argh. This again was an opportunity where we let it get away from us." And we’re going to be paying for it down the road.
S: All right. We’ll muddle along.
Brain on a Chip (16:05)
S: Jay, you’re going to start the News Item segment by telling us about this new brain on a chip.
J: So engineers at MIT have designed a brain on a chip that is actually smaller than a piece of confetti.
E: All right, you have to define "brain" for us, Jay. Go ahead.
J: Yeah. Well, here we go. It’s kind of like a brain. So it’s made up of tens of thousands of silicon-based brain synapses known as memristors. Memristors.
J: —That’s a portmanteau.
C: Resistor, but memory?
J: Memory transistors.
B: Memristor’s a portmanteau of memory and resistor.
C: Ah! I was right. It’s resistor, not transistor.
J: Oh, I’m sorry. And resistor. You’re right, Bob. So the engineers published the results of their research in the journal Nature, Nanotechnology, and I just like the fact that we’re [in] an age where we have journals that are named with the word "nanotechnology" in them, and it’s legit.
E: Yeah, well, Nature, Phrenology kind of closed awhile ago, so…
B: Yeah, they’ve been out for a while, man.
J: Keep in mind that there have been many advances in this exact area, right? And this is just another incremental advancement that was made with memristors out of alloys of silver, copper, and silicon. Now, previous versions were made with un-alloyed elements and didn’t function as well as the new one. And that is, basically, it as its core, but let me give you come of the details because there’s some cool stuff that they’re doing here.
So, memristors are called neuromorphic devices, which means they mimic the human brain’s neural architecture. "Neuro-morphic." I love that. A "neuromorph." That could’ve been one of our patrons' names, right, Bob? These connections are an essential component in neuromorphic computing. So even though it serves as a [resistor] in a circuit, it’s actually a lot more like a real human brain synapse. It’s the connection between two neurons, right? That’s really what this memory transistor is doing. [Jay describes transistors, forgetting that memristors are not close to transistors, but rather to resistors.] In a traditional transistor, you either have on or off. And when we think, "What does an actual synapse do?" A synapse isn’t on or off. A synapse has to do with the amplitude or the volume of the signal that’s coming through. And they were—
C: —Yeah, but it becomes all or nothing. That’s how neurons work.
S: In a way, Cara.
C: There’s a threshold. There’s a cutoff. [inaudible]
S: There’s a threshold effect, and the neuron either fires or not, but what really makes a difference is the frequency at which the neuron fires. Is it firing fast or slow? And that’s analog.
C: That’s true.
S: So, yeah, each individual firing is all or nothing, but the rate at which it fires—that’s the variable. And that’s something a lot of people don’t know. They think, "Oh, the signal goes through. The neuron just fires once." No, it’s like—
C: —No, they fire—
S: —it’s a train of firing. In that way, the signal’s analog. So this is a way—the memristor’s a way of doing the same thing where it’s not just on or off. It’s not a 1 or a 0. It’s a range in between.
J: A memristor could send a varying strength of signal depending on the strength of the signal received. It kind of just repeats the signal that it’s got.
C: Yeah, so that is really similar, actually.
J: This makes it function more like a real synapse, and if you think of that like the difference between on or off—which, essentially, is two options—a memristor would have many, many more options, making its possible range of operations much bigger. So another interesting thing is that a memristor would be able to remember a value that was associated with a particular signal strength and re-create the same, exact signal the next time it receives that specific signal. And that’s where this thing dives into super complexity because when you have—
C: —Because it can learn?
J: Well, yeah, it’s able to have memory, which—I’m sure, physically, it’s got memory. But I guess what they’re saying is when it gets the same strength of signal, it’ll access the memory that it had. Kind of replicating what’s going on in the brain, in a way. I guess this—
C: —Yeah, sort of, at the synaptic level, it’s replicating it.
J: Yeah. So previous memristors designs—they didn’t use alloys, like I said before. And they had a problem with ions that would flow through them, actually wandering off track and getting kind of lost. So it’s kind of like thinking about a hose that has water going through it, and there’s a little dribble of a hole that’s letting water out, and some of the water gets lost. Well, that water is crucial. Why?
C: It’s funny because that happens all the freakin' time in the brain or in body cells. (laughs) That’s exactly how cells work.
J: They’re trying to get it so the accuracy is there. It’s consistent because when, like the hose, when water leaks out, then the amount of water going from point A to point B is not the correct amount, which isn’t soliciting the correct response or reaction by everything else around it. It’s a little complicated, but—
C: —It is, but the funny thing is, I’ll be impressed when it still works, even though it’s leaky as hell, which is how our brains are. (laughs)
J: Well, let’s get at close to perfection as we can first, and then we’ll let it get freaky [?]. So they’re calling it signal strength. So when some of these ions get lost, the signal strength drops, and then that was a problem because things weren’t predictable and weren’t acting in a way that they wanted. So the engineers looked to known metallurgy techniques to solve the problem, which was genius, if you think about it. So they figured out that copper was the right metal to add to the silver and silicon mixture. So now they—
E: —"That’s right, copper!" (James Cagney voice?)
J: "That’s right, copper!"
J: And because they knew from metallurgy techniques, they knew that copper would help retain the ions inside the flow. And, you know what? It worked. So the researchers said that "in performance tests, the new hardware worked better than its predecessors" and they were able to do a lot of really cool things with them working much better. You know, they were using it to sharpen an out-of-focus image, which I thought was really cool. This is not truly novel because software programmers have already created artificial synapse networks with software, right? But that is just not the same. And the research team wants to build a physical neural network that’s hardware-based.
So this could be used, when they really get this slick, as a portable artificial intelligence system. Now, I don’t think it’s going to—it’s not going to be conscious. Let me explain what this means. This would come in handy, [as] an example, if you would want to operate without an internet connection. Like today, you talk into your phone and it tells you—it types out what you said. Your phone can’t do that. Your phone is sending what you’re saying to their server farm with really amazing software on the other end that’s translating what you’re saying and sending back the translation, right? It’s not happening on your phone.
B: I didn’t say they could do that!
C: Yes you did when you checked that box.
B: Oh shit.
C & E: (laughs)
J: Well this is considered—
E: —I didn’t read all that.
J: —this is considered heavy processing. Why would you want everybody’s phone to have to even come—they couldn’t. Your phone couldn’t handle the amount of processing that needs to be done to do this. It’s very smart, from a software perspective, to have all the phones be sending back to the main server. The main server has all the juice to do the processing and then just gives you back the answer; that’s fine. But there are circumstances where you want things to happen super fast, and you don’t want to be relying on the internet. Like, for smart cars as an example, you want cameras on a smart car to be able to see something and get an instantaneous, a millisecond response, which you can’t get fast enough over the internet. [So] then you have to do it locally. And this would be the way that they would be able to take that super dense and process-heavy computations locally and shrink them down so we can have them in a computer that’s in your car or your cellphone or handheld devices at some point.
So, they’re calling it "having a resident neural network," and they’re saying that it would be independent from the internet. It would operate in real time, as opposed to having to wait to get the response back from some server somewhere. If the internet goes down and the space shuttle or the dragon capsule is using this, they’re screwed. You know what I mean?
J: So you have the intelligence onboard. So, it’s an incremental move. It’s really cool because they were able to make a milestone change here, even though it didn’t lurch things forward so much that we should be like, "Hey, five to ten [years]!" Nobody’s saying five to ten on this. But I find it interesting because as we mimic human brain—as we kind of reverse engineer what the brain is doing, and we’re pulling more and more pieces out that they’re going to try to fit into this hardware, what are we going to get? And will there be—I put it to all of you guys—will there be some type of emergent consciousness?
S: So the next news item which I want to cover is also about kind of a narrow, technical thing, but I thought this was just too cool not to mention. Have any of you heard the word "twistronics?"
J: Not yet. No.
C: No, not at all.
B: I think I did.
E: Sounds like a video game.
S: Yeah. So it’s like this entirely new technology, which could—
E: —(laughs) Entirely new?
S: —could come to nothing, could transform our technological world.
C: (laughs) You know, one or the other!
E: I’ll take four of them!
S: This is going to be quick. This is what it is in a nutshell. You take a two-dimensional material—
J: —That is cool, Steve! I mean, who would think of that?
E: Two-dimensional material, okay.
S: —Like graphene—
S: The graphene is a two-dimensional material made of carbon, where you have chicken-wire, hexagonal configuration, right?
E: I love it. Like a hex map, yeah!
S: Yeah, exactly.
J: (laughs) Ev…
S: And this has—we’ve been talking for years about the fact that these two-dimensional materials and these graphene materials have really interesting properties in terms of their conduction of electricity, their conduction of heat, their strength. They’re lightweight. You could make nano-fibers out of them, nano-tubes, all kinds of things. You can dope them with other material to alter their properties. And this has been—
C: Ooo. "Dope" them.
S: —a very, very active in material science research over the last decade. And we’re starting to see applications at the basic end, like mixing little nano-fibers, these carbon nano-fibers, into materials so that it’s stronger, for example. But we’ve had a hard time manufacturing it at a massive industrial scale, especially at the level of quality that we need because even one kink or rip or whatever could really destroy the physical properties, right? It could unzip. So researchers have been researching what happens when you take two sheets of graphene and then you rotate them or twist them slightly relative to each other—
J: —So they touch each other in certain places? Is that what you’re saying?
S: No, it’s more that—so you lay them directly on top of each other, but then one is rotated with respect to the other. So they’re not—
C: —Oh, so there’s almost a cross-hatch pattern.
S: Yeah, exactly.
B: That’s cool.
E: Oh, you double, triple, quadruple the strength or something?
S: So theorists predicted that there might be a magic angle. And that at that magic angle, really new physics may happen. It’s kind of like a meta-material, right, Bob?
B: Yay, new physics! Yes.
E & C: (laughs)
C: Got his attention!
S: And then in 2018, the experimental physicists demonstrated that the magic angle actually exists. It’s 1.1º, so it’s a very, very small angle.
S: So if you take two sheets of graphene and you rotate them with respect to each other by 1.1º, then it becomes—what do you guess?
E: Ten times?
C: Yeah, ten times as strong.
S: —becomes a superconductor.
(Rogues express amazement.)
C: No way!
S: A superconductor—
J: Whoa. Wait, wait. A legit superconductor?
S: —legit superconductor—
B: —But what temperature, though?
S: —one of the holy grails of materials science.
B: What temperature?
C: Still super cold?
S: It’s a superconductor up to 1.7K.
C & B: (laughs)
E: Up to 1.7K. That’s cold.
S: Yeah, that’s cold.
B: So, kind of hovering very close to absolute zero.
S: Yeah, but it’s more about the proof of concept—
S: —than anything else.
B: And who knows? They may be able to tweak that, but just learning that such a simple thing can create a superconductor can explode our knowledge on superconductors in general.
S: So it’s tunable. Basically, the resistance is tunable by twisting the layers. The recent study that was done, though, extended this one notch. They looked at the graphene, but they included in it molybdenum trioxide.
B: (mumbling and stumbling with the word)
S: Or "moly" trioxide. And with this two-dimensional material, they were able to make it a superconductor of light. But not only that—
B: —Wait, what does that mean?
C: I don’t know.
S: Okay. This is what it means. So normally light would basically propagate in every direction, would diffract quite a bit, but what they found at the magic angle: [if] light traveled in straight lines without diffracting, it would propagate in just a single direction. So why would that be important? That would be important because this could, potentially, be useful for photonics.
S: For basically using electrical devices—electronic devices—but with light instead of electrons.
E: Oh. Everything’s fiberoptic at that [inaudible].
S: No, not fiberoptic. This is not just for information. This is photonics. This is like your computer—
B: —Your computer running on photons instead of electrons.
S: —running on photons instead of electrons, exactly.
B: That’s what I said.
E: The actual [inaudible]. Think beyond the connection. Think the…
S: It’s photon’'ic versus electronic. Think about it that way.
J: (sing-songs) Photonic!
C: Cool. Is anything photonic right now?
B: I think so…
S: I don’t know if there’s any actual application. It’s more of a research thing because—again, we’re trying to find materials that will allow us to control light with enough precision—
C: —Man, that’s cool!
S: —in order to make a device out of it. So this is, again, along those lines.
E: What’s the byproduct?
B: Efficiency and speed, I think, because imagine this: I’m not only operating at the speed of light—
S: —It’s much less [inaudible]
B: —Right, and that’s because the photons don’t interact. They go right through each other, whereas electrons do!
C: Yeah. They line up in a little line.
B: You could be crossing the streams as much as you want, and you’re not going to get any of the interference that you would get with electrons, so that’s huge.
S: Right. So a theoretical photonic device would be faster, smaller, and cooler than an equivalent electronic device.
C: And you mean temperature-cooler, not just like cooler?
S: Yes. Temperature-cooler.
E: Well, both, actually.
B: Generically cooler.
S: It’s important if you’re trying to implant a device in your brain, for example. You don’t want it to produce any waste heat. And the smaller and faster is also important as well. So implantable devices might be one of the target applications for a photonic, computational device, for example. So that’s basically it. This is an incremental advance. It’s a proof-of-concept thing. And it’s just opening up this new world of tuning the physical properties of these two-dimensional materials simply by manipulating the twisted angle of one layer on top of another. And there are these magic angles where new physics happens. And, again, we’re at the very, very beginning of this. In 30 years, this will be a laboratory curiosity, or it will transform our technology. It’s hard to say.
B: Yeah. Now, Steve, do you consider this a meta-material?
S: It is considered a meta-material.
B: Okay. It makes sense.
E: Does it lose its 2-D definition when it becomes twisted, when you start layering these things?
S: No, no.
E: It’s still considered 2-D?
S: Still 2-D. Yeah, still considered two-dimensional.
C: Still flatter than paper!
S: So, Bob, what do you think is—
B: [inaudible] One!
S: —the important force at work at that scale between the two twisted two-dimensional layers.
B: Van der Waals?
S: Very good! Excellent.
B: Yeah, baby!
S: Van der Waals. That is it. That’s the same force that’s—
S: —Yeah, gecko. It’s a very close [inaudible] like between surfaces or between particles, etc. It also deals with a polariton—Bob, do you know what a polariton is?
B: I have heard of them, yes, I have.
S: It’s a quasiparticle that emerges—
B: —Quasiparticle. Kind of like a hole, almost, right? Some of these quasiparticles—
S: —Quasiparticles are not real particles. They’re just phenomena that behave like particles because they can move and track and whatever, but—so it’s a quasiparticle that emerges when you excite a photon. So they’re also important. The polaritons, I think, are what are being propagated in the single direction through the twisted two-dimensional material. And it’s anisotropic, which I believe was one of our words, wasn’t it, Cara? Anisotropic?[v 1]
C: Umm… maybe.
S: It just means that it has different properties in different directions or different configurations.
C: Yes! It was. It was. It was. We talked about being high on a mountain, and—
S: —As opposed to isotropic, where it’s the same in every direction.
C: Mmhmm. Uh, it sounds like you’re just making a lot of this up, but that’s science. (laughs)
B: No. I’m backing him up. It’s not bullshit.
C: It’s like there is that line for the naive listener, who’s like, "Is this sci-fi?" It’s almost sci-fi.
S: Oh, you want sci-fi, Cara? Here’s the actual article, okay? Listen to this.
S: "Following our previous discovery, published in 2018, we found that bi-axial Van der Waals semiconductors (like the chemicals used) represent an emergent family of materials supporting exotic polaritonic behaviors."
C: (laughs) I love it. I love it.
J: Cara, listen to me very carefully.
J: The Emperor made everything that happened to Anakin Skywalker happen to him. He—
J: —he funneled him into becoming a Sith Lord, and then when he had him emotionally, he made him kill everyone that he could, and then he took advantage of the fact that Obi-Wan cut his arms and legs off, and that’s when he made him become Darth Vader. He basically built a prison around Anakin Skywalker.
C: Jay, you’re not using enough of the lingo. You have to throw in some of those words that Steve just used.
S: Like polaritonic, Jay, come on.
C: Polaritonic! Put a "polaritonic field" around him.
J: It was a miesotropic dual-transmission protobiotus tube that they put around him.
E: That’s better. (laughs)
C: I love it.
S: Right. All right, Bob—
J: —Cara goes, "I love it!"
Drake Remix (35:19)
S: All right, you want nerdy, Cara?
C: Yep! (Evan laughs.)
S: Bob’s going to tell us about yet another paper trying to squeeze another paper out of the Drake Equation.
B: Yeah, or, kind of related to Drake, but I shall mention that. So yes, we have another prediction about how much life there is in the universe. This time, it’s couched in terms of active alien civilizations in our Milky Way galaxy right now. Researchers claim that there’s likely to be at least—wait for it—36. Anticlimactic, right, a little bit? Thirty-six active alien—
C: —That seems like for just our galaxy.
B: Oh, yeah? Okay. So—
S: —Let’s see. There’s the Klingons, the Romulans…
S: There’s the Cardassians…
E: The Vulcans…
B: The Borlans.
S: The Dominion. Yep.
B: There’s a lot of 'em.
E: Romanians. [?]
B: So how confident is this assessment, and what about the Drake Equation? This comes from scientists at the University of Nottingham, and it published in a paper, recently, in the Astrophysical Journal. So this whole talk likely brings to mind the famous Drake Equation, as Steve mentioned. This is proposed by radio astronomer Frank Drake in 1961. Since we don’t know if there’s life out there—we never got a positive hit in our searches—we don’t know for sure. You have to infer it to try wrap your mind around it. And that’s kind of what the Drake Equation does: it infers the number of planets that could host a technological civilization that we could detect. And it brings to mind—to my mind, anyway—Sheldon, from The Big Bang Theory, who gave one of the most succinct description of the Frank Equation that I’ve ever heard.
E: (The Drake Equation.)
B: Now, Steve, he said, "The one that estimates the odds of making contact with extraterrestrials by calculating the product of an increasingly restrictive series of fractional values, such as those stars with planets, and those planets likely to develop life." And then he says the actual equation: N = R …….. I just remember that from years ago. It was just such a pithy way to describe it. The problem, of course, is that—
C: —(laughs) That’s so pithy. I love it.
B: —It is! It’s just encapsulates it beautifully. So the problem, of course, is that scientists don’t know precisely what those numbers are. How many planets are there in the universe or in the galaxy that could host life, and, then, that could host life similar enough to Earth-life that can develop a technology that we’re familiar with. All that stuff. How do you really wrap your head around those numbers. Very difficult. We don’t know what those numbers are. And, of course, we’ve been refining them. We have a much better idea now that we’ve discovered all of these exoplanets, but we’re not anywhere near a decent confidence level. And we probably never will be. This is fun speculation, but I don’t think really we’re ever going to get an answer.
So this is kind of what these people are trying to do. Dr. Tom Wesby , first author of this latest study, said that, "Our new study simplifies these assumptions." He’s comparing it to the Drake Equation. "…simplifies these assumptions, using new data, giving us a solid estimate of the number of civilizations in our galaxy." "A solid estimate" is how he describes it. Interesting. So, this new research from these physics and astronomy scientists takes more of an evolutionary approach to determining how many times CETI—C-E-T-I, or Communicating Extraterrestrial Intelligent—civilizations arise. To do that, they need to make assumptions, right, because we just know so little. You’ve have to make some assumptions, and studies like these are usually all about the assumptions.
So their paper starts with a general assumption, the big, general assumption, and that is, if you take an Earth-like planet at an Earth-like distance from its star and you also take a Sun-like star, and you put them in a bag and shake 'em for five billion years, then it’s likely that life will form, right, without the bag, of course. I just threw that in there. It’s very likely that life will form, if you replicate some of the big-picture descriptions of our system. So that’s the big assumption. Doesn’t sound like a crazy assumption, but there it is.
But, then, they come up with ranges of predictions because the 36 that you see in these news items, that’s just one of many different numbers that they could have said. There’s lots of different ways to look at this. So they come up with ranges of predictions based on whether they apply a strong or a weak assumption on top of that original assumption, right? So you got the original assumption, and then they have another assumption that they can lay on top that’s either strong or weak, and from there, you get different numbers, depending on if you put the weak or the strong assumption. The number, 36, came after applying the strong assumption on top of that original one that they call the Astrobiological Copernican Strong Condition. Now, that means that a planet-star combination similar to the Earth and Sun must—not "will likely" create life—but must create intelligent life between 4.5 and 5 billion years. That’s the overarching, major assumption that they’re making here, and, then, they add another little assumption on top of that, that the resulting civilization should be actively communicating for 100 years. Just like us, right? A lot of this is based on the only data point that we have: life on Earth. So they’re throwing everything that we know about life on Earth, this one data point that we have, and they’re generalizing it to the entire galaxy or universe. So that’s the scenario because once you take all those assumptions that I mentioned—that life probably started on an analog to Earth’s Sun, and the other assumption was that it must have created intelligent life, not likely, but must have, between 4.5 and 5 billion years, and then you have 100 years of a resulting civilization, where they can communicate using technology—you add all of that together and it spits out a number, 36, or a range of numbers—
S: —Why just 100 years?
B: Because that’s—yeah! Right? I don’t know. Because—
C: —Are they saying at least 100 years?
B: It says 100. But it—
E: —is it because we’ve had 100 years of radio transmissions?
B: Yes, that’s why! Because, at the very least, we’re going to have—if we destroy ourselves tomorrow, we had at least 100 years of communication like that. So that’s what they’re doing. They’re modeling this after what we know, which, I think is—how do you generalized from this one thing? That’s where 36 came from, but the 36 was actually a huge range, and 36 was just the slightly more likely than some of the other numbers. If they applied the weak assumption to their general assumption, the answer would have been something like 928 civilizations existing right now, instead of 36. Okay? Now, the huge problem here is that the assumptions create these huge uncertainties. They’re so large that their answers could reasonable range from zero all the way to 2,908 existing right now in our galaxy, depending on which strong or weak assumption you layered on top. All I can say about that—that’s just, wait, all right, let me just calm down a second—
C & E: (laughs)
B: So it’s those uncertainties—it’s the huge size of these uncertainties that, I think, kills this entire idea and makes these numbers—I mean, if a reasonable answer to your query is zero, then, I mean, how useful is this equation? If it can—
C: —It’s a little like "Garbage in, garbage out." It’s not that it’s garbage, it’s just such speculation that you’re putting in, that, of course, that’s going to magnify the speculation getting out of the algorithm.
B: And, yeah, making it kind of worthless. I tried to think of analogy. One I came up with—it reminds me of Darwin trying to infer the Theory of Evolution from one finch. Like, "Okay, here we go. I got a finch." Trying to come up with the Theory of Evolution—you’re not going to do it. That’s one data point. Speculating can be a lot of fun; we speculate all the time. But coming up with anything that can be characterized as a solid estimate the way they described it, I think, it just pure folly. And I’m curious to see what some other scientists say, people that are real scientists.
S: It’s a pure guess!
B: Yeah, it’s a pure guess. They didn’t even really throw out—if you read their paper, sure. All these details are in there, but a lot of these—and they actually do an interesting job of trying to think about all the various scenarios and metallicities of stars and all these different characteristics—so it’s interesting, in a way, if you can get through the paper—and it’s not as difficult as many other papers i’ve read—but this is fairly technical. But a lot of these news items that these science outlets are talking [about]—they put the headline out there, "There’s 36 alien civilizations in the Milky Way," and they really are not doing it justice and really letting you know all the assumptions and huge uncertainties that are involved. So yeah, yet again, we’re seeing more science news where the title really sucks, and they really just don’t go into the detail that they should.
S: Again, it’s kind of like it’s a best of fun thought experiment at parties.
S: That’s about the level that it is. It doesn’t change anything. It doesn’t really inform anything because—
E: —Yeah, it doesn’t advance the ball for down the field.
S: "The answer could be zero, it could be thousands."
B: Right, and even if there are 36 alien civilizations throughout the Milky Way, they estimate that they’d be, on average, 17,000 light years away, and probably would be a planet orbiting a dim star that would be next to impossible for us to even find. So even then, even in a best case scenario, almost, it’s kind of like, "Oh, shit. Even though there might be 36, we probably couldn’t communicate with them anyway," based on what they were saying. But it was interesting—
E: —Not with radio transmissions.
B: —to research.
S: Yeah, but if civilizations, on average, survive for 100,000 years, then there could be 36 thousand civilizations.
B: Well that’s just it. And they mention that. If we find, somehow—if we can determine that some of these civilizations can last for many thousands of—like for millennia—then that would bode for us. Or, if this theory shows to us that, for example, that there’s only one or two of these civilizations in the entire galaxy, that would mean that they don’t. They don’t last that long. Maybe there is some big filter.
S: Well not necessarily, because of the other assumptions, because maybe most planets that are like Earth—and with the Earth-Sun combination—maybe most of them don’t produce intelligent species. Maybe intelligent technological type of species are more rare than our one data point might suggest.
C: And also maybe there’s a—we know there’s a different kind of Goldilocks Zone, so it’s weird that they’re using this rando Earth-type planet close to the Sun, when most astrobiologists have moved on, especially in our solar system, to looking at things that are much farther away that use tidal energy, for example.
B: Exactly. There could be thriving civilizations underneath miles of ice on more planets than carbon-based life on the Earth. There could be things that we would never detect. And those are the things that are lost in all of their assumptions. That could be the cause of so much life in the universe that we’ll never find with equations like this.
C: Yeah, and they could affect the outcome of the equation by orders of magnitude.
S: Orders of magnitude, exactly.
E: I like that one of the variables of the Drake Equation is the "civilization has to survive itself." It can’t blow itself up in the whole process. Or you have to account that—
C: It has to actually exist.
E: —some of them might do that. You get to the point of nuclear weapons; they may destroy themselves.
C: Or just one bad disease.
B: Or some asteroid hitting the planet or some anti-matter nanotech fusion generated type of weirdness that destroys civilizations.
E: Too many twistronics that they built.
C: Or they just get—
B: —Great filter. Great filter, man.
C: —They could just get eaten by another organism. You know what I mean? There’s a lot of ways—
S: —New robots always take over.
C: —species can die early on. Or yeah, the robots.
E: Ah, the robots.
B: Synthetic life, using their artificial neural networks…
S: All right. Thanks, Bob.
Editing Human Embryos (47:41)
S: Cara, tell us about this study on editing human embryos. How’s that going?
C: Right. So one thing that’s important to point out at the very beginning is that this is not the first time that researchers have been doing CRISPR experiments with human embryos, and it probably won’t be the last. But this specific research team at the Francis Crick Institute, led Kathy Nyacan, did do something a little bit different this time, and doing that little bit different thing resulted in, (bracingly) let’s just say a not-great outcome. So it’s a little bit of a harbinger. Here’s a quote from a person who is not involved in this study but a professor of molecular and cell bio at UC Berkeley. He said, "There’s not sugarcoating this: This is a restraining order for all genome editors to stay the living daylights away from embryo editing." Oh, that’s strong language.
(ur): Oh boy.
C: So let’s talk about what happened. All right. So there have been, like I said, previous studies where researchers have looked at embryos that are donated. So, usually, these are from in-vitro fertilization and, then, they’re unused embryos. They’re donated for research, and there are really strict laws around how to do this. They’re usually destroyed, I think, just after 14 days. So they don’t really get to develop, except for a little while beyond the genomic editing. This study—by the way, a little bit of a caveat—has not been peer reviewed yet. So this was published on the Bio Archive, which means it is—It’s basically when somebody has a finding that they think is important enough that they want [others in] the research community to look at it. They’ll publish there, and then it’ll go through the peer review process because that’s, obviously, a very slow process. Now, usually, journalists shouldn’t really be reporting on things that haven’t gone through peer review, but in certain instances, it’s deemed kind of necessary, especially when the results are sort of like red flag results. So, yes, they may pan out. They may not. But what we’re talking about here is putting on the brakes, not accelerating. And so for that reason, it’s actually important that these results are made more widely available.
So, long story short—and this part is kind of like not as important—but the researchers were looking at a pluripotency factor called Oct-4. So that’s like a protein that is going to have these cells divide. And it’s encoded by what I want to call the "Poof 1" gene. I’m sure that’s not how you pronounce it. It’s actually POU5F1, but when you look at, it looks like "POUF 1."
E: (laughs) I love it.
C: So, for the purposes of this story, I’m going to call it the POUF 1 gene. So what they were looking is this POUF 1 gene, which encodes for this pluripotency factor, Oct-4—and what they wanted to do is manipulate it. They took a sample of these embryos and then they submitted most of them to this gene editing at this specific locus. And they left the other ones as a control. And so then they decided, "Okay. Now we’re going to look and see what happened." And that’s what most experiments prior to this have done, right? I’m going to do some sort of knockout or some sort of deletion or some sort of change using CRISPR. I’m going to do a gene edit, and then I’m going to look at the gene and see what happened to it. But they said, "We want to go one step further, and instead of just looking at what happens to the gene that we’re specifically targeting and manipulating, we want to see if anything happens to the rest of the genome," which is, actually, really hard to do. They had to develop a whole protocol, like an actual procedure. And that procedure is based on something called a "loss of heterozygosity." I don’t know if you guys have heard of a loss of heterozygosity, an "LOH," if you come across that when you’re reading these kinds of articles. What that is, is it’s actually common in nature, but they were inducing it. It also has a wide involvement in cancer.
Brief Biology Review (51:41)
C: But it’s basically the fact—let’s do a quick background. We know the way that—how should we do this? Okay. Have you ever seen a karyotype?
C: Yeah. You have. Anybody else?
J: I have. Yeah, sure.
C: Okay. So imagine a karyotype—if you’ve never seen one, you might want to Google one—and you’ll see that the human karyotype, for example, it has how many? Twenty-three? Chromosomes. 1 through 22 are somatic chromosome and, of course, the 23rd is the XY or XX or some combination or variation based on what happened during myosis. And so those chromosomes are rolled up DNA packaged which a bunch of proteins, and within those chromosomes, there are tons of different genes, right? So the genes are at a specific locus on the chromosome. That’s the site of where the gene is. Does that make sense?
E: "Locus" as in "location."
C: Yeah. Like L-O-C-U-S. A gene’s locus. Or you’ll hear "loci" when you’re referring to more than one. So that’s on the chromosome. It’s a specific point on the chromosome where there’s a gene. And all a gene is is just a coding region. It’s just something that codes for something on your DNA.
B: A protein.
C: Right. Yeah, usually it’s going to code for a protein; sometimes it’ll be another nucleic acid or something. And so when we talk about alleles, we often talk about pairs of alleles, and that’s because you have two chromosomes—one from Mom, one from Dad. You can either be homozygotic or heterozygotic, meaning you can either have two of the same allele or two different alleles. Remember back to your biology class Punnet Square. So the big R and the little r, and Mendell and pea pods and all that good stuff. Usually, you’re going to be heterozygous—not always, but usually—Loss of heterozygosity means that they’re able to look specifically for events where one of those things got knocked out, and now there’s only one thing, which forces homozygosity. Does that make sense?
Back to News Item (53:35)
C: The two alleles are no longer two; it’s only one. So you have a loss of heterozygosity. Those are the specific events that these researchers were looking for. And, remember, they were snipping out the POUF 1 gene. And in 25 embryos—I found my numbers—they snipped it out in 18 of them. The other seven were left as controls. And then, instead of just looking at that gene locus, they decided to analyze the entire genome of every embryo because, historically, studies have shown that there are problems even at the site of the gene. Okay? So you’re trying to knock it out, but maybe in some you end up gaining a little piece of DNA or a piece of the gene. Maybe in some you knock it out but you also knocked out some of the regions near it. Well, that could be bad if something near the gene codes for something super important, right? So that’s a problem. But these researchers said, "We need to know if it’s even more of a problem than that." So they looked at the entire genome of all the different embryos, and they found out, "Ooo, not looking good." So ten of them were normal—okay, so 10 out of 25, or I guess 10 out of 18 because we’re only looking at the experimental ones—that’s…good, but it’s just over half. But they found that eight had abnormalities on chromosome 6, and four had either deletions or additions to DNA that was not the edited gene; it was next to the edited gene. So we’re calling these "off-target effects," and we’ve known for awhile, right, that these off-target effects can be problematic. But here they’re finding that they’re happening on whole chromosomes, which is really, really, really worrisome. And, of course, this isn’t happening in a vacuum because we all remember—we talked about it in a live show in D.C., I think—when that researcher He Jiankui—remember the Chinese researcher? He went rogue.
E: Yeah. CRISPR [inaudible].
C: Yeah. He went rogue, and he decided to edit some babies or to edit some embryos. And—
E: —Haven’t heard much from him lately.
C: Yeah. Unfortunately. And, of course, this was not only totally in violation of the ethics, the well-established ethics of the entire field; it was also not even a necessary thing. He wasn’t trying to save a life; he was trying to prevent these kids from getting HIV, but there are perfectly good drugs that do that. So, of course, there was international outcry [and] a lot of changes to policy based on that. But the interesting thing that I didn’t realize until I read some of the coverage of this news article is that in the US, if you were to implant a gene-edited embryo a woman, you would go to jail. It’s illegal. Over two dozen other countries also have either laws or certifications or whatever that prohibit this, but a lot of countries don’t have those kinds of laws. And, for example, there’s a guy in Russia who has published multiple times that he absolutely has plans to do germline gene editing because he wants to cure deafness, which, of course, didn’t just get outcries from the research community but also from the deaf community, because there are a lot of issues around the idea that deafness is a disability because it’s got so much culture that’s steeped into it.
So, this is not an esoteric conversation that we’re having. The truth of the matter is although we have the technology to do germline edits, it’s not perfect technology, and there’s a really decent-looking chance—each new study shows us that there’s a decent-looking chance—that we would not, necessarily, be producing children that were otherwise healthy if we did these kinds of edits. One more quote from another researcher who is not involved in the study: "Nobody has any business using genome editing to try to make modifications in the germline. We’re nowhere close to having the scientific ability to do this in a safe way." And so this is a real warning, and, of course, there are organizations around the world that are working on developing international standards because this—you know, often we talk about how technology moves faster than regulation, and this is one of those situation where we really need these regulations in place because one rogue actor can do something that’s potentially really unethical. And we’re talking power of life and death here. We’re not just talking "interesting science experiment in the lab." So that’s really important to remember.
S: So we’ve got to more carefully because the technology is not there yet in terms of safety.
C: Right. It doesn’t mean we shouldn’t be doing the kinds of experiments that these researchers are doing at the—this is how we know, right? But there are strict protocols. Like you do the research on these embryos, and at 14 days, the embryos are disposed of. So they never develop into children. And they’re happening in petri dishes; they’re not happening in a womb. They never would live on their own. So they’re doing it just for added safety and probably because of pressure, international pressure, there are a lot of religious, cultural values, pressures to not let these embryos divide so many times that they start to resemble a fetus. So, of course, there are rules in place. The kind of research that’s being done is very, very important. But to actually use this as some sort of medical technology at this point—not only is it way too soon, but it’s really troubling. I don’t think that the tools that we have right now—even though they’re a game changer, and they’re amazing—it seems like they’re not sufficient without some additional technological improvements.
E: They call that "not ready for primetime."
S: All right. Thank you, Cara.
Who's That Noisy? (59:34)
- Answer to last week’s Noisy: Golden Gate Bridge New Sidewalk Railing Slats
(Transcriptionist’s note: really cool, eerie sound!)
S: Who’s That Noisy time!
C: (small voice) Noisy.
J: (same small voice) It’s a noisy. (regular voice) Last week I played this noisy: [Birds chirping in background of resonant airy vibration sounds]
It’s funny that people always email me and mention the background noises.
C: That’s all we could recognize.
E: You mean the birds?
C: The birds.
J: The typical email would be like, (obnoxious voice) "Well, I can hear birds in the background."
J: It’s like, yeah, that’s not the Noisy. Okay? Don’t even get (Boston accent) started.
All right. Here we go. The first guess was sent by a listener named Kevin Malfa, and he says, "Hey, Jay. This week’s Noisy is so subtle, but I’ll take a guess. Was this a pool vacuum pump? Why is my guess pool-related? I don’t know. Probably because I need to replace mine." So anyway.
J: It’s not a pool pump. I don’t even know why, Kevin, why you picked a pool pump. Eh, you know. I’ve heard pool pumps. They do make a kind of whiney noise like that, I guess, but that one is incorrect. Jim Kelly wrote in: "Hey, Jay. I think this week’s Noisy is a crystal-singing bowl being played outdoors." Jim, many people guessed this; you were the first. It does kind of sound like that. I don’t disagree. If you listen to these crystal-singing bowls that kind of have that monotone "aohm" type of thing going on… But that is not what it is.
Visto Tutti, my Australian Roman god! Now, this guy—anyway, he says, "That bird I have heard before. It is a European LBB. That stands for Little Brown Bird, which means I can’t identify it. The tone with low frequency modulation brings to mind a time when I was beside an oil pipeline out in the Donga. With a quiet environment, one can hear the oil being pumped through the pipes." A donga is like a little living abode, that is, like a tent or a hut or a squat shack, that type of deal. That is not correct, but interesting.
S: Jay, by the way. You know what we call LBBs in the US? And this is true.
S: They’re called LBJs.
C: Little Brown Jays?
S: Little Brown Job.
J: Oh, okay.
S: It’s basically an unidentifiable brown bird, probably a sparrow. But there’s so many little brown birds, but just because of LBJ we call them LBJs.
J: That’s cool.
J: Brandi Hernandez wrote in. She said, "100% the Golden Gate Bridge on a recent windy day." And she happens to be the winner. She’s right.
J: This is what she wrote: "There are new sidewalk railing slats just installed—" which they did. They just updated the bridge and added some styles to it. "—KQED wrote an article, and it turns out the bridge is now a giant kazoo. Good thing it’s never windy on the Golden Gate Bridge. Just kidding. It’s windy all the time." (Jay and Cara laugh)
So yeah. They added some things to it—I think to make it a little more aerodynamic—and they hum now. And it’s new. It’s a new feature. So this is really cool.
C: Is it a feature or is it a bug?
J: I mean, you know, I don’t have to deal with it. (Cara laughs) So, you know what I mean.
E: But what’s with the bird getting in there, audio-bombing this Noisy?
J: Oh, I know! The birds, they’re going crazy with noise all the time, Ev.
C: How dare that bird.
J: So I’d like to thank Erin Carlson for sending that Noisy in. Very cool Noisy.
New Noisy (1:03:06)
J: I have a new Noisy this week. This was a Noisy sent in from a listener named Dan Whitaker [sp?]. [intermittent buzzing, crackling, whirring] So if you think you know what this week’s Noisy is or if you heard something cool, you can email me at WTN@theskepticsguide.org.
J: Now, Steve, every week, for the last couple of months, I’ve been telling people about a thing that’s going to happen online. It’s called NECCS. And I'm telling you, there’s a few things going on that people really won’t appreciate until they see it, and I'm telling you to trust me: the Gameshow Night on Friday night is going to be awesome. It’s going to be awesome. Steve—
S: —I agree.
J: —me, George, and Brian Wect [sp?] just had our big, incredibly detailed meeting and everything with Ian. And we got just some amazing stuff coming for that gameshow. It’s going to be a lot of fun. So I’m telling you, if you like gameshows and you want to laugh, you’re going to want to come to this. That night we got Bill Nye, Leann Lord, and Andrea Jones Roy, as contestants and me, Steve, George, and Brian are going to be the gamemasters. Cara is trying to arrange her schedule where she can be one. She’s going to in—
C: (funny voice) —Aw, yeah!
J: —Where are you going to be, Cara?
C: I’ll be overseas. I might be in Scotland. Trying to work that out right now.
J: Yeah. So as soon as Cara figures out where she’s going to be and if we can pull it off—because timing is actually important, here, and I don’t know if that’s going to have an impact. We’ll have to test it out, Cara.
C: Yeah. We have to see what the delay is, and also if I really want to be awake at 4 in the morning.
J: Yeah, that’s the other thing.
C: But I love you guys, so I will be.
J: But it’s going to be two days in a row, then, Cara, and I want you to just be honest with yourself.
C: I’ll take the later, the early stuff.
J: But I really want you. So, anyway, it’s going to be a lot of fun. And, then, all day the next day, which is August 1, we are going to have a full day of conferences from 10 AM to probably past 8 PM Eastern Daylight Time. We have a ton of people on the website that you can go see. We have Richard Wiseman.
C: A new addition to the lineup is my very good friend Dr. Shane Campbell Staten, from UCLA. And he is also the host, or I think the co-host, of a podcast called "The Biology of Superheroes."
S: Yeah. It should be cool.
E: Mmhmm. Cool.
J: That’s right up my alley.
J: So I’m very excited. It’s a lot of fun. Please do join us. You not only will be supporting skepticism in general, but you’ll also be helping us prepare for next year’s conference, which will be in Atlantic City. And anything that you spend on your tickets this year will be credited to you for your tickets next year. So please do join us. If you don’t mind, go to—
C: —That’s a good deal!
J: —You know, it is a damn good deal—
J: —for a day and a half of programming.
S: Yeah, it’s basically free if you’re going to both conferences.
C: Yeah. I think so. It’s free. Yeah!
J: So go to NECSS.org, and we will see you online July 31 and August 1 of this year.
S: Thanks, brother.
S: All right. I think we’re only going to have time for one email this week.
Question #1: Policing and Race (1:06:11)
S: We had numerous, numerous questions on this study, which is why I think we need to break it down a little bit. So this has to do with statistics on police use of force and police killings. I think, unfortunately, one slice of this study is being often discussed without putting it in the full context of the whole study, which is what I wanted to do. This was actually a Harvard professor—an economics professor, who used an economics model, and also did a lot of data collection for this study—focusing on different cities around the United States. So it’s not using federal-level data, and it’s important to note that the one thing that researchers all agree on is we don’t have enough data; we need better tracking systems for all police use of force as well as police shootings and people who die at the hands of police so that we can do good statistical analysis. But this was a big study. And the professor and people who did this study were actually a little bit surprised by the results, which I always find is a good thing. It means that they were looking at the data and not—you know, when it goes against their preconceived notions, it’s harder to think that they were working backwards from their conclusion.
So, in any case, the thing that’s been widely reported—and what we got most of the questions on—was that the study—if you look just at people who are killed by police officers during the time of the study in the United States, and then you look at all the different factors that might influence—that correlate with that, they were unable to show that the race of the victim, the person who was killed, is an independent variable. In other words, in this data set, cops were not killing black people at a higher rate than white people once you control for a lot of other things, such as the number of times people are interacting with the police, the crime rate, all those things. So that’s what people are saying. Like, "Therefore, is it not true, then, that there isn’t evidence of systemic racism in policing?" (Transcriptionist’s note: Absence of evidence is not evidence of absence!) That conclusion goes way beyond that one slice of data, and it’s interesting that if you just read through this study, there’s lots of other things that it reports on that show very clearly that there is [systemic] racism in policing.[note 1] The same data, for example, showed a very robust signal that the use of non-lethal force is consistently higher against Black Americans than not-Black Americans—White Americans, for example.
C: And also, Steve, that’s just reported uses. You have to remember that, too. Yeah, that’s only what we know.
S: Yeah, so that’s, of course, one of the caveats in the data. The data is not—although these researchers did go to great lengths to scour those reports—but, yes, there’s no system of systematically reporting such things, and so—
C: —Yeah, the thing is, if it was never reported, they were never going to have that.
S: Yeah, right. It’s missing from the data. But even in the data that we have, it’s across the board: for every kind of use of non-lethal force—such as handcuffing, tasing, physically touching, pushing to the ground, all those things—there were higher [instances], across the cities in the study, for Black Americans than White Americans. And that was an independent variable that could not be explained by all the other variables that they looked at. It looked like a completely independent variable. But here’s the other thing: there was much more data, there were many, many more instances of use of non-lethal force than the use of lethal force. Also, you could break the use of lethal force down into two categories: looking at instances where the person killed was armed—so the police were shooting an armed suspect—and you could break out just situations where the person was unarmed. Now, that’s a smaller group, right?
C: Oh is it? Interesting. That’s smaller.
S: Oh, it’s much smaller! Yeah, most of the shootings—
C: —Because it’s also a massive mistake or, I don’t know if you want to call it a mistake, but it should not happen, ever.
S: It should never happen. Pretty much.
C: Yeah, exactly.
S: Have to be pretty extraordinary circumstances. But it’s relatively rare. So the absolute numbers are fairly low. It’s about—depending on the year—it could be anywhere from 500 to 1,000 people a year are killed by police. Most of those—like 700, 800—are armed suspects, and then there’s a couple hundred that are unarmed.
C: It’s amazing, though, that we consider that a low number. It’s just like, by comparison—
S: —I know. It’s just crazy high when you compare it to other countries, which is another angle here. It’s orders of magnitude higher than, say, the UK, for example. In that group, the researchers basically said, "We can’t draw any conclusions. There’s just way too little data to do statistical analysis." The researchers themselves were very clear to say, even in the shooting data, that even where it showed—Again, it failed to show that there was a racial difference as an independent variable. But you always have to be careful when a study fails to show a relationship because it could always be that it just wasn’t a powerful enough study, like there wasn’t enough data to show, to statistical significance, that the effect was there. And that’s basically what the researcher was saying: "It’s not that we proved there isn’t; it’s just that, in this data, we couldn’t say that there was." But it’s hard to know how generalizable is this data, and if it’s just that a more robust data set would be able to show it. And they couldn’t even make any statements about the shooting of unarmed suspect because that was way too little data, statistically.
C: And we have to remember, too, a couple of things: (1) this is aggregate data, so we know that there are pockets, like Minneapolis—
C: —or Minnesota as a whole, right, where we’re seeing significant difference in the number—
S: —So, yeah. Exactly. There are some regional differences. For Minnesota, for example—or I think it says "Minneapolis"—in Minneapolis—
C: —Yeah, I think it’s just Minneapolis.
S: —the police use of nonlethal force is seven times higher, controlling for these other variables—meaning, like number of interactions with the police—seven times higher for African Americans than it is for Caucasians. And so that’s dramatic. That’s obviously going to vary region by region. And, of course, a lot of people cite the figure that if you’re a Black, you’re 2.5 times more likely to be killed by police than if you’re White, just to get the risk of, like—
C: —Right, because they’re also looking at the population. There’s only—
S: —That’s federal-level data.
C: Yeah, because 13% of the general population is Black, and so if the number is the same, then that means more Black people are getting killed.
S: Yeah, it’s per capita.
C: Per capita!
S: The lifetime risk for a Black male getting killed by the police is one in a thousand, which is not insignificant, and it’s also one of the higher causes of death for young African American males.
C: Ugh. And also just going to jail, like you see that those [numbers] are really, really high. But the other thing, I think, that’s not left out but that’s important to remember—and again, I’m not saying this—you could very easily, I think I’m being conspiratorial, but I’m not—there is no incentive to be candid about force, right?
C: Police are only going to report the minimum of what they have to report by law if they’re even being scrupulous.
J: I totally agree. I mean, of course! Of course they are.
C: Just like every single time a police officer kills somebody who’s unarmed, the original report almost always says it’s in self-defense first or that "they saw a weapon" or that whatever—there’s always reasoning that’s built into the report that then later is disproved.
S: Yeah. Now that we’re getting a lot of video evidence—
S: —the data is shifting, definitely
C: So we have to remember that, too, the data is probably charitable at best.
S: But even if we take it at face value—so let’s just do this.
S: Let’s be charitable and say, "All right. This data is accurate for what it is." And even given all the caveats let’s just assume I take it at face value. What would that mean about policing in the United States? First of all it does not mean, in my opinion, it does not mean that there isn’t an issue of [systemic] racism in policing because, again, the evidence from the nonlethal use of force is the most clear, and clearly shows that there’s a racial bias as an independent variable. And it also doesn’t capture a lot of things that wouldn’t necessarily be considered "use of force", just like pulling somebody over who’s "driving while black", things like that. You turn—
C: —And there are so many studies of those things—
S: —Yes. Absolutely.
C: —that show, over and over, that Blacks are significantly more likely to get pulled over—
S: —Stop and frisk.
C: —stop and frisk, have their cars searched, have to just get handcuffed and then released; there are a lot of studies on traffic stops and things like that.
S: A lot of the emails that we’ve been getting indicate that, I think, that there’s a misunderstanding by what we mean by [systemic] racism. That doesn’t mean that every cop is racist. It in fact—
C: —It doesn’t even mean that any cop is racist.
S: —It specifically means that that’s not the case. It just means that there are factors at work that make the experience of Black Americans with police different, [systemically] different—
S: —in a bad way, yeah—than White Americans. And it could be partly just socioeconomic. Historical factors come in to play: under-policing and over-policing in different neighborhoods, the neighborhoods themselves, the effects of red-lining over decades, things like that. And so all of that comes into play. There’s all these societal disparities. But the problem is that policing seems to be magnifying those societal disparities, when, in fact, if anything, it should be doing the opposite. It should be seeking to mitigate or minimize them.
C: Right. I think I saw a really good tweet that somebody wrote—I can’t remember—a social media post that said, and I’m paraphrasing, but something along the lines of "Institutional racism doesn’t mean that everybody in the institution is racist. That means that even if there were no racist people in the institution, it would still negatively harm Black people."
S: It’s just the way things are done.
C: That’s the point.
S: Yeah, absolutely.
C: The system itself has—
B: —It’s baked in.
C: —racist shit built into it.
S: I think the other big issue, here, is just that the use of force by American police compared to our industrialized nations' colleagues is orders of magnitude greater, and we need to figure out why that is; how much of that is just, again, the culture of policing; how much of it is the gun culture that we have in our country, always raising the stakes; how much of it is how policing is organized and structured and what’s required of them. So—
C: —Yeah, the militarization of the police forces. I think there’s a lot of argumentation around that being a huge problem—
S: —Yes. Absolutely.
C: —is that if you’ve got a hammer, everything’s going to be a nail. Well, if you’ve got tanks and automatic weapons, you might need to find an excuse to use them.
S: Yeah, there’s two factors, I think, that are playing into that. One is that in areas where social problems go out of control—like we have an opiate problem, a drug abuse problem, we’re not really taking care of the mentally ill, we’ve deinstitutionalized a lot of mentally ill [people]—that increases the societal demands on a city, a county, a state, and a lot of that falls to the police. And so they are, then, being stretched thin and being sort of the first line dealing with things like mental illness, and that creates this revolving door approach. And this something that we’ve talked about or has been talked about for decades, literally, since—
C: —Everything that falls through the cracks falls on their shoulders.
S: —And I’ve seen this personally as a physician. I’ve had patients who are caught in this cycle. You’re mentally ill. You create a disturbance or you threaten somebody or an incident. The police respond. What can they do? The only thing they really can do is arrest you, so they do. You go to prison, right? And then, when you’re done with prison, you go to a hospital. And then they treat you temporarily, but then they have no place to send you, so then you go back out on the street until you commit another offense. And you get arrested again. And that cycle—
C: —Yeah. Once you’ve been arrested, you’re completely disincentivized from entering society. You would think that everything is rehabilitative, but it’s actually a massive block to reentering society. You can’t get funding. You can’t get housing. You can’t get educational loans. You can’t get a good job, especially if you’re a felon. And, unfortunately, our drug laws—which have been historically also very racist—they breed felons, non-violent felons.
S: Yep. So you end up with the problem of mental illness being dealt with in the criminal legal system, with police sort of on the front lines, and then you have, combined with that, again, this militarization of police, which has been shown to increase the use of force by police. There are studies which showed that. And so, anyway, we have to—there’s a lot of recommendations about what we can do to improve the situation. So I just made a list of the most common ones that I’ve been seeing out there: ban choke holds; ban "no-knock warrants"; rebalance qualified immunity, which means—right now, police are almost immune from being sued for abuse of their power, their authority; a national registry of police offenders so they can’t just go to the next department; emphasize deescalation in police training, police use of force as a last result; police must report every time they draw their weapon or point a weapon at a civilian; mandated use of body cameras; better data collection across the board for all forms of police use of force. So you’ll notice that none of those have anything to do directly with race. This is all just about deescalating and reducing police use of force.
And, of course, if we could get it down to levels that are comparable to other, similar countries, or just to much lower levels, that will reduce, in absolute numbers, any use of force against historically oppressed populations and magnifying any implicit rational biases that are in the system, et cetera, et cetera. So it’s all good. But, in addition, there are some specifically race-focused recommendations like, "Yeah, you probably should make an effort so that the police force reflects the demographics of the community it’s policing." It’s probably a good thing.
C: Yeah. But there’s also good research to show that that’s not—it’s not enough. It doesn’t—
S: —It’s not enough. Absolutely. There’s no single panacea.[v 2] But we definitely have to get away from—listen, we’re not trying to say that individual police officers are bad people or [that] they’re any different than the population in general. It’s just people in that situation. And maybe we do need to think how they’re being structured, how they’re being trained, what kind of resources that they have. And I think we really, really, really need to emphasize how to minimize excessive use of force by our police. Systemically.
C: Like incentivization structures, code of silence issues, things like that, which have, historically, been really, really problematic. This idea that when something bad happens—I mean, you saw it in the video of the old man who was knocked down, and another cop was like, "Should we"—he reached for him, and the other cop pulled him away. And you also saw it in George Floyd’s video, when the cop had his knee on his neck and the other cop was like "Should we, like, roll him over?" And he was like, "No." It’s this culture of silence, complicity. These kinds of things have to change and, unfortunately, sometimes, this is why I think you’re seeing a lot of the protesters talking about "defunding" the police; it doesn’t mean "disband" the police. It means take some of the money that we’re putting towards police efforts and put them into social systems, or social programs.
S: Just completely restructure it. So I was very interested—
C: —Or restructure, yeah.
S: —in the Camden, New Jersey, experience, which was done under a Republican governor. They basically dissolved the police and then reconstituted them in order to restructure them from the ground up. And they focused on community-based policing. And the crime rate plummeted. So that’s the thing. It’s not just that we’re reducing police use of force. It’s actually better for the community. There’s lower crime. So this idea of like, "What do you want?"
S: "You need to have aggressive, militarized police in order to reduce crime." No! It’s the opposite. It actually worsens the situation.
C: Yeah. That exacerbates it. It instigates it. It makes people more twitchy. It makes people more afraid to actually call the police.
C: Obviously there’s a long history of distrust, for good reason.
E: They’re looked at as almost like a paramilitary organization.
C: Oh, absolutely.
E: That’s not a good way of looking at that. I mean, it’s bad.
S: I would like to get beyond this, sort of, confrontational, either-or thing: you either support the police or you don’t, or whatever.
C: I know.
S: No, it’s that we want everyone to be functioning better. We want it to make lives better for the police and for all the citizens and, of course, we want to minimize and eradicate any inequities in the system. Racism, sexism, whatever. And we can do all of that. We can accomplish all of that. This is a situation—again, to be positive—it seems like we’re at a moment, at least, where there’s bipartisan support—
S: —for a lot of specific fixes—
C: —Changes. Yeah. And accountability!
S: —We actually know what to do. We actually know what to do now. It’s not like we’re not sure what to do about it. Anyway, so I’m hoping that we’ll see some—and there are things winding their way through Congress. And we’ll see what happens.
C: That would be great.
S: But I just want to emphasize one big misconception, just to drive this point home. Saying that there is systemic racism in policing doesn’t mean we have to read the mind of one cop and say that they had racial animus or intent in a specific act that they did. That’s not what we mean. That’s not what we’re talking about.
S: We’re just talking about all of these complex factors [conspiring] together to make the experience much worse for that segment of our population. And it’s really—it grinds people down in such a way that it’s horrible. And it needs to stop, and we could make it stop. And that needs to be a priority.
C: You know there’s a—coming in, as I always do, with the doc recommendations—there’s a great doc on PBS right now. The doc is called "The Talk." And what it’s really about is the talk, sadly, that African-American mothers have to have with their boys about how to conduct themselves—
E: —Yeah, I’ve read about that.
C: —When they are pulled over—
B: —Oh, wow.
C: —inevitably. Like what do you do to not get killed. And the thing is, we are privileged that we don’t have to have those kinds of conversations.
E: I’ve seen a lot of interviews with athletes, among other people, who have all alluded to that very thing.
C: Mmhmm. And it’s just really emotional, but it’s also really telling about these everyday individual experiences that you’re talking about, Steve, that add up to this concept that we are talking about of systemic racism in policing. So I highly recommend it because it just gives you, I think, a little bit more of that emotional connection to the problem, especially if you’re somebody who’s lucky enough to have never had to deal with it or think about it.
E: Yup. Count our blessings, and try to be more empathetic, definitely.
S: All right, guys. Let’s go on with Science or Fiction.
Science or Fiction (1:26:37)
Voiceover: It’s time for Science or Fiction.
Item #1: About 50% of the heat given off by the Earth’s interior is generated from radioactive decay.
Item #2: Earth is the densest planet in the solar system.
Item #3: The driest place on Earth is the Atacama Desert in Chile and Peru, which receives on average 15 mm of rainfall per year.
Steve Explains Item #1
Steve Explains Item #2
Steve Explains Item #3
Skeptical Quote of the Week ()
Science is elegant and beautiful, but it requires an effort to understand. This is a golden opportunity to educate people. Any person with a scientific temperament, not necessarily a scientist, cannot support these types of messages.
– Hasan Al Hariri, CEO of the Dubai Astronomy Group, when asked about the June 21, 2020, doomsday insanity
S: —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 firstname.lastname@example.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.
Today I Learned
- Fact/Description, possibly with an article reference
- In this segment, Steve often misidentifies "systemic racism" as "systematic racism." They both exist, but in the context of investigating racial biases in policing, the racism is much more a systemic problem than a systematic one. This article does a good job breaking down the differences: Learn the Difference Between 'Systemic' and 'Systematic'
- Gulf News: Will the world come to an end on June 21?
- ScienceDaily: Engineers put tens of thousands of artificial brain synapses on a single chip
- Neurologica: What is Twistronics?
- Forbes: There Are At Least 36 Intelligent Alien Civilizations In Our Galaxy, Say Scientists
- OneZero: Scientists Edited Human Embryos in the Lab, and It Was a Disaster
- KQED: Why the Golden Gate Bridge Is Now a Giant Orange Wheezing Kazoo
- Neurologica: The Stats on Police Killings
- Physics World: Radioactive decay accounts for half of Earth’s heat
- Universe Today: How Dense Are The Planets?
- Universe Today: What is the Driest Place on Earth?
- [url for TIL, publication: title]