Latest / From First Principles / Black Hole Movies, Digital Heart Twins, and World Cup Tech (EP 48)

Transcript

Black Hole Movies, Digital Heart Twins, and World Cup Tech (EP 48) · From First Principles

• 0:00
  If you took like one of those mini donuts from Dunkin Donuts and you put it on the moon, that's how big those black hole donuts would be.
• 0:11
  What they've done is create a digital twin, a personalized 3D computer simulation of the patient's heart.
• 0:19
  Okay, here's how they do it.
• 0:20
  It's also like fairly clear from the video that it hit something.
• 0:25
  Right?
• 0:26
  How do we explain?
• 0:27
  It's like, I don't know.
• 0:29
  It's fairly clear from the video that there's a change in momentum.
• 0:32
  How is it that the IMU on the ball did not pick it up?
• 0:35
  Both things can be true.
• 0:37
  Hello, Internet.
• 0:38
  This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
• 0:47
  We are back for one of our first questions.
• 0:51
  rundown episodes after all of our specials.
• 0:54
  If you haven't caught our America 250 or our World Cup episode, be sure to check that.
• 1:00
  This week, we're going to cover some of our favorite discoveries and stories in science over the last few months while we've been away, including astronomers who caught a supermassive black hole reversing its magnetic field, researchers developing a digital twin,
• 1:18
  for your human heart to kind of help physicians with diagnostics, a new ultrasonic CT scanner that uses nothing but sound waves and a pool of water.
• 1:31
  And we have two follow-up stories on the World Cup, including the Norway versus England ball tracking controversy.
• 1:40
  And we may...
• 1:41
  They sneak in a little segment of Are You Smarter Than a Scientist?
• 1:46
  Oh, and the other World Cup story is the science behind favoritism and home field advantage, which was clearly not in effect for any of the host nations in this World Cup.
• 1:57
  As always, we are going to talk about the science from the ground up today, because this is From First Principle.
• 2:19
  So our first story comes from the Event Horizon Telescope.
• 2:23
  I'm sure you remember back in 2019, they imaged a black hole for the first time.
• 2:31
  And there were so many memes, cat memes.
• 2:34
  It was the first black hole that was ever photographed.
• 2:36
  I think the first image actually is the cat meme that I want to show everyone.
• 2:40
  It's the black hole.
• 2:42
  And then as you zoom out, it's the cat eyes.
• 2:45
  I thought that was pretty funny.
• 2:46
  It was all over social media because it's such a big deal, right?
• 2:50
  For the longest time, black holes are things of theory.
• 2:55
  It's confined to the theory departments in physics and no one else really like messed with it.
• 3:03
  In the late 1990s, Andrea Ghez, among others, started imaging the black hole at the center of our Milky Way.
• 3:10
  But what I mean by that is they were really imaging the stars around the black hole, right?
• 3:14
  And they were looking at the star trails, and you could see that there was some mass that was massive, millions of times the mass of the sun, that was ringing these stars around in these Keplerian orbits.
• 3:29
  And so there had to be a black hole in there.
• 3:32
  But to actually image a black hole, right, where you've got an event horizon, you've got the accretion disk around it, that is going to take an insane amount of technology and an insane amount of coordination from the entire globe.
• 3:45
  So this is a follow-up story to that 2019 photograph, where now you can imagine in 2019 they photographed for the first time the black hole.
• 3:58
  It's been about seven years.
• 4:00
  You can image over and over and create a movie of a black hole, of the accretion disk revolving around a black hole and all of the weird physics that happens around that black hole.
• 4:11
  Right.
• 4:12
  So now you can start testing the theories.
• 4:14
  And it's such a cool thing that we are able to do because of all the technology that we have.
• 4:20
  So if you start from the beginning, right, most of the photos that you see of the black hole, like that cat meme, that is of M87, which is this galaxy, 55 million light years away.
• 4:32
  It was first discovered in 1781 as part of the Messier catalog.
• 4:37
  Charles Messier was a French comet astronomer, and he was basically like trying to hunt for comets.
• 4:43
  That was his big passion.
• 4:46
  And when you hunt for comets, what do you do?
• 4:48
  You basically point the telescope at the sky and you look for fuzzy objects.
• 4:53
  But those fuzzy objects have to move.
• 4:56
  So in order to distinguish the ones that are moving from the ones that are not, he created a catalog of all the things that he should be ignoring.
• 5:03
  And that became the Messier catalog, which is now much more important than all of his comet research.
• 5:11
  You know, he was trying to basically find comets and he made a list of stuff to ignore.
• 5:16
  Like...
• 5:17
  You know when you're coding or something and it's like ignore this flag, ignore this flag?
• 5:21
  That was him with the entire Night Sky.
• 5:24
  He created this giant catalog and turns out he cataloged
• 5:27
  I think something like on the order of hundreds, if not maybe a thousand objects that are now like nebulae, galaxies, other things that are close to the Milky Way and close to the sun.
• 5:38
  This is like a classic story in science, which is kind of like our Hubble story with the deep field we did a few episodes ago, which is let's point this at a...
• 5:48
  A space of nothing.
• 5:49
  Yeah.
• 5:49
  Yeah.
• 5:49
  And see what happens.
• 5:50
  And everyone's like, what?
• 5:52
  And it became one of the most important things ever.
• 5:53
  And it's like sometimes, you know, you stumble upon discoveries and just being serendipitous is really important in the process because it does still.
• 6:04
  Again, in this case, it's a little bit annoying because he's like, I care about this thing and I'm known for this other thing.
• 6:09
  But it's like you still did the work.
• 6:11
  No, and I don't think he would mind if he was alive today that all of these objects have the prefix M and then the object right in front.
• 6:20
  Everybody knows M87 in astrophysics because this is the closest large supermassive active galactic nuclei to the Milky Way.
• 6:30
  It's only 55 million light years away, which is actually quite close.
• 6:34
  The Andromeda galaxy is 2 million light years away.
• 6:37
  So this is only like, what, 20 to 30 times as distant as the Andromeda galaxy?
• 6:43
  So it's really in our local, you know, we're talking about tens of millions.
• 6:47
  The universe is tens of billions, right?
• 6:49
  So this is very local to us.
• 6:52
  It's in the constellation Virgo.
• 6:54
  And, you know, to your point, it's one of those things where...
• 7:00
  cataloging negative results was actually a good thing.
• 7:04
  In science, everybody...
• 7:05
  If you want to publish in science and nature and sell, you have to come up with results and positive results.
• 7:11
  It's like, oh, I did an experiment.
• 7:13
  I had a hypothesis.
• 7:14
  The hypothesis worked.
• 7:15
  Here, he was just cataloging negative things because he thought, these are the stuff that I want to ignore.
• 7:21
  And it turns out the negative results in this particular scientific endeavor...
• 7:26
  was the most important thing that he did in his life, right?
• 7:28
  Kind of cool.
• 7:29
  Right.
• 7:30
  So anyways, it turns out to be one of the largest galaxies in our neighborhood, and it's the most studied supermassive black hole in history.
• 7:39
  If you go back to that photo, actually.
• 7:42
  Photo number two.
• 7:42
  Yes, photo number two.
• 7:44
  Notice, it's an elliptical galaxy, so it doesn't have a lot of structure, which means it's full of old stars and it's massive.
• 7:53
  It's not a spiral galaxy in the sense that the Milky Way or the Andromeda galaxy is.
• 7:57
  And also, there's this giant jet.
• 8:02
  It's like a hose coming out of that center, right?
• 8:07
  When people saw that using the radio astronomy that was...
• 8:13
  built up after World War II, people were like, that's really weird.
• 8:17
  Because, one...
• 8:19
  You can actually measure the speed of that jet, and it's very, very close to the speed of light.
• 8:25
  At some point, people were actually saying super luminous.
• 8:28
  It was higher than the speed of light.
• 8:30
  Turns out it was like, it's not an error, but there's like some weird physics going on that like makes it seem like the stuff is moving faster than the speed of light, but really it's like kind of a group velocity type thing.
• 8:41
  The actual stuff is not actually moving faster than the speed of light, but maybe some phenomenon is.
• 8:46
  Yeah.
• 8:48
  When people saw that, they were like, there's got to be something really energetic at the center of that thing that is causing a giant jet to form that's like many light years ago.
• 9:02
  In size.
• 9:03
  What is creating the inciting, what is creating the force that is enabling us to image this particular situation?
• 9:11
  Yeah, like if you've got a hose, right, and the hose produces a jet that's light years long.
• 9:18
  It's a pretty strong hose.
• 9:19
  The hose has to be insanely strong.
• 9:21
  And so that's where we get the idea of active galactic nuclei.
• 9:24
  The idea that there could be a supermassive black hole at the center that is creating so much energy.
• 9:29
  from accreting matter into the black hole, that that heat and that magnetic field and that plasma is shooting out a bunch of material at this incredibly high speed.
• 9:42
  So this is the first sort of indication that, yes, black holes are real.
• 9:45
  And perhaps...
• 9:48
  we can study it in a really, really precise way.
• 9:51
  Okay?
• 9:53
  Then in 2019, we got two images.
• 9:56
  These amazing images that popped up all over social media, took over the entire globe.
• 10:01
  On the left is the black hole of M87.
• 10:05
  The center is probably where the event horizon is.
• 10:08
  Okay?
• 10:09
  Right.
• 10:09
  That's where...
• 10:10
  The dark spot in the middle.
• 10:11
  Yeah, that's really what the black hole is.
• 10:13
  But obviously, a black hole is somewhere, is a region of space where nothing can escape, not even light.
• 10:20
  So if light can't escape, how do you image it?
• 10:22
  Well, what you're really imaging is the accretion disk around the black hole.
• 10:27
  And I want to take a quick moment for our audio listeners.
• 10:30
  You know, many of you will have seen this image.
• 10:33
  It looks like a glazed donut.
• 10:35
  Right.
• 10:36
  Right.
• 10:36
  You have that orange glow, the black center.
• 10:39
  And it was very big in this time period.
• 10:42
  But that is the image we're looking at, too.
• 10:45
  But that is one of the two that we're looking at.
• 10:47
  And then you'll get to the second here.
• 10:48
  Exactly.
• 10:49
  And so the one on the left is the one at M87.
• 10:52
  That's 55 million light years away.
• 10:54
  OK. OK.
• 10:55
  The one on the right.
• 10:57
  And you can see the size of that relative to the one on the left.
• 11:01
  Yes.
• 11:01
  Right?
• 11:02
  The one on the right, you can see the inset.
• 11:04
  It would fit very neatly inside the black hole, inside the event horizon of M87.
• 11:10
  The one on the right is our puny little black hole of the Milky Way.
• 11:14
  Ah, okay.
• 11:14
  Okay?
• 11:15
  Yes.
• 11:15
  And it's quite amazing that...
• 11:20
  The M87 black hole is so much bigger than the Milky Way.
• 11:24
  Right?
• 11:25
  It's probably in the way these images are scaled, we would say maybe it's...
• 11:31
  Two and a half to three and a half times.
• 11:34
  Yeah.
• 11:34
  Scale-wise.
• 11:35
  Mm-hmm.
• 11:36
  I will say that the R is...
• 11:38
  I think it's... Is it Sagittarius A?
• 11:40
  Hey, Sagittarius A.
• 11:41
  Exactly.
• 11:41
  Sagittarius A.
• 11:42
  Kind of looks like a bagel.
• 11:44
  Yeah.
• 11:45
  A little more bagel-shaped.
• 11:46
  Yeah, compared to the neat donut.
• 11:48
  Donut shape.
• 11:49
  Yeah, yeah, yeah.
• 11:50
  And that's probably...
• 11:54
  It's probably a feature of the fact that M87 is so big that the accretion disc is more uniform and it's farther away.
• 12:01
  So you're not getting the nitty-gritty details.
• 12:03
  Yeah, makes sense.
• 12:04
  Now, the relative size of these photos, you can see that in terms of the relative size, they look about the same.
• 12:11
  Yeah, yeah, yeah.
• 12:12
  That's because in the sky...
• 12:14
  relative size wise they look about the same it's just that the milky way black hole sagittarius a star by the way it's not sagittarius a the star star means that we're talking about the the black hole that's that's always like that's all the m87 you see the m87 star yes that means we're talking about the black hole and when we say star we we mean asterisk asterisk yeah yeah but in in colloquial language 100 yeah just for people who might be viewing it that's what we're referring to as the exactly yeah so whenever you see like that that means we're talking about the black hole at the
• 12:44
  And the two are about the same size in the sky.
• 12:47
  So this is how big they would look if we could like zoom in, zoom in, zoom in.
• 12:51
  It's just that the Milky Way, even though it's so much smaller, it's so much closer to us.
• 12:56
  It's only 100,000 light years, right?
• 12:58
  Compared to the 55 million light years.
• 13:00
  So even though the M87 is actually way bigger, it could fit the entire Milky Way black hole inside.
• 13:06
  Because it's so far away, the size of it looks about the same.
• 13:09
  So now that brings me to my second point.
• 13:12
  That photograph is really, really small in the night sky.
• 13:16
  Okay.
• 13:17
  That thing is about 50 micro arc seconds across.
• 13:21
  So let's get to how actually big that is.
• 13:24
  Right.
• 13:25
  So this is from the Babylonian time.
• 13:28
  I want to quickly.
• 13:30
  40 micro, not 50.
• 13:31
  Oh, yeah.
• 13:32
  Well, 40 to 50.
• 13:34
  There's like a scale bar in there that says 40.
• 13:36
  It's like.
• 13:37
  It's nebulous like, okay, if I measure this way, it's like 60.
• 13:40
  If I measure this way, it's 40.
• 13:42
  You know what I mean?
• 13:43
  Yeah, yeah, yeah.
• 13:43
  No, no, totally fair.
• 13:44
  Totally fair.
• 13:45
  Yeah.
• 13:45
  But at the end of the day, it's something like tens of micro arc seconds across, right?
• 13:51
  Yes.
• 13:51
  Now, how big actually is that in the night sky?
• 13:54
  So astronomers use the degree system that we have inherited from the Babylonians to
• 14:00
  And they just took that to the extreme.
• 14:02
  Shout out to the Babylonians.
• 14:03
  Yeah, shout out to the Babylonians way back.
• 14:05
  They said a full circle is 360 degrees.
• 14:08
  So the astronomers said, okay, a full circle in the night sky is 360 degrees.
• 14:13
  So a single degree is 1 360th of a full circle.
• 14:19
  That makes sense.
• 14:20
  The moon is about half a degree.
• 14:22
  which means it would take 720 moons lined up to make a full circle.
• 14:27
  Does that make sense?
• 14:28
  Yes.
• 14:28
  Right?
• 14:28
  So the moon is about half a degree.
• 14:30
  Each degree has 60 arc minutes, and each minute has 60 arc seconds, right?
• 14:36
  The same way that we use hours and minutes and seconds in, like, normal timekeeping.
• 14:42
  So that's the idea.
• 14:44
  You've got...
• 14:47
  You got half a degree, so that's 30 arc minutes is the moon, right?
• 14:51
  Because an hour would be a full degree.
• 14:53
  Yes.
• 14:54
  Okay.
• 14:54
  So the moon is 30 arc minutes, which means it's 60 arc seconds to a minute.
• 15:00
  So that's 1,800 arc seconds.
• 15:04
  Yes.
• 15:04
  Right?
• 15:05
  So imagine taking the moon and dividing it up into 1,800.
• 15:08
  That's a single arc second.
• 15:10
  And then now a micro arc second is a millionth of that.
• 15:14
  To give you an idea, you see that donut?
• 15:16
  Remember that donut?
• 15:18
  If you took one of those mini donuts from Dunkin' Donuts and you put it on the moon, that's how big those black hole donuts would be.
• 15:29
  Does that make sense?
• 15:30
  That's crazy!
• 15:32
  That's crazy that that's what we're imaging.
• 15:34
  Right.
• 15:35
  Like, if you want to put it into terrestrial terms.
• 15:39
  You're saying we put a munchkin on the moon.
• 15:43
  Yeah.
• 15:44
  And then we imaged it from here.
• 15:46
  From Earth.
• 15:46
  From ground.
• 15:47
  And we're looking at a munchkin on the moon.
• 15:49
  Yeah.
• 15:50
  That's what we're talking about.
• 15:50
  That's what we're talking about.
• 15:52
  And if you want to put it into terrestrial terms, it's like an ant.
• 15:56
  Yeah.
• 16:00
  on the empire state building viewed from los angeles which for those who are not familiar with the u.s geography is that's across the continent it's across the continent yeah yeah okay or like or like from new york to paris okay you could do you could do that if you want which is so i think the point you're trying to point out here is the level of precision over the scale of distance is outrageous on its face
• 16:26
  Yes.
• 16:27
  It's incredible.
• 16:29
  And the thing is, that donut, it wasn't just a donut.
• 16:31
  We could see details in the donut.
• 16:33
  So the resolution we're getting is like insane.
• 16:37
  Right?
• 16:37
  We're getting sprinkles on the donut.
• 16:39
  Like which side has sprinkles?
• 16:40
  Is it a Boston cream?
• 16:41
  Is it sprinkles?
• 16:43
  What is the one that has the little crumpled edges on it?
• 16:46
  Oh, I hate those.
• 16:47
  I only like glazed.
• 16:49
  Plain glazed.
• 16:50
  Plain glazed.
• 16:52
  But...
• 16:53
  It's incredible that we're able to do this, right?
• 16:55
  And to give you an idea, right, we've actually covered the tyranny of resolution when it comes to astronomy plenty of times on this podcast.
• 17:07
  There's a fundamental physics issue with trying to resolve stuff, okay?
• 17:12
  Yeah.
• 17:12
  Yeah.
• 17:12
  So, light at the end of the day is a wave, right?
• 17:16
  And so, if I have two, let's say I have two light bulbs that are right next to each other.
• 17:21
  If they're right in front of my face, of course I can discern them, right?
• 17:25
  Because my eye acts like a tiny little telescope and it creates an image in the back of my retina.
• 17:32
  And if the light bulb A goes to a certain part of my retina and light bulb B goes to another part of my retina, then I can discern that there are two light bulbs.
• 17:43
  Because these rods and cones are saying, hey, I'm seeing a light bulb over here.
• 17:47
  These rods and cones are saying, hey, I see a light bulb over here, right?
• 17:51
  And so the two light bulbs are not overlapping in my detector.
• 17:55
  And therefore, I can discern the two.
• 17:58
  Now, what?
• 17:59
  There comes something called the rally limit, which is if I take those two light bulbs, those two points of light, let's say tiny little LEDs, and I take them really, really far away, all of a sudden they merge into one LED.
• 18:10
  Why?
• 18:11
  Right.
• 18:11
  Why?
• 18:12
  Because the LEDs, the image in my retina is now getting overlapped.
• 18:17
  Because of the wave nature of light.
• 18:18
  My pupil is only this big, and so the wave nature of light is going to, like, diffract in my pupil, and that's going to cause a blurring effect that is just purely due to the wave nature of light.
• 18:32
  There's nothing I can do about it.
• 18:33
  There's no, like, oh, put it in a vacuum, put it in cold.
• 18:37
  Yeah.
• 18:38
  Nothing.
• 18:39
  Because of the distance and the fundamentals of how light travels over distance.
• 18:47
  As you increase that distance away from our own detectors, which are our eyes, the farther the distance away, the more overlap will happen between two independent light sources.
• 18:59
  They could be very far away.
• 19:00
  Yeah.
• 19:01
  But the farther you go away, it becomes hard to discern that there's a gap between these two.
• 19:07
  Yes.
• 19:07
  And so how do we do anything, right?
• 19:10
  Well, we make the detector bigger, right?
• 19:13
  We make our telescope bigger.
• 19:14
  The eye is a terrible detector.
• 19:16
  It's only about like less than a centimeter in diameter.
• 19:20
  That's why we have large telescopes.
• 19:22
  The larger the telescope...
• 19:24
  The smaller the angular resolution that you can have, right?
• 19:29
  Or I should say the greater the angular resolution, the smaller the angular distance you can discern between two independent points.
• 19:35
  Which is why we continue to speak.
• 19:37
  Why do we need to make bigger and bigger telescopes?
• 19:40
  Right.
• 19:40
  Because the bigger we make them, this is why, for example, between Hubble and JWST, we have fuzzy versus less fuzzy.
• 19:49
  There's different wavelengths in there and there's details.
• 19:51
  Right.
• 19:51
  Yes.
• 19:52
  But fundamentally speaking, we have giant Magellan that's coming out versus regular Magellan.
• 19:59
  Why do you need to make a bigger Magellan?
• 20:01
  Yeah.
• 20:01
  Because we like 4K TV.
• 20:03
  Yes.
• 20:03
  We don't like 720p.
• 20:05
  No.
• 20:05
  We don't like fuzzy CRT TV.
• 20:08
  We want to see every blade of grass.
• 20:10
  We want to see every teardrop that comes from the face.
• 20:13
  And you can get that from making your detector larger in order to resolve light.
• 20:19
  Yeah.
• 20:20
  to more of a level a better level of granularity exactly and if we're not going to settle for 720p on our tvs we shouldn't settle for it in astronomy you know you know what i'm saying yes come on guys 100 um so so the rally limit that's that's that's the fundamental trade-off that we have okay right now there's two ways to make your resolution better okay
• 20:43
  The smallest angular resolution, the smallest angular distance that you can discern is given by the Rayleigh limit.
• 20:49
  It's effectively the wavelength of the light divided by the diameter of your aperture, the diameter of your telescope.
• 20:55
  This is something that we've been over a lot, right?
• 20:57
  And it kind of makes sense.
• 20:58
  The smaller the wavelength of the light, the less it's going to, like, bend around your detector.
• 21:03
  And so you can maybe discern smaller and smaller things.
• 21:06
  The bigger the detector, the smaller the thing you can discern.
• 21:09
  That's why the detector is in the denominator.
• 21:11
  The wavelength is in the numerator.
• 21:13
  Okay.
• 21:14
  So...
• 21:15
  Suppose we go for radio wavelengths, like millimeter wavelengths.
• 21:19
  So it's not quite radio.
• 21:20
  It's like millimeter.
• 21:21
  So it's like shortwave radio.
• 21:24
  Yeah.
• 21:24
  Okay.
• 21:24
  So about like 1.3 millimeters.
• 21:27
  And I'll get into why we want to do radio and not optical, which is at the hundreds of nanometers.
• 21:32
  Right.
• 21:33
  So suppose we want to do radio wavelengths and we want to image a donut on the moon.
• 21:42
  Okay, we can calculate, okay, we want to image at the level of like single micro arc seconds.
• 21:51
  Plug that into the theta of the Raleigh criterion.
• 21:55
  The lambda is 1.3 millimeters.
• 21:58
  So how big should my diameter be?
• 22:00
  The diameter turns out to be 10,000 kilometers.
• 22:06
  That's quite large.
• 22:08
  Quite large.
• 22:09
  What it's saying is in order to image a donut on the moon with radio wavelengths,
• 22:17
  I need a detector, a radio telescope the size of the Earth.
• 22:22
  10,000 kilometers.
• 22:23
  Take it or leave it.
• 22:25
  That's what physics says.
• 22:26
  Let's get a Dyson sphere going, baby.
• 22:29
  Well, these guys created like a telescope sphere around the Earth, effectively.
• 22:34
  Okay?
• 22:35
  The next video shows you how they did it.
• 22:37
  They said, okay, this is what it is, right?
• 22:39
  We need a telescope the size of the Earth.
• 22:41
  Yes.
• 22:41
  We can't build a single telescope.
• 22:43
  What we're going to do is rig up.
• 22:45
  a bunch of radio telescopes all across the globe, and they're all going to communicate with each other and take independent images of this thing.
• 22:54
  Now, our light-gathering power is not going to be that high because the detector is not the size of the Earth.
• 23:00
  But our resolution capacity is going to be the size of the Earth because I'm able to take...
• 23:06
  light from this part of the detector and this part of the detector.
• 23:10
  So there's, there's two detectors in Chile.
• 23:13
  There's one in Tucson.
• 23:15
  There's one in Hawaii.
• 23:17
  There's one in Northern Africa, California,
• 23:20
  And as the Earth rotates, you use the Earth's rotation to image the black hole.
• 23:26
  Isn't that sick?
• 23:28
  And this is what is so brilliant about this is how resourceful it is, which is we're saying we don't have Dyson spheres.
• 23:35
  We're not going to build a 10.
• 23:37
  Yeah, we're not civilization stage for whatever.
• 23:40
  Whatever it is.
• 23:41
  Right, right.
• 23:42
  In the comments, tell me what it is.
• 23:44
  We don't have enough political social collaboration to pull something like that off at this moment in humanity.
• 23:48
  So we say, okay, we have these detection platforms all over the surface of the earth.
• 23:56
  And because they are covering the surface of the earth in these different latitude, longitude positions, we can image the same object at distance.
• 24:07
  Yeah.
• 24:07
  But from these different points on the planet.
• 24:10
  Yeah.
• 24:11
  And then we can basically blend the results from that to create what would effectively be the same thing as having a 10 kilometer wide singular device.
• 24:22
  Yeah.
• 24:22
  You would have to take more time because you don't have as much light gathering power.
• 24:26
  Right.
• 24:27
  Like you don't have as many photons coming in because you've only got these individual detectives.
• 24:31
  If you had something the size of the Earth, then you could just, boom, take an image.
• 24:34
  But we're not harvesting all of Mercury's mass to create a giant radio telescope.
• 24:42
  Maybe in our future as humanity, we will do that.
• 24:44
  Fingers crossed.
• 24:46
  Yeah, we will destroy the planet of Mercury and create a giant telescope.
• 24:51
  I'm sure there's going to be people that really don't want that to happen, right?
• 24:55
  Mercury retrograde is a very big thing.
• 24:57
  It's very popular.
• 24:58
  I heard with the horoscopes.
• 24:59
  Yeah, but that's effectively what's happening.
• 25:02
  I mean, you've been to the very large array in New Mexico, right?
• 25:05
  That's the same thing.
• 25:06
  It's an interferometer.
• 25:08
  That's what this is called.
• 25:08
  This is called radio interferometry.
• 25:11
  The idea is to use interference between all of your detectors to create an image with an exquisite resolution that is effectively given by the size of how far apart your farthest telescopes are.
• 25:26
  Right.
• 25:27
  And in this case, it's the Earth.
• 25:28
  One more thing I want to point out with that video, actually, if you don't mind.
• 25:33
  So notice, the Earth is rotating, right?
• 25:35
  And this is from the vantage point of M87.
• 25:38
  So it's like, I'm at M87 looking at the Earth.
• 25:41
  Now the Earth is tilted.
• 25:43
  And so notice, Antarctica is always facing M87, right?
• 25:48
  Because the Earth is tilted, so Antarctica is always facing the constellation of Virgo.
• 25:54
  So the telescope in Antarctica on the South Pole is called the South Pole Telescope.
• 26:00
  It's just always staring at M87.
• 26:03
  And then the other guys are like picking and choosing, filling in the data.
• 26:08
  At time in the day, things like that.
• 26:09
  But it's just cool that no matter what part of the year, right, because the Earth's tilt doesn't change.
• 26:14
  So no matter what part of the year, it's just going to keep staring at that spot in the sky.
• 26:18
  And fortunately with radio astronomy, it doesn't really mind staring at the sky during the daytime.
• 26:24
  Right.
• 26:25
  Right.
• 26:25
  So the sun isn't really affecting radio astronomy as much as optical.
• 26:31
  Optical.
• 26:31
  Yeah.
• 26:32
  Optical.
• 26:32
  Obviously we can't see any stars during the day.
• 26:34
  Radio, if you look, you'll be able to see stuff.
• 26:37
  We have our atmosphere, and there's blocking happening, and light, and it's interfering.
• 26:42
  Yeah, like the sun is still a source of radio waves, don't get me wrong, but it's not like getting scattered as much by the atmosphere as compared to, you know, like optical light.
• 26:52
  Right.
• 26:52
  So as long as you don't like just stare directly at the sun, you're good to go.
• 26:55
  100%.
• 26:56
  I just think that's, it's such a cool thing that we've done as humanity.
• 27:00
  It's also a cool thing that we've figured out, right?
• 27:02
  Because you can just be like, oh, we can't build a 10 kilometer wide telescope.
• 27:06
  I'm going to give up because this problem is insurmountable for us because we're not going to have the engineering capacity, the funding and whatever.
• 27:12
  And it's like, this is, I think one of the beauties of the process that we talk about so much on the show is how can you do more with less?
• 27:22
  Yeah.
• 27:23
  And the ingenuity that arrives from if we can't do X, can we maybe do Y ultimately to get the same end result?
• 27:31
  And this is a perfect example of how, you know, we're not going to get the funding to do what we want to do, but we already have the resources to be.
• 27:40
  So pragmatic in getting to the same end result, which is this imaging task.
• 27:46
  Exactly.
• 27:47
  Which has given us this M87, this beautiful, beautiful, beautiful image.
• 27:53
  Beautiful image.
• 27:54
  Right.
• 27:54
  And so now the other point that I just want to make is why radio, right?
• 27:59
  If we can do this with like just light at the end of the day, right?
• 28:04
  Light is light.
• 28:05
  Why don't we just like wait for the nighttime and then image with optical?
• 28:08
  Right.
• 28:09
  Right, because the argument would be like some of our optical stuff is pretty tough.
• 28:12
  It's pretty tough, right?
• 28:14
  But we don't have optical interferometers.
• 28:17
  Not really.
• 28:17
  We've got a few.
• 28:19
  Which is meaning more like this large array of optical telescopes.
• 28:25
  So the VLA being the very large array of radio telescopes, which are lower cost, right?
• 28:33
  And maybe you could argue lower data stream complexity and downstream...
• 28:38
  That's the main one.
• 28:39
  Okay.
• 28:39
  It's the data stream, not the complexity in the sense of computational complexity.
• 28:45
  No.
• 28:45
  In terms of literal physics, radio is slow.
• 28:50
  Ah, okay.
• 28:50
  That makes sense.
• 28:51
  Radio is slow.
• 28:52
  The radio wavelengths are longer, which means the cycle of the light is slower.
• 28:58
  Okay?
• 28:58
  Now, here's the key thing.
• 29:00
  With interferometry, what I need to do is...
• 29:03
  interfere the light signals, which means a wave is coming in over here, a wave is coming in over here.
• 29:09
  I need to know the exact timing of these waves so that I can add them up and destructively and constructively interfere them.
• 29:16
  Now with radio astronomy, like something like the VLA, right, I'm going to
• 29:23
  you're looking at about the cycle, the cycle between these things is going to be about 10 to the minus 12 to 10 to the minus 10 seconds.
• 29:31
  So like, you know,
• 29:35
  0.009 or 10 zeros and then one second is going to be the cycle time of this thing.
• 29:40
  Okay?
• 29:41
  This is in the megahertz to gigahertz range.
• 29:45
  Right.
• 29:45
  And that's something that we know how to do.
• 29:46
  We know how to handle it.
• 29:47
  Right?
• 29:47
  Radio, like literally the radio in your car.
• 29:49
  Right.
• 29:49
  Right?
• 29:49
  We know how to tune stuff at that range.
• 29:52
  Okay?
• 29:52
  Okay.
• 29:54
  And what that means is we've got the timing just right that we can add them up and subtract them and so on and so forth.
• 29:59
  Yes.
• 30:01
  So what you can do with radio astronomy is you can take data at the South Pole and take data in Spain and take data in Hawaii and stuff like that.
• 30:10
  Rig it up to a clock that is accurate enough.
• 30:13
  And then when you put it all together with like your petabytes and petabytes of data, you have the tag of like when the signal came and you can you can you can match and you can do the computational.
• 30:24
  You can make the math math.
• 30:25
  You can make the math math.
• 30:26
  Right.
• 30:27
  With with optical now, you run into the block that the cycle time is faster than your clock.
• 30:34
  Like the light that's coming in is faster than the reliable clock that you have to record the thing.
• 30:40
  So you can't actually line up the signals.
• 30:44
  That makes total sense.
• 30:45
  Right?
• 30:45
  And this is why, so there is a famous optical interferometer in Chile called the Very Large Telescope, VLT, compared to the VLA.
• 30:55
  And what they do in order to do the interference, they don't do computational interference.
• 31:00
  They literally have tunnels underneath the ground that are routing the light physically so that the light physically interferes.
• 31:09
  Right.
• 31:10
  Right?
• 31:10
  Because that's like, okay, you can't record the data and do it computationally.
• 31:15
  Hardware is king.
• 31:16
  Right?
• 31:16
  And even when it comes to astronomy, hardware is king.
• 31:20
  As we've discussed multiple times on the show.
• 31:23
  And so VLT does it that way.
• 31:25
  But if I wanted to rig up an optical array that's the size of the Earth, currently it's not possible.
• 31:30
  Yeah.
• 31:30
  Because we just don't have the timing complexity.
• 31:33
  Now, there are optical clocks.
• 31:34
  Right.
• 31:35
  that are getting to that level of accuracy.
• 31:38
  Right.
• 31:38
  But like now deploying it across all of these telescopes and it's a challenge, right?
• 31:43
  Because it's still a very new technology.
• 31:46
  But this is, just to be clear, this is an engineering problem, not necessarily a fundamental science or physics problem at this point.
• 31:55
  Like we would know, we have potential paths well identified.
• 31:58
  Yes, the potential paths are well identified.
• 32:00
  I think realizing those paths, there still might be some physics issues.
• 32:03
  Okay.
• 32:03
  You know, like creating an optical clock that is stable enough.
• 32:09
  Like, I don't know much about, and I think we're going to do future episodes on optical clocks, right?
• 32:13
  This is a good one.
• 32:13
  Comment below if you want this one.
• 32:16
  Because people always talk about, oh, we have atomic clocks.
• 32:19
  But what we're saying is there are not enough decimal clocks.
• 32:21
  Even the atomic clocks are like, yeah.
• 32:23
  There's not enough decimal places.
• 32:24
  Yeah, yeah.
• 32:25
  And if there aren't enough decimal places, it's like only in Boulder, Colorado.
• 32:28
  Yeah.
• 32:29
  Right.
• 32:29
  There's like there's like a few groups in Boulder, Colorado that have this thing working.
• 32:33
  We have it for the nukes, but not for science.
• 32:36
  Let's make the clocks for nukes work for science.
• 32:39
  That's what we want.
• 32:40
  So so and I do also want to shout out there's there's a team at Mount Wilson that we covered that also had.
• 32:47
  Sure.
• 32:49
  An optical interferometer.
• 32:50
  And they're the guys that found sunspots on other stars.
• 32:53
  We covered that episode.
• 32:55
  But they also do the hardware thing.
• 32:56
  They've got a tunnel with a bunch of lasers.
• 32:59
  And they're interfering it optically.
• 33:01
  Yes.
• 33:02
  So doing this, which is really a computational challenge, we haven't been able to figure out for optical telescopes.
• 33:10
  And the description is really important.
• 33:12
  Because basically what we're saying is radio waves travel through space
• 33:18
  slower than light does, which is what we mean when we say optical.
• 33:23
  And so we have a work... No, I should be clear.
• 33:27
  Radio waves travel at the same speed as optical.
• 33:30
  The cycle...
• 33:31
  The cycle...
• 33:33
  The wave goes up and down...
• 33:35
  Slower.
• 33:35
  Slower.
• 33:36
  Right?
• 33:36
  The speed of light is the same.
• 33:37
  People are going to come at us.
• 33:38
  No, no, no, no.
• 33:39
  No, no.
• 33:39
  That's a fair distinction.
• 33:41
  And excuse my misnomer there.
• 33:42
  That's a fair distinction.
• 33:44
  And as a crude analogy, it's like we're a police with a police radar watching people go by.
• 33:50
  But the rate at which we can see the up and down go is too slow for the police.
• 33:54
  for the Dodge Charger, whatever the Dodge.
• 33:57
  The Bugatti.
• 33:59
  The Dodge Charger, it's going to be fine.
• 34:00
  Wait, hold on.
• 34:01
  Some people are going to be mad because there's the Trackhawks.
• 34:04
  There's some label of them that are fast.
• 34:06
  But the point is... Yeah, you've got to like ring it up.
• 34:11
  But we just are not able to move as quickly to have enough discrete granularity from a measurement perspective and timing to do anything valuable.
• 34:24
  And that's kind of the – and once we could be able to do that, it's a very big deal.
• 34:29
  Yeah.
• 34:30
  Because then we can start resolving crazy things.
• 34:33
  Not just the black hole, right?
• 34:35
  But just imagine resolving features in Andromeda.
• 34:40
  All of the nebulae that we have, like the Horsehead Nebula, the Orion Nebula, the Pillars of Creation, all of that is in the Milky Way.
• 34:49
  Imagine being able to resolve...
• 34:52
  those types of star-creating regions in Andromeda, for example, and seeing similar structures in M87 even.
• 35:00
  I think it would be insane.
• 35:02
  So that's kind of a frontier of astronomy that is just ripe.
• 35:06
  But it involves physics, right?
• 35:09
  And engineering to happen first.
• 35:11
  To happen first.
• 35:12
  Anyways, it was a long sojourn.
• 35:14
  So now we've got the Event Horizon Telescope.
• 35:16
  2019, it came out with that photo.
• 35:18
  But it's been seven years.
• 35:19
  So it's been taking data this whole time.
• 35:21
  Yes.
• 35:21
  And now, finally, it has released a video of our black hole.
• 35:29
  The M87 black hole.
• 35:30
  Which looks beautiful.
• 35:32
  Yeah.
• 35:32
  This is a video.
• 35:34
  This is a video over six years.
• 35:37
  Of the black hole at the center of the M87 galaxy.
• 35:40
  This is so outrageous.
• 35:41
  Billions of light years.
• 35:43
  This is so outrageous.
• 35:44
  I mean, sorry, billions of solar masses.
• 35:46
  Right, right, right.
• 35:49
  55 million light years away.
• 35:50
  When you say solar masses...
• 35:52
  Yeah, it's the sun.
• 35:53
  Our sun.
• 35:54
  Billions of suns.
• 35:55
  Billions of our sun.
• 35:56
  Inside a black hole that is like accreting.
• 35:59
  And all of those lines that you see, this is what's very special about this new finding.
• 36:04
  Those lines are the polarization of the radio waves from that part of the accretion disk.
• 36:11
  Now, why is that important?
• 36:12
  Why?
• 36:12
  polarization is a property of light that has to do with the fact that, you know, light is a wave.
• 36:17
  So, well, it could oscillate this way or it could oscillate this way, or it could oscillate anywhere in between.
• 36:21
  Right.
• 36:22
  There's a, there's a 2d degree of freedom.
• 36:25
  The direction of the polarization tells you something about the direction and the strength of the magnetic field.
• 36:32
  Right.
• 36:33
  Okay.
• 36:34
  And black holes have intense magnetic fields around them because you've got all this plasma, which is a bunch of charged particles, charged particles moving around, and,
• 36:41
  Maxwell's equations, you get giant magnetic fields.
• 36:44
  The giant magnetic fields are the reason why M87 has that giant jet that we saw earlier.
• 36:49
  And so studying that magnetic field is extremely important for figuring out the properties of not only the black hole, but also the properties of elliptical galaxies in general.
• 37:00
  I don't know if you remember the Carnegie episode that we had with Michael Blanton.
• 37:05
  Yes.
• 37:07
  We asked him, what's the one...
• 37:09
  like question that you would want answered at the end towards the end of the episode 100 and he said i'd really like to know why these large elliptical galaxies and like these large old galaxies don't create any new stars he was like for some reason yeah for some reason for some reason yeah and there's like there's like 10 different theories and everyone's got an idea right right but no one knows no one knows this happens yeah but it's just for some reason and
• 37:34
  Yeah, and every single one of those theories, the culprit is the black hole.
• 37:39
  Okay?
• 37:40
  The black hole does something, and then all of the stars stop forming.
• 37:45
  No new babies in the galaxy because of the black hole.
• 37:48
  Right.
• 37:50
  Studying the black hole, like M87, is going to give us clues as to whether one...
• 37:56
  Theory is correct versus the other.
• 37:58
  So, I mean, first of all, just like the technological achievement of creating this video of a black hole that's 55 million years, 55 million light years away is insane.
• 38:13
  You can see the magnetic field changing.
• 38:15
  It's even flipping in some directions, which is kind of crazy, which means that like...
• 38:21
  The stuff in the accretion disk is moving at near the speed of light.
• 38:24
  And the size of that accretion disk is about where if the sun was at the center, Voyager would be where the main donut is.
• 38:32
  So it's outside the realm of Pluto.
• 38:35
  But it's not that big, which means if this stuff is going at near the speed of light, this is a highly dynamic system where we are going to see changes on a yearly basis.
• 38:45
  And if we can get that...
• 38:46
  time down to even monthly, we are going to see changes on a monthly basis.
• 38:51
  The system is highly turbulent.
• 38:52
  Yes.
• 38:54
  It's a highly active, relatively, maybe not dense is the right word.
• 38:59
  No, of course it's dense.
• 39:00
  Relatively dense.
• 39:03
  And the amount of activity that's happening is,
• 39:07
  We've never been able to visualize in motion.
• 39:10
  No, we never have.
• 39:11
  In motion.
• 39:12
  Right, and most things in astronomy just stay put.
• 39:15
  They, like, the pillars of creation that were photographed by Hubble, and then we photographed with James Webb, they look the same.
• 39:22
  Right.
• 39:23
  And it's like 20 years later.
• 39:25
  It looks about the same because the stuff is not moving at relativistic speeds, and the size of the stuff is light years across.
• 39:32
  But here, you've got something that's light hours to days, and the stuff is moving at light speed.
• 39:37
  So you're going to have a lot of motion.
• 39:39
  It's extremely exciting for theorists, for astronomers, for people in AI that are like…
• 39:46
  like putting algorithms to the test with like how to make this data better because you've got petabytes and petabytes of data, right?
• 39:54
  It's a really cool, cool thing.
• 39:56
  If you're looking for motion in the universe, check out M87.
• 39:59
  That's right.
• 40:00
  Because that's where the motion is.
• 40:03
  M87 has motion, as the kids would say.
• 40:06
  Yeah.
• 40:07
  I think it's an incredible story.
• 40:09
  Yeah.
• 40:10
  Okay, so moving on to our second story.
• 40:12
  Yeah, this is a good one.
• 40:13
  So that was our first one on the sort of magnetic field related to black holes and our ability to actually see it now.
• 40:21
  Yeah.
• 40:22
  We're moving on to our digital heart twin.
• 40:24
  Yes.
• 40:25
  Not to be confused with the FIFA twin that's created when you get scanned in for VAR.
• 40:31
  Yeah.
• 40:31
  The VAR, the very not truthful.
• 40:34
  Yeah.
• 40:36
  So we talked about in our previous episode, you get every player gets scanned into this FIFA system.
• 40:41
  So when they do the VAR animations, it's not just a random stick figure.
• 40:45
  It's their actual scan.
• 40:46
  Yeah.
• 40:46
  We're doing something not dissimilar for our hearts.
• 40:51
  Not dissimilar, exactly.
• 40:53
  For some medicinal benefit and to assist with how physicians do different diagnosis.
• 41:01
  I'm actually very curious about this because obviously in recent news, unfortunately, we've seen the passing of Senator Lindsey Graham.
• 41:10
  And he seems to have had a cardiac-related diagnosis.
• 41:15
  cause of death for those who are not looking at conspiracy theories.
• 41:18
  Oh, okay.
• 41:19
  And so there's this... My Instagram is only conspiracy theories.
• 41:23
  No, I'm just kidding.
• 41:24
  But yeah.
• 41:25
  So precision medicine for the heart being one of the number one and two and three maybe leading causes of death in America at least.
• 41:34
  Hugely, hugely beneficial.
• 42:04
  So it's basically you've got like a fast heart rhythm that originates somewhere in your heart, usually in your lower chambers, okay, in the ventricles, like the two that are on the bottom.
• 42:16
  And historically what doctors do when they want to treat this is they stick a catheter and they do a catheter ablation.
• 42:23
  So basically you thread a wire into the heart, into the pulmonary arteries, like the arteries that are around the heart, that are keeping the heart going, right?
• 42:34
  Because the heart is a muscle, so it needs blood.
• 42:36
  The blood comes from these veins and arteries.
• 42:38
  So you thread a wire through these arteries, and you effectively try to look for where the arrhythmia is happening.
• 42:47
  Okay, and it's like a needle in a haystack type of problem.
• 42:50
  You're trying to find this thing, and then when you finally find it, you burn, you ablate a tiny patch of that tissue, and that hopefully stops the erratic signal, the erratic electrical signal.
• 43:02
  You're trying to disturb the system somehow.
• 43:03
  Yeah, you're trying to just like, there's like some cells in there.
• 43:07
  There's like some like pacemaker neurons or like whatever neurons that are part of the contracting of the heart that are like doing weird things.
• 43:13
  You burn them off.
• 43:15
  You kill them.
• 43:15
  And then the rest of the heart kind of just like moves on.
• 43:18
  Okay.
• 43:20
  But as I said, it's like finding a needle in a haystack, right?
• 43:23
  So this new procedure...
• 43:26
  It was published in the New England Journal of Medicine.
• 43:29
  We love to see it.
• 43:30
  Yes.
• 43:30
  Still waiting for our subscription.
• 43:32
  Yeah.
• 43:32
  I still had to call in favors from my doctor friends to get access to this journal article.
• 43:40
  But at the end of the day, the procedure that usually takes hours, because the doctors are literally prodding and looking for where this thing is happening.
• 43:48
  Now they've brought it down to 30 minutes.
• 43:51
  And if the doctor misses the exact spot...
• 43:54
  This thing finds it.
• 43:55
  What they've done is create a digital twin, a personalized 3D computer simulation of the patient's heart.
• 44:03
  Okay?
• 44:03
  Here's how they do it.
• 44:05
  They first take a bunch of really high-resolution MRI scans, and they create a 3D digital twin of the patient's heart.
• 44:13
  So every patient gets his or her own digital twin.
• 44:18
  Okay?
• 44:19
  Then what you do...
• 44:20
  is you reconstruct that in your software, okay?
• 44:24
  All of those 2D images from the MRI scans become this 3D, really high-resolution image, and then you simulate what is happening in that heart, because now you know enough about heart cardiology, about heart physiology, about how these neurons are working, and the model is then assigned these electrical properties...
• 44:44
  And the researchers pace that digital heart in the same way that the pacemaker cells create this electrical gradient that causes the heart to contract and then push out blood to the lungs, take in blood from the lungs, so on and so forth.
• 45:00
  And what you can do here is you can do a virtual surgery.
• 45:03
  So here's what's happening in the video.
• 45:06
  Oh, that's fascinating.
• 45:07
  Yeah.
• 45:07
  So you've got the heart, you've created this 3D model.
• 45:11
  Now you're simulating the electrical signals in that heart.
• 45:15
  And you're trying to figure out where, just according to the physiology of the heart...
• 45:21
  There should be that arrhythmia.
• 45:24
  And there you see it in the VT, that circle that you see.
• 45:26
  You're seeing the current kind of loop around that center.
• 45:32
  It shouldn't be doing that.
• 45:33
  It's the eye of the hurricane.
• 45:34
  Yes, it's an eye of the hurricane.
• 45:36
  It shouldn't be doing that.
• 45:37
  There should be no hurricane.
• 45:38
  It should be cleanly diffusing and creating the sort of contracting that we all know and love that keeps our blood going.
• 45:46
  So in the simulation, you can identify exactly where it's happening.
• 45:50
  And then in the simulation, you can ablate that part and see what the resulting pattern of electrical stimulation is going to do.
• 45:59
  And if you ablate that part, is it going to work?
• 46:02
  If it works, then finally you go to the surgeon and you're like, yep, we've got a best strategy.
• 46:07
  Out of all of the different strategies that we've tried, we've got a best strategy that we found on the computer.
• 46:12
  Now you're the surgeon.
• 46:13
  Go in and just do it.
• 46:17
  30 minutes instead of hours.
• 46:19
  And I think what we're sort of saying is particularly for having a patient under during that time period where you have to be searching, doing search and rescue.
• 46:27
  You no longer have to have the patient under to do search and rescue.
• 46:30
  You've...
• 46:31
  theoretically de-risk.
• 46:33
  But it's like way lower.
• 46:36
  The de-risk.
• 46:37
  It's way lower.
• 46:39
  You've been able to do enough pre-work here where the patient risk profile is also lower.
• 46:47
  Yeah, exactly.
• 46:48
  And it's so fascinating because we've been studying the heart vigorously for a very, very long time now.
• 46:55
  And so we have a lot of knowledge as it relates to this particular organ.
• 47:00
  And it builds, this is built on all of that knowledge.
• 47:03
  It's not just out of thin air.
• 47:04
  Yep.
• 47:05
  Right.
• 47:05
  And I think that's a really important point here, which is like we can do things like this now because we've spent so much time gathering data about the heart, building simulations around the heart.
• 47:15
  And then so you can now go to an individual patient and then apply all of that history of knowledge.
• 47:21
  Create a personalized solution to them and basically run the surgery in silica.
• 47:30
  Yeah, exactly.
• 47:32
  Before you go in vivo.
• 47:35
  Yes, that's exactly right.
• 47:36
  That's incredible.
• 47:37
  Yeah, and in a 10-patient trial, so this was all FDA approved, by the way.
• 47:41
  Yes.
• 47:42
  The trial was FDA approved.
• 47:44
  And in the 10-patient trial, all participants were free of the sustained arrhythmias at the follow-up.
• 47:50
  Incredible.
• 47:51
  And that's far exceeding the typical rate, which is like 60% success rate, right?
• 47:54
  So for 10 people to have success...
• 47:57
  Like at 60%, that's 0.6 to the 10th power, which is about 0.006.
• 48:03
  So like that happening by chance among like a normal with normal procedures is like less than 1%.
• 48:11
  But here it was happening, right?
• 48:13
  100% of the time.
• 48:14
  Yeah.
• 48:14
  Every time.
• 48:15
  Exactly.
• 48:16
  So here you don't even.
• 48:17
  Yeah, this isn't.
• 48:18
  You can't really argue on it.
• 48:20
  The other thing that was crazy is the eight of the patients were entirely off antiarrhythmia medications.
• 48:26
  They didn't even have to take medications.
• 48:28
  Two of them had lower doses, but eight of them were just like done.
• 48:31
  We're good.
• 48:32
  Yeah.
• 48:32
  They did it.
• 48:33
  And I think one of the things, technology understandably is very complicated in our modern era in consumer applications and a variety of other contexts and the economics that relates to people who make technological discoveries.
• 48:49
  Yeah.
• 48:56
  Is hugely incredible for patient outcomes.
• 48:59
  Yeah.
• 49:00
  The healthcare system is not what we're talking about when we talk about just the raw ability to get the solution.
• 49:07
  And then we have to deal with how do we make this more accessible.
• 49:10
  And there's sort of the public health, healthcare insurance paradigm that's.
• 49:16
  needs to be dealt with.
• 49:17
  But our ability to have real solutions to these problems that are this effective is so, again, I'm sure many people who are listening, us personally, others, have had friends, family, who have had maybe not this specific heart-related issue, but adjacent-related heart-related issues.
• 49:38
  And it's the number one killer of people in the U.S. And we're slowly chipping away at the tool set
• 49:46
  to start to solve these problems.
• 49:49
  Again, it's from hours to 30 minutes.
• 49:54
  If anyone's been to the hospital, you don't want to be in there.
• 49:56
  No, that's insane.
• 49:57
  I mean, the surgeons don't want to do a surgery for hours when they can do something in 30 minutes.
• 50:03
  One funny thing I will say is, so I saw an interview with Natalia Treonova, who is the professor of biomedical engineering at Johns Hopkins.
• 50:12
  And
• 50:15
  her team there i think they're like trying to work on a desktop app now that is um that is going to make this accessible on a desktop that's and so doctors can have this information in minutes pretty soon they're going to make an app store like on the iphone i think that's what
• 50:31
  I think that was kind of funny.
• 50:32
  It's like now it's just like we're just going to miniaturize it and put it on a laptop.
• 50:36
  Yeah.
• 50:37
  And like there's no like that's kind of the I mean ultimately eventually basically you'll have the tooling and then your end surface a laptop or a phone.
• 50:48
  And right now some of us have.
• 50:50
  watches or aura rings yeah do very rudimentary yeah it's just yeah the monitoring is going to go up the technology is going to go up this is great the time is going to go down down which which matters in terms of saving lives yeah frankly um this is very cool super super fascinating great story that's our story number two i'm going to queue up a story number three for you
• 51:15
  Which, interestingly, is related to our story number two.
• 51:21
  We didn't plan this, but we should.
• 51:22
  Right, right, right, right, right, right, right.
• 51:24
  This ended up being convenient, even though.
• 51:27
  So, as you guys know, with the rundown, we're not going super deep into any particular story, except the black hole story.
• 51:33
  Yeah.
• 51:33
  That was good.
• 51:34
  There's still so much more I could talk about.
• 51:36
  Like, we only spent 30 minutes on it.
• 51:40
  I could have spent an hour and a half.
• 51:41
  You guys know me, if you're regular listeners.
• 51:45
  But we try to use the rundown as a way in which to give the listeners a little taste of many of the things happening at the frontier.
• 51:54
  As we mentioned at the beginning of this episode, we're trying to cover a couple of stories we weren't able to get to earlier in the year as we get back into now doing our normal weekly rundown.
• 52:04
  So we're hitting what's happening as it's happening because it's happening very fast.
• 52:10
  And one story that I saw that was fascinating to me
• 52:13
  was the introduction of MidJourney Medical.
• 52:18
  And some of you who are listening may be familiar with MidJourney.
• 52:23
  For those who don't know, MidJourney is primarily known for being an image generation tool.
• 52:29
  It initially started as a Discord-only image generation thing.
• 52:35
  So they had a bot on Discord.
• 52:38
  Right.
• 52:39
  I remember using your Discord account to try to mess around with it.
• 52:43
  There was no website.
• 52:44
  There was no interface.
• 52:45
  And they were very early, even before a lot of the big model providers opened AI and thought, but in terms of leaning into...
• 52:56
  Image generation.
• 52:57
  So the question might be, what does mid-journey medical have to do with AI image generation?
• 53:02
  And the underlying company has created this, what they're calling an ultra CT scanner.
• 53:08
  They've launched a healthcare division.
• 53:10
  And they've now announced this full body ultrasound.
• 53:14
  Yeah.
• 53:15
  scanner that aims to image the entire body in about 60 seconds using sound waves and water without radiation or magnetic fields, which would be the MRI would be the comparative here, which I talked about a lot recently on the podcast.
• 53:30
  And so a lot of people's spidey senses went up.
• 53:35
  Historically, a lot of tech companies make a lot of claims.
• 53:38
  There's not been a single of the modern
• 53:41
  tech architecture that has moved into medical imaging right uh at any time and the founder of midjourney has done a couple of different endeavors that he's really tried to lean into saying how can we utilize something like ai image generation to create a funding vehicle for greater good purposes right instead of having to raise money all the time so this is kind of
• 54:05
  what his vision has been for some time, and MidJourney broke through.
• 54:09
  So the main idea here is it's a first-generation prototype with no FDA regulatory clearance yet, and it's built on licensed technology from Butterfly Network rather than obviously MidJourney's generative AI.
• 54:22
  But at launch, they want to offer full-body composition maps rather than diagnostics with a first location planned for late 2027 in San Francisco.
• 54:35
  So the idea here is Mid Journey wants to create these Mid Journey spas.
• 54:41
  We're now looking at this video of their prototype that they brought forth at this demo that they brought for other people where you stand in what is like a circular platform that lowers
• 54:52
  of water, you're surrounded in a soup by these circular micron level sized sensors.
• 54:59
  There's like thousands of millions of these sensors that are sending these radio waves at you and you're getting this real time, because it's ultrasound, or it's Zemar, you're getting this real time sort of cross-sectional scan of your body that they then through
• 55:15
  a few algorithmic systems, then compose a 3D image afterwards.
• 55:19
  And we're gonna get into some of the technical details for a second.
• 55:23
  So when you step in the water, you're staying on the platform.
• 55:27
  It's connected to a tube to these rails and the gates of the gate, moving into the center and gently lower your power to patches over five centimeters, or simply some of these one-way phrases, and then out of the water.
• 55:45
  doing here.
• 55:46
  As you descend, you pass through a made of about half a billion tiny squares, each the size of a fine grain of sand, each capable
• 56:09
  Oh, very nice.
• 56:11
  In all these directions, they're doing this super, super high frequency.
• 56:15
  Ah, very nice.
• 56:16
  Right.
• 56:16
  And so this is now how they're trying to create a different implementation of the underlying concept.
• 56:28
  No, so that's really cool.
• 56:30
  Yeah, you've got...
• 56:31
  It's the microphone and speaker that you were talking about, right?
• 56:34
  Yeah, you're sending out ultrasound and then the...
• 56:39
  Yeah, it reminds me of how we mapped the Earth's interior using seismic waves.
• 56:46
  You know what I mean?
• 56:47
  Exactly.
• 56:47
  Like, we figured out that there's a mantle and a core and an inner and outer core and all that other kind of stuff because we looked at how seismic waves, earthquake waves, bent around in the interior of space and we deduced that there has to be like...
• 57:04
  these layers in the earth's crust or it's under the earth's crust and here what you're doing is effectively that but you're creating fake earthquakes right you're creating fake little earthquakes where you can control the size and the frequency of them and then you have a bunch of like sensors that's crazy that's so clever each of the squares creates ultrasonic waves and records the earth plus back millions of times
• 57:32
  The challenge is the processing pipeline.
• 57:35
  Right.
• 57:36
  It's not just sensing, but it's also how you process the data.
• 57:41
  So take a look here now at the next one, which is so what we're looking at is sort of one of the 3D images that it creates.
• 57:49
  And on the labels on each side, you kind of see the different.
• 57:52
  parts that it's identifying.
• 57:53
  As waves travel through the water, your body and your body, they kind of change shape.
• 57:58
  And the shapes change.
• 58:00
  Whenever they change, it helps them understand the density and stiffness, going from water to fat to muscle to bone, of what the underlying structure is by looking at how the shapes of all the waves change.
• 58:12
  They can reconstruct
• 58:14
  Right.
• 58:14
  This detail map.
• 58:15
  It's not dissimilar to other medical imaging technologies.
• 58:18
  It's just kind of a little bit of a different medium.
• 58:22
  And then so all of these images will then come together to create this 3D mockup of your body.
• 58:31
  So we see in this graphic here there you're looking at two legs.
• 58:36
  Right.
• 58:37
  Right.
• 58:38
  And then they're basically stretching out these two legs.
• 58:40
  It's like slices.
• 58:41
  Spreading out to the different slices that are these individual images.
• 58:45
  And so what they're putting, they're sort of saying their pieces, the software pipeline to go from the slices to the 3D imaging, the time span to be able to do so, the processing system.
• 58:57
  And their goal is quite interesting here.
• 59:01
  So let's talk about what exists now.
• 59:04
  Yeah.
• 59:04
  And what doesn't.
• 59:06
  A working Gen 1 prototype demonstrated live, as we saw.
• 59:10
  And that's what we saw.
• 59:11
  That's what we saw.
• 59:12
  It does body composition mapping.
• 59:14
  That's the stated launch capability.
• 59:16
  And they did this with a licensing deal with Butterfly Network, which has some of the underlying image sensing technology pieces.
• 59:23
  They kind of put the pipeline together to make it this end solution.
• 59:27
  And the public announcement and marketing site was...
• 59:29
  What is claimed or planned from MidJourney Medical is image quality superior to MRI, which many in the radiological and scientific community have some feedback about, which I'll talk about in a second.
• 59:44
  Diagnostic capability pending FDA submissions.
• 59:48
  They want to have 50,000 scanners worldwide over a six-year period, and they want to be doing a billion full-body scans per month.
• 59:58
  That's their stated goal for this system and platform.
• 1:00:02
  So like what is the so what here?
• 1:00:04
  Like, okay, great.
• 1:00:05
  Like why does this kind of like really matter?
• 1:00:08
  Ultrasound is generally excellent for a wide range of problems, including assessing abdominal organs, blood vessels, the heart, pregnancy, and many other soft tissue problems.
• 1:00:19
  Yeah, yeah.
• 1:00:19
  Right?
• 1:00:20
  In real time, without radiation.
• 1:00:23
  Okay, great.
• 1:00:24
  What it doesn't do straightforwardly and can't replace kind of what MRI does currently—
• 1:00:30
  is the soft tissue contrast for the brain and the spinal cord.
• 1:00:34
  Not quite as good in those contexts.
• 1:00:35
  Characterization of joints and certain tumors.
• 1:00:38
  And then validation pathways for certain platforms like DEXA for bone density.
• 1:00:42
  So it's not quite as able to be great in those areas.
• 1:00:47
  And when I looked at some of the back and forth,
• 1:00:50
  People said medical imaging technology is about a toolbox of instruments.
• 1:00:55
  It's not a one instrument to rule them all.
• 1:00:57
  And depending on your use case, you have better options than others.
• 1:01:01
  So the idea is a whole new body ultrasound system can do some things very well, and it might not answer every problem.
• 1:01:09
  But doing some things really well that makes it more accessible is super valuable.
• 1:01:13
  Yeah.
• 1:01:14
  In and of itself.
• 1:01:17
  And so that's kind of I thought this was interesting because it's this, you know, we have a lot of the tech folks come in to say we want to help save the world.
• 1:01:26
  This seems to be an actual practice, something that could be really practical.
• 1:01:30
  If they can go through the FDA process and get approval here and fit into the toolbox.
• 1:01:35
  of medical imaging.
• 1:01:36
  It would be the first new medical imaging device.
• 1:01:39
  I believe it's in like over 50 years.
• 1:01:41
  Wow.
• 1:01:42
  That's really, you know, has to gain adoption and get buy-in.
• 1:01:46
  But I think there's some interesting.
• 1:01:48
  I mean, that is, that is really cool in the fundamentals of, of what they're doing here.
• 1:01:53
  Yeah, yeah, definitely.
• 1:01:55
  I mean, the only ultrasound that I've ever really experienced is like, you know, for like pregnancies and things like that, right?
• 1:02:02
  So, but obviously, yeah, doing soft tissue probing is going to be huge.
• 1:02:09
  The one thing that I'm like kind of skeptical about, I would say, is the billion full body scans per month.
• 1:02:16
  There's only 7 billion people on the earth.
• 1:02:19
  So like what?
• 1:02:21
  I guess a single person could have multiple full body scans because this is, as you said, a real time thing.
• 1:02:28
  And they're trying to make it a lifestyle spa concept.
• 1:02:31
  Then they got to make it cheap.
• 1:02:32
  Which I agree with you.
• 1:02:33
  Right?
• 1:02:34
  No, which I agree with you.
• 1:02:34
  A billion a month is like that already you're saturating the developed world.
• 1:02:44
  You know what I mean?
• 1:02:45
  Now you got to tap into like a lot of countries that don't have the kind of capital for the health care that we do here in the States.
• 1:02:54
  I don't know if we were up and things like that.
• 1:02:56
  We don't need a spa like this in Zimbabwe, for example.
• 1:02:59
  Exactly.
• 1:03:00
  I don't think that's the right entry point.
• 1:03:01
  So a billion full body scans per month.
• 1:03:04
  What?
• 1:03:04
  So a single person is doing multiple per month.
• 1:03:08
  month yeah and then also it's like well if one scan is really good why do i need to be doing exactly like how much is my body really changing month per month um anyways that's the only number that i'm like kind of skeptical about um 50 000 scanners worldwide that makes sense that's not that there's there's a lot of cities in the world that are relative that have a relatively well to do enough population to like want to want something like this in one of their hospitals
• 1:03:34
  We could do a dozen to two dozen in L.A. alone.
• 1:03:36
  Exactly.
• 1:03:37
  So, yeah.
• 1:03:40
  I still think, yeah, the fact that it's a new medical imaging device.
• 1:03:44
  I think that's what the interesting thing is.
• 1:03:46
  It's just at least we're pushing, you know, again, there's both this balance between what's tried and true and then also integrating like CRISPR, right?
• 1:03:56
  As an example, it's a new medical technology in a totally different context.
• 1:04:00
  And we're now trying to figure out how do we make this safe?
• 1:04:03
  How do we scale it?
• 1:04:04
  And I think it is good to push the envelope.
• 1:04:06
  The weird part about this story is, at least in part, the source, which is coming from a nontraditional player.
• 1:04:14
  Yeah, yeah, yeah.
• 1:04:14
  Mid-journey.
• 1:04:16
  In the medical imaging space, which has created a lot of skepticism.
• 1:04:19
  Yeah.
• 1:04:20
  But again, the proof's in the pudding.
• 1:04:21
  It's falsifiable.
• 1:04:22
  They can put it to the test.
• 1:04:24
  And if the FDA approves it...
• 1:04:25
  we will have a great new solution.
• 1:04:28
  We are going to take this moment after our first three stories.
• 1:04:32
  We still have, I believe it is two stories left.
• 1:04:34
  Yeah, but those are really kind of fake stories.
• 1:04:38
  They're going to be fun because we're going to end on some World Cup hype.
• 1:04:41
  But before we get there, I'm going to do a little bit of housekeeping.
• 1:04:44
  For those who are listening, as you know, with myself and Krishna, it is us two on the pod.
• 1:04:51
  No network, no extra people coming around.
• 1:04:55
  We really do this, just the two of us.
• 1:04:57
  We really appreciate the support from our patrons who help support bringing you the best entertaining science and deep dive content that you can find here.
• 1:05:08
  In the world, in English language, you get it here on FFP.
• 1:05:11
  If you want to help support the podcast, there's a couple ways you can support.
• 1:05:15
  You can become a donor by going to ffppod.com backslash donate.
• 1:05:19
  We have a variety of different options one time and monthly.
• 1:05:22
  If you cannot support monetarily in this economy, please share it with a friend, bring it to journal club, post it on your Instagram, your socials.
• 1:05:31
  Any kind of promotion is helpful to help us get put up in the
• 1:05:36
  billionaire algorithms which we do not control and science and science education and news is so important especially in this day and age which is it is progressing faster than ever and it is could not be more it could not be uh less understood in an era where it's growing so quickly and so any support that you could give us is super appreciated
• 1:06:00
  All of our shows are on FFBpod.com.
• 1:06:01
  You can listen to us on all of the podcast networks.
• 1:06:05
  Our clips are on social.
• 1:06:07
  If you have found yourself stumbling upon this episode after watching some of our World Cup content in the past couple of weeks, welcome.
• 1:06:15
  This is what this show is like when we're not talking about football.
• 1:06:19
  We will talk about it a little bit at the end of the episode.
• 1:06:21
  But what you just experienced in the first half is really what the show is about.
• 1:06:26
  It's great for a car ride, a commute to work.
• 1:06:29
  It's great if you want, you have some kids getting to the teenage years.
• 1:06:34
  You want to get them into some content that's about science.
• 1:06:37
  But everything else is boring.
• 1:06:39
  We have Drake memes.
• 1:06:40
  We understand what's going on on the interwebs, what the kids are doing these days.
• 1:06:45
  There's something about 6'7".
• 1:06:47
  Who knows?
• 1:06:48
  It's a great way to get everyone started and engaged with science.
• 1:06:52
  It's fun, entertaining, but still detailed about the fundamentals and the facts.
• 1:06:58
  And with that, what we are going to do, which has been a long time since we have done it, is bring back one of my favorite segments, which is Are You Smarter Than a Scientist?
• 1:07:18
  our favorite game show oh okay with our resident phd krishna chowdhury as we know the world cup is about to end everyone is gonna have to find something to entertain themselves and to end the world cup journey we have our question this week is to name the 10 most common injuries
• 1:07:45
  And I will just note that this is based on UEFA Elite Club's injury study, which is a long-running European pro club data study that was first published in 2009 with newer findings from the 2021 and 2022 seasons.
• 1:08:05
  including their latest ECIS update in the British Journal of Sports Medicine in 2023.
• 1:08:13
  So this is not just us making up what it is.
• 1:08:17
  This is published data.
• 1:08:18
  I'm trying to give you time to think because now you are on the spot.
• 1:08:23
  Okay.
• 1:08:24
  Common.
• 1:08:25
  Can I just name like the specific body parts?
• 1:08:30
  You're going to need to name a body part and potentially the type of thing that's happening with the body part.
• 1:08:39
  Because I initially made this as just body part only.
• 1:08:43
  But that was too easy.
• 1:08:44
  Okay.
• 1:08:45
  Shit.
• 1:08:45
  So...
• 1:08:48
  All right.
• 1:08:49
  What about like the Achilles tendon?
• 1:08:52
  Achilles tendon.
• 1:08:54
  Like the Achilles, you know, the...
• 1:08:56
  Your Achilles heel.
• 1:08:57
  The heel.
• 1:08:57
  Achilles heel.
• 1:08:59
  So the question is, is Achilles heel an answer on the list?
• 1:09:05
  And unfortunately... No?
• 1:09:06
  Oh, my God.
• 1:09:08
  I'm already... That is going to be your first trick early in the game.
• 1:09:11
  Early in the game.
• 1:09:12
  I don't...
• 1:09:13
  This is the first time.
• 1:09:14
  I don't even have one on there.
• 1:09:17
  Okay, one actually, okay, I'm pretty sure even though this is European football and American soccer, there should probably be concussion on here, no?
• 1:09:29
  Like, people are getting hit with yellow cards.
• 1:09:32
  Like, yeah, I would say, okay, let's do concussion.
• 1:09:35
  Is concussion on the list?
• 1:09:41
  I really hate to say it.
• 1:09:42
  No?
• 1:09:43
  No?
• 1:09:44
  But it is not in the top 10 most common.
• 1:09:47
  It's not 10.
• 1:09:47
  Okay.
• 1:09:48
  In the most common.
• 1:09:49
  You were more directionally correct with your first.
• 1:09:53
  It's got to do with legs, huh?
• 1:09:55
  All right.
• 1:09:56
  I'm just going to say that.
• 1:10:01
  You know, hamstring.
• 1:10:03
  Okay.
• 1:10:04
  So we're saying hamstring.
• 1:10:06
  Yeah.
• 1:10:06
  Yeah.
• 1:10:08
  And we're going to be on the board with a hamstring strain.
• 1:10:11
  Okay.
• 1:10:11
  I'll give you that.
• 1:10:12
  Okay.
• 1:10:13
  So very common.
• 1:10:14
  Very common.
• 1:10:14
  Yeah.
• 1:10:15
  I think you've even mentioned it.
• 1:10:17
  Yes.
• 1:10:17
  At 24%.
• 1:10:19
  It's kind of the meme.
• 1:10:21
  Celebrations have been made over people grabbing their hamstring.
• 1:10:24
  Yeah.
• 1:10:25
  Like after they score a goal, pretending they have a hamstring injury.
• 1:10:28
  Okay.
• 1:10:28
  So we're on the board.
• 1:10:29
  Okay.
• 1:10:30
  So we're cooking.
• 1:10:32
  You'll get some.
• 1:10:33
  You'll get some.
• 1:10:33
  Okay.
• 1:10:34
  Okay.
• 1:10:36
  I'm going to need some lifelines, though, because I'm already on two.
• 1:10:38
  Okay.
• 1:10:39
  Okay.
• 1:10:40
  So you're going to have to give me a few.
• 1:10:42
  What I would say here is think about...
• 1:10:46
  Football is a sport where we run long distances, right?
• 1:10:51
  And there's a part of the body over those long distances that is very active in being a – try not to be too specific here – in being a facilitator of other parts of the lower body moving over long distances.
• 1:11:09
  Right?
• 1:11:13
  No, I'm thinking of the heart.
• 1:11:14
  No, no, no.
• 1:11:15
  But that doesn't make sense.
• 1:11:16
  The lower, okay.
• 1:11:19
  Think about the different component parts, and something has to be a translator between... Oh, like the knee?
• 1:11:28
  I don't know.
• 1:11:28
  I'm just saying.
• 1:11:29
  Okay, so...
• 1:11:30
  I'm just saying.
• 1:11:30
  Wait, what's in the knee?
• 1:11:31
  Oh, the ACLU.
• 1:11:32
  No, that's the... That's the... No, but you know what I mean?
• 1:11:38
  The thingy.
• 1:11:40
  There are three letters there that are correct.
• 1:11:42
  The ACL.
• 1:11:42
  The ACL.
• 1:11:44
  Yeah.
• 1:11:45
  ACL tear.
• 1:11:46
  Okay, there we go.
• 1:11:47
  Is our number 10 answer.
• 1:11:48
  And a little bit lower than many would expect at only 2%.
• 1:11:51
  Okay.
• 1:11:54
  But like that's the knee.
• 1:11:55
  That's the knee.
• 1:11:55
  Okay, that's the knee.
• 1:11:56
  We're getting a tear in the knee.
• 1:11:58
  All right, we got hamstring, which is I think thighs.
• 1:12:00
  Now we got knees.
• 1:12:01
  Okay.
• 1:12:02
  No, there's got to be something in the foot.
• 1:12:04
  The guy from Canada broke his...
• 1:12:07
  Didn't he break something because of the Qatari red card?
• 1:12:11
  I plead the fifth.
• 1:12:13
  That's got to be something.
• 1:12:14
  I plead the fifth.
• 1:12:16
  I'll give you another lifeline, which is I would stay in...
• 1:12:23
  muscles more than bones.
• 1:12:25
  More than bones, okay.
• 1:12:26
  Only Christian Pulisic gets a microfracture.
• 1:12:29
  Right.
• 1:12:31
  Well, that's actually not true.
• 1:12:32
  Yeah.
• 1:12:32
  And so let me not, that's not, it may be in our answers, so that's not true.
• 1:12:36
  But I think being more muscle oriented would be the correct approach here.
• 1:12:42
  I know there's people who are watching at home and they're like,
• 1:12:45
  Bing, bing, bong, boom, bing, bing.
• 1:12:47
  And they're just going through this list.
• 1:12:49
  I've gotten actually probably six to eight of these injuries myself.
• 1:12:53
  Oh, really?
• 1:12:54
  Yeah.
• 1:12:55
  This muscle...
• 1:12:58
  Let's see.
• 1:12:59
  No, hamstring is done.
• 1:13:00
  The calves, calf strain.
• 1:13:03
  We will go ahead and put calf strain at number seven.
• 1:13:07
  Nice.
• 1:13:07
  With 4%.
• 1:13:08
  We are on the board.
• 1:13:10
  Again, you're starting to...
• 1:13:11
  Okay.
• 1:13:11
  You're starting to wind it.
• 1:13:13
  It's been a while since we've done it.
• 1:13:14
  I will note, Christian is...
• 1:13:17
  a biophysicist physicist because biophysicists people get annoyed about it yeah mostly like at the molecular level not uh molecular and cellular level not at a full organism um no calves hamstrings hamstring um you're on the board you're on the board aclu is the there's got to be something with the foot how about um like uh
• 1:13:41
  No, I already said Achilles.
• 1:13:42
  That didn't work.
• 1:13:45
  Again, think about soccer being a sport where we run a lot.
• 1:13:49
  Yeah.
• 1:13:49
  Okay.
• 1:13:50
  So, calves.
• 1:13:51
  What else?
• 1:13:52
  What is this thing called?
• 1:13:53
  The thigh?
• 1:13:53
  No.
• 1:13:54
  The thigh muscle?
• 1:13:55
  The quad.
• 1:13:57
  Quad.
• 1:13:57
  Quad strain.
• 1:13:58
  We got it on the board.
• 1:14:00
  Number six.
• 1:14:01
  Quad strain.
• 1:14:03
  With five percent.
• 1:14:04
  Okay.
• 1:14:04
  Now I just got to...
• 1:14:05
  Okay.
• 1:14:06
  How many muscles can I think of that are in the lower body?
• 1:14:11
  we're cooking we're missing number two three four five eight and nine there's a lot of items on the board well it's drained damn i really thought concussion was gonna be on here um thankfully we don't make contact with our heads that often cte is not a problem in the game of football
• 1:14:33
  How about... Now I'm just blanking.
• 1:14:38
  I literally have no idea.
• 1:14:39
  I'm going to say heel.
• 1:14:41
  The heel.
• 1:14:42
  The heel.
• 1:14:43
  The heel muscle.
• 1:14:44
  The heel muscle.
• 1:14:46
  Is it on the board for our last strike?
• 1:14:51
  Unfortunately...
• 1:14:54
  That is not on the board.
• 1:14:55
  Yeah, that's fair.
• 1:14:56
  This was a tough one.
• 1:14:58
  Yeah, it just shows you how little I know about the human body.
• 1:15:01
  I was really trying to mostly rage bait the audience.
• 1:15:04
  We're screaming the answers at home right now.
• 1:15:08
  I kind of put you in a tough spot.
• 1:15:10
  But we're going to go through with the correct answers.
• 1:15:13
  In our number two spot...
• 1:15:15
  was a contusion at 17%, which you can get bruising, bone contusion.
• 1:15:21
  Okay.
• 1:15:21
  Very common.
• 1:15:23
  Number three is an injury I currently have after going to, shout out to Ben, Ben Fong's birthday party, where I got a groin strain at 9%.
• 1:15:36
  I should have done groin, yeah.
• 1:15:39
  Very common.
• 1:15:41
  I know you said Achilles.
• 1:15:43
  It was close.
• 1:15:45
  But not quite the ankle sprain.
• 1:15:47
  Oh.
• 1:15:48
  Which was the answer we were looking for at 7%.
• 1:15:51
  That's probably an Achilles in there.
• 1:15:53
  I don't think that's correct.
• 1:15:55
  No.
• 1:15:55
  I'm shocked we didn't get the ankle sprain.
• 1:15:59
  Now, we talked about the ACLU tear.
• 1:16:01
  Yes.
• 1:16:03
  But we did not talk about the MCL sprain, which is another type of...
• 1:16:10
  abbreviation sprain.
• 1:16:12
  I'm not a doctor.
• 1:16:14
  I know there's ACL, MCL.
• 1:16:16
  Everyone talks about it.
• 1:16:18
  It's so common.
• 1:16:19
  Our number eight.
• 1:16:22
  which is more in the direction of the former captain of the U.S. Men's National Team, Christian Pulisic, with a fracture at 4%.
• 1:16:33
  And our last option, I was trying to talk about the connector, is a meniscus tear.
• 1:16:40
  Oh, I could have gotten that.
• 1:16:41
  Which was at 3%.
• 1:16:42
  We did not get a...
• 1:16:47
  this episode.
• 1:16:48
  No, I didn't deserve that.
• 1:16:49
  It was not that well, but we really do appreciate all of our listeners joining for another great installment of Are You Smarter Than a Scientist?
• 1:17:00
  I did put Krishna on the spot today and the stream deck worked perfectly with no interruptions, so we are not rusty at all as we are now pushing an hour and 15 minutes for what was supposed to be a short episode.
• 1:17:17
  But as you know us, we like to yap on this show.
• 1:17:21
  And so we're going to go now into the last of our two stories, which are going to be fun.
• 1:17:25
  It's going to be relaxing.
• 1:17:27
  We're going to wrap this up pretty quick here.
• 1:17:29
  We are going into our England versus Norway World Cup goal.
• 1:17:34
  Ball camera.
• 1:17:36
  Cable.
• 1:17:37
  Hey, Jude.
• 1:17:38
  Bellingham story.
• 1:17:40
  Yes.
• 1:17:40
  So.
• 1:17:42
  Norway versus England in the quarterfinals.
• 1:17:46
  The goalie of Norway kicks a ball, and I think we've got a video of it on Fox Sports.
• 1:17:51
  This is video 14.
• 1:17:54
  This is on Fox Sports, right?
• 1:17:56
  He kicks the ball.
• 1:17:57
  Apparently, it hits a wire, one of the wires that holds up the sky cam, and then it lands directly on an English player who then dribbles, gives it to Bellingham somewhere, and then Bellingham scores.
• 1:18:11
  I think that's Anthony Gordon, $80 million Barcelona player, eat your heart out, Marcus Rashford.
• 1:18:17
  And then the Norway team's like, it definitely hit.
• 1:18:19
  And Fox Sports, there's Erling Haaland saying it definitely hit.
• 1:18:24
  And Fox Sports on their Instagram says it hit a wire.
• 1:18:29
  So they went on the record to say it hit a wire, right?
• 1:18:33
  Now let's go to the next video.
• 1:18:36
  This is from TSN, which is, I think, the Canadian ESPN.
• 1:18:42
  And they show from the side, it hits the spider cam, and then it goes down, changes the trajectory...
• 1:18:51
  Looks a little Infantino-ish.
• 1:18:53
  A little sketch.
• 1:18:54
  It looks a little sketchy.
• 1:18:55
  It looks like, I mean, they're doing this whole, like, you know, zoom in and things like that, right?
• 1:19:00
  The next video we've got is just a clean video that I found of the ball going up.
• 1:19:05
  And then it definitely, like, falls down.
• 1:19:08
  Bruh, bruh.
• 1:19:09
  It definitely falls down.
• 1:19:12
  The camera itself is moving, which is why I can't really get a good handle on what's going on.
• 1:19:20
  But it seems like it goes up, and then it falls faster than it should.
• 1:19:25
  This is not a rainbow where there's gold at the end of the rainbow.
• 1:19:29
  No, right?
• 1:19:30
  And it seems more than air resistance.
• 1:19:33
  Yeah.
• 1:19:33
  Because I do understand that, like, you know, it's not going to be a perfect parabola.
• 1:19:38
  The parabola is going to get squished as you keep going.
• 1:19:40
  It just doesn't look quite like air resistance.
• 1:19:44
  Right?
• 1:19:44
  It's a little sketch.
• 1:19:46
  Right?
• 1:19:46
  Now, the ball has a sensor.
• 1:19:48
  That's the story.
• 1:19:49
  Which we talked about.
• 1:19:50
  Yes.
• 1:19:50
  We talked about the ball has a sensor, and it's taking data at 500 hertz.
• 1:19:55
  It's got this IMU inertial mass unit right under the surface of the ball.
• 1:20:02
  Yeah.
• 1:20:03
  It goes off and it's sending data at 500 times a second, right?
• 1:20:08
  This thing is measuring acceleration in three directions.
• 1:20:11
  So FIFA puts out this video of the ball.
• 1:20:16
  There's the data from the IMU.
• 1:20:18
  In the bottom left corner, we see it like what looks like if you watch ER or the pit or any hospital shows, the little thing that shows your heartbeat.
• 1:20:26
  Yeah.
• 1:20:27
  This is flatlined.
• 1:20:28
  It's flatlined, except for when the goalie kicks it.
• 1:20:33
  That's it.
• 1:20:33
  It's flatlining.
• 1:20:34
  There's a little bit of undulation, you can actually tell.
• 1:20:37
  If you look closely, there's a tiny bit of undulation.
• 1:20:39
  It's not totally flat.
• 1:20:41
  It's not a line.
• 1:20:41
  It's not a line, which tells me, because there's a theory that's going out there saying that, oh, perhaps the ball cut off.
• 1:20:49
  It was far enough away from the receivers to where the data packet wasn't going through.
• 1:20:55
  I don't see that because I'm seeing it's still, it's continues to continue.
• 1:20:59
  Like it's not flat lining.
• 1:21:00
  Right.
• 1:21:00
  There's still, there's still like salient data going through a consistent signal.
• 1:21:05
  That is not, it's not just a, whatever, uh,
• 1:21:08
  y equals zero.
• 1:21:10
  Exactly.
• 1:21:10
  Yeah.
• 1:21:12
  So now this brings me to my next point, right?
• 1:21:14
  Which is, and I'd like you to stay on this, right?
• 1:21:16
  Okay.
• 1:21:16
  Well, we're going to keep this up.
• 1:21:17
  Yeah.
• 1:21:18
  So it brings me to my next point, which is the ball is now traveling through the air.
• 1:21:22
  Yeah.
• 1:21:23
  Right?
• 1:21:23
  It's experiencing free fall.
• 1:21:25
  It's also spinning because as with any ball, and I think you can see in the video, the ball is actually spinning, but this thing is flatlined or almost.
• 1:21:34
  There is a tiny undulation, which I think is caused by the spin.
• 1:21:37
  Yeah.
• 1:21:38
  But what I think is happening is that there is post-processing algorithms that are taking the raw data.
• 1:21:49
  Because what we're seeing right now is...
• 1:21:52
  Just a 1D trace.
• 1:21:54
  First of all, it's infuriating to me that there's no axes labels.
• 1:21:57
  Yeah.
• 1:21:59
  Okay?
• 1:21:59
  My PhD advisor would have my neck if I presented him any data that looked like that.
• 1:22:05
  And for good reason.
• 1:22:06
  Right.
• 1:22:06
  Okay?
• 1:22:06
  Right.
• 1:22:07
  You got to have labels on your axes.
• 1:22:08
  Yeah.
• 1:22:09
  Tell me like a second.
• 1:22:10
  Right.
• 1:22:11
  And what the y-axis even means, is it a meters per second?
• 1:22:14
  Am I looking at acceleration?
• 1:22:16
  Am I looking at angular acceleration?
• 1:22:18
  What am I even looking at?
• 1:22:19
  Is it jerk?
• 1:22:20
  Is it the derivative of acceleration?
• 1:22:22
  So on and so forth, right?
• 1:22:23
  So anyways, there's got to be, there's like a sensitivity threshold, right?
• 1:22:29
  For when things get triggered.
• 1:22:30
  Right.
• 1:22:31
  And I think there's got to be some post-processing of the data that is happening.
• 1:22:36
  Right.
• 1:22:36
  Which is true of a lot of...
• 1:22:39
  sense of platforms where you have the level of volume of data.
• 1:22:42
  There's just so much noise.
• 1:22:43
  What are you looking for?
• 1:22:45
  Yeah, and in any case, the IMU is measuring three units of acceleration.
• 1:22:50
  It's an X, Y, and Z in acceleration.
• 1:22:52
  If something is spinning, you're going to get acceleration in X, Y, and Z, and all of those are going to cycle through, which is going to cause the spinning.
• 1:23:00
  So the fact that I'm seeing this flatlining means that there is some post-processing going on.
• 1:23:05
  Fundamentally.
• 1:23:06
  Right?
• 1:23:06
  Fundamentally.
• 1:23:07
  We're not even seeing the...
• 1:23:09
  Anyway, we would see a three-dimensional thing.
• 1:23:11
  Yeah, I would see three traces at least, but I'm seeing only one.
• 1:23:14
  Now, perhaps they're signaling out one, but in any case, I'm not seeing the whole thing.
• 1:23:18
  Okay?
• 1:23:19
  We can all agree that this is not raw data that I'm seeing.
• 1:23:23
  100%.
• 1:23:23
  Okay?
• 1:23:23
  With no labels.
• 1:23:24
  Yeah, with no labels.
• 1:23:26
  Okay.
• 1:23:27
  Now, the second thing is...
• 1:23:29
  Now, if the ball is spinning and it's subject to this constant acceleration, right, there's got to be an algorithm that's looking for a discrete touch, like the one that the goalie had, like the one when he fell.
• 1:23:41
  Or the one where it hit the hair of the one player on that one team.
• 1:23:44
  Yes, and actually that's the next one.
• 1:23:45
  If you go to the next one, we've got Croatia versus Portugal.
• 1:23:49
  There's a little bump of it hitting the guy, his hair, and then all of a sudden Croatia's goal is disallowed, right?
• 1:23:57
  Right.
• 1:23:58
  Now, there's not a lot of information about the post-processing of the data.
• 1:24:06
  There's a lot of information out there on the FIFA website about the 500 hertz, about the IMU.
• 1:24:12
  But at the end of the day, fundamentally, what is the data that I'm seeing on my TV that they're presenting as evidence?
• 1:24:20
  I don't know.
• 1:24:21
  Now, the worst case scenario would be that there is an AI algorithm.
• 1:24:28
  that is behind this, right, that has been trained on player touches, that has been trained on what to look for when I kick a ball, when a hand feels a ball, when it hits my head, right?
• 1:24:44
  These are things that the AI algorithm would have been trained for.
• 1:24:47
  And perhaps the signature... Perchance.
• 1:24:51
  Perchance.
• 1:24:51
  Perchance the signature of the ball hitting a wire...
• 1:24:55
  is a very different signature in that IMU compared to hitting a human body because there's a springiness in us compared to, like, the wire.
• 1:25:03
  But I guess the wire also has a springiness.
• 1:25:05
  But, like, maybe the timing is, like, small enough where, like, the algorithm thought it was just noise.
• 1:25:12
  It's, like, some type of thing, right?
• 1:25:15
  I'm just saying I don't think this is a good way...
• 1:25:18
  It's not in the training data set.
• 1:25:19
  Yeah, perhaps it's not in the training data set.
• 1:25:22
  And so now...
• 1:25:23
  And it gets filtered out?
• 1:25:24
  It gets filtered out because it thinks it's noise.
• 1:25:28
  Look.
• 1:25:28
  I don't know.
• 1:25:29
  So we do know after our Match and Momentum video that there are folks who've worked on some of FIFA's graphics and digital analytics tools that are displayed in-game.
• 1:25:44
  We would love to actually understand this question.
• 1:25:48
  It's in the weeds.
• 1:25:50
  It's in the weeds, but it's also fairly clear from the video that it hit something.
• 1:25:56
  Right?
• 1:25:56
  How do we explain?
• 1:25:57
  It's like, I don't know.
• 1:25:59
  It's fairly clear from the video that there's a change in momentum.
• 1:26:03
  How is it that the IMU on the ball did not pick it up?
• 1:26:06
  Both things can be true.
• 1:26:07
  Right.
• 1:26:07
  Right?
• 1:26:08
  Right.
• 1:26:08
  Right.
• 1:26:09
  It can be true in saying, well, we have these ball sensors.
• 1:26:12
  And I don't, yeah, like nothing got triggered.
• 1:26:15
  And so the referee didn't see it.
• 1:26:16
  Okay.
• 1:26:17
  However, we have visuals from multiple angles that don't align with that description of events.
• 1:26:24
  Yeah.
• 1:26:25
  And so this is a great learning opportunity.
• 1:26:28
  Yeah.
• 1:26:28
  We'd be happy to have you on and to discuss.
• 1:26:33
  Is there a filter?
• 1:26:35
  Is there sort of an AI algorithm that helps to distill down these three axes into what we see in a 2D plane with no labels?
• 1:26:44
  And I get it.
• 1:26:44
  It's sports.
• 1:26:46
  People don't want to read labels.
• 1:26:48
  And fair play to the Norway team.
• 1:26:50
  They said, let's not make this a big deal.
• 1:26:52
  That's not why we lost.
• 1:26:53
  Let's play.
• 1:26:55
  However...
• 1:26:56
  It is... Yeah.
• 1:26:59
  I'd like to know.
• 1:27:00
  Right?
• 1:27:01
  And I also think, like, it's also just a bad way to do...
• 1:27:04
  If you were in an experimental lab and you were only relying on a single sensor that has a heavy amount of data manipulation, that's just a bad way to do things.
• 1:27:13
  Right.
• 1:27:14
  You should always have coincident data.
• 1:27:16
  And so, like, and I'm pretty sure FIFA has a bunch of cameras, as we were talking about.
• 1:27:20
  Right?
• 1:27:20
  So...
• 1:27:22
  There's got to be other perhaps that modality of data sensing is not in real time.
• 1:27:28
  And and so and as such, you can't stop.
• 1:27:31
  And perhaps only the sensor is the thing that is like affecting real time decisions.
• 1:27:36
  And if there's a filter there, at least at a minimum, we can say, hey, yes, we need to expand the filter a little bit.
• 1:27:43
  Yeah.
• 1:27:44
  To account for this incidental event type that was not in our initial problem set when we defined how to define the filters.
• 1:27:52
  Yeah.
• 1:27:52
  And that's okay.
• 1:27:53
  This is just like, this is fun.
• 1:27:55
  Yeah.
• 1:27:55
  I just, I don't know.
• 1:27:56
  Like, we don't know.
• 1:27:58
  I'm literally just speculating, but like, given what I've seen.
• 1:28:02
  It doesn't add up.
• 1:28:04
  The math is not mathing.
• 1:28:06
  And so we would appreciate an answer.
• 1:28:09
  FIFA, we are here to give you a platform to explain if you would like to.
• 1:28:15
  And if you would not like to, we will still get the answer one way or another.
• 1:28:19
  Yeah.
• 1:28:19
  And so... We'll figure it out.
• 1:28:21
  It is just...
• 1:28:23
  These are the challenges that happen when you put new technology into production.
• 1:28:29
  And it happens in hardware.
• 1:28:30
  It happens in software.
• 1:28:32
  We now it's the US.
• 1:28:34
  We have these crazy sky cams.
• 1:28:37
  Blah, blah, blah.
• 1:28:38
  They're moving quickly.
• 1:28:40
  Players can kick the ball higher now.
• 1:28:42
  All this stuff.
• 1:28:43
  We're going to move to our second one, which is another interesting aspect of the World Cup.
• 1:28:49
  Yes.
• 1:28:49
  I'm sure you've seen the entire world turn on Argentina.
• 1:28:54
  It has happened.
• 1:28:55
  It has happened, right?
• 1:28:57
  This is the BBC, classic BBC.
• 1:28:59
  Classic BBC.
• 1:29:00
  Ahead of the Argentina-England match.
• 1:29:03
  Are Argentina being treated favorably at the World Cup?
• 1:29:07
  Is Messi and Argentina being favored by FIFA, right?
• 1:29:10
  So there's a lot of just like rhetoric out there.
• 1:29:14
  A lot of anecdotal evidence.
• 1:29:16
  A lot of people watching the Egypt game.
• 1:29:20
  Witness testimony, one would say.
• 1:29:22
  What's going on?
• 1:29:23
  People watching the Swiss game saying what's going on.
• 1:29:26
  There is a...
• 1:29:28
  Guy from a Northeastern, from the Northeastern Sports Statistics NetSI Sports Research Group, Brennan Klein.
• 1:29:37
  So he compiled some data.
• 1:29:40
  We love data.
• 1:29:41
  We love data.
• 1:29:41
  Okay.
• 1:29:42
  So here's a chart that's created by him.
• 1:29:45
  What he's showing is this year's World Cup, all of the teams that have seen the most favorable VAR outcomes, that's in blue, and those that have seen the least favorable VAR outcomes using a baseline of per hundred fouls.
• 1:30:01
  So he's normalizing for the number of fouls.
• 1:30:07
  How many VAR decisions were turned in favor of me and how many VAR decisions were turned in the favor of...
• 1:30:14
  Per 100 fouls.
• 1:30:15
  Yes.
• 1:30:15
  Per 100 fouls.
• 1:30:16
  Yeah, yeah.
• 1:30:17
  Per 100 fouls is just a way of saying I'm normalizing by the number of... Like I'm just dividing by the number of fouls, right?
• 1:30:23
  I got you.
• 1:30:23
  So it's like a per foul type thing, right?
• 1:30:26
  That makes sense.
• 1:30:26
  But this becomes a percentage because now you have 100 in the denominator.
• 1:30:30
  I got you.
• 1:30:30
  So, okay.
• 1:30:31
  Okay.
• 1:30:33
  Mexico actually tops that list.
• 1:30:36
  And then it's Argentina.
• 1:30:38
  Okay.
• 1:30:39
  Okay.
• 1:30:39
  And then it's a bunch of other countries.
• 1:30:42
  England is somewhere in the middle.
• 1:30:43
  France is somewhere in the middle.
• 1:30:45
  But France is also net positive.
• 1:30:46
  There's no net negatives against them.
• 1:30:49
  Yeah.
• 1:30:50
  There's another plot that's there.
• 1:30:52
  I just want to note that the U.S. is net negative.
• 1:30:56
  Yeah.
• 1:30:57
  VAR does not support the US.
• 1:30:59
  No, not at all.
• 1:31:00
  Or Canada, for that matter.
• 1:31:02
  Or Belgium, so I can't complain about the Belgium game.
• 1:31:05
  Yeah.
• 1:31:06
  And so the next plot is something that I found on Reddit.
• 1:31:14
  Here, the Y-axis is the opposition fouls per yellow card.
• 1:31:19
  Okay?
• 1:31:19
  Okay.
• 1:31:19
  So fouls per yellow card that you're getting, meaning how many fouls your opponent commits until they receive a yellow.
• 1:31:27
  Okay?
• 1:31:28
  What's the, like, the threshold?
• 1:31:29
  Mm-hmm.
• 1:31:30
  And the lower on the y-axis, the more favorable the team is.
• 1:31:36
  Okay, the x-axis is the delta between penalties won and penalties conceded.
• 1:31:41
  So that's how you get 3, 2, 1.
• 1:31:44
  Okay, Argentina, the bubble size, by the way, the size of the bubble shows how many fouls the team commits until they are shown a yellow card.
• 1:31:54
  So the bigger the size of the bubble is,
• 1:31:58
  the more tolerant the referees are for that team.
• 1:32:01
  Argentina's on the bottom right corner, right?
• 1:32:07
  And so... With a pretty big bubble.
• 1:32:09
  With a pretty big bubble, right?
• 1:32:10
  Which means the bigger the bubble, the more tolerant the referees are, so to speak, in a very crude, crude example, right?
• 1:32:19
  And it naively would suggest that the referees are being biased.
• 1:32:27
  Argentina won most penalties while its opponents receive a yellow card after five fouls committed.
• 1:32:32
  They get a yellow after 19 fouls.
• 1:32:34
  Yeah.
• 1:32:35
  Yeah.
• 1:32:35
  So Argentina needs 19 fouls to commit to get a yellow.
• 1:32:40
  Their opponents need about five.
• 1:32:42
  I don't know.
• 1:32:43
  You're right.
• 1:32:44
  And that's what I would say, too.
• 1:32:46
  I don't know.
• 1:32:46
  Because I want to believe.
• 1:32:48
  Look.
• 1:32:49
  I want to believe.
• 1:32:50
  But...
• 1:32:52
  I'm going to also have to put on my data scientist hat on.
• 1:32:56
  Okay.
• 1:32:56
  Okay?
• 1:32:57
  Okay.
• 1:32:57
  And just do some good hygiene.
• 1:32:59
  Okay.
• 1:32:59
  Okay?
• 1:33:00
  Okay.
• 1:33:01
  First of all...
• 1:33:02
  Yes.
• 1:33:03
  It's an incredibly small sample size.
• 1:33:05
  Okay.
• 1:33:06
  All right?
• 1:33:06
  If you go back to that...
• 1:33:08
  This is 22?
• 1:33:09
  Yeah.
• 1:33:09
  If you go back to that...
• 1:33:10
  Okay.
• 1:33:11
  The x-axis is discretized by negative 1, 0, 1, 2, and 3.
• 1:33:18
  But they've made the plot...
• 1:33:20
  fatter on the x-axis so it seems like the x-axis is the more salient dimension i see that but like there's nothing there's no way to have a 2.5 yeah right right i see what you're saying okay so so already there's there's a bit of like movie magic happening there's a little bit of manipulation of the visual representation now the other thing is they're treating every foul equally
• 1:33:44
  Right?
• 1:33:45
  That's not completely true.
• 1:33:47
  Like, if you've got a team like Argentina that presses all the time, you've got someone like Messi who's, like, always in your space, then, like, if you commit a foul against them, there's a higher chance of, like, that resulting in a penalty.
• 1:34:01
  That's not truly accounted for here.
• 1:34:02
  Okay.
• 1:34:03
  Right?
• 1:34:04
  So style of play perhaps is not something that is accounted for.
• 1:34:09
  Like, Cape Verde, for example, like, those dudes had a great defense for quite a while.
• 1:34:15
  So part of what you're saying I think style of play I think that example May be contested Generally speaking This idea of if you play Haram ball which is what Arsenal Play and you just sit And it's 11 of you in your own box And then you just wait to counter attack Like necessarily you don't have A lot of opportunities Theoretically as many opportunities to be fouled Egypt certainly did that Because you're not on the ball
• 1:34:45
  Right?
• 1:34:45
  So that's not something that has been taken into account.
• 1:34:48
  Also, the types of fouls, like Argentina, the fact that they require 19 fouls to get a yellow, perhaps it's because they're doing smaller technical fouls.
• 1:34:58
  Now, anecdotally, I would say that's not...
• 1:35:02
  True, probably.
• 1:35:03
  The comments are going to go crazy.
• 1:35:06
  But there's all sorts of like, I think with all of this stuff, and I think the author of the Northeastern paper, right, or the Northeastern study actually said this.
• 1:35:17
  Like, this kind of exercise, given the small data size and the heat...
• 1:35:25
  of what we're doing, the exercise is fraught with confirmation bias.
• 1:35:30
  But at the end of the day, it's the World Cup, and I personally want FIFA to be biased to Argentina.
• 1:35:39
  So at the end of the day, all I'm going to say is, look, is this truly convincing data that Argentina...
• 1:35:47
  Sorry.
• 1:35:48
  Is this truly convincing data that there is a bias for Argentina by FIFA?
• 1:35:52
  No.
• 1:35:52
  No.
• 1:35:53
  But do I care?
• 1:35:55
  Also, no.
• 1:35:56
  All right?
• 1:35:57
  So, yeah.
• 1:35:58
  Hashtag rigged.
• 1:36:00
  Hashtag stop the steal.
• 1:36:02
  Better call Infantino.
• 1:36:04
  I just want everyone to know that I am not an Argentina supporter, and I do not condone any rigging.
• 1:36:11
  However, I will say the comment thread is going to be full of the shot of Messi giving a red card foul raise on the calf.
• 1:36:20
  One of the main injuries we saw from our Are You Smarter Than a Scientist game show segment.
• 1:36:24
  Yeah.
• 1:36:24
  And say, oh, how is this not a foul?
• 1:36:27
  Argentina never gets called.
• 1:36:28
  Like that's, I'm just saying it because I already know.
• 1:36:31
  Yeah.
• 1:36:32
  It's going to be the image that is all in the clips of this.
• 1:36:36
  Yeah.
• 1:36:36
  And let's put this at the end of the clip.
• 1:36:38
  Yeah.
• 1:36:38
  So that people who comment it before reaching the end of the video, which apparently no one watches 90 seconds of content.
• 1:36:46
  Yeah.
• 1:36:47
  Anymore.
• 1:36:47
  before just rage.
• 1:36:49
  I just want you guys to know your brain is rotting because this is a 90 second clip and you saw Christian say something about Argentina that was favorable and then we came in here and said yes we know about the messy foul with the thingy and his plate in the calf and you're going to put the image of it and you're going to look stupid.
• 1:37:09
  I just want you to know that.
• 1:37:11
  I just got to get that off my chest.
• 1:37:14
  Yeah and the point basically being like
• 1:37:18
  Look, I don't know how much of me thinking that there's an Argentina bias.
• 1:37:22
  It's just because of all the stuff that I'm seeing on social media.
• 1:37:26
  But for the purposes of me enjoying this England versus Argentina game, I am believing 100% of it.
• 1:37:33
  And I want Argentina to go down.
• 1:37:37
  And that is rare.
• 1:37:38
  Coming from an Indian.
• 1:37:41
  A hundred percent.
• 1:37:42
  Of Indian descent.
• 1:37:42
  You've seen the memes with the map where it's like the two countries that care.
• 1:37:45
  It's like Argentina and India.
• 1:37:46
  Yeah.
• 1:37:46
  That want India to win and the rest of the world is England.
• 1:37:48
  No, like I'm.
• 1:37:49
  It's a big deal.
• 1:37:50
  I want England to win.
• 1:37:51
  No, it's a big deal.
• 1:37:52
  Rule Britannia, as they say.
• 1:37:54
  Again, we'll put this at the end of my part of the clip.
• 1:37:58
  So people also.
• 1:37:59
  No, but this.
• 1:38:00
  It'll be fun.
• 1:38:00
  It's great.
• 1:38:00
  It'll be fun.
• 1:38:01
  The World Cup is great.
• 1:38:02
  You know, the U.S. is out, which we're not going to comment on.
• 1:38:06
  But yeah.
• 1:38:06
  Yeah.
• 1:38:08
  I, as a Chelsea fan, up the Chels, I want to see Tuchel be successful because my club unceremoniously sacked him after he brought us to Champions League with nobodies.
• 1:38:20
  Wow.
• 1:38:21
  Like, he brought us to Champions League with nobodies.
• 1:38:25
  with like, like with, with anyway, I'm not, can't think of analogy.
• 1:38:29
  Like we did not have the best players on paper.
• 1:38:33
  Yeah.
• 1:38:33
  Right.
• 1:38:34
  And then there's some, and you got to the chip and we won the champions league.
• 1:38:38
  And we won.
• 1:38:39
  That's quite good.
• 1:38:40
  Captain America.
• 1:38:41
  Christian Pulisic has a champions league.
• 1:38:43
  Thanks to Thomas Tuchel, the current England gaffer.
• 1:38:47
  So I know he's good.
• 1:38:50
  I know he can do a lot with a little there's talent on the England squad.
• 1:38:53
  Yeah.
• 1:38:55
  I'm so conflicted about this game.
• 1:38:57
  Yeah.
• 1:38:58
  Because I have disdain for both for different reasons.
• 1:39:04
  And it's like, what's the lesser of two evils?
• 1:39:08
  I will say, as a fan of the Prem, I will likely have to say I would prefer to see England win this one.
• 1:39:19
  And then losing the final.
• 1:39:21
  It can't come home right now.
• 1:39:23
  We can't have us lose.
• 1:39:25
  Let me not.
• 1:39:25
  I'm not going to talk.
• 1:39:26
  Yeah.
• 1:39:26
  At America's 250th.
• 1:39:27
  Yeah.
• 1:39:28
  Yeah.
• 1:39:28
  Like we can't.
• 1:39:29
  This is supposed to be our celebration.
• 1:39:31
  Yeah.
• 1:39:31
  And then they win it.
• 1:39:32
  Yeah.
• 1:39:33
  We just can't have our.
• 1:39:34
  Especially after we didn't just lose.
• 1:39:36
  We like got embarrassed.
• 1:39:37
  Embarrassed.
• 1:39:37
  And then the Europeans rub it in our face.
• 1:39:39
  Yeah.
• 1:39:40
  And like we deserve it.
• 1:39:42
  And we deserve it.
• 1:39:43
  Because we had the whole red card ball.
• 1:39:45
  Like it got reversed.
• 1:39:46
  It was just like.
• 1:39:47
  It's just like.
• 1:39:48
  It was like the worst way to go out, dude.
• 1:39:51
  It's bad management.
• 1:39:52
  Yeah.
• 1:39:53
  It was so bad.
• 1:39:53
  None of us asked for anything to be rescinded.
• 1:39:56
  All right.
• 1:39:57
  I know Kwanzaa had the two game suspension, which doesn't make sense.
• 1:40:01
  And Balogun got it removed.
• 1:40:02
  This is getting in the weeds.
• 1:40:03
  Yeah.
• 1:40:03
  This is not a sports podcast.
• 1:40:05
  However.
• 1:40:06
  So the last thing that I want to say, okay, is like because I was like going in a deep dive about like what could it be, right?
• 1:40:12
  Right.
• 1:40:13
  I have a theory about the Argentina bias.
• 1:40:16
  Okay.
• 1:40:16
  That I think is new.
• 1:40:17
  Ooh, new Argentina bias theory.
• 1:40:20
  So it could be that there is a bias for Argentina and FIFA has nothing to do with it.
• 1:40:26
  Okay.
• 1:40:26
  Okay.
• 1:40:27
  It could be that they have a home court advantage at all of these games.
• 1:40:31
  Because I don't know if you've seen in all of these Argentina games, the stands are flooded with Argentina fans.
• 1:40:38
  It's the messy effect.
• 1:40:40
  Yeah.
• 1:40:40
  Everybody has an Argentina jersey, mostly a messy jersey.
• 1:40:45
  I see what you're saying.
• 1:40:46
  There are studies, so this is in 2007, Boiko and other researchers, what they did was try to quantify the home field advantage.
• 1:41:00
  which is a well-documented phenomenon, right?
• 1:41:02
  Like there's a home field advantage for like the players, they get riled up.
• 1:41:06
  There's a home field advantage for refereeing.
• 1:41:09
  There is a referee bias that contributes to home field advantage in the English Premier League.
• 1:41:14
  And it has to do with just like psychology, right?
• 1:41:16
  Like the referee doesn't want to give a bad decision to the thousands and tens of thousands of people that are watching him.
• 1:41:26
  Now, it didn't quite work with the Balogun.
• 1:41:28
  Right.
• 1:41:29
  Situation.
• 1:41:30
  But it did work when Argentina was in Miami, where Messi plays in the MLS in Miami, which is home field effectively.
• 1:41:40
  Exactly.
• 1:41:40
  And like even the Kansas City game, right, against Switzerland.
• 1:41:43
  Packed.
• 1:41:43
  Packed with Argentinian fans.
• 1:41:45
  100%.
• 1:41:45
  Like every single one of these Argentina games just has so many fans, right?
• 1:41:50
  And so what these guys did was, you know, it's hard to disentangle the...
• 1:41:57
  the performance of the team with the performance of the referees.
• 1:42:00
  But what they did was separate out referees and look at individual referees, whether they were judging a home game versus another game, and look at individual referees.
• 1:42:10
  So now you've washed out the effect of whether the team is at home or not.
• 1:42:15
  And in the next figure, we'll show you.
• 1:42:17
  This is a figure from the...
• 1:42:21
  From the paper, on the X-axis, all the individual referees, and this is the mean goal differential that they could have contributed to based on their yellow cards and all things like that, all of it is positive.
• 1:42:32
  Meaning, like, the referee bias is always positive for the home team.
• 1:42:37
  That's very...
• 1:42:38
  Even then, you could be like, it's really hard to conflate the home field advantage from the referee and the home field advantage from the team.
• 1:42:47
  Like the team is more inspired.
• 1:42:49
  Maybe they're more violent.
• 1:42:51
  The 12th man, the fan, all these things.
• 1:42:53
  Right.
• 1:42:54
  They play their every whatever.
• 1:42:56
  Exactly.
• 1:42:56
  So what we really need to do as a control would be to remove the 12th man.
• 1:43:01
  Right.
• 1:43:02
  Right.
• 1:43:03
  And lo and behold, the COVID pandemic happens.
• 1:43:09
  Oh, wait, this is perfect.
• 1:43:11
  It's the only time you'd ever be able to get this data.
• 1:43:14
  Yes.
• 1:43:15
  It's the only time where you're playing football and there's no one in the stands.
• 1:43:19
  There's nobody there.
• 1:43:20
  Right?
• 1:43:22
  Wait, this is great.
• 1:43:23
  So now you've got to control.
• 1:43:25
  That's why I wanted to bring this up because I thought it was so cool that we've got a 2007 study that is showing that there is a home field advantage for referee bias, right?
• 1:43:35
  Referees are biased for the home team.
• 1:43:36
  Yes.
• 1:43:37
  But you could always be like, it's so hard to conflate.
• 1:43:39
  100%.
• 1:43:40
  Right?
• 1:43:40
  Okay.
• 1:43:41
  The next paper is the natural experiment by Arendelle in the frontiers of behavioral economics.
• 1:43:49
  Yes.
• 1:43:50
  And they looked at the same pattern, but now for the games during the COVID-19 pandemic.
• 1:43:55
  This is so good.
• 1:43:56
  Because you can't, there is no, there is effectively, at least you could say the pitch still, but really it's about the fans and the energy and the ambiance.
• 1:44:07
  No.
• 1:44:08
  Yeah.
• 1:44:08
  Zero.
• 1:44:09
  And they confirmed that referee bias.
• 1:44:11
  They looked at 7,000 matches across Europe, and they showed that when the crowd goes silent, there's no crowd.
• 1:44:17
  The referee bias for the home team is eliminated.
• 1:44:20
  That's incredible.
• 1:44:23
  Yeah.
• 1:44:24
  And again, anecdotally, everyone's believed this to be true.
• 1:44:28
  Yeah, forever.
• 1:44:29
  This is why home field advantage is even a concept.
• 1:44:31
  Yeah, it's like a home field advantage not only in the psyche of the players, but now they're showing it's in the psyche of the referee himself or herself.
• 1:44:39
  There's bias in the...
• 1:44:41
  I thought this was so cool.
• 1:44:42
  No, that's really good because, again, we will probably never really ever get.
• 1:44:48
  No, we're never going to get that experiment again.
• 1:44:50
  I mean, God willing, right?
• 1:44:52
  Right, right, right.
• 1:44:52
  Let's hope so.
• 1:44:53
  Right, right.
• 1:44:53
  Especially with the skit.
• 1:44:54
  Because you could argue like, oh, well, one team can be banned from having fans in the stands for some period of time.
• 1:44:59
  Yeah.
• 1:44:59
  But we're saying it was everybody.
• 1:45:01
  Yeah, for a whole year.
• 1:45:02
  Right?
• 1:45:03
  And the amount of data that they've used, it's like 5,000 games, 7,000 games.
• 1:45:07
  Now you're getting a statistically significant effect.
• 1:45:09
  That is so...
• 1:45:12
  People are so glad.
• 1:45:13
  Yeah, I thought it was good.
• 1:45:14
  Because they also built on the other one.
• 1:45:15
  It was like, well, let's double.
• 1:45:17
  And so the point is, it was positive in the study where the fans were there.
• 1:45:22
  And then you took away the fans.
• 1:45:23
  When the fans were not there.
• 1:45:25
  Which means, understandably, the refs want to get home safe.
• 1:45:28
  Yeah.
• 1:45:28
  And so they might be a little.
• 1:45:31
  And just psychologically, right?
• 1:45:33
  There's something in your psyche when there's 50,000 people booing you.
• 1:45:36
  You don't want it.
• 1:45:41
  Even though you're supposed to be neutral and whatever, there's going to be part of you that wants to be liked by the 70,000 people.
• 1:45:48
  You want to hear cheers for when you do this and you say no penalty or penalty or whatever.
• 1:45:53
  So...
• 1:45:55
  This is going to do numbers for the Argentina bias community.
• 1:46:01
  Yeah, so that's my theory, which I think it's a new theory.
• 1:46:05
  And so this is an out for FIFA because FIFA doesn't have anything to do with this.
• 1:46:09
  This is just like the home field advantage that Argentina has.
• 1:46:12
  Again, this is terrible data science because clearly it didn't work for America.
• 1:46:16
  Right.
• 1:46:17
  However, one would argue that our fans don't instill the same fear in referees that footballing nation fans do.
• 1:46:29
  The English fans, the Argentinian fans.
• 1:46:31
  Like, these Americans, they can't even... USA, who's scared of that?
• 1:46:35
  You know what I mean?
• 1:46:36
  Yeah, and then we stop caring.
• 1:46:38
  Yeah, right.
• 1:46:39
  Immediately.
• 1:46:40
  They're not going to do anything.
• 1:46:41
  They're going to watch the TV.
• 1:46:44
  Fascinating.
• 1:46:45
  This has been...
• 1:46:46
  A blockbuster rundown, our longest rundown by literally 2X.
• 1:46:52
  Yeah.
• 1:46:53
  Because we just love yapping and we love talking to you guys.
• 1:46:56
  And there's so many good things and exciting things happening.
• 1:47:00
  We covered Black Holes, Digital Heart Twin, the ultrasound CT scanner from MidJourney.
• 1:47:08
  Two wonderful World Cup stories.
• 1:47:11
  First, about the controversy about the camera, which, again, FIFA, if you'd like to come on and explain yourself, you have a platform to do so.
• 1:47:22
  And where does it?
• 1:47:24
  It wasn't about the Argentina bias specifically, but home field advantage.
• 1:47:28
  Is it statistically significant?
• 1:47:30
  Referee bias being actually the thing that creates the Argentina bias is
• 1:47:34
  I think is a very clever hypothesis.
• 1:47:38
  We will see what the algorithm and the comments on the internet say
• 1:47:45
  My name is Lester Nare, joined as always by my co-host and our resident PhD and Argentina-biased hypothesis creator, Krishna Chowdhury.
• 1:47:58
  We really appreciate you all, as always, for joining us, meandering through our friendship in a journey of curiosity and science.
• 1:48:06
  We will see you all next week.