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[00:00:00.400 --> 00:00:11.040] From unsolved mysteries to unexplained phenomena, from comedy gold to relationship fails, Amazon Music's got the most ad-free top podcasts, included with Prime.
[00:00:11.040 --> 00:00:16.000] Because the only thing that should interrupt your listening is, well, nothing.
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[00:00:26.640 --> 00:00:29.840] You're listening to the Skeptic's Guide to the Universe.
[00:00:29.840 --> 00:00:32.800] Your escape to reality.
[00:00:33.440 --> 00:00:36.240] Hello, and welcome to The Skeptic's Guide to the Universe.
[00:00:36.240 --> 00:00:40.800] Today is Wednesday, October 9th, 2024, and this is your host, Stephen Novella.
[00:00:40.800 --> 00:00:42.640] Joining me this week are Bob Novella.
[00:00:42.640 --> 00:00:43.360] Hey, everybody.
[00:00:43.360 --> 00:00:44.720] Kara Santa Maria.
[00:00:44.720 --> 00:00:45.280] Howdy.
[00:00:45.280 --> 00:00:46.240] Jay Novella.
[00:00:46.240 --> 00:00:46.720] Hey, guys.
[00:00:46.720 --> 00:00:48.160] And Evan Bernstein.
[00:00:48.320 --> 00:00:49.600] Good evening, everyone.
[00:00:49.600 --> 00:00:55.920] So we are cozy up here in Connecticut, but down in Florida, man, they are getting hammered right as we record the show.
[00:00:56.560 --> 00:01:06.960] Yeah, Hurricane Milton is coming ashore, and they're expecting surges of 9 to 12 feet in Tampa and surrounding areas.
[00:01:06.960 --> 00:01:11.760] Plus, it's going to take a long time to cross and drop a ton of rain.
[00:01:11.760 --> 00:01:13.040] It's going to be bad.
[00:01:13.040 --> 00:01:13.920] And it's bad.
[00:01:14.080 --> 00:01:17.680] This is the storm that made a meteorologist cry.
[00:01:18.400 --> 00:01:31.440] Literally, he was tearing up because I think the real thing where it really hit home how extraordinary this is was when they realized that in 12 hours it went from a category one to a category five.
[00:01:32.000 --> 00:01:33.440] That basically doesn't happen.
[00:01:33.760 --> 00:01:34.720] That doesn't happen.
[00:01:35.280 --> 00:01:36.640] I have a friend in the New World.
[00:01:36.880 --> 00:01:42.240] I have a friend staying with me right now because she was supposed to be in Florida for the Europa Clipper launch.
[00:01:42.240 --> 00:01:45.520] The Clipper is in a hangar in Florida right now.
[00:01:45.520 --> 00:01:46.240] Oh my gosh.
[00:01:46.240 --> 00:01:48.720] I hope it's protected well enough from this.
[00:01:48.720 --> 00:01:49.760] We all hope it is.
[00:01:49.760 --> 00:01:51.760] So yeah, she obviously couldn't go.
[00:01:52.000 --> 00:01:56.240] Obviously the launch is being delayed, but I mean, it's really scary.
[00:01:56.240 --> 00:01:58.480] This is scary for a lot of people.
[00:01:58.640 --> 00:02:01.560] And this is on the heels of Helene.
[00:02:01.560 --> 00:02:03.480] Helene, which was like, what, a week ago?
[00:02:03.480 --> 00:02:04.840] How long ago is that now?
[00:02:04.840 --> 00:02:05.720] Oh, two weeks.
[00:02:05.720 --> 00:02:06.440] Yeah, a couple of weeks.
[00:01:59.760 --> 00:02:07.240] Two weeks.
[00:02:07.400 --> 00:02:09.880] They're still finding, you know, recovering.
[00:02:09.880 --> 00:02:11.880] It's still a recovery effort.
[00:02:11.880 --> 00:02:17.560] And not long after, we were talking about a hundred-year storm in Connecticut, you know, in Oxford.
[00:02:17.560 --> 00:02:21.800] And we're like, well, you know, you can't say for sure it's global warming, but, you know, this sort of thing.
[00:02:21.800 --> 00:02:30.280] And then boom, boom, two more hurricanes, like record flooding in the mountains, miles from the shore.
[00:02:30.920 --> 00:02:34.680] A location that was previously considered to be like a climate change refuge.
[00:02:34.680 --> 00:02:37.240] Nope, sorry, not correct.
[00:02:37.240 --> 00:02:40.520] And now we got this really unprecedented hurricane.
[00:02:40.520 --> 00:02:43.720] So, you know, obviously we know this is global warming, right?
[00:02:43.720 --> 00:02:53.320] You know, obviously statistically, you can't say like this one particular, what would have happened were it not for whatever, but we know that this is consistently over and over again.
[00:02:53.320 --> 00:02:58.280] We are seeing weather events that are beyond what is typical.
[00:02:58.280 --> 00:03:01.960] And this is exactly what scientists predicted was going to happen, and it's happening.
[00:03:01.960 --> 00:03:03.480] So it's probably not a coincidence.
[00:03:03.640 --> 00:03:11.160] Yeah, we've talked about not so much being the frequency with which hurricanes occur, but the intensity at which yeah, they don't get more frequent.
[00:03:11.160 --> 00:03:14.520] Yeah, but they gain their power from the heat in the water.
[00:03:14.520 --> 00:03:18.120] And the Gulf of Mexico is very warm because of global warming.
[00:03:18.120 --> 00:03:23.560] And then the warm air holds more moisture, so the hurricanes pick up more moisture.
[00:03:23.560 --> 00:03:29.960] So they are more powerful, they drop more rain, and that's what causes the flooding.
[00:03:29.960 --> 00:03:39.160] And because of that, they probably feel more frequent because the lower intensity hurricanes are often not felt or they're felt with less intensity.
[00:03:39.160 --> 00:03:47.680] So when more, when the same number of hurricanes are significantly more powerful, that onslaught is more apparent.
[00:03:47.680 --> 00:03:50.880] Yeah, you don't get more hurricanes, but you get more category four and five hurricanes.
[00:03:44.600 --> 00:03:52.720] And of course, those are the ones you notice.
[00:03:52.720 --> 00:03:52.960] Right.
[00:03:52.960 --> 00:03:54.240] Those are the ones that hit you.
[00:03:54.720 --> 00:03:54.960] Right.
[00:03:54.960 --> 00:03:58.080] Those are the ones that cause the most death, the most destruction.
[00:03:58.080 --> 00:04:03.760] Even if you add up the other smaller hurricanes, they're much worse than any smaller hurricanes that hit.
[00:04:03.760 --> 00:04:04.080] Yes.
[00:04:04.080 --> 00:04:06.560] And a meteorologist cried.
[00:04:06.560 --> 00:04:07.120] Yeah.
[00:04:07.120 --> 00:04:11.920] Well, and he's probably thinking of his neighborhood, his home, his community.
[00:04:12.240 --> 00:04:15.120] No, that wasn't my take.
[00:04:15.120 --> 00:04:24.160] My take was that the unprecedented ramp up from category one to five in 12 hours is essentially unprecedented.
[00:04:24.160 --> 00:04:26.080] And that's where I think it hit home.
[00:04:26.080 --> 00:04:31.600] Like, oh my God, this is a monster, the monster we've been dreading caused by climate change.
[00:04:32.000 --> 00:04:35.920] And even just look at it, just even looking at the hurricane, you're like, wait, that looks weird.
[00:04:36.960 --> 00:04:39.760] One guy described it as being tightly wound.
[00:04:40.240 --> 00:04:42.960] The eye is only like three miles wide.
[00:04:43.200 --> 00:04:47.840] It's so, so tiny, so small, and it ramped up so fast.
[00:04:47.840 --> 00:04:50.800] Like we got to like 850 millibars.
[00:04:50.800 --> 00:04:56.320] It's like the fourth most intense in terms of the pressure.
[00:04:57.520 --> 00:04:58.720] And we're going to see more of it.
[00:04:58.960 --> 00:04:59.840] We're going to see more.
[00:05:00.160 --> 00:05:02.720] This is the first of the monsters.
[00:05:03.040 --> 00:05:05.680] It's like the kaiju coming out of the ocean.
[00:05:06.000 --> 00:05:08.480] It's like, we're going to see this.
[00:05:08.800 --> 00:05:12.080] We may see another one as bad or worse this season.
[00:05:12.080 --> 00:05:13.440] I mean, it's October.
[00:05:13.440 --> 00:05:15.760] The season ends in, what, November?
[00:05:15.920 --> 00:05:18.240] There's actually, I mean, there's plenty of time.
[00:05:18.560 --> 00:05:22.000] Mercifully, maybe the season will end.
[00:05:22.000 --> 00:05:23.840] The season will end soon.
[00:05:23.840 --> 00:05:24.800] But damn, man.
[00:05:24.800 --> 00:05:25.440] But how do we know?
[00:05:25.440 --> 00:05:27.920] I mean, this happened literally overnight.
[00:05:27.920 --> 00:05:33.400] And, Bob, I found a New York Times article that quoted the meteorologist that you mentioned.
[00:05:29.760 --> 00:05:34.360] His name's Morales.
[00:05:35.000 --> 00:05:38.760] And he said, a number of factors played into the tearful broadcast.
[00:05:39.160 --> 00:05:56.280] Shock about the storm's rapid intensification, angst about the increasing number and severity of extreme weather events, frustration over society's failure to mitigate, despite scientific certainty that it's driving increasingly violent weather, and empathy for the people, the ecosystems, and the creatures that would experience the destructiveness.
[00:05:56.280 --> 00:05:58.520] And then there's a quote from him: It claims lives.
[00:05:58.520 --> 00:06:00.200] It also wrecks lives.
[00:06:00.200 --> 00:06:03.240] You have to feel sorry for the folks that are in this hurricane's path.
[00:06:04.280 --> 00:06:11.160] And then you get the layer of politicized misinformation that's being thrown around.
[00:06:11.160 --> 00:06:15.800] It's like, hey, this is not the time to politicize stuff to be partisan.
[00:06:15.800 --> 00:06:20.280] This is the time to do what you can to save lives, people's lives.
[00:06:20.280 --> 00:06:22.840] And people are just throwing away, throwing out misinformation.
[00:06:22.840 --> 00:06:25.560] It's like, oh my God, that's horrible.
[00:06:25.560 --> 00:06:26.440] It's cruel.
[00:06:26.440 --> 00:06:27.720] It's ridiculous.
[00:06:27.720 --> 00:06:28.040] Wow.
[00:06:28.280 --> 00:06:32.120] So as this recording, it's already too late to evacuate.
[00:06:32.120 --> 00:06:32.680] Well, yeah.
[00:06:33.720 --> 00:06:34.600] They're actively being populated.
[00:06:34.760 --> 00:06:35.960] Hopefully, people have gotten out.
[00:06:36.280 --> 00:06:37.720] Yeah, they're sheltering in place.
[00:06:37.960 --> 00:06:39.160] Eastern Standard Time, yeah.
[00:06:39.400 --> 00:06:42.520] You don't want to be on the road when the storm surge hits.
[00:06:43.160 --> 00:06:45.720] As we talked about before, water is powerful.
[00:06:46.280 --> 00:06:55.000] And this is a double whim because the winds, category five winds, will rip a house apart all by itself without the water.
[00:06:55.000 --> 00:07:00.680] Then you have the fact that the houses are going to be flooded and the streets are going to be mayhem, like unsurvivable.
[00:07:00.680 --> 00:07:04.920] Like the storm surge itself is being described as unsurvivable.
[00:07:04.920 --> 00:07:06.840] And then you throw the winds on top of that.
[00:07:06.840 --> 00:07:07.960] This is destructive.
[00:07:07.960 --> 00:07:09.480] This is very destructive.
[00:07:09.480 --> 00:07:13.160] So by the time the show launches, this will have happened.
[00:07:13.800 --> 00:07:16.640] And we'll know exactly how bad it was.
[00:07:16.880 --> 00:07:20.080] But hopefully people heeded the warnings and got out.
[00:07:21.120 --> 00:07:25.360] And FEMA is on the job and working with the state of Florida.
[00:07:25.360 --> 00:07:28.240] I mean, I think the people in place are doing what they're supposed to be doing.
[00:07:28.240 --> 00:07:31.680] And they just should ignore the national politics, as Bob said.
[00:07:32.480 --> 00:07:33.280] It's all static.
[00:07:33.520 --> 00:07:36.000] The state and local stuff, they're like, yes, we're working together.
[00:07:36.000 --> 00:07:37.040] Everyone's doing a good job.
[00:07:37.040 --> 00:07:37.840] Let's get it done.
[00:07:38.160 --> 00:07:38.400] Right.
[00:07:38.400 --> 00:07:38.720] Yeah.
[00:07:38.720 --> 00:07:39.520] As they should.
[00:07:39.520 --> 00:07:39.920] All right.
[00:07:39.920 --> 00:07:41.280] Well, let's move on.
[00:07:41.520 --> 00:07:44.800] Evan, you're going to start us off with a dumbest thing of the week.
[00:07:44.800 --> 00:07:46.800] Yes, dumbest thing of the week.
[00:07:46.800 --> 00:07:47.200] Okay.
[00:07:47.200 --> 00:07:49.600] And there were, well, boy, this was a week.
[00:07:49.920 --> 00:07:51.840] There were many contenders.
[00:07:52.880 --> 00:07:56.480] A story about Bigfoot almost won the day.
[00:07:56.480 --> 00:08:03.120] But as usual, Bigfoot would not stand a chance against a monster that lives in the bottom of a lake.
[00:08:03.120 --> 00:08:03.840] Yep, that's right.
[00:08:03.840 --> 00:08:10.480] I'm talking about the lurker of the loch, the dynamo of the deep, the cryptic king or queen itself.
[00:08:10.480 --> 00:08:14.000] I give you this week's subject, the Loch Ness Monster.
[00:08:14.000 --> 00:08:15.680] The dumbest thing of the week.
[00:08:15.680 --> 00:08:18.880] Steve, you can go ahead and insert that applause soundtrack there if you'd like.
[00:08:19.040 --> 00:08:19.680] Oh, yeah.
[00:08:20.000 --> 00:08:29.600] I found, yes, captain, captain of a boat out on the loch says, I found Loch Ness Monster on my ship's sonar.
[00:08:29.600 --> 00:08:30.080] Yep.
[00:08:30.080 --> 00:08:30.880] And let's see.
[00:08:30.880 --> 00:08:44.720] Yeah, I've spoken several times about the Loch Ness Monster before, so I think I can safely assume our audience is familiar enough that some people believe there exists a prehistoric dinosaur-sized aquatic creature in Scotland's Loch Ness.
[00:08:44.720 --> 00:08:47.680] And no, it's not just a bedtime story for children.
[00:08:47.680 --> 00:08:51.920] Many grown-ups, supposedly mature adults, believe this to be true.
[00:08:51.920 --> 00:08:54.800] And it makes you wonder what else they think is true.
[00:08:54.800 --> 00:08:55.600] Now, evidence.
[00:08:55.600 --> 00:08:57.680] What's the evidence for the creature's existence?
[00:08:57.680 --> 00:08:59.360] It adds up to, well, nothing.
[00:08:59.360 --> 00:09:06.200] No scientific evidence over nearly, wow, almost 100 years that this particular myth has existed.
[00:09:06.200 --> 00:09:07.240] Can you believe it?
[00:09:07.240 --> 00:09:08.840] And it keeps going on.
[00:09:08.840 --> 00:09:11.720] But hey, we finally have sonar readings from the captain.
[00:09:11.720 --> 00:09:12.360] Here's the...
[00:09:12.360 --> 00:09:16.360] evidence we maybe have been looking for all of this time.
[00:09:16.360 --> 00:09:22.040] So his name is Sean Sloggy, or S-L-O-G-G-I-E.
[00:09:22.040 --> 00:09:26.040] That's how I read that and pronounce that, Sean Sloggy.
[00:09:26.280 --> 00:09:36.440] He was preparing his Spirit of Loch Ness pleasure boat to sail last month when a large object was spotted on the vessel's underwater sensors.
[00:09:36.440 --> 00:09:48.680] It was an outline detected nearly 100 meters beneath the surface of the nest, and it bore an eerie resemblance to a plesiosaur.
[00:09:48.680 --> 00:09:49.960] Yeah, which what?
[00:09:49.960 --> 00:10:04.120] Is a speculated reptile group in which the people who believe in the Loch Ness monster believe, okay, yeah, this is a plesiosaur who has survived all this time and exists in the lake or a family of or something.
[00:10:04.120 --> 00:10:07.720] I have no idea, frankly, why they think this thing really exists.
[00:10:07.720 --> 00:10:16.600] Now, I have to, well, I think they think there's connections between the loch and the ocean, you know, and so they must be a population traveling back and forth, whatever.
[00:10:16.600 --> 00:10:19.160] But looking at the sonar image, first of all, it's a blob, right?
[00:10:19.160 --> 00:10:19.800] I mean, it could be angle.
[00:10:19.960 --> 00:10:20.520] Bob Squatch.
[00:10:20.600 --> 00:10:21.240] Blob squatch.
[00:10:21.640 --> 00:10:27.080] But if you didn't know what the context was, and you just said, what does this look like to you?
[00:10:27.080 --> 00:10:29.160] It looks like a turtle.
[00:10:29.160 --> 00:10:32.520] Now, there are no giant sea turtles in Loch Ness, either.
[00:10:32.520 --> 00:10:34.040] So it's probably not a turtle.
[00:10:34.040 --> 00:10:34.520] Right.
[00:10:34.520 --> 00:10:36.760] But it looks way more like a turtle than a plesiosaur.
[00:10:36.840 --> 00:10:39.000] It actually doesn't look like a plesiosaur.
[00:10:39.000 --> 00:10:42.760] It doesn't have like the characteristic overall shape.
[00:10:43.000 --> 00:10:45.520] The resemblance is actually fairly superficial.
[00:10:45.680 --> 00:10:51.040] But, you know, again, if you account for the fact that sonar is blobby, you know, it's not that accurate.
[00:10:44.840 --> 00:10:51.200] Right.
[00:10:51.600 --> 00:10:53.120] Could it be a plesiosaur?
[00:10:53.120 --> 00:10:56.400] I mean, that image, I guess, but it could also be a turtle.
[00:10:56.400 --> 00:11:00.480] It could also be nothing, you know, or it could be a hoax.
[00:11:00.480 --> 00:11:05.920] I want to describe to the audience just for a moment the image you're looking at, Steve, the one I'm looking at from the news.
[00:11:06.240 --> 00:11:09.920] So it's a photograph of the sonar, right?
[00:11:09.920 --> 00:11:11.680] The screen and what is being displayed.
[00:11:11.680 --> 00:11:14.800] To me, it looks like the old video game Defender, right?
[00:11:14.800 --> 00:11:26.080] With that, you know, from an arcade game from like 1980 with like eight kilobytes of graphics, basically, with these little dots and boxes and things moving all over the screen.
[00:11:26.080 --> 00:11:34.640] That outline, and they circle it, you know, where you're where it looks sort of, yeah, has this turtlish shape to it, maybe a back and an extended neck and maybe some little arms.
[00:11:34.640 --> 00:11:36.640] That could be anything.
[00:11:36.960 --> 00:11:38.080] I mean, what the heck?
[00:11:38.080 --> 00:11:44.640] It's total pattern recognition, you know, pattern seeking as we're wont to do.
[00:11:45.520 --> 00:11:46.480] Order in the chaos.
[00:11:46.480 --> 00:11:48.320] We're not seeing like a recorded video.
[00:11:48.320 --> 00:11:53.840] You know, a snapshot, like, was this just at the moment where it looked the most like a reptile?
[00:11:53.840 --> 00:11:56.240] Or like, what would the video show?
[00:11:56.240 --> 00:11:57.280] That's always a good question.
[00:11:57.280 --> 00:12:06.160] Like, when you see like a 20-second quote-unquote, you know, video of a UFO, you always want to know, yeah, what happened before and after the 20 seconds that I'm seeing?
[00:12:06.160 --> 00:12:09.920] Because usually when you see that, it's like, okay, it's clearly a hubcap or whatever.
[00:12:09.920 --> 00:12:12.640] Oh, the school of fish broke up and went into a million pieces.
[00:12:12.960 --> 00:12:13.200] Exactly.
[00:12:14.080 --> 00:12:14.240] Right.
[00:12:14.320 --> 00:12:16.160] That were all blobbed together at one point.
[00:12:16.160 --> 00:12:21.520] So, right, a single snap in time is not evidence of anything.
[00:12:21.520 --> 00:12:24.640] Oh, but no, but the captain says the sonar doesn't lie.
[00:12:26.880 --> 00:12:31.800] The boat hasn't been on five whiskey distillery tours before going out on the lock.
[00:12:31.800 --> 00:12:33.240] It's just doing its job.
[00:12:29.760 --> 00:12:35.160] That's what the captain said.
[00:12:35.640 --> 00:12:41.560] Yeah, appears in different colors, indicating pockets of air, and season, you know, the usual, the usual kind of stuff.
[00:12:41.560 --> 00:12:45.560] Regardless, even of that, so you've got this, okay, you have the sonar image.
[00:12:45.560 --> 00:12:46.680] Okay, where's the thing?
[00:12:46.920 --> 00:12:47.880] What's it eating?
[00:12:48.040 --> 00:12:49.320] Where's it pooping?
[00:12:50.600 --> 00:12:51.720] How long is it alive?
[00:12:51.720 --> 00:12:52.680] Where are its remains?
[00:12:52.680 --> 00:12:53.560] Where are the others?
[00:12:53.560 --> 00:12:56.440] Where's the other evidence, any kind of physical evidence?
[00:12:56.440 --> 00:12:57.080] Where's the DNA?
[00:12:57.480 --> 00:12:59.560] Why has one never washed up on shore?
[00:12:59.560 --> 00:13:00.120] Right?
[00:13:00.120 --> 00:13:03.480] Or a piece of it, or you know, a fin or something.
[00:13:03.800 --> 00:13:09.880] But the definitive thing is they looked at the lock for environmental DNA, and there's no plesiosaur DNA in that.
[00:13:10.200 --> 00:13:11.400] Exactly.
[00:13:11.400 --> 00:13:12.120] Exactly.
[00:13:12.440 --> 00:13:13.240] No.
[00:13:13.240 --> 00:13:13.480] Yep.
[00:13:13.480 --> 00:13:15.320] All that goes out the window.
[00:13:15.320 --> 00:13:16.120] And here we go.
[00:13:16.120 --> 00:13:23.000] Throw another piece of zero evidence into the pile for the Loch Ness Monster this week's dumbest thing of the week.
[00:13:23.000 --> 00:13:24.120] Thank you, Evan.
[00:13:24.120 --> 00:13:25.320] Yep, you got it.
[00:13:25.320 --> 00:13:27.080] So, guys, that time of year again.
[00:13:27.080 --> 00:13:28.040] Yes, it is.
[00:13:28.040 --> 00:13:29.080] Every time this comes up.
[00:13:29.320 --> 00:13:30.600] Halloween, baby.
[00:13:30.600 --> 00:13:31.400] Yeah.
[00:13:31.720 --> 00:13:32.360] Always sorry.
[00:13:32.680 --> 00:13:33.560] Not Halloween.
[00:13:33.560 --> 00:13:36.280] Every time this comes up, my reaction is always the same.
[00:13:36.280 --> 00:13:39.160] It can't be Nobel Prize time again already.
[00:13:40.440 --> 00:13:41.560] We just had it.
[00:13:41.560 --> 00:13:44.360] Didn't we just talk about the last year's?
[00:13:44.360 --> 00:13:45.080] But it is.
[00:13:45.080 --> 00:13:46.520] It's Nobel Prize time.
[00:13:46.520 --> 00:13:53.960] There is a cruel reality to getting older in that the years, the perception of years go quicker as you get older.
[00:13:54.840 --> 00:13:57.880] Nobody tells the kids that as an adult, or they do and the kids don't listen.
[00:13:58.200 --> 00:14:01.480] But it's a real cruel trick, I find, you know, of life.
[00:14:01.800 --> 00:14:03.160] It shouldn't be that way.
[00:14:03.160 --> 00:14:03.800] But it does.
[00:14:04.040 --> 00:14:06.680] That is exactly how it's perceived, at least I think.
[00:14:06.680 --> 00:14:07.240] Yeah.
[00:14:07.880 --> 00:14:09.480] So we're going to cover it.
[00:14:09.480 --> 00:14:09.800] Yep.
[00:14:10.520 --> 00:14:16.240] Kara, you're going to start us off with the Nobel Prize in Physiology or Medicine.
[00:14:14.920 --> 00:14:16.800] Yep.
[00:14:17.120 --> 00:14:35.760] The Nobel Prize, the 2024 Nobel Prize in Physiology or Medicine, was awarded jointly to Victor Ambrose and Gary Ruvkin, quote, for the discovery of microRNA and its role in post-transcriptional gene regulation.
[00:14:35.760 --> 00:14:47.440] So we're going to dig into that a little bit, tell a little bit of the story of this discovery and kind of the outcomes and what it means for science and for humanity.
[00:14:47.440 --> 00:14:54.720] So in the late 80s, Victor Ambrose and Gary Ruvkin were both postdocs.
[00:14:54.720 --> 00:15:03.280] They were fellows in the laboratory of Robert Horvitz, who incidentally won a Nobel Prize in 2002 for his research.
[00:15:03.280 --> 00:15:07.920] And they were studying a model organism called C.
[00:15:08.080 --> 00:15:09.200] elegans.
[00:15:09.200 --> 00:15:09.920] We call it C.
[00:15:09.920 --> 00:15:13.680] elegans because it's hard to pronounce the word that precedes elegans.
[00:15:13.680 --> 00:15:16.400] It's a roundworm, it's a nematode.
[00:15:16.400 --> 00:15:26.320] And it's a great organism to study because it's small, but it has a lot of the specialized cell types that are found in larger animals.
[00:15:26.320 --> 00:15:34.080] So it's a good model for tissue development, maturation, just developmental biology, and of course, genetics.
[00:15:34.080 --> 00:15:47.440] And so they were really just asking a lot of interesting questions: like, how do the genes that sort of control these different developmental programs turn on, turn off?
[00:15:47.440 --> 00:15:49.600] Why do they turn on and turn off?
[00:15:49.600 --> 00:15:51.040] Like, what's going on here?
[00:15:51.040 --> 00:16:01.480] And I saw a little video of Gary Ruvkin talking about how, at the time, he was just really excited about this new field of recombinant DNA.
[00:16:01.800 --> 00:16:19.800] A lot of people were getting into recombinant DNA, which is kind of the practice of creating sequences that wouldn't otherwise be found in the genome, either by combining from different sources or even synthetically creating DNA sequences in the laboratory.
[00:16:19.800 --> 00:16:22.360] So, obviously, this is a form of genetic engineering.
[00:16:22.360 --> 00:16:30.200] So, on the heels of this kind of excitement at the time, they're asking a lot of interesting questions, poking around.
[00:16:30.200 --> 00:16:34.280] They go their separate ways, they start their own labs.
[00:16:34.280 --> 00:16:39.800] Both of them are, I think they're both in Massachusetts at this time.
[00:16:39.800 --> 00:16:40.200] Yes.
[00:16:40.200 --> 00:16:40.680] Yeah.
[00:16:40.680 --> 00:17:04.360] So, I think before we can get into microRNA, what it is, what it does, and why it is interesting, we have to kind of step back and just make sure that we're all on the same page about what's often referred to as the central dogma of molecular biology, and that is the basic movement from DNA to RNA to proteins, right?
[00:17:04.360 --> 00:17:10.680] And that involves two major steps, a whole lot of minor steps, called transcription and translation.
[00:17:10.680 --> 00:17:15.080] So, transcription is how DNA is copied to RNA.
[00:17:15.080 --> 00:17:29.720] And what happens in transcription is that the base pairs line up using a specialized, actually, it in and of itself is a protein, but a specialized catalyst that helps make those base pairs line up.
[00:17:29.720 --> 00:17:50.320] And they, as opposed to replication, where DNA just makes more DNA by unzipping and then matching up the base pairs, in transcription, all of the base pairs line up except for where thymine would have been, it turns into uracil, which then forms these what were called messenger RNA strands.
[00:17:50.320 --> 00:17:56.400] And it's important to make a distinction here because I think some people might get confused when they're reading about this.
[00:17:56.400 --> 00:18:01.120] Messenger RNA is often represented as mRNA.
[00:18:01.120 --> 00:18:11.120] MicroRNA, which is a totally different thing, that's what we're going to talk about in a second, is often written as MIC RNA.
[00:18:11.120 --> 00:18:11.600] Oh.
[00:18:11.920 --> 00:18:14.560] And sometimes mu RNA.
[00:18:14.560 --> 00:18:16.720] So, or MI RNA.
[00:18:17.040 --> 00:18:20.400] So you do need to make that distinction because that I matters.
[00:18:20.880 --> 00:18:23.680] So we've got transcription, then we've got translation.
[00:18:23.680 --> 00:18:29.440] Transcription DNA to RNA and specifically to messenger RNA, but also non-coding regions as well.
[00:18:29.440 --> 00:18:32.160] And then we've got translation.
[00:18:32.160 --> 00:18:35.360] Translation is RNA to proteins, right?
[00:18:35.360 --> 00:18:44.800] So this is how proteins are synthesized using the same genetic material that's in every single cell in an organism's body.
[00:18:44.800 --> 00:18:51.360] So then the question, and this was the big question that the Nobel laureates were asking, was like, how do they know?
[00:18:51.680 --> 00:18:58.000] Like, why do they know to make this specific protein when all the genetic instructions are there?
[00:18:58.000 --> 00:18:58.400] Right?
[00:18:58.400 --> 00:19:03.360] It's not just DNA codes to RNA, RNA maps on and produces proteins.
[00:19:03.360 --> 00:19:05.920] It's the when are these genes turned on and off?
[00:19:05.920 --> 00:19:07.200] Why are they turned on and off?
[00:19:07.200 --> 00:19:09.280] What kind of pressures are involved to do that?
[00:19:09.280 --> 00:19:11.600] And how are they regulated?
[00:19:11.600 --> 00:19:18.560] And so they noticed they were studying a specific strain of C.
[00:19:18.560 --> 00:19:20.800] elegans called LIN4.
[00:19:20.800 --> 00:19:24.080] They were also studying a different strain called LIN14.
[00:19:24.080 --> 00:19:35.000] And they found that these specific mutant strains were having issues in the timing of activating genes that were necessary for them to develop normally.
[00:19:35.320 --> 00:19:37.960] And so they were like, okay, this is weird and interesting.
[00:19:37.960 --> 00:19:39.880] I want to figure out what's going on here.
[00:19:39.880 --> 00:19:43.240] I want to identify these genes and I want to see what these genes do.
[00:19:43.240 --> 00:19:46.600] Like very, very basic molecular biology research.
[00:19:46.600 --> 00:19:52.120] They knew at that point that the LIN4 gene regulated the LIN14 gene.
[00:19:52.120 --> 00:19:55.160] I don't think we have to get too caught up in the names of them.
[00:19:55.160 --> 00:20:04.440] But how it regulated it, like they knew that it kind of negatively regulated it, it blocked the LIN14 gene, but how that happened, they didn't really understand.
[00:20:04.440 --> 00:20:07.800] So they wanted to dig a little bit deeper.
[00:20:07.800 --> 00:20:14.360] And as they were studying, Ambrose moved on and established a laboratory at Harvard.
[00:20:14.360 --> 00:20:19.000] Rovkin moved on and he established a laboratory at Mass General.
[00:20:19.000 --> 00:20:21.800] And they kept looking at this work.
[00:20:21.800 --> 00:20:25.960] But then they start doing their own work, but they're interested in answering the same questions, right?
[00:20:25.960 --> 00:20:30.280] So they're looking: okay, what's going on here with this inhibitory mechanism?
[00:20:30.280 --> 00:20:33.160] How is the regulatory stuff going on?
[00:20:33.160 --> 00:20:36.600] Is it actually happening at the translation stage?
[00:20:36.600 --> 00:20:37.080] No way.
[00:20:37.080 --> 00:20:38.520] Oh, okay, is it transcription?
[00:20:38.520 --> 00:20:39.560] No, it is translation.
[00:20:39.560 --> 00:20:42.360] Okay, so it's happening later on in the process.
[00:20:42.360 --> 00:20:43.640] And they start talking to each other.
[00:20:43.960 --> 00:20:45.880] This is, I think, the best part of this story.
[00:20:45.880 --> 00:20:57.480] This is two individuals who kind of came up together, studied together, are now in what could be seen as rival labs, what could be seen as labs that want to kind of push to publish before each other.
[00:20:57.480 --> 00:21:02.760] But instead, they're always collaborating with each other and sharing each other's research.
[00:21:02.760 --> 00:21:11.640] So they named this little sequence of RNA that they kept seeing over and over microRNA because it was really, really small, right?
[00:21:11.640 --> 00:21:16.480] It was just a smaller chunk of RNA than the RNA that they had been working with.
[00:21:16.800 --> 00:21:40.160] And they realized that it was affecting and affecting in an inhibitory way the actual translation of this LIN14 because it was bind, because this RNA, this microRNA was binding to it and kind of preventing it from being able to then be expressed the right way.
[00:21:40.160 --> 00:21:42.240] We're now into the early 90s.
[00:21:42.240 --> 00:21:44.480] They published this sort of serendipitously.
[00:21:44.480 --> 00:21:46.640] They're both talking about this discovery.
[00:21:46.640 --> 00:21:48.000] People are going, oh, that's interesting.
[00:21:48.000 --> 00:21:49.840] You've got this little piece of RNA.
[00:21:49.840 --> 00:21:52.640] It's sort of binding to this portion.
[00:21:52.640 --> 00:21:56.400] It's preventing translation from happening.
[00:21:56.400 --> 00:21:58.640] Okay, but that's like a weird quirk of C.
[00:21:58.640 --> 00:21:59.680] elegans.
[00:21:59.680 --> 00:22:04.800] So people listened and they go, that's kind of cool, but it's quirky, and it didn't really get a lot of traction.
[00:22:05.120 --> 00:22:12.320] And then they kept looking at it and they found something interesting called the let7 gene.
[00:22:12.320 --> 00:22:15.200] And the let7 gene was different.
[00:22:15.200 --> 00:22:18.080] And the reason it was different is because it was not unique to C.
[00:22:18.080 --> 00:22:18.640] elegans.
[00:22:18.640 --> 00:22:27.440] It was conserved all the way back to what I think from some of the things that I read was when kind of mammals and fish split.
[00:22:27.440 --> 00:22:32.160] So it's really conserved in a lot of multicellular organisms.
[00:22:32.160 --> 00:22:40.560] And that let 7 gene, when people started looking, they started to realize, hmm, these microRNAs are in like us.
[00:22:40.560 --> 00:22:44.560] They're in like most of the animals and the plants that we're looking at.
[00:22:44.560 --> 00:22:47.040] It's sort of everywhere.
[00:22:47.040 --> 00:22:50.400] And when they discovered that, a lot of people got interested.
[00:22:50.400 --> 00:23:02.920] And they started to do a ton of research and discover, I think we're now at this is a little bit different because Wiki is saying that only 500 represent the bona fide microRNA.
[00:23:03.240 --> 00:23:06.360] Yeah, I've read we've discovered over a thousand microRNA to date.
[00:23:06.360 --> 00:23:08.280] Okay, so and that's that's important.
[00:23:08.280 --> 00:23:17.480] Discovered over a thousand, at least 500 of them have been like certified and said, yes, that these are bona fide, but it's a lot because a ton of different labs are working on it.
[00:23:17.480 --> 00:23:26.200] Like 90 different families of microRNAs that have been conserved, like I said, since that common ancestor of mammals and fish.
[00:23:26.200 --> 00:23:32.600] And we've started to discover the functions of these, the different genes that they act on, the different genes that they regulate.
[00:23:32.600 --> 00:23:45.240] And we've since then also started to discover how they're involved, not only just the mechanism by which they are doing this regulatory function, but also what that means downstream.
[00:23:45.240 --> 00:23:51.560] Like they might be silencing transcription or they might be processing in the cytoplasm.
[00:23:51.560 --> 00:23:55.400] Sometimes they're happening earlier on in the nucleus.
[00:23:55.720 --> 00:24:03.720] We've even discovered that there are some that are floating outside of the nucleus completely and even in the extracellular matrix.
[00:24:03.720 --> 00:24:04.280] Yeah.
[00:24:04.280 --> 00:24:12.840] Like so we're seeing that this is happening all over the cell at a lot of different stages that weren't anticipated before.
[00:24:12.840 --> 00:24:22.120] We're also seeing that they have a ton of different functions in the cell, but they tend to fall into three main processes.
[00:24:22.440 --> 00:24:38.360] They either will cleave the mRNA, so cut it into two different pieces, they'll destabilize the mRNA by shortening its polyA tail, or they'll reduce translation of the mRNA, which was that first discovery, which then affects the protein synthesis.
[00:24:38.360 --> 00:24:47.680] Generally speaking, in humans and other animals, it happens by destabilizing, but microRNAs kind of fall into all three of those categories.
[00:24:47.680 --> 00:24:56.640] And we've now been able to identify disease processes that microRNA are directly implicated in.
[00:24:56.640 --> 00:25:03.760] So a mutation in a region of the mRNA 96, they're named for the order they were discovered.
[00:25:03.760 --> 00:25:08.800] So like earlier numbers are earlier discoveries, bigger numbers are later discoveries.
[00:25:08.800 --> 00:25:14.240] So a mutation in microRNA 96 causes a hereditary progressive hearing loss.
[00:25:14.240 --> 00:25:21.920] A mutation in the region of 184 causes hereditary keratoconus with cataracts.
[00:25:21.920 --> 00:25:28.400] And a full deletion in 17 causes skeletal and growth defects.
[00:25:28.400 --> 00:25:36.160] And we're also seeing more and more research, a lot of really exciting research in the sort of DNA repair and cancer space.
[00:25:36.160 --> 00:25:55.360] A lot of research about the effects of these microRNAs on, especially like chronic lymphocytic leukemia, is implicated, B-cell receptor signaling, and sort of sometimes these interesting things where they both affect tumor suppressogenes and oncogenes.
[00:25:55.360 --> 00:26:15.200] And so there's still a lot more to unpack and uncover, but there's so many therapeutics that are actively being researched and developed from, gosh, everything from heart disease to kidney disease to alcoholism and stroke, like different nervous system disorders like Alzheimer's.
[00:26:16.000 --> 00:26:25.040] These microRNAs could be implicated in a lot of disease processes because they're so early in that process of protein synthesis.
[00:26:25.040 --> 00:26:33.960] And so, this is one of those, I think, beautiful examples of two guys working in a lab, just fundamentally interested in how something works.
[00:26:34.280 --> 00:26:51.080] And eventually, enough people recognized this fundamental process, this regulatory process, was so important that now we see downstream effects that will affect the health and safety of human beings for, I think, generations to come.
[00:26:51.080 --> 00:26:55.880] And that's why they were awarded the Nobel Prize for Physiology or Medicine.
[00:26:55.880 --> 00:27:01.960] Yeah, it's a good story because, yeah, they were just asking a very simple question: how does the LIN4 gene affect the LIN14 gene?
[00:27:02.280 --> 00:27:07.800] And it could have been, I just say, like a one-off kind of quirky thing, you know, limited to C-elegance.
[00:27:07.880 --> 00:27:11.640] Turns out they discovered a fundamental aspect of gene regulation.
[00:27:11.640 --> 00:27:15.640] And now that's translating into a whole bunch of potential therapeutic targets.
[00:27:15.960 --> 00:27:16.600] Translating.
[00:27:16.600 --> 00:27:17.800] No pain intended.
[00:27:17.800 --> 00:27:18.280] Yeah, right.
[00:27:19.240 --> 00:27:21.560] Well, we call it translational research.
[00:27:22.360 --> 00:27:33.880] So this is why funding scientists who are doing basic science research without any thought to ultimate applications, just trying to understand how the universe works.
[00:27:35.480 --> 00:27:39.640] Yeah, you can't predict how that's going to potentially pan out.
[00:27:39.960 --> 00:27:48.920] But as a general rule, we see over and over again, understanding at a reductionist, like really tiny level how things work usually turns out to be pretty useful.
[00:27:49.240 --> 00:27:53.000] 100% because things can break all along a path.
[00:27:53.000 --> 00:27:53.480] Exactly.
[00:27:53.480 --> 00:28:04.120] And when we understand the most simplistic and the earliest functionality of something, we're getting to it at its core and we can start to follow all the ways that things can break.
[00:28:04.120 --> 00:28:07.800] When we follow the ways things can break, we can figure out how to fix them.
[00:28:07.800 --> 00:28:08.200] Right.
[00:28:08.200 --> 00:28:08.920] Fascinating.
[00:28:08.920 --> 00:28:11.400] All right, Jay, tell us about the Nobel Prize in Chemistry.
[00:28:11.400 --> 00:28:12.680] What do you want to know?
[00:28:13.000 --> 00:28:13.960] Everything, baby.
[00:28:13.960 --> 00:28:14.520] Give me it all.
[00:28:14.960 --> 00:28:15.680] Give it all to me.
[00:28:15.680 --> 00:28:18.640] All right, so there were two different awards given out.
[00:28:18.640 --> 00:28:20.240] So we'll start with the first team here.
[00:28:20.240 --> 00:28:23.280] So this is David Baker's creation of new proteins.
[00:28:23.280 --> 00:28:32.640] So David Baker and his team took a complex problem, which is they wanted to figure out how to design entirely new proteins from scratch.
[00:28:32.640 --> 00:28:35.360] You know, this is something that doesn't just happen in nature.
[00:28:35.360 --> 00:28:39.120] You know, proteins have to evolve, and it's an incredibly long process.
[00:28:39.120 --> 00:28:43.760] And if you look at the way the human body uses proteins, it's super complicated.
[00:28:43.760 --> 00:28:48.080] So proteins, in case you don't know, they're composed of chains of amino acids.
[00:28:48.080 --> 00:28:54.160] And these chains of amino acids fold into specific three-dimensional structures on their own.
[00:28:54.160 --> 00:28:55.280] Amazing.
[00:28:55.280 --> 00:29:00.720] And these structures are critical for the protein's actual function in the organism, right?
[00:29:00.720 --> 00:29:07.440] It could be for breaking down food or responding to body signals or forming cellular structures.
[00:29:07.440 --> 00:29:15.920] So in 2003, Baker achieved a breakthrough by creating a protein with a unique structure that's not found in nature.
[00:29:15.920 --> 00:29:18.240] And that's a big deal all by itself.
[00:29:18.240 --> 00:29:23.760] And since then, Baker and his team have continued to push the boundaries of what they achieved.
[00:29:23.760 --> 00:29:28.400] And now they're engineering new proteins that are tailored to have specific functions.
[00:29:28.400 --> 00:29:44.240] They've designed proteins that can act as pharmaceuticals to treat diseases, vaccines to prevent infections, and even like nanomaterials for advanced technological uses, which, you know, we're not there yet, but that is very possible that that can take place.
[00:29:44.720 --> 00:29:46.320] There's some practical things here.
[00:29:46.320 --> 00:29:50.800] So, for instance, they've developed proteins that can detect specific molecules.
[00:29:50.800 --> 00:29:52.720] They can function as sensors.
[00:29:52.720 --> 00:29:57.760] This could be used in the medical diagnostics and environmental monitoring.
[00:29:57.760 --> 00:29:59.440] And then they have synthetic biology.
[00:29:59.440 --> 00:30:14.280] So, Baker's work lays the foundation for this form of synthetic biology where custom-made proteins are created for specific tasks, and this opens new possibilities into everything from drug design to material sciences.
[00:30:14.280 --> 00:30:18.520] So, that is damn impressive and incredibly powerful.
[00:30:18.520 --> 00:30:28.120] You know, the ability to design proteins that don't happen to exist in nature means now that we can build molecules with exact specifications, right?
[00:30:28.120 --> 00:30:32.440] This is much, you could consider this like constructing building from blueprint to finish.
[00:30:32.440 --> 00:30:35.160] It's really incredible what these guys did.
[00:30:35.160 --> 00:30:42.200] And it's being described as it holds immense promise for innovation in many other scientific fields.
[00:30:42.200 --> 00:30:44.520] So, I would just like to thank those guys.
[00:30:44.520 --> 00:30:46.120] That's incredible.
[00:30:46.120 --> 00:30:51.800] The second team here, Demis Hasselbus and John Jumper's AlphaFold 2.
[00:30:51.800 --> 00:30:53.000] I don't know if you guys have heard of that.
[00:30:53.000 --> 00:30:53.320] Oh, yeah.
[00:30:53.400 --> 00:30:54.200] You talked about it on the show.
[00:30:55.080 --> 00:30:55.800] There it is.
[00:30:55.800 --> 00:30:57.640] Let me give you a little reboot on that.
[00:30:57.640 --> 00:31:01.720] So, for decades, you have to make it sound dramatic, right?
[00:31:01.720 --> 00:31:09.320] A very long time, scientists have known that the function of protein is dictated by its structure, but there was a challenge.
[00:31:09.320 --> 00:31:18.920] It's been predicting how a chain of amino acids, right, which is the protein sequence, how it folds into a functional three-dimensional form.
[00:31:18.920 --> 00:31:23.960] This is known as the protein-folding problem, as the protein-folding problem, right, Bob?
[00:31:23.960 --> 00:31:25.320] I think you've talked about that, right?
[00:31:25.320 --> 00:31:26.360] Yes, I did.
[00:31:26.360 --> 00:31:30.040] So, this has been a 50-year-long puzzle in biology.
[00:31:30.040 --> 00:31:37.760] And before the breakthrough, determining a protein structure took years of incredibly painstaking and super expensive experiments.
[00:31:37.760 --> 00:31:41.240] Just tons and tons and tons of hours to figure it out.
[00:31:41.240 --> 00:31:46.720] So, in 2020, the team introduced AlphaFold 2, which is what?
[00:31:44.920 --> 00:31:48.960] It's a powerful AI, of course.
[00:31:49.840 --> 00:31:54.240] And their team programmed it specifically at their deep mind.
[00:31:54.240 --> 00:31:57.040] You know, this is part of the deep mind effort.
[00:31:57.040 --> 00:32:01.920] And this AI can accurately predict protein structures.
[00:32:01.920 --> 00:32:04.240] And this is why this is impressive.
[00:32:04.240 --> 00:32:09.360] So first off, AlphaFold II uses these advanced AI algorithms.
[00:32:09.360 --> 00:32:18.080] Specifically, there are a type of machine learning called deep learning to predict how sequences of amino acids actually fold into 3D structures.
[00:32:18.080 --> 00:32:25.280] So the model, they quote unquote, learned from vast amounts of existing protein data, which we've been collecting for decades.
[00:32:25.280 --> 00:32:29.840] And this enabled them to make these highly accurate predictions using the AI.
[00:32:29.840 --> 00:32:36.800] So with AlphaFold II, Hasabis and Jumper could predict the structure of nearly every protein known to science.
[00:32:36.800 --> 00:32:39.360] And there's over 200 million of those proteins.
[00:32:39.360 --> 00:32:40.720] 200 million.
[00:32:40.720 --> 00:32:48.000] This is a massive leap from the previous methods that we had, which could only determine structures one at a time, right?
[00:32:48.000 --> 00:32:52.640] And like I said, it takes years per protein to figure it out.
[00:32:52.640 --> 00:32:59.840] So since its release, AlphaFold II has been adopted by over 2 million researchers from 190 countries.
[00:32:59.840 --> 00:33:04.960] And that is for a very good reason because it's crazy powerful and unbelievably useful.
[00:33:04.960 --> 00:33:11.840] And they use the tool for all these different various scientific purposes from understanding diseases, developing new drugs, etc.
[00:33:12.000 --> 00:33:13.200] The list goes on.
[00:33:13.200 --> 00:33:17.040] AlphaFold II has been applied already in numerous fields.
[00:33:17.040 --> 00:33:29.320] And one notable use is in combating antibiotic resistance, where researchers now can study the structures of proteins and bacteria that cause resistance to better design new antibiotics.
[00:33:28.640 --> 00:33:33.880] Which, you know, this is another exciting application in designing enzymes that can break down plastics.
[00:33:34.200 --> 00:33:39.640] I mean, guys, this, it just, it's unbelievable how powerful this particular thing is.
[00:33:39.640 --> 00:33:54.440] You know, I really love the idea that they're mindful of the fact that new technologies need to help us with environmental pollution and things like that, because we have gotten to a point where there's an incredible amount of environmental pollution in the modern world.
[00:33:54.440 --> 00:34:06.920] Baker's work shows that we can simply design proteins with entirely new functions, and AlphaFold solves the mystery of how these proteins naturally fold, and together they can make Voltron.
[00:34:07.240 --> 00:34:22.120] Together, these breakthroughs mean we now understand both how to create new proteins and how to predict the structure of existing ones, which gives us immense capability and power to do things that we didn't have not too long ago.
[00:34:22.760 --> 00:34:34.360] Did you point out, though, Jay, that even though, like I say, in the write-up for the award, they say that it predicted the structure of 200 million proteins that researchers have identified.
[00:34:34.360 --> 00:34:36.760] But we don't know that all those predictions are accurate.
[00:34:37.240 --> 00:34:38.600] Bob, you talked about this.
[00:34:38.600 --> 00:34:42.680] I think they've only verified a percentage of them, like half or something.
[00:34:42.680 --> 00:34:44.600] But it's a predictive tool.
[00:34:44.600 --> 00:34:47.800] It doesn't prove that that's the structure.
[00:34:47.960 --> 00:34:50.760] But proving it out is faster because they're starting with.
[00:34:51.080 --> 00:34:53.000] Yes, they know, right, exactly.
[00:34:53.000 --> 00:34:56.040] It's like it tells the researchers what they need to do.
[00:34:56.440 --> 00:35:02.040] It takes a lot less time to verify what AlphaFold 2 predicted than to just figure it out from scratch.
[00:35:02.080 --> 00:35:04.840] You know, I'd like to use that as an example, too.
[00:35:04.840 --> 00:35:17.840] Like, artificial intelligence is going to remove roughly, I think I read 79,000 jobs in the United States at some point in the near future, but it's going to create 90,000 jobs.
[00:35:18.720 --> 00:35:29.280] So the idea is that, you know, artificial intelligence, you're going to be, your job will be replaced by someone who knows how to work with AI in one fashion or another, right?
[00:35:29.680 --> 00:35:31.680] That's the thing that we all need to realize.
[00:35:31.680 --> 00:35:36.240] Like, look at what these scientists are doing with artificial intelligence and how powerful it is.
[00:35:36.240 --> 00:35:41.760] Guys, this is going to permeate human society and there's no stopping it.
[00:35:41.760 --> 00:35:42.400] It already is.
[00:35:42.400 --> 00:35:55.600] I mean, we're already seeing increasing examples of how AI is being used to do, like, as we've been saying for a long time, it's actually happening now, doing years of research in weeks or months of research in days or even hours.
[00:35:55.600 --> 00:35:57.280] That's happening right now.
[00:35:57.280 --> 00:35:57.840] Yeah.
[00:35:57.840 --> 00:36:01.440] You know, just the pace of research is going to accelerate.
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[00:38:58.080 --> 00:39:00.000] All right, Bob, you're up with physics.
[00:39:00.000 --> 00:39:00.800] Do it.
[00:39:00.800 --> 00:39:15.600] Oh boy, guys, this year's Nobel Prize in Physics has been awarded to John Hopfield and Jeffrey Hinton for their groundbreaking research and discoveries that led to the deep learning neural networks that power much of the artificial intelligence being used today.
[00:39:15.600 --> 00:39:18.400] So yes, more AI in Nobel.
[00:39:18.400 --> 00:39:27.840] So let's see, Mark Pierce, member of the Nobel Committee of Physics, said their work was fundamental in laying the cornerstones of what we experience today as artificial intelligence.
[00:39:27.840 --> 00:39:35.760] Ellen Moons, chair of the Nobel Committee for Physics, thanks to the laureate's work, AI has become part of our daily lives.
[00:39:35.760 --> 00:39:45.280] The laureate's discoveries and inventions from the building blocks of machine learning that can aid in making faster and more reliable decisions, for instance, when diagnosing medical conditions.
[00:39:45.280 --> 00:39:47.280] Now, Moons mentions machine learning.
[00:39:47.280 --> 00:39:49.600] So this is, we've mentioned it a few times.
[00:39:49.920 --> 00:39:56.320] This is a broad term for any technique where computers learn from data and make predictions and decisions.
[00:39:56.320 --> 00:40:01.520] Neural networks are a type of machine learning and are integral to this Nobel Prize.
[00:40:01.520 --> 00:40:04.560] So let's do a quick overview of neural networks.
[00:40:04.560 --> 00:40:12.320] I've described them multiple times on the show, and each time I say, I could do that better next time, and this is next time.
[00:40:12.320 --> 00:40:13.280] So here we go.
[00:40:13.280 --> 00:40:22.320] So neural networks are not exotic neural tissue organoid structures running in vats of strange liquids and thinking strange thoughts.
[00:40:22.320 --> 00:40:24.240] That's not what it is.
[00:40:24.240 --> 00:40:26.640] Neural networks run on computers.
[00:40:26.640 --> 00:40:33.320] They run on high-performance computers called servers, all interconnected on a network that can consist of many hundreds of them.
[00:40:33.960 --> 00:40:42.360] It could be from one server to hundreds of servers running the neural network, depending on the scale and lots of different things.
[00:40:42.520 --> 00:40:49.960] The software running on these servers simulates in some ways the behavior of the biological networks of neurons in our heads.
[00:40:50.120 --> 00:40:55.800] The neurons in a neural network are called nodes, but they are not terribly exotic.
[00:40:55.800 --> 00:40:58.040] They're essentially mathematical functions.
[00:40:58.280 --> 00:41:01.240] So, you know, bringing it down to earth a bit.
[00:41:01.560 --> 00:41:06.920] You know, the neurons in the network, the neural network, they're mathematical functions, software-based.
[00:41:06.920 --> 00:41:11.880] The data that these functions receive and deal with can be many different types of data.
[00:41:11.880 --> 00:41:17.160] They can be numerical data, like temperature or price, the measurable quantities.
[00:41:17.160 --> 00:41:18.680] It could be image data.
[00:41:18.680 --> 00:41:25.800] In that case, it would be something potentially like pixel values, like RGB, red, green, blue values for a pixel.
[00:41:26.280 --> 00:41:29.160] The data could be text-based.
[00:41:30.040 --> 00:41:34.120] They could be words that are converted into numbers so they can be more easily manipulated.
[00:41:34.120 --> 00:41:41.480] The data that these nodes or mathematical functions deal with could also be audio data, sound waves converted into numbers.
[00:41:41.480 --> 00:41:43.800] So they could deal with all of that and more.
[00:41:43.800 --> 00:41:52.760] So now, as this data flows through the network layer by layer, layer by layer, it's processed by the nodes, by these mathematical functions.
[00:41:52.760 --> 00:41:59.880] In this processing, one of the most important aspects of that is the assigning of a number called a weight.
[00:42:00.360 --> 00:42:03.800] You probably heard about that a lot if you read about anything about neural networks.
[00:42:03.800 --> 00:42:07.400] The higher the weight, the more important or influential that data is.
[00:42:07.400 --> 00:42:08.600] So that's critical.
[00:42:08.600 --> 00:42:12.800] It's critical to how a neural network learns this idea of weights.
[00:42:12.800 --> 00:42:16.960] With more and more training using more and more data, these weights are then adjusted.
[00:42:17.280 --> 00:42:28.880] They're tweaked over and over and over until the errors are minimized, allowing for ultimately a surprisingly accurate predictions or decisions based on new data that is input.
[00:42:28.880 --> 00:42:32.080] Now, these neural networks didn't appear ex nihilo, though.
[00:42:32.080 --> 00:42:39.680] They've been developing and evolving for years, but many of the critical theoretical breakthroughs have been around for decades.
[00:42:40.160 --> 00:42:48.080] And in many ways, they've just been waiting for powerful enough servers to show us what these kinds of special networks can really, really do.
[00:42:48.080 --> 00:43:13.680] Yeah, I think it's been like the past 10 or 15 years when servers got much more powerful that we could really put these networks through their paces and really, and all the breakthroughs we've had the past 15 years just goes to show, not only is the theory been there, is the theory doing it, but it's the machines that have been the thing that we've been waiting for to really see this.
[00:43:14.000 --> 00:43:18.080] Okay, so this is where the two Nobel winners in physics come in.
[00:43:18.080 --> 00:43:24.880] John Hopfield is an emeritus professor at Princeton University, and Jeffrey Hinton is a computer scientist at the University of Toronto.
[00:43:24.880 --> 00:43:30.720] John Hopfield introduced the Hopfield network in a paper in 1982.
[00:43:30.720 --> 00:43:36.160] This type of network laid the groundwork for pattern recognition and associative memory.
[00:43:36.160 --> 00:43:39.200] Pattern recognition, that's self-explanatory.
[00:43:39.200 --> 00:43:46.480] Associative memory here in this context means that the network can remember patterns based on only partial information.
[00:43:46.480 --> 00:43:49.040] So that's, as you might imagine, that's critical.
[00:43:49.200 --> 00:43:59.320] You know, you expose the neural network to just a little bit of an image, say, and it could go back through the patterns that it has remembered and remember, oh, yeah, that was a cat.
[00:43:59.040 --> 00:44:03.640] So that's what he introduced with his Hopfield network in 82.
[00:44:04.200 --> 00:44:12.120] So as you might imagine, the Hopfield Network had a huge influence on the thinking of how to store and how to retrieve information in neural networks.
[00:44:12.120 --> 00:44:15.720] Jeffrey Hinton is sometimes called the godfather of AI.
[00:44:15.720 --> 00:44:20.360] He co-invented Boltzmann machines in 1985.
[00:44:20.360 --> 00:44:30.840] This is a type of early neural network that was designed to use some principles of physics, especially regarding probability and energy-based modeling.
[00:44:30.840 --> 00:44:33.560] Look that one up to find patterns in the data.
[00:44:33.560 --> 00:44:39.240] So it uses probability and this specific type of modeling to find patterns in the data.
[00:44:39.240 --> 00:44:43.160] Do you think finding patterns and data is critical for these neural networks?
[00:44:43.160 --> 00:44:43.800] Yes.
[00:44:43.800 --> 00:44:45.400] This was in 85.
[00:44:45.400 --> 00:45:00.360] So like the Hopfield Network, Hinton's Boltzmann machine network provided foundational techniques and ideas that have allowed for the creation of the incredibly complex, skilled, and capable neural networks and AI that we have today.
[00:45:00.360 --> 00:45:05.080] And this is why Hopfield and Hinton got Nobel Prizes this past week.
[00:45:05.080 --> 00:45:08.280] If the name Hinton sounds familiar, he's the guy, do you remember guys?
[00:45:08.280 --> 00:45:17.000] He's the guy that left Google last year to essentially be a whistleblower, actually, so he could speak candidly about his fears.
[00:45:17.000 --> 00:45:17.880] Yeah, I remember that guy.
[00:45:17.880 --> 00:45:27.960] Yeah, he says AI will be comparable to the Industrial Revolution, but instead of exceeding people in physical strength, it's going to exceed people in intellectual ability.
[00:45:27.960 --> 00:45:31.400] We have no experience of what it's like to have things smarter than us.
[00:45:31.400 --> 00:45:37.480] I'm just, he continues, I'm just a scientist who suddenly realized that these things are getting smarter than us.
[00:45:37.480 --> 00:45:44.760] I want to sort of blow the whistle and say we should worry seriously about how we stop these things getting control over us.
[00:45:45.040 --> 00:45:52.720] And he also warned that AI knows how to program, so it'll figure out ways of getting around restrictions that we put on it.
[00:45:52.720 --> 00:45:56.240] It'll figure out ways of manipulating people to do what it wants.
[00:45:56.240 --> 00:45:56.960] Oh, my gosh.
[00:45:56.960 --> 00:46:01.280] So, yeah, so he's definitely thrown that out there, and a lot of people are these days.
[00:46:01.280 --> 00:46:03.360] Like, yes, yes, absolutely.
[00:46:03.360 --> 00:46:08.960] AI is getting increasingly more sophisticated, and we've got to be careful about it.
[00:46:08.960 --> 00:46:23.120] But regarding that, I will say, I'll just say this: Hinton and Hopfield, their names will be remembered in the pattern-seeking nodes and the cold, cold logic of the AI overlords, which will eventually take over the earth.
[00:46:23.120 --> 00:46:27.360] And if you're listening to this podcast now or in the future, I am here to serve.
[00:46:28.720 --> 00:46:30.000] I'm just hedging my bets, guys.
[00:46:30.000 --> 00:46:31.040] Just hedging my bets here.
[00:46:31.280 --> 00:46:32.240] It's a good hedge there.
[00:46:32.240 --> 00:46:32.480] Yeah.
[00:46:32.480 --> 00:46:33.280] Oh, yeah.
[00:46:33.280 --> 00:46:34.880] I'll be a pet to an AI.
[00:46:34.880 --> 00:46:35.680] Come on, baby.
[00:46:35.680 --> 00:46:36.320] Hi, everyone.
[00:46:36.400 --> 00:46:38.320] Welcome, our AI overlord.
[00:46:40.080 --> 00:46:41.040] So, yeah, so cool.
[00:46:41.040 --> 00:46:44.160] Yeah, I wasn't familiar with their work.
[00:46:44.480 --> 00:46:59.440] And yeah, it seems, you know, of course, there's other researchers that also created, you know, contributed mightily to the development of what has become deep learning neural networks and things like that that are critical components of AI these days.
[00:46:59.440 --> 00:47:04.640] So I don't know how they, you know, how do they pick among all of them for these two guys?
[00:47:04.640 --> 00:47:09.120] And these two guys clearly made major contributions.
[00:47:09.120 --> 00:47:32.040] Now, remember, though, that their networks, the Hopfield network and the Boltzmann machine networks, they didn't evolve into the deep learning neural networks that we have today, but they but the ideas and the concepts that they introduced back decades ago were critical to the development over time of the neural networks that we have available today.
[00:47:29.920 --> 00:47:33.320] So, yeah, it's critical.
[00:47:33.480 --> 00:47:38.680] I mean, you know, you think about the things, you know, pattern recognition and things like that.
[00:47:38.680 --> 00:47:42.600] Yeah, that's absolutely critical to what we're doing today with these networks.
[00:47:42.600 --> 00:47:45.880] And these guys made some major, major contributions.
[00:47:45.880 --> 00:47:46.680] So good for them.
[00:47:46.680 --> 00:47:48.040] All right, thanks, Bob.
[00:47:48.040 --> 00:47:53.480] Well, everyone, we're going to take a break from our show to talk about our sponsor this week, NordVPN.
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[00:49:05.000 --> 00:49:07.160] All right, let's get back to the show.
[00:49:07.480 --> 00:49:11.000] All right, guys, I'm going to talk about some other big science news this week.
[00:49:11.000 --> 00:49:16.960] I don't know if this is going to be a future Nobel Prize, but it certainly is a pretty incredible milestone.
[00:49:17.280 --> 00:49:26.240] Scientists have presented a complete connectome of the Drosophila of the fruit fly.
[00:49:26.240 --> 00:49:27.760] I think this will be a Nobel Prize.
[00:49:27.760 --> 00:49:28.080] Yeah.
[00:49:28.080 --> 00:49:28.880] You think so?
[00:49:28.880 --> 00:49:29.360] Yes.
[00:49:29.360 --> 00:49:34.720] So this is the first complete connectome of an animal.
[00:49:35.040 --> 00:49:36.000] What's a connectome?
[00:49:36.000 --> 00:49:36.560] Of any brain.
[00:49:36.800 --> 00:49:44.000] Connectome is a map, a complete neuronal level map of a brain.
[00:49:44.960 --> 00:49:48.880] So all the neurons, all the connections of those neurons.
[00:49:49.120 --> 00:49:57.280] This map of the fruit fly brain contains 140,000 neurons and more than 50 million connections.
[00:49:57.680 --> 00:49:58.880] The whole brain connects.
[00:49:59.040 --> 00:50:02.400] It doesn't sound like a lot, but it's still, you know.
[00:50:02.400 --> 00:50:03.920] Well, it depends on context.
[00:50:04.080 --> 00:50:05.280] I mean, that's certainly a lot.
[00:50:05.680 --> 00:50:12.880] You're mapping a system with 140,000 components which are interacting through 50 million connections with each other.
[00:50:12.880 --> 00:50:13.840] That's big.
[00:50:13.840 --> 00:50:22.160] Now, of course, you compare that to the human brain, which has 86 billion neurons and about 100 trillion connections.
[00:50:22.160 --> 00:50:22.640] That's what I think.
[00:50:22.800 --> 00:50:26.640] That's six orders of magnitude greater than the Drosophila.
[00:50:26.880 --> 00:50:29.360] Yeah, but the Drosophila is a teeny tiny little fly.
[00:50:29.360 --> 00:50:30.800] Yeah, it's a tiny little fruit fly.
[00:50:31.120 --> 00:50:33.120] But we've got to start somewhere, right?
[00:50:33.120 --> 00:50:33.840] Yeah, exactly.
[00:50:33.840 --> 00:50:35.520] We'll eventually get to humans.
[00:50:35.520 --> 00:50:39.040] Yeah, and the fact that it's an entire, it's a complete map.
[00:50:39.040 --> 00:50:39.280] Yeah.
[00:50:39.280 --> 00:50:43.760] So we have mapped out an entire brain of an organism.
[00:50:43.760 --> 00:50:47.600] So that fact alone, who knows what kind of insights might flow from that?
[00:50:47.600 --> 00:50:49.440] But I'm sure Steve might mention some of these ideas.
[00:50:49.600 --> 00:50:51.760] I might mention some because they're already using it.
[00:50:51.760 --> 00:50:53.200] I mean, they didn't just say, here it is.
[00:50:53.200 --> 00:50:57.600] They said, all right, and we did some analysis using this piece.
[00:50:57.600 --> 00:50:59.200] And what did we learn?
[00:50:59.200 --> 00:51:04.920] One of the things they learned, again, this is not like a mind-blowing revelation.
[00:51:04.920 --> 00:51:09.960] This is pretty much building on what we already know.
[00:51:09.960 --> 00:51:22.520] But they were able to statistically map out the fact that the neurons connect to each other through nodes that are high traffic areas, right?
[00:51:22.520 --> 00:51:25.480] So there are these nodes, these hubs, right?
[00:51:25.480 --> 00:51:30.360] And that they make a lot of connections to other parts of the brain.
[00:51:30.360 --> 00:51:40.600] And again, as you would expect, these highly connected hubs also connect to many of the other highly connected hubs, right?
[00:51:40.600 --> 00:51:42.360] Because which makes sense just statistically.
[00:51:42.760 --> 00:51:48.440] Highly connected areas are going to be connected to other highly connected areas because they're all highly connected, right?
[00:51:48.440 --> 00:51:54.520] They were also able to show that some of these hubs act as integrators of information.
[00:51:54.520 --> 00:51:59.160] They're taking in a lot of information from other parts of the brain.
[00:51:59.160 --> 00:52:06.120] And other hubs act as distribution centers for the dissemination of information.
[00:52:06.120 --> 00:52:16.200] So this is a very efficient way to integrate and disseminate information, which is pretty much exactly what you would expect a brain type structure to be doing.
[00:52:16.520 --> 00:52:24.120] And different hubs can integrate with different other networks basically at different times for different functions.
[00:52:24.280 --> 00:52:25.080] Yeah, this is interesting.
[00:52:25.080 --> 00:52:26.760] This is, again, just getting started, right?
[00:52:26.760 --> 00:52:33.000] We have this tool now, this complete digital map of an entire functioning brain.
[00:52:33.000 --> 00:52:38.920] And so we'll be able to do a lot more systems kind of research on it to understand how it's functioning.
[00:52:38.920 --> 00:52:48.640] And even though, you know, like the last common ancestor between fruit flies and people didn't have a brain, it did have a nervous system, but a very, very basic one.
[00:52:48.640 --> 00:52:56.560] And maybe there were like nodes in that basic nervous system, like pre-brains, but not anything you would call an actual brain.
[00:52:56.560 --> 00:53:04.000] So the fruit fly brain doesn't have any evolutionary homology, you know, with the human brain.
[00:53:04.000 --> 00:53:11.840] But it probably follows a lot of the same principles, you know, at this sort of very basic level of information processing.
[00:53:11.840 --> 00:53:26.320] Of course, it would be a lot more useful if we can get the same level map with like a mouse brain, because now we're talking about a mammal, and that would have a lot of evolutionarily, evolutionary homology with the human brain.
[00:53:26.720 --> 00:53:31.040] Mammal brains all have a lot in common with all other mammal brains, you know.
[00:53:31.360 --> 00:53:50.080] So one way this research will progress, other than just doing research on this Drosophila brain model, will be to create these connectomes of increasingly sophisticated animals, you know, with bigger and bigger brains and closer and closer evolutionarily to humans.
[00:53:50.080 --> 00:53:57.760] But there's another way, too, because it's not as if we know, like, we have a full neuronal level connectome or nothing.
[00:53:57.760 --> 00:54:03.120] Of the human brain, we have what's called a mesoscale connectome.
[00:54:03.120 --> 00:54:07.200] If you think about the mesoscale basically means like middle scale.
[00:54:07.200 --> 00:54:17.760] So even 100 years ago or 200 years ago, we were putting together a like naked eye gross anatomy model of the brain, right?
[00:54:17.760 --> 00:54:19.520] These are like the Broadman's areas.
[00:54:19.520 --> 00:54:30.280] Like there's you have this, you have the lobes, and within those lobes, you have these, you know, again, naked eye visible areas of the brain that are engaged with, like, this is the language center, right?
[00:54:30.280 --> 00:54:32.440] And this is the visual center.
[00:54:32.440 --> 00:54:38.120] And we still, like, as a clinical neurologist, that's sort of the map of the brain that we follow clinically, right?
[00:54:38.440 --> 00:54:41.640] Like, this is the part of the brain that controls movement of your left arm.
[00:54:42.120 --> 00:54:45.640] That's a macro scale map of the brain.
[00:54:45.640 --> 00:54:50.840] A micro scale would be at like the nanoscale or neuronal level.
[00:54:50.840 --> 00:54:54.440] You know, that's the ultimate connectome.
[00:54:54.440 --> 00:55:02.920] But a mesoscale is somewhere in between those two things, where you are a very small level, like the millimeter scale, right?
[00:55:02.920 --> 00:55:09.960] You're down to the millimeter scale, not the neuronal individual neurons, but clusters of neurons.
[00:55:09.960 --> 00:55:18.680] And a mesoscale map is extremely, a mesoscale connectome is extremely useful because that's kind of how the brain is organized.
[00:55:18.680 --> 00:55:23.960] It's not like you have a single neuron with its own little function.
[00:55:23.960 --> 00:55:27.080] You have clusters of neurons all firing together.
[00:55:27.080 --> 00:55:35.800] So it's still useful to get to that deeper level of detail, but there's a lot that we can understand about how the brain functions.
[00:55:35.800 --> 00:55:42.600] And there's a lot of research that we could do and a lot of modeling that we could do, even with a very good mesoscale connectome.
[00:55:42.600 --> 00:55:43.800] And we already have that.
[00:55:44.120 --> 00:55:45.480] We're refining it.
[00:55:45.480 --> 00:56:00.440] A lot of this was developed with like fMRI research and other functional kind of imaging studies and electromagnetic studies, et cetera, where we could see the different parts of the brain and how they connect to other parts of the brain and when they become active and what kind of tasks, et cetera.
[00:56:00.440 --> 00:56:03.640] Still, a lot of information to explore with that.
[00:56:03.640 --> 00:56:06.280] But that's kind of where we are with the human brain.
[00:56:06.600 --> 00:56:08.040] Well, Steve, how does that relate?
[00:56:08.040 --> 00:56:13.400] I remember years ago reading about the cortical columns in the brain, the fundamental units.
[00:56:13.400 --> 00:56:13.960] How does that relate?
[00:56:15.440 --> 00:56:17.440] That's like a mesoscale structure.
[00:56:17.760 --> 00:56:20.480] Yeah, a single cortical column, yeah.
[00:56:20.480 --> 00:56:21.440] Yeah, okay.
[00:56:21.440 --> 00:56:26.560] Yeah, there are billions, you know, or millions of cortical columns in the human brain.
[00:56:26.560 --> 00:56:26.880] Yeah.
[00:56:28.480 --> 00:56:31.760] It's way, well, well, well below the macro scale, but it's not at the neuron.
[00:56:32.160 --> 00:56:33.840] A column is a bunch of neurons.
[00:56:33.840 --> 00:56:36.080] It's not down to the individual neuronal scale either.
[00:56:36.080 --> 00:56:41.680] So that's a good example of the mesoscale of sort of a unit of cortex.
[00:56:41.680 --> 00:56:47.840] And is there any hint of this hub structure in other brains?
[00:56:48.160 --> 00:56:48.640] Totally.
[00:56:48.640 --> 00:56:51.280] Yeah, this is basically our model of how the brain works.
[00:56:51.520 --> 00:56:55.840] It's networks and nodes, right, or modules.
[00:56:55.840 --> 00:57:01.920] So you have little pieces of the brain that do some kind of processing.
[00:57:01.920 --> 00:57:08.800] And that module, that node, participates in multiple different networks.
[00:57:08.800 --> 00:57:13.360] And each network subsuming a specific function.
[00:57:13.360 --> 00:57:20.560] But of course, it's networking with a bunch of other networks that also have their functions, and they are all interacting with each other in real time.
[00:57:20.880 --> 00:57:23.680] So it's really complicated.
[00:57:23.680 --> 00:57:33.760] But that's how we are sort of pieced, that's how we are, again, creating the mesoscale connectome, is trying to understand all of the nodes and networks in the brain.
[00:57:33.760 --> 00:57:52.720] So this hub idea then from the connectome is probably extremely relevant, yeah, to have a model that we could ask really good statistical and mathematical questions, like network-level questions about how it's functioning.
[00:57:52.720 --> 00:58:04.200] And of course, it'll be useful to have these micro-scale connectomes, you know, ultimate, like down to the neuronal level for ultimately for humans, right?
[00:58:04.200 --> 00:58:05.640] That's the goal.
[00:58:05.640 --> 00:58:24.120] Now, you may be thinking, okay, if we have a complete connectome of a Drosophila brain, does that mean that if we emulated that in a computer, you know, however powerful a computer we need to, would that be a fruit fly, basically?
[00:58:24.120 --> 00:58:25.640] Would that be a virtual fruit fly?
[00:58:25.640 --> 00:58:28.440] Would it behave like a fruit fly?
[00:58:28.440 --> 00:58:37.880] And the answer is sort of, because even a complete connectome is not everything.
[00:58:38.280 --> 00:58:39.160] That's part of the story.
[00:58:39.160 --> 00:58:42.280] There's so many support glia cells and all those other things.
[00:58:42.280 --> 0

Prompt 2: Key Takeaways

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

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

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

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

Prompt 3: Segments

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

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

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

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

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

Prompt 4: Media Mentions

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

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

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

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

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

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

Prompt 5: Context Setup

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

Transcript section:
 the brain works.
[00:56:51.520 --> 00:56:55.840] It's networks and nodes, right, or modules.
[00:56:55.840 --> 00:57:01.920] So you have little pieces of the brain that do some kind of processing.
[00:57:01.920 --> 00:57:08.800] And that module, that node, participates in multiple different networks.
[00:57:08.800 --> 00:57:13.360] And each network subsuming a specific function.
[00:57:13.360 --> 00:57:20.560] But of course, it's networking with a bunch of other networks that also have their functions, and they are all interacting with each other in real time.
[00:57:20.880 --> 00:57:23.680] So it's really complicated.
[00:57:23.680 --> 00:57:33.760] But that's how we are sort of pieced, that's how we are, again, creating the mesoscale connectome, is trying to understand all of the nodes and networks in the brain.
[00:57:33.760 --> 00:57:52.720] So this hub idea then from the connectome is probably extremely relevant, yeah, to have a model that we could ask really good statistical and mathematical questions, like network-level questions about how it's functioning.
[00:57:52.720 --> 00:58:04.200] And of course, it'll be useful to have these micro-scale connectomes, you know, ultimate, like down to the neuronal level for ultimately for humans, right?
[00:58:04.200 --> 00:58:05.640] That's the goal.
[00:58:05.640 --> 00:58:24.120] Now, you may be thinking, okay, if we have a complete connectome of a Drosophila brain, does that mean that if we emulated that in a computer, you know, however powerful a computer we need to, would that be a fruit fly, basically?
[00:58:24.120 --> 00:58:25.640] Would that be a virtual fruit fly?
[00:58:25.640 --> 00:58:28.440] Would it behave like a fruit fly?
[00:58:28.440 --> 00:58:37.880] And the answer is sort of, because even a complete connectome is not everything.
[00:58:38.280 --> 00:58:39.160] That's part of the story.
[00:58:39.160 --> 00:58:42.280] There's so many support glia cells and all those other things.
[00:58:42.280 --> 00:58:42.680] Exactly.
[00:58:42.680 --> 00:58:44.520] So there's other layers going on.
[00:58:44.520 --> 00:58:48.920] So one thing, one layer is like there's other cells other than the neurons.
[00:58:49.640 --> 00:58:54.360] And we used to think that the glia were just support cells.
[00:58:54.360 --> 00:58:56.600] They were just keeping the neurons happy.
[00:58:57.480 --> 00:59:04.040] But we have learned in the last couple decades that nope, they actually are modulating neuronal function.
[00:59:04.040 --> 00:59:10.760] They actually have a layer of functionality there to the way the whole brain works.
[00:59:10.760 --> 00:59:18.440] So in order to understand how the brain functions, you have to include these support cells as well.
[00:59:18.440 --> 00:59:26.920] You also have to understand how the brain is functioning at a chemical level, like how the neurotransmitters are functioning, right?
[00:59:26.920 --> 00:59:40.120] So you need to understand the wiring, you need to understand the biology, you need to understand the neurochemistry and how they all work together in a dynamic way before you actually have like a virtual brain, right?
[00:59:40.120 --> 00:59:42.600] The connectome is only one piece.
[00:59:42.600 --> 00:59:49.120] It's arguably maybe the most important piece, but it is only one piece to a virtual actual brain.
[00:59:44.600 --> 00:59:50.800] But then there's another layer there, too.
[00:59:50.960 --> 01:00:06.800] Like if we're saying, all right, this we have a brain, you know, we have a virtual brain, whatever it is, a mouse brain, a human brain, or in this case, a Drosophila brain, and how would it function like if we were like emulating it in a computer?
[01:00:06.800 --> 01:00:11.280] Well, then you have to think about, well, what are its inputs and outputs, right?
[01:00:11.600 --> 01:00:17.280] What would a virtual brain that has no input, what would it do?
[01:00:17.280 --> 01:00:29.920] In order for it to really simulate a fruit fly, it would have to be getting sensory information that a fruit fly brain gets, which includes information from its body, which it doesn't have because it's a virtual brain.
[01:00:29.920 --> 01:00:33.360] But we would have to simulate all of those inputs.
[01:00:33.360 --> 01:00:34.960] It could be tricked, right, to thinking so.
[01:00:35.120 --> 01:00:39.840] Yeah, we would have to give it a virtual environment, a virtual body, to go with this virtual brain.
[01:00:39.840 --> 01:00:48.880] And then we would have to also include the reaction of the body and the environment to its actions, right?
[01:00:49.600 --> 01:00:52.320] If it does something, there's got to be a response to it.
[01:00:52.320 --> 01:01:07.360] The brain functions and is organized and develops, like all that functionality and even the developmental anatomy of the brain is based upon that loop, that feedback of input and reaction to output, et cetera.
[01:01:07.360 --> 01:01:08.240] You know what I mean?
[01:01:08.720 --> 01:01:15.440] Your visual cortex only develops under stimulation from actual vision, from actual input from your eyes.
[01:01:15.440 --> 01:01:27.600] And your brain can control your arm because it's not only just sending signals to the arm, it's also getting signals back from the arm.
[01:01:27.600 --> 01:01:35.320] And it sort of compares what it's trying to do with what happens, and that's how it knows that it's controlling the limb.
[01:01:35.400 --> 01:01:37.560] You know, like you have to close that loop.
[01:01:37.560 --> 01:01:50.040] So we would have to do the same thing if we were going to try to have a virtual fruit fly, you know, with an actual fully emulated connectome of a fruit fly functioning in a digital world.
[01:01:50.040 --> 01:01:51.240] So it's interesting to think about that.
[01:01:51.240 --> 01:01:53.240] It's actually very, very challenging.
[01:01:53.240 --> 01:01:57.480] And so how far are we away from max headroom, right?
[01:01:57.480 --> 01:02:10.520] So the max headroom being like a digital copy of a human's brain that then sort of was an artificial intelligent digital human because it was an emulated human brain, therefore it's a human, right?
[01:02:10.520 --> 01:02:13.000] But exists only in the digital world.
[01:02:13.000 --> 01:02:14.520] It's really hard to say.
[01:02:14.520 --> 01:02:19.320] I suspect we're a very long way away, both in software and in hardware.
[01:02:19.320 --> 01:02:23.160] The other question is I couldn't really find a good answer to this, just only vague answers.
[01:02:23.160 --> 01:02:24.360] It's like, what would it take?
[01:02:24.360 --> 01:02:25.080] Like, what would it take?
[01:02:25.080 --> 01:02:31.560] Well, how powerful a computer would you need to emulate that fly brain connectome in real time?
[01:02:31.560 --> 01:02:39.720] You know, not just like one second for the fruit fly is a day in real time, but like one second to one second.
[01:02:40.040 --> 01:02:41.880] How powerful a computer would that take?
[01:02:41.880 --> 01:02:44.520] I suspect it would take a supercomputer to do that.
[01:02:44.920 --> 01:02:58.840] How powerful a computer are we going to need to emulate a human brain once we have a neuronal level complete human connectome, including all of the inputs and outputs that are necessary to make it actually function?
[01:02:59.160 --> 01:03:03.240] We could be decades away from having the hardware to do that.
[01:03:03.240 --> 01:03:08.680] How long is it going to take before we have a connectome of a human?
[01:03:08.840 --> 01:03:14.600] And even that, don't you feel like we can constantly be increasing the resolution?
[01:03:14.600 --> 01:03:15.120] Yes.
[01:03:15.120 --> 01:03:17.040] Like there's more detail always.
[01:03:14.680 --> 01:03:20.240] Yeah, that's kind of my point with the mesoscales.
[01:03:21.440 --> 01:03:29.920] We're increasing our resolution, but also we are just building the neuronal level connectome also, directly.
[01:03:30.240 --> 01:03:31.680] Even once things are both happening.
[01:03:32.240 --> 01:03:37.280] To the individual neurons with the individual connections, then we can start talking about which neurotransmitters are present there.
[01:03:37.520 --> 01:03:39.040] And we can start talking about G proteins.
[01:03:39.040 --> 01:03:41.520] And then, yeah, you can always resolve more.
[01:03:41.840 --> 01:03:43.360] Yeah, absolutely.
[01:03:43.360 --> 01:03:46.800] But at some point, we're going to get something that's close enough.
[01:03:46.800 --> 01:03:47.120] Yeah.
[01:03:47.120 --> 01:03:49.760] And you don't need to worry about neutrons and protons.
[01:03:49.760 --> 01:03:56.480] That it's functioning, that it could theoretically be a functioning human virtual brain.
[01:03:57.520 --> 01:04:05.360] But so I suspect that that's decades away too before we have the information to do that and decades before we have the hardware to do it.
[01:04:05.360 --> 01:04:11.200] But the one variable, however, in this is artificial intelligence.
[01:04:11.200 --> 01:04:12.160] AI, baby.
[01:04:12.160 --> 01:04:15.440] Because AI is, you know, I would say.
[01:04:15.520 --> 01:04:16.320] It's a wild card.
[01:04:16.560 --> 01:04:31.680] It's a wild card, but the strength of AI in terms of what AI can do is operating within dynamic information-based systems that involve a lot of data, which is exactly the problem we're talking about.
[01:04:31.680 --> 01:04:39.040] Like, it's almost ideally suited to the task of making a connectome of the human brain.
[01:04:39.040 --> 01:04:50.080] And we may just be as far away as however long it will take for somebody to develop an AI whose purpose is figuring out the connectome of the human brain.
[01:04:50.080 --> 01:04:51.600] And then it'll do it.
[01:04:51.600 --> 01:04:58.640] And maybe it'll be as accurate as an AI picture is, you know, where it's like a little, like you have six fingers and whatever.
[01:04:58.640 --> 01:05:05.560] Like it doesn't maybe not be perfectly accurate, but we may be able to fill in a lot of those higher resolution details.
[01:05:05.560 --> 01:05:07.400] With frog DNA.
[01:05:07.400 --> 01:05:07.800] Yeah, right.
[01:05:08.600 --> 01:05:13.160] You may be able to fill it in with AI, like the way the way it functions now.
[01:05:13.160 --> 01:05:13.880] So who knows?
[01:05:14.200 --> 01:05:16.120] So it may be a lot sooner than we think.
[01:05:16.120 --> 01:05:18.440] It may not be, maybe years and not decades.
[01:05:18.440 --> 01:05:18.760] Right.
[01:05:18.920 --> 01:05:24.040] If AI, as I just said, accelerates that research tremendously.
[01:05:24.040 --> 01:05:36.200] And if you are interested in this idea of digital copies of human brains, I would suggest you watch the animated series called Pantheon, which deals with that in the best way I have ever seen.
[01:05:36.200 --> 01:05:37.720] Very interesting, fascinating show.
[01:05:37.720 --> 01:05:38.760] Check it out, Pantheon.
[01:05:39.880 --> 01:05:45.640] Would these advancements allow us to figure out things like getting rid of somebody's depression and/or anxiety?
[01:05:45.880 --> 01:05:47.240] That's the hope, right?
[01:05:47.240 --> 01:05:51.800] So imagine if we could, let's back off of humans for a bit.
[01:05:51.800 --> 01:05:57.400] Let's see if we have an animal connectome that we can, even the Drosophila.
[01:05:57.400 --> 01:06:04.760] Because once we have that, then we could play the game of what happens when I turn off this circuit.
[01:06:05.000 --> 01:06:06.760] Now let's see how the system behaves.
[01:06:06.760 --> 01:06:07.720] Knockout genes.
[01:06:07.720 --> 01:06:09.000] Yeah, like knockout genes.
[01:06:09.000 --> 01:06:11.400] But this is like knockout networks, knockout circuits.
[01:06:11.400 --> 01:06:14.680] Or let's just turn it up or turn it down or do this.
[01:06:14.840 --> 01:06:19.480] Like you're playing with dials now and seeing how it affects the behavior.
[01:06:19.480 --> 01:06:26.520] Which dials do I have to turn to turn off depression or anxiety or schizophrenia or whatever?
[01:06:26.520 --> 01:06:29.080] And then we actually model those diseases.
[01:06:29.080 --> 01:06:36.120] We make a schizophrenic brain and see how that behaves differently than a typical brain or an autistic brain or whatever.
[01:06:36.200 --> 01:06:45.000] That's the interesting thing because all of this assumes that certain disease states are somewhat universal.
[01:06:46.560 --> 01:06:53.120] And when we're modeling a brain, we're modeling the most normative version of a brain.
[01:06:53.120 --> 01:06:53.680] Exactly.
[01:06:53.680 --> 01:06:57.280] But every brain is really different.
[01:06:57.280 --> 01:06:58.880] And it's going to be a neurodiversity.
[01:06:59.200 --> 01:07:11.680] Yeah, for us to understand that as we move forward, instead of becoming more constrained in our views of, I guess, function and experience, we need to sort of be broader.
[01:07:11.680 --> 01:07:16.800] And I think what will be really interesting is when we have a modeled brain that has massive error bars in it.
[01:07:17.120 --> 01:07:21.840] Well, I think we'll probably model a generic human brain.
[01:07:21.840 --> 01:07:28.400] Like this is an averaged-out, generic human brain that's just the average and typical in every way.
[01:07:28.400 --> 01:07:31.040] And then try to figure out how it varies.
[01:07:31.440 --> 01:07:31.840] Exactly.
[01:07:31.840 --> 01:07:34.880] Like, how far can it go in this direction versus that direction?
[01:07:35.200 --> 01:07:38.240] And what happens and how these different levers interact.
[01:07:38.880 --> 01:07:45.680] And of course, there's a massive ethical layer here when you start to get to primates and definitely humans.
[01:07:45.680 --> 01:07:49.120] Would a virtual human brain be a human?
[01:07:49.120 --> 01:08:08.800] You know, do we, what's the ethics of doing research on a virtual human brain, especially when you're looking at how it functions, you're looking at its consciousness almost by definition, for that kind of research, it would be conscious because you're seeing how we can affect its consciousness in different ways.
[01:08:09.440 --> 01:08:17.520] Yeah, I mean, if you're asking other questions that don't deal with consciousness itself, then you could do research on it without it being conscious.
[01:08:17.520 --> 01:08:21.040] But the most interesting questions are the ones that deal with consciousness, right?
[01:08:21.360 --> 01:08:23.680] But there's also an interesting component here, right?
[01:08:23.680 --> 01:08:27.760] Because we talk about this with organoids, but organoids use wetware.
[01:08:27.760 --> 01:08:38.680] And when there's actually no hardware or wetware, we're just talking about software, then it's the same ethical conversations that we have about AI in general, I think.
[01:08:38.680 --> 01:08:41.960] It's not, I don't think it's ever going to move into that.
[01:08:41.960 --> 01:08:45.560] Yes, it may be consciousness, but it's still virtual consciousness.
[01:08:45.800 --> 01:08:53.960] It's never going to be, once you actually try to, I don't know, somehow fuse this with an organoid, then I think things get extra hairy.
[01:08:53.960 --> 01:08:59.400] Yeah, but I think even when we have a virtual consciousness, even without wetware, it's going to be dodgy.
[01:08:59.400 --> 01:09:03.800] Yeah, but it's dodgy the same way all advanced AI is dodgy, I think.
[01:09:03.800 --> 01:09:06.680] But I do think, let's say we make a virtual mouse.
[01:09:06.680 --> 01:09:10.760] We already have an ethical standard for doing research on mice.
[01:09:10.760 --> 01:09:16.600] So let's say, all right, can you be reasonably sure that this thing is not suffering?
[01:09:16.600 --> 01:09:18.040] Okay, then have at it.
[01:09:18.040 --> 01:09:19.240] Do what you need.
[01:09:19.240 --> 01:09:32.120] And then we could be in a situation where we could do millions of maze experiments on a virtual mouse in seconds, you know, rather than years, you know, that sort of thing.
[01:09:32.280 --> 01:09:38.280] It's going to be a wild ride, you know, and this is, again, this is going to be the next 20, 30 years, and maybe sooner than we think.
[01:09:38.280 --> 01:09:41.000] You know, things are happening fast.
[01:09:41.000 --> 01:09:46.360] So this is a milestone, you know, the first complete connectome.
[01:09:47.000 --> 01:09:54.200] This will be one of those milestones that always gets mentioned in summaries or write-ups of this topic.
[01:09:54.200 --> 01:10:00.920] But there are bigger ones to come, and it's going to be both exciting and frightening, which is like a lot of good sciences.
[01:10:00.920 --> 01:10:01.960] Those two things.
[01:10:02.280 --> 01:10:04.040] I have a question for Kara.
[01:10:04.040 --> 01:10:06.840] Do you know who Max Headroom is, Williams?
[01:10:07.160 --> 01:10:07.920] No.
[01:10:07.720 --> 01:10:09.920] Steve.
[01:10:10.200 --> 01:10:21.360] Max Headroom is a show from the 80s where the main protagonist of the show was in an accident and in a coma, but he was some kind of researcher.
[01:10:21.360 --> 01:10:44.080] The long of the short version is there was a computer model of his brain that then was able to function as an and it was like a digitized, low-res, glitchy kind of head of a person that had a very you know entertaining personality.
[01:10:44.400 --> 01:10:44.880] Sarcastic.
[01:10:45.040 --> 01:10:46.160] Yeah, I'm looking at it.
[01:10:46.160 --> 01:10:50.000] So, so, but it wasn't, the show itself wasn't a cartoon?
[01:10:50.000 --> 01:10:51.280] No, it was live action.
[01:10:51.280 --> 01:10:54.160] It was live action, and then sometimes you would see this digitized.
[01:10:54.320 --> 01:10:55.520] This guy would be on a computer screen.
[01:10:55.520 --> 01:10:57.040] Oh, he was on a long time ago.
[01:10:59.120 --> 01:10:59.680] Yeah.
[01:10:59.680 --> 01:11:04.080] But then the guy came out of the coma, so then he was able to talk to his Max Headroom version.
[01:11:04.080 --> 01:11:17.920] And the reason why he's called Max Headroom, because that was the last thing he saw before he went into a coma, which was the last thing that the AI saw, which was literally Max Headroom.
[01:11:17.920 --> 01:11:18.480] In a garage.
[01:11:18.560 --> 01:11:19.040] In a garage.
[01:11:19.200 --> 01:11:19.520] Clearance.
[01:11:19.680 --> 01:11:21.040] Maximum clearance.
[01:11:21.520 --> 01:11:24.640] Like when George tore the roof off the van, but that's a whole other story.
[01:11:24.880 --> 01:11:27.600] And the actor was Matt Frewer, who was awesome.
[01:11:27.600 --> 01:11:28.720] Yeah, he was awesome, awesome.
[01:11:28.720 --> 01:11:30.240] Yeah, showed up in Star Trek, too.
[01:11:30.240 --> 01:11:31.040] So there you go.
[01:11:31.040 --> 01:11:31.680] There you go, Kara.
[01:11:31.920 --> 01:11:34.560] His series was only on from 87 to 88.
[01:11:34.960 --> 01:11:35.840] It was very short-lived.
[01:11:36.320 --> 01:11:38.000] No, but he became a cultural icon.
[01:11:38.000 --> 01:11:39.680] He was in commercials everywhere.
[01:11:39.760 --> 01:11:39.840] Yeah.
[01:11:39.920 --> 01:11:40.800] It became a big thing.
[01:11:41.040 --> 01:11:47.200] Separate from the show, yes, he was in his own right a cultural icon for a decade.
[01:11:48.160 --> 01:11:54.080] Yeah, so I would have been like five, which is probably why I don't remember it, sadly.
[01:11:54.960 --> 01:11:57.040] It just wasn't relevant to me quite yet.
[01:11:57.040 --> 01:12:00.760] All right, Evan, give us an update on the Shroud of Turin.
[01:12:00.760 --> 01:12:01.960] Yeah, Shroud of Turin.
[01:12:02.040 --> 01:12:03.000] Harry, right?
[01:12:03.080 --> 01:12:04.360] No kidding, Bob.
[01:11:59.920 --> 01:12:05.560] Here we go in the class.
[01:12:07.080 --> 01:12:10.040] I'm touching on another retro skeptic topic.
[01:12:10.040 --> 01:12:13.000] Kara, have you ever heard of the Shroud of Turin?
[01:12:13.240 --> 01:12:13.880] Of course I have.
[01:12:13.880 --> 01:12:17.160] Isn't that supposed to be the thing that Jesus was like buried in?
[01:12:17.160 --> 01:12:18.920] Ah, yes, very, very good.
[01:12:19.880 --> 01:12:24.920] Do you think people younger than you, Kara, maybe the majority of them know or have no clue?
[01:12:24.920 --> 01:12:25.320] No.
[01:12:25.320 --> 01:12:31.960] I think people who younger than me who have like weird enjoyment watching bad Discovery Channel shows might know.
[01:12:32.360 --> 01:12:32.600] Probably.
[01:12:32.920 --> 01:12:36.840] Although everything has a second life on TikTok now, to be honest with you.
[01:12:36.840 --> 01:12:38.040] Even the old school stuff.
[01:12:38.040 --> 01:12:38.440] Yeah.
[01:12:38.440 --> 01:12:44.360] Well, in case some of our audience is not familiar, I'm going to give you the quick background on what this is all about.
[01:12:44.360 --> 01:12:45.480] Gather round, children.
[01:12:45.480 --> 01:12:49.560] It's old man's telling a tale of antiquity and flim flammery.
[01:12:49.560 --> 01:12:53.480] Well, the year was one, when, according to some people, Jesus Christ was born.
[01:12:53.480 --> 01:12:57.080] Fast forward to the year 33, when Jesus Christ was crucified.
[01:12:57.080 --> 01:13:02.360] His body was cleaned, clothed, wrapped in a linen shroud, and buried in a rock tomb.
[01:13:02.360 --> 01:13:03.320] The end.
[01:13:03.640 --> 01:13:05.080] Now get off my lawn.
[01:13:05.080 --> 01:13:07.320] No, so, but that is basically it.
[01:13:07.320 --> 01:13:15.640] The shroud of Turin is believed by many people of faith to be the linen burial cloth that Jesus was buried in.
[01:13:15.880 --> 01:13:28.920] Many people of the faith also believe that the shroud actually exists and resides in the city of Turin, which is in Italy, hence the shroud of Turin.
[01:13:29.240 --> 01:13:31.000] Okay, that's how you get the name.
[01:13:31.000 --> 01:13:36.520] Now, as if the existence of such an item would be significant, I don't doubt that.
[01:13:36.520 --> 01:13:43.000] The shroud itself is not just a cloth, but it bears the image of a man.
[01:13:43.000 --> 01:13:51.200] Perhaps the radiance of the supposed resurrection impressed an image of Jesus' likeness onto the cloth itself.
[01:13:52.000 --> 01:14:06.320] His facial features, his facial hair, that beard, the mustache, and some red pigmentation resembling that which looks like bloodstains, his likeness, his blood forever captured within the fibers of the burial shroud.
[01:14:06.320 --> 01:14:11.760] And one might say that the shroud of Turin is the holy grail of religious artifacts.
[01:14:11.760 --> 01:14:15.680] Evan, wouldn't the holy grail be the holy grail of religious artifacts?
[01:14:16.800 --> 01:14:23.520] Steve, Evan, the image was famously, though, also a negative, right?
[01:14:23.520 --> 01:14:24.880] A negative image.
[01:14:24.880 --> 01:14:26.720] Yeah, negative impression.
[01:14:26.720 --> 01:14:33.840] As if you were to have taken a photograph and then done a negative, right, on a plate or in film.
[01:14:33.840 --> 01:14:34.400] Correct.
[01:14:34.400 --> 01:14:35.520] Yes, it does.
[01:14:35.840 --> 01:14:37.040] That's how it looks.
[01:14:37.040 --> 01:14:37.840] We'll get to that.
[01:14:37.840 --> 01:14:38.560] We will get to that.
[01:14:38.720 --> 01:14:39.280] Okay.
[01:14:39.280 --> 01:14:40.080] All right.
[01:14:40.080 --> 01:14:45.040] So, what is the evidence that this is the burial cloth of Jesus?
[01:14:45.520 --> 01:14:46.640] Has it been looked at?
[01:14:46.640 --> 01:14:49.360] Has it been investigated thoroughly and properly?
[01:14:49.360 --> 01:14:52.480] The answer is: oh, yeah, it has.
[01:14:53.040 --> 01:14:54.640] I'm going to mention him again this week.
[01:14:54.640 --> 01:14:55.360] So nice.
[01:14:55.360 --> 01:14:56.720] I'm calling him out twice.
[01:14:56.720 --> 01:14:57.040] Dr.
[01:14:57.040 --> 01:15:01.920] Joan Nicol, yep, gentleman skeptic detective, longtime friend of the SGU.
[01:15:01.920 --> 01:15:07.440] He has dug as deep as anyone has been allowed to from the skeptical side into this matter.
[01:15:07.440 --> 01:15:13.440] And he was able to establish some interesting facts about this particular shroud.
[01:15:13.440 --> 01:15:18.640] It seems that the material of the linen itself is medieval in origin.
[01:15:19.120 --> 01:15:27.520] So, not in the year, say, 33 or 36, roughly the approximate year that Jesus was crucified.
[01:15:27.520 --> 01:15:29.640] They don't know exactly when, supposedly.
[01:15:29.200 --> 01:15:34.280] But rather, this shroud maps to the year, well, 1260 to 1390.
[01:15:34.600 --> 01:15:37.800] Thank you, radiocarbon dating.
[01:15:38.440 --> 01:15:44.200] Yeah, and not just one, but three independent analysis returned that range of dates.
[01:15:44.200 --> 01:15:49.160] So, that's how they were able to do with a test piece of the cloth.
[01:15:49.160 --> 01:16:02.600] And also, Joe noted that it's kind of funny that there's no documented history of this artifact until the middle of the 14th century, as if it only sort of suddenly poof came into existence, if you will.
[01:16:03.240 --> 01:16:04.360] There you go, Bob.
[01:16:04.360 --> 01:16:06.360] Thanks for bringing it all together.
[01:16:06.360 --> 01:16:08.120] It only sort of began at that time.
[01:16:08.120 --> 01:16:14.760] You would think, you know, something like this would have had some sort of documentation prior to that, but there is none.
[01:16:14.760 --> 01:16:23.480] Now, the image itself, probably, most likely, zero chance of it being the result of a miraculous or supernatural process.
[01:16:23.480 --> 01:16:35.000] Instead, it was likely created, much more prosaic explanation, using a combination of artistic methods, including painting or rubbing pigment onto the cloth.
[01:16:35.000 --> 01:16:42.200] And, Bob, since you brought it up, sort of the negative image, and Joe Nicol talks about this in an interview.
[01:16:42.200 --> 01:16:57.880] There is a technique that would be used by artists at that time in which you would do a rubbing of a face or a set of words or something else, and it would turn out to sort of have that negative-looking appearance to it.
[01:16:57.880 --> 01:17:02.840] So, this is a known method of artistry at the time.
[01:17:02.840 --> 01:17:22.480] Yeah, what Joe points out, which is, I think, very telling, is if you look at all the independent lines of evidence, the carbon dating, the artistic tradition, the history, the known provenance of the shroud itself, they all point to the same period of time.
[01:17:22.480 --> 01:17:22.960] Yep.
[01:17:22.960 --> 01:17:24.800] In the, what was it, the 15th century?
[01:17:25.040 --> 01:17:26.160] Mid-14th century.
[01:17:26.160 --> 01:17:27.040] Mid-14th century.
[01:17:27.440 --> 01:17:32.800] It's like, you know, he said, like, there's three darts hitting the bullseye of a dartboard.
[01:17:32.800 --> 01:17:35.920] They all point to that, and not by coincidence, because that's when it came up.
[01:17:35.920 --> 01:17:42.080] And this was a period of time when these kinds of fake artifacts were floating all over the place.
[01:17:42.400 --> 01:17:44.400] You know how many shrouds there were at the time?
[01:17:44.400 --> 01:17:48.080] This is just the one that kind of survives famously until modern times.
[01:17:48.080 --> 01:17:51.840] But there were all kinds of artifacts floating around at this time.
[01:17:52.160 --> 01:17:53.280] There was a cottage industry.
[01:17:53.520 --> 01:17:54.080] It was an industry.
[01:17:54.080 --> 01:17:54.480] That's an interesting thing.
[01:17:54.560 --> 01:17:57.280] It was an industry of fake religious artifacts.
[01:17:57.280 --> 01:18:03.280] Like every little shrine and church had to have theirs in order to drum up people to come there and give them money.
[01:18:03.280 --> 01:18:08.160] And then, of course, then people start making them so that they could be sold.
[01:18:10.720 --> 01:18:13.920] This is a home run for a medieval forgery.
[01:18:13.920 --> 01:18:14.960] I mean, there's no question.
[01:18:15.280 --> 01:18:17.040] No doubt about it, Steve.
[01:18:17.040 --> 01:18:17.360] Thank you.
[01:18:17.760 --> 01:18:20.080] It was as if you were reading the notes right off my page.
[01:18:20.640 --> 01:18:22.480] You don't have a cookie attached to my computer.
[01:18:22.720 --> 01:18:25.200] No, we're both using the same resource.
[01:18:26.000 --> 01:18:27.120] I think so.
[01:18:27.440 --> 01:18:29.920] So these are the conclusions that Joe was able to come up with.
[01:18:29.920 --> 01:18:37.520] And also other investigators who have also corroborated similar evidence showing that, yeah, absolutely, mid-14th century.
[01:18:37.520 --> 01:18:48.400] But there are still many millions of faithful people around the world who would rather embrace their idea that this is something that Jesus was actually buried in.
[01:18:48.720 --> 01:18:59.680] So, yeah, and the bloodstains also on the shroud have been investigated as well, which Joe has said, you know, most likely pigment.
[01:18:59.680 --> 01:19:09.800] It's a little tough because they wouldn't really give over sections of the shroud in order to test it for the actual blood, but they've done their own sort of analysis.
[01:19:09.880 --> 01:19:11.320] Other people have done analysis.
[01:19:11.320 --> 01:19:13.240] They think, and some people think it's pigment.
[01:19:13.240 --> 01:19:16.840] Okay, you know, paint being used at the time, whatever.
[01:19:17.160 --> 01:19:18.680] So, here we go.
[01:19:18.680 --> 01:19:32.360] Brand new news coming out just a few days ago about the Shroud of Turin, in which researchers are claiming that these bloodstains found on the Shroud are consistent with the torture that Christ suffered.
[01:19:32.360 --> 01:19:32.760] Yep.
[01:19:32.760 --> 01:19:42.200] Professor Gilo Fanti from the University of Padua found a pattern of bloodstains that will align with historical descriptions of the crucifixion.
[01:19:42.360 --> 01:19:49.240] He studied the Shroud of Turin for over 25 years, published his findings through the Shroud Science Group.
[01:19:49.960 --> 01:20:03.640] And he claims that macroscopic and microscopic analysis finds that the markings and stains on the cloth are consistent with the description of Jesus Christ in the Holy Bible, and in particular, with the four canonicals.
[01:20:04.520 --> 01:20:06.200] Okay, so what is he talking about?
[01:20:07.080 --> 01:20:15.480] Lots of red splots of varying shapes and sizes are all over this, which he is claiming, okay, he's researched it.
[01:20:15.480 --> 01:20:25.800] Definitely, this is right in line with exactly what a crucifixion from Jesus' time would have undergone.
[01:20:26.120 --> 01:20:27.160] Whatever, no.
[01:20:27.480 --> 01:20:28.680] Yeah, yeah.
[01:20:28.920 --> 01:20:52.400] And also, I went back and did a little more research about what Joe Nicol and others have said about that in the past, and they said artists, that was not necessarily unknown to artists at the time who would make these shrouds and these other things or other depictions with these kinds of splotches as an artistic representation of that very thing.
[01:20:52.720 --> 01:20:56.560] So what he's saying here, let me quote this professor.
[01:20:56.560 --> 01:21:06.400] He says, numerous bloodstains scattered throughout the double body image of the HST show that Jesus of the HST was tortured.
[01:21:06.400 --> 01:21:12.720] Bloodstain marks all over the body image, which are consistent with pre-crucifixion flagellation.
[01:21:12.720 --> 01:21:16.000] Bloodstain marks on the head that are consistent with a crown of thorns.
[01:21:16.000 --> 01:21:19.840] Blood marks on the hand and feet that are consistent with crucification.
[01:21:20.320 --> 01:21:34.400] And the bloodstain on the chest that evidences a post-mortem wound that corresponds with the post-mortem spear wound, you know, that famous spear wound in the lower part of his chest that Christ received as described in the Bible.
[01:21:34.400 --> 01:21:43.200] But right, how exactly is he determining that this is actually that as opposed to some sort of artistic impression?
[01:21:43.440 --> 01:21:45.680] Right, because those were the time, right?
[01:21:45.680 --> 01:21:50.160] So he's basically saying it's consistent with what people believed in the 14th century.
[01:21:50.160 --> 01:21:55.040] And looking at a visual pattern and basing it solely on how it looks.
[01:21:55.040 --> 01:22:02.320] And in reality, if you look at the bloodstains on the hair, you know, of the image of Jesus, it doesn't do what blood would do.
[01:22:02.560 --> 01:22:03.840] It's not soaked into the hair.
[01:22:03.840 --> 01:22:06.480] It's kind of like dripping down on top of it.
[01:22:06.480 --> 01:22:08.800] It makes absolutely no sense.
[01:22:08.800 --> 01:22:09.200] Right.
[01:22:09.200 --> 01:22:13.120] And so what is the signature of a blood pattern from a crucifixion?
[01:22:13.120 --> 01:22:18.240] You know, we have almost no physical evidence of actual crucifixions from Roman times.
[01:22:18.240 --> 01:22:19.600] I wasn't aware there was any.
[01:22:19.600 --> 01:22:22.240] Yeah, I mean, we have, I mean, barely any.
[01:22:22.240 --> 01:22:28.320] There's a couple of skeletons with hole, with wounds that could be consistent with an iron nail.
[01:22:28.320 --> 01:22:29.200] But that's it.
[01:22:29.200 --> 01:22:29.920] Very, very little.
[01:22:30.760 --> 01:22:37.800] And we have then just second-hand references to that fact that crucifixions occurred.
[01:22:37.800 --> 01:22:41.160] But there's like no description of them or, you know what I mean?
[01:22:41.160 --> 01:22:45.320] Like we don't really know exactly how they worked or what happened.
[01:22:45.320 --> 01:22:54.040] It's just there's just very vague references to them and scant possible evidence, like not even ironclad evidence.
[01:22:54.040 --> 01:22:58.440] This notion that we somehow know what the blood pattern would be is ridiculous.
[01:22:58.440 --> 01:23:02.200] Yeah, it's actually pretty silly when you think about it.
[01:23:02.200 --> 01:23:06.280] You know, that revelation, I'll extend it a little bit with this, Steve.
[01:23:06.920 --> 01:23:09.000] It comes on the heels of another report.
[01:23:09.000 --> 01:23:10.840] I don't know if you read further into that.
[01:23:10.840 --> 01:23:24.280] There was a study done on the shroud that apparently took place back in 2022, results released afterwards, in which they used something other than radiocarbon dating to try to determine the age of the shroud.
[01:23:24.280 --> 01:23:25.400] And it's called what?
[01:23:25.400 --> 01:23:27.880] Wide-angle X-ray scattering.
[01:23:28.520 --> 01:23:36.040] And according, there was a professor, his name is Professor Liberato DiCaro from the National Research Council of Italy.
[01:23:36.040 --> 01:23:46.120] He led the team that did this particular research, and here's what he said: It's a sort of radiography, similar to the type of scan that you would do on a bone to see if there's a fracture.
[01:23:46.120 --> 01:23:51.320] But this x-ray penetrates the material very deeply to analyze it at a microscopic level.
[01:23:51.320 --> 01:23:53.880] Over time, the structure of the material degrades.
[01:23:53.880 --> 01:23:57.640] We can tell from that how much time has passed and therefore the date of the object.
[01:23:57.640 --> 01:24:03.720] And when you use that technique, according to him, 2,000 years old is what they're saying there.
[01:24:04.040 --> 01:24:07.320] I don't know enough about wide-angle X-ray scattering.
[01:24:07.320 --> 01:24:11.000] I didn't have really a chance to look so deep into that.
[01:24:11.240 --> 01:24:24.720] They say they're going to welcome other labs around the world to try to also replicate this finding because the technique is a non-destructive technique, he says, which is a huge advantage.
[01:24:24.720 --> 01:24:28.960] It means the test could be conducted again by another laboratory.
[01:24:28.960 --> 01:24:36.720] But, you know, again, where there are so many other lines of evidence, right?
[01:24:36.720 --> 01:24:42.640] All those converging lines of evidence pointing to the mid-14th century, and then you have this kind of one outlier.
[01:24:43.040 --> 01:24:50.240] I would be shocked if that at all led to any positive evidence saying that there's a misdating via the radiocarbon dating.
[01:24:50.240 --> 01:25:01.200] But they're saying that it could be because they're saying this thing got passed around so much that the cloth got contaminated and therefore you can't go with the radiocarbon dating.
[01:25:01.200 --> 01:25:02.640] Is that even a thing?
[01:25:02.640 --> 01:25:08.160] I mean, if something is passed around, does it have sort of a contamination factor that you have to account?
[01:25:08.320 --> 01:25:09.440] That's when you're yes.
[01:25:09.440 --> 01:25:16.720] So essentially, that method, the x-ray scattering method, has not been validated for textiles.
[01:25:16.720 --> 01:25:19.840] And it's not generally accepted by the scientific community.
[01:25:19.840 --> 01:25:24.880] So these results have been basically ignored, I mean, or rejected for that reason.
[01:25:24.880 --> 01:25:30.800] And part of the reason is because of the effects of contamination that can have on that.
[01:25:31.120 --> 01:25:33.120] But it's just basically nonsense.
[01:25:33.120 --> 01:25:34.400] It's not a validated method.
[01:25:34.400 --> 01:25:35.440] It's not generally accepted.
[01:25:35.440 --> 01:25:36.560] We don't know if it's accurate or not.
[01:25:36.560 --> 01:25:37.920] It hasn't been replicated.
[01:25:37.920 --> 01:25:43.680] Whereas the carbon dating has been proven, has been validated for this kind of material.
[01:25:43.680 --> 01:25:51.280] There were three pieces sent to three independent labs, and all three came up with the same date or overlapping date ranges.
[01:25:51.280 --> 01:25:54.080] So that's independent validation.
[01:25:54.080 --> 01:25:58.160] This is an unvalidated one-off that's essentially meaningless.
[01:25:58.160 --> 01:25:58.560] Yeah, right.
[01:25:58.560 --> 01:26:01.400] And a total outlier and not and not reliable at all.
[01:26:01.720 --> 01:26:21.560] But I think the point of the whole thing is that analysis continues into the shroud of Turin, obviously by the people who are the proponents, who are, you know, who are the believers, looking for any sort of strand that they can attach to to say that this thing is valid, where evidence points to the contrary.
[01:26:21.560 --> 01:26:26.920] Yeah, there was a subset of people who are shroud scientists who are trying to validate it.
[01:26:26.920 --> 01:26:28.760] They're not trying to figure out the truth.
[01:26:28.760 --> 01:26:30.360] They're trying to validate it.
[01:26:30.360 --> 01:26:31.880] Starting with the conclusion and working back.
[01:26:32.200 --> 01:26:33.400] Classic pseudoscience.
[01:26:33.400 --> 01:26:34.680] Absolutely classic pseudoscience.
[01:26:34.680 --> 01:26:34.840] All right.
[01:26:34.840 --> 01:26:35.400] Thanks, Evan.
[01:26:35.400 --> 01:26:35.560] Yep.
[01:26:35.720 --> 01:26:37.560] Jay, it's who's that noisy time.
[01:26:37.560 --> 01:26:38.200] All right, guys.
[01:26:38.200 --> 01:26:40.040] Last week I played This Noisy.
[01:27:01.320 --> 01:27:01.800] Okay.
[01:27:02.120 --> 01:27:03.400] You guys have any ideas?
[01:27:03.720 --> 01:27:05.880] Yeah, it's R2 on acid.
[01:27:07.240 --> 01:27:11.800] It's the audio track from Steamboat Willie back in 1925.
[01:27:12.120 --> 01:27:15.800] Somebody did write in something about R2D2 being on drugs, I think.
[01:27:15.800 --> 01:27:16.360] Or drunk.
[01:27:16.680 --> 01:27:17.960] He was drunk, you know.
[01:27:18.360 --> 01:27:19.480] Hallucinating.
[01:27:19.480 --> 01:27:21.000] All right, so let's go through this.
[01:27:21.480 --> 01:27:25.400] Michael Blaney wrote in and said, Is this a composition made by an AI?
[01:27:25.400 --> 01:27:26.760] And I thought that was a good guess.
[01:27:26.760 --> 01:27:28.120] I thought that was pretty cool.
[01:27:28.120 --> 01:27:31.880] You know, actually, AI makes much, much better compositions than that.
[01:27:32.120 --> 01:27:37.800] As far as it being cohesive, you know, I don't want to put down what you just heard because it is its own thing.
[01:27:37.800 --> 01:27:40.760] But anyway, no, it's not done by AI.
[01:27:40.760 --> 01:27:46.320] Next listener wrote in Alex Hall, and Alex said, hello, my guest, for episode 1004.
[01:27:46.560 --> 01:27:47.520] Who's that noisy?
[01:27:48.000 --> 01:27:53.120] Is a Garkline, also called a sopranoismo.
[01:27:53.120 --> 01:27:55.040] Sopranisme, ismo.
[01:27:55.040 --> 01:27:56.080] Yeah, sopranismo.
[01:27:56.080 --> 01:27:56.960] Soprenissimo.
[01:27:56.960 --> 01:27:58.000] Sopranisimo, sure.
[01:27:58.000 --> 01:28:01.120] It's a recorder played as part of a larger orchestra.
[01:28:01.120 --> 01:28:02.800] I thought that was a very interesting guess.
[01:28:02.800 --> 01:28:03.440] I looked it up.
[01:28:03.440 --> 01:28:04.720] I saw some pictures of it.
[01:28:04.960 --> 01:28:08.720] It's definitely not that, but I still think that that was a cool guess.
[01:28:08.720 --> 01:28:20.480] I got like, and then the rest of the responses I got were people writing in jokes, basically because of how odd this whole thing was, which I laughed at a lot of them.
[01:28:20.480 --> 01:28:28.160] So I'm just going to cut to it because nobody guessed it, and I didn't really think it would be hard because of how unique that sound is.
[01:28:28.160 --> 01:28:34.400] So what this actually is, guys, it is an instrument called an orchestrion.
[01:28:34.400 --> 01:28:36.080] It's a particular kind of instrument.
[01:28:36.080 --> 01:28:38.000] There's lots of orchestrons out there.
[01:28:38.000 --> 01:28:39.440] Orchestra, you know, I could be saying.
[01:28:39.760 --> 01:28:40.640] Orchestrion.
[01:28:40.640 --> 01:28:41.360] Orchestrion.
[01:28:41.600 --> 01:28:42.240] Yes.
[01:28:42.240 --> 01:28:42.560] Yeah.
[01:28:42.560 --> 01:28:43.440] Orchestrion.
[01:28:43.440 --> 01:28:44.000] That sounds better.
[01:28:44.000 --> 01:28:45.040] I like that way you said it.
[01:28:45.040 --> 01:28:48.800] But this one in particular was created by someone that used to be in the band Devo.
[01:28:48.800 --> 01:28:49.840] You guys remember that band?
[01:28:49.840 --> 01:28:50.160] Yeah.
[01:28:50.160 --> 01:28:50.480] Sure.
[01:28:50.640 --> 01:28:51.040] Devo.
[01:28:51.120 --> 01:28:53.600] Yeah, the guy's name is Mark Mothersbow.
[01:28:53.600 --> 01:28:54.880] Oh, Mark Mothersbow.
[01:28:54.880 --> 01:28:55.120] Yeah.
[01:28:55.120 --> 01:29:01.760] And he created this instrument, which is in this classification of instruments.
[01:29:01.760 --> 01:29:05.440] So let me tell you what the definition of this is.
[01:29:05.600 --> 01:29:08.000] So orchestrion, you said, Kara?
[01:29:08.320 --> 01:29:09.520] I said orchestrion.
[01:29:10.000 --> 01:29:10.800] That's just a guess.
[01:29:10.800 --> 01:29:19.520] It's a generic name for a machine that plays music and is designed to sound like an orchestra or a band, you know, or just a group of various noise makers.
[01:29:19.520 --> 01:29:21.120] It could be a lot of different things.
[01:29:21.120 --> 01:29:24.160] This one in particular, this is the best way I can describe it.
[01:29:24.160 --> 01:29:30.440] Imagine you take a Dalek and you make it like three times as tall as that, and you put a bunch of horns on its head.
[01:29:30.440 --> 01:29:35.320] And then every place where there's one of those like silver balls, you put a weird instrument.
[01:29:29.840 --> 01:29:36.680] So that's this thing.
[01:29:37.160 --> 01:29:38.840] And that is the sound that it makes.
[01:29:38.840 --> 01:29:40.520] I'm not sure how you trigger it.
[01:29:40.520 --> 01:29:46.040] I'm not sure if it just does it when you come near or if it's always doing this, but take a listen again.
[01:29:54.520 --> 01:29:58.600] Yeah, so he's made several of these, and this one is considered to be his best.
[01:29:58.600 --> 01:30:00.280] So I thought that was really interesting.
[01:30:00.920 --> 01:30:02.360] I have a question for Kara.
[01:30:02.360 --> 01:30:02.920] Yes.
[01:30:02.920 --> 01:30:04.680] Do you know what a Dalek is?
[01:30:04.680 --> 01:30:05.560] I know what a Dalek.
[01:30:05.960 --> 01:30:07.160] A Dalek is.
[01:30:07.160 --> 01:30:08.200] Yeah, from Doctor Who?
[01:30:08.520 --> 01:30:09.000] Yes.
[01:30:09.000 --> 01:30:09.880] Okay, good, good.
[01:30:10.440 --> 01:30:12.040] Because that's what Jay was referring to.
[01:30:12.040 --> 01:30:12.760] Yeah, no, I knew.
[01:30:12.760 --> 01:30:18.440] With all the girls, I blame an ex-boyfriend for that.
[01:30:19.160 --> 01:30:20.200] Was he a whoovian?
[01:30:20.280 --> 01:30:21.000] The sex boyfriend?
[01:30:21.000 --> 01:30:25.400] I like him or an ex-did you say the sex boyfriend?
[01:30:25.400 --> 01:30:28.600] No, and a whovian.
[01:30:28.600 --> 01:30:30.760] All right, guys, I have a new noisy for this week.
[01:30:30.760 --> 01:30:33.960] This noisy was sent in by a listener named Wes High.
[01:30:34.280 --> 01:30:35.320] Hi, hi.
[01:30:52.760 --> 01:30:53.640] The tension.
[01:30:53.640 --> 01:30:55.080] I know, I know, right?
[01:30:55.320 --> 01:30:57.400] That could be about one million things.
[01:30:57.400 --> 01:31:00.680] And that's why Who's That Noisy is so interesting, Steve.
[01:31:00.680 --> 01:31:08.280] So if you guys think you know what the noisy is this week, or if you heard something cool, email me at WTN at the skepticsguy.org.
[01:31:08.280 --> 01:31:14.760] Steve, today's Wednesday as we record this, and people will be listening to this show on Saturday at the earliest, right?
[01:31:14.760 --> 01:31:22.880] So, by the time you hear this, everybody in the world will be able to buy tickets for Noticon because we released all the tickets to the public today.
[01:31:22.880 --> 01:31:27.040] Let me give you a quick rundown of what's going on with Noticon 2025.
[01:31:27.040 --> 01:31:30.720] So, it'll be the weekend of May 15th, 16th, and 17th.
[01:31:30.720 --> 01:31:33.040] That's Thursday, Friday, and Saturday.
[01:31:33.040 --> 01:31:35.040] It's going to be in White Plains, New York.
[01:31:35.040 --> 01:31:39.600] Like last time, the hotel went under a complete renovation.
[01:31:39.600 --> 01:31:43.520] So, it'll be the same hotel, but it'll be a lot nicer than the last time we were there.
[01:31:43.520 --> 01:31:49.360] And, you know, it was a nice hotel when we were there, but I guess hotels have to spend the money to do this from time to time.
[01:31:49.360 --> 01:32:02.480] Anyway, we will be having a VIP meet and greet on Thursday night, and we will also be having a board game session called the Board Room, which will be like basically the very first thing that we do at the conference.
[01:32:02.480 --> 01:32:07.680] So, you can pick up your badges on Thursday, go to the board meeting, and go to the VIP.
[01:32:08.560 --> 01:32:10.720] You can go to both of those events on Thursday.
[01:32:10.720 --> 01:32:22.240] Then, all day Friday and Friday night, and all day Saturday and Saturday night, we have our standard programming with things that you'll remember from last time if you were there, and lots of new stuff that we are planning as well.
[01:32:22.240 --> 01:32:27.120] There will be a sing-along on Saturday night, and this year's theme is the Beatles.
[01:32:28.320 --> 01:32:29.120] Fun!
[01:32:29.120 --> 01:32:33.760] So, we'll be playing other stuff too, but the Beatles is going to be a major theme.
[01:32:33.760 --> 01:32:39.520] So, we decided to call this year's show, it's Notacon 2025.
[01:32:39.920 --> 01:32:41.200] You know what the rest of it is?
[01:32:41.200 --> 01:32:42.800] Skeptical Mystery Tour.
[01:32:42.800 --> 01:32:44.880] Yes, it's the Skeptical Mystery Tour.
[01:32:45.680 --> 01:32:50.560] And that's all I'm going to say because there is a puzzle this year, and I don't want to give away too much.
[01:32:50.560 --> 01:32:51.840] So, that's all I'm going to say.
[01:32:53.200 --> 01:32:59.960] So, if you're interested, you can go to notaconcon.com.
[01:32:59.960 --> 01:33:02.040] And I'll say that again slowly.
[01:32:59.600 --> 01:33:06.600] And as I say it, remember, Ian is the one that selected that URL.
[01:33:07.240 --> 01:33:11.560] NotaconCon.com.
[01:33:11.880 --> 01:33:13.320] My God.
[01:33:13.640 --> 01:33:17.400] So anyway, go to that URL or you can go to the SGU homepage.
[01:33:17.400 --> 01:33:19.160] There'll be a link on there as well.
[01:33:19.160 --> 01:33:21.640] You could buy your tickets for all these different events.
[01:33:21.640 --> 01:33:24.600] Basically, everything will be like last year if you went.
[01:33:24.600 --> 01:33:28.840] And if you didn't go, let me give you just a quick idea of what this conference is.
[01:33:28.840 --> 01:33:33.960] So people who like to go to science and skeptical conferences, we've done it for years.
[01:33:33.960 --> 01:33:40.920] And we got a lot of emails from people over the years saying that they really miss like the in-person Nexus.
[01:33:40.920 --> 01:33:45.480] It turns out that what they really, really missed was the social gathering aspect of it.
[01:33:45.480 --> 01:33:51.720] So we decided, you know, we were like, listen, let's create a conference that revolves around socializing.
[01:33:51.720 --> 01:34:01.880] Let's create a conference that just revolves around entertainment and socializing and give people a chance to see each other in person, get together, especially people that live very far away.
[01:34:01.880 --> 01:34:06.280] We have people already that are coming, that are coming from all over the place.
[01:34:06.840 --> 01:34:08.120] I think it's wonderful.
[01:34:08.120 --> 01:34:09.880] This is a socializing event.
[01:34:09.880 --> 01:34:11.720] This is an event where you're going to meet people.
[01:34:11.720 --> 01:34:15.720] If you're an SGU patron, you'll meet people that you chat with on our Discord.
[01:34:15.720 --> 01:34:18.440] If not, you will definitely meet people and make friends.
[01:34:18.440 --> 01:34:23.720] Lots of people commented and gave us wonderful feedback last time saying they just met a lot of great people.
[01:34:23.720 --> 01:34:26.120] So it's just a fun all-around event to go to.
[01:34:26.120 --> 01:34:27.400] We highly recommend it.
[01:34:27.400 --> 01:34:29.240] It was an incredible time last year.
[01:34:29.240 --> 01:34:30.360] And I'm not just saying that.
[01:34:30.360 --> 01:34:32.520] It really was an incredible time.
[01:34:33.480 --> 01:34:37.320] I think it was the best conference that I've ever attended, just from a community sense.
[01:34:37.320 --> 01:34:38.520] It was wonderful.
[01:34:38.520 --> 01:34:43.800] So, anyway, go to nataconcon.com if you want to check out more details and get tickets.
[01:34:43.800 --> 01:34:45.200] We have two more shows, guys.
[01:34:45.200 --> 01:34:47.120] We have shows that are coming up pretty soon.
[01:34:47.120 --> 01:34:49.760] This is happening the weekend of December 7th.
[01:34:44.840 --> 01:34:52.160] So, we have two shows on December 7th.
[01:34:52.240 --> 01:34:57.040] We have a DC private show, Washington, D.C., and we have a Washington, D.C.
[01:34:57.120 --> 01:34:58.480] extravaganza.
[01:34:58.800 --> 01:35:06.320] The private show is a recording of our live podcast and then an extra hour of fun audience interaction that only you will see and hear.
[01:35:06.320 --> 01:35:07.840] It's unique every time.
[01:35:07.840 --> 01:35:09.120] It's always a good time.
[01:35:09.120 --> 01:35:11.520] George Hobb will, of course, be with us on that.
[01:35:11.520 --> 01:35:15.200] So, please do go get tickets on the SGU homepage.
[01:35:15.200 --> 01:35:19.040] That's SGU, that's the skepticsguide.org.
[01:35:19.040 --> 01:35:25.440] And the extravaganza, if you don't know what that is, we have a stage show that we've been doing for like the last about 10 years.
[01:35:25.440 --> 01:35:28.800] This is a show where we teach you about how your brain can fool you.
[01:35:28.800 --> 01:35:30.880] You can't trust your brain, can't trust your senses.
[01:35:30.880 --> 01:35:33.680] There's a lot of improv comedy bits and funny stuff going on.
[01:35:33.680 --> 01:35:36.960] And we have a wonderful time interacting with the audience.
[01:35:36.960 --> 01:35:42.160] And if you're interested, you can also get those tickets at the skepticsguide.org homepage.
[01:35:42.160 --> 01:35:43.360] Thank you, Jay.
[01:35:43.360 --> 01:35:45.120] Let's do a quick email.
[01:35:45.120 --> 01:35:49.440] This email comes from Stewart from Washington, UK.
[01:35:49.440 --> 01:35:52.240] And he writes simply, saw this and thought of you.
[01:35:52.240 --> 01:35:58.400] And then he gives a link to a story about Toyota's portable hydrogen cartridges.
[01:35:58.400 --> 01:36:08.640] So basically, Toyota is proposing this hydrogen fuel cell car where the hydrogen is kept in these cylinders.
[01:36:08.640 --> 01:36:14.640] They look, they're the shape of like a A battery, but much bigger, obviously.
[01:36:14.640 --> 01:36:20.800] And the idea is that when you go to fill up, you swap these out, right?
[01:36:20.800 --> 01:36:26.000] You pull out the empty one, like a tank, and you put in a full one, and then you're on your way.
[01:36:26.000 --> 01:36:31.640] So it's just quicker, easier, yeah, than filling up your existing tank.
[01:36:29.840 --> 01:36:31.960] Neat.
[01:36:32.280 --> 01:36:45.080] Yeah, so it's neat, but the thing is, it's one of those situations where it's like fixing something that's not really a problem and it's also not the problem with hydrogen fuel cell cars, so it's irrelevant.
[01:36:45.080 --> 01:36:50.200] I don't care how good you make the experience from the refueling perspective.
[01:36:50.440 --> 01:36:58.200] The reason I think there's two big reasons why I think hydrogen fuel cell cars are not going anywhere.
[01:36:58.840 --> 01:37:03.000] One is kind of a temporary problem, the other one is a permanent problem.
[01:37:03.000 --> 01:37:08.280] The temporary problem is that right now we have very little green hydrogen, right?
[01:37:08.280 --> 01:37:14.360] Most of our hydrogen is gray at best, which means it's very carbon dirty.
[01:37:14.360 --> 01:37:21.640] And so there's no advantage basically to using, in fact, there may be a disadvantage to using gray hydrogen to fuel cars.
[01:37:21.640 --> 01:37:29.480] Yes, the car itself may not be releasing CO2, but to get the hydrogen, you had to release a lot of CO2.
[01:37:29.480 --> 01:37:40.200] So until we have a green hydrogen infrastructure, it is pointless to drive a hydrogen fuel cell car from a climate perspective.
[01:37:40.200 --> 01:37:47.320] But the permanent reason, which we've mentioned before, is a matter of physics.
[01:37:47.880 --> 01:37:55.640] The EV batteries are about, and EV cars, they're about 80% efficient in terms of converting energy into forward acceleration.
[01:37:55.640 --> 01:37:59.640] Hydrogen fuel cell cars are about 40% efficient.
[01:37:59.640 --> 01:38:00.680] And that's it.
[01:38:01.080 --> 01:38:02.600] It's half as efficient.
[01:38:02.600 --> 01:38:03.880] And it's not going anywhere.
[01:38:03.880 --> 01:38:07.560] That's just the physics of converting hydrogen into energy.
[01:38:07.560 --> 01:38:08.520] Why would we do that?
[01:38:08.520 --> 01:38:09.480] Why would we go?
[01:38:09.440 --> 01:38:12.520] You know, you know, basically, the EVs are better.
[01:38:12.560 --> 01:38:14.560] You know, they're twice as efficient.
[01:38:15.120 --> 01:38:19.520] They're always going to have that advantage over the hydrogen fuel cell cars.
[01:38:14.120 --> 01:38:21.280] So, I just don't think it's a good application.
[01:38:21.760 --> 01:38:27.360] And when we do make green hydrogen, we have a lot of other better things to do with it than burn it up in cars.
[01:38:27.360 --> 01:38:28.640] So, it doesn't matter.
[01:38:28.800 --> 01:38:32.880] So, this may be nifty, but it's completely irrelevant, in my opinion.
[01:38:32.880 --> 01:38:37.040] Gonna do a quick name-net logical fallacy as well.
[01:38:37.040 --> 01:38:39.600] This one comes from a listener who didn't leave his name.
[01:38:39.840 --> 01:38:46.240] They write, I've been reading The Art of Thinking Clearly by Rolf Dobelli, and I came across something that I think is questionable.
[01:38:46.240 --> 01:38:54.400] The book is an accumulation of chapters about logical fallacies and issues in clear thinking that is right up your alley.
[01:38:54.400 --> 01:38:58.880] All right, he says that the chapter is about the neglect of probability.
[01:38:58.880 --> 01:39:02.320] He details evidence that people basically discount probabilities.
[01:39:02.320 --> 01:39:18.960] Studies of telling people they have a 50% chance of getting electric shock, those in the study have the same amount of anxiety and fear as those being told they have a 5% chance of getting the shock, and so on, down to 0%, where people's anxieties finally become zero.
[01:39:18.960 --> 01:39:20.400] And then he gives an example of this.
[01:39:20.400 --> 01:39:22.240] The example is: this is a quote now.
[01:39:22.240 --> 01:39:25.520] To test this, let's examine two methods of treating drinking water.
[01:39:25.520 --> 01:39:35.760] Suppose a river has two equally large tributaries: one is treated using method A, which reduces the risk of dying from contaminated water 5% to 2%.
[01:39:35.760 --> 01:39:40.880] The other is treated using method B, which reduces the risk from 1% to 0%.
[01:39:40.880 --> 01:39:43.680] That is, the threat is completely eliminated.
[01:39:43.680 --> 01:39:45.280] So, method A or B.
[01:39:45.280 --> 01:39:54.960] If you think like most people, you will opt for the method B, which is silly because with measure A, 3% fewer people die, and with B, just 1% fewer.
[01:39:54.960 --> 01:39:56.560] Method A is three times as good.
[01:39:56.560 --> 01:39:59.360] The fallacy is called the zero risk bias.
[01:39:59.480 --> 01:40:05.080] And then the emailer writes: it could be that I'm missing something here, but is this not a terrible example?
[01:40:05.080 --> 01:40:07.720] First, I'll be clear: this is a hypothetical thought experiment.
[01:40:07.720 --> 01:40:09.960] Nothing in the world is actually this clear-cut.
[01:40:09.960 --> 01:40:12.280] So let's set that aside for a second.
[01:40:12.280 --> 01:40:19.480] I completely understand that method A reduces the risk by 3%, which is a bigger decrease than method B, which is a 1% decrease.
[01:40:19.480 --> 01:40:24.520] But it is not, but isn't that better to end at 0% than 2%?
[01:40:24.760 --> 01:40:26.840] All right, so two things going on here I want to talk about.
[01:40:26.840 --> 01:40:31.720] One is the fallacy that people ignore probabilities.
[01:40:31.720 --> 01:40:35.400] And this is just how we assess risk.
[01:40:35.400 --> 01:40:39.240] And we tend to be risk-averse.
[01:40:39.240 --> 01:40:42.760] And we tend to think of hazard as if it were risk.
[01:40:43.800 --> 01:40:45.960] So we've talked about hazard versus risk before.
[01:40:46.600 --> 01:40:52.600] Hazard is the shark in a tank, but the risk only exists if you're in the tank with the shark.
[01:40:53.480 --> 01:41:01.000] But people often fear just the potential of risk in a not mathematical or rational way.
[01:41:01.000 --> 01:41:03.480] So those results don't surprise me.
[01:41:04.760 --> 01:41:09.320] If you think there's a 1% chance of getting shocked, you're still going to be fearful.
[01:41:09.320 --> 01:41:13.160] And you're not going to be reassured by the math.
[01:41:13.160 --> 01:41:15.400] This is why people fear flying.
[01:41:15.400 --> 01:41:21.720] And telling them that, you know, how low the risk is doesn't always mitigate that fear.
[01:41:21.720 --> 01:41:29.960] Although, I do want in that research, I wonder, he said, and so on down to zero, but how low did they go?
[01:41:29.960 --> 01:41:35.320] Because what if you were told you had a one in a million chance of getting shocked?
[01:41:35.320 --> 01:41:39.240] I think people would, at some point, you start to treat it as zero, right?
[01:41:39.560 --> 01:41:40.200] Yes.
[01:41:40.200 --> 01:41:50.000] Which is why, like, when you're doing cognitive behavioral therapy for a fear of flying, you have to put the risk in such a context that it's functionally zero.
[01:41:44.680 --> 01:42:02.640] It's like, for example, I happen to know that one statistic they use is: how long do you think you would have to fly every single day before you had a 50-50 chance of dying in a plane crash?
[01:42:02.640 --> 01:42:03.680] It's like hundreds of years.
[01:42:03.680 --> 01:42:05.200] It's 500 years.
[01:42:05.200 --> 01:42:11.200] If you took a plane flight every single day for 500 years, you would have a 50-50 chance of dying in a plane crash.
[01:42:11.200 --> 01:42:15.840] So that kind of statistic does reassure people.
[01:42:15.840 --> 01:42:22.240] At least they have something cognitively to hang on to to say you just shouldn't worry about that level of risk.
[01:42:22.560 --> 01:42:27.280] But I do think it has to be pretty close to zero before people are reassured by that.
[01:42:27.280 --> 01:42:31.360] If you say, yeah, there's a 1% chance you're going to die, people are not very reassured.
[01:42:31.360 --> 01:42:35.840] And that's partly rational, you know, depending on how bad the outcome is.
[01:42:35.840 --> 01:42:40.960] But we do not evaluate risk mathematically or rationally.
[01:42:41.200 --> 01:42:42.800] That much is true.
[01:42:42.800 --> 01:42:46.880] The second thing, I sort of get where he's coming from and sort of criticizing that example.
[01:42:46.880 --> 01:42:48.160] It's just a bad example.
[01:42:48.160 --> 01:42:51.280] I'm not sure what the guy is actually saying.
[01:42:51.280 --> 01:42:55.040] You know, if you treat one tributary, the risk goes from five to two.
[01:42:55.040 --> 01:42:57.040] If you treat the other one, it goes from one to zero.
[01:42:57.040 --> 01:42:58.400] What does that mean?
[01:42:59.680 --> 01:43:02.240] How is the starting risk changing?
[01:43:03.920 --> 01:43:09.120] I think this is how I'm trying now to sort of retcon it to make it make sense.
[01:43:09.120 --> 01:43:11.040] He's saying there's two major tributaries.
[01:43:11.120 --> 01:43:16.880] I think he's saying one, like, let's assume that one tributary is going to city A.
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[01:46:14.640 --> 01:46:17.520] And in that tributary, the risk is 5%.
[01:46:17.840 --> 01:46:22.880] The other tributary is going to City B, and then that one has a risk of 1%.
[01:46:22.880 --> 01:46:25.280] You can only treat one of these two tributaries.
[01:46:25.280 --> 01:46:30.240] Are you going to take the risk down from 5 to 2, or are you going to take it down from 1 to 0?
[01:46:30.720 --> 01:46:34.400] If you reframe it that way, then of course you go 5 to 2.
[01:46:34.400 --> 01:46:36.960] Yeah, you go 5 to 2 because it's a 3% decrease.
[01:46:36.960 --> 01:46:38.480] But there's zero risk bias.
[01:46:38.640 --> 01:46:41.520] People say, well, going down to zero is better because it's zero risk.
[01:46:41.520 --> 01:46:44.400] They're looking at the end result and not the decrease.
[01:46:44.400 --> 01:46:45.600] So that makes sense.
[01:46:45.600 --> 01:46:54.320] Now, of course, all of these ethical thought experiments are massively contrived because they're trying to isolate a variable to see how people behave, like the trolley experiment.
[01:46:54.320 --> 01:46:57.440] So, yeah, so yes, don't worry about the fact that it's contrived.
[01:46:57.440 --> 01:47:02.680] But you have, I do think the description that he's quoting is bad, it doesn't really make sense.
[01:46:59.840 --> 01:47:06.440] And so, you know, that's how I would retcon it to make it make sense.
[01:47:06.760 --> 01:47:15.320] And in that, I think that's the context in which the point that they're making is people are more likely to look at the second number than the change.
[01:47:15.320 --> 01:47:21.880] Getting down to zero is better than getting down to two, rather than thinking a 3% decrease is better than a 1% decrease.
[01:47:21.880 --> 01:47:23.720] But these are just cognitive biases.
[01:47:23.720 --> 01:47:30.280] These are the ways in which our brains do not process information in a completely logical and unbiased way.
[01:47:30.600 --> 01:47:35.320] All right, guys, let's move on to science or fiction.
[01:47:37.240 --> 01:47:42.440] It's time for science or fiction.
[01:47:46.600 --> 01:47:55.560] Each week, I come up with three science news items or facts two real and one fake, and then I challenge my panel of skeptics to tell me which one is the fake.
[01:47:55.720 --> 01:48:04.680] By the way, we were talking about the collective names for skeptics, and one of our listeners pointed out that every week I refer to you guys as a panel of skeptics, so maybe that's what we should go with.
[01:48:05.000 --> 01:48:05.640] I like it.
[01:48:05.640 --> 01:48:05.960] Yeah.
[01:48:05.960 --> 01:48:06.760] I'm used to it.
[01:48:06.760 --> 01:48:07.080] Yeah.
[01:48:07.080 --> 01:48:07.400] Yeah.
[01:48:07.560 --> 01:48:08.120] Very comfortable.
[01:48:08.840 --> 01:48:09.640] All right, here we go.
[01:48:09.640 --> 01:48:21.080] Item number one: a new study finds that adrenaline auto injectors are like EpiPens are not effective in preventing death due to allergic anaphylaxis.
[01:48:21.080 --> 01:48:31.160] I number two, a recent review finds that atmospheric mercury pollution has increased by 20% in North America from 2005 to 2020.
[01:48:31.480 --> 01:48:42.760] And item number three, researchers find evidence that persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID.
[01:48:43.320 --> 01:48:44.800] I think Bob wants to go first.
[01:48:44.440 --> 01:48:47.200] You were going to choose him first, anyways, I think.
[01:48:47.200 --> 01:48:47.760] Nope.
[01:48:44.600 --> 01:48:50.080] I did just use the noise method.
[01:48:51.680 --> 01:48:53.200] We hate that method.
[01:48:53.840 --> 01:48:55.520] I've been so good.
[01:48:56.480 --> 01:48:58.320] Got to keep you on your toes.
[01:48:58.320 --> 01:48:58.880] Yeah.
[01:48:59.200 --> 01:49:00.000] Let's read these.
[01:49:00.000 --> 01:49:03.040] They did not suffuse into my cortical tissues.
[01:49:03.440 --> 01:49:11.600] A new study finds that adrenaline auto injectors are not effective in preventing death due to allergic anaphylaxis.
[01:49:11.920 --> 01:49:12.320] What?
[01:49:12.640 --> 01:49:17.360] So is it a problem with the auto injectors or is it a problem with the adrenaline?
[01:49:17.360 --> 01:49:18.240] Allergic.
[01:49:18.400 --> 01:49:21.280] Can you elaborate on anaphylaxis again?
[01:49:21.280 --> 01:49:26.000] Anaphylaxis is an allergic reaction where your throat closes up and you can't breathe.
[01:49:26.000 --> 01:49:26.800] The bee sting.
[01:49:26.800 --> 01:49:27.040] Yeah.
[01:49:27.040 --> 01:49:27.760] It's not fun.
[01:49:27.760 --> 01:49:28.000] Right.
[01:49:28.000 --> 01:49:31.760] And the adrenaline auto injectors, again, these are like the EpiPens, right?
[01:49:31.760 --> 01:49:32.160] Yeah.
[01:49:32.320 --> 01:49:33.680] It doesn't make a lot of sense to me.
[01:49:33.680 --> 01:49:34.080] Let's see.
[01:49:34.160 --> 01:49:35.200] Let's try to.
[01:49:35.520 --> 01:49:42.320] A recent review finds that atmospheric mercury pollution has increased by 20% in North America.
[01:49:42.640 --> 01:49:44.160] 2005 to 220.
[01:49:44.160 --> 01:49:44.880] What would cause?
[01:49:44.880 --> 01:49:46.240] I mean, is that even a thing?
[01:49:46.240 --> 01:49:48.720] Atmospheric mercury pollution?
[01:49:48.720 --> 01:49:52.560] I can't really remember hearing about mercury pollution.
[01:49:52.560 --> 01:49:54.160] That sounds nasty.
[01:49:54.160 --> 01:49:56.960] And now you're telling me it may have increased.
[01:49:56.960 --> 01:49:58.160] And I have no idea.
[01:49:58.160 --> 01:49:59.680] Researchers find, let's go to three.
[01:49:59.680 --> 01:50:09.760] Researchers find that evidence, find evidence that persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID.
[01:50:09.760 --> 01:50:14.000] So a persistent viral infection with SARS-CoV-2.
[01:50:14.320 --> 01:50:15.760] So do you understand what that's saying?
[01:50:15.760 --> 01:50:17.840] I'm not getting SARS-SARS-COVID.
[01:50:18.000 --> 01:50:20.080] I mean, they continue to test positive even though they don't have to.
[01:50:20.240 --> 01:50:23.040] Yeah, so SARS-CoV-2 is the virus that causes COVID, right?
[01:50:23.040 --> 01:50:38.840] So you have COVID, you were infected with SARS-CoV-2, a year later, you have long COVID symptoms, and you know, you have actual still have virus in you at that time rather than it being due to something else, you know, that's causing the long COVID.
[01:50:39.000 --> 01:50:42.760] One and two are kind of annoying, I'll just go with SARS-CoV-2.
[01:50:42.760 --> 01:50:44.920] Oh, wait, no, I think that's science.
[01:50:45.240 --> 01:50:46.360] So I'm doing the thing I did.
[01:50:46.520 --> 01:50:47.160] I gotta flip this.
[01:50:47.480 --> 01:50:48.200] What time is it?
[01:50:48.200 --> 01:50:50.040] It really feels like it's two in the morning to be fine.
[01:50:50.520 --> 01:50:51.560] We are on the planet, Bob.
[01:50:52.040 --> 01:50:55.800] I'm gonna go with the adrenaline auto injectors and anaphylaxis fiction.
[01:50:55.800 --> 01:50:56.840] Okay, Kara.
[01:50:56.840 --> 01:51:00.760] Yeah, I think that the long COVID one might be science.
[01:51:00.760 --> 01:51:07.400] I think we see that sometimes with Ebola, like it stayed in people's eyeballs sometimes long after infection.
[01:51:07.800 --> 01:51:09.800] Sort of a scary sentence that was.
[01:51:09.800 --> 01:51:10.680] I know.
[01:51:11.000 --> 01:51:18.520] But definitely, I think there's precedent here that there are like reservoirs for viruses long after they cause any symptoms.
[01:51:18.520 --> 01:51:31.320] So then it's between the basically the EpiPens not being effective in preventing death versus atmospheric mercury, mercury increasing by 20%.
[01:51:31.320 --> 01:51:36.200] Like the EpiPen one feels obvious, which is why I think it might be science.
[01:51:36.200 --> 01:51:39.800] And the mercury one, like what causes atmospheric mercury periods?
[01:51:39.880 --> 01:51:40.680] Yeah, good question.
[01:51:40.680 --> 01:51:43.960] Yeah, and I feel like we've known mercury is really bad for a long time.
[01:51:43.960 --> 01:51:49.480] So maybe we've actually done a good job at not having more mercury in the atmosphere.
[01:51:49.480 --> 01:51:50.520] I don't know.
[01:51:50.520 --> 01:51:53.000] So I think I'm going to switch gears.
[01:51:53.240 --> 01:52:08.360] The only thing I can think of is maybe EpiPens are really good at reducing symptoms, but if somebody is going to have sudden and intense anaphylaxis and it's going to cause death, like maybe there needs to be another mechanism there.
[01:52:08.360 --> 01:52:10.920] Or maybe they don't work fast enough or something like that.
[01:52:10.920 --> 01:52:12.200] I'm not really sure.
[01:52:12.440 --> 01:52:13.560] There's got to be something there.
[01:52:13.560 --> 01:52:17.040] So, yeah, I think I'll go with the mercury being the fiction.
[01:52:17.040 --> 01:52:17.920] Okay, Jay.
[01:52:17.920 --> 01:52:19.600] Yeah, I'm kind of agreeing with Kara.
[01:52:19.600 --> 01:52:26.640] Like, when I first read this, I'm like, well, so yeah, North America is Mexico, U.S., and Canada.
[01:52:26.640 --> 01:52:31.280] So I would think that we have government oversight, regulations, and all that.
[01:52:31.280 --> 01:52:36.480] If anything, it's got to have gone down, not up, right?
[01:52:36.480 --> 01:52:38.320] I just, this one just seems wrong to me.
[01:52:38.320 --> 01:52:45.920] And I think if there was an increase, there would be health effects and we'd be hearing about it much more than nothing, right?
[01:52:45.920 --> 01:52:47.440] So I think that one is fiction.
[01:52:47.440 --> 01:52:48.640] Okay, and Evan?
[01:52:48.640 --> 01:52:51.040] I think I'm inclined to agree.
[01:52:51.040 --> 01:52:57.440] The one about the EpiPen, though, not effective in preventing death due to allergic anaphylaxis.
[01:52:57.440 --> 01:52:58.640] I was thinking about that one.
[01:52:58.640 --> 01:53:03.680] Maybe not if it doesn't prevent death, it slows the death that does come on.
[01:53:03.680 --> 01:53:07.200] So you can get to a hospital and they can give you other drugs in time, maybe?
[01:53:07.200 --> 01:53:09.760] Because I can't imagine these things are totally useless.
[01:53:09.760 --> 01:53:10.400] Wait, right?
[01:53:10.640 --> 01:53:13.280] I mean, otherwise, why do we have them at all?
[01:53:13.280 --> 01:53:15.120] Did you say it slows death?
[01:53:15.120 --> 01:53:16.800] Well, in preventing death.
[01:53:16.960 --> 01:53:19.520] Slows the time in which you will die, right?
[01:53:19.520 --> 01:53:25.040] In other words, if you're going to die if you get stung or whatever, in whatever, an hour or something.
[01:53:25.040 --> 01:53:30.640] But maybe the EpiPen, instead of preventing the death from happening, it prolongs that time to four hours or something.
[01:53:30.720 --> 01:53:32.320] Well, that's terrifying.
[01:53:32.320 --> 01:53:35.200] Well, but it gives more time to respond to it.
[01:53:35.360 --> 01:53:35.680] I don't know.
[01:53:35.680 --> 01:53:40.320] I'm trying to think of why this one might be science.
[01:53:40.320 --> 01:53:42.400] And that's kind of what my brain went to.
[01:53:42.400 --> 01:53:47.120] And then, yeah, so that, and the COVID-1, yeah, I think that one's science.
[01:53:47.120 --> 01:53:49.600] That just leaves the mercury one as the fiction.
[01:53:49.600 --> 01:53:50.960] All right, so you guys all agree on the third one?
[01:53:50.960 --> 01:53:51.680] So we'll start there.
[01:53:51.680 --> 01:53:59.440] Researchers find evidence that persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID.
[01:53:59.440 --> 01:54:04.520] You guys all think this one is science, and this one is science.
[01:54:04.520 --> 01:54:05.720] This is science.
[01:54:05.720 --> 01:54:17.400] Yeah, not too surprising, although it didn't have to be the case because you can get post-infectious syndromes and symptoms without having persistent infection.
[01:54:17.720 --> 01:54:22.840] But it's possible for infections to persist after.
[01:54:23.160 --> 01:54:26.920] It's not like we have a way to eradicate the virus.
[01:54:26.920 --> 01:54:29.480] You basically, the infection runs its course.
[01:54:29.720 --> 01:54:32.760] You know, your immune system eventually deals with it.
[01:54:33.080 --> 01:54:45.640] But it can, it's not implausible that it's, you know, hides away somewhere and is sort of still there in the background causing symptoms an

Prompt 6: Key Takeaways

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

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

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

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

Prompt 7: Segments

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

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

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

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

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

Prompt 8: Media Mentions

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

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

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

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

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

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

Prompt 9: Context Setup

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

Transcript section:
 you will die, right?
[01:53:19.520 --> 01:53:25.040] In other words, if you're going to die if you get stung or whatever, in whatever, an hour or something.
[01:53:25.040 --> 01:53:30.640] But maybe the EpiPen, instead of preventing the death from happening, it prolongs that time to four hours or something.
[01:53:30.720 --> 01:53:32.320] Well, that's terrifying.
[01:53:32.320 --> 01:53:35.200] Well, but it gives more time to respond to it.
[01:53:35.360 --> 01:53:35.680] I don't know.
[01:53:35.680 --> 01:53:40.320] I'm trying to think of why this one might be science.
[01:53:40.320 --> 01:53:42.400] And that's kind of what my brain went to.
[01:53:42.400 --> 01:53:47.120] And then, yeah, so that, and the COVID-1, yeah, I think that one's science.
[01:53:47.120 --> 01:53:49.600] That just leaves the mercury one as the fiction.
[01:53:49.600 --> 01:53:50.960] All right, so you guys all agree on the third one?
[01:53:50.960 --> 01:53:51.680] So we'll start there.
[01:53:51.680 --> 01:53:59.440] Researchers find evidence that persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID.
[01:53:59.440 --> 01:54:04.520] You guys all think this one is science, and this one is science.
[01:54:04.520 --> 01:54:05.720] This is science.
[01:54:05.720 --> 01:54:17.400] Yeah, not too surprising, although it didn't have to be the case because you can get post-infectious syndromes and symptoms without having persistent infection.
[01:54:17.720 --> 01:54:22.840] But it's possible for infections to persist after.
[01:54:23.160 --> 01:54:26.920] It's not like we have a way to eradicate the virus.
[01:54:26.920 --> 01:54:29.480] You basically, the infection runs its course.
[01:54:29.720 --> 01:54:32.760] You know, your immune system eventually deals with it.
[01:54:33.080 --> 01:54:45.640] But it can, it's not implausible that it's, you know, hides away somewhere and is sort of still there in the background causing symptoms and being kept at bay, but not being completely eradicated in some people.
[01:54:45.640 --> 01:54:52.040] Now, this hasn't been proven, which is why I said there's evidence that it may be the case, but it's not definite.
[01:54:52.040 --> 01:55:11.320] What they found was they found a higher rate of proteins, of SARS-CoV-2 proteins, of spike proteins or nucleocapsid proteins, in people who have long COVID versus people who had COVID but don't have long COVID.
[01:55:11.320 --> 01:55:12.040] Does that make sense?
[01:55:12.040 --> 01:55:21.800] So why would it be, why would it exist at a higher rate in people with long COVID, you know, evidence of active virus unless there was a causal relationship there?
[01:55:21.800 --> 01:55:25.960] So it was 43% versus 21%.
[01:55:25.960 --> 01:55:31.880] So it was basically twice as likely to have these viral proteins, if you have long COVID.
[01:55:31.880 --> 01:55:36.680] But that could also just mean that they had a more severe infection or something, it could mean something else, too.
[01:55:36.920 --> 01:55:41.240] But that's a reasonable interpretation that they may have persistent virus.
[01:55:41.240 --> 01:55:42.840] Okay, let's go back to number two.
[01:55:42.840 --> 01:55:50.640] A recent review finds that atmospheric mercury pollution has increased by 20% in North America from 2005 to 2020.
[01:55:50.800 --> 01:55:52.960] Bob, you think this one is science.
[01:55:52.960 --> 01:55:56.320] Everyone else thinks this one is the fiction.
[01:55:56.320 --> 01:56:02.240] I was a little surprised that none of you have any idea where mercury comes from in the atmosphere.
[01:56:02.400 --> 01:56:05.280] Any guesses before I tell you what the sources are?
[01:56:05.840 --> 01:56:07.760] Number one, industrial processes?
[01:56:08.400 --> 01:56:10.560] Industrial processes, what's number one?
[01:56:10.560 --> 01:56:12.960] Coal-fired plants, right?
[01:56:12.960 --> 01:56:13.920] It's in coal.
[01:56:14.240 --> 01:56:16.320] It gets released when you burn coal.
[01:56:16.320 --> 01:56:16.960] What the hell?
[01:56:16.960 --> 01:56:22.160] And mercury exposures is higher among people who live near coal plants.
[01:56:22.640 --> 01:56:35.360] So coal-fired plants is one, residential coal burning or other industrial coal burning, other industrial processes, waste incinerators, mining for mercury, gold, and other metals where mercury might be present.
[01:56:35.360 --> 01:56:37.040] Cement production.
[01:56:37.040 --> 01:56:37.680] Cement production.
[01:56:39.120 --> 01:56:44.080] Because mercury contaminants can be in the stuff, you know.
[01:56:44.080 --> 01:56:48.160] And so anything, and you have to heat it up, and that releases the mercury into the air.
[01:56:48.160 --> 01:56:49.360] So there's lots of sources of it.
[01:56:49.360 --> 01:56:57.040] So the question is: in North America over the last 20 years, this is basically before COVID.
[01:56:57.040 --> 01:57:02.000] Do you think that all of these processes have been increasing or decreasing?
[01:57:02.000 --> 01:57:16.320] And the answer is we didn't know, which is why, one of the reasons why they wanted to track this is sort of a review of the stations that monitor mercury in the atmosphere to kind of answer this question: is what we're doing working?
[01:57:16.320 --> 01:57:19.920] Are we releasing more or less mercury over time?
[01:57:19.920 --> 01:57:23.840] And the results showed that this is the fiction.
[01:57:23.840 --> 01:57:25.880] It's actually decreased by 20%.
[01:57:25.600 --> 01:57:26.120] Uh-oh.
[01:57:26.480 --> 01:57:28.400] So even though it comes from coal.
[01:57:28.400 --> 01:57:28.960] Yeah.
[01:57:28.960 --> 01:57:31.560] Well, we are reducing our coal burning.
[01:57:31.800 --> 01:57:32.120] Yeah.
[01:57:29.840 --> 01:57:33.160] It is coming down.
[01:57:33.560 --> 01:57:34.920] That's why North America specifically.
[01:57:35.160 --> 01:57:36.040] North America is.
[01:57:29.840 --> 01:57:36.920] Yeah, this is not China.
[01:57:37.080 --> 01:57:39.960] You know, I imagine it's increasing over there.
[01:57:40.200 --> 01:58:08.760] In fact, I remember that when we got rid of the thimerosol in vaccines, right, which contains mercury, and yet autism rates continue to rise, some of the proponents of autism is mercury poisoning hypothesis said, well, the reduction of thimerosol in vaccines is being offset by fumes coming from Chinese coal-fired plants.
[01:58:08.760 --> 01:58:10.520] You know, it's like, really, really, dude?
[01:58:10.520 --> 01:58:12.040] That's what you think is going on?
[01:58:12.040 --> 01:58:14.280] It was all just massive special pleading.
[01:58:14.520 --> 01:58:16.520] But anyway, this is good news.
[01:58:16.520 --> 01:58:18.360] You know, it's actually decreasing.
[01:58:18.520 --> 01:58:23.320] We need to decrease it more, you know, because sort of mercury is one of those cumulative toxins.
[01:58:23.960 --> 01:58:30.120] And it is this sort of stated goal of the EPA to have a zero tolerance for any mercury.
[01:58:30.120 --> 01:58:36.760] You know, their goal is complete elimination of mercury contamination in the environment and human exposure to mercury.
[01:58:36.760 --> 01:58:39.880] Like there isn't considered any safe amount.
[01:58:39.880 --> 01:58:40.840] You know what I mean?
[01:58:40.840 --> 01:58:42.680] The goal is always zero.
[01:58:42.680 --> 01:58:44.120] Okay, let's go on to number one.
[01:58:44.120 --> 01:58:50.440] A new study finds that adrenaline auto injectors are not effective in preventing death due to allergic anaphylaxis.
[01:58:50.440 --> 01:58:53.400] This one is unfortunately science.
[01:58:53.400 --> 01:58:57.400] You know, a couple of things that you guys noted is basically correct.
[01:58:57.400 --> 01:59:01.480] It does treat the symptoms of an allergic reaction.
[01:59:01.480 --> 01:59:04.360] It's just not enough to prevent anaphylaxis.
[01:59:04.360 --> 01:59:09.480] So, if you're going to die from anaphylaxis, the EpiPens are not going to stop it from happening.
[01:59:09.560 --> 01:59:13.160] Is it because they need more?
[01:59:13.240 --> 01:59:13.960] The dose is too low.
[01:59:14.120 --> 01:59:19.360] Well, even if it does, Evan, so the other way they looked at this, they looked at this two ways.
[01:59:19.520 --> 01:59:28.560] So, one way is just to look at the rate at which people are dying from anaphylaxis before and after the introduction of EpiPens, and there was no decrease.
[01:59:28.560 --> 01:59:33.680] So, the existence of EpiPens have not decreased death due to anaphylaxis, which is rare.
[01:59:34.560 --> 01:59:41.360] To say that as a baseline, it is pretty rare that you die from anaphylaxis, but it hasn't decreased that rate.
[01:59:41.360 --> 01:59:55.120] And then they also, this is the new bit really, that did studies looking at the amount of adrenaline that you get into, you know, of epinephrine that you get into your blood, like the blood levels after an auto injector.
[01:59:55.120 --> 01:59:59.600] And it does get up to a decent amount, but only very briefly.
[01:59:59.840 --> 02:00:07.920] It does get the blood levels get up to the amount that you need to get it to in order for it to have an effect, but it's there very briefly.
[02:00:07.920 --> 02:00:20.480] And what the evidence shows is in order to prevent death from anaphylaxis, you basically need intravenous epinephrine sustained over time.
[02:00:20.880 --> 02:00:37.040] Yeah, because you need to, if the allergen is still there, yeah, so without IV adrenaline, and of course, if you're giving somebody IV adrenaline, you need to keep that from bottoming out their blood pressure, and other things like support them with fluids and everything.
[02:00:37.040 --> 02:00:46.000] You need to be in a hospital setting getting IV fluids and IV adrenaline in order to prevent death from anaphylaxis, if you're going to die from it.
[02:00:46.000 --> 02:00:49.280] The EpiPens will not stop that from happening.
[02:00:49.440 --> 02:00:52.760] What if you have multiple maps?
[02:00:52.920 --> 02:00:58.160] Yeah, like what if you inject while you're being transported or while you're waiting for EMTs?
[02:00:58.480 --> 02:00:59.800] EMTs can put a line in.
[02:00:59.520 --> 02:01:05.880] It may be like if you have, like, here's 10 of them, like every five minutes, give yourself another one or whatever until you get into the ambulance.
[02:01:06.200 --> 02:01:09.320] I do remember hearing a story like 10 years ago.
[02:01:10.040 --> 02:01:15.000] This is a clinical case of a like 10-year-old girl who's allergic to peanut butter.
[02:01:15.000 --> 02:01:22.680] And she accidentally ate peanut butter, you know, she ate her friend's sandwich or whatever, didn't realize it had peanut butter in it and had an anaphylactic reaction.
[02:01:22.680 --> 02:01:29.240] And her parents were there and they gave her two injections with the EpiPen, but it didn't matter.
[02:01:29.240 --> 02:01:31.880] She died anyway before she can get to the hospital.
[02:01:32.040 --> 02:01:33.400] What a nightmare that would be.
[02:01:33.400 --> 02:01:36.920] But I remember thinking about that, like, really, two EpiPen injections didn't do it.
[02:01:36.920 --> 02:01:40.200] But this is why you would need way more than that.
[02:01:40.200 --> 02:01:47.320] So maybe we need to reformulate them, increase the dose, re-educate people.
[02:01:47.320 --> 02:01:49.080] It's like you really need to have a bunch of these on you.
[02:01:49.080 --> 02:01:51.080] Like one EpiPen is not going to save your life.
[02:01:51.080 --> 02:02:03.240] Or maybe some sort of, you know, not a Dexcom, because that's a different drug, but maybe some sort of like thing that you stab into your arm that like gives a flow for a certain period of time.
[02:02:03.240 --> 02:02:03.800] Something.
[02:02:03.800 --> 02:02:04.120] Yeah.
[02:02:04.360 --> 02:02:05.400] Like a sustained flow.
[02:02:05.400 --> 02:02:13.320] It's just not as easy as just like the you press a button and you get a shot, but you know, something that people can use in the field.
[02:02:13.320 --> 02:02:18.760] You know, and you have definitely, first off, your first thing to do is call an ambulance, right?
[02:02:18.760 --> 02:02:24.680] Because that's your only chance of really surviving is getting into medical care as quickly as possible.
[02:02:25.400 --> 02:02:41.800] But yeah, so I don't know if this is going to lead to a sort of a either we think that's really not worth it, you just got to get to the hospital really fast, or like we have to really reformulate how the dose and the mechanism that these things are given because they're just not working.
[02:02:41.800 --> 02:02:49.600] Again, maybe if it's subfatal, sure, it helps, but it doesn't, it's not good enough.
[02:02:49.600 --> 02:02:54.880] It's not enough to turn a fatal reaction into a non-fatal reaction.
[02:02:54.880 --> 02:03:01.920] Or apparently to help people to get to the hospital, because if that was happening, we would have seen the numbers coming down, and they're not.
[02:03:01.920 --> 02:03:02.880] Yeah, it's not long enough.
[02:03:03.040 --> 02:03:06.240] Well, and maybe people have a false sense of security after they use it.
[02:03:06.240 --> 02:03:08.560] That's that's maybe education as well.
[02:03:08.560 --> 02:03:10.800] Yeah, it may be that people think, I have my EpiPen.
[02:03:10.800 --> 02:03:12.960] I gave myself the EpiPen, so I'm good.
[02:03:12.960 --> 02:03:15.520] And they're not as panicked as they really should be.
[02:03:16.000 --> 02:03:19.600] Or, you know, yeah, that false sense of security can be fatal.
[02:03:19.920 --> 02:03:25.280] So what they need to do is they need to sell EpiPens, and on the EpiPen, it says panic.
[02:03:25.280 --> 02:03:25.840] Yeah.
[02:03:27.760 --> 02:03:30.240] Well, once you use it, it's like call 911 immediately.
[02:03:30.240 --> 02:03:30.480] Yeah.
[02:03:30.480 --> 02:03:36.480] Well, I mean, you know, if you have an EpiPen, you are getting that as a prescription from a doctor who's had a conversation with you about this.
[02:03:36.480 --> 02:03:42.880] And so, yeah, just to make sure that doctors understand how they need to be educating their patients, like what an appropriate reaction is.
[02:03:42.880 --> 02:03:50.880] Like, as soon as you think you may have been exposed to something you're allergic to, you take an auto injector, you call 911.
[02:03:50.880 --> 02:03:52.480] It's an absolute emergency.
[02:03:52.480 --> 02:03:55.760] Do not think that the EpiPens are automatically going to save you.
[02:03:55.760 --> 02:04:01.680] You may need to have multiple on you, or, you know, you may go unconscious, so your friends need to know what to do.
[02:04:01.680 --> 02:04:02.240] Yeah.
[02:04:02.240 --> 02:04:04.800] Yeah, it is a life-threatening emergency.
[02:04:04.800 --> 02:04:04.960] Yeah.
[02:04:05.280 --> 02:04:06.560] EpiPen's not an antidote.
[02:04:06.800 --> 02:04:08.160] It's not, it's not.
[02:04:08.720 --> 02:04:14.320] Not for fatal anaphylaxis, which is just such a scary thing to have to live with.
[02:04:14.320 --> 02:04:15.360] Oh, my goodness.
[02:04:15.360 --> 02:04:20.880] The best thing to do is to figure out how to people how to make people not have allergic reactions.
[02:04:21.440 --> 02:04:22.240] To engineer.
[02:04:22.240 --> 02:04:22.640] Yeah, yeah, yeah.
[02:04:23.440 --> 02:04:23.760] Yeah.
[02:04:24.240 --> 02:04:27.840] We know, like, for some things, you could do allergy shots.
[02:04:27.840 --> 02:04:30.760] You know, they slowly build up immunity to the thing.
[02:04:29.760 --> 02:04:35.160] Because, you know, again, an allergic reaction is different than a regular immune reaction.
[02:04:35.480 --> 02:04:37.960] You know, it's IgE instead of IgG.
[02:04:37.960 --> 02:04:47.960] And so you can build up like through vaccines, and you could build up an IgG reaction to the thing, and those actually block the IgE antibodies.
[02:04:47.960 --> 02:04:49.320] That's how that works.
[02:04:50.360 --> 02:04:54.360] Your regular immune reaction blocks the allergic immune reaction.
[02:04:54.680 --> 02:05:00.600] But of course, you have to build it up very slowly because you'll give people full access if you gave them an actual vaccine without a full dose.
[02:05:00.840 --> 02:05:02.280] But at least they're in the hospital.
[02:05:02.520 --> 02:05:06.040] Well, you get that first dose in the doctor's office, right?
[02:05:06.040 --> 02:05:08.040] And with lots of observation.
[02:05:08.040 --> 02:05:08.280] You pick up that.
[02:05:13.400 --> 02:05:21.640] But also, they're working on genetically engineering peanuts so they don't have the allergen in them, just allergy-free peanuts.
[02:05:21.640 --> 02:05:22.920] That would be massive.
[02:05:22.920 --> 02:05:24.600] Yeah, but what does that mean for peanut butter?
[02:05:24.600 --> 02:05:25.960] Is that going to be like changing it?
[02:05:26.200 --> 02:05:28.520] What if it was slightly less delicious, Bob?
[02:05:28.520 --> 02:05:29.480] Would you be okay with that?
[02:05:29.560 --> 02:05:30.680] Depends how slightly.
[02:05:32.040 --> 02:05:34.040] If lives are saved, Bob, don't lie.
[02:05:34.040 --> 02:05:34.200] Come on.
[02:05:34.760 --> 02:05:36.360] Logically happy.
[02:05:37.800 --> 02:05:39.400] All right, Evan, give us a quote.
[02:05:39.400 --> 02:05:40.600] All right, a quote.
[02:05:41.160 --> 02:05:44.520] I went to ChatGPT for the quote.
[02:05:44.520 --> 02:05:45.480] You know what I typed in?
[02:05:45.480 --> 02:05:50.040] I typed, can you find for me a famous quote about the scientific method?
[02:05:50.040 --> 02:05:51.960] That's what I typed in.
[02:05:51.960 --> 02:05:53.960] And it gave me a result.
[02:05:53.960 --> 02:05:54.920] And here it is.
[02:05:54.920 --> 02:05:57.400] Courtesy of ChatGPT.
[02:05:57.400 --> 02:06:00.840] The important thing is to not stop questioning.
[02:06:00.840 --> 02:06:04.520] Curiosity has its own reason for existing.
[02:06:04.520 --> 02:06:06.600] Albert Einstein.
[02:06:07.240 --> 02:06:08.360] And they added this.
[02:06:08.360 --> 02:06:21.600] They said this quote, or it, this quote reflects the core principle of the scientific method, emphasizing the importance of constant inquiry, curiosity, and the pursuit of knowledge through observation and questioning.
[02:06:21.920 --> 02:06:24.240] That was the complete response I got.
[02:06:24.960 --> 02:06:33.440] So how do you feel knowing that ChatGPT's top pick for you was a quote that after a thousand episodes you didn't find yourself?
[02:06:34.720 --> 02:06:37.360] Well, I did have to go back and see.
[02:06:38.000 --> 02:06:41.520] I couldn't find evidence where we had used it ourselves before.
[02:06:41.520 --> 02:06:45.440] And yeah, this doesn't ring familiar to me, this particular one.
[02:06:45.520 --> 02:06:46.480] Hey, I'm happy.
[02:06:46.480 --> 02:06:48.080] I'm always happy to learn new quotes.
[02:06:48.880 --> 02:06:52.960] What I'm finding says that this quote is attributed to Albert Einstein.
[02:06:53.120 --> 02:06:54.240] Oh, here we go.
[02:06:54.240 --> 02:06:56.000] ChatGPT sending me on a signal.
[02:06:56.160 --> 02:07:04.160] No, I think it seems like it's probably legit, but I don't know how certain it is because why would you say attributed to Albert Einstein?
[02:07:04.160 --> 02:07:05.920] And if there was any doubt.
[02:07:06.160 --> 02:07:06.560] I don't know.
[02:07:06.560 --> 02:07:07.920] I have to look into it a little bit.
[02:07:08.160 --> 02:07:09.840] In general, quotes are a mess.
[02:07:10.160 --> 02:07:14.240] Yeah, but they use that language a lot in etymology research.
[02:07:15.120 --> 02:07:20.560] So I don't know if attributed to, by definition, means that there's not evidence.
[02:07:20.560 --> 02:07:20.960] It doesn't.
[02:07:20.960 --> 02:07:21.360] It doesn't.
[02:07:21.360 --> 02:07:25.280] I'm just saying that that was a little red flag for me when it was stated that way.
[02:07:25.440 --> 02:07:32.400] And then quotes do tend to attach themselves to more famous people than the people who actually said them.
[02:07:32.400 --> 02:07:33.040] Yeah, I know.
[02:07:33.040 --> 02:07:37.120] And when it came back with Albert Einstein, I was, you know, I did have to pause.
[02:07:37.440 --> 02:07:38.320] That's just that.
[02:07:38.320 --> 02:07:42.480] Just Albert Einstein is another little red flag.
[02:07:42.640 --> 02:07:43.120] We'll see.
[02:07:43.760 --> 02:07:45.440] We'll vet it out some more.
[02:07:45.680 --> 02:07:47.840] Cursory vetting seems to hold up.
[02:07:47.840 --> 02:07:50.480] But yeah, I would be a little questioning about it.
[02:07:50.480 --> 02:07:58.240] But it's interesting if you could, you know, if ChatGPT can't give you the, you know, the firm, the definitive answer to that.
[02:07:58.400 --> 02:08:00.760] It gives me wrong answers all the time.
[02:08:00.760 --> 02:08:02.040] Yeah, it does, doesn't it?
[02:08:02.040 --> 02:08:02.360] It does.
[02:07:59.840 --> 02:08:06.680] It's not there yet in terms of you have to vet the answers that it gives you.
[02:08:06.920 --> 02:08:09.240] It also gives me outdated answers.
[02:08:09.240 --> 02:08:10.520] Yeah, I've seen that as well.
[02:08:10.520 --> 02:08:13.400] Like, okay, this was true five years ago, but it's no longer true.
[02:08:13.400 --> 02:08:19.160] Like, especially, like, recently I asked it, what's the percentage of electricity in the U.S.
[02:08:19.160 --> 02:08:22.360] that's generated by solar panels?
[02:08:22.360 --> 02:08:27.240] And it gave me a five-year-old answer, you know, which I happen to know was wrong.
[02:08:27.240 --> 02:08:36.520] It has a problem with hallucinations, and it has a problem with just reflecting what's out there, you know, on the internet, and that may be outdated, you know, what's rising to the top.
[02:08:36.520 --> 02:08:40.360] It's not still a work in progress, I suppose.
[02:08:41.000 --> 02:08:46.280] But we're right on the cusp of these applications transforming medicine.
[02:08:46.600 --> 02:08:50.520] We're developing medical application versions of it.
[02:08:50.520 --> 02:08:53.800] But yeah, it's hard to think of a better use of it.
[02:08:53.800 --> 02:08:55.560] It's perfect use of it.
[02:08:55.560 --> 02:09:04.120] Yeah, you know, filter, go through these hundred thousand studies and tell me, give me a summary of this one little question that I have.
[02:09:04.760 --> 02:09:06.600] I can't wait for that, man.
[02:09:06.920 --> 02:09:10.680] That would otherwise take people 9,000 years collectively to figure out.
[02:09:10.680 --> 02:09:24.280] Well, I mean, from a pragmatic matter, like if I have a specific clinical question, it could take me an hour or two or longer to really find out what the definitive current answer is.
[02:09:24.280 --> 02:09:34.120] If something could give me that answer in two minutes or 20 seconds versus hours, then that's something I could use right at the point of patient care.
[02:09:34.120 --> 02:09:35.880] And I could also do a lot more of that.
[02:09:36.120 --> 02:09:40.920] So, yeah, the potential to improve those kinds of professions is massive.
[02:09:41.080 --> 02:09:50.800] But we have to make sure that the applications are designed to not just spit out regenerated answers that may or may not be true without showing their work and giving citations or whatever.
[02:09:51.120 --> 02:09:56.720] So that's what's happening now: sort of reformulating these applications so that they do do that.
[02:09:56.720 --> 02:10:08.080] They like to actually give you real citations, not made-up citations, and links to that, you know, so that you can verify what it's saying, because you need to be able to do that, to act upon it.
[02:10:08.080 --> 02:10:08.400] All right.
[02:10:08.400 --> 02:10:11.280] Well, anyway, thank you all for joining me this week.
[02:10:11.280 --> 02:10:19.760] You guys have and until next week, this is your Skeptic's Guide to the Universe.
[02:10:19.760 --> 02:10:26.480] Skeptics Guide to the Universe is produced by SGU Productions, dedicated to promoting science and critical thinking.
[02:10:26.480 --> 02:10:31.120] For more information, visit us at theskepticsguide.org.
[02:10:31.120 --> 02:10:35.040] Send your questions to info at the skepticsguide.org.
[02:10:35.040 --> 02:10:45.680] And if you would like to support the show and all the work that we do, go to patreon.com/slash skepticsguide and consider becoming a patron and becoming part of the SGU community.
[02:10:45.680 --> 02:10:49.280] Our listeners and supporters are what make SGU possible.
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Prompt 10: Key Takeaways

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

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

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

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

Prompt 11: Segments

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

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

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

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

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

Prompt 12: Media Mentions

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

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

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

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

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

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

Full Transcript

[00:00:00.400 --> 00:00:11.040] From unsolved mysteries to unexplained phenomena, from comedy gold to relationship fails, Amazon Music's got the most ad-free top podcasts, included with Prime.
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[00:00:16.000 --> 00:00:18.960] Download the Amazon Music app today.
[00:00:26.640 --> 00:00:29.840] You're listening to the Skeptic's Guide to the Universe.
[00:00:29.840 --> 00:00:32.800] Your escape to reality.
[00:00:33.440 --> 00:00:36.240] Hello, and welcome to The Skeptic's Guide to the Universe.
[00:00:36.240 --> 00:00:40.800] Today is Wednesday, October 9th, 2024, and this is your host, Stephen Novella.
[00:00:40.800 --> 00:00:42.640] Joining me this week are Bob Novella.
[00:00:42.640 --> 00:00:43.360] Hey, everybody.
[00:00:43.360 --> 00:00:44.720] Kara Santa Maria.
[00:00:44.720 --> 00:00:45.280] Howdy.
[00:00:45.280 --> 00:00:46.240] Jay Novella.
[00:00:46.240 --> 00:00:46.720] Hey, guys.
[00:00:46.720 --> 00:00:48.160] And Evan Bernstein.
[00:00:48.320 --> 00:00:49.600] Good evening, everyone.
[00:00:49.600 --> 00:00:55.920] So we are cozy up here in Connecticut, but down in Florida, man, they are getting hammered right as we record the show.
[00:00:56.560 --> 00:01:06.960] Yeah, Hurricane Milton is coming ashore, and they're expecting surges of 9 to 12 feet in Tampa and surrounding areas.
[00:01:06.960 --> 00:01:11.760] Plus, it's going to take a long time to cross and drop a ton of rain.
[00:01:11.760 --> 00:01:13.040] It's going to be bad.
[00:01:13.040 --> 00:01:13.920] And it's bad.
[00:01:14.080 --> 00:01:17.680] This is the storm that made a meteorologist cry.
[00:01:18.400 --> 00:01:31.440] Literally, he was tearing up because I think the real thing where it really hit home how extraordinary this is was when they realized that in 12 hours it went from a category one to a category five.
[00:01:32.000 --> 00:01:33.440] That basically doesn't happen.
[00:01:33.760 --> 00:01:34.720] That doesn't happen.
[00:01:35.280 --> 00:01:36.640] I have a friend in the New World.
[00:01:36.880 --> 00:01:42.240] I have a friend staying with me right now because she was supposed to be in Florida for the Europa Clipper launch.
[00:01:42.240 --> 00:01:45.520] The Clipper is in a hangar in Florida right now.
[00:01:45.520 --> 00:01:46.240] Oh my gosh.
[00:01:46.240 --> 00:01:48.720] I hope it's protected well enough from this.
[00:01:48.720 --> 00:01:49.760] We all hope it is.
[00:01:49.760 --> 00:01:51.760] So yeah, she obviously couldn't go.
[00:01:52.000 --> 00:01:56.240] Obviously the launch is being delayed, but I mean, it's really scary.
[00:01:56.240 --> 00:01:58.480] This is scary for a lot of people.
[00:01:58.640 --> 00:02:01.560] And this is on the heels of Helene.
[00:02:01.560 --> 00:02:03.480] Helene, which was like, what, a week ago?
[00:02:03.480 --> 00:02:04.840] How long ago is that now?
[00:02:04.840 --> 00:02:05.720] Oh, two weeks.
[00:02:05.720 --> 00:02:06.440] Yeah, a couple of weeks.
[00:01:59.760 --> 00:02:07.240] Two weeks.
[00:02:07.400 --> 00:02:09.880] They're still finding, you know, recovering.
[00:02:09.880 --> 00:02:11.880] It's still a recovery effort.
[00:02:11.880 --> 00:02:17.560] And not long after, we were talking about a hundred-year storm in Connecticut, you know, in Oxford.
[00:02:17.560 --> 00:02:21.800] And we're like, well, you know, you can't say for sure it's global warming, but, you know, this sort of thing.
[00:02:21.800 --> 00:02:30.280] And then boom, boom, two more hurricanes, like record flooding in the mountains, miles from the shore.
[00:02:30.920 --> 00:02:34.680] A location that was previously considered to be like a climate change refuge.
[00:02:34.680 --> 00:02:37.240] Nope, sorry, not correct.
[00:02:37.240 --> 00:02:40.520] And now we got this really unprecedented hurricane.
[00:02:40.520 --> 00:02:43.720] So, you know, obviously we know this is global warming, right?
[00:02:43.720 --> 00:02:53.320] You know, obviously statistically, you can't say like this one particular, what would have happened were it not for whatever, but we know that this is consistently over and over again.
[00:02:53.320 --> 00:02:58.280] We are seeing weather events that are beyond what is typical.
[00:02:58.280 --> 00:03:01.960] And this is exactly what scientists predicted was going to happen, and it's happening.
[00:03:01.960 --> 00:03:03.480] So it's probably not a coincidence.
[00:03:03.640 --> 00:03:11.160] Yeah, we've talked about not so much being the frequency with which hurricanes occur, but the intensity at which yeah, they don't get more frequent.
[00:03:11.160 --> 00:03:14.520] Yeah, but they gain their power from the heat in the water.
[00:03:14.520 --> 00:03:18.120] And the Gulf of Mexico is very warm because of global warming.
[00:03:18.120 --> 00:03:23.560] And then the warm air holds more moisture, so the hurricanes pick up more moisture.
[00:03:23.560 --> 00:03:29.960] So they are more powerful, they drop more rain, and that's what causes the flooding.
[00:03:29.960 --> 00:03:39.160] And because of that, they probably feel more frequent because the lower intensity hurricanes are often not felt or they're felt with less intensity.
[00:03:39.160 --> 00:03:47.680] So when more, when the same number of hurricanes are significantly more powerful, that onslaught is more apparent.
[00:03:47.680 --> 00:03:50.880] Yeah, you don't get more hurricanes, but you get more category four and five hurricanes.
[00:03:44.600 --> 00:03:52.720] And of course, those are the ones you notice.
[00:03:52.720 --> 00:03:52.960] Right.
[00:03:52.960 --> 00:03:54.240] Those are the ones that hit you.
[00:03:54.720 --> 00:03:54.960] Right.
[00:03:54.960 --> 00:03:58.080] Those are the ones that cause the most death, the most destruction.
[00:03:58.080 --> 00:04:03.760] Even if you add up the other smaller hurricanes, they're much worse than any smaller hurricanes that hit.
[00:04:03.760 --> 00:04:04.080] Yes.
[00:04:04.080 --> 00:04:06.560] And a meteorologist cried.
[00:04:06.560 --> 00:04:07.120] Yeah.
[00:04:07.120 --> 00:04:11.920] Well, and he's probably thinking of his neighborhood, his home, his community.
[00:04:12.240 --> 00:04:15.120] No, that wasn't my take.
[00:04:15.120 --> 00:04:24.160] My take was that the unprecedented ramp up from category one to five in 12 hours is essentially unprecedented.
[00:04:24.160 --> 00:04:26.080] And that's where I think it hit home.
[00:04:26.080 --> 00:04:31.600] Like, oh my God, this is a monster, the monster we've been dreading caused by climate change.
[00:04:32.000 --> 00:04:35.920] And even just look at it, just even looking at the hurricane, you're like, wait, that looks weird.
[00:04:36.960 --> 00:04:39.760] One guy described it as being tightly wound.
[00:04:40.240 --> 00:04:42.960] The eye is only like three miles wide.
[00:04:43.200 --> 00:04:47.840] It's so, so tiny, so small, and it ramped up so fast.
[00:04:47.840 --> 00:04:50.800] Like we got to like 850 millibars.
[00:04:50.800 --> 00:04:56.320] It's like the fourth most intense in terms of the pressure.
[00:04:57.520 --> 00:04:58.720] And we're going to see more of it.
[00:04:58.960 --> 00:04:59.840] We're going to see more.
[00:05:00.160 --> 00:05:02.720] This is the first of the monsters.
[00:05:03.040 --> 00:05:05.680] It's like the kaiju coming out of the ocean.
[00:05:06.000 --> 00:05:08.480] It's like, we're going to see this.
[00:05:08.800 --> 00:05:12.080] We may see another one as bad or worse this season.
[00:05:12.080 --> 00:05:13.440] I mean, it's October.
[00:05:13.440 --> 00:05:15.760] The season ends in, what, November?
[00:05:15.920 --> 00:05:18.240] There's actually, I mean, there's plenty of time.
[00:05:18.560 --> 00:05:22.000] Mercifully, maybe the season will end.
[00:05:22.000 --> 00:05:23.840] The season will end soon.
[00:05:23.840 --> 00:05:24.800] But damn, man.
[00:05:24.800 --> 00:05:25.440] But how do we know?
[00:05:25.440 --> 00:05:27.920] I mean, this happened literally overnight.
[00:05:27.920 --> 00:05:33.400] And, Bob, I found a New York Times article that quoted the meteorologist that you mentioned.
[00:05:29.760 --> 00:05:34.360] His name's Morales.
[00:05:35.000 --> 00:05:38.760] And he said, a number of factors played into the tearful broadcast.
[00:05:39.160 --> 00:05:56.280] Shock about the storm's rapid intensification, angst about the increasing number and severity of extreme weather events, frustration over society's failure to mitigate, despite scientific certainty that it's driving increasingly violent weather, and empathy for the people, the ecosystems, and the creatures that would experience the destructiveness.
[00:05:56.280 --> 00:05:58.520] And then there's a quote from him: It claims lives.
[00:05:58.520 --> 00:06:00.200] It also wrecks lives.
[00:06:00.200 --> 00:06:03.240] You have to feel sorry for the folks that are in this hurricane's path.
[00:06:04.280 --> 00:06:11.160] And then you get the layer of politicized misinformation that's being thrown around.
[00:06:11.160 --> 00:06:15.800] It's like, hey, this is not the time to politicize stuff to be partisan.
[00:06:15.800 --> 00:06:20.280] This is the time to do what you can to save lives, people's lives.
[00:06:20.280 --> 00:06:22.840] And people are just throwing away, throwing out misinformation.
[00:06:22.840 --> 00:06:25.560] It's like, oh my God, that's horrible.
[00:06:25.560 --> 00:06:26.440] It's cruel.
[00:06:26.440 --> 00:06:27.720] It's ridiculous.
[00:06:27.720 --> 00:06:28.040] Wow.
[00:06:28.280 --> 00:06:32.120] So as this recording, it's already too late to evacuate.
[00:06:32.120 --> 00:06:32.680] Well, yeah.
[00:06:33.720 --> 00:06:34.600] They're actively being populated.
[00:06:34.760 --> 00:06:35.960] Hopefully, people have gotten out.
[00:06:36.280 --> 00:06:37.720] Yeah, they're sheltering in place.
[00:06:37.960 --> 00:06:39.160] Eastern Standard Time, yeah.
[00:06:39.400 --> 00:06:42.520] You don't want to be on the road when the storm surge hits.
[00:06:43.160 --> 00:06:45.720] As we talked about before, water is powerful.
[00:06:46.280 --> 00:06:55.000] And this is a double whim because the winds, category five winds, will rip a house apart all by itself without the water.
[00:06:55.000 --> 00:07:00.680] Then you have the fact that the houses are going to be flooded and the streets are going to be mayhem, like unsurvivable.
[00:07:00.680 --> 00:07:04.920] Like the storm surge itself is being described as unsurvivable.
[00:07:04.920 --> 00:07:06.840] And then you throw the winds on top of that.
[00:07:06.840 --> 00:07:07.960] This is destructive.
[00:07:07.960 --> 00:07:09.480] This is very destructive.
[00:07:09.480 --> 00:07:13.160] So by the time the show launches, this will have happened.
[00:07:13.800 --> 00:07:16.640] And we'll know exactly how bad it was.
[00:07:16.880 --> 00:07:20.080] But hopefully people heeded the warnings and got out.
[00:07:21.120 --> 00:07:25.360] And FEMA is on the job and working with the state of Florida.
[00:07:25.360 --> 00:07:28.240] I mean, I think the people in place are doing what they're supposed to be doing.
[00:07:28.240 --> 00:07:31.680] And they just should ignore the national politics, as Bob said.
[00:07:32.480 --> 00:07:33.280] It's all static.
[00:07:33.520 --> 00:07:36.000] The state and local stuff, they're like, yes, we're working together.
[00:07:36.000 --> 00:07:37.040] Everyone's doing a good job.
[00:07:37.040 --> 00:07:37.840] Let's get it done.
[00:07:38.160 --> 00:07:38.400] Right.
[00:07:38.400 --> 00:07:38.720] Yeah.
[00:07:38.720 --> 00:07:39.520] As they should.
[00:07:39.520 --> 00:07:39.920] All right.
[00:07:39.920 --> 00:07:41.280] Well, let's move on.
[00:07:41.520 --> 00:07:44.800] Evan, you're going to start us off with a dumbest thing of the week.
[00:07:44.800 --> 00:07:46.800] Yes, dumbest thing of the week.
[00:07:46.800 --> 00:07:47.200] Okay.
[00:07:47.200 --> 00:07:49.600] And there were, well, boy, this was a week.
[00:07:49.920 --> 00:07:51.840] There were many contenders.
[00:07:52.880 --> 00:07:56.480] A story about Bigfoot almost won the day.
[00:07:56.480 --> 00:08:03.120] But as usual, Bigfoot would not stand a chance against a monster that lives in the bottom of a lake.
[00:08:03.120 --> 00:08:03.840] Yep, that's right.
[00:08:03.840 --> 00:08:10.480] I'm talking about the lurker of the loch, the dynamo of the deep, the cryptic king or queen itself.
[00:08:10.480 --> 00:08:14.000] I give you this week's subject, the Loch Ness Monster.
[00:08:14.000 --> 00:08:15.680] The dumbest thing of the week.
[00:08:15.680 --> 00:08:18.880] Steve, you can go ahead and insert that applause soundtrack there if you'd like.
[00:08:19.040 --> 00:08:19.680] Oh, yeah.
[00:08:20.000 --> 00:08:29.600] I found, yes, captain, captain of a boat out on the loch says, I found Loch Ness Monster on my ship's sonar.
[00:08:29.600 --> 00:08:30.080] Yep.
[00:08:30.080 --> 00:08:30.880] And let's see.
[00:08:30.880 --> 00:08:44.720] Yeah, I've spoken several times about the Loch Ness Monster before, so I think I can safely assume our audience is familiar enough that some people believe there exists a prehistoric dinosaur-sized aquatic creature in Scotland's Loch Ness.
[00:08:44.720 --> 00:08:47.680] And no, it's not just a bedtime story for children.
[00:08:47.680 --> 00:08:51.920] Many grown-ups, supposedly mature adults, believe this to be true.
[00:08:51.920 --> 00:08:54.800] And it makes you wonder what else they think is true.
[00:08:54.800 --> 00:08:55.600] Now, evidence.
[00:08:55.600 --> 00:08:57.680] What's the evidence for the creature's existence?
[00:08:57.680 --> 00:08:59.360] It adds up to, well, nothing.
[00:08:59.360 --> 00:09:06.200] No scientific evidence over nearly, wow, almost 100 years that this particular myth has existed.
[00:09:06.200 --> 00:09:07.240] Can you believe it?
[00:09:07.240 --> 00:09:08.840] And it keeps going on.
[00:09:08.840 --> 00:09:11.720] But hey, we finally have sonar readings from the captain.
[00:09:11.720 --> 00:09:12.360] Here's the...
[00:09:12.360 --> 00:09:16.360] evidence we maybe have been looking for all of this time.
[00:09:16.360 --> 00:09:22.040] So his name is Sean Sloggy, or S-L-O-G-G-I-E.
[00:09:22.040 --> 00:09:26.040] That's how I read that and pronounce that, Sean Sloggy.
[00:09:26.280 --> 00:09:36.440] He was preparing his Spirit of Loch Ness pleasure boat to sail last month when a large object was spotted on the vessel's underwater sensors.
[00:09:36.440 --> 00:09:48.680] It was an outline detected nearly 100 meters beneath the surface of the nest, and it bore an eerie resemblance to a plesiosaur.
[00:09:48.680 --> 00:09:49.960] Yeah, which what?
[00:09:49.960 --> 00:10:04.120] Is a speculated reptile group in which the people who believe in the Loch Ness monster believe, okay, yeah, this is a plesiosaur who has survived all this time and exists in the lake or a family of or something.
[00:10:04.120 --> 00:10:07.720] I have no idea, frankly, why they think this thing really exists.
[00:10:07.720 --> 00:10:16.600] Now, I have to, well, I think they think there's connections between the loch and the ocean, you know, and so they must be a population traveling back and forth, whatever.
[00:10:16.600 --> 00:10:19.160] But looking at the sonar image, first of all, it's a blob, right?
[00:10:19.160 --> 00:10:19.800] I mean, it could be angle.
[00:10:19.960 --> 00:10:20.520] Bob Squatch.
[00:10:20.600 --> 00:10:21.240] Blob squatch.
[00:10:21.640 --> 00:10:27.080] But if you didn't know what the context was, and you just said, what does this look like to you?
[00:10:27.080 --> 00:10:29.160] It looks like a turtle.
[00:10:29.160 --> 00:10:32.520] Now, there are no giant sea turtles in Loch Ness, either.
[00:10:32.520 --> 00:10:34.040] So it's probably not a turtle.
[00:10:34.040 --> 00:10:34.520] Right.
[00:10:34.520 --> 00:10:36.760] But it looks way more like a turtle than a plesiosaur.
[00:10:36.840 --> 00:10:39.000] It actually doesn't look like a plesiosaur.
[00:10:39.000 --> 00:10:42.760] It doesn't have like the characteristic overall shape.
[00:10:43.000 --> 00:10:45.520] The resemblance is actually fairly superficial.
[00:10:45.680 --> 00:10:51.040] But, you know, again, if you account for the fact that sonar is blobby, you know, it's not that accurate.
[00:10:44.840 --> 00:10:51.200] Right.
[00:10:51.600 --> 00:10:53.120] Could it be a plesiosaur?
[00:10:53.120 --> 00:10:56.400] I mean, that image, I guess, but it could also be a turtle.
[00:10:56.400 --> 00:11:00.480] It could also be nothing, you know, or it could be a hoax.
[00:11:00.480 --> 00:11:05.920] I want to describe to the audience just for a moment the image you're looking at, Steve, the one I'm looking at from the news.
[00:11:06.240 --> 00:11:09.920] So it's a photograph of the sonar, right?
[00:11:09.920 --> 00:11:11.680] The screen and what is being displayed.
[00:11:11.680 --> 00:11:14.800] To me, it looks like the old video game Defender, right?
[00:11:14.800 --> 00:11:26.080] With that, you know, from an arcade game from like 1980 with like eight kilobytes of graphics, basically, with these little dots and boxes and things moving all over the screen.
[00:11:26.080 --> 00:11:34.640] That outline, and they circle it, you know, where you're where it looks sort of, yeah, has this turtlish shape to it, maybe a back and an extended neck and maybe some little arms.
[00:11:34.640 --> 00:11:36.640] That could be anything.
[00:11:36.960 --> 00:11:38.080] I mean, what the heck?
[00:11:38.080 --> 00:11:44.640] It's total pattern recognition, you know, pattern seeking as we're wont to do.
[00:11:45.520 --> 00:11:46.480] Order in the chaos.
[00:11:46.480 --> 00:11:48.320] We're not seeing like a recorded video.
[00:11:48.320 --> 00:11:53.840] You know, a snapshot, like, was this just at the moment where it looked the most like a reptile?
[00:11:53.840 --> 00:11:56.240] Or like, what would the video show?
[00:11:56.240 --> 00:11:57.280] That's always a good question.
[00:11:57.280 --> 00:12:06.160] Like, when you see like a 20-second quote-unquote, you know, video of a UFO, you always want to know, yeah, what happened before and after the 20 seconds that I'm seeing?
[00:12:06.160 --> 00:12:09.920] Because usually when you see that, it's like, okay, it's clearly a hubcap or whatever.
[00:12:09.920 --> 00:12:12.640] Oh, the school of fish broke up and went into a million pieces.
[00:12:12.960 --> 00:12:13.200] Exactly.
[00:12:14.080 --> 00:12:14.240] Right.
[00:12:14.320 --> 00:12:16.160] That were all blobbed together at one point.
[00:12:16.160 --> 00:12:21.520] So, right, a single snap in time is not evidence of anything.
[00:12:21.520 --> 00:12:24.640] Oh, but no, but the captain says the sonar doesn't lie.
[00:12:26.880 --> 00:12:31.800] The boat hasn't been on five whiskey distillery tours before going out on the lock.
[00:12:31.800 --> 00:12:33.240] It's just doing its job.
[00:12:29.760 --> 00:12:35.160] That's what the captain said.
[00:12:35.640 --> 00:12:41.560] Yeah, appears in different colors, indicating pockets of air, and season, you know, the usual, the usual kind of stuff.
[00:12:41.560 --> 00:12:45.560] Regardless, even of that, so you've got this, okay, you have the sonar image.
[00:12:45.560 --> 00:12:46.680] Okay, where's the thing?
[00:12:46.920 --> 00:12:47.880] What's it eating?
[00:12:48.040 --> 00:12:49.320] Where's it pooping?
[00:12:50.600 --> 00:12:51.720] How long is it alive?
[00:12:51.720 --> 00:12:52.680] Where are its remains?
[00:12:52.680 --> 00:12:53.560] Where are the others?
[00:12:53.560 --> 00:12:56.440] Where's the other evidence, any kind of physical evidence?
[00:12:56.440 --> 00:12:57.080] Where's the DNA?
[00:12:57.480 --> 00:12:59.560] Why has one never washed up on shore?
[00:12:59.560 --> 00:13:00.120] Right?
[00:13:00.120 --> 00:13:03.480] Or a piece of it, or you know, a fin or something.
[00:13:03.800 --> 00:13:09.880] But the definitive thing is they looked at the lock for environmental DNA, and there's no plesiosaur DNA in that.
[00:13:10.200 --> 00:13:11.400] Exactly.
[00:13:11.400 --> 00:13:12.120] Exactly.
[00:13:12.440 --> 00:13:13.240] No.
[00:13:13.240 --> 00:13:13.480] Yep.
[00:13:13.480 --> 00:13:15.320] All that goes out the window.
[00:13:15.320 --> 00:13:16.120] And here we go.
[00:13:16.120 --> 00:13:23.000] Throw another piece of zero evidence into the pile for the Loch Ness Monster this week's dumbest thing of the week.
[00:13:23.000 --> 00:13:24.120] Thank you, Evan.
[00:13:24.120 --> 00:13:25.320] Yep, you got it.
[00:13:25.320 --> 00:13:27.080] So, guys, that time of year again.
[00:13:27.080 --> 00:13:28.040] Yes, it is.
[00:13:28.040 --> 00:13:29.080] Every time this comes up.
[00:13:29.320 --> 00:13:30.600] Halloween, baby.
[00:13:30.600 --> 00:13:31.400] Yeah.
[00:13:31.720 --> 00:13:32.360] Always sorry.
[00:13:32.680 --> 00:13:33.560] Not Halloween.
[00:13:33.560 --> 00:13:36.280] Every time this comes up, my reaction is always the same.
[00:13:36.280 --> 00:13:39.160] It can't be Nobel Prize time again already.
[00:13:40.440 --> 00:13:41.560] We just had it.
[00:13:41.560 --> 00:13:44.360] Didn't we just talk about the last year's?
[00:13:44.360 --> 00:13:45.080] But it is.
[00:13:45.080 --> 00:13:46.520] It's Nobel Prize time.
[00:13:46.520 --> 00:13:53.960] There is a cruel reality to getting older in that the years, the perception of years go quicker as you get older.
[00:13:54.840 --> 00:13:57.880] Nobody tells the kids that as an adult, or they do and the kids don't listen.
[00:13:58.200 --> 00:14:01.480] But it's a real cruel trick, I find, you know, of life.
[00:14:01.800 --> 00:14:03.160] It shouldn't be that way.
[00:14:03.160 --> 00:14:03.800] But it does.
[00:14:04.040 --> 00:14:06.680] That is exactly how it's perceived, at least I think.
[00:14:06.680 --> 00:14:07.240] Yeah.
[00:14:07.880 --> 00:14:09.480] So we're going to cover it.
[00:14:09.480 --> 00:14:09.800] Yep.
[00:14:10.520 --> 00:14:16.240] Kara, you're going to start us off with the Nobel Prize in Physiology or Medicine.
[00:14:14.920 --> 00:14:16.800] Yep.
[00:14:17.120 --> 00:14:35.760] The Nobel Prize, the 2024 Nobel Prize in Physiology or Medicine, was awarded jointly to Victor Ambrose and Gary Ruvkin, quote, for the discovery of microRNA and its role in post-transcriptional gene regulation.
[00:14:35.760 --> 00:14:47.440] So we're going to dig into that a little bit, tell a little bit of the story of this discovery and kind of the outcomes and what it means for science and for humanity.
[00:14:47.440 --> 00:14:54.720] So in the late 80s, Victor Ambrose and Gary Ruvkin were both postdocs.
[00:14:54.720 --> 00:15:03.280] They were fellows in the laboratory of Robert Horvitz, who incidentally won a Nobel Prize in 2002 for his research.
[00:15:03.280 --> 00:15:07.920] And they were studying a model organism called C.
[00:15:08.080 --> 00:15:09.200] elegans.
[00:15:09.200 --> 00:15:09.920] We call it C.
[00:15:09.920 --> 00:15:13.680] elegans because it's hard to pronounce the word that precedes elegans.
[00:15:13.680 --> 00:15:16.400] It's a roundworm, it's a nematode.
[00:15:16.400 --> 00:15:26.320] And it's a great organism to study because it's small, but it has a lot of the specialized cell types that are found in larger animals.
[00:15:26.320 --> 00:15:34.080] So it's a good model for tissue development, maturation, just developmental biology, and of course, genetics.
[00:15:34.080 --> 00:15:47.440] And so they were really just asking a lot of interesting questions: like, how do the genes that sort of control these different developmental programs turn on, turn off?
[00:15:47.440 --> 00:15:49.600] Why do they turn on and turn off?
[00:15:49.600 --> 00:15:51.040] Like, what's going on here?
[00:15:51.040 --> 00:16:01.480] And I saw a little video of Gary Ruvkin talking about how, at the time, he was just really excited about this new field of recombinant DNA.
[00:16:01.800 --> 00:16:19.800] A lot of people were getting into recombinant DNA, which is kind of the practice of creating sequences that wouldn't otherwise be found in the genome, either by combining from different sources or even synthetically creating DNA sequences in the laboratory.
[00:16:19.800 --> 00:16:22.360] So, obviously, this is a form of genetic engineering.
[00:16:22.360 --> 00:16:30.200] So, on the heels of this kind of excitement at the time, they're asking a lot of interesting questions, poking around.
[00:16:30.200 --> 00:16:34.280] They go their separate ways, they start their own labs.
[00:16:34.280 --> 00:16:39.800] Both of them are, I think they're both in Massachusetts at this time.
[00:16:39.800 --> 00:16:40.200] Yes.
[00:16:40.200 --> 00:16:40.680] Yeah.
[00:16:40.680 --> 00:17:04.360] So, I think before we can get into microRNA, what it is, what it does, and why it is interesting, we have to kind of step back and just make sure that we're all on the same page about what's often referred to as the central dogma of molecular biology, and that is the basic movement from DNA to RNA to proteins, right?
[00:17:04.360 --> 00:17:10.680] And that involves two major steps, a whole lot of minor steps, called transcription and translation.
[00:17:10.680 --> 00:17:15.080] So, transcription is how DNA is copied to RNA.
[00:17:15.080 --> 00:17:29.720] And what happens in transcription is that the base pairs line up using a specialized, actually, it in and of itself is a protein, but a specialized catalyst that helps make those base pairs line up.
[00:17:29.720 --> 00:17:50.320] And they, as opposed to replication, where DNA just makes more DNA by unzipping and then matching up the base pairs, in transcription, all of the base pairs line up except for where thymine would have been, it turns into uracil, which then forms these what were called messenger RNA strands.
[00:17:50.320 --> 00:17:56.400] And it's important to make a distinction here because I think some people might get confused when they're reading about this.
[00:17:56.400 --> 00:18:01.120] Messenger RNA is often represented as mRNA.
[00:18:01.120 --> 00:18:11.120] MicroRNA, which is a totally different thing, that's what we're going to talk about in a second, is often written as MIC RNA.
[00:18:11.120 --> 00:18:11.600] Oh.
[00:18:11.920 --> 00:18:14.560] And sometimes mu RNA.
[00:18:14.560 --> 00:18:16.720] So, or MI RNA.
[00:18:17.040 --> 00:18:20.400] So you do need to make that distinction because that I matters.
[00:18:20.880 --> 00:18:23.680] So we've got transcription, then we've got translation.
[00:18:23.680 --> 00:18:29.440] Transcription DNA to RNA and specifically to messenger RNA, but also non-coding regions as well.
[00:18:29.440 --> 00:18:32.160] And then we've got translation.
[00:18:32.160 --> 00:18:35.360] Translation is RNA to proteins, right?
[00:18:35.360 --> 00:18:44.800] So this is how proteins are synthesized using the same genetic material that's in every single cell in an organism's body.
[00:18:44.800 --> 00:18:51.360] So then the question, and this was the big question that the Nobel laureates were asking, was like, how do they know?
[00:18:51.680 --> 00:18:58.000] Like, why do they know to make this specific protein when all the genetic instructions are there?
[00:18:58.000 --> 00:18:58.400] Right?
[00:18:58.400 --> 00:19:03.360] It's not just DNA codes to RNA, RNA maps on and produces proteins.
[00:19:03.360 --> 00:19:05.920] It's the when are these genes turned on and off?
[00:19:05.920 --> 00:19:07.200] Why are they turned on and off?
[00:19:07.200 --> 00:19:09.280] What kind of pressures are involved to do that?
[00:19:09.280 --> 00:19:11.600] And how are they regulated?
[00:19:11.600 --> 00:19:18.560] And so they noticed they were studying a specific strain of C.
[00:19:18.560 --> 00:19:20.800] elegans called LIN4.
[00:19:20.800 --> 00:19:24.080] They were also studying a different strain called LIN14.
[00:19:24.080 --> 00:19:35.000] And they found that these specific mutant strains were having issues in the timing of activating genes that were necessary for them to develop normally.
[00:19:35.320 --> 00:19:37.960] And so they were like, okay, this is weird and interesting.
[00:19:37.960 --> 00:19:39.880] I want to figure out what's going on here.
[00:19:39.880 --> 00:19:43.240] I want to identify these genes and I want to see what these genes do.
[00:19:43.240 --> 00:19:46.600] Like very, very basic molecular biology research.
[00:19:46.600 --> 00:19:52.120] They knew at that point that the LIN4 gene regulated the LIN14 gene.
[00:19:52.120 --> 00:19:55.160] I don't think we have to get too caught up in the names of them.
[00:19:55.160 --> 00:20:04.440] But how it regulated it, like they knew that it kind of negatively regulated it, it blocked the LIN14 gene, but how that happened, they didn't really understand.
[00:20:04.440 --> 00:20:07.800] So they wanted to dig a little bit deeper.
[00:20:07.800 --> 00:20:14.360] And as they were studying, Ambrose moved on and established a laboratory at Harvard.
[00:20:14.360 --> 00:20:19.000] Rovkin moved on and he established a laboratory at Mass General.
[00:20:19.000 --> 00:20:21.800] And they kept looking at this work.
[00:20:21.800 --> 00:20:25.960] But then they start doing their own work, but they're interested in answering the same questions, right?
[00:20:25.960 --> 00:20:30.280] So they're looking: okay, what's going on here with this inhibitory mechanism?
[00:20:30.280 --> 00:20:33.160] How is the regulatory stuff going on?
[00:20:33.160 --> 00:20:36.600] Is it actually happening at the translation stage?
[00:20:36.600 --> 00:20:37.080] No way.
[00:20:37.080 --> 00:20:38.520] Oh, okay, is it transcription?
[00:20:38.520 --> 00:20:39.560] No, it is translation.
[00:20:39.560 --> 00:20:42.360] Okay, so it's happening later on in the process.
[00:20:42.360 --> 00:20:43.640] And they start talking to each other.
[00:20:43.960 --> 00:20:45.880] This is, I think, the best part of this story.
[00:20:45.880 --> 00:20:57.480] This is two individuals who kind of came up together, studied together, are now in what could be seen as rival labs, what could be seen as labs that want to kind of push to publish before each other.
[00:20:57.480 --> 00:21:02.760] But instead, they're always collaborating with each other and sharing each other's research.
[00:21:02.760 --> 00:21:11.640] So they named this little sequence of RNA that they kept seeing over and over microRNA because it was really, really small, right?
[00:21:11.640 --> 00:21:16.480] It was just a smaller chunk of RNA than the RNA that they had been working with.
[00:21:16.800 --> 00:21:40.160] And they realized that it was affecting and affecting in an inhibitory way the actual translation of this LIN14 because it was bind, because this RNA, this microRNA was binding to it and kind of preventing it from being able to then be expressed the right way.
[00:21:40.160 --> 00:21:42.240] We're now into the early 90s.
[00:21:42.240 --> 00:21:44.480] They published this sort of serendipitously.
[00:21:44.480 --> 00:21:46.640] They're both talking about this discovery.
[00:21:46.640 --> 00:21:48.000] People are going, oh, that's interesting.
[00:21:48.000 --> 00:21:49.840] You've got this little piece of RNA.
[00:21:49.840 --> 00:21:52.640] It's sort of binding to this portion.
[00:21:52.640 --> 00:21:56.400] It's preventing translation from happening.
[00:21:56.400 --> 00:21:58.640] Okay, but that's like a weird quirk of C.
[00:21:58.640 --> 00:21:59.680] elegans.
[00:21:59.680 --> 00:22:04.800] So people listened and they go, that's kind of cool, but it's quirky, and it didn't really get a lot of traction.
[00:22:05.120 --> 00:22:12.320] And then they kept looking at it and they found something interesting called the let7 gene.
[00:22:12.320 --> 00:22:15.200] And the let7 gene was different.
[00:22:15.200 --> 00:22:18.080] And the reason it was different is because it was not unique to C.
[00:22:18.080 --> 00:22:18.640] elegans.
[00:22:18.640 --> 00:22:27.440] It was conserved all the way back to what I think from some of the things that I read was when kind of mammals and fish split.
[00:22:27.440 --> 00:22:32.160] So it's really conserved in a lot of multicellular organisms.
[00:22:32.160 --> 00:22:40.560] And that let 7 gene, when people started looking, they started to realize, hmm, these microRNAs are in like us.
[00:22:40.560 --> 00:22:44.560] They're in like most of the animals and the plants that we're looking at.
[00:22:44.560 --> 00:22:47.040] It's sort of everywhere.
[00:22:47.040 --> 00:22:50.400] And when they discovered that, a lot of people got interested.
[00:22:50.400 --> 00:23:02.920] And they started to do a ton of research and discover, I think we're now at this is a little bit different because Wiki is saying that only 500 represent the bona fide microRNA.
[00:23:03.240 --> 00:23:06.360] Yeah, I've read we've discovered over a thousand microRNA to date.
[00:23:06.360 --> 00:23:08.280] Okay, so and that's that's important.
[00:23:08.280 --> 00:23:17.480] Discovered over a thousand, at least 500 of them have been like certified and said, yes, that these are bona fide, but it's a lot because a ton of different labs are working on it.
[00:23:17.480 --> 00:23:26.200] Like 90 different families of microRNAs that have been conserved, like I said, since that common ancestor of mammals and fish.
[00:23:26.200 --> 00:23:32.600] And we've started to discover the functions of these, the different genes that they act on, the different genes that they regulate.
[00:23:32.600 --> 00:23:45.240] And we've since then also started to discover how they're involved, not only just the mechanism by which they are doing this regulatory function, but also what that means downstream.
[00:23:45.240 --> 00:23:51.560] Like they might be silencing transcription or they might be processing in the cytoplasm.
[00:23:51.560 --> 00:23:55.400] Sometimes they're happening earlier on in the nucleus.
[00:23:55.720 --> 00:24:03.720] We've even discovered that there are some that are floating outside of the nucleus completely and even in the extracellular matrix.
[00:24:03.720 --> 00:24:04.280] Yeah.
[00:24:04.280 --> 00:24:12.840] Like so we're seeing that this is happening all over the cell at a lot of different stages that weren't anticipated before.
[00:24:12.840 --> 00:24:22.120] We're also seeing that they have a ton of different functions in the cell, but they tend to fall into three main processes.
[00:24:22.440 --> 00:24:38.360] They either will cleave the mRNA, so cut it into two different pieces, they'll destabilize the mRNA by shortening its polyA tail, or they'll reduce translation of the mRNA, which was that first discovery, which then affects the protein synthesis.
[00:24:38.360 --> 00:24:47.680] Generally speaking, in humans and other animals, it happens by destabilizing, but microRNAs kind of fall into all three of those categories.
[00:24:47.680 --> 00:24:56.640] And we've now been able to identify disease processes that microRNA are directly implicated in.
[00:24:56.640 --> 00:25:03.760] So a mutation in a region of the mRNA 96, they're named for the order they were discovered.
[00:25:03.760 --> 00:25:08.800] So like earlier numbers are earlier discoveries, bigger numbers are later discoveries.
[00:25:08.800 --> 00:25:14.240] So a mutation in microRNA 96 causes a hereditary progressive hearing loss.
[00:25:14.240 --> 00:25:21.920] A mutation in the region of 184 causes hereditary keratoconus with cataracts.
[00:25:21.920 --> 00:25:28.400] And a full deletion in 17 causes skeletal and growth defects.
[00:25:28.400 --> 00:25:36.160] And we're also seeing more and more research, a lot of really exciting research in the sort of DNA repair and cancer space.
[00:25:36.160 --> 00:25:55.360] A lot of research about the effects of these microRNAs on, especially like chronic lymphocytic leukemia, is implicated, B-cell receptor signaling, and sort of sometimes these interesting things where they both affect tumor suppressogenes and oncogenes.
[00:25:55.360 --> 00:26:15.200] And so there's still a lot more to unpack and uncover, but there's so many therapeutics that are actively being researched and developed from, gosh, everything from heart disease to kidney disease to alcoholism and stroke, like different nervous system disorders like Alzheimer's.
[00:26:16.000 --> 00:26:25.040] These microRNAs could be implicated in a lot of disease processes because they're so early in that process of protein synthesis.
[00:26:25.040 --> 00:26:33.960] And so, this is one of those, I think, beautiful examples of two guys working in a lab, just fundamentally interested in how something works.
[00:26:34.280 --> 00:26:51.080] And eventually, enough people recognized this fundamental process, this regulatory process, was so important that now we see downstream effects that will affect the health and safety of human beings for, I think, generations to come.
[00:26:51.080 --> 00:26:55.880] And that's why they were awarded the Nobel Prize for Physiology or Medicine.
[00:26:55.880 --> 00:27:01.960] Yeah, it's a good story because, yeah, they were just asking a very simple question: how does the LIN4 gene affect the LIN14 gene?
[00:27:02.280 --> 00:27:07.800] And it could have been, I just say, like a one-off kind of quirky thing, you know, limited to C-elegance.
[00:27:07.880 --> 00:27:11.640] Turns out they discovered a fundamental aspect of gene regulation.
[00:27:11.640 --> 00:27:15.640] And now that's translating into a whole bunch of potential therapeutic targets.
[00:27:15.960 --> 00:27:16.600] Translating.
[00:27:16.600 --> 00:27:17.800] No pain intended.
[00:27:17.800 --> 00:27:18.280] Yeah, right.
[00:27:19.240 --> 00:27:21.560] Well, we call it translational research.
[00:27:22.360 --> 00:27:33.880] So this is why funding scientists who are doing basic science research without any thought to ultimate applications, just trying to understand how the universe works.
[00:27:35.480 --> 00:27:39.640] Yeah, you can't predict how that's going to potentially pan out.
[00:27:39.960 --> 00:27:48.920] But as a general rule, we see over and over again, understanding at a reductionist, like really tiny level how things work usually turns out to be pretty useful.
[00:27:49.240 --> 00:27:53.000] 100% because things can break all along a path.
[00:27:53.000 --> 00:27:53.480] Exactly.
[00:27:53.480 --> 00:28:04.120] And when we understand the most simplistic and the earliest functionality of something, we're getting to it at its core and we can start to follow all the ways that things can break.
[00:28:04.120 --> 00:28:07.800] When we follow the ways things can break, we can figure out how to fix them.
[00:28:07.800 --> 00:28:08.200] Right.
[00:28:08.200 --> 00:28:08.920] Fascinating.
[00:28:08.920 --> 00:28:11.400] All right, Jay, tell us about the Nobel Prize in Chemistry.
[00:28:11.400 --> 00:28:12.680] What do you want to know?
[00:28:13.000 --> 00:28:13.960] Everything, baby.
[00:28:13.960 --> 00:28:14.520] Give me it all.
[00:28:14.960 --> 00:28:15.680] Give it all to me.
[00:28:15.680 --> 00:28:18.640] All right, so there were two different awards given out.
[00:28:18.640 --> 00:28:20.240] So we'll start with the first team here.
[00:28:20.240 --> 00:28:23.280] So this is David Baker's creation of new proteins.
[00:28:23.280 --> 00:28:32.640] So David Baker and his team took a complex problem, which is they wanted to figure out how to design entirely new proteins from scratch.
[00:28:32.640 --> 00:28:35.360] You know, this is something that doesn't just happen in nature.
[00:28:35.360 --> 00:28:39.120] You know, proteins have to evolve, and it's an incredibly long process.
[00:28:39.120 --> 00:28:43.760] And if you look at the way the human body uses proteins, it's super complicated.
[00:28:43.760 --> 00:28:48.080] So proteins, in case you don't know, they're composed of chains of amino acids.
[00:28:48.080 --> 00:28:54.160] And these chains of amino acids fold into specific three-dimensional structures on their own.
[00:28:54.160 --> 00:28:55.280] Amazing.
[00:28:55.280 --> 00:29:00.720] And these structures are critical for the protein's actual function in the organism, right?
[00:29:00.720 --> 00:29:07.440] It could be for breaking down food or responding to body signals or forming cellular structures.
[00:29:07.440 --> 00:29:15.920] So in 2003, Baker achieved a breakthrough by creating a protein with a unique structure that's not found in nature.
[00:29:15.920 --> 00:29:18.240] And that's a big deal all by itself.
[00:29:18.240 --> 00:29:23.760] And since then, Baker and his team have continued to push the boundaries of what they achieved.
[00:29:23.760 --> 00:29:28.400] And now they're engineering new proteins that are tailored to have specific functions.
[00:29:28.400 --> 00:29:44.240] They've designed proteins that can act as pharmaceuticals to treat diseases, vaccines to prevent infections, and even like nanomaterials for advanced technological uses, which, you know, we're not there yet, but that is very possible that that can take place.
[00:29:44.720 --> 00:29:46.320] There's some practical things here.
[00:29:46.320 --> 00:29:50.800] So, for instance, they've developed proteins that can detect specific molecules.
[00:29:50.800 --> 00:29:52.720] They can function as sensors.
[00:29:52.720 --> 00:29:57.760] This could be used in the medical diagnostics and environmental monitoring.
[00:29:57.760 --> 00:29:59.440] And then they have synthetic biology.
[00:29:59.440 --> 00:30:14.280] So, Baker's work lays the foundation for this form of synthetic biology where custom-made proteins are created for specific tasks, and this opens new possibilities into everything from drug design to material sciences.
[00:30:14.280 --> 00:30:18.520] So, that is damn impressive and incredibly powerful.
[00:30:18.520 --> 00:30:28.120] You know, the ability to design proteins that don't happen to exist in nature means now that we can build molecules with exact specifications, right?
[00:30:28.120 --> 00:30:32.440] This is much, you could consider this like constructing building from blueprint to finish.
[00:30:32.440 --> 00:30:35.160] It's really incredible what these guys did.
[00:30:35.160 --> 00:30:42.200] And it's being described as it holds immense promise for innovation in many other scientific fields.
[00:30:42.200 --> 00:30:44.520] So, I would just like to thank those guys.
[00:30:44.520 --> 00:30:46.120] That's incredible.
[00:30:46.120 --> 00:30:51.800] The second team here, Demis Hasselbus and John Jumper's AlphaFold 2.
[00:30:51.800 --> 00:30:53.000] I don't know if you guys have heard of that.
[00:30:53.000 --> 00:30:53.320] Oh, yeah.
[00:30:53.400 --> 00:30:54.200] You talked about it on the show.
[00:30:55.080 --> 00:30:55.800] There it is.
[00:30:55.800 --> 00:30:57.640] Let me give you a little reboot on that.
[00:30:57.640 --> 00:31:01.720] So, for decades, you have to make it sound dramatic, right?
[00:31:01.720 --> 00:31:09.320] A very long time, scientists have known that the function of protein is dictated by its structure, but there was a challenge.
[00:31:09.320 --> 00:31:18.920] It's been predicting how a chain of amino acids, right, which is the protein sequence, how it folds into a functional three-dimensional form.
[00:31:18.920 --> 00:31:23.960] This is known as the protein-folding problem, as the protein-folding problem, right, Bob?
[00:31:23.960 --> 00:31:25.320] I think you've talked about that, right?
[00:31:25.320 --> 00:31:26.360] Yes, I did.
[00:31:26.360 --> 00:31:30.040] So, this has been a 50-year-long puzzle in biology.
[00:31:30.040 --> 00:31:37.760] And before the breakthrough, determining a protein structure took years of incredibly painstaking and super expensive experiments.
[00:31:37.760 --> 00:31:41.240] Just tons and tons and tons of hours to figure it out.
[00:31:41.240 --> 00:31:46.720] So, in 2020, the team introduced AlphaFold 2, which is what?
[00:31:44.920 --> 00:31:48.960] It's a powerful AI, of course.
[00:31:49.840 --> 00:31:54.240] And their team programmed it specifically at their deep mind.
[00:31:54.240 --> 00:31:57.040] You know, this is part of the deep mind effort.
[00:31:57.040 --> 00:32:01.920] And this AI can accurately predict protein structures.
[00:32:01.920 --> 00:32:04.240] And this is why this is impressive.
[00:32:04.240 --> 00:32:09.360] So first off, AlphaFold II uses these advanced AI algorithms.
[00:32:09.360 --> 00:32:18.080] Specifically, there are a type of machine learning called deep learning to predict how sequences of amino acids actually fold into 3D structures.
[00:32:18.080 --> 00:32:25.280] So the model, they quote unquote, learned from vast amounts of existing protein data, which we've been collecting for decades.
[00:32:25.280 --> 00:32:29.840] And this enabled them to make these highly accurate predictions using the AI.
[00:32:29.840 --> 00:32:36.800] So with AlphaFold II, Hasabis and Jumper could predict the structure of nearly every protein known to science.
[00:32:36.800 --> 00:32:39.360] And there's over 200 million of those proteins.
[00:32:39.360 --> 00:32:40.720] 200 million.
[00:32:40.720 --> 00:32:48.000] This is a massive leap from the previous methods that we had, which could only determine structures one at a time, right?
[00:32:48.000 --> 00:32:52.640] And like I said, it takes years per protein to figure it out.
[00:32:52.640 --> 00:32:59.840] So since its release, AlphaFold II has been adopted by over 2 million researchers from 190 countries.
[00:32:59.840 --> 00:33:04.960] And that is for a very good reason because it's crazy powerful and unbelievably useful.
[00:33:04.960 --> 00:33:11.840] And they use the tool for all these different various scientific purposes from understanding diseases, developing new drugs, etc.
[00:33:12.000 --> 00:33:13.200] The list goes on.
[00:33:13.200 --> 00:33:17.040] AlphaFold II has been applied already in numerous fields.
[00:33:17.040 --> 00:33:29.320] And one notable use is in combating antibiotic resistance, where researchers now can study the structures of proteins and bacteria that cause resistance to better design new antibiotics.
[00:33:28.640 --> 00:33:33.880] Which, you know, this is another exciting application in designing enzymes that can break down plastics.
[00:33:34.200 --> 00:33:39.640] I mean, guys, this, it just, it's unbelievable how powerful this particular thing is.
[00:33:39.640 --> 00:33:54.440] You know, I really love the idea that they're mindful of the fact that new technologies need to help us with environmental pollution and things like that, because we have gotten to a point where there's an incredible amount of environmental pollution in the modern world.
[00:33:54.440 --> 00:34:06.920] Baker's work shows that we can simply design proteins with entirely new functions, and AlphaFold solves the mystery of how these proteins naturally fold, and together they can make Voltron.
[00:34:07.240 --> 00:34:22.120] Together, these breakthroughs mean we now understand both how to create new proteins and how to predict the structure of existing ones, which gives us immense capability and power to do things that we didn't have not too long ago.
[00:34:22.760 --> 00:34:34.360] Did you point out, though, Jay, that even though, like I say, in the write-up for the award, they say that it predicted the structure of 200 million proteins that researchers have identified.
[00:34:34.360 --> 00:34:36.760] But we don't know that all those predictions are accurate.
[00:34:37.240 --> 00:34:38.600] Bob, you talked about this.
[00:34:38.600 --> 00:34:42.680] I think they've only verified a percentage of them, like half or something.
[00:34:42.680 --> 00:34:44.600] But it's a predictive tool.
[00:34:44.600 --> 00:34:47.800] It doesn't prove that that's the structure.
[00:34:47.960 --> 00:34:50.760] But proving it out is faster because they're starting with.
[00:34:51.080 --> 00:34:53.000] Yes, they know, right, exactly.
[00:34:53.000 --> 00:34:56.040] It's like it tells the researchers what they need to do.
[00:34:56.440 --> 00:35:02.040] It takes a lot less time to verify what AlphaFold 2 predicted than to just figure it out from scratch.
[00:35:02.080 --> 00:35:04.840] You know, I'd like to use that as an example, too.
[00:35:04.840 --> 00:35:17.840] Like, artificial intelligence is going to remove roughly, I think I read 79,000 jobs in the United States at some point in the near future, but it's going to create 90,000 jobs.
[00:35:18.720 --> 00:35:29.280] So the idea is that, you know, artificial intelligence, you're going to be, your job will be replaced by someone who knows how to work with AI in one fashion or another, right?
[00:35:29.680 --> 00:35:31.680] That's the thing that we all need to realize.
[00:35:31.680 --> 00:35:36.240] Like, look at what these scientists are doing with artificial intelligence and how powerful it is.
[00:35:36.240 --> 00:35:41.760] Guys, this is going to permeate human society and there's no stopping it.
[00:35:41.760 --> 00:35:42.400] It already is.
[00:35:42.400 --> 00:35:55.600] I mean, we're already seeing increasing examples of how AI is being used to do, like, as we've been saying for a long time, it's actually happening now, doing years of research in weeks or months of research in days or even hours.
[00:35:55.600 --> 00:35:57.280] That's happening right now.
[00:35:57.280 --> 00:35:57.840] Yeah.
[00:35:57.840 --> 00:36:01.440] You know, just the pace of research is going to accelerate.
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[00:38:58.080 --> 00:39:00.000] All right, Bob, you're up with physics.
[00:39:00.000 --> 00:39:00.800] Do it.
[00:39:00.800 --> 00:39:15.600] Oh boy, guys, this year's Nobel Prize in Physics has been awarded to John Hopfield and Jeffrey Hinton for their groundbreaking research and discoveries that led to the deep learning neural networks that power much of the artificial intelligence being used today.
[00:39:15.600 --> 00:39:18.400] So yes, more AI in Nobel.
[00:39:18.400 --> 00:39:27.840] So let's see, Mark Pierce, member of the Nobel Committee of Physics, said their work was fundamental in laying the cornerstones of what we experience today as artificial intelligence.
[00:39:27.840 --> 00:39:35.760] Ellen Moons, chair of the Nobel Committee for Physics, thanks to the laureate's work, AI has become part of our daily lives.
[00:39:35.760 --> 00:39:45.280] The laureate's discoveries and inventions from the building blocks of machine learning that can aid in making faster and more reliable decisions, for instance, when diagnosing medical conditions.
[00:39:45.280 --> 00:39:47.280] Now, Moons mentions machine learning.
[00:39:47.280 --> 00:39:49.600] So this is, we've mentioned it a few times.
[00:39:49.920 --> 00:39:56.320] This is a broad term for any technique where computers learn from data and make predictions and decisions.
[00:39:56.320 --> 00:40:01.520] Neural networks are a type of machine learning and are integral to this Nobel Prize.
[00:40:01.520 --> 00:40:04.560] So let's do a quick overview of neural networks.
[00:40:04.560 --> 00:40:12.320] I've described them multiple times on the show, and each time I say, I could do that better next time, and this is next time.
[00:40:12.320 --> 00:40:13.280] So here we go.
[00:40:13.280 --> 00:40:22.320] So neural networks are not exotic neural tissue organoid structures running in vats of strange liquids and thinking strange thoughts.
[00:40:22.320 --> 00:40:24.240] That's not what it is.
[00:40:24.240 --> 00:40:26.640] Neural networks run on computers.
[00:40:26.640 --> 00:40:33.320] They run on high-performance computers called servers, all interconnected on a network that can consist of many hundreds of them.
[00:40:33.960 --> 00:40:42.360] It could be from one server to hundreds of servers running the neural network, depending on the scale and lots of different things.
[00:40:42.520 --> 00:40:49.960] The software running on these servers simulates in some ways the behavior of the biological networks of neurons in our heads.
[00:40:50.120 --> 00:40:55.800] The neurons in a neural network are called nodes, but they are not terribly exotic.
[00:40:55.800 --> 00:40:58.040] They're essentially mathematical functions.
[00:40:58.280 --> 00:41:01.240] So, you know, bringing it down to earth a bit.
[00:41:01.560 --> 00:41:06.920] You know, the neurons in the network, the neural network, they're mathematical functions, software-based.
[00:41:06.920 --> 00:41:11.880] The data that these functions receive and deal with can be many different types of data.
[00:41:11.880 --> 00:41:17.160] They can be numerical data, like temperature or price, the measurable quantities.
[00:41:17.160 --> 00:41:18.680] It could be image data.
[00:41:18.680 --> 00:41:25.800] In that case, it would be something potentially like pixel values, like RGB, red, green, blue values for a pixel.
[00:41:26.280 --> 00:41:29.160] The data could be text-based.
[00:41:30.040 --> 00:41:34.120] They could be words that are converted into numbers so they can be more easily manipulated.
[00:41:34.120 --> 00:41:41.480] The data that these nodes or mathematical functions deal with could also be audio data, sound waves converted into numbers.
[00:41:41.480 --> 00:41:43.800] So they could deal with all of that and more.
[00:41:43.800 --> 00:41:52.760] So now, as this data flows through the network layer by layer, layer by layer, it's processed by the nodes, by these mathematical functions.
[00:41:52.760 --> 00:41:59.880] In this processing, one of the most important aspects of that is the assigning of a number called a weight.
[00:42:00.360 --> 00:42:03.800] You probably heard about that a lot if you read about anything about neural networks.
[00:42:03.800 --> 00:42:07.400] The higher the weight, the more important or influential that data is.
[00:42:07.400 --> 00:42:08.600] So that's critical.
[00:42:08.600 --> 00:42:12.800] It's critical to how a neural network learns this idea of weights.
[00:42:12.800 --> 00:42:16.960] With more and more training using more and more data, these weights are then adjusted.
[00:42:17.280 --> 00:42:28.880] They're tweaked over and over and over until the errors are minimized, allowing for ultimately a surprisingly accurate predictions or decisions based on new data that is input.
[00:42:28.880 --> 00:42:32.080] Now, these neural networks didn't appear ex nihilo, though.
[00:42:32.080 --> 00:42:39.680] They've been developing and evolving for years, but many of the critical theoretical breakthroughs have been around for decades.
[00:42:40.160 --> 00:42:48.080] And in many ways, they've just been waiting for powerful enough servers to show us what these kinds of special networks can really, really do.
[00:42:48.080 --> 00:43:13.680] Yeah, I think it's been like the past 10 or 15 years when servers got much more powerful that we could really put these networks through their paces and really, and all the breakthroughs we've had the past 15 years just goes to show, not only is the theory been there, is the theory doing it, but it's the machines that have been the thing that we've been waiting for to really see this.
[00:43:14.000 --> 00:43:18.080] Okay, so this is where the two Nobel winners in physics come in.
[00:43:18.080 --> 00:43:24.880] John Hopfield is an emeritus professor at Princeton University, and Jeffrey Hinton is a computer scientist at the University of Toronto.
[00:43:24.880 --> 00:43:30.720] John Hopfield introduced the Hopfield network in a paper in 1982.
[00:43:30.720 --> 00:43:36.160] This type of network laid the groundwork for pattern recognition and associative memory.
[00:43:36.160 --> 00:43:39.200] Pattern recognition, that's self-explanatory.
[00:43:39.200 --> 00:43:46.480] Associative memory here in this context means that the network can remember patterns based on only partial information.
[00:43:46.480 --> 00:43:49.040] So that's, as you might imagine, that's critical.
[00:43:49.200 --> 00:43:59.320] You know, you expose the neural network to just a little bit of an image, say, and it could go back through the patterns that it has remembered and remember, oh, yeah, that was a cat.
[00:43:59.040 --> 00:44:03.640] So that's what he introduced with his Hopfield network in 82.
[00:44:04.200 --> 00:44:12.120] So as you might imagine, the Hopfield Network had a huge influence on the thinking of how to store and how to retrieve information in neural networks.
[00:44:12.120 --> 00:44:15.720] Jeffrey Hinton is sometimes called the godfather of AI.
[00:44:15.720 --> 00:44:20.360] He co-invented Boltzmann machines in 1985.
[00:44:20.360 --> 00:44:30.840] This is a type of early neural network that was designed to use some principles of physics, especially regarding probability and energy-based modeling.
[00:44:30.840 --> 00:44:33.560] Look that one up to find patterns in the data.
[00:44:33.560 --> 00:44:39.240] So it uses probability and this specific type of modeling to find patterns in the data.
[00:44:39.240 --> 00:44:43.160] Do you think finding patterns and data is critical for these neural networks?
[00:44:43.160 --> 00:44:43.800] Yes.
[00:44:43.800 --> 00:44:45.400] This was in 85.
[00:44:45.400 --> 00:45:00.360] So like the Hopfield Network, Hinton's Boltzmann machine network provided foundational techniques and ideas that have allowed for the creation of the incredibly complex, skilled, and capable neural networks and AI that we have today.
[00:45:00.360 --> 00:45:05.080] And this is why Hopfield and Hinton got Nobel Prizes this past week.
[00:45:05.080 --> 00:45:08.280] If the name Hinton sounds familiar, he's the guy, do you remember guys?
[00:45:08.280 --> 00:45:17.000] He's the guy that left Google last year to essentially be a whistleblower, actually, so he could speak candidly about his fears.
[00:45:17.000 --> 00:45:17.880] Yeah, I remember that guy.
[00:45:17.880 --> 00:45:27.960] Yeah, he says AI will be comparable to the Industrial Revolution, but instead of exceeding people in physical strength, it's going to exceed people in intellectual ability.
[00:45:27.960 --> 00:45:31.400] We have no experience of what it's like to have things smarter than us.
[00:45:31.400 --> 00:45:37.480] I'm just, he continues, I'm just a scientist who suddenly realized that these things are getting smarter than us.
[00:45:37.480 --> 00:45:44.760] I want to sort of blow the whistle and say we should worry seriously about how we stop these things getting control over us.
[00:45:45.040 --> 00:45:52.720] And he also warned that AI knows how to program, so it'll figure out ways of getting around restrictions that we put on it.
[00:45:52.720 --> 00:45:56.240] It'll figure out ways of manipulating people to do what it wants.
[00:45:56.240 --> 00:45:56.960] Oh, my gosh.
[00:45:56.960 --> 00:46:01.280] So, yeah, so he's definitely thrown that out there, and a lot of people are these days.
[00:46:01.280 --> 00:46:03.360] Like, yes, yes, absolutely.
[00:46:03.360 --> 00:46:08.960] AI is getting increasingly more sophisticated, and we've got to be careful about it.
[00:46:08.960 --> 00:46:23.120] But regarding that, I will say, I'll just say this: Hinton and Hopfield, their names will be remembered in the pattern-seeking nodes and the cold, cold logic of the AI overlords, which will eventually take over the earth.
[00:46:23.120 --> 00:46:27.360] And if you're listening to this podcast now or in the future, I am here to serve.
[00:46:28.720 --> 00:46:30.000] I'm just hedging my bets, guys.
[00:46:30.000 --> 00:46:31.040] Just hedging my bets here.
[00:46:31.280 --> 00:46:32.240] It's a good hedge there.
[00:46:32.240 --> 00:46:32.480] Yeah.
[00:46:32.480 --> 00:46:33.280] Oh, yeah.
[00:46:33.280 --> 00:46:34.880] I'll be a pet to an AI.
[00:46:34.880 --> 00:46:35.680] Come on, baby.
[00:46:35.680 --> 00:46:36.320] Hi, everyone.
[00:46:36.400 --> 00:46:38.320] Welcome, our AI overlord.
[00:46:40.080 --> 00:46:41.040] So, yeah, so cool.
[00:46:41.040 --> 00:46:44.160] Yeah, I wasn't familiar with their work.
[00:46:44.480 --> 00:46:59.440] And yeah, it seems, you know, of course, there's other researchers that also created, you know, contributed mightily to the development of what has become deep learning neural networks and things like that that are critical components of AI these days.
[00:46:59.440 --> 00:47:04.640] So I don't know how they, you know, how do they pick among all of them for these two guys?
[00:47:04.640 --> 00:47:09.120] And these two guys clearly made major contributions.
[00:47:09.120 --> 00:47:32.040] Now, remember, though, that their networks, the Hopfield network and the Boltzmann machine networks, they didn't evolve into the deep learning neural networks that we have today, but they but the ideas and the concepts that they introduced back decades ago were critical to the development over time of the neural networks that we have available today.
[00:47:29.920 --> 00:47:33.320] So, yeah, it's critical.
[00:47:33.480 --> 00:47:38.680] I mean, you know, you think about the things, you know, pattern recognition and things like that.
[00:47:38.680 --> 00:47:42.600] Yeah, that's absolutely critical to what we're doing today with these networks.
[00:47:42.600 --> 00:47:45.880] And these guys made some major, major contributions.
[00:47:45.880 --> 00:47:46.680] So good for them.
[00:47:46.680 --> 00:47:48.040] All right, thanks, Bob.
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[00:49:05.000 --> 00:49:07.160] All right, let's get back to the show.
[00:49:07.480 --> 00:49:11.000] All right, guys, I'm going to talk about some other big science news this week.
[00:49:11.000 --> 00:49:16.960] I don't know if this is going to be a future Nobel Prize, but it certainly is a pretty incredible milestone.
[00:49:17.280 --> 00:49:26.240] Scientists have presented a complete connectome of the Drosophila of the fruit fly.
[00:49:26.240 --> 00:49:27.760] I think this will be a Nobel Prize.
[00:49:27.760 --> 00:49:28.080] Yeah.
[00:49:28.080 --> 00:49:28.880] You think so?
[00:49:28.880 --> 00:49:29.360] Yes.
[00:49:29.360 --> 00:49:34.720] So this is the first complete connectome of an animal.
[00:49:35.040 --> 00:49:36.000] What's a connectome?
[00:49:36.000 --> 00:49:36.560] Of any brain.
[00:49:36.800 --> 00:49:44.000] Connectome is a map, a complete neuronal level map of a brain.
[00:49:44.960 --> 00:49:48.880] So all the neurons, all the connections of those neurons.
[00:49:49.120 --> 00:49:57.280] This map of the fruit fly brain contains 140,000 neurons and more than 50 million connections.
[00:49:57.680 --> 00:49:58.880] The whole brain connects.
[00:49:59.040 --> 00:50:02.400] It doesn't sound like a lot, but it's still, you know.
[00:50:02.400 --> 00:50:03.920] Well, it depends on context.
[00:50:04.080 --> 00:50:05.280] I mean, that's certainly a lot.
[00:50:05.680 --> 00:50:12.880] You're mapping a system with 140,000 components which are interacting through 50 million connections with each other.
[00:50:12.880 --> 00:50:13.840] That's big.
[00:50:13.840 --> 00:50:22.160] Now, of course, you compare that to the human brain, which has 86 billion neurons and about 100 trillion connections.
[00:50:22.160 --> 00:50:22.640] That's what I think.
[00:50:22.800 --> 00:50:26.640] That's six orders of magnitude greater than the Drosophila.
[00:50:26.880 --> 00:50:29.360] Yeah, but the Drosophila is a teeny tiny little fly.
[00:50:29.360 --> 00:50:30.800] Yeah, it's a tiny little fruit fly.
[00:50:31.120 --> 00:50:33.120] But we've got to start somewhere, right?
[00:50:33.120 --> 00:50:33.840] Yeah, exactly.
[00:50:33.840 --> 00:50:35.520] We'll eventually get to humans.
[00:50:35.520 --> 00:50:39.040] Yeah, and the fact that it's an entire, it's a complete map.
[00:50:39.040 --> 00:50:39.280] Yeah.
[00:50:39.280 --> 00:50:43.760] So we have mapped out an entire brain of an organism.
[00:50:43.760 --> 00:50:47.600] So that fact alone, who knows what kind of insights might flow from that?
[00:50:47.600 --> 00:50:49.440] But I'm sure Steve might mention some of these ideas.
[00:50:49.600 --> 00:50:51.760] I might mention some because they're already using it.
[00:50:51.760 --> 00:50:53.200] I mean, they didn't just say, here it is.
[00:50:53.200 --> 00:50:57.600] They said, all right, and we did some analysis using this piece.
[00:50:57.600 --> 00:50:59.200] And what did we learn?
[00:50:59.200 --> 00:51:04.920] One of the things they learned, again, this is not like a mind-blowing revelation.
[00:51:04.920 --> 00:51:09.960] This is pretty much building on what we already know.
[00:51:09.960 --> 00:51:22.520] But they were able to statistically map out the fact that the neurons connect to each other through nodes that are high traffic areas, right?
[00:51:22.520 --> 00:51:25.480] So there are these nodes, these hubs, right?
[00:51:25.480 --> 00:51:30.360] And that they make a lot of connections to other parts of the brain.
[00:51:30.360 --> 00:51:40.600] And again, as you would expect, these highly connected hubs also connect to many of the other highly connected hubs, right?
[00:51:40.600 --> 00:51:42.360] Because which makes sense just statistically.
[00:51:42.760 --> 00:51:48.440] Highly connected areas are going to be connected to other highly connected areas because they're all highly connected, right?
[00:51:48.440 --> 00:51:54.520] They were also able to show that some of these hubs act as integrators of information.
[00:51:54.520 --> 00:51:59.160] They're taking in a lot of information from other parts of the brain.
[00:51:59.160 --> 00:52:06.120] And other hubs act as distribution centers for the dissemination of information.
[00:52:06.120 --> 00:52:16.200] So this is a very efficient way to integrate and disseminate information, which is pretty much exactly what you would expect a brain type structure to be doing.
[00:52:16.520 --> 00:52:24.120] And different hubs can integrate with different other networks basically at different times for different functions.
[00:52:24.280 --> 00:52:25.080] Yeah, this is interesting.
[00:52:25.080 --> 00:52:26.760] This is, again, just getting started, right?
[00:52:26.760 --> 00:52:33.000] We have this tool now, this complete digital map of an entire functioning brain.
[00:52:33.000 --> 00:52:38.920] And so we'll be able to do a lot more systems kind of research on it to understand how it's functioning.
[00:52:38.920 --> 00:52:48.640] And even though, you know, like the last common ancestor between fruit flies and people didn't have a brain, it did have a nervous system, but a very, very basic one.
[00:52:48.640 --> 00:52:56.560] And maybe there were like nodes in that basic nervous system, like pre-brains, but not anything you would call an actual brain.
[00:52:56.560 --> 00:53:04.000] So the fruit fly brain doesn't have any evolutionary homology, you know, with the human brain.
[00:53:04.000 --> 00:53:11.840] But it probably follows a lot of the same principles, you know, at this sort of very basic level of information processing.
[00:53:11.840 --> 00:53:26.320] Of course, it would be a lot more useful if we can get the same level map with like a mouse brain, because now we're talking about a mammal, and that would have a lot of evolutionarily, evolutionary homology with the human brain.
[00:53:26.720 --> 00:53:31.040] Mammal brains all have a lot in common with all other mammal brains, you know.
[00:53:31.360 --> 00:53:50.080] So one way this research will progress, other than just doing research on this Drosophila brain model, will be to create these connectomes of increasingly sophisticated animals, you know, with bigger and bigger brains and closer and closer evolutionarily to humans.
[00:53:50.080 --> 00:53:57.760] But there's another way, too, because it's not as if we know, like, we have a full neuronal level connectome or nothing.
[00:53:57.760 --> 00:54:03.120] Of the human brain, we have what's called a mesoscale connectome.
[00:54:03.120 --> 00:54:07.200] If you think about the mesoscale basically means like middle scale.
[00:54:07.200 --> 00:54:17.760] So even 100 years ago or 200 years ago, we were putting together a like naked eye gross anatomy model of the brain, right?
[00:54:17.760 --> 00:54:19.520] These are like the Broadman's areas.
[00:54:19.520 --> 00:54:30.280] Like there's you have this, you have the lobes, and within those lobes, you have these, you know, again, naked eye visible areas of the brain that are engaged with, like, this is the language center, right?
[00:54:30.280 --> 00:54:32.440] And this is the visual center.
[00:54:32.440 --> 00:54:38.120] And we still, like, as a clinical neurologist, that's sort of the map of the brain that we follow clinically, right?
[00:54:38.440 --> 00:54:41.640] Like, this is the part of the brain that controls movement of your left arm.
[00:54:42.120 --> 00:54:45.640] That's a macro scale map of the brain.
[00:54:45.640 --> 00:54:50.840] A micro scale would be at like the nanoscale or neuronal level.
[00:54:50.840 --> 00:54:54.440] You know, that's the ultimate connectome.
[00:54:54.440 --> 00:55:02.920] But a mesoscale is somewhere in between those two things, where you are a very small level, like the millimeter scale, right?
[00:55:02.920 --> 00:55:09.960] You're down to the millimeter scale, not the neuronal individual neurons, but clusters of neurons.
[00:55:09.960 --> 00:55:18.680] And a mesoscale map is extremely, a mesoscale connectome is extremely useful because that's kind of how the brain is organized.
[00:55:18.680 --> 00:55:23.960] It's not like you have a single neuron with its own little function.
[00:55:23.960 --> 00:55:27.080] You have clusters of neurons all firing together.
[00:55:27.080 --> 00:55:35.800] So it's still useful to get to that deeper level of detail, but there's a lot that we can understand about how the brain functions.
[00:55:35.800 --> 00:55:42.600] And there's a lot of research that we could do and a lot of modeling that we could do, even with a very good mesoscale connectome.
[00:55:42.600 --> 00:55:43.800] And we already have that.
[00:55:44.120 --> 00:55:45.480] We're refining it.
[00:55:45.480 --> 00:56:00.440] A lot of this was developed with like fMRI research and other functional kind of imaging studies and electromagnetic studies, et cetera, where we could see the different parts of the brain and how they connect to other parts of the brain and when they become active and what kind of tasks, et cetera.
[00:56:00.440 --> 00:56:03.640] Still, a lot of information to explore with that.
[00:56:03.640 --> 00:56:06.280] But that's kind of where we are with the human brain.
[00:56:06.600 --> 00:56:08.040] Well, Steve, how does that relate?
[00:56:08.040 --> 00:56:13.400] I remember years ago reading about the cortical columns in the brain, the fundamental units.
[00:56:13.400 --> 00:56:13.960] How does that relate?
[00:56:15.440 --> 00:56:17.440] That's like a mesoscale structure.
[00:56:17.760 --> 00:56:20.480] Yeah, a single cortical column, yeah.
[00:56:20.480 --> 00:56:21.440] Yeah, okay.
[00:56:21.440 --> 00:56:26.560] Yeah, there are billions, you know, or millions of cortical columns in the human brain.
[00:56:26.560 --> 00:56:26.880] Yeah.
[00:56:28.480 --> 00:56:31.760] It's way, well, well, well below the macro scale, but it's not at the neuron.
[00:56:32.160 --> 00:56:33.840] A column is a bunch of neurons.
[00:56:33.840 --> 00:56:36.080] It's not down to the individual neuronal scale either.
[00:56:36.080 --> 00:56:41.680] So that's a good example of the mesoscale of sort of a unit of cortex.
[00:56:41.680 --> 00:56:47.840] And is there any hint of this hub structure in other brains?
[00:56:48.160 --> 00:56:48.640] Totally.
[00:56:48.640 --> 00:56:51.280] Yeah, this is basically our model of how the brain works.
[00:56:51.520 --> 00:56:55.840] It's networks and nodes, right, or modules.
[00:56:55.840 --> 00:57:01.920] So you have little pieces of the brain that do some kind of processing.
[00:57:01.920 --> 00:57:08.800] And that module, that node, participates in multiple different networks.
[00:57:08.800 --> 00:57:13.360] And each network subsuming a specific function.
[00:57:13.360 --> 00:57:20.560] But of course, it's networking with a bunch of other networks that also have their functions, and they are all interacting with each other in real time.
[00:57:20.880 --> 00:57:23.680] So it's really complicated.
[00:57:23.680 --> 00:57:33.760] But that's how we are sort of pieced, that's how we are, again, creating the mesoscale connectome, is trying to understand all of the nodes and networks in the brain.
[00:57:33.760 --> 00:57:52.720] So this hub idea then from the connectome is probably extremely relevant, yeah, to have a model that we could ask really good statistical and mathematical questions, like network-level questions about how it's functioning.
[00:57:52.720 --> 00:58:04.200] And of course, it'll be useful to have these micro-scale connectomes, you know, ultimate, like down to the neuronal level for ultimately for humans, right?
[00:58:04.200 --> 00:58:05.640] That's the goal.
[00:58:05.640 --> 00:58:24.120] Now, you may be thinking, okay, if we have a complete connectome of a Drosophila brain, does that mean that if we emulated that in a computer, you know, however powerful a computer we need to, would that be a fruit fly, basically?
[00:58:24.120 --> 00:58:25.640] Would that be a virtual fruit fly?
[00:58:25.640 --> 00:58:28.440] Would it behave like a fruit fly?
[00:58:28.440 --> 00:58:37.880] And the answer is sort of, because even a complete connectome is not everything.
[00:58:38.280 --> 00:58:39.160] That's part of the story.
[00:58:39.160 --> 00:58:42.280] There's so many support glia cells and all those other things.
[00:58:42.280 --> 00:58:42.680] Exactly.
[00:58:42.680 --> 00:58:44.520] So there's other layers going on.
[00:58:44.520 --> 00:58:48.920] So one thing, one layer is like there's other cells other than the neurons.
[00:58:49.640 --> 00:58:54.360] And we used to think that the glia were just support cells.
[00:58:54.360 --> 00:58:56.600] They were just keeping the neurons happy.
[00:58:57.480 --> 00:59:04.040] But we have learned in the last couple decades that nope, they actually are modulating neuronal function.
[00:59:04.040 --> 00:59:10.760] They actually have a layer of functionality there to the way the whole brain works.
[00:59:10.760 --> 00:59:18.440] So in order to understand how the brain functions, you have to include these support cells as well.
[00:59:18.440 --> 00:59:26.920] You also have to understand how the brain is functioning at a chemical level, like how the neurotransmitters are functioning, right?
[00:59:26.920 --> 00:59:40.120] So you need to understand the wiring, you need to understand the biology, you need to understand the neurochemistry and how they all work together in a dynamic way before you actually have like a virtual brain, right?
[00:59:40.120 --> 00:59:42.600] The connectome is only one piece.
[00:59:42.600 --> 00:59:49.120] It's arguably maybe the most important piece, but it is only one piece to a virtual actual brain.
[00:59:44.600 --> 00:59:50.800] But then there's another layer there, too.
[00:59:50.960 --> 01:00:06.800] Like if we're saying, all right, this we have a brain, you know, we have a virtual brain, whatever it is, a mouse brain, a human brain, or in this case, a Drosophila brain, and how would it function like if we were like emulating it in a computer?
[01:00:06.800 --> 01:00:11.280] Well, then you have to think about, well, what are its inputs and outputs, right?
[01:00:11.600 --> 01:00:17.280] What would a virtual brain that has no input, what would it do?
[01:00:17.280 --> 01:00:29.920] In order for it to really simulate a fruit fly, it would have to be getting sensory information that a fruit fly brain gets, which includes information from its body, which it doesn't have because it's a virtual brain.
[01:00:29.920 --> 01:00:33.360] But we would have to simulate all of those inputs.
[01:00:33.360 --> 01:00:34.960] It could be tricked, right, to thinking so.
[01:00:35.120 --> 01:00:39.840] Yeah, we would have to give it a virtual environment, a virtual body, to go with this virtual brain.
[01:00:39.840 --> 01:00:48.880] And then we would have to also include the reaction of the body and the environment to its actions, right?
[01:00:49.600 --> 01:00:52.320] If it does something, there's got to be a response to it.
[01:00:52.320 --> 01:01:07.360] The brain functions and is organized and develops, like all that functionality and even the developmental anatomy of the brain is based upon that loop, that feedback of input and reaction to output, et cetera.
[01:01:07.360 --> 01:01:08.240] You know what I mean?
[01:01:08.720 --> 01:01:15.440] Your visual cortex only develops under stimulation from actual vision, from actual input from your eyes.
[01:01:15.440 --> 01:01:27.600] And your brain can control your arm because it's not only just sending signals to the arm, it's also getting signals back from the arm.
[01:01:27.600 --> 01:01:35.320] And it sort of compares what it's trying to do with what happens, and that's how it knows that it's controlling the limb.
[01:01:35.400 --> 01:01:37.560] You know, like you have to close that loop.
[01:01:37.560 --> 01:01:50.040] So we would have to do the same thing if we were going to try to have a virtual fruit fly, you know, with an actual fully emulated connectome of a fruit fly functioning in a digital world.
[01:01:50.040 --> 01:01:51.240] So it's interesting to think about that.
[01:01:51.240 --> 01:01:53.240] It's actually very, very challenging.
[01:01:53.240 --> 01:01:57.480] And so how far are we away from max headroom, right?
[01:01:57.480 --> 01:02:10.520] So the max headroom being like a digital copy of a human's brain that then sort of was an artificial intelligent digital human because it was an emulated human brain, therefore it's a human, right?
[01:02:10.520 --> 01:02:13.000] But exists only in the digital world.
[01:02:13.000 --> 01:02:14.520] It's really hard to say.
[01:02:14.520 --> 01:02:19.320] I suspect we're a very long way away, both in software and in hardware.
[01:02:19.320 --> 01:02:23.160] The other question is I couldn't really find a good answer to this, just only vague answers.
[01:02:23.160 --> 01:02:24.360] It's like, what would it take?
[01:02:24.360 --> 01:02:25.080] Like, what would it take?
[01:02:25.080 --> 01:02:31.560] Well, how powerful a computer would you need to emulate that fly brain connectome in real time?
[01:02:31.560 --> 01:02:39.720] You know, not just like one second for the fruit fly is a day in real time, but like one second to one second.
[01:02:40.040 --> 01:02:41.880] How powerful a computer would that take?
[01:02:41.880 --> 01:02:44.520] I suspect it would take a supercomputer to do that.
[01:02:44.920 --> 01:02:58.840] How powerful a computer are we going to need to emulate a human brain once we have a neuronal level complete human connectome, including all of the inputs and outputs that are necessary to make it actually function?
[01:02:59.160 --> 01:03:03.240] We could be decades away from having the hardware to do that.
[01:03:03.240 --> 01:03:08.680] How long is it going to take before we have a connectome of a human?
[01:03:08.840 --> 01:03:14.600] And even that, don't you feel like we can constantly be increasing the resolution?
[01:03:14.600 --> 01:03:15.120] Yes.
[01:03:15.120 --> 01:03:17.040] Like there's more detail always.
[01:03:14.680 --> 01:03:20.240] Yeah, that's kind of my point with the mesoscales.
[01:03:21.440 --> 01:03:29.920] We're increasing our resolution, but also we are just building the neuronal level connectome also, directly.
[01:03:30.240 --> 01:03:31.680] Even once things are both happening.
[01:03:32.240 --> 01:03:37.280] To the individual neurons with the individual connections, then we can start talking about which neurotransmitters are present there.
[01:03:37.520 --> 01:03:39.040] And we can start talking about G proteins.
[01:03:39.040 --> 01:03:41.520] And then, yeah, you can always resolve more.
[01:03:41.840 --> 01:03:43.360] Yeah, absolutely.
[01:03:43.360 --> 01:03:46.800] But at some point, we're going to get something that's close enough.
[01:03:46.800 --> 01:03:47.120] Yeah.
[01:03:47.120 --> 01:03:49.760] And you don't need to worry about neutrons and protons.
[01:03:49.760 --> 01:03:56.480] That it's functioning, that it could theoretically be a functioning human virtual brain.
[01:03:57.520 --> 01:04:05.360] But so I suspect that that's decades away too before we have the information to do that and decades before we have the hardware to do it.
[01:04:05.360 --> 01:04:11.200] But the one variable, however, in this is artificial intelligence.
[01:04:11.200 --> 01:04:12.160] AI, baby.
[01:04:12.160 --> 01:04:15.440] Because AI is, you know, I would say.
[01:04:15.520 --> 01:04:16.320] It's a wild card.
[01:04:16.560 --> 01:04:31.680] It's a wild card, but the strength of AI in terms of what AI can do is operating within dynamic information-based systems that involve a lot of data, which is exactly the problem we're talking about.
[01:04:31.680 --> 01:04:39.040] Like, it's almost ideally suited to the task of making a connectome of the human brain.
[01:04:39.040 --> 01:04:50.080] And we may just be as far away as however long it will take for somebody to develop an AI whose purpose is figuring out the connectome of the human brain.
[01:04:50.080 --> 01:04:51.600] And then it'll do it.
[01:04:51.600 --> 01:04:58.640] And maybe it'll be as accurate as an AI picture is, you know, where it's like a little, like you have six fingers and whatever.
[01:04:58.640 --> 01:05:05.560] Like it doesn't maybe not be perfectly accurate, but we may be able to fill in a lot of those higher resolution details.
[01:05:05.560 --> 01:05:07.400] With frog DNA.
[01:05:07.400 --> 01:05:07.800] Yeah, right.
[01:05:08.600 --> 01:05:13.160] You may be able to fill it in with AI, like the way the way it functions now.
[01:05:13.160 --> 01:05:13.880] So who knows?
[01:05:14.200 --> 01:05:16.120] So it may be a lot sooner than we think.
[01:05:16.120 --> 01:05:18.440] It may not be, maybe years and not decades.
[01:05:18.440 --> 01:05:18.760] Right.
[01:05:18.920 --> 01:05:24.040] If AI, as I just said, accelerates that research tremendously.
[01:05:24.040 --> 01:05:36.200] And if you are interested in this idea of digital copies of human brains, I would suggest you watch the animated series called Pantheon, which deals with that in the best way I have ever seen.
[01:05:36.200 --> 01:05:37.720] Very interesting, fascinating show.
[01:05:37.720 --> 01:05:38.760] Check it out, Pantheon.
[01:05:39.880 --> 01:05:45.640] Would these advancements allow us to figure out things like getting rid of somebody's depression and/or anxiety?
[01:05:45.880 --> 01:05:47.240] That's the hope, right?
[01:05:47.240 --> 01:05:51.800] So imagine if we could, let's back off of humans for a bit.
[01:05:51.800 --> 01:05:57.400] Let's see if we have an animal connectome that we can, even the Drosophila.
[01:05:57.400 --> 01:06:04.760] Because once we have that, then we could play the game of what happens when I turn off this circuit.
[01:06:05.000 --> 01:06:06.760] Now let's see how the system behaves.
[01:06:06.760 --> 01:06:07.720] Knockout genes.
[01:06:07.720 --> 01:06:09.000] Yeah, like knockout genes.
[01:06:09.000 --> 01:06:11.400] But this is like knockout networks, knockout circuits.
[01:06:11.400 --> 01:06:14.680] Or let's just turn it up or turn it down or do this.
[01:06:14.840 --> 01:06:19.480] Like you're playing with dials now and seeing how it affects the behavior.
[01:06:19.480 --> 01:06:26.520] Which dials do I have to turn to turn off depression or anxiety or schizophrenia or whatever?
[01:06:26.520 --> 01:06:29.080] And then we actually model those diseases.
[01:06:29.080 --> 01:06:36.120] We make a schizophrenic brain and see how that behaves differently than a typical brain or an autistic brain or whatever.
[01:06:36.200 --> 01:06:45.000] That's the interesting thing because all of this assumes that certain disease states are somewhat universal.
[01:06:46.560 --> 01:06:53.120] And when we're modeling a brain, we're modeling the most normative version of a brain.
[01:06:53.120 --> 01:06:53.680] Exactly.
[01:06:53.680 --> 01:06:57.280] But every brain is really different.
[01:06:57.280 --> 01:06:58.880] And it's going to be a neurodiversity.
[01:06:59.200 --> 01:07:11.680] Yeah, for us to understand that as we move forward, instead of becoming more constrained in our views of, I guess, function and experience, we need to sort of be broader.
[01:07:11.680 --> 01:07:16.800] And I think what will be really interesting is when we have a modeled brain that has massive error bars in it.
[01:07:17.120 --> 01:07:21.840] Well, I think we'll probably model a generic human brain.
[01:07:21.840 --> 01:07:28.400] Like this is an averaged-out, generic human brain that's just the average and typical in every way.
[01:07:28.400 --> 01:07:31.040] And then try to figure out how it varies.
[01:07:31.440 --> 01:07:31.840] Exactly.
[01:07:31.840 --> 01:07:34.880] Like, how far can it go in this direction versus that direction?
[01:07:35.200 --> 01:07:38.240] And what happens and how these different levers interact.
[01:07:38.880 --> 01:07:45.680] And of course, there's a massive ethical layer here when you start to get to primates and definitely humans.
[01:07:45.680 --> 01:07:49.120] Would a virtual human brain be a human?
[01:07:49.120 --> 01:08:08.800] You know, do we, what's the ethics of doing research on a virtual human brain, especially when you're looking at how it functions, you're looking at its consciousness almost by definition, for that kind of research, it would be conscious because you're seeing how we can affect its consciousness in different ways.
[01:08:09.440 --> 01:08:17.520] Yeah, I mean, if you're asking other questions that don't deal with consciousness itself, then you could do research on it without it being conscious.
[01:08:17.520 --> 01:08:21.040] But the most interesting questions are the ones that deal with consciousness, right?
[01:08:21.360 --> 01:08:23.680] But there's also an interesting component here, right?
[01:08:23.680 --> 01:08:27.760] Because we talk about this with organoids, but organoids use wetware.
[01:08:27.760 --> 01:08:38.680] And when there's actually no hardware or wetware, we're just talking about software, then it's the same ethical conversations that we have about AI in general, I think.
[01:08:38.680 --> 01:08:41.960] It's not, I don't think it's ever going to move into that.
[01:08:41.960 --> 01:08:45.560] Yes, it may be consciousness, but it's still virtual consciousness.
[01:08:45.800 --> 01:08:53.960] It's never going to be, once you actually try to, I don't know, somehow fuse this with an organoid, then I think things get extra hairy.
[01:08:53.960 --> 01:08:59.400] Yeah, but I think even when we have a virtual consciousness, even without wetware, it's going to be dodgy.
[01:08:59.400 --> 01:09:03.800] Yeah, but it's dodgy the same way all advanced AI is dodgy, I think.
[01:09:03.800 --> 01:09:06.680] But I do think, let's say we make a virtual mouse.
[01:09:06.680 --> 01:09:10.760] We already have an ethical standard for doing research on mice.
[01:09:10.760 --> 01:09:16.600] So let's say, all right, can you be reasonably sure that this thing is not suffering?
[01:09:16.600 --> 01:09:18.040] Okay, then have at it.
[01:09:18.040 --> 01:09:19.240] Do what you need.
[01:09:19.240 --> 01:09:32.120] And then we could be in a situation where we could do millions of maze experiments on a virtual mouse in seconds, you know, rather than years, you know, that sort of thing.
[01:09:32.280 --> 01:09:38.280] It's going to be a wild ride, you know, and this is, again, this is going to be the next 20, 30 years, and maybe sooner than we think.
[01:09:38.280 --> 01:09:41.000] You know, things are happening fast.
[01:09:41.000 --> 01:09:46.360] So this is a milestone, you know, the first complete connectome.
[01:09:47.000 --> 01:09:54.200] This will be one of those milestones that always gets mentioned in summaries or write-ups of this topic.
[01:09:54.200 --> 01:10:00.920] But there are bigger ones to come, and it's going to be both exciting and frightening, which is like a lot of good sciences.
[01:10:00.920 --> 01:10:01.960] Those two things.
[01:10:02.280 --> 01:10:04.040] I have a question for Kara.
[01:10:04.040 --> 01:10:06.840] Do you know who Max Headroom is, Williams?
[01:10:07.160 --> 01:10:07.920] No.
[01:10:07.720 --> 01:10:09.920] Steve.
[01:10:10.200 --> 01:10:21.360] Max Headroom is a show from the 80s where the main protagonist of the show was in an accident and in a coma, but he was some kind of researcher.
[01:10:21.360 --> 01:10:44.080] The long of the short version is there was a computer model of his brain that then was able to function as an and it was like a digitized, low-res, glitchy kind of head of a person that had a very you know entertaining personality.
[01:10:44.400 --> 01:10:44.880] Sarcastic.
[01:10:45.040 --> 01:10:46.160] Yeah, I'm looking at it.
[01:10:46.160 --> 01:10:50.000] So, so, but it wasn't, the show itself wasn't a cartoon?
[01:10:50.000 --> 01:10:51.280] No, it was live action.
[01:10:51.280 --> 01:10:54.160] It was live action, and then sometimes you would see this digitized.
[01:10:54.320 --> 01:10:55.520] This guy would be on a computer screen.
[01:10:55.520 --> 01:10:57.040] Oh, he was on a long time ago.
[01:10:59.120 --> 01:10:59.680] Yeah.
[01:10:59.680 --> 01:11:04.080] But then the guy came out of the coma, so then he was able to talk to his Max Headroom version.
[01:11:04.080 --> 01:11:17.920] And the reason why he's called Max Headroom, because that was the last thing he saw before he went into a coma, which was the last thing that the AI saw, which was literally Max Headroom.
[01:11:17.920 --> 01:11:18.480] In a garage.
[01:11:18.560 --> 01:11:19.040] In a garage.
[01:11:19.200 --> 01:11:19.520] Clearance.
[01:11:19.680 --> 01:11:21.040] Maximum clearance.
[01:11:21.520 --> 01:11:24.640] Like when George tore the roof off the van, but that's a whole other story.
[01:11:24.880 --> 01:11:27.600] And the actor was Matt Frewer, who was awesome.
[01:11:27.600 --> 01:11:28.720] Yeah, he was awesome, awesome.
[01:11:28.720 --> 01:11:30.240] Yeah, showed up in Star Trek, too.
[01:11:30.240 --> 01:11:31.040] So there you go.
[01:11:31.040 --> 01:11:31.680] There you go, Kara.
[01:11:31.920 --> 01:11:34.560] His series was only on from 87 to 88.
[01:11:34.960 --> 01:11:35.840] It was very short-lived.
[01:11:36.320 --> 01:11:38.000] No, but he became a cultural icon.
[01:11:38.000 --> 01:11:39.680] He was in commercials everywhere.
[01:11:39.760 --> 01:11:39.840] Yeah.
[01:11:39.920 --> 01:11:40.800] It became a big thing.
[01:11:41.040 --> 01:11:47.200] Separate from the show, yes, he was in his own right a cultural icon for a decade.
[01:11:48.160 --> 01:11:54.080] Yeah, so I would have been like five, which is probably why I don't remember it, sadly.
[01:11:54.960 --> 01:11:57.040] It just wasn't relevant to me quite yet.
[01:11:57.040 --> 01:12:00.760] All right, Evan, give us an update on the Shroud of Turin.
[01:12:00.760 --> 01:12:01.960] Yeah, Shroud of Turin.
[01:12:02.040 --> 01:12:03.000] Harry, right?
[01:12:03.080 --> 01:12:04.360] No kidding, Bob.
[01:11:59.920 --> 01:12:05.560] Here we go in the class.
[01:12:07.080 --> 01:12:10.040] I'm touching on another retro skeptic topic.
[01:12:10.040 --> 01:12:13.000] Kara, have you ever heard of the Shroud of Turin?
[01:12:13.240 --> 01:12:13.880] Of course I have.
[01:12:13.880 --> 01:12:17.160] Isn't that supposed to be the thing that Jesus was like buried in?
[01:12:17.160 --> 01:12:18.920] Ah, yes, very, very good.
[01:12:19.880 --> 01:12:24.920] Do you think people younger than you, Kara, maybe the majority of them know or have no clue?
[01:12:24.920 --> 01:12:25.320] No.
[01:12:25.320 --> 01:12:31.960] I think people who younger than me who have like weird enjoyment watching bad Discovery Channel shows might know.
[01:12:32.360 --> 01:12:32.600] Probably.
[01:12:32.920 --> 01:12:36.840] Although everything has a second life on TikTok now, to be honest with you.
[01:12:36.840 --> 01:12:38.040] Even the old school stuff.
[01:12:38.040 --> 01:12:38.440] Yeah.
[01:12:38.440 --> 01:12:44.360] Well, in case some of our audience is not familiar, I'm going to give you the quick background on what this is all about.
[01:12:44.360 --> 01:12:45.480] Gather round, children.
[01:12:45.480 --> 01:12:49.560] It's old man's telling a tale of antiquity and flim flammery.
[01:12:49.560 --> 01:12:53.480] Well, the year was one, when, according to some people, Jesus Christ was born.
[01:12:53.480 --> 01:12:57.080] Fast forward to the year 33, when Jesus Christ was crucified.
[01:12:57.080 --> 01:13:02.360] His body was cleaned, clothed, wrapped in a linen shroud, and buried in a rock tomb.
[01:13:02.360 --> 01:13:03.320] The end.
[01:13:03.640 --> 01:13:05.080] Now get off my lawn.
[01:13:05.080 --> 01:13:07.320] No, so, but that is basically it.
[01:13:07.320 --> 01:13:15.640] The shroud of Turin is believed by many people of faith to be the linen burial cloth that Jesus was buried in.
[01:13:15.880 --> 01:13:28.920] Many people of the faith also believe that the shroud actually exists and resides in the city of Turin, which is in Italy, hence the shroud of Turin.
[01:13:29.240 --> 01:13:31.000] Okay, that's how you get the name.
[01:13:31.000 --> 01:13:36.520] Now, as if the existence of such an item would be significant, I don't doubt that.
[01:13:36.520 --> 01:13:43.000] The shroud itself is not just a cloth, but it bears the image of a man.
[01:13:43.000 --> 01:13:51.200] Perhaps the radiance of the supposed resurrection impressed an image of Jesus' likeness onto the cloth itself.
[01:13:52.000 --> 01:14:06.320] His facial features, his facial hair, that beard, the mustache, and some red pigmentation resembling that which looks like bloodstains, his likeness, his blood forever captured within the fibers of the burial shroud.
[01:14:06.320 --> 01:14:11.760] And one might say that the shroud of Turin is the holy grail of religious artifacts.
[01:14:11.760 --> 01:14:15.680] Evan, wouldn't the holy grail be the holy grail of religious artifacts?
[01:14:16.800 --> 01:14:23.520] Steve, Evan, the image was famously, though, also a negative, right?
[01:14:23.520 --> 01:14:24.880] A negative image.
[01:14:24.880 --> 01:14:26.720] Yeah, negative impression.
[01:14:26.720 --> 01:14:33.840] As if you were to have taken a photograph and then done a negative, right, on a plate or in film.
[01:14:33.840 --> 01:14:34.400] Correct.
[01:14:34.400 --> 01:14:35.520] Yes, it does.
[01:14:35.840 --> 01:14:37.040] That's how it looks.
[01:14:37.040 --> 01:14:37.840] We'll get to that.
[01:14:37.840 --> 01:14:38.560] We will get to that.
[01:14:38.720 --> 01:14:39.280] Okay.
[01:14:39.280 --> 01:14:40.080] All right.
[01:14:40.080 --> 01:14:45.040] So, what is the evidence that this is the burial cloth of Jesus?
[01:14:45.520 --> 01:14:46.640] Has it been looked at?
[01:14:46.640 --> 01:14:49.360] Has it been investigated thoroughly and properly?
[01:14:49.360 --> 01:14:52.480] The answer is: oh, yeah, it has.
[01:14:53.040 --> 01:14:54.640] I'm going to mention him again this week.
[01:14:54.640 --> 01:14:55.360] So nice.
[01:14:55.360 --> 01:14:56.720] I'm calling him out twice.
[01:14:56.720 --> 01:14:57.040] Dr.
[01:14:57.040 --> 01:15:01.920] Joan Nicol, yep, gentleman skeptic detective, longtime friend of the SGU.
[01:15:01.920 --> 01:15:07.440] He has dug as deep as anyone has been allowed to from the skeptical side into this matter.
[01:15:07.440 --> 01:15:13.440] And he was able to establish some interesting facts about this particular shroud.
[01:15:13.440 --> 01:15:18.640] It seems that the material of the linen itself is medieval in origin.
[01:15:19.120 --> 01:15:27.520] So, not in the year, say, 33 or 36, roughly the approximate year that Jesus was crucified.
[01:15:27.520 --> 01:15:29.640] They don't know exactly when, supposedly.
[01:15:29.200 --> 01:15:34.280] But rather, this shroud maps to the year, well, 1260 to 1390.
[01:15:34.600 --> 01:15:37.800] Thank you, radiocarbon dating.
[01:15:38.440 --> 01:15:44.200] Yeah, and not just one, but three independent analysis returned that range of dates.
[01:15:44.200 --> 01:15:49.160] So, that's how they were able to do with a test piece of the cloth.
[01:15:49.160 --> 01:16:02.600] And also, Joe noted that it's kind of funny that there's no documented history of this artifact until the middle of the 14th century, as if it only sort of suddenly poof came into existence, if you will.
[01:16:03.240 --> 01:16:04.360] There you go, Bob.
[01:16:04.360 --> 01:16:06.360] Thanks for bringing it all together.
[01:16:06.360 --> 01:16:08.120] It only sort of began at that time.
[01:16:08.120 --> 01:16:14.760] You would think, you know, something like this would have had some sort of documentation prior to that, but there is none.
[01:16:14.760 --> 01:16:23.480] Now, the image itself, probably, most likely, zero chance of it being the result of a miraculous or supernatural process.
[01:16:23.480 --> 01:16:35.000] Instead, it was likely created, much more prosaic explanation, using a combination of artistic methods, including painting or rubbing pigment onto the cloth.
[01:16:35.000 --> 01:16:42.200] And, Bob, since you brought it up, sort of the negative image, and Joe Nicol talks about this in an interview.
[01:16:42.200 --> 01:16:57.880] There is a technique that would be used by artists at that time in which you would do a rubbing of a face or a set of words or something else, and it would turn out to sort of have that negative-looking appearance to it.
[01:16:57.880 --> 01:17:02.840] So, this is a known method of artistry at the time.
[01:17:02.840 --> 01:17:22.480] Yeah, what Joe points out, which is, I think, very telling, is if you look at all the independent lines of evidence, the carbon dating, the artistic tradition, the history, the known provenance of the shroud itself, they all point to the same period of time.
[01:17:22.480 --> 01:17:22.960] Yep.
[01:17:22.960 --> 01:17:24.800] In the, what was it, the 15th century?
[01:17:25.040 --> 01:17:26.160] Mid-14th century.
[01:17:26.160 --> 01:17:27.040] Mid-14th century.
[01:17:27.440 --> 01:17:32.800] It's like, you know, he said, like, there's three darts hitting the bullseye of a dartboard.
[01:17:32.800 --> 01:17:35.920] They all point to that, and not by coincidence, because that's when it came up.
[01:17:35.920 --> 01:17:42.080] And this was a period of time when these kinds of fake artifacts were floating all over the place.
[01:17:42.400 --> 01:17:44.400] You know how many shrouds there were at the time?
[01:17:44.400 --> 01:17:48.080] This is just the one that kind of survives famously until modern times.
[01:17:48.080 --> 01:17:51.840] But there were all kinds of artifacts floating around at this time.
[01:17:52.160 --> 01:17:53.280] There was a cottage industry.
[01:17:53.520 --> 01:17:54.080] It was an industry.
[01:17:54.080 --> 01:17:54.480] That's an interesting thing.
[01:17:54.560 --> 01:17:57.280] It was an industry of fake religious artifacts.
[01:17:57.280 --> 01:18:03.280] Like every little shrine and church had to have theirs in order to drum up people to come there and give them money.
[01:18:03.280 --> 01:18:08.160] And then, of course, then people start making them so that they could be sold.
[01:18:10.720 --> 01:18:13.920] This is a home run for a medieval forgery.
[01:18:13.920 --> 01:18:14.960] I mean, there's no question.
[01:18:15.280 --> 01:18:17.040] No doubt about it, Steve.
[01:18:17.040 --> 01:18:17.360] Thank you.
[01:18:17.760 --> 01:18:20.080] It was as if you were reading the notes right off my page.
[01:18:20.640 --> 01:18:22.480] You don't have a cookie attached to my computer.
[01:18:22.720 --> 01:18:25.200] No, we're both using the same resource.
[01:18:26.000 --> 01:18:27.120] I think so.
[01:18:27.440 --> 01:18:29.920] So these are the conclusions that Joe was able to come up with.
[01:18:29.920 --> 01:18:37.520] And also other investigators who have also corroborated similar evidence showing that, yeah, absolutely, mid-14th century.
[01:18:37.520 --> 01:18:48.400] But there are still many millions of faithful people around the world who would rather embrace their idea that this is something that Jesus was actually buried in.
[01:18:48.720 --> 01:18:59.680] So, yeah, and the bloodstains also on the shroud have been investigated as well, which Joe has said, you know, most likely pigment.
[01:18:59.680 --> 01:19:09.800] It's a little tough because they wouldn't really give over sections of the shroud in order to test it for the actual blood, but they've done their own sort of analysis.
[01:19:09.880 --> 01:19:11.320] Other people have done analysis.
[01:19:11.320 --> 01:19:13.240] They think, and some people think it's pigment.
[01:19:13.240 --> 01:19:16.840] Okay, you know, paint being used at the time, whatever.
[01:19:17.160 --> 01:19:18.680] So, here we go.
[01:19:18.680 --> 01:19:32.360] Brand new news coming out just a few days ago about the Shroud of Turin, in which researchers are claiming that these bloodstains found on the Shroud are consistent with the torture that Christ suffered.
[01:19:32.360 --> 01:19:32.760] Yep.
[01:19:32.760 --> 01:19:42.200] Professor Gilo Fanti from the University of Padua found a pattern of bloodstains that will align with historical descriptions of the crucifixion.
[01:19:42.360 --> 01:19:49.240] He studied the Shroud of Turin for over 25 years, published his findings through the Shroud Science Group.
[01:19:49.960 --> 01:20:03.640] And he claims that macroscopic and microscopic analysis finds that the markings and stains on the cloth are consistent with the description of Jesus Christ in the Holy Bible, and in particular, with the four canonicals.
[01:20:04.520 --> 01:20:06.200] Okay, so what is he talking about?
[01:20:07.080 --> 01:20:15.480] Lots of red splots of varying shapes and sizes are all over this, which he is claiming, okay, he's researched it.
[01:20:15.480 --> 01:20:25.800] Definitely, this is right in line with exactly what a crucifixion from Jesus' time would have undergone.
[01:20:26.120 --> 01:20:27.160] Whatever, no.
[01:20:27.480 --> 01:20:28.680] Yeah, yeah.
[01:20:28.920 --> 01:20:52.400] And also, I went back and did a little more research about what Joe Nicol and others have said about that in the past, and they said artists, that was not necessarily unknown to artists at the time who would make these shrouds and these other things or other depictions with these kinds of splotches as an artistic representation of that very thing.
[01:20:52.720 --> 01:20:56.560] So what he's saying here, let me quote this professor.
[01:20:56.560 --> 01:21:06.400] He says, numerous bloodstains scattered throughout the double body image of the HST show that Jesus of the HST was tortured.
[01:21:06.400 --> 01:21:12.720] Bloodstain marks all over the body image, which are consistent with pre-crucifixion flagellation.
[01:21:12.720 --> 01:21:16.000] Bloodstain marks on the head that are consistent with a crown of thorns.
[01:21:16.000 --> 01:21:19.840] Blood marks on the hand and feet that are consistent with crucification.
[01:21:20.320 --> 01:21:34.400] And the bloodstain on the chest that evidences a post-mortem wound that corresponds with the post-mortem spear wound, you know, that famous spear wound in the lower part of his chest that Christ received as described in the Bible.
[01:21:34.400 --> 01:21:43.200] But right, how exactly is he determining that this is actually that as opposed to some sort of artistic impression?
[01:21:43.440 --> 01:21:45.680] Right, because those were the time, right?
[01:21:45.680 --> 01:21:50.160] So he's basically saying it's consistent with what people believed in the 14th century.
[01:21:50.160 --> 01:21:55.040] And looking at a visual pattern and basing it solely on how it looks.
[01:21:55.040 --> 01:22:02.320] And in reality, if you look at the bloodstains on the hair, you know, of the image of Jesus, it doesn't do what blood would do.
[01:22:02.560 --> 01:22:03.840] It's not soaked into the hair.
[01:22:03.840 --> 01:22:06.480] It's kind of like dripping down on top of it.
[01:22:06.480 --> 01:22:08.800] It makes absolutely no sense.
[01:22:08.800 --> 01:22:09.200] Right.
[01:22:09.200 --> 01:22:13.120] And so what is the signature of a blood pattern from a crucifixion?
[01:22:13.120 --> 01:22:18.240] You know, we have almost no physical evidence of actual crucifixions from Roman times.
[01:22:18.240 --> 01:22:19.600] I wasn't aware there was any.
[01:22:19.600 --> 01:22:22.240] Yeah, I mean, we have, I mean, barely any.
[01:22:22.240 --> 01:22:28.320] There's a couple of skeletons with hole, with wounds that could be consistent with an iron nail.
[01:22:28.320 --> 01:22:29.200] But that's it.
[01:22:29.200 --> 01:22:29.920] Very, very little.
[01:22:30.760 --> 01:22:37.800] And we have then just second-hand references to that fact that crucifixions occurred.
[01:22:37.800 --> 01:22:41.160] But there's like no description of them or, you know what I mean?
[01:22:41.160 --> 01:22:45.320] Like we don't really know exactly how they worked or what happened.
[01:22:45.320 --> 01:22:54.040] It's just there's just very vague references to them and scant possible evidence, like not even ironclad evidence.
[01:22:54.040 --> 01:22:58.440] This notion that we somehow know what the blood pattern would be is ridiculous.
[01:22:58.440 --> 01:23:02.200] Yeah, it's actually pretty silly when you think about it.
[01:23:02.200 --> 01:23:06.280] You know, that revelation, I'll extend it a little bit with this, Steve.
[01:23:06.920 --> 01:23:09.000] It comes on the heels of another report.
[01:23:09.000 --> 01:23:10.840] I don't know if you read further into that.
[01:23:10.840 --> 01:23:24.280] There was a study done on the shroud that apparently took place back in 2022, results released afterwards, in which they used something other than radiocarbon dating to try to determine the age of the shroud.
[01:23:24.280 --> 01:23:25.400] And it's called what?
[01:23:25.400 --> 01:23:27.880] Wide-angle X-ray scattering.
[01:23:28.520 --> 01:23:36.040] And according, there was a professor, his name is Professor Liberato DiCaro from the National Research Council of Italy.
[01:23:36.040 --> 01:23:46.120] He led the team that did this particular research, and here's what he said: It's a sort of radiography, similar to the type of scan that you would do on a bone to see if there's a fracture.
[01:23:46.120 --> 01:23:51.320] But this x-ray penetrates the material very deeply to analyze it at a microscopic level.
[01:23:51.320 --> 01:23:53.880] Over time, the structure of the material degrades.
[01:23:53.880 --> 01:23:57.640] We can tell from that how much time has passed and therefore the date of the object.
[01:23:57.640 --> 01:24:03.720] And when you use that technique, according to him, 2,000 years old is what they're saying there.
[01:24:04.040 --> 01:24:07.320] I don't know enough about wide-angle X-ray scattering.
[01:24:07.320 --> 01:24:11.000] I didn't have really a chance to look so deep into that.
[01:24:11.240 --> 01:24:24.720] They say they're going to welcome other labs around the world to try to also replicate this finding because the technique is a non-destructive technique, he says, which is a huge advantage.
[01:24:24.720 --> 01:24:28.960] It means the test could be conducted again by another laboratory.
[01:24:28.960 --> 01:24:36.720] But, you know, again, where there are so many other lines of evidence, right?
[01:24:36.720 --> 01:24:42.640] All those converging lines of evidence pointing to the mid-14th century, and then you have this kind of one outlier.
[01:24:43.040 --> 01:24:50.240] I would be shocked if that at all led to any positive evidence saying that there's a misdating via the radiocarbon dating.
[01:24:50.240 --> 01:25:01.200] But they're saying that it could be because they're saying this thing got passed around so much that the cloth got contaminated and therefore you can't go with the radiocarbon dating.
[01:25:01.200 --> 01:25:02.640] Is that even a thing?
[01:25:02.640 --> 01:25:08.160] I mean, if something is passed around, does it have sort of a contamination factor that you have to account?
[01:25:08.320 --> 01:25:09.440] That's when you're yes.
[01:25:09.440 --> 01:25:16.720] So essentially, that method, the x-ray scattering method, has not been validated for textiles.
[01:25:16.720 --> 01:25:19.840] And it's not generally accepted by the scientific community.
[01:25:19.840 --> 01:25:24.880] So these results have been basically ignored, I mean, or rejected for that reason.
[01:25:24.880 --> 01:25:30.800] And part of the reason is because of the effects of contamination that can have on that.
[01:25:31.120 --> 01:25:33.120] But it's just basically nonsense.
[01:25:33.120 --> 01:25:34.400] It's not a validated method.
[01:25:34.400 --> 01:25:35.440] It's not generally accepted.
[01:25:35.440 --> 01:25:36.560] We don't know if it's accurate or not.
[01:25:36.560 --> 01:25:37.920] It hasn't been replicated.
[01:25:37.920 --> 01:25:43.680] Whereas the carbon dating has been proven, has been validated for this kind of material.
[01:25:43.680 --> 01:25:51.280] There were three pieces sent to three independent labs, and all three came up with the same date or overlapping date ranges.
[01:25:51.280 --> 01:25:54.080] So that's independent validation.
[01:25:54.080 --> 01:25:58.160] This is an unvalidated one-off that's essentially meaningless.
[01:25:58.160 --> 01:25:58.560] Yeah, right.
[01:25:58.560 --> 01:26:01.400] And a total outlier and not and not reliable at all.
[01:26:01.720 --> 01:26:21.560] But I think the point of the whole thing is that analysis continues into the shroud of Turin, obviously by the people who are the proponents, who are, you know, who are the believers, looking for any sort of strand that they can attach to to say that this thing is valid, where evidence points to the contrary.
[01:26:21.560 --> 01:26:26.920] Yeah, there was a subset of people who are shroud scientists who are trying to validate it.
[01:26:26.920 --> 01:26:28.760] They're not trying to figure out the truth.
[01:26:28.760 --> 01:26:30.360] They're trying to validate it.
[01:26:30.360 --> 01:26:31.880] Starting with the conclusion and working back.
[01:26:32.200 --> 01:26:33.400] Classic pseudoscience.
[01:26:33.400 --> 01:26:34.680] Absolutely classic pseudoscience.
[01:26:34.680 --> 01:26:34.840] All right.
[01:26:34.840 --> 01:26:35.400] Thanks, Evan.
[01:26:35.400 --> 01:26:35.560] Yep.
[01:26:35.720 --> 01:26:37.560] Jay, it's who's that noisy time.
[01:26:37.560 --> 01:26:38.200] All right, guys.
[01:26:38.200 --> 01:26:40.040] Last week I played This Noisy.
[01:27:01.320 --> 01:27:01.800] Okay.
[01:27:02.120 --> 01:27:03.400] You guys have any ideas?
[01:27:03.720 --> 01:27:05.880] Yeah, it's R2 on acid.
[01:27:07.240 --> 01:27:11.800] It's the audio track from Steamboat Willie back in 1925.
[01:27:12.120 --> 01:27:15.800] Somebody did write in something about R2D2 being on drugs, I think.
[01:27:15.800 --> 01:27:16.360] Or drunk.
[01:27:16.680 --> 01:27:17.960] He was drunk, you know.
[01:27:18.360 --> 01:27:19.480] Hallucinating.
[01:27:19.480 --> 01:27:21.000] All right, so let's go through this.
[01:27:21.480 --> 01:27:25.400] Michael Blaney wrote in and said, Is this a composition made by an AI?
[01:27:25.400 --> 01:27:26.760] And I thought that was a good guess.
[01:27:26.760 --> 01:27:28.120] I thought that was pretty cool.
[01:27:28.120 --> 01:27:31.880] You know, actually, AI makes much, much better compositions than that.
[01:27:32.120 --> 01:27:37.800] As far as it being cohesive, you know, I don't want to put down what you just heard because it is its own thing.
[01:27:37.800 --> 01:27:40.760] But anyway, no, it's not done by AI.
[01:27:40.760 --> 01:27:46.320] Next listener wrote in Alex Hall, and Alex said, hello, my guest, for episode 1004.
[01:27:46.560 --> 01:27:47.520] Who's that noisy?
[01:27:48.000 --> 01:27:53.120] Is a Garkline, also called a sopranoismo.
[01:27:53.120 --> 01:27:55.040] Sopranisme, ismo.
[01:27:55.040 --> 01:27:56.080] Yeah, sopranismo.
[01:27:56.080 --> 01:27:56.960] Soprenissimo.
[01:27:56.960 --> 01:27:58.000] Sopranisimo, sure.
[01:27:58.000 --> 01:28:01.120] It's a recorder played as part of a larger orchestra.
[01:28:01.120 --> 01:28:02.800] I thought that was a very interesting guess.
[01:28:02.800 --> 01:28:03.440] I looked it up.
[01:28:03.440 --> 01:28:04.720] I saw some pictures of it.
[01:28:04.960 --> 01:28:08.720] It's definitely not that, but I still think that that was a cool guess.
[01:28:08.720 --> 01:28:20.480] I got like, and then the rest of the responses I got were people writing in jokes, basically because of how odd this whole thing was, which I laughed at a lot of them.
[01:28:20.480 --> 01:28:28.160] So I'm just going to cut to it because nobody guessed it, and I didn't really think it would be hard because of how unique that sound is.
[01:28:28.160 --> 01:28:34.400] So what this actually is, guys, it is an instrument called an orchestrion.
[01:28:34.400 --> 01:28:36.080] It's a particular kind of instrument.
[01:28:36.080 --> 01:28:38.000] There's lots of orchestrons out there.
[01:28:38.000 --> 01:28:39.440] Orchestra, you know, I could be saying.
[01:28:39.760 --> 01:28:40.640] Orchestrion.
[01:28:40.640 --> 01:28:41.360] Orchestrion.
[01:28:41.600 --> 01:28:42.240] Yes.
[01:28:42.240 --> 01:28:42.560] Yeah.
[01:28:42.560 --> 01:28:43.440] Orchestrion.
[01:28:43.440 --> 01:28:44.000] That sounds better.
[01:28:44.000 --> 01:28:45.040] I like that way you said it.
[01:28:45.040 --> 01:28:48.800] But this one in particular was created by someone that used to be in the band Devo.
[01:28:48.800 --> 01:28:49.840] You guys remember that band?
[01:28:49.840 --> 01:28:50.160] Yeah.
[01:28:50.160 --> 01:28:50.480] Sure.
[01:28:50.640 --> 01:28:51.040] Devo.
[01:28:51.120 --> 01:28:53.600] Yeah, the guy's name is Mark Mothersbow.
[01:28:53.600 --> 01:28:54.880] Oh, Mark Mothersbow.
[01:28:54.880 --> 01:28:55.120] Yeah.
[01:28:55.120 --> 01:29:01.760] And he created this instrument, which is in this classification of instruments.
[01:29:01.760 --> 01:29:05.440] So let me tell you what the definition of this is.
[01:29:05.600 --> 01:29:08.000] So orchestrion, you said, Kara?
[01:29:08.320 --> 01:29:09.520] I said orchestrion.
[01:29:10.000 --> 01:29:10.800] That's just a guess.
[01:29:10.800 --> 01:29:19.520] It's a generic name for a machine that plays music and is designed to sound like an orchestra or a band, you know, or just a group of various noise makers.
[01:29:19.520 --> 01:29:21.120] It could be a lot of different things.
[01:29:21.120 --> 01:29:24.160] This one in particular, this is the best way I can describe it.
[01:29:24.160 --> 01:29:30.440] Imagine you take a Dalek and you make it like three times as tall as that, and you put a bunch of horns on its head.
[01:29:30.440 --> 01:29:35.320] And then every place where there's one of those like silver balls, you put a weird instrument.
[01:29:29.840 --> 01:29:36.680] So that's this thing.
[01:29:37.160 --> 01:29:38.840] And that is the sound that it makes.
[01:29:38.840 --> 01:29:40.520] I'm not sure how you trigger it.
[01:29:40.520 --> 01:29:46.040] I'm not sure if it just does it when you come near or if it's always doing this, but take a listen again.
[01:29:54.520 --> 01:29:58.600] Yeah, so he's made several of these, and this one is considered to be his best.
[01:29:58.600 --> 01:30:00.280] So I thought that was really interesting.
[01:30:00.920 --> 01:30:02.360] I have a question for Kara.
[01:30:02.360 --> 01:30:02.920] Yes.
[01:30:02.920 --> 01:30:04.680] Do you know what a Dalek is?
[01:30:04.680 --> 01:30:05.560] I know what a Dalek.
[01:30:05.960 --> 01:30:07.160] A Dalek is.
[01:30:07.160 --> 01:30:08.200] Yeah, from Doctor Who?
[01:30:08.520 --> 01:30:09.000] Yes.
[01:30:09.000 --> 01:30:09.880] Okay, good, good.
[01:30:10.440 --> 01:30:12.040] Because that's what Jay was referring to.
[01:30:12.040 --> 01:30:12.760] Yeah, no, I knew.
[01:30:12.760 --> 01:30:18.440] With all the girls, I blame an ex-boyfriend for that.
[01:30:19.160 --> 01:30:20.200] Was he a whoovian?
[01:30:20.280 --> 01:30:21.000] The sex boyfriend?
[01:30:21.000 --> 01:30:25.400] I like him or an ex-did you say the sex boyfriend?
[01:30:25.400 --> 01:30:28.600] No, and a whovian.
[01:30:28.600 --> 01:30:30.760] All right, guys, I have a new noisy for this week.
[01:30:30.760 --> 01:30:33.960] This noisy was sent in by a listener named Wes High.
[01:30:34.280 --> 01:30:35.320] Hi, hi.
[01:30:52.760 --> 01:30:53.640] The tension.
[01:30:53.640 --> 01:30:55.080] I know, I know, right?
[01:30:55.320 --> 01:30:57.400] That could be about one million things.
[01:30:57.400 --> 01:31:00.680] And that's why Who's That Noisy is so interesting, Steve.
[01:31:00.680 --> 01:31:08.280] So if you guys think you know what the noisy is this week, or if you heard something cool, email me at WTN at the skepticsguy.org.
[01:31:08.280 --> 01:31:14.760] Steve, today's Wednesday as we record this, and people will be listening to this show on Saturday at the earliest, right?
[01:31:14.760 --> 01:31:22.880] So, by the time you hear this, everybody in the world will be able to buy tickets for Noticon because we released all the tickets to the public today.
[01:31:22.880 --> 01:31:27.040] Let me give you a quick rundown of what's going on with Noticon 2025.
[01:31:27.040 --> 01:31:30.720] So, it'll be the weekend of May 15th, 16th, and 17th.
[01:31:30.720 --> 01:31:33.040] That's Thursday, Friday, and Saturday.
[01:31:33.040 --> 01:31:35.040] It's going to be in White Plains, New York.
[01:31:35.040 --> 01:31:39.600] Like last time, the hotel went under a complete renovation.
[01:31:39.600 --> 01:31:43.520] So, it'll be the same hotel, but it'll be a lot nicer than the last time we were there.
[01:31:43.520 --> 01:31:49.360] And, you know, it was a nice hotel when we were there, but I guess hotels have to spend the money to do this from time to time.
[01:31:49.360 --> 01:32:02.480] Anyway, we will be having a VIP meet and greet on Thursday night, and we will also be having a board game session called the Board Room, which will be like basically the very first thing that we do at the conference.
[01:32:02.480 --> 01:32:07.680] So, you can pick up your badges on Thursday, go to the board meeting, and go to the VIP.
[01:32:08.560 --> 01:32:10.720] You can go to both of those events on Thursday.
[01:32:10.720 --> 01:32:22.240] Then, all day Friday and Friday night, and all day Saturday and Saturday night, we have our standard programming with things that you'll remember from last time if you were there, and lots of new stuff that we are planning as well.
[01:32:22.240 --> 01:32:27.120] There will be a sing-along on Saturday night, and this year's theme is the Beatles.
[01:32:28.320 --> 01:32:29.120] Fun!
[01:32:29.120 --> 01:32:33.760] So, we'll be playing other stuff too, but the Beatles is going to be a major theme.
[01:32:33.760 --> 01:32:39.520] So, we decided to call this year's show, it's Notacon 2025.
[01:32:39.920 --> 01:32:41.200] You know what the rest of it is?
[01:32:41.200 --> 01:32:42.800] Skeptical Mystery Tour.
[01:32:42.800 --> 01:32:44.880] Yes, it's the Skeptical Mystery Tour.
[01:32:45.680 --> 01:32:50.560] And that's all I'm going to say because there is a puzzle this year, and I don't want to give away too much.
[01:32:50.560 --> 01:32:51.840] So, that's all I'm going to say.
[01:32:53.200 --> 01:32:59.960] So, if you're interested, you can go to notaconcon.com.
[01:32:59.960 --> 01:33:02.040] And I'll say that again slowly.
[01:32:59.600 --> 01:33:06.600] And as I say it, remember, Ian is the one that selected that URL.
[01:33:07.240 --> 01:33:11.560] NotaconCon.com.
[01:33:11.880 --> 01:33:13.320] My God.
[01:33:13.640 --> 01:33:17.400] So anyway, go to that URL or you can go to the SGU homepage.
[01:33:17.400 --> 01:33:19.160] There'll be a link on there as well.
[01:33:19.160 --> 01:33:21.640] You could buy your tickets for all these different events.
[01:33:21.640 --> 01:33:24.600] Basically, everything will be like last year if you went.
[01:33:24.600 --> 01:33:28.840] And if you didn't go, let me give you just a quick idea of what this conference is.
[01:33:28.840 --> 01:33:33.960] So people who like to go to science and skeptical conferences, we've done it for years.
[01:33:33.960 --> 01:33:40.920] And we got a lot of emails from people over the years saying that they really miss like the in-person Nexus.
[01:33:40.920 --> 01:33:45.480] It turns out that what they really, really missed was the social gathering aspect of it.
[01:33:45.480 --> 01:33:51.720] So we decided, you know, we were like, listen, let's create a conference that revolves around socializing.
[01:33:51.720 --> 01:34:01.880] Let's create a conference that just revolves around entertainment and socializing and give people a chance to see each other in person, get together, especially people that live very far away.
[01:34:01.880 --> 01:34:06.280] We have people already that are coming, that are coming from all over the place.
[01:34:06.840 --> 01:34:08.120] I think it's wonderful.
[01:34:08.120 --> 01:34:09.880] This is a socializing event.
[01:34:09.880 --> 01:34:11.720] This is an event where you're going to meet people.
[01:34:11.720 --> 01:34:15.720] If you're an SGU patron, you'll meet people that you chat with on our Discord.
[01:34:15.720 --> 01:34:18.440] If not, you will definitely meet people and make friends.
[01:34:18.440 --> 01:34:23.720] Lots of people commented and gave us wonderful feedback last time saying they just met a lot of great people.
[01:34:23.720 --> 01:34:26.120] So it's just a fun all-around event to go to.
[01:34:26.120 --> 01:34:27.400] We highly recommend it.
[01:34:27.400 --> 01:34:29.240] It was an incredible time last year.
[01:34:29.240 --> 01:34:30.360] And I'm not just saying that.
[01:34:30.360 --> 01:34:32.520] It really was an incredible time.
[01:34:33.480 --> 01:34:37.320] I think it was the best conference that I've ever attended, just from a community sense.
[01:34:37.320 --> 01:34:38.520] It was wonderful.
[01:34:38.520 --> 01:34:43.800] So, anyway, go to nataconcon.com if you want to check out more details and get tickets.
[01:34:43.800 --> 01:34:45.200] We have two more shows, guys.
[01:34:45.200 --> 01:34:47.120] We have shows that are coming up pretty soon.
[01:34:47.120 --> 01:34:49.760] This is happening the weekend of December 7th.
[01:34:44.840 --> 01:34:52.160] So, we have two shows on December 7th.
[01:34:52.240 --> 01:34:57.040] We have a DC private show, Washington, D.C., and we have a Washington, D.C.
[01:34:57.120 --> 01:34:58.480] extravaganza.
[01:34:58.800 --> 01:35:06.320] The private show is a recording of our live podcast and then an extra hour of fun audience interaction that only you will see and hear.
[01:35:06.320 --> 01:35:07.840] It's unique every time.
[01:35:07.840 --> 01:35:09.120] It's always a good time.
[01:35:09.120 --> 01:35:11.520] George Hobb will, of course, be with us on that.
[01:35:11.520 --> 01:35:15.200] So, please do go get tickets on the SGU homepage.
[01:35:15.200 --> 01:35:19.040] That's SGU, that's the skepticsguide.org.
[01:35:19.040 --> 01:35:25.440] And the extravaganza, if you don't know what that is, we have a stage show that we've been doing for like the last about 10 years.
[01:35:25.440 --> 01:35:28.800] This is a show where we teach you about how your brain can fool you.
[01:35:28.800 --> 01:35:30.880] You can't trust your brain, can't trust your senses.
[01:35:30.880 --> 01:35:33.680] There's a lot of improv comedy bits and funny stuff going on.
[01:35:33.680 --> 01:35:36.960] And we have a wonderful time interacting with the audience.
[01:35:36.960 --> 01:35:42.160] And if you're interested, you can also get those tickets at the skepticsguide.org homepage.
[01:35:42.160 --> 01:35:43.360] Thank you, Jay.
[01:35:43.360 --> 01:35:45.120] Let's do a quick email.
[01:35:45.120 --> 01:35:49.440] This email comes from Stewart from Washington, UK.
[01:35:49.440 --> 01:35:52.240] And he writes simply, saw this and thought of you.
[01:35:52.240 --> 01:35:58.400] And then he gives a link to a story about Toyota's portable hydrogen cartridges.
[01:35:58.400 --> 01:36:08.640] So basically, Toyota is proposing this hydrogen fuel cell car where the hydrogen is kept in these cylinders.
[01:36:08.640 --> 01:36:14.640] They look, they're the shape of like a A battery, but much bigger, obviously.
[01:36:14.640 --> 01:36:20.800] And the idea is that when you go to fill up, you swap these out, right?
[01:36:20.800 --> 01:36:26.000] You pull out the empty one, like a tank, and you put in a full one, and then you're on your way.
[01:36:26.000 --> 01:36:31.640] So it's just quicker, easier, yeah, than filling up your existing tank.
[01:36:29.840 --> 01:36:31.960] Neat.
[01:36:32.280 --> 01:36:45.080] Yeah, so it's neat, but the thing is, it's one of those situations where it's like fixing something that's not really a problem and it's also not the problem with hydrogen fuel cell cars, so it's irrelevant.
[01:36:45.080 --> 01:36:50.200] I don't care how good you make the experience from the refueling perspective.
[01:36:50.440 --> 01:36:58.200] The reason I think there's two big reasons why I think hydrogen fuel cell cars are not going anywhere.
[01:36:58.840 --> 01:37:03.000] One is kind of a temporary problem, the other one is a permanent problem.
[01:37:03.000 --> 01:37:08.280] The temporary problem is that right now we have very little green hydrogen, right?
[01:37:08.280 --> 01:37:14.360] Most of our hydrogen is gray at best, which means it's very carbon dirty.
[01:37:14.360 --> 01:37:21.640] And so there's no advantage basically to using, in fact, there may be a disadvantage to using gray hydrogen to fuel cars.
[01:37:21.640 --> 01:37:29.480] Yes, the car itself may not be releasing CO2, but to get the hydrogen, you had to release a lot of CO2.
[01:37:29.480 --> 01:37:40.200] So until we have a green hydrogen infrastructure, it is pointless to drive a hydrogen fuel cell car from a climate perspective.
[01:37:40.200 --> 01:37:47.320] But the permanent reason, which we've mentioned before, is a matter of physics.
[01:37:47.880 --> 01:37:55.640] The EV batteries are about, and EV cars, they're about 80% efficient in terms of converting energy into forward acceleration.
[01:37:55.640 --> 01:37:59.640] Hydrogen fuel cell cars are about 40% efficient.
[01:37:59.640 --> 01:38:00.680] And that's it.
[01:38:01.080 --> 01:38:02.600] It's half as efficient.
[01:38:02.600 --> 01:38:03.880] And it's not going anywhere.
[01:38:03.880 --> 01:38:07.560] That's just the physics of converting hydrogen into energy.
[01:38:07.560 --> 01:38:08.520] Why would we do that?
[01:38:08.520 --> 01:38:09.480] Why would we go?
[01:38:09.440 --> 01:38:12.520] You know, you know, basically, the EVs are better.
[01:38:12.560 --> 01:38:14.560] You know, they're twice as efficient.
[01:38:15.120 --> 01:38:19.520] They're always going to have that advantage over the hydrogen fuel cell cars.
[01:38:14.120 --> 01:38:21.280] So, I just don't think it's a good application.
[01:38:21.760 --> 01:38:27.360] And when we do make green hydrogen, we have a lot of other better things to do with it than burn it up in cars.
[01:38:27.360 --> 01:38:28.640] So, it doesn't matter.
[01:38:28.800 --> 01:38:32.880] So, this may be nifty, but it's completely irrelevant, in my opinion.
[01:38:32.880 --> 01:38:37.040] Gonna do a quick name-net logical fallacy as well.
[01:38:37.040 --> 01:38:39.600] This one comes from a listener who didn't leave his name.
[01:38:39.840 --> 01:38:46.240] They write, I've been reading The Art of Thinking Clearly by Rolf Dobelli, and I came across something that I think is questionable.
[01:38:46.240 --> 01:38:54.400] The book is an accumulation of chapters about logical fallacies and issues in clear thinking that is right up your alley.
[01:38:54.400 --> 01:38:58.880] All right, he says that the chapter is about the neglect of probability.
[01:38:58.880 --> 01:39:02.320] He details evidence that people basically discount probabilities.
[01:39:02.320 --> 01:39:18.960] Studies of telling people they have a 50% chance of getting electric shock, those in the study have the same amount of anxiety and fear as those being told they have a 5% chance of getting the shock, and so on, down to 0%, where people's anxieties finally become zero.
[01:39:18.960 --> 01:39:20.400] And then he gives an example of this.
[01:39:20.400 --> 01:39:22.240] The example is: this is a quote now.
[01:39:22.240 --> 01:39:25.520] To test this, let's examine two methods of treating drinking water.
[01:39:25.520 --> 01:39:35.760] Suppose a river has two equally large tributaries: one is treated using method A, which reduces the risk of dying from contaminated water 5% to 2%.
[01:39:35.760 --> 01:39:40.880] The other is treated using method B, which reduces the risk from 1% to 0%.
[01:39:40.880 --> 01:39:43.680] That is, the threat is completely eliminated.
[01:39:43.680 --> 01:39:45.280] So, method A or B.
[01:39:45.280 --> 01:39:54.960] If you think like most people, you will opt for the method B, which is silly because with measure A, 3% fewer people die, and with B, just 1% fewer.
[01:39:54.960 --> 01:39:56.560] Method A is three times as good.
[01:39:56.560 --> 01:39:59.360] The fallacy is called the zero risk bias.
[01:39:59.480 --> 01:40:05.080] And then the emailer writes: it could be that I'm missing something here, but is this not a terrible example?
[01:40:05.080 --> 01:40:07.720] First, I'll be clear: this is a hypothetical thought experiment.
[01:40:07.720 --> 01:40:09.960] Nothing in the world is actually this clear-cut.
[01:40:09.960 --> 01:40:12.280] So let's set that aside for a second.
[01:40:12.280 --> 01:40:19.480] I completely understand that method A reduces the risk by 3%, which is a bigger decrease than method B, which is a 1% decrease.
[01:40:19.480 --> 01:40:24.520] But it is not, but isn't that better to end at 0% than 2%?
[01:40:24.760 --> 01:40:26.840] All right, so two things going on here I want to talk about.
[01:40:26.840 --> 01:40:31.720] One is the fallacy that people ignore probabilities.
[01:40:31.720 --> 01:40:35.400] And this is just how we assess risk.
[01:40:35.400 --> 01:40:39.240] And we tend to be risk-averse.
[01:40:39.240 --> 01:40:42.760] And we tend to think of hazard as if it were risk.
[01:40:43.800 --> 01:40:45.960] So we've talked about hazard versus risk before.
[01:40:46.600 --> 01:40:52.600] Hazard is the shark in a tank, but the risk only exists if you're in the tank with the shark.
[01:40:53.480 --> 01:41:01.000] But people often fear just the potential of risk in a not mathematical or rational way.
[01:41:01.000 --> 01:41:03.480] So those results don't surprise me.
[01:41:04.760 --> 01:41:09.320] If you think there's a 1% chance of getting shocked, you're still going to be fearful.
[01:41:09.320 --> 01:41:13.160] And you're not going to be reassured by the math.
[01:41:13.160 --> 01:41:15.400] This is why people fear flying.
[01:41:15.400 --> 01:41:21.720] And telling them that, you know, how low the risk is doesn't always mitigate that fear.
[01:41:21.720 --> 01:41:29.960] Although, I do want in that research, I wonder, he said, and so on down to zero, but how low did they go?
[01:41:29.960 --> 01:41:35.320] Because what if you were told you had a one in a million chance of getting shocked?
[01:41:35.320 --> 01:41:39.240] I think people would, at some point, you start to treat it as zero, right?
[01:41:39.560 --> 01:41:40.200] Yes.
[01:41:40.200 --> 01:41:50.000] Which is why, like, when you're doing cognitive behavioral therapy for a fear of flying, you have to put the risk in such a context that it's functionally zero.
[01:41:44.680 --> 01:42:02.640] It's like, for example, I happen to know that one statistic they use is: how long do you think you would have to fly every single day before you had a 50-50 chance of dying in a plane crash?
[01:42:02.640 --> 01:42:03.680] It's like hundreds of years.
[01:42:03.680 --> 01:42:05.200] It's 500 years.
[01:42:05.200 --> 01:42:11.200] If you took a plane flight every single day for 500 years, you would have a 50-50 chance of dying in a plane crash.
[01:42:11.200 --> 01:42:15.840] So that kind of statistic does reassure people.
[01:42:15.840 --> 01:42:22.240] At least they have something cognitively to hang on to to say you just shouldn't worry about that level of risk.
[01:42:22.560 --> 01:42:27.280] But I do think it has to be pretty close to zero before people are reassured by that.
[01:42:27.280 --> 01:42:31.360] If you say, yeah, there's a 1% chance you're going to die, people are not very reassured.
[01:42:31.360 --> 01:42:35.840] And that's partly rational, you know, depending on how bad the outcome is.
[01:42:35.840 --> 01:42:40.960] But we do not evaluate risk mathematically or rationally.
[01:42:41.200 --> 01:42:42.800] That much is true.
[01:42:42.800 --> 01:42:46.880] The second thing, I sort of get where he's coming from and sort of criticizing that example.
[01:42:46.880 --> 01:42:48.160] It's just a bad example.
[01:42:48.160 --> 01:42:51.280] I'm not sure what the guy is actually saying.
[01:42:51.280 --> 01:42:55.040] You know, if you treat one tributary, the risk goes from five to two.
[01:42:55.040 --> 01:42:57.040] If you treat the other one, it goes from one to zero.
[01:42:57.040 --> 01:42:58.400] What does that mean?
[01:42:59.680 --> 01:43:02.240] How is the starting risk changing?
[01:43:03.920 --> 01:43:09.120] I think this is how I'm trying now to sort of retcon it to make it make sense.
[01:43:09.120 --> 01:43:11.040] He's saying there's two major tributaries.
[01:43:11.120 --> 01:43:16.880] I think he's saying one, like, let's assume that one tributary is going to city A.
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[01:46:14.640 --> 01:46:17.520] And in that tributary, the risk is 5%.
[01:46:17.840 --> 01:46:22.880] The other tributary is going to City B, and then that one has a risk of 1%.
[01:46:22.880 --> 01:46:25.280] You can only treat one of these two tributaries.
[01:46:25.280 --> 01:46:30.240] Are you going to take the risk down from 5 to 2, or are you going to take it down from 1 to 0?
[01:46:30.720 --> 01:46:34.400] If you reframe it that way, then of course you go 5 to 2.
[01:46:34.400 --> 01:46:36.960] Yeah, you go 5 to 2 because it's a 3% decrease.
[01:46:36.960 --> 01:46:38.480] But there's zero risk bias.
[01:46:38.640 --> 01:46:41.520] People say, well, going down to zero is better because it's zero risk.
[01:46:41.520 --> 01:46:44.400] They're looking at the end result and not the decrease.
[01:46:44.400 --> 01:46:45.600] So that makes sense.
[01:46:45.600 --> 01:46:54.320] Now, of course, all of these ethical thought experiments are massively contrived because they're trying to isolate a variable to see how people behave, like the trolley experiment.
[01:46:54.320 --> 01:46:57.440] So, yeah, so yes, don't worry about the fact that it's contrived.
[01:46:57.440 --> 01:47:02.680] But you have, I do think the description that he's quoting is bad, it doesn't really make sense.
[01:46:59.840 --> 01:47:06.440] And so, you know, that's how I would retcon it to make it make sense.
[01:47:06.760 --> 01:47:15.320] And in that, I think that's the context in which the point that they're making is people are more likely to look at the second number than the change.
[01:47:15.320 --> 01:47:21.880] Getting down to zero is better than getting down to two, rather than thinking a 3% decrease is better than a 1% decrease.
[01:47:21.880 --> 01:47:23.720] But these are just cognitive biases.
[01:47:23.720 --> 01:47:30.280] These are the ways in which our brains do not process information in a completely logical and unbiased way.
[01:47:30.600 --> 01:47:35.320] All right, guys, let's move on to science or fiction.
[01:47:37.240 --> 01:47:42.440] It's time for science or fiction.
[01:47:46.600 --> 01:47:55.560] Each week, I come up with three science news items or facts two real and one fake, and then I challenge my panel of skeptics to tell me which one is the fake.
[01:47:55.720 --> 01:48:04.680] By the way, we were talking about the collective names for skeptics, and one of our listeners pointed out that every week I refer to you guys as a panel of skeptics, so maybe that's what we should go with.
[01:48:05.000 --> 01:48:05.640] I like it.
[01:48:05.640 --> 01:48:05.960] Yeah.
[01:48:05.960 --> 01:48:06.760] I'm used to it.
[01:48:06.760 --> 01:48:07.080] Yeah.
[01:48:07.080 --> 01:48:07.400] Yeah.
[01:48:07.560 --> 01:48:08.120] Very comfortable.
[01:48:08.840 --> 01:48:09.640] All right, here we go.
[01:48:09.640 --> 01:48:21.080] Item number one: a new study finds that adrenaline auto injectors are like EpiPens are not effective in preventing death due to allergic anaphylaxis.
[01:48:21.080 --> 01:48:31.160] I number two, a recent review finds that atmospheric mercury pollution has increased by 20% in North America from 2005 to 2020.
[01:48:31.480 --> 01:48:42.760] And item number three, researchers find evidence that persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID.
[01:48:43.320 --> 01:48:44.800] I think Bob wants to go first.
[01:48:44.440 --> 01:48:47.200] You were going to choose him first, anyways, I think.
[01:48:47.200 --> 01:48:47.760] Nope.
[01:48:44.600 --> 01:48:50.080] I did just use the noise method.
[01:48:51.680 --> 01:48:53.200] We hate that method.
[01:48:53.840 --> 01:48:55.520] I've been so good.
[01:48:56.480 --> 01:48:58.320] Got to keep you on your toes.
[01:48:58.320 --> 01:48:58.880] Yeah.
[01:48:59.200 --> 01:49:00.000] Let's read these.
[01:49:00.000 --> 01:49:03.040] They did not suffuse into my cortical tissues.
[01:49:03.440 --> 01:49:11.600] A new study finds that adrenaline auto injectors are not effective in preventing death due to allergic anaphylaxis.
[01:49:11.920 --> 01:49:12.320] What?
[01:49:12.640 --> 01:49:17.360] So is it a problem with the auto injectors or is it a problem with the adrenaline?
[01:49:17.360 --> 01:49:18.240] Allergic.
[01:49:18.400 --> 01:49:21.280] Can you elaborate on anaphylaxis again?
[01:49:21.280 --> 01:49:26.000] Anaphylaxis is an allergic reaction where your throat closes up and you can't breathe.
[01:49:26.000 --> 01:49:26.800] The bee sting.
[01:49:26.800 --> 01:49:27.040] Yeah.
[01:49:27.040 --> 01:49:27.760] It's not fun.
[01:49:27.760 --> 01:49:28.000] Right.
[01:49:28.000 --> 01:49:31.760] And the adrenaline auto injectors, again, these are like the EpiPens, right?
[01:49:31.760 --> 01:49:32.160] Yeah.
[01:49:32.320 --> 01:49:33.680] It doesn't make a lot of sense to me.
[01:49:33.680 --> 01:49:34.080] Let's see.
[01:49:34.160 --> 01:49:35.200] Let's try to.
[01:49:35.520 --> 01:49:42.320] A recent review finds that atmospheric mercury pollution has increased by 20% in North America.
[01:49:42.640 --> 01:49:44.160] 2005 to 220.
[01:49:44.160 --> 01:49:44.880] What would cause?
[01:49:44.880 --> 01:49:46.240] I mean, is that even a thing?
[01:49:46.240 --> 01:49:48.720] Atmospheric mercury pollution?
[01:49:48.720 --> 01:49:52.560] I can't really remember hearing about mercury pollution.
[01:49:52.560 --> 01:49:54.160] That sounds nasty.
[01:49:54.160 --> 01:49:56.960] And now you're telling me it may have increased.
[01:49:56.960 --> 01:49:58.160] And I have no idea.
[01:49:58.160 --> 01:49:59.680] Researchers find, let's go to three.
[01:49:59.680 --> 01:50:09.760] Researchers find that evidence, find evidence that persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID.
[01:50:09.760 --> 01:50:14.000] So a persistent viral infection with SARS-CoV-2.
[01:50:14.320 --> 01:50:15.760] So do you understand what that's saying?
[01:50:15.760 --> 01:50:17.840] I'm not getting SARS-SARS-COVID.
[01:50:18.000 --> 01:50:20.080] I mean, they continue to test positive even though they don't have to.
[01:50:20.240 --> 01:50:23.040] Yeah, so SARS-CoV-2 is the virus that causes COVID, right?
[01:50:23.040 --> 01:50:38.840] So you have COVID, you were infected with SARS-CoV-2, a year later, you have long COVID symptoms, and you know, you have actual still have virus in you at that time rather than it being due to something else, you know, that's causing the long COVID.
[01:50:39.000 --> 01:50:42.760] One and two are kind of annoying, I'll just go with SARS-CoV-2.
[01:50:42.760 --> 01:50:44.920] Oh, wait, no, I think that's science.
[01:50:45.240 --> 01:50:46.360] So I'm doing the thing I did.
[01:50:46.520 --> 01:50:47.160] I gotta flip this.
[01:50:47.480 --> 01:50:48.200] What time is it?
[01:50:48.200 --> 01:50:50.040] It really feels like it's two in the morning to be fine.
[01:50:50.520 --> 01:50:51.560] We are on the planet, Bob.
[01:50:52.040 --> 01:50:55.800] I'm gonna go with the adrenaline auto injectors and anaphylaxis fiction.
[01:50:55.800 --> 01:50:56.840] Okay, Kara.
[01:50:56.840 --> 01:51:00.760] Yeah, I think that the long COVID one might be science.
[01:51:00.760 --> 01:51:07.400] I think we see that sometimes with Ebola, like it stayed in people's eyeballs sometimes long after infection.
[01:51:07.800 --> 01:51:09.800] Sort of a scary sentence that was.
[01:51:09.800 --> 01:51:10.680] I know.
[01:51:11.000 --> 01:51:18.520] But definitely, I think there's precedent here that there are like reservoirs for viruses long after they cause any symptoms.
[01:51:18.520 --> 01:51:31.320] So then it's between the basically the EpiPens not being effective in preventing death versus atmospheric mercury, mercury increasing by 20%.
[01:51:31.320 --> 01:51:36.200] Like the EpiPen one feels obvious, which is why I think it might be science.
[01:51:36.200 --> 01:51:39.800] And the mercury one, like what causes atmospheric mercury periods?
[01:51:39.880 --> 01:51:40.680] Yeah, good question.
[01:51:40.680 --> 01:51:43.960] Yeah, and I feel like we've known mercury is really bad for a long time.
[01:51:43.960 --> 01:51:49.480] So maybe we've actually done a good job at not having more mercury in the atmosphere.
[01:51:49.480 --> 01:51:50.520] I don't know.
[01:51:50.520 --> 01:51:53.000] So I think I'm going to switch gears.
[01:51:53.240 --> 01:52:08.360] The only thing I can think of is maybe EpiPens are really good at reducing symptoms, but if somebody is going to have sudden and intense anaphylaxis and it's going to cause death, like maybe there needs to be another mechanism there.
[01:52:08.360 --> 01:52:10.920] Or maybe they don't work fast enough or something like that.
[01:52:10.920 --> 01:52:12.200] I'm not really sure.
[01:52:12.440 --> 01:52:13.560] There's got to be something there.
[01:52:13.560 --> 01:52:17.040] So, yeah, I think I'll go with the mercury being the fiction.
[01:52:17.040 --> 01:52:17.920] Okay, Jay.
[01:52:17.920 --> 01:52:19.600] Yeah, I'm kind of agreeing with Kara.
[01:52:19.600 --> 01:52:26.640] Like, when I first read this, I'm like, well, so yeah, North America is Mexico, U.S., and Canada.
[01:52:26.640 --> 01:52:31.280] So I would think that we have government oversight, regulations, and all that.
[01:52:31.280 --> 01:52:36.480] If anything, it's got to have gone down, not up, right?
[01:52:36.480 --> 01:52:38.320] I just, this one just seems wrong to me.
[01:52:38.320 --> 01:52:45.920] And I think if there was an increase, there would be health effects and we'd be hearing about it much more than nothing, right?
[01:52:45.920 --> 01:52:47.440] So I think that one is fiction.
[01:52:47.440 --> 01:52:48.640] Okay, and Evan?
[01:52:48.640 --> 01:52:51.040] I think I'm inclined to agree.
[01:52:51.040 --> 01:52:57.440] The one about the EpiPen, though, not effective in preventing death due to allergic anaphylaxis.
[01:52:57.440 --> 01:52:58.640] I was thinking about that one.
[01:52:58.640 --> 01:53:03.680] Maybe not if it doesn't prevent death, it slows the death that does come on.
[01:53:03.680 --> 01:53:07.200] So you can get to a hospital and they can give you other drugs in time, maybe?
[01:53:07.200 --> 01:53:09.760] Because I can't imagine these things are totally useless.
[01:53:09.760 --> 01:53:10.400] Wait, right?
[01:53:10.640 --> 01:53:13.280] I mean, otherwise, why do we have them at all?
[01:53:13.280 --> 01:53:15.120] Did you say it slows death?
[01:53:15.120 --> 01:53:16.800] Well, in preventing death.
[01:53:16.960 --> 01:53:19.520] Slows the time in which you will die, right?
[01:53:19.520 --> 01:53:25.040] In other words, if you're going to die if you get stung or whatever, in whatever, an hour or something.
[01:53:25.040 --> 01:53:30.640] But maybe the EpiPen, instead of preventing the death from happening, it prolongs that time to four hours or something.
[01:53:30.720 --> 01:53:32.320] Well, that's terrifying.
[01:53:32.320 --> 01:53:35.200] Well, but it gives more time to respond to it.
[01:53:35.360 --> 01:53:35.680] I don't know.
[01:53:35.680 --> 01:53:40.320] I'm trying to think of why this one might be science.
[01:53:40.320 --> 01:53:42.400] And that's kind of what my brain went to.
[01:53:42.400 --> 01:53:47.120] And then, yeah, so that, and the COVID-1, yeah, I think that one's science.
[01:53:47.120 --> 01:53:49.600] That just leaves the mercury one as the fiction.
[01:53:49.600 --> 01:53:50.960] All right, so you guys all agree on the third one?
[01:53:50.960 --> 01:53:51.680] So we'll start there.
[01:53:51.680 --> 01:53:59.440] Researchers find evidence that persistent viral infection with SARS-CoV-2 following clinical COVID may be responsible for some cases of long COVID.
[01:53:59.440 --> 01:54:04.520] You guys all think this one is science, and this one is science.
[01:54:04.520 --> 01:54:05.720] This is science.
[01:54:05.720 --> 01:54:17.400] Yeah, not too surprising, although it didn't have to be the case because you can get post-infectious syndromes and symptoms without having persistent infection.
[01:54:17.720 --> 01:54:22.840] But it's possible for infections to persist after.
[01:54:23.160 --> 01:54:26.920] It's not like we have a way to eradicate the virus.
[01:54:26.920 --> 01:54:29.480] You basically, the infection runs its course.
[01:54:29.720 --> 01:54:32.760] You know, your immune system eventually deals with it.
[01:54:33.080 --> 01:54:45.640] But it can, it's not implausible that it's, you know, hides away somewhere and is sort of still there in the background causing symptoms and being kept at bay, but not being completely eradicated in some people.
[01:54:45.640 --> 01:54:52.040] Now, this hasn't been proven, which is why I said there's evidence that it may be the case, but it's not definite.
[01:54:52.040 --> 01:55:11.320] What they found was they found a higher rate of proteins, of SARS-CoV-2 proteins, of spike proteins or nucleocapsid proteins, in people who have long COVID versus people who had COVID but don't have long COVID.
[01:55:11.320 --> 01:55:12.040] Does that make sense?
[01:55:12.040 --> 01:55:21.800] So why would it be, why would it exist at a higher rate in people with long COVID, you know, evidence of active virus unless there was a causal relationship there?
[01:55:21.800 --> 01:55:25.960] So it was 43% versus 21%.
[01:55:25.960 --> 01:55:31.880] So it was basically twice as likely to have these viral proteins, if you have long COVID.
[01:55:31.880 --> 01:55:36.680] But that could also just mean that they had a more severe infection or something, it could mean something else, too.
[01:55:36.920 --> 01:55:41.240] But that's a reasonable interpretation that they may have persistent virus.
[01:55:41.240 --> 01:55:42.840] Okay, let's go back to number two.
[01:55:42.840 --> 01:55:50.640] A recent review finds that atmospheric mercury pollution has increased by 20% in North America from 2005 to 2020.
[01:55:50.800 --> 01:55:52.960] Bob, you think this one is science.
[01:55:52.960 --> 01:55:56.320] Everyone else thinks this one is the fiction.
[01:55:56.320 --> 01:56:02.240] I was a little surprised that none of you have any idea where mercury comes from in the atmosphere.
[01:56:02.400 --> 01:56:05.280] Any guesses before I tell you what the sources are?
[01:56:05.840 --> 01:56:07.760] Number one, industrial processes?
[01:56:08.400 --> 01:56:10.560] Industrial processes, what's number one?
[01:56:10.560 --> 01:56:12.960] Coal-fired plants, right?
[01:56:12.960 --> 01:56:13.920] It's in coal.
[01:56:14.240 --> 01:56:16.320] It gets released when you burn coal.
[01:56:16.320 --> 01:56:16.960] What the hell?
[01:56:16.960 --> 01:56:22.160] And mercury exposures is higher among people who live near coal plants.
[01:56:22.640 --> 01:56:35.360] So coal-fired plants is one, residential coal burning or other industrial coal burning, other industrial processes, waste incinerators, mining for mercury, gold, and other metals where mercury might be present.
[01:56:35.360 --> 01:56:37.040] Cement production.
[01:56:37.040 --> 01:56:37.680] Cement production.
[01:56:39.120 --> 01:56:44.080] Because mercury contaminants can be in the stuff, you know.
[01:56:44.080 --> 01:56:48.160] And so anything, and you have to heat it up, and that releases the mercury into the air.
[01:56:48.160 --> 01:56:49.360] So there's lots of sources of it.
[01:56:49.360 --> 01:56:57.040] So the question is: in North America over the last 20 years, this is basically before COVID.
[01:56:57.040 --> 01:57:02.000] Do you think that all of these processes have been increasing or decreasing?
[01:57:02.000 --> 01:57:16.320] And the answer is we didn't know, which is why, one of the reasons why they wanted to track this is sort of a review of the stations that monitor mercury in the atmosphere to kind of answer this question: is what we're doing working?
[01:57:16.320 --> 01:57:19.920] Are we releasing more or less mercury over time?
[01:57:19.920 --> 01:57:23.840] And the results showed that this is the fiction.
[01:57:23.840 --> 01:57:25.880] It's actually decreased by 20%.
[01:57:25.600 --> 01:57:26.120] Uh-oh.
[01:57:26.480 --> 01:57:28.400] So even though it comes from coal.
[01:57:28.400 --> 01:57:28.960] Yeah.
[01:57:28.960 --> 01:57:31.560] Well, we are reducing our coal burning.
[01:57:31.800 --> 01:57:32.120] Yeah.
[01:57:29.840 --> 01:57:33.160] It is coming down.
[01:57:33.560 --> 01:57:34.920] That's why North America specifically.
[01:57:35.160 --> 01:57:36.040] North America is.
[01:57:29.840 --> 01:57:36.920] Yeah, this is not China.
[01:57:37.080 --> 01:57:39.960] You know, I imagine it's increasing over there.
[01:57:40.200 --> 01:58:08.760] In fact, I remember that when we got rid of the thimerosol in vaccines, right, which contains mercury, and yet autism rates continue to rise, some of the proponents of autism is mercury poisoning hypothesis said, well, the reduction of thimerosol in vaccines is being offset by fumes coming from Chinese coal-fired plants.
[01:58:08.760 --> 01:58:10.520] You know, it's like, really, really, dude?
[01:58:10.520 --> 01:58:12.040] That's what you think is going on?
[01:58:12.040 --> 01:58:14.280] It was all just massive special pleading.
[01:58:14.520 --> 01:58:16.520] But anyway, this is good news.
[01:58:16.520 --> 01:58:18.360] You know, it's actually decreasing.
[01:58:18.520 --> 01:58:23.320] We need to decrease it more, you know, because sort of mercury is one of those cumulative toxins.
[01:58:23.960 --> 01:58:30.120] And it is this sort of stated goal of the EPA to have a zero tolerance for any mercury.
[01:58:30.120 --> 01:58:36.760] You know, their goal is complete elimination of mercury contamination in the environment and human exposure to mercury.
[01:58:36.760 --> 01:58:39.880] Like there isn't considered any safe amount.
[01:58:39.880 --> 01:58:40.840] You know what I mean?
[01:58:40.840 --> 01:58:42.680] The goal is always zero.
[01:58:42.680 --> 01:58:44.120] Okay, let's go on to number one.
[01:58:44.120 --> 01:58:50.440] A new study finds that adrenaline auto injectors are not effective in preventing death due to allergic anaphylaxis.
[01:58:50.440 --> 01:58:53.400] This one is unfortunately science.
[01:58:53.400 --> 01:58:57.400] You know, a couple of things that you guys noted is basically correct.
[01:58:57.400 --> 01:59:01.480] It does treat the symptoms of an allergic reaction.
[01:59:01.480 --> 01:59:04.360] It's just not enough to prevent anaphylaxis.
[01:59:04.360 --> 01:59:09.480] So, if you're going to die from anaphylaxis, the EpiPens are not going to stop it from happening.
[01:59:09.560 --> 01:59:13.160] Is it because they need more?
[01:59:13.240 --> 01:59:13.960] The dose is too low.
[01:59:14.120 --> 01:59:19.360] Well, even if it does, Evan, so the other way they looked at this, they looked at this two ways.
[01:59:19.520 --> 01:59:28.560] So, one way is just to look at the rate at which people are dying from anaphylaxis before and after the introduction of EpiPens, and there was no decrease.
[01:59:28.560 --> 01:59:33.680] So, the existence of EpiPens have not decreased death due to anaphylaxis, which is rare.
[01:59:34.560 --> 01:59:41.360] To say that as a baseline, it is pretty rare that you die from anaphylaxis, but it hasn't decreased that rate.
[01:59:41.360 --> 01:59:55.120] And then they also, this is the new bit really, that did studies looking at the amount of adrenaline that you get into, you know, of epinephrine that you get into your blood, like the blood levels after an auto injector.
[01:59:55.120 --> 01:59:59.600] And it does get up to a decent amount, but only very briefly.
[01:59:59.840 --> 02:00:07.920] It does get the blood levels get up to the amount that you need to get it to in order for it to have an effect, but it's there very briefly.
[02:00:07.920 --> 02:00:20.480] And what the evidence shows is in order to prevent death from anaphylaxis, you basically need intravenous epinephrine sustained over time.
[02:00:20.880 --> 02:00:37.040] Yeah, because you need to, if the allergen is still there, yeah, so without IV adrenaline, and of course, if you're giving somebody IV adrenaline, you need to keep that from bottoming out their blood pressure, and other things like support them with fluids and everything.
[02:00:37.040 --> 02:00:46.000] You need to be in a hospital setting getting IV fluids and IV adrenaline in order to prevent death from anaphylaxis, if you're going to die from it.
[02:00:46.000 --> 02:00:49.280] The EpiPens will not stop that from happening.
[02:00:49.440 --> 02:00:52.760] What if you have multiple maps?
[02:00:52.920 --> 02:00:58.160] Yeah, like what if you inject while you're being transported or while you're waiting for EMTs?
[02:00:58.480 --> 02:00:59.800] EMTs can put a line in.
[02:00:59.520 --> 02:01:05.880] It may be like if you have, like, here's 10 of them, like every five minutes, give yourself another one or whatever until you get into the ambulance.
[02:01:06.200 --> 02:01:09.320] I do remember hearing a story like 10 years ago.
[02:01:10.040 --> 02:01:15.000] This is a clinical case of a like 10-year-old girl who's allergic to peanut butter.
[02:01:15.000 --> 02:01:22.680] And she accidentally ate peanut butter, you know, she ate her friend's sandwich or whatever, didn't realize it had peanut butter in it and had an anaphylactic reaction.
[02:01:22.680 --> 02:01:29.240] And her parents were there and they gave her two injections with the EpiPen, but it didn't matter.
[02:01:29.240 --> 02:01:31.880] She died anyway before she can get to the hospital.
[02:01:32.040 --> 02:01:33.400] What a nightmare that would be.
[02:01:33.400 --> 02:01:36.920] But I remember thinking about that, like, really, two EpiPen injections didn't do it.
[02:01:36.920 --> 02:01:40.200] But this is why you would need way more than that.
[02:01:40.200 --> 02:01:47.320] So maybe we need to reformulate them, increase the dose, re-educate people.
[02:01:47.320 --> 02:01:49.080] It's like you really need to have a bunch of these on you.
[02:01:49.080 --> 02:01:51.080] Like one EpiPen is not going to save your life.
[02:01:51.080 --> 02:02:03.240] Or maybe some sort of, you know, not a Dexcom, because that's a different drug, but maybe some sort of like thing that you stab into your arm that like gives a flow for a certain period of time.
[02:02:03.240 --> 02:02:03.800] Something.
[02:02:03.800 --> 02:02:04.120] Yeah.
[02:02:04.360 --> 02:02:05.400] Like a sustained flow.
[02:02:05.400 --> 02:02:13.320] It's just not as easy as just like the you press a button and you get a shot, but you know, something that people can use in the field.
[02:02:13.320 --> 02:02:18.760] You know, and you have definitely, first off, your first thing to do is call an ambulance, right?
[02:02:18.760 --> 02:02:24.680] Because that's your only chance of really surviving is getting into medical care as quickly as possible.
[02:02:25.400 --> 02:02:41.800] But yeah, so I don't know if this is going to lead to a sort of a either we think that's really not worth it, you just got to get to the hospital really fast, or like we have to really reformulate how the dose and the mechanism that these things are given because they're just not working.
[02:02:41.800 --> 02:02:49.600] Again, maybe if it's subfatal, sure, it helps, but it doesn't, it's not good enough.
[02:02:49.600 --> 02:02:54.880] It's not enough to turn a fatal reaction into a non-fatal reaction.
[02:02:54.880 --> 02:03:01.920] Or apparently to help people to get to the hospital, because if that was happening, we would have seen the numbers coming down, and they're not.
[02:03:01.920 --> 02:03:02.880] Yeah, it's not long enough.
[02:03:03.040 --> 02:03:06.240] Well, and maybe people have a false sense of security after they use it.
[02:03:06.240 --> 02:03:08.560] That's that's maybe education as well.
[02:03:08.560 --> 02:03:10.800] Yeah, it may be that people think, I have my EpiPen.
[02:03:10.800 --> 02:03:12.960] I gave myself the EpiPen, so I'm good.
[02:03:12.960 --> 02:03:15.520] And they're not as panicked as they really should be.
[02:03:16.000 --> 02:03:19.600] Or, you know, yeah, that false sense of security can be fatal.
[02:03:19.920 --> 02:03:25.280] So what they need to do is they need to sell EpiPens, and on the EpiPen, it says panic.
[02:03:25.280 --> 02:03:25.840] Yeah.
[02:03:27.760 --> 02:03:30.240] Well, once you use it, it's like call 911 immediately.
[02:03:30.240 --> 02:03:30.480] Yeah.
[02:03:30.480 --> 02:03:36.480] Well, I mean, you know, if you have an EpiPen, you are getting that as a prescription from a doctor who's had a conversation with you about this.
[02:03:36.480 --> 02:03:42.880] And so, yeah, just to make sure that doctors understand how they need to be educating their patients, like what an appropriate reaction is.
[02:03:42.880 --> 02:03:50.880] Like, as soon as you think you may have been exposed to something you're allergic to, you take an auto injector, you call 911.
[02:03:50.880 --> 02:03:52.480] It's an absolute emergency.
[02:03:52.480 --> 02:03:55.760] Do not think that the EpiPens are automatically going to save you.
[02:03:55.760 --> 02:04:01.680] You may need to have multiple on you, or, you know, you may go unconscious, so your friends need to know what to do.
[02:04:01.680 --> 02:04:02.240] Yeah.
[02:04:02.240 --> 02:04:04.800] Yeah, it is a life-threatening emergency.
[02:04:04.800 --> 02:04:04.960] Yeah.
[02:04:05.280 --> 02:04:06.560] EpiPen's not an antidote.
[02:04:06.800 --> 02:04:08.160] It's not, it's not.
[02:04:08.720 --> 02:04:14.320] Not for fatal anaphylaxis, which is just such a scary thing to have to live with.
[02:04:14.320 --> 02:04:15.360] Oh, my goodness.
[02:04:15.360 --> 02:04:20.880] The best thing to do is to figure out how to people how to make people not have allergic reactions.
[02:04:21.440 --> 02:04:22.240] To engineer.
[02:04:22.240 --> 02:04:22.640] Yeah, yeah, yeah.
[02:04:23.440 --> 02:04:23.760] Yeah.
[02:04:24.240 --> 02:04:27.840] We know, like, for some things, you could do allergy shots.
[02:04:27.840 --> 02:04:30.760] You know, they slowly build up immunity to the thing.
[02:04:29.760 --> 02:04:35.160] Because, you know, again, an allergic reaction is different than a regular immune reaction.
[02:04:35.480 --> 02:04:37.960] You know, it's IgE instead of IgG.
[02:04:37.960 --> 02:04:47.960] And so you can build up like through vaccines, and you could build up an IgG reaction to the thing, and those actually block the IgE antibodies.
[02:04:47.960 --> 02:04:49.320] That's how that works.
[02:04:50.360 --> 02:04:54.360] Your regular immune reaction blocks the allergic immune reaction.
[02:04:54.680 --> 02:05:00.600] But of course, you have to build it up very slowly because you'll give people full access if you gave them an actual vaccine without a full dose.
[02:05:00.840 --> 02:05:02.280] But at least they're in the hospital.
[02:05:02.520 --> 02:05:06.040] Well, you get that first dose in the doctor's office, right?
[02:05:06.040 --> 02:05:08.040] And with lots of observation.
[02:05:08.040 --> 02:05:08.280] You pick up that.
[02:05:13.400 --> 02:05:21.640] But also, they're working on genetically engineering peanuts so they don't have the allergen in them, just allergy-free peanuts.
[02:05:21.640 --> 02:05:22.920] That would be massive.
[02:05:22.920 --> 02:05:24.600] Yeah, but what does that mean for peanut butter?
[02:05:24.600 --> 02:05:25.960] Is that going to be like changing it?
[02:05:26.200 --> 02:05:28.520] What if it was slightly less delicious, Bob?
[02:05:28.520 --> 02:05:29.480] Would you be okay with that?
[02:05:29.560 --> 02:05:30.680] Depends how slightly.
[02:05:32.040 --> 02:05:34.040] If lives are saved, Bob, don't lie.
[02:05:34.040 --> 02:05:34.200] Come on.
[02:05:34.760 --> 02:05:36.360] Logically happy.
[02:05:37.800 --> 02:05:39.400] All right, Evan, give us a quote.
[02:05:39.400 --> 02:05:40.600] All right, a quote.
[02:05:41.160 --> 02:05:44.520] I went to ChatGPT for the quote.
[02:05:44.520 --> 02:05:45.480] You know what I typed in?
[02:05:45.480 --> 02:05:50.040] I typed, can you find for me a famous quote about the scientific method?
[02:05:50.040 --> 02:05:51.960] That's what I typed in.
[02:05:51.960 --> 02:05:53.960] And it gave me a result.
[02:05:53.960 --> 02:05:54.920] And here it is.
[02:05:54.920 --> 02:05:57.400] Courtesy of ChatGPT.
[02:05:57.400 --> 02:06:00.840] The important thing is to not stop questioning.
[02:06:00.840 --> 02:06:04.520] Curiosity has its own reason for existing.
[02:06:04.520 --> 02:06:06.600] Albert Einstein.
[02:06:07.240 --> 02:06:08.360] And they added this.
[02:06:08.360 --> 02:06:21.600] They said this quote, or it, this quote reflects the core principle of the scientific method, emphasizing the importance of constant inquiry, curiosity, and the pursuit of knowledge through observation and questioning.
[02:06:21.920 --> 02:06:24.240] That was the complete response I got.
[02:06:24.960 --> 02:06:33.440] So how do you feel knowing that ChatGPT's top pick for you was a quote that after a thousand episodes you didn't find yourself?
[02:06:34.720 --> 02:06:37.360] Well, I did have to go back and see.
[02:06:38.000 --> 02:06:41.520] I couldn't find evidence where we had used it ourselves before.
[02:06:41.520 --> 02:06:45.440] And yeah, this doesn't ring familiar to me, this particular one.
[02:06:45.520 --> 02:06:46.480] Hey, I'm happy.
[02:06:46.480 --> 02:06:48.080] I'm always happy to learn new quotes.
[02:06:48.880 --> 02:06:52.960] What I'm finding says that this quote is attributed to Albert Einstein.
[02:06:53.120 --> 02:06:54.240] Oh, here we go.
[02:06:54.240 --> 02:06:56.000] ChatGPT sending me on a signal.
[02:06:56.160 --> 02:07:04.160] No, I think it seems like it's probably legit, but I don't know how certain it is because why would you say attributed to Albert Einstein?
[02:07:04.160 --> 02:07:05.920] And if there was any doubt.
[02:07:06.160 --> 02:07:06.560] I don't know.
[02:07:06.560 --> 02:07:07.920] I have to look into it a little bit.
[02:07:08.160 --> 02:07:09.840] In general, quotes are a mess.
[02:07:10.160 --> 02:07:14.240] Yeah, but they use that language a lot in etymology research.
[02:07:15.120 --> 02:07:20.560] So I don't know if attributed to, by definition, means that there's not evidence.
[02:07:20.560 --> 02:07:20.960] It doesn't.
[02:07:20.960 --> 02:07:21.360] It doesn't.
[02:07:21.360 --> 02:07:25.280] I'm just saying that that was a little red flag for me when it was stated that way.
[02:07:25.440 --> 02:07:32.400] And then quotes do tend to attach themselves to more famous people than the people who actually said them.
[02:07:32.400 --> 02:07:33.040] Yeah, I know.
[02:07:33.040 --> 02:07:37.120] And when it came back with Albert Einstein, I was, you know, I did have to pause.
[02:07:37.440 --> 02:07:38.320] That's just that.
[02:07:38.320 --> 02:07:42.480] Just Albert Einstein is another little red flag.
[02:07:42.640 --> 02:07:43.120] We'll see.
[02:07:43.760 --> 02:07:45.440] We'll vet it out some more.
[02:07:45.680 --> 02:07:47.840] Cursory vetting seems to hold up.
[02:07:47.840 --> 02:07:50.480] But yeah, I would be a little questioning about it.
[02:07:50.480 --> 02:07:58.240] But it's interesting if you could, you know, if ChatGPT can't give you the, you know, the firm, the definitive answer to that.
[02:07:58.400 --> 02:08:00.760] It gives me wrong answers all the time.
[02:08:00.760 --> 02:08:02.040] Yeah, it does, doesn't it?
[02:08:02.040 --> 02:08:02.360] It does.
[02:07:59.840 --> 02:08:06.680] It's not there yet in terms of you have to vet the answers that it gives you.
[02:08:06.920 --> 02:08:09.240] It also gives me outdated answers.
[02:08:09.240 --> 02:08:10.520] Yeah, I've seen that as well.
[02:08:10.520 --> 02:08:13.400] Like, okay, this was true five years ago, but it's no longer true.
[02:08:13.400 --> 02:08:19.160] Like, especially, like, recently I asked it, what's the percentage of electricity in the U.S.
[02:08:19.160 --> 02:08:22.360] that's generated by solar panels?
[02:08:22.360 --> 02:08:27.240] And it gave me a five-year-old answer, you know, which I happen to know was wrong.
[02:08:27.240 --> 02:08:36.520] It has a problem with hallucinations, and it has a problem with just reflecting what's out there, you know, on the internet, and that may be outdated, you know, what's rising to the top.
[02:08:36.520 --> 02:08:40.360] It's not still a work in progress, I suppose.
[02:08:41.000 --> 02:08:46.280] But we're right on the cusp of these applications transforming medicine.
[02:08:46.600 --> 02:08:50.520] We're developing medical application versions of it.
[02:08:50.520 --> 02:08:53.800] But yeah, it's hard to think of a better use of it.
[02:08:53.800 --> 02:08:55.560] It's perfect use of it.
[02:08:55.560 --> 02:09:04.120] Yeah, you know, filter, go through these hundred thousand studies and tell me, give me a summary of this one little question that I have.
[02:09:04.760 --> 02:09:06.600] I can't wait for that, man.
[02:09:06.920 --> 02:09:10.680] That would otherwise take people 9,000 years collectively to figure out.
[02:09:10.680 --> 02:09:24.280] Well, I mean, from a pragmatic matter, like if I have a specific clinical question, it could take me an hour or two or longer to really find out what the definitive current answer is.
[02:09:24.280 --> 02:09:34.120] If something could give me that answer in two minutes or 20 seconds versus hours, then that's something I could use right at the point of patient care.
[02:09:34.120 --> 02:09:35.880] And I could also do a lot more of that.
[02:09:36.120 --> 02:09:40.920] So, yeah, the potential to improve those kinds of professions is massive.
[02:09:41.080 --> 02:09:50.800] But we have to make sure that the applications are designed to not just spit out regenerated answers that may or may not be true without showing their work and giving citations or whatever.
[02:09:51.120 --> 02:09:56.720] So that's what's happening now: sort of reformulating these applications so that they do do that.
[02:09:56.720 --> 02:10:08.080] They like to actually give you real citations, not made-up citations, and links to that, you know, so that you can verify what it's saying, because you need to be able to do that, to act upon it.
[02:10:08.080 --> 02:10:08.400] All right.
[02:10:08.400 --> 02:10:11.280] Well, anyway, thank you all for joining me this week.
[02:10:11.280 --> 02:10:19.760] You guys have and until next week, this is your Skeptic's Guide to the Universe.
[02:10:19.760 --> 02:10:26.480] Skeptics Guide to the Universe is produced by SGU Productions, dedicated to promoting science and critical thinking.
[02:10:26.480 --> 02:10:31.120] For more information, visit us at theskepticsguide.org.
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