Quasar Quirks & Sky Surveys with Matt O’Dowd

22:23And so to us, we only know where the light comes from. So it looks like there are two or four images of the same quasar.

22:30Neil deGrasse Tyson:Was that called the Einstein cross? The most famous one is called the Einstein cross. Yeah, because it was a quadruple-y lens object. One of the early ones that was found. And so what you're saying is there's four pictures and they're the same because it's the same, source is making the four pictures. One key difference is that they're often at different times because the path lengths are different. So you're seeing the same quasar with offsets of somewhere between hours and weeks and that's crazy powerful. Wait, hold on. You only know that if something happened on the quasar that you can start the clock.

23:09Neil deGrasse Tyson:If it's just a static image, you wouldn't know. Exactly. That's fantastic. Isn't it? I mean, that's just freaking fantastic. That you can predict. Yes. You can say, oh, this burped over here. Right. Watch this right here. Wait for it. Wait for it. That's amazing. Wait for it. It's almost like you have like a tiny little reset time machine that you're able to observe what's happening. You know? Well, let me see that again. They're called gravitational lenses because it's gravity kind of acting as this galaxy size lens in addition to your telescope. It's a crappy lens because it's made up of stars and, you know, it's not very well ground.

23:43and so what you see is a little messy, right? You see magnification, yes, very helpful, but you also see new fluctuations in the quasar due to the fact that the galaxy itself is made of stars and the stars are moving compared to the galaxy and so you see all these new effects of a kind of crappy lens. But if you can model all of it, then you can use the gravitational lens to actually map the inner regions of the quasar, which is still very hard because they're still very tiny and very far away. Gotcha. Wait a minute.

24:19Neil deGrasse Tyson:You're saying the lens gives you access, deeper access to the quasar than you would otherwise have with just an image of a quasar. Very much. So it's a telescope booster. It's a telescope booster, yeah. Oh, nice. Very good, Chuck. Yeah. Cool, man. It's not... Or an enhancer, I would say. Yeah. I mean, you've got to do a little bit of work. You know, it's like when they first put the Hubble mirror up and it was ground wrong and they had to do a lot of work to recover the images. Likewise, these crappy lenses. When you say the word ground, you mean when you take the shape of the mirror? The geometry needs to...

24:54Neil deGrasse Tyson:Wait, that happened? Oh, God. You don't remember that? It had the wrong shape. Where were you? Where was I? I'll tell you where I was. Hubble had the wrong... I was watching Japanese anime. That's where Hubble was. It had the wrong shape. By the way, it had the wrong shape, but it was perfectly ground to the wrong shape. So rather than replace the mirror, we took the other optics and compensated for that other shape and then put it right back in business. Like plastic surgery. Okay. Yeah. Okay. The reason why that happened, not to get so off topic, is the mirror was tested in situ and it had a perfect shape.

25:34Neil deGrasse Tyson:Okay. And the other lenses were tested in situ, but they were not tested together. Okay. Okay, until it was too late. Yeah, still a dumbass mistake. But go ahead. So, does that mean now, the farther away the quasar, the more likely you'll have lensing opportunities for things to be in your line of sight? Why, yes, that would make sense. But now it's farther away, but now it's dimmer. Yeah, I mean, you also want, like there's a perfect configuration of distance to the lens, distance to the quasar, and so there's some factors. But it's true that very nearby ones are very unlikely to be lensed. and so because there's less stuff less chance of stuff being between you and it and nearby would be how far away uh I don't know downtown New York no nearest quasars how far out are those I mean the nearest ones uh you know so M51 let me ask you another way let me take it off of that when I see maps of the large scale structure of the universe the quasars are the most distant objects in these maps.

26:40Neil deGrasse Tyson:Is that because they are the most distant or is it because they're the only things you can see that far away, but other galaxies, ordinary galaxies, are populated among them and you just can't see them? Yes. Okay. No, let me, so yes, yes, and one more thing. So first of all, there was a quasar epoch when quasars were, the brightest quasars were the most common. and this is like the middle third of the age of the universe, basically. And we're now post that. There are still some big ones locally. The other thing is that the brightest quasars are quite rare. And so you just, by statistics, you have to look a long way to see the first one, right?

27:20Okay. Okay, so if you're in a sparse forest, the nearest tree is likely far away. And lastly, they are the things that we see to the greatest distances. So when you see these surveys and we can't see the galaxies, we see these pinpoints of light. Right. Oh, there's a galaxy there, but we see it because of the quasar. Right, and you can't see the galaxy because it's not as bright as the quasar. Exactly. Okay.

27:41Neil deGrasse Tyson:But there are galaxies we wouldn't otherwise seen were it not for the fact that they were far away and lensed. That's also true. Yeah. Yes. Whoa. I mean, we see galaxies far away now. So you're getting a real assist from the universe itself. Yes. Yeah, no, lensing's very powerful. That's crazy. It's the best. Predicted by Einstein. Another crumb that just fell off of Einstein. And he wasn't even thinking about it. He wasn't even thinking about it. He was like, you know what? but there might be something called gravitational lensing. You know, F that, who cares? That is exactly how that went down.

28:10Neil deGrasse Tyson:Wow. No, Matt, you know that's how that went down. He actually didn't think we would ever observe like gravitational lensing out in space. He thought it would be too weak and too far away. Or I think too rare because he was only thinking of it in an exact alignment of two objects. Okay. And then you get an Einstein ring. Right. Because if you're slightly off, then it splits the image. It distorts the image. But if it's exactly on it, then the light is equally likely in any direction coming around it, and you get a ring. I think he figured an exact lineup was rare. But also in 1915, when General Relativity was published, we didn't even know that the universe existed outside the Milky Way galaxy.

28:48Very good point. Holy crap. Maybe Einstein did, but he wasn't telling us. Hubble, 15 years later. 29, yeah. Hubble came along and was just like, yeah, there's a lot more.

28:59Neil deGrasse Tyson:All right, so thanks for catching me up on quasars. So these sound like complicated problems that need more than just I to solve. Well, I spent my research life staring at these things with my human eyes, but until recently, it's been possible. When I started out, we had maybe 100 of these lensed quasars, then 300, but we're about to find many thousands. This comes to you from surveys. It's all going to change with Rubin, which will discover many thousands of these lensed quasars and countless other things. And so there aren't enough... So is normal eye, normal intelligence, not good enough to handle this problem?

29:44It's barely good enough to do it even when you have one because the systems are so complicated. You can't model it easily. You've got these stars in the galaxy moving around. You don't know where the stars are. You have to do that kind of statistically. Within the quasar.

29:59Neil deGrasse Tyson:Stars moving around within the quasar. Stars moving around within the lensing galaxy that changes the way the lensing works. Oh, oh. It's very messy. Oh. But it's powerful because those stars, they like sweep across the inner structure of the quasar like this kind of radar, vroom, vroom, and they can map it. They can map it at the same resolution as the Event Horizon Telescope, but for thousands. Wait, wait, so I misunderstood. You're saying the galaxies that are lensing the quasar, the movement of stars within those galaxies give you varying patterns in the quasar itself. Exactly, yeah. That's what you were saying.

30:42Neil deGrasse Tyson:That's what I'm saying. Oh, my gosh. Yeah, you have a quasar. You don't really know what's happening in there. Okay, now I have to freak out. Oh, my gosh. That's what you're saying. Right? Right? Right. And imagine this distant quasar. It's very small, but you've got this lens, and you're kind of sweeping these complicated magnification bands across it. So you see different parts of the quasar change over different times. You can even see when it sweeps across the black hole in principle and see it darken for a little bit. All of this is going to be seen by Ruben. Wow. But. But. Okay. Like P.B.

31:14Herman said, everyone has a big butt. Okay, go. So our big butt is big data. It's the fact that we need to now model thousands of these things and we could barely do one. And so we are indeed turning to AI machine learning.

31:32Neil deGrasse Tyson:So not just AI, intelligence. Yeah. Artificial intelligence. Artificial, exactly. Which presumes that the artificiality of it is better at it than you are. Yeah, I mean, it's such a catch-all term. And we're not putting them into ChatGPT where Bill's out. Exactly, right. sophisticated neural networks of different types to do various of these. I mean, I don't want to sound glib, but it's kind of a matter of just pattern recognition at that point, right? You're not glib. That's exactly what it is. Okay. These things can be very good at pattern recognition. Better than we are? They can find... Can they see Jesus in a piece of toast?

32:13If you train... No, but in tortillas, you get them all the time. Oh, it's a tortilla. He's just eating a tortilla. The answer is absolutely, they're particularly good at that. But when we come to some data and we look for the patterns, we look for the patterns that we think are going to be there, right? Or the relationships between the parameters that we think are important. With different types of AI model, you can throw in the data and it will find the patterns, even if they're patterns that weren't expected.

32:42Neil deGrasse Tyson:But my question, too, is we are excellent pattern-recognizing creatures. so good at it that we will see patterns even that aren't there. Right. So is AI equally as susceptible? Is it that good that it's as bad as we are? Is it so good as it's as bad as we are? That's a sentence I wanted to stay, but that's it. Is it so good, it's bad? So there is that, and there's also the fact that it will find what it's expecting to find, just like we will. And we see what we expect to see. Do you remember, I mean, it's only a few years ago, but it's now like the ancient era of AI when you train these neural networks to recognize whether something's a cat or a dog.

33:20Yes. And you show it a cat, 100 % of the time it knows it's a cat, a dog, it knows it's a dog. Right. If you show it a chipmunk, it will definitely say it's either a cat or a dog. Right? Yeah. So it'll see what, it all depends on how you train it. It just said chipmunk.

33:34Neil deGrasse Tyson:Yeah. Okay, we trained it on chipmunks. Okay, we trained it on, so these are some of the challenges. It's as good as what you put in. Yeah, but if you are training it for what you expect, It's not going to find something that nobody expects. Unless - Which is the serendipity on the frontier of science that we all cherish so highly. But what you could do if, I mean, I'm just spitballing here, but you could just allow it to find whatever pattern it wants. Like whatever pattern is there, just find me a pattern. You have to know what pattern means. But it already does. It can extrapolate from the patterns that you already trained it on.

34:11So then what you can do is -

34:12Neil deGrasse Tyson:But then it's still extrapolating from a given pattern. Can you find a pattern for which there is no template? Oh, wow. That's the question. There we go. Okay, so can AI find a pattern on which it has not been trained? Yes. And that can be an authentic pattern, not something in its imagination, like when we find patterns that aren't there. So there is a power to, in our case, it's putting the physics in and training it on what you think the physics is. That's actually quite a powerful approach. Exactly. Like this sort of simulation-based. Because it's foundational. Yeah, and it tells you what is happening in the context of what you put in.

34:49But what if you didn't know what was in there? So we talk about supervised learning and unsupervised learning, and there are techniques for unsupervised learning where you can tell it anything. You just said, what do you see? What do you see? And then it'll tell you something, and then it's your job to interpret what that means and why those patterns emerged. And has the answer ever come back, a bunny rabbit? It's usually chipmunks, actually. Well done, sir. So is that precisely how you're using your AI? We do a bunch of things. And Ruben and the other surveys do a bunch of things. In our case, we use variational auto-encoders to take these, in our case, it's the fluctuation over time of these light curves, compact them down into a much more compact space in what we call the latent space, and then use that latent space to try to reproduce the data.

35:40In that latent space, we know the patterns are hidden, and so we have further neural networks to extract things like what is the mass of the black hole, how fast is it spinning, all of this good stuff. Because in principle, the network has learned what the fundamental parameters that went into generating those fluctuations were. Right, because you need to know the physics of everything going on. If you miss some physics, you don't know what you're doing. Yeah, but you can expand the input physics well beyond what you think is in reality. And we try to do that. We try to say, all right, well, what is the span of all possible physics of these quasars and lenses, et cetera?

36:18Let's go much bigger than that to make sure we encompass the true space. And then you can also test how brittle it is. Like you can break it and see if it still gives you reasonable answers. So there's ways around the chipmunk problem.

36:30Neil deGrasse Tyson:You're checking the sensitivity of the system. Sensitivity and brittleness. And see how you... Exactly right, yeah. Okay. That's pretty wild, man. I mean, by the way, I want to say, these are all done by my graduate students and postdocs. Good for you. Are you in Bahamas while they're doing this? Well, Bahamas did come into it, actually. Let's know the story. I don't need to hear your Bahamas stories. That's fine. If anything, I found just the smartest people to do this. I mean, these days, I learned programming on Fortran, right? As did I, I confess. Yes. Fortran 4 for me. It's quite difficult for me to really grok the ins and outs of all of this.

37:15So I just try to— You and Olfart, it doesn't make a difference. You already did your time. I did my time. Yeah, you did your time. That's how it works. You did your time, and now you get to nod wisely at group meetings. That's right, and take credit for their work. That's how it goes. And one day they'll get to do that to the next generation. That's how it goes.

37:31Neil deGrasse Tyson:You know what I mean? It's beautiful, really. So we've seen photos of the Vera Rubin telescope in the Andes Mountains of Chile, where we have a lot of telescopes. And there's the dome, and then there's this whole other section sticking out the side. The shoe, yeah. To me, it always looked like a shoe. It looks like a shoe sticking out because it has a rounded front. I presume that's where all the data, the big data, is happening. Yeah, I think they stack the data in there. I think there's a lot of - And then they fly it up to whatever. There's a lot of on-site processing because you can't just ship it all.

38:09Neil deGrasse Tyson:So is it because this telescope is uniquely in need of data processing support that it was conceived and designed this way? Well, there's also, you know, there are people there doing important engineering things. They haven't been replaced by AI like we have. So engineering support, but also the computing facilities that are there. And yeah, I've never been. I want to go and take a tour. Of the shoe. Of the shoe. Yeah, yeah. So what it seems to me, I don't want to speak for you, but tell me if I'm correct, that most people's fear of AI is that it'll take their job. Whereas when you're a scientist on the frontier, such as yourself, the AI allows you to step where you could not have stepped at all.

39:01Neil deGrasse Tyson:So it's not the same thing as replacing a job. It is empowering you to think more creatively about your thoughts on the scientific frontier. Is that a fair characterization? It's such early days in this revolution that it's hard to say where it's going to land. Right now, it's insanely powerful in many, many respects. it takes away work that we didn't want to be doing anyway. It's a lot of grunt work. It's incredibly powerful. But it's also the new reasoning models are able to do things that previously graduate students were doing. Uh-oh. The hope is, oh, well, now graduate students can be freed up to do better things.

39:50Yeah, more creative things. That's the hope, but the reality might be that the AI is like, Look at you, dumbass. That's what you said. I can't believe you thought that this was something viable. God, who hired this dude? That's a little scary. I mean, on the other hand, the stuff that graduate students used to have to do, which is stare at this boring data forever. Right. You know, an AGI, Artificial Graduate Student Intelligence. That's what you said for. Very good. That was nice. do that. They can be occupied. Do a thesis in an afternoon, essentially. And so the hope is that the professors won't say, oh, I don't need graduate students anymore.

40:30They'll say, oh, graduate students. Now do this. I don't get this stuff. Please do this. Now do this. Now let me ask you this. Is there any benefit to the graduate student doing the grunt work? Is there something that can come out of that

40:45Neil deGrasse Tyson:for our brains? I'm going to say no. Here's why. Really? In my day. In your day. Okay. Pre-AI, but computer power was growing exponentially. There used to be a course in graduate school on spherical trigonometry. Which nobody needs now. Because the computer does it. Exactly. Okay, spherical trigonometry. You know, trigonometry normally on a flat piece of paper. But on the dome, you have angles between stars and moving the telescopes. And what's the shortest slew path between two? That's all spherical trigonometry. Gone. We just push a button. And it's done. The telescope calculates it. Now, let me ask you this.

41:24Neil deGrasse Tyson:You took spherical, but you took it, right? No, no, no. It was like two years before I got there. We stopped teaching it. And do you understand spherical trigonometry? That's the real question. I mean, the question is, are there intuitions that we miss now? I think there could be intuitions. It's an intuition thing. Yeah. That's really what I'm talking about. Going through the wax on, wax off of graduate school. Right. Oh, what were you learning? I don't know, but now I know Kung Fu. We don't know what we lose, I guess. Interesting. So I, okay, let me give a counter story to that. All right. Okay, when I was in graduate school, a member of our department, a faculty member, was world's expert on galaxy classification, okay?

42:10Neil deGrasse Tyson:Okay. World's expert. Okay. There's never been an expert such as him either before or since. Okay. Okay? And so in one of his classes, we are classifying galaxies. It's like, this is stupid. Why am I doing this? And nowadays, computers classify galaxies. You don't need to do this. But when I look at a galaxy that you just took a picture of, I have a whole other relationship with it that you don't. I'm feeling it. Because I've looked at hundreds and hundreds of these, maybe thousands of these, and so it's in me in a different way. It's almost a muscle memory of what it is and why it looks that way.

42:51And to what I was saying earlier, who knows what inspiration that is inspired by just that reservoir of seemingly useless knowledge that you are holding.

43:06Neil deGrasse Tyson:Like he said, the wax on, wax off. That's precisely the... And knowing what's under the hood, knowing how the sausage is made. You know, these days, future generations of graduate students won't know how to code because they talk to the computer, they vibe code. AI will code. And they never make an if statement. What do we lose? Maybe nothing. But I feel like there's something about knowing what's under the hood. That kind of helps. Well, it helps you know what the true capabilities are. Do you know how your smartphone works? No, you don't. Yeah, like you don't know what the vulnerabilities are if it's just a black box, I guess.

43:44Neil deGrasse Tyson:But if it's a perfect black box, there are no vulnerabilities. Okay, so we just need to build that. Yeah.

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46:58And by that, I mean StarTalk. Hey, this is Kevin the Sommelier, and I support StarTalk on Patreon. You're listening to StarTalk with Neil deGrasse Tyson.

47:15Neil deGrasse Tyson:Let me just bring closure to this data challenge that we now have. We are awash in data. The data rate for the Rubin telescope will exceed that of any previous telescope in our portfolio. Orders of magnitude. By orders of magnitude. Wow. I'm reminded of what was the first time they turned on the telescope, and they discovered like a thousand new asteroids crossing the sky. Right, yeah. And you can do that because it's taking its repeated imagery, which is the only way you'll know if something moves. Yeah. Because otherwise it's a still spring. It's a static shot. It's a static shot. And is that a star, or is it something that goes bump in the night?

47:54Neil deGrasse Tyson:Yeah. So that forced us, our field, our community, to innovate in ways we didn't have to before, right? So it was all good? It headed in the right direction there? Yeah. Do you mean as astronomers or as a conversation? All of the above. Oh, that's great. You were so incredulous. You're like, yeah. I'm offended. No, Neo, I'm wasting my life. it's funny because it's true I only say that because we had needs for imaging the universe that exceeded what Kodak now many decades ago routinely produced and so we they put a special team on our needs and created special emulsions that were more sensitive that were larger that and so when CCDs came out we were the first to fully exploit them and before anyone even knew what they were.

48:55Neil deGrasse Tyson:And poor Kodak did not take advantage of that. Kodak, it's just a flash in the pan. They're like, we're going to do this for these nerds. People love film. Yeah, these eggheads need these great feedies. They'll love film all the time. So only when it became a commodity were we no longer the leading edge of that. But it seems now with this level of data, is this more data than anyone has had to think about before? Or are advertisers mining data off of social media accounts? And is that a greater repository of data for them to sell us product? It's greater for them. In terms of bits, I don't know if it's greater.

49:37So it's enormous. It's, for the first time, taking a full image of the entire southern sky every three nights for 10 years. I can give you some numbers about how much data that is. Like the CCD is enormous. It takes like 400 HDTVs to show one image. 400. 400. And that's one image, and the southern sky is like 3 ,000, 4 ,000 of those images.

50:06Neil deGrasse Tyson:Just to be clear, the Hubble telescope field of view is a fraction the size of the full moon. Wow. And this is like 40 times the size for one of those images. Of the full moon. Yeah. That's amazing. So that's how you can, so if you said, Hubble, give me an image of the whole sky. Okay. Call me in 30 years when I finally, because I'm going to have to stitch this all together. I'm going to mosaic this. Right. And so. Call me in three days. It's pretty fast. Three days for the whole sky. Wow. That's the very rule of the time. Okay. But like you said, it's a movie of the sky. So we see things changing, things going bump in the night.

50:40We see the quasars flickering at the edge of the universe, all of it. And it's all just going to be like mainlined for 10 years and what do we do with it? Well, we work very hard. You guys are making like a flip book of the universe. It's a flip book of the universe, it really is. Holy crap. But it's a big flip book. It's hard to put the pages. It's like several football fields. To rub your thumb across the flip book. You put the thumb across the page edges. Dude, that's funny. I got a paper cut. Yeah, I lost my hand.

51:15Neil deGrasse Tyson:so just to anchor this in proper context at the risk of repeating myself in my day you go to the telescope you take a picture and you take the picture home and analyze it if something moved you would have no idea and it was not that big a problem because stars live 10 billion years 5 billion years and you're there getting a 30 minute exposure of it you're not expecting fireworks in that moment you're looking at it but maybe there are somewhere in the universe. Well, there definitely are. We know there are. We know a lot of what the fireworks are, but there's a lot that we don't know also. The universe is pretty violent.

51:52It's quite dynamic. Yeah.

51:54Neil deGrasse Tyson:Yeah. It's tough. It doesn't bump. So it'd be a shame for us to sit this close to each other and not compare notes. So you've got a YouTube channel. You bring delivered content that's fun and interesting and exciting to hear. as a mix of not only what is frontier science, but what also is fun science. So it's clear that you're doing some cherry picking of what you could be talking about. Plus, you look like the sexy professor that one might daydream about. Oh, why thank you. The other thing that we go. So share with me some of your tools and tactics that you've found most potent in your efforts to bring the universe down to Earth.

52:39You know, so PBS Space Time fills a particular niche, I think, which is that we do go hard. Like, you know, we've covered the holographic universe and quantum mechanics. You've been out there, yeah. All the way to the edge of the holographic universe and, you know, as well as, you know, more traditional space stuff. So we found very early that there's a huge appetite for kind of seeing under the hood, like how science happens, how scientists actually talk. And I think for the longest time, people have felt a little bit babied by a lot of popular science media. And they know when they're being talked down to.

53:21They know when they're being talked down to. And so I think one thing that I do well is I have a good jargon detector. I know when something's jargon. And jargon doesn't have to be like a specialized word. It can even be a specialized use of a word. And so the point is that so much of science, even the stuff that's hard, is accessible to human language and can be talked about in human language. And I think that scientists are not the best people at doing that because they talk professionally. They talk in professional. You're not trained for that.

53:49Neil deGrasse Tyson:It's all shop talk for scientists. It's all shop talk. Yeah, but all professions have shop talk. Exactly. Right, right. So what's different about science? Science is hyper, hyper specialized just because it's old. We know so much about the world that to make any progress, we have to dig deep and narrow to make any progress. And so the language around each subfield tends to be very specialized. And every time you get a subfield over subfield over subfield. It's excluding of others because of the language and because it's, you know, it attracts a certain type of people, nerds, who are really into.

54:24No, it's true. And they enjoy going granular on information. Exactly, yeah. So I think over time science has become, I kind of think of it as genre-fied. So something is science-y if it's hyper-focused and detail-oriented, when really it's about curiosity about the world. So I think the beginning of science and what it really is, is just it's curiosity with organization. And this is where we are now after centuries and millennia of doing this. We know a lot, right? But a side effect of that is the siloing of fields between each other and between scientists and non-scientists and just the fact that the scientist and science feels like an other type of thing.

55:07It's a certain type of person who becomes a scientist and is into science, whereas one thing we try to do is to make things sound a little more collective. I rarely say... Inclusive. Inclusive and collective in the sense that yes, we should be very proud of Albert Einstein for coming up with general relativity, but we should be proud of humanity also for coming up with, for one of us, for figuring this stuff out. We figured this out.

55:38Neil deGrasse Tyson:I think about that all the time. That's why I have an active disinterest in my genealogy. Because I want to be what I want to be based on what I know humans are capable of. Not based on somebody before you. Right, right, right. But also, it's lazy when people do that. It's completely lazy. Very lazy, because what you're saying, it's like when people say, we did it! We won the World Series! Who's we? And I'm like, really? What, now, when's your contract up? Yeah, yeah, yeah, yeah. Like, shut the hell up. No, no, but you're allowed to participate as a species in the achievements of your species.

56:16Neil deGrasse Tyson:Yes. I think you're allowed to do that. Without a doubt, but the fact is that - I'm gonna say, we figured out how to build the suspension bridge and figure out how to go to the moon. Yes. And I don't want you coming behind me saying, well, what part of the project did you work on? Well, no, there's a different story. That's different though. And here's why. Because it's my tax money that went in? Not even that. You did. Your tax money did help build that. But the thing is that science is not so specialized like a sport or something else like that, that you can't do it. You can actually do it. if you want, you can understand, and believe me, I'm speaking from experience.

56:55You can understand this stuff. It's a little difficult, takes a little bit of work, but once you do that, it's like, oh my God. But I think the public sense is the opposite. I think the public sense is that sport is more accessible than science is. And that's my point, but my point is this. Sport is not more accessible. Of course. You can never hit a home run in a major league park. I give a damn what you think you can do. It's never going to happen.

57:21Neil deGrasse Tyson:So it's like the person graduating high school saying, or college saying, I want to be a professional basketball player, let's say. And they're way more neurosurgeons or aerospace engineers. Exactly. So that's too hard. I want to do this easy route. I want to do this easy route. And it's the hardest thing in the world. You're never going to do it. But the thing is, kids play basketball, and you're not really into, so you're not genre-fied for liking basketball. You're a kid. Right. And you play it, not because you might one day make the NBA, but because it's fun. But science doesn't have that nearly as much.

57:58Right. The kids who do science, they're the nerds. But you guys do science. I think you're not giving yourselves enough credit here. Okay? What you do is make science like basketball. And quite frankly, there's an audience out there that really feels that and they appreciate it greatly. Well, thank you, Chuck. I feel like that is a big part of the goal. I think we have a lot of work to do.

58:26Neil deGrasse Tyson:Can I tell you my transition? Thank you for saying that. That was insightful. Oh, cool. Listen, even a broken clock is right twice. Okay, broken clock. So you got one more. That's right. One more. Number one coming. Okay. so in my sort of rise in visibility in the early days people would come and say are you Neil Tyson I said yes I am he said okay tell me about black holes and what you said the other day so all I was was a food for them there wasn't the fame was the fact that I had excited their curiosity okay they didn't care what my favorite color was okay as my visibility got higher and higher more and more people would just say oh can I get your autograph pre-selfies.

59:12Neil deGrasse Tyson:Can I get your autograph? And wouldn't it? Don't you want to ask me about the universe? No, I just want your autograph. So I felt cheap. When was the last time you told someone about black holes? Has it been a while? I mean, everyone you tell about black holes. So this continues, and more and more people, now they want to take selfies. That's fine. And I oblige. But I felt incomplete by this until someone told me. I said, Neil, do you realize you're a scientist? and people want your autograph? Right. You're a scientist, and people want to take a selfie with you? Like a rock star or athlete? And so I had to, because they're not going to go to the athlete and say, please explain your, they just want to.

59:58They just want to be, they want to connect with the athlete.

1:00:00Neil deGrasse Tyson:They just want to connect in that one way. And so I realized, oh my gosh. Yeah. The science is sharing the space that had only previously been occupied. Yeah. And it's happening more and more. By the way, I got it. Alex, our producer, you were not here. I've never said this to you, but I'll say it now because he was here and we were talking about you. And I said, we were talking about you. And I said to Alex, do you know how hard it is to be world famous? A f***ing science?

1:00:40Am I lying out?

1:00:45Neil deGrasse Tyson:Yeah, there might be just a few dozen scientists who became. It's what I'm saying. But what's great is I love what you said about the accessibility and not talking down and allowing people to find the wonder in science. Because I think that not only is it necessary for science to continue to thrive socially, but I think it's imperative for democracy, for our species, for the future of not just the country, but the world with respect to the things that information that we know that will save us as a species like climate. and all these things really are very deeply connected to science and scientific literacy and that's the truly important thing that you guys are doing.

1:01:42He's running for office soon.

1:01:44Neil deGrasse Tyson:No, I'm sorry. You got my vote. But it's not just what science can do for us, it's this suite of modes of thinking and tools of thought and just ways to apply curiosity and rationality that are absolutely not restricted to what we now think of as science. It's just thinking. It's just thinking carefully. And it's so important in all works. A state of mind, a state of thought, a state of curiosity. I love it. Yeah. The last thing I'll say here is I had to learn this after I stopped teaching classrooms and started writing books and doing podcasts. There's no obligation in a person's first encounter with your expertise for them to learn everything on your syllabus.

1:02:26Neil deGrasse Tyson:Throw out the stuff that's boring or uninteresting. Yeah, it's got to be there in a classroom because you need the sequencing. but pick the stuff that's really cool. Teach them that. Then they say, this is cool. I want to learn more. And then they're at a level where they can get the nuances that you left out in the first place. Yeah, yeah. But also like throwing in a few like little deep views is nice also. It's like teasers for the future. Oh, yeah, yeah, yeah, yeah. But I'm just saying often there's detail that's just simply unnecessary. Sure, yeah. At the first pass. And it's hard to know how to spot that and excise it.

1:03:04We're very precious about our details, us nerds. Yeah, it is.

1:03:07Neil deGrasse Tyson:It is. Dude, we got to end it there. Yes. Oh, yeah. Matt. This was fun. Thanks, Matt. Always good having Matt on the show. It's nice to come down to the fifth floor and chat about quasars. All right. Chuck, always good to have you, man. Always a pleasure. And your special is still, your comedy special, still airing on our YouTube channel. YouTube channel. And it's called what? Chuck Nice. Just Smart Enough. because I'm sitting between these two. So that's about as good as it's going to be for me. I love that. I think you're smarter than just smart enough. All right. This has been another installment of StarTalk.

1:03:41Neil deGrasse Tyson:The Matt O'Dowd edition. Yes. Until next time, keep looking up.

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