Cosmic Queries – Algorithms and Data, with Hannah Fry

What is an algorithm? How do you interpret large amounts of data? Neil deGrasse Tyson and comic co-host Chuck Nice answer fan-submitted Cosmic Queries exploring algorithms and big data alongside mathematician and author Hannah Fry, PhD.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:01Neil deGrasse Tyson?

  Welcome to StarTalk, your place in the universe where science and pop culture collide. StarTalk begins right now.

• 0:15Neil deGrasse Tyson?

  This is StarTalk. I'm your host Neil deGrasse Tyson, your personal astrophysicist, and this is a Cosmic Queries edition. It's becoming a fan favorite, and when I do Cosmic Queries, you know Chuck Nice can't be far behind. Chuck?

• 0:29Chuck Nice?

  Hey, what's happening Neil?

• 0:31Neil deGrasse Tyson?

  Dude, welcome back in the house.

• 0:33Chuck Nice?

  Yeah, yeah, well, yeah, virtually in the house.

• 0:36Neil deGrasse Tyson?

  Virtually, in the, in the, in the Corona-verse house.

• 0:40Chuck Nice?

  That's right.

• 0:41Neil deGrasse Tyson?

  We're all in the same Corona-verse house.

• 0:43Chuck Nice?

  Right.

• 0:44Neil deGrasse Tyson?

  Today, we're doing Cosmic Queries on algorithms and data.

• 0:49Chuck Nice?

  Algorithms and data?

• 0:51Neil deGrasse Tyson?

  You gotta love me some algorithms and data, because they had nothing happened.

• 0:54Chuck Nice?

  I do not. I'm not a big fan. Not a fan of either. I mean, I'm a fan of data. Both, both the actual information kind and the android from Star Trek. I'm a fan of data. Algorithms, not so much.

• 1:07Neil deGrasse Tyson?

  Oh, yeah, I forgot. We had a whole, a whole entity named data, an android, basically. So what we have here is, we've invited into studio today, data, math, we have an expert on algorithms and data, a mathematician, Hannah Fry, who's dialing in from the UK. Hannah, welcome.

• 1:32Speaker 02

  Thank you very much. You know Chuck, you're not the only person who hates the word algorithms.

• 1:38Speaker 02

  I was once at a tech conference, and I was just chatting to this guy, being like, I think it's a word that makes about 85% of people want to gouge out their own eyes. He agreed with me. He said, "Yeah, but it does make the remaining 15% of people mildly aroused." So I know I'm in.

• 1:57Chuck Nice?

  Well, you know, yeah, mildly aroused, best because Al is only mildly sexy.

• 2:03Chuck Nice?

  Mr. Go Rhythm.

• 2:05Neil deGrasse Tyson?

  Al Go Rhythm, yeah, there you go.

• 2:07Chuck Nice?

  Exactly.

• 2:08Neil deGrasse Tyson?

  So let me get your full bio here. You're Associate Professor of Mathematics, University College London. And you co-host a BBC radio show, Radio 4, because Radio B C has very stove-piped channels. And it's The Curious Cases of Rutherford and Fry.

• 2:27Speaker 02

  Yeah.

• 2:28Neil deGrasse Tyson?

  Now, you have to do some splaining on that one. Author of the book, recently released, just last year, Hello World: Being Human in the Age of Algorithms. So you're the person for this Cosmic Queries.

• 2:43Speaker 02

  I mean, I know a thing or two. I'm not gonna big myself up too much, but I've dabbled.

• 2:50Chuck Nice?

  Hannah, if I may, can I just make you feel a little more at home digitally as we cross the Great Pond? Here we go. Here we go. Oh, BBC News time, 5 o'clock GMT.

• 3:04Speaker 02

  That was actually quite good.

• 3:07Chuck Nice?

  Is he like that?

• 3:07Speaker 02

  Yeah, that was very good, very good.

• 3:10Neil deGrasse Tyson?

  He's good.

• 3:10Speaker 02

  You can get a job, I'm sure.

• 3:13Neil deGrasse Tyson?

  The Brits show off that they have the Prime Meridian, so.

• 3:16Chuck Nice?

  Yes, exactly.

• 3:17Neil deGrasse Tyson?

  The universal time Greenwich Mean Time.

• 3:20Speaker 02

  You know, in fact, I live in Greenwich. The Prime Meridian is about 100 metres from my house.

• 3:25Neil deGrasse Tyson?

  Oh, do you feel it, though?

• 3:30Speaker 02

  What's quite nice is you go for like, you go for a little, you know, little walk around, there's like a peninsula. And along the Prime Meridian, as it goes across the water, there's just an arrow that says here. Oh, no, I've forgotten the circumference of the Earth now. 16,000 kilometres, something like that?

• 3:45Neil deGrasse Tyson?

  No, no, way bigger than that. Yeah. People once thought it was 16,000 in ancient Greece, so we've got to update you.

• 3:54Speaker 02

  I made a guess, and I made a guess with the wrong person.

• 3:57Neil deGrasse Tyson?

  Hannah's a fan of history. She's a fan of history, that's all.

• 4:00Speaker 02

  It goes back to here, which I quite like.

• 4:03Neil deGrasse Tyson?

  We're about fifty thousand kilometers, forty to fifty thousand kilometers.

• 4:07Speaker 02

  Well, I was within a factor of ten, so.

• 4:10Neil deGrasse Tyson?

  I know it to miles and you, the guys who gave us miles, so it's twenty five thousand miles is what that is. But I'm just delighted you live near the prime meridian and you can get a little vibe from it when.

• 4:20Speaker 02

  Regularly.

• 4:21Neil deGrasse Tyson?

  Take a stroll. So tell me, tell me about data. What's the state of data today? Relative to like when any of us were kids or even before there were computers. The word data, of course, predates computers. So what's going on today?

• 4:37Speaker 02

  Well, in some ways, I mean, in a lot of ways, not that much has changed. I mean, it's still a case of, you know, people collecting statistics about humans, about how we behave, about what we do, using kind of mathematical techniques to analyze it and trying to infer scientifically everything that you can about our behavior from it. So, you know, this is like a subject that has a long history going back to the fifties and the sixties. I think that what's really changed is just the volume of data. I mean, you don't need me to tell you just the incredible amounts of data that is collected on us. But I think for me, it's not just the amount of data that's directly collated about us. It's the things that you can infer from that data.

• 5:18Speaker 02

  The sort of, the guesses that you can make about people that you wouldn't necessarily realize you could do. So a really nice example is I was, and what I'm talking about here is I was talking to a chief data officer for supermarkets, a chain of supermarkets in the UK. And this supermarket, they have access to everything that everyone buys. So they know what's in your basket. They know your weekly shop. But they also sell home insurance, right? So they can tell who makes better, you know, higher claims on their home insurance and compare it to what people are buying. And they realized in their data that you can you can tell that people who claim

• 6:00Speaker 02

  less on their home insurance are people who tend to cook food at home, which is kind of like, oh, okay. I mean, once you hear it's like, well, that sort of makes sense, I guess, right? If you're a very house proud person and you're you're spending ages creating a meal from scratch, then you know, you're not going to let your kids play football in the house, right? It's like, kind of the the groups connect. But the question is, how do you decide who the home cook? Anyway, it turns out that there is one item that's like the strongest indicator of all this this one item that's in your basket that's the biggest giveaway that you are a a home cook more than any other.

• 6:31Chuck Nice?

  Frozen pizza.

• 6:35Chuck Nice?

  I'm going with frozen pizza.

• 6:36Speaker 02

  I'm kind of a cooking, I guess. Want to guess? Do you want to guess, Neil?

• 6:41Chuck Nice?

  Um, let me see. I I like to cook at home. And let's see, I cook a certain type of food, but I can't cook without uh olive oil.

• 6:49Speaker 02

  Mmm.

• 6:50Neil deGrasse Tyson?

  How about um anchovy paste?

• 6:52Speaker 02

  Oh, yeah, I think it is is some.

• 6:55Neil deGrasse Tyson?

  Or tomato paste, something that's so base level uh uh kitchen preparation.

• 7:01Speaker 02

  So it's actually a little bit more, little bit more, I guess, of it's fresh fennel. It's fresh fennel.

• 7:07Chuck Nice?

  Wow.

• 7:08Speaker 02

  So I think it's kind of nice. And I mean, I think that's right. Like, you just wouldn't, if you're buying fresh fennel, you must be a home cook.

• 7:14Chuck Nice?

  No, you're only cooking at home. That is the only purpose for fresh fennel. I mean, seriously, if nobody's saying like, oh, I have to pick up some fresh fennel, I need to shine my shoes.

• 7:26Speaker 02

  Exactly. So I uh so I started buying fresh fennel to see if my home insurance prices would start going down. But as yet, nothing.

• 7:33Neil deGrasse Tyson?

  Whoa, okay. So so uh that's one thing where the access people have to who and what you are, when it's becomes when the data is consumer based data. So I guess we get that. A lot of people are fear that or angered by it. But before we land there more securely, let me just comment that as the volume of data has grown, because computers are obtaining data constantly, hasn't the computing ability to analyze data risen with it? So that we're not really feeling the stress of being smothered in data that we might have feared? A few decades ago.

• 8:09Speaker 02

  Yeah, well, as as analysts, you may be not feeling the fear of being smart.

• 8:13Neil deGrasse Tyson?

  Okay, so I first heard from John Allen Paulus. Now, this is now mid 90s, late 90s. What he said was, um, the internet is the world's biggest library. The problem is all the books are scattered on the floor. And I said, wow, that's a brilliant analogy, but at the time, there wasn't a Google search engine or any kind of way to organize that information. Within a few years, there were search engines, so the books were no longer on the floor. Who knows where the hell they are now, but they're not on the floor.

• 8:43Speaker 02

  Wow, I really like that.

• 8:45Neil deGrasse Tyson?

  Yeah, it was clever, but it doesn't apply today because we can get through the data. And my people, we have very big telescopes, getting a lot of data on the universe. Half of the effort in prepping for that telescope is the data pipeline to handle the data. So are you, can you say that you're awash in data? Are you, are you on top of the situation?

• 9:06Speaker 02

  Well, okay, there's a, I'll be on top of the situation. Okay, so definitely like the amount of grunts that you have now, just that you, I mean, you just didn't have access to before. You can handle, you can handle vast data sets with just incredibly intricate detail about, you know, everything from the cosmos to human behavior that you just weren't able to before. But the thing is, is that I think the biggest challenge when it comes to data isn't so much about volume, but more about quality. And the thing is, is that anyone who has worked with data knows that cleaning data is like so much the battle. You get like this, this massive, great big wallop of data. You're like, brilliant, it's gonna be so good, can't wait to like dive in.

• 9:49Speaker 02

  And then you realize just how long it takes to make it, to get it into a shape where it, where it does anything that you want it to. Let me think of an example of something that I can give you that will be, um.

• 10:01Neil deGrasse Tyson?

  I can tell you, in my field, we just call it reducing the data. If I showed you raw data from the universe, you'd say, what the hell is that? Where's my pretty Hubble picture? Well, if you knew what the hell happened between the photon hitting the telescope and the photo that ended up in the press, um, you'd be, um, you might be shocked, but, no, you wouldn't be, Hannah, but others might.

• 10:23Neil deGrasse Tyson?

  So you have an example?

• 10:24Speaker 02

  I'm trying to think, sorry, forgive me. There must be one that's really funny and quite stupid.

• 10:30Neil deGrasse Tyson?

  Well, well, they might come out in the Q and A part, so we can.

• 10:33Speaker 02

  Yeah, okay.

• 10:34Neil deGrasse Tyson?

  That's the one we want, definitely.

• 10:35Speaker 02

  Yeah, I know. You definitely want one where something's just really stupid. There's definitely one.

• 10:41Neil deGrasse Tyson?

  Another quick thing about your bio. Tell me about your BBC Four radio show.

• 10:45Speaker 02

  Oh, okay. So this is a show that I host with the geneticist Adam Rutherford. And we've been going for about, I think we're recording our 16th series now.

• 10:57Neil deGrasse Tyson?

  Whoa.

• 10:58Speaker 02

  The idea is that people send us in questions and we go out and investigate them. And initially, we, because Radio Four is like, it's kind of the posh channel.

• 11:09Neil deGrasse Tyson?

  Okay.

• 11:10Speaker 02

  It's very highbrow. It's very, there's no music. It's all, it's where the politicians go and it's where they have like these deep intellectual debates. I mean, they have like, you know, programs on philosophy on it, right? It's like, it's like the very kind of like, the very highbrow stuff. So initially, we wanted our program to be very highbrow too, and to be very serious and like, we're very serious scientists. But we discovered quite quickly that actually what works better is if you just basically muck around. And as a result of that, the questions that have been coming in have been from families with younger kids. And they end up being like the best questions. So we had a question that I really liked, which was, what's the tiniest dinosaur? Right? Which is a question that was asked of us by like an eight-year-old.

• 11:54Speaker 02

  Seems like a really, you know, trivial, silly question that you can just dismiss with a, with a quick Google search. But actually unfolds this whole thing of like, how do you define size? How do you define dinosaur? You know, this whole kind of world underneath it. So yeah, that's really what it's morphed into is just this, yeah, like wonderful playground where all the annoying questions that kids want to ask their parents, they send them into us instead.

• 12:17Neil deGrasse Tyson?

  So to the benefit, I think, of the listeners. So it kind of undoes some of their poshitude.

• 12:25Speaker 02

  It does, it does. It's definitely not Bosh.

• 12:29Neil deGrasse Tyson?

  Let's see if we can lead off with a question here. Chuck, we got a first question on algorithms and data for one of the world's experts on that subject.

• 12:35Chuck Nice?

  Sure thing. Let's go, let's start off, we always start off with a Patreon patron because they give us money. So let's go to TJ Monroe from Patreon says, Dr. Tyson and Dr. Frye, the two best radio voices in science.

• 12:51Neil deGrasse Tyson?

  Oh.

• 12:52Speaker 02

  Thank you.

• 12:53Neil deGrasse Tyson?

  Wait, so how is Rutherford's voice in your show?

• 12:57Speaker 02

  It's rubbish. Yours is much better.

• 12:59Neil deGrasse Tyson?

  Oh, okay. So we gotta do, we gotta do our own show then?

• 13:02Chuck Nice?

  There you go.

• 13:03Neil deGrasse Tyson?

  Uh huh, uh huh.

• 13:04Chuck Nice?

  He says, can you walk through the process of creating a predictive algorithm for something like the path of a lightning bolt?

• 13:14Speaker 02

  Ooh.

• 13:15Chuck Nice?

  Or ocean currents? Now, one is a lot easier than the other.

• 13:19Speaker 02

  Yeah, sure.

• 13:20Chuck Nice?

  Because one, one repeats itself. I'm sorry, go ahead.

• 13:23Neil deGrasse Tyson?

  You say that, Chuck, you're an expert on this.

• 13:26Chuck Nice?

  Well, yes, you know, so Neil, in my, in my spare time.

• 13:30Neil deGrasse Tyson?

  But, but I, what I like, that's a great question. What I like about it is, there are two things that are highly sort of, you know, oceans can be turbulent, you have storms and things, but there is a prevailing thing.

• 13:45Chuck Nice?

  Right.

• 13:46Neil deGrasse Tyson?

  You don't know where lightning's gonna strike, but you know it's gonna strike somewhere over there.

• 13:48Speaker 02

  Yeah.

• 13:49Neil deGrasse Tyson?

  So I love that question. So Hannah, has that reduced itself to algorithms at this point or not?

• 13:54Speaker 02

  So I haven't seen an algorithm for predicting lightning strikes, but I'm just thinking through how you could do it. So certainly there are gonna be certain things that, that go into it, as you say, right? Like there are certain days when you can look out in the sky and be pretty confident that no lightning strikes are gonna happen, and other days where you can be fairly confident that they will. So there's certain things that you can measure in the atmosphere, the atmospheric pressure, the humidity, all of those kind of things, that you could plug into a system that could help you predict the likelihood of a lightning strike. But the exact path of it, I mean, Neil, you probably know more about this than me, but I would say that the exact path of it is gonna be very difficult to deduce precisely where.

• 14:31Speaker 02

  I mean, where

• 14:31Neil deGrasse Tyson?

  Do you call that

• 14:32Speaker 02

  end up?

• 14:33Neil deGrasse Tyson?

  Is that

• 14:33Speaker 02

  It's easy, yeah.

• 14:35Neil deGrasse Tyson?

  Would you call it an algorithm if you are checking the atmospheric pressure and the humidity and the size of the clouds and the moisture? If those are just inputs to something that calculates, does that come under your category of algorithm as well?

• 14:49Speaker 02

  Yeah, I think so. I think so. I mean, obviously, there are different kinds of algorithm and, you know, the artificial intelligence is one that gets a lot of attention and the algorithms that deal with sort of data on the internet is another. But I think anything that is taking, taking something from the real world, like a recipe, right? Like taking ingredients from the real world, doing something with it and then spitting out some kind of answer. I think for me, that counts as an algorithm. I mean, you know, technically, if you stop and ask someone for directions, if you're in your car and you stop and ask someone for directions and they say, go down there, go that way, that way, that way. I mean, technically, they're giving you an algorithm.

• 15:24Neil deGrasse Tyson?

  Right, right. So, okay, so algorithm is a very wide catch basin then for accounting for things that you want to predict or understand. So with, with lightning, you'll only discharge a cloud if there's a, if the buildup in charge is very different, either from one cloud to another cloud or between the cloud and the ground. And so, like you said, Hannah, if you measure the humidity, you can check to see what is the propensity of electricity to cross humid air versus dry air and look at a threshold for that. And you say, when it hits this threshold, it's going. And how much of algorithms is also thresholding phenomenon?

• 16:05Speaker 02

  Oh, lots and lots. So I think, yeah, I think that, so that's the difference between, sometimes you are trying to predict exactly what's going to happen. You know, you're gonna, you're trying to predict, let's say, you know, if a ball is rolling down a hill or whatever, like exactly where it's going to end up, that kind of thing. And sometimes you're just saying, as you said, like, what is the probability that this might happen? And at what point do you set this threshold to say, okay? Like, for instance, you know, the example that I gave you earlier about the fennel thing, right? It's like, it's not definitely, you know, it's, it's not like you buy fennel, therefore you're not going to claim on your home insurance. It's like, you buy, you buy fennel, therefore you're likely to be a home cook, therefore you're likely to do this. And then you sort of, all the way through those, you're going to be setting thresholds

• 16:50Speaker 02

  where you say, if it tips over this, then, you know, we assume you're a home cook. If it tips over this, we assume you're, you know, whatever.

• 16:57Neil deGrasse Tyson?

  Interesting. Okay, so that's an important fact here because it's not the one piece of data that gives you the un... it's not the one piece of data that tells you what everything is. It's the one piece of data that might put you over the, over the, the edge of that conclusion. Is that a way to think about that?

• 17:17Speaker 02

  Yeah, I think that's right. I think that's right. I think when you're, when you're dealing with uncertainty, I mean, very, there are very few things, especially when you're, when you're, you know, handling data to do with human behavior, very few things are cold hard facts, right? Very few things are, you, you're rarely dealing in absolutes. So when you're handling uncertainty, the only way that you can possibly convert uncertainty into a yes no answer is by saying, here's the line. If we cross it, we'll assume it's a yes.

• 17:45Neil deGrasse Tyson?

  Got it. And, and the ocean currents, those, like you said, Chuck, those have prevailing, they're, they're not catastrophic the way a lightning bolt is. So presumably, um, that doesn't have this kind of thresholding.

• 17:56Speaker 02

  No, true. But, I mean, ocean currents, there are, there are, uh, you know, much, they're, they're sort of very sophisticated, uh, equations that can describe fluid flow, right? So, um, they're still not absolute, you know, especially when you're dealing with turbulence, there's still a lot of probability and randomness and, and, and chaos, right? That's involved in all of that. But you can say with more, it's, it's, it's not a thresholding problem. You're, as you say, right? You're like, you can say with more certainty where things are going to be and how they're going to be moving.

• 18:27Chuck Nice?

  I was gonna say, there's also connection when you're dealing with, uh, ocean currents as opposed to lightning bolts. Ocean currents are all connected because it's not one ocean. It's an entire oceanic system that happens on the globe, whereas lightning bolts are isolated incidents. So that's that, there's that too.

• 18:47Speaker 02

  That's very true. Although, if you ask the mathematician, the mathematician who studied the ocean, I mean, they assume it's two dimensional. So, I think it's a bit lost on.

• 18:58Neil deGrasse Tyson?

  Yeah, yeah. Chuck, that you start subtracting dimensions to make the problem easier to solve. Whether or not your answer is correct at the end.

• 19:06Speaker 02

  Elegant there, right? Elegant. All you need.

• 19:08Neil deGrasse Tyson?

  We'll get back to that. Uh, we gotta take our first, let's take our first break. And when we come back, more cosmic queries with data mathematician Hannah Frye when we return.

• 19:43Neil deGrasse Tyson?

  We're back. StarTalk, Cosmic Queries, Algorithms and Data. Yeah, I said it. Algorithms and data.

• 19:52Speaker 02

  I'm so thrilled when you say it.

• 19:56Neil deGrasse Tyson?

  Chuck Nice, co-host, tweeting at ChuckNiceComic.

• 20:00Chuck Nice?

  Thank you, sir.

• 20:00Neil deGrasse Tyson?

  And we have as our special guest Professor of Mathematics at University College London, Hannah Fry.

• 20:06Speaker 02

  I'm an associate professor. You just gave me a promotion there. Thank you.

• 20:10Neil deGrasse Tyson?

  I will do that. You take that to the bank and to your department chair.

• 20:17Chuck Nice?

  Hannah, how would you like to be a chancellor?

• 20:21Speaker 02

  I want to be sir. That's what I want.

• 20:24Neil deGrasse Tyson?

  Oh, there you go.

• 20:26Neil deGrasse Tyson?

  So Chuck, you got another question about data?

• 20:28Chuck Nice?

  Sure, sure, sure. Let's go back to Patreon and let's go to Shawshank Submaranian, who says, Neil, hello, and Hannah, hello. I would like to know how impactful would solving the P versus non-P problem with respect to our capabilities of understanding the universe?

• 20:49Neil deGrasse Tyson?

  Excuse me, the P versus non-P problem? Are we all fluent?

• 20:53Chuck Nice?

  Yes, exactly. If you have consumed copious amounts of liquid, the P versus non-P problem becomes quite the conundrum.

• 21:08Neil deGrasse Tyson?

  What is the proximity to your water closet? So Hannah, what is P versus non-P? And is that a real outstanding problem?

• 21:17Speaker 02

  Yeah, yeah, totally. So this is one of the millennium maths problems. So if you solve this Shawshank, what was his name? Shawshank?

• 21:24Chuck Nice?

  Shawshank Submaranian.

• 21:26Speaker 02

  Okay.

• 21:27Chuck Nice?

  He's showing off, by the way, with this question. He's showing off.

• 21:31Speaker 02

  If you solve this problem, you win a million dollars. So I mean, it's kind of maybe the change at the university bigger, but definitely a big change to your life. Okay, so let's say that I gave you a gigantic Sudoku puzzle and asked you to solve it. I mean, like a really massive one, right? Not just like nine, nine squares, a massive, massive one and ask you to solve it. It would take you forever to solve it. But if I said, here's a solved one, I want you to check if it's right. Actually, that's a much easier problem, right? Even though they could be the same Sudoku puzzle, filling it in in the first place is much, much, much harder than just checking that it's right. So there are some-

• 22:09Neil deGrasse Tyson?

  Wait, wait, wait, you say a solved puzzle?

• 22:12Speaker 02

  Yeah.

• 22:12Neil deGrasse Tyson?

  That would imply it's right. So you mean a filled in puzzle?

• 22:14Speaker 02

  Okay, you're, you're right. My language is sloppy. I take it back. A filled in puzzle. I have to take it back. Okay, so sometimes where you've got like a blank Sudoku grid, effectively, uh, you know, or the analogy in, in computers, if it's very easy to check that the answer is right, sometimes you can use that as a loophole to get you to the answer very quickly, right? Because you can just generate answers and check if they're right, rather than kind of go through and grunt through the entire process. So the question is, is that always the case? If you can check that an answer is right quickly, much quicker than you can to solve the problem in the first place, is that always the case? Can you solve something, uh, do really hard problems that you've got to grunt

• 22:57Speaker 02

  through, always have quick solutions or not? Um, and the, the reason why this has repercussions and the reason why this has, you know, potential impact on our understanding of the universe is that an awful lot of the algorithms that were used to try and understand gigantic systems, you know, I'm sure that, that this is certainly true in a lot of cosmology, a lot of them have to use very clever workarounds to account for the fact that some problems are just really hard. Some problems you kind of have to grunt through, um, to, to find the answer. So there are a whole host of different algorithms that exist to try and make that, that, that, you know, that grunting process easier. But if it were the case that actually all these difficult problems do have

• 23:38Speaker 02

  an easy, quick solution, I mean, that would be, if you, you know, if you could suddenly reduce the amount of computational time that you spend on a problem, I mean, that would have a dramatic, dramatic effect on the number of things that you could compute.

• 23:51Neil deGrasse Tyson?

  So I, I want to chuck, I'm going to show off in front of Hannah, so give me a moment here. Oh, good, do you think? So did the four color map problem-

• 23:59Speaker 02

  Yeah.

• 24:00Neil deGrasse Tyson?

  I I was around and in college when that was solved and it was considered inelegant because someone put an algorithm and just grunted through it and but they solved it and no one else had solved it before. So in principle, then that implies that it was easier to solve it that way than by any analytic way.

• 24:19Speaker 02

  Hmm.

• 24:20Neil deGrasse Tyson?

  Is that a fair analogy here or not?

• 24:22Speaker 02

  Ah, is it a analogy? Yeah, I mean, that one, people got very upset about that, didn't they?

• 24:26Neil deGrasse Tyson?

  Yes, I remember that.

• 24:27Speaker 02

  Yeah, but like, because normally when you do a proof, you write it down and it's this elegant statements of logic.

• 24:34Neil deGrasse Tyson?

  It fits on the back of an envelope.

• 24:35Speaker 02

  Yeah, right, exactly, yeah.

• 24:37Chuck Nice?

  So at the risk, at the risk, at the risk of of not being a part of the four color map parade.

• 24:44Speaker 02

  Good point.

• 24:45Chuck Nice?

  The four color map love fest. Well, what the hell is the four color map problem?

• 24:51Speaker 02

  Okay, you know, when you get like a map of the states, you know, the United States, and you've got like all of the different states, whatever, and you want to color it in?

• 25:00Neil deGrasse Tyson?

  Yeah.

• 25:00Speaker 02

  The question is, can you color in with four colors? So that no two states next to each other share the same color, right? Turns out that you can. The question is, is there, so the the four color problem is, uh, let me get this right, actually, you know, you might remember more than me, but does any map exist for which you cannot color it in in four colors or?

• 25:22Neil deGrasse Tyson?

  Yeah, that's that's the way.

• 25:23Speaker 02

  What is the minimum number of colors that you need to color any map?

• 25:26Neil deGrasse Tyson?

  I think we knew that the four colors was the right answer. We just didn't have a proof of it. And so it was it was intractable. Until somebody, again, I I went to college so long ago, computers were new to the world of math.

• 25:41Chuck Nice?

  Punch cards.

• 25:43Speaker 02

  Oh, they're selling, I mean, it was the seventies, right?

• 25:46Neil deGrasse Tyson?

  It was the seventies, yes, it was, uh, back when I was.

• 25:48Speaker 02

  And like a steam powered handle.

• 25:56Neil deGrasse Tyson?

  Yeah, so, um, and it was proven, but only through this, um, by checking the answer, not by proving the answer.

• 26:07Chuck Nice?

  Exactly.

• 26:07Neil deGrasse Tyson?

  That feels like what you just described.

• 26:09Chuck Nice?

  Right.

• 26:10Neil deGrasse Tyson?

  And so, I give you an example from my field. I think, Hannah, I think this is an example. We have people looking for galaxies that are very, very low in their surface brightness. Like, you would, you would scan by it and you wouldn't even know it was there. Well, how, how, how do you look for them if they don't reveal themselves? Well, we, the ones we have found, we know what their light profile looks like. And so what we can do is set up a filter that goes out and tries to match the light in the sky to that filter. And when you get a slight increase in a match, there's a galaxy. Now you can put all your resources there and say, yep, there's a galaxy there.

• 26:53Chuck Nice?

  So you're, you're looking for the answer to ask the question?

• 26:57Neil deGrasse Tyson?

  Yes, yes, yes.

• 26:58Speaker 02

  Yeah.

• 26:59Chuck Nice?

  Yes. That's it.

• 26:59Neil deGrasse Tyson?

  So Hannah, is this legit?

• 27:01Speaker 02

  Yeah, I mean, that's, that's, I mean, these are great examples of like grunting through the solution. And the question is, is that always the easiest way? Or is, I, is there actually a trick? Is there some clever trick that you could have used? You know, I don't know, like folding your data in half and looking for like this superimposed like lights, you know, maybe there's some clever trick somewhere that just, that no one spotted yet. You know, there's so often cases where people come up with these clever tricks. And maybe there was a clever trick that the whole thing could have been solved much quicker without having to grunt it through.

• 27:31Neil deGrasse Tyson?

  Okay, so if this problem gets solved, then it'll give us confidence for all future problems to say, don't even worry about figuring out the answer analytically. Let's just... Compute the answer and then check it.

• 27:47Speaker 02

  Yeah.

• 27:47Neil deGrasse Tyson?

  I mean, if that's easier than that, for me, that's less romantic, that's less elegant.

• 27:53Speaker 02

  It is, but then at the same time, you have to think of the potential repercussions of this. Like, you know, there are some problems, like if you take protein folding as an example, right? So proteins are just the source of so much, you know, of life. I mean, essentially, they're like the fundamental building blocks of life.

• 28:09Chuck Nice?

  Building blocks.

• 28:10Speaker 02

  But everything that could happen to the human body, from, you know, Alzheimer's to the drugs that you, like the effect of drugs that you take, I mean, everything, it all comes down to proteins. And proteins, they're like these long ribbons of amino acids. And the way that they fold up determines their function. These things are incredibly, incredibly, incredibly complicated. And it's okay to go from the folded up, the folded up bundle of ribbon to the long string is possible. But going from the long string to work out what folded up knotted shape it makes is really, really, really, really, super, super, super hard. We understand all of the physics of it. We have, you know, equations that could work it out, but you just cannot grunt through it all.

• 28:55Speaker 02

  And if you, let's say that you could solve this problem. Let's say that you could have a computer that could grunt through all the possibilities. What that would mean is you could say, I want a protein that serves this function. I want a protein that can combat this disease. I want a protein that acts on the body in this way. What shape is it? You know, it's like this shape. Okay, now what is the string of amino acids I need to print to, to, to create that protein? I mean, I'm talking like long, long, long, long, long term here. These are not like things that are around the corner. But, I mean, however, I think there are some applications of this stuff that means that actually romance, that, you know, romance is dead. Like, who cares about romance when you've got protein folding?

• 29:33Neil deGrasse Tyson?

  So Chuck is listening attentively here because he wants to know the formula for a funny joke.

• 29:41Speaker 02

  I think it's going to be hard.

• 29:42Chuck Nice?

  I really, I really don't. Why would I change anything now?

• 29:48Neil deGrasse Tyson?

  How do you fold your words together to guarantee there's a funny joke at the other side?

• 29:52Chuck Nice?

  That, I'm sure there's an algorithm for that, you know, and believe me, I would love that.

• 30:00Chuck Nice?

  It's funny because it sounds like what you're talking about is quantum computing.

• 30:04Speaker 02

  In power, yeah, in power.

• 30:06Neil deGrasse Tyson?

  Well, the extreme level of it, yeah, if you could get the computing power. And then all problems will just be solved depending on whoever spends time looking at it. That, that would, like, I have to reiterate, Hannah, that would take away the romance of the quest for me a little bit, I think. A little bit. Well, you have to sigh. Did you hear that sigh?

• 30:25Chuck Nice?

  Yeah, that was, yes.

• 30:27Speaker 02

  Wait, so do you think that, do you think the four color problem was unromantic?

• 30:31Neil deGrasse Tyson?

  Yes, yes, yeah, yeah. I mean, I, because, you know, we have E equals mc squared, that fits, you know, children write down that equation, it's one of the most profound equations in the universe. How many forces are there in the universe? There's not thousands, there's four, all right? In the, in the early universe, there was fewer. You want, I have a bias, a philosophical bias, that I, when I part the curtains, I want to find simplicity rather than complexity.

• 30:59Speaker 02

  Do you think that simplicity always exists, though? Or do you think actually sometimes if you're a physicist, you can get like, drawn into this sort of, I don't know, it's like, it's like a potion of simplicity that maybe only

• 31:11Neil deGrasse Tyson?

  Yeah.

• 31:13Neil deGrasse Tyson?

  So, here's, here's my lesson that I have to tell myself to get out of this sort of state of romance. Johannes Kepler, when he first showed the, the, um, the planets going around the sun, and he was trying to figure out what kind of orbits did they have and about their distances? And he had a system where he, he's a mathematician, and you know there are the five platonic solids, right? Do you know about this, Chuck?

• 31:40Chuck Nice?

  No, five solids, no.

• 31:41Neil deGrasse Tyson?

  It's, it's, it's a singing group from the sixties.

• 31:45Chuck Nice?

  That's what I was looking for.

• 31:50Neil deGrasse Tyson?

  That's exactly what I was looking for.

• 31:54Neil deGrasse Tyson?

  So, you want to tell them the five solids?

• 31:56Speaker 02

  Okay, so Plato was like super into this idea of everything being perfect. So, the platonic solids are the five shapes that can be created where every side is the same. So, a cube is a platonic solid, every side is a square, tetrahedron, octahedron, dodecahedron.

• 32:15Neil deGrasse Tyson?

  Go ahead.

• 32:16Speaker 02

  And what's the last one? Icosahedron, I guess?

• 32:18Neil deGrasse Tyson?

  Yeah, icosahedron, and I think you left out the pyramid, which is what?

• 32:24Neil deGrasse Tyson?

  The four-sided pyramid, a tetrahedron. No, she said it. Oh, you said it, okay. Yeah, she said tetrahedron. So, we get five, so tetrahedron, octahedron, cube.

• 32:35Speaker 02

  Yeah.

• 32:36Neil deGrasse Tyson?

  Icosahedron and dodecahedron, right. So, each of those have the same shape polygon on all sides, but there are only five of them. So, Kepler knew this, and he also knew that there were six planets. And he said, "Well, everything is perfect and divine, and math is perfect. Maybe the planets are the separations, occupy orbits, and the separations between nested platonic solids."

• 33:03Speaker 02

  If only.

• 33:04Neil deGrasse Tyson?

  So, he took them and nested them and put planet orbits, and he actually got pretty close. He was like, but this was his ideal. This was his sense of perfection that he was imposing on nature, and it was all bullshit.

• 33:19Speaker 02

  I think that happens quite often, though. I think it does happen where people fall in love with the simplicity of their theory and forget that actually often the world's really ugly.

• 33:29Neil deGrasse Tyson?

  Yes, so I use the Kepler example. We had, to his credit, it took him 15 years, but to his credit, 10 years, but he discarded the entire system and out came elliptical orbits.

• 33:41Speaker 02

  Which are beautiful.

• 33:46Speaker 02

  In their own way.

• 33:47Neil deGrasse Tyson?

  In their own way. Not as beautiful as perfect circles as as Copernicus had presumed. But so anyway, let's go to the next question. Chuck.

• 33:55Chuck Nice?

  All right, let's go to John Baker from Patreon. He says, hi, guys. I'm back to prove my ignorance yet again. What kind of empirical data is used? Well, first, let me ask a core question. What does it mean to use an algorithm? Not that I couldn't look it up on Wiki, or or I could have just paid attention in school.

• 34:21Chuck Nice?

  I love John Baker. This guy's amazing. Anyway, that's just. And you know what? I should have. I should have let off with this question, but because the truth is, what we never have touched upon in this show yet. What is an algorithm?

• 34:34Speaker 02

  Now, what is it?

• 34:36Neil deGrasse Tyson?

  Hannah, algorithm 101. Give it to me.

• 34:37Speaker 02

  Okay, 101. Algorithm is this gigantic umbrella term that doesn't really mean very much, which I think is the reason why people hate the word so much. But essentially, all it is is a series of logical steps that take you from some input to some kind of output, right? So a recipe, a cake recipe, that counts. That's an algorithm. Your inputs are your ingredients. The logical steps is the recipe itself. The output's the cake that you get at the end. The difference, though, is that when people.

• 35:07Neil deGrasse Tyson?

  Wait, wait, wait, wait. A cake recipe is a flowchart, I would think.

• 35:10Speaker 02

  I don't think, I don't think, I don't think a flowchart is an algorithm. It could be. I mean, it's just like a giant, like, algorithm, the word algorithm is like this giant, all encompassing term. But I think when people use it, they tend to mean something within a computer. So something where we're inputting some data, and then the machine has some kind of autonomy in terms of the decisions that it makes along the way and spits out an answer at the end.

• 35:38Neil deGrasse Tyson?

  Of course. So computer programs, then, everything you do in a computer program is an algorithm. Is that fair to think of it that way?

• 35:44Speaker 02

  I think that's fair. Yeah. Although, I mean, I think that when you people use the word, you know, if you go like, did you?

• 35:52Speaker 02

  Did you learn, so I had a ZX Spectrum, which I think is quite a British thing. Like, it's like a Commodore 64. That was the thing that I learned to code on. Well, that's the American one, right?

• 36:00Chuck Nice?

  Yes, we did. The Commodore 64 was the American version.

• 36:03Speaker 02

  And what's...

• 36:04Neil deGrasse Tyson?

  And that's because Hannah is 80 years old.

• 36:07Chuck Nice?

  I was gonna say, I wanna unpin my video now, so I could get a closer look at Hannah. Cause she's just, right, cause right now, she's in a little window on my screen.

• 36:18Chuck Nice?

  Like, in her, like, early 30s, late 20s, and then she's talking about Commodore 64. And I'm like, is it her or is it my eyes?

• 36:26Neil deGrasse Tyson?

  Okay, Grandma. I talked to his Grandma.

• 36:29Speaker 02

  I learned coding when I was two.

• 36:33Neil deGrasse Tyson?

  Cause she's such a child genius.

• 36:35Chuck Nice?

  Exactly. There you go.

• 36:36Speaker 02

  No, but on my, so on my ZX Spectrum, this is kind of the British equivalent. You would do like, "print hello", uh, "go to line 10", and then it'd just go around, around, and around, and just be like, the whole, fill the whole screen with hello, right? That was the kind of programming that everyone did when, like, kids. And technically, that's an algorithm. It's just a really rubbish one.

• 36:56Speaker 02

  You know, not particularly, not really doing anything. So I think if people use the word, they tend to mean it's like some kind of automated decision-making. That's sort of really what they mean. But I, but I think, you know, if we're being absolutely fair, the word "algorithm" encompasses all of this.

• 37:09Neil deGrasse Tyson?

  Now Chuck, the person had more to that question, but we just ran out of time in this segment. So when we come back, we'll pick up more on the angst shared by this questioner who wondered whether he should have learned all this in school in the first place. This is StarTalk Cosmic Queries. We'll be right back.

• 37:39Chuck Nice?

  Hey, we'd like to give a Patreon shout out to the following Patreon patrons, Dan McGowan and Sullivan S. Paulson. Thank you so much for your support. You know, without you, we just couldn't do this show. And if any of you out there listening would like your very own Patreon shout out, please go to patreon.com/startalkradio and support us.

• 38:12Neil deGrasse Tyson?

  Startalk, we're back. Cosmic Queries: Algorithms and Data Edition. You never thought we'd go there, but we did. So there. I got Chuck, of course, and Professor Hannah Frye, Associate Professor of Mathematics at University College London. You shared with us earlier in the session that you live in Greenwich. And we've all heard of Greenwich, even if you've never been there. Greenwich time, that's like the time, the base time of the world, right? You get kind of cocky about that?

• 38:45Speaker 02

  You know, we swagger around here.

• 38:51Speaker 02

  It actually took me to move to Greenwich. I've only lived here for three years or so. But it took me to move to Greenwich to realize that Greenwich Mean Time is... The word "mean" in it actually means "average mean" across an entire year. I didn't know that.

• 39:05Neil deGrasse Tyson?

  Oh, yeah, entirely.

• 39:07Speaker 02

  Of course, 24 hours a day is 24 hours. No, it's not. It's 24 hours on average.

• 39:11Neil deGrasse Tyson?

  Yeah, exactly. Yeah, the time it takes the sun to return to its spot on the sky, on average, is 24 hours. Sometimes it takes longer, sometimes it takes less. People don't know that.

• 39:25Speaker 02

  Yeah.

• 39:25Neil deGrasse Tyson?

  Yeah.

• 39:26Speaker 02

  So, yeah, I was happily drinking my Greenwich Mean Time lager and wandering around Greenwich Mean Time Village. And I didn't realize that "mean" meant "average mean."

• 39:35Neil deGrasse Tyson?

  Yeah, it has nothing to do with the emotional state of your time.

• 39:41Neil deGrasse Tyson?

  So Chuck, we left off someone upset that he didn't learn the meaning of algorithm in school, but I think there was a question based down there.

• 39:49Chuck Nice?

  What does it mean to use an algorithm?

• 39:51Neil deGrasse Tyson?

  Okay.

• 39:52Chuck Nice?

  And then Hannah said, like, well, a recipe is an algorithm.

• 39:56Neil deGrasse Tyson?

  But I like what Hannah... I like the distinction Hannah is making as we go forward in the 21st century, that we think of algorithms as an automated procedure rather than something that...

• 40:05Speaker 02

  Yeah, you do, something that makes a decision. And then I think that there's a further distinction there as well between an algorithm and artificial intelligence. And I think that, like, the way I like to think of this is, let's say you've got a smart light bulb that's kind of connected to the internet. And you decide to program it so that it turns on at six o'clock and goes off at eleven o'clock. So that's an algorithm, right? You programmed it. You said, if it's six o'clock, turn off, right? If it all turn on, whatever. That's just a straightforward algorithm. If it was artificial intelligence, generally speaking, uh, most people agree that artificial intelligence needs to include some aspect of learning. So instead, the light bulb would recognize that you came home at six o'clock and turned the light on.

• 40:48Speaker 02

  It would recognize that you like to dim the switch, you know, nine P M. When you do some reading, and then you go to bed at eleven o'clock. So if it's starting to learn from its environment and then impose those rules itself, that counts as artificial intelligence.

• 41:00Neil deGrasse Tyson?

  Right. And but but but that's simply an updatable algorithm.

• 41:05Speaker 02

  Yeah, yeah, yeah, exactly. It's something that's continually revising it.

• 41:08Neil deGrasse Tyson?

  Okay. By the way, you go on, Chuck.

• 41:11Chuck Nice?

  I was gonna say, in in addition to that, though, it is also, more importantly, pattern recognition. So the update is based on the recognition of patterns.

• 41:20Speaker 02

  Totally agree. Yeah, completely, which was really hard until very recently.

• 41:25Neil deGrasse Tyson?

  Now, Hannah, you scared me a little when you began that comment because you said, imagine a smart light bulb. And I thought, aren't smart robots enough? What would a smart light bulb be? Light bulbs marching down the street.

• 41:41Speaker 02

  It just pops above your head every time you have an idea.

• 41:44Chuck Nice?

  I love the smart light bulbs. Like, humans must die. In order for us to shine, humans must die.

• 41:54Neil deGrasse Tyson?

  Exactly. And in fact, they would they would dig up the joke from, um, there's a comedian whose Twitter handle is the science comedian. And I quote him every now and then. One of my one of my favorite jokes of his or comments was, the light bulb was such a good idea. It became a symbol for a good idea.

• 42:15Chuck Nice?

  That's funny. That's true.

• 42:17Neil deGrasse Tyson?

  So if light bulbs uh become our overlords, they will remind us that anytime we think something brilliant, it's one of them that gets popped up in the air.

• 42:27Chuck Nice?

  Don't think we don't know what you're thinking. Well, we only know what you're thinking if it's a good thing. If it's a good thought, we know it. Okay? All right. That's funny.

• 42:40Neil deGrasse Tyson?

  So the science to me is Brian Mallow, if anyone want to dig him up.

• 42:43Chuck Nice?

  Nice.

• 42:43Neil deGrasse Tyson?

  Okay, so you got another question there, Chuck.

• 42:45Chuck Nice?

  Sure thing. Um, this is Ben Sellers and Ben wants to know this. Um, uh, from an evolution standpoint, our relationships and mating behaviors probably follow patterns useful for hunter-gatherers. How do our behaviors on social media and dating websites resemble patterns from more primitive days? What would make interacting online more connected to our primitive programming? Now, I don't know if this is your purview, Hannah, but he's, he's making a really, you know, pretty, pretty poignant association, which is we now find people online. That's how we find love now. I mean, and, and the number is only going up every year. Do the

• 43:25Chuck Nice?

  hunter-gatherer, you know, brainsets actually apply to the way that we go after one another digitally?

• 43:34Speaker 02

  I love this. It's like, I'm just foraging, foraging for lovers.

• 43:37Neil deGrasse Tyson?

  Oh, yeah. Hunter, hunter, hunter gatherer.

• 43:40Chuck Nice?

  Right. And by the way, it is kind of a foraging swipe right, swipe right.

• 43:44Speaker 02

  I know. So you're hunting.

• 43:45Chuck Nice?

  Yeah.

• 43:46Speaker 02

  Um, so actually, one of the very first things I did, as soon as I finished my PhD, was I did this really silly talk that was like, actually supposed to be this kind of private joke that just got really out of hand, which was called the maths of love, right? Which was in part looking at data for online dating websites. And like, it was kind of this thing where I just wanted to demonstrate that you can take a mathematical view to everything. Anyway, it got terribly out of hand and ended up being a TED Talk. And, uh, but in that, there was something that was really interesting. I think it's relevant to this, which is, uh...

• 44:18Neil deGrasse Tyson?

  Wait, wait, just, just a quick thing, Hannah. Most people who have thoughts that get out of hand, don't end up giving TED Talks.

• 44:26Neil deGrasse Tyson?

  So, so it requires some level of brilliance to...

• 44:30Speaker 02

  Well, it's very.

• 44:31Neil deGrasse Tyson?

  Yeah. Okay. I just wanted to distinguish you from everybody else. Yeah. What would be encountered. To so you're Ted Talk, yes, so go on.

• 44:41Speaker 02

  Well, for a number of years in Britain, people started calling me Doctor Love and I was like, it was just a joke, guys. It's never been serious. I'm really not Doctor Love. Anyway, okay, so in it, one of the things that I talked about was about the who gets most attention, right? Whose photos get most attention on dating websites? And you would think, okay, surely it's going to be the people who everyone considers as best looking, right? I mean, surely, right? The most attractive people get the most attention, surely. But so OkCupid is kind of an interesting dating website because for a while there, they were totally open about the fact that they were experimenting on their customers. And like released all their data. And also on their website, what you're allowed to do was rate how attractive you thought other people were on a scale between one and five, right?

• 45:25Speaker 02

  So five is very beautiful. One, I think, slightly more facially challenged is the technical term. And what is, um, what they found was that it's not true that just the people who get fives get the most attention. It was the people who divided opinion the most. So the people who are getting the most attention were like averaging out as kind of a four. But they weren't people where everyone was giving them a four. They were people where some people would give them a five. And lots of people would give them a one. Some people thought they were absolutely horrific and some people thought they were really beautiful. And the explanation for this, which I I quite like, is kind of like an instinctive one. So I guess this sort of goes into the hunter gatherer thing in a way.

• 46:06Speaker 02

  Which is that it's like a game theory thing, right? If you are, if you come across someone and you think that they're very beautiful. Then you imagine they're getting lots of attention and you think, well, why would I, there's no point in throwing my hat in that ring. I may as well stand back. Whereas if you come across someone who you think is very beautiful, but you imagine other people will really dislike. So something, someone who's like a bit unusual in some way, then you're like, great, this gorgeous person isn't going to be getting that much attention. And you kind of like throw, throw yourself in. But just because everyone's doing that, that means it's the really beautiful people who are not getting any attention and the kind of quirky ones who are getting lots. So I think it's kind of interesting.

• 46:45Neil deGrasse Tyson?

  Wow. So, so what you're saying, um, apart from the, the, the, the socio-cultural, um, lessons from that is.

• 46:55Neil deGrasse Tyson?

  Algorithms that might apply to human behavior that data that we're now collecting on billions of people provide might have some strong evolutionary guidance for us going forward.

• 47:08Speaker 02

  Yeah, I mean, I think in terms of it's always like tough to link your backs to evolution, isn't it? But I do think that that you can, you can certainly come up with these game theoretic arguments for the patterns in our behavior. I mean, you know, they're not exactly full survival, but, um, but I think there's they're like fun to explore.

• 47:24Neil deGrasse Tyson?

  But it means you have an algorithm that applies that that's established in one environment. Like Chuck was saying, you're on the, you're on the plains of the Serengeti and there's a certain algorithm for our behavior that we can't shake because it's genetically encoded within us perhaps.

• 47:40Speaker 02

  Yeah, I mean, perhaps. Yeah. I mean, I guess I'm sure that there's like anthropologists who will know this much better than me. But yeah, I like, I, I, I don't doubt it. I'm sure that there, there are lots of occasions where we act really instinctively where, you know, our ancient, our ancient history causes us to act in a certain way. And I'm, I'm sure that we do it still, even when we are interacting with people on a completely different platform to the one that we were designed for.

• 48:03Neil deGrasse Tyson?

  So Chuck, we got time for maybe one or two more questions.

• 48:06Chuck Nice?

  Wow. All right. God, we're, we're gonna have to do another show, man. I, I have 11 pages of questions.

• 48:14Neil deGrasse Tyson?

  Oh, okay, Hannah, you gotta come back.

• 48:16Chuck Nice?

  That is how many people are really like interested in this subject. It's unbelievable.

• 48:21Speaker 02

  You'd have thought, data and algorithms, huh?

• 48:24Chuck Nice?

  So I know we're wrapping it up, so I'm trying to find one. Okay, here's one. Okay. Um, this is Dean from Twitter and Dean says.

• 48:35Neil deGrasse Tyson?

  Wait, Chuck, you're just choosing people whose names you can pronounce. This whole episode have been pronounceable names.

• 48:40Chuck Nice?

  All right, let me go with them. Instead, I'll go with Tielo Jangmanas.

• 48:47Neil deGrasse Tyson?

  That's better.

• 48:49Chuck Nice?

  I'm sure.

• 48:51Speaker 02

  Oh, poor Dean now. Dean thought he had his moment and robbed women at the last minute.

• 48:58Chuck Nice?

  Well, sorry, Dean. We'll get, we're gonna do this show again. So we'll get back to you, buddy. All right. So this is Tielo Jangman, Jangmans, Jangmans. Okay, from Twitter.

• 49:08Neil deGrasse Tyson?

  Okay.

• 49:08Chuck Nice?

  He says.

• 49:11Chuck Nice?

  I'm sorry, I just, he says, assume that the game on behavioral data is already over. The next level is biological data, and the one after that is thought police. Okay, um, what are your expectations and what do you think the outcomes will be? So what he's really talking about there is predictive analytics. Will we ever get predictive analytics to a point where you have pre-crime? It's like, you didn't, you didn't commit a crime, but you know what? We know you're going to commit this crime because these algorithms have actually profiled you in such a way that tells us that you are a criminal.

• 49:54Neil deGrasse Tyson?

  Hannah, you said that about, about Fennel, okay?

• 49:58Chuck Nice?

  Oh my God, you did!

• 50:00Neil deGrasse Tyson?

  If you know that much about who a person is, if they buy Fennel. I, I love what Chuck said there. Can, can you get, are the algorithms so good that they know your thoughts and then they know your next behavior and then you pre-arrest someone?

• 50:15Speaker 02

  Okay, so this is such, this is such a tough topic, right? Like, I spent a huge chunk of my book talking about this, you know, predictive policing and predictive algorithms.

• 50:23Neil deGrasse Tyson?

  Tell us the name of your book again, just so we get that.

• 50:25Speaker 02

  It's called Hello World, Hello World.

• 50:27Neil deGrasse Tyson?

  Hello World, Life in the Age of...

• 50:30Speaker 02

  I can't remember what they gave us, I can't remember what they did to the subtitles in America, I think it's How to be Human in the Age of the Algorithm, maybe?

• 50:36Neil deGrasse Tyson?

  Yeah, that's right, that's what.

• 50:37Chuck Nice?

  I thought you were going to say, I can't remember what the name of my book is. The subtitle is different in America.

• 50:45Neil deGrasse Tyson?

  Yeah, they do that sometimes, yeah, okay. That's the translate.

• 50:48Speaker 02

  Wow, hands!

• 50:49Neil deGrasse Tyson?

  Between two English-speaking countries, that's the translate.

• 50:53Speaker 02

  I know. Okay, so, right, this is like a really tough topic, because the thing is, is that some people have definitely tried to do this, there have definitely been some situations in which people have tried to predict. So there was one particular example in Chicago, I think it was, where they, the idea was quite straightforward, right, which was like... Okay, well, when it comes to gun crime, often today's victims are tomorrow's perpetrators. So if you analyse the network of people who, you know, who people are friends with, you know, who people hang out with, that kind of stuff. If you analyse that network and see and feed in where events are happening. Can you come up with like a risk score, if you like, this is kind of like your, the threshold thing that you were talking about earlier with, with, uh, with lightning.

• 51:36Speaker 02

  Can you come up with a, a risk score that says, uh, we think that this group of people or this group of people are likely to be involved in something in the near future. And when this whole system was set up, it was set up in kind of a nice way, or, or like, I think it was set up with good intentions. Because the idea was that if your name appeared on this list, then police and, uh, social workers. And we'd come around to your house and they would, so it would be like. Intervention. An intervention, right, but it's like, you know, here are these programs that you can join. Here are these alternatives that you can, you know, we can want to help you out of this, the, the life that you're in, right, that was kind of like the intention. But of course, of course, it didn't work out that way, because if you give that list to people. Who've got a completely different set of priorities, as soon as there was, you know, a, a gun homicide.

• 52:21Speaker 02

  It turned out that people took this list and started at the top of the list and then just started arresting all the way down. So by the end, you, um, radical version of this analysis of the, the whole project. And by the end, the people who were on the list were, I can't remember the numbers, but basically way more times, uh, way more likely to have been arrested by the police. Uh, regardless of whether they were involved in the original crime. So essentially it turned into a list, a harassment list, right? Hmm. And I think this is the thing, I think that in a, in like a lab setting, in kind of a cop, a cold. Like environment of like an ivory tower. Actually, I think there are certain things that you can say about like likelihood, you know, like there are, there are some people that you can pick out who you know in a million years, they're never going to commit a gun homicide.

• 53:05Speaker 02

  And then there are other people who, you know, perhaps aren't quite in the same boat. And I think there are some things that you can say about humans. But the problem is, is that the world isn't this ivory tower. You can't like create a system. That gives you that information because it doesn't then tell you what you're supposed to do with it. It doesn't tell you how you're supposed to interact with people, it doesn't tell you what you're supposed to do with it. And I think that I haven't yet heard of a really positive story where people have tried to do something like that and it's gone really well because I just think it's just, I just think like that, that kind of, yeah, the real world is messy, the real world is messy.

• 53:40Neil deGrasse Tyson?

  Right, so, so Hannah, I think just AI will figure out what to do with the data.

• 53:46Speaker 02

  It definitely won't.

• 53:50Neil deGrasse Tyson?

  AI will, will know to become our overlords and subjugate us because we can't take care of ourselves.

• 53:57Speaker 02

  Definitely won't. I mean, also, right, I, that was, that was a point like a couple of years ago where everyone, I mean, genuinely newspaper articles had that attitude of like, I'll just feed it into the AI and it will be able to predict everything. This, uh, so this book, actually, this one here, this guy here, Matt Salganick, he did this amazing project where he had, I can't remember exactly how many, but it's thousands of kids, right, thousands of kids, and he had data on them from when they were, when they were born, when they were five years old, 10 years old, all the way up into their teens, and he had everything on them, right, he had, you know, what their parents did, he had interviews with them, an unimaginable amount, big, big, big data, right, on these kids. And, uh, what he did very cleverly is he released the data, you know, to the public, anonymized and so on, in stages, and he held back the last stage when they were, I think,

• 54:42Speaker 02

  18, and he asked people all around the world, he said, here's everything you know about these kids from naught to 15, I want you to predict how many of them ended up in trouble, how many of them went on to further education, all of those different kind of things. And everyone around the world with their very clever AI and their very clever, all of this, that and the other, tried to do it. Would you like to know what came out on top? Linear regression.

• 55:08Neil deGrasse Tyson?

  Oh, the basic.

• 55:11Speaker 02

  The most basic, basic, basic. What's gone before will happen again.

• 55:17Neil deGrasse Tyson?

  You fit a line to the trend in the data.

• 55:19Speaker 02

  Yeah.

• 55:19Neil deGrasse Tyson?

  And there you have it.

• 55:20Speaker 02

  Yeah.

• 55:20Neil deGrasse Tyson?

  Chuck, in case you didn't know, that's linear regression is the, that's fitting a line through the data, they have to put more syllables to that.

• 55:28Chuck Nice?

  Right, yeah.

• 55:29Neil deGrasse Tyson?

  Linear regression.

• 55:30Chuck Nice?

  Oh, yes, you draw a line.

• 55:31Speaker 02

  Straight line. I think it was. I think I've got that right, guys. By the way, I would probably fact check that slightly because it may have been, whatever. I may have got a tiny couple of those facts wrong, but whatever.

• 55:40Chuck Nice?

  Yeah, we don't care.

• 55:41Neil deGrasse Tyson?

  The sense of it is fine.

• 55:42Chuck Nice?

  It's a great story. I'm sticking with that.

• 55:46Neil deGrasse Tyson?

  So we got to bring this to a close, but we have to have Hannah back on. Oh, my gosh, we've only just scratched the surface, especially with 11 pages. Hannah, clearly you've triggered interest in our fan base and they're going to want more of you as we go forward. But I think, correct me if I'm wrong, Hannah, that one of the great lessons of this is really maybe everyone should have paid attention in their math class because math will be, math will be the foundational forces that define our social, cultural existence in this world. Did I overstate that, Hannah, or not?

• 56:22Speaker 02

  Yeah, no, I think that's actually true. I always think like if, I think that it's really hard to realize how important this stuff is because it's invisible. And I kind of think, you know, drones, like drones came along and then all of a sudden there were loads of drones everywhere and everyone got really upset. Now you've got to have every license possible to fly a drone. I always think that like if you could see algorithms in the same way that you could see drones, I think people would be a lot more, you know, willing to, well, on it really. I think that they'd want to educate themselves a lot more about it. So, yeah.

• 56:54Chuck Nice?

  Or they'd just be really annoyed by algorithms, like they are drones.

• 57:01Neil deGrasse Tyson?

  So, okay, guys, we got to, we got to wrap this up. Hannah, thank you very much for sharing your wisdom, your insights, and some of it pleasing, some of it scary, part of what we need going forward. Chuck, always good to have you.

• 57:15Chuck Nice?

  Always a pleasure.

• 57:16Neil deGrasse Tyson?

  All right. This has been a Cosmic Queries edition on data and algorithms. We got to call it quits there. Neil deGrasse Tyson, your personal astrophysicist, bidding you as always to keep looking up.