Alan Turing’s Final Theory Was About Leopards

28:45Really? I'm feeling them right now with my tongue. You're feeling them right now. That is Turing, Turing, a Turing pattern right there. We know fingerprints, by the way, the ridges on your fingers. It's exactly the same thing. It's about week 10 of pregnancy. You get these chemical waves that follow the Turing patterns. I was thinking of fingerprints the whole time, by the way. Were you? As soon as you mentioned that the geneticists were like, no, it's all according to the rules of DNA. I was like, ah, but you know what? Not fingerprints. Identical twins share DNA, but they do not have the same fingerprints because those are formed in this way that Tyrion discovered that's much more of an order out of chaos.

29:27Exactly. Exactly. If you have a twin, they can bleed to cover up your crimes, but their fingerprints will give it away. You cannot blame them unless you've got their blood. You know, even fingers and toes, right? You see, if you think of your hand as like, it's almost like a wave of like, yes, no, yes, no, yes, no, yes, no. It's during baton. No kidding. Yes. It's like this wave of proteins that oscillate across your embryonic hand that are telling, yes, that are telling you when to build bone, which is your activator and when to sort of die off and create gaps between your fingers, which is the inhibitors.

30:01It's like activator, finger, inhibitor, gap. It's like, it's all during. It's all during. They came up with this in his mind. You know, he didn't even do biology. What's sticking with me is that they were able to do this on mice. It's like, okay, we could do math on paper or on a computer, and then the screen will output the image. But instead of a screen, they used a mouse's body. They do all the little math, they create the things, and then the mouse is printed out, and it matches the equations that they have seeded. Exactly. Exactly. Which is, it's wild to imagine. And it's sort of like, it's sort of which way around is it?

30:44Is it that your body is doing these equations or is it that the equations are just unreasonably good at describing what your body is doing? It's probably the latter. But I mean, there are so few of these, like by the time the 1950s comes along, you know, physics is sort of like Einstein's been along and done all of the space stuff. You know, chemistry's got all the periodic table. There are so few of these unbelievably beautiful, elegant descriptions of reality that are left to find. During had one. If he had only done this, he would have been one of the most important scientists of the last century.

31:20Well, yeah, because after the periodic table is filled out and we've got relativity, then yeah, we really moved into order, chaos, complexity, and that's right where Turing was. Exactly. So this idea then of how does the egg know what to do? How does the embryo know what to do? This is what it comes down to essentially, is that if you have a normal physical system, then you get a tiny fluctuation, like a little ripple, then it just diffuses, it fades away, the water goes flat again. But when you have a Turing system, a tiny instability, just a random bump of the activator chemical ends up hitting this positive feedback loop.

32:03And because the activator's job is to make more of itself, you end up starting this process that ends up building these biological creatures. And when the sperm pierces the egg, right, it physically snaps the surface tension of the cell. And that is enough of a fluctuation in some creatures to end up dictating the main axis of the creature, the axis of head to butt, essentially. Wow. So there is a worm. We know this. Humans are just a little bit more complicated, right? There's a bit more going on. But there is a worm where just that fluctuation of where the sperm enters is enough to say, here's the head, here's the butt.

32:48I should tell you the sperm enters and that's where the butt is. In case you're interested, that's going to be the butt. In the frog, it's the belly. Where the sperm enters, it's the belly. It's a shame. I looked this up. I was looking this up yesterday. So it is a shame because I really wanted it to be that you could say in a human that you know that the sperm from your dad entered and it turned out to be your ear. I really wanted that to be the case, but it isn't. Not quite that. No, because human eggs, you can split them and then they still, it's not like this half becomes the butt and this becomes the head, you know, like.

33:23Oh, I guess that's true. They still have the potential. you couldn't take a human egg rip it in half reverse it and make a butthead person i think that's the plot of human caterpillar isn't it you will oh no centipede damn it i'm gonna run yeah and also it's not the plot of human centipede but it could be the plot of human centipede 4 i have to confess i've never watched a single one of them not even a trailer i watched the first one did you well yeah with the premise that it has, how could you not? You know? I didn't need that mental image of my life, frankly. Hey, there hasn't been a four. I nailed it.

34:01You guys. There's been two and three. Quick. Just kidding. I'm a huge human centipede fan and I've watched them all. We own that now. If anyone does make that blood. It's got to be about in utero, like cell changing in order to create like a donut human. Yeah. A human centipede with one person. Where you alter your own butt, like perpetual motion. But it sounds like you couldn't create these, you couldn't, you couldn't create the human centipede for people using just genetic changes. You would need to also alter the properties of these chemicals that create the tearing patterns and interact with each other in these ways that produce order.

34:46Yeah. I mean, I think that the humans are just so much more complicated, right? There's a lot going on when when it comes to the sort of complexity of a human. Not for the scientists in our movie. They're going to figure it out. Could you have, instead of human centipede, could you have, would worm centipede be interesting? Oh, not really. What? Oh, how about this? How about centipede centipede? I would rather watch that, frankly. I would rather watch that. What is the snake that eats itself, the Ouroboros? That's what I'm imagining. If you do happen to be in charge of mega Hollywood budgets, give us a call.

35:23We've got many more ideas where this came from. Okay, I think that's a good place for us to take a break. But when we come back, I'm going to be talking about people who have found Turing patterns in other unexpected places and what they have done with them. And I'll be honest with you, it ends up going in quite a dark direction. Oh my goodness. All right, I'll be there. You better be. I'm not too down right now. I better be. I'm contractually obligated to be there, but you out there listening, you're in it. You're in it for the kicks. See you after the break.

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37:20Welcome back. So far we have talked about Alan Turing and the stripes of a zebra, the spots of a leopard and human centipedes. But there's even more, even more directions that this idea has taken us. And Hannah, I want to hear it. If you go back to that analogy of the forest, right, and just seeing that you get these patches, of fires. It's a little like hotspot, as it were, of fires. For a really long time, you know, probably since the 1970s, people have looked at the mathematics of Turing patterns and been like, I wonder if you see that in human systems too. I wonder if you get that in urban settings.

37:57And probably I think the clearest example of this, there was a paper that actually wasn't that long ago, 2019. And there was an urban planner called Peter Pels. And he was looking at slums in the global south okay and his idea was look i don't think these things end up forming randomly i they actually have this really distinctive spatial pattern and so he applied turing's equations and then found that you get the same kind of dynamics because if you think about it right you what why do you end up getting slums in the global south where they do and part of it is because of local attraction. So you get a little bit of a demand for low income workers.

38:41So they settle maybe near an industrial area or like a transit hub. And then once they're there, they create a network. And then it means that, you know, they open food stalls, they, you know, offer informal labor, whatever it might be, they're sort of pulling other people in towards them. It's local activation, like an attraction for other people. But as you get more people who crowd into that area, then the land becomes really scarce and then the living conditions, you know, maybe get a bit worse because these residents don't necessarily have like mobility. They can't, you know, pick up and move and go somewhere else.

39:12You end up with sort of a moat effectively that appears around, around these, these areas. And meanwhile, the wealthy population who can commute, they're kind of pushing back, they're driving up land passes and surrounding rings. So you end up with like a city that organizes itself into a Turing pattern where you get these intense, highly localized clusters of poverty, the sort of spots, as it were, that are surrounded by these much wider rings of, you know, high income or commercial areas outside of it. That's so fascinating. So the spots on a leopard, the rosettes on a leopard, for those pedants out there.

39:50They're arranged in the same way that poverty spots the earth. So that's the theory, right? But it's definitely more descriptive. I mean, there's no chemistry going on here. There's no sort of like, there's no WTC and DKK. But instead of atoms, it's people. Where does it possibly fall apart, though, this similarity? Well, it's quite the question that you've just asked, because some people think that it doesn't, or certainly thought that it didn't. I think if you're looking for kind of localised hotspots in an urban environment, then, especially some that have a dynamic equilibrium that sort of like stay there over time, then actually crime looks like a really sensible place to look for touring patterns.

40:39because, you know, let's take burglary as an example. You've got burglars who are wandering around the city who are looking for opportunities, which is like diffusion in a way. It's like the foxes that you had before. You've also got police who are patrolling, trying to prevent crime. So you've got that reaction between the two groups. The difference slightly to other systems is that you know that burglars are also communicating with each other about high value targets. we've known the criminologists have known this for a really long time by the way that point at which you are most likely to be burgled is when you've just been burgled okay um because people replace their valuables because burglars know the layout of your house maybe there's something there that they want to come back for but also often in a street you get um houses that are similar structure to one another so burglars will repeatedly target the same area until people people kind of notice.

41:35So you get this like spike, this like this moment of attractiveness of a particular area where burglars are all drawn to that area, the hotspot essentially. So in 2008, there was this group of mathematicians who released this absolutely gorgeous paper. And I've used this paper with my students loads because it's just like the mathematics in it is really lovely. It's based loosely around the Turing idea of reaction and diffusion. and it's on this idealized street network. It's like it's a grid structure. It's kind of this perfect mathematical model. And they show that if you have this system, this kind of reaction and diffusion between burglars and police, you end up seeing hotspots pop up and disappear in the same way that you do across a real city.

42:22It's like you look at it and you're like, oh, that looks a lot like what you see in the city. Kind of in the same way that Turing looked at zebra patterns and was like, well, that's sort of what you end up seeing in real life. The thing is, in 2008, this was an era when people were very excited about data becoming available. You know, we hadn't really had really, really good data on people before this point. And now you had mobile phones, you had like city reports, you had cameras, etc. And so people were wondering, well, maybe there is a way that you can do physics with people. Maybe there is a way that there are underlying equations that dictate how people are moving.

43:00Sure. Yeah. Model them as a bottled gas. Let's see what happens. Exactly. And the police agreed that maybe this was something that could work. So the Los Angeles Police Department, they looked at this paper and they were like, oh, there might be something in this. Maybe, maybe maths can tell us where these hotspots are going to be next, you know, not just where they are. Now we're predicting crime. Now we're predicting crime. Future crime. future crime or at the very least how are these hotspots moving and changing and and how might they move in future so one of the people on that original paper um this guy called jeffrey brantingham he took this this like what was a really beautiful theoretical paper and turned into a company which was called predpol predictive policing and then in the early 2010s what they would do is they were working with police officers in la and they would print out these maps at the beginning of their shifts and the maps add like a little 500 by 500 foot boxes on them and that box would say this is where there's likely to be crime this evening this is where we expect crime to be right the maths by this point had like i mean i just want to make sure that i'm really accurate this is like it's moved on a couple of steps from turing's reaction diffusion in order to get it to work for that setting but that like right at the heart of it then kind of the nugget of the idea is the same thing that you have these two systems that are diffusing across the space.

44:30And I have to say, right, they did a randomized control trial on this in Kent in the UK. And it's the closest that anyone had been to like double checking if it worked. And to be honest, it actually did work. It did work, this Kent study. But it didn't work in Los Angeles. Well, they didn't do a proper test of it in Los Angeles, or at the very least, they didn't publish a proper test of it in Los Angeles. But in Kent, they did. They published a proper trial of it. So they had, and it was double blind as well. So they had two teams, two sets of patrol maps. One of them was like professional crime analysts who were doing it, humans using their intuition.

45:10And the other was this program, this software. And what they did, the algorithm won, basically. The algorithm was 10 times more accurate predicting the exact 500 square foot box. Where crime would occur. And Kent, they reported an 8.5 % drop in street crime during this trial period. I'm assuming what you do is you look at this grid that the software has filled in with potential hotspots tonight and you put officers there or cameras. Here in the States, we've got these big portable poles with blinking blue lights covered in cameras that just tell you we're watching here. I mean, it could get really complicated because if you dissuade the criminals from where they would have been operating that night, they're just going to go somewhere else that same night.

46:02You have very smartly landed on the real vulnerability of this because when you're observing leopard spots or retrospectively looking at where slums have appeared in the global south, You are not in there interfering with the system as it happens. The real problem with PredPol was that if you are sending cars into a particular neighborhood and you're saying this is where crime is going to occur, you've got people in those cars, right? You've got like real police, real humans. and if they are expecting to find crime in a particular area of the city, they're going to find crime. They're going to find it, yeah.

46:50And maybe it's not the crime that the algorithm was talking about. Maybe it's not burglary, but maybe it's, I don't know, like even someone jaywalking or whatever it might be. But then the issue also is that the algorithm relies on knowing where the crime was already. Right. And so the more crime that you find, the more crime you're putting into the system, the more that the algorithm thinks that that place is already a hotspot, the more it's going to send police back into that area. And the more and more and more you end up finding more crimes. And it will not surprise you when I tell you that in LA in particular, the neighborhoods which were disproportionately flagged as hotspots by this algorithm were neighborhoods that disproportionately contained African -American residents.

47:42And so what ended up happening essentially is that this opened the door to what was automated harassment of particular communities. Right. There's a big difference between watching how spots form or hair follicles on a mouse versus actually putting law enforcement officers in a particular part of the city where they're looking for and interacting with the very chemical reaction you're trying to predict. they're going to mess up the results by being there and noticing, oh, I saw some jaywalking. I saw a car without plates. Things that normally would not have been noticed, wouldn't have been reported, are now suddenly getting fed into the algorithm.

48:26And that's changing up not only what they think will happen, but how they're treating everyone who lives in this whole city. Right. And thus you come to kind of a conundrum, because actually I have to confess that I sort of have like a bit of a, a bit of a connection to this type of work. Right. So I have published papers on mathematics of burglary. I have like these mathematicians who published that initial paper, you know, I said that I've like used it for my students. I've like met and worked with them. And in the early days, I, especially after the Kent randomized control trial, it was like, you know, I, I really didn't immediately see what the problem was.

49:03I didn't immediately see the potential ethical concerns of this stuff. Because here is the conundrum. You can, to a certain extent, better than random chance, predict where crime is going to happen. You can, right? The algorithms work. The problem is, what on earth do you do with that information? Yes. What do you do with it? Because a crime that might happen isn't a crime yet. If you, through surveillance and presence, cause it to not happen, then what does the algorithm do for the next night? You're dealing with probabilities here, you know? Yeah. I mean, the same in biology, the same in forest fires, the same in all of this.

49:46You're not saying 100 % definitely this area is going to be part of the hotspot or not. You are handling with uncertainty front and center. And so it's one thing to say this area, this individual, this group of people are going to be implicated in a crime in future. It's another thing altogether to say, maybe they will, maybe they won't be. Should we or should we not intervene? Right. I should tell you now the way that the UK have dealt with this, I imagine it sounds like it's something similar in the US, is that if there has been a burglary in your neighborhood and your chances of being burglar to have increased, they will put a leaflet through your door saying there is an increased likelihood that your house may be targeted.

50:34Make sure that you keep your security up. And that does, that again, has had a randomized controlled trial. And that again has ended up with a really positive impact. But it's interesting anyway. I just, this is one of those areas where I feel like I was really there as it was all going on with the academics as people were trying to apply mathematics to areas of policing. And really there when the backlash and the naughty repercussions of it came through. I mean, I should also say one of the things about PredPol, the reason why it was dropped in particular by the LAPD was that actually internal reports said it just didn't work.

51:11It just didn't work very well. Well, part of it could be the name too, PredPol. it sounds like a dark organization in a movie. You know, it's a little bit too powerful sounding. It does a little bit. It's a little bit minority report, isn't it? Yes. Not that the name was the biggest problem, but hindsight being 2020, you can go, you know what? If I was a screenplay writer, I would call the organization that doesn't quite work right bread bowl. I mean, absolutely. Has anyone thought of calling it bread bowl? Because that sounds delicious. See, this is this is the kind of commentary I'm here to add, Hannah.

51:55And I appreciate every moment. I think there's a better bread bowl joke there than what I came up with. But, you know, you guys in the comments can give it a better setup. This is fascinating. It's so it's so amazing that you worked on this mathematics and worked with the mathematicians. so what's the state of this field now I have to be honest with you I mean and there's like there's like seven different directions I could go in with this and I was thinking about this last night and I still haven't made up my mind which I probably should have done in advance of this conversation there are all sorts of examples of this right the birdie one is not alone the one piece of work that I particularly did actually um that really got me out of this whole space was I was working with the police in, this is 2011, and there had been these big riots across the UK, really, really out of control, right?

52:46There was looting, there was arson, there was all kinds of assaults. It was really, the police were very shocked by how quickly things had descended, how out of hand things had got over the course of five days. This is when I was, you know, just finished my PhD. This is like the first thing that I was working on, this collaboration with the police. And what they did was they collected all the data of everybody who'd been arrested in connection with these riots and they handed it over to this group of mathematicians and criminologists and said okay see what patterns you can find what we did and didn't do and what we could have done differently so that things didn't get quite so out of hand in the uk our police are not perfect by any stretch of the imagination but i get the impression that we have a slightly better relationship with them than perhaps some people do in the States, right?

53:34There's sort of more, you get, there's more of a kind of feeling of community. Well, yours are called Bobbies. They're called Bobbies, yeah. They're not without flaws. It's important for me to say that, but on the whole, they tend to be, you know, they do a lot of really good work. So we did that. We published this paper and as part of the paper, as well as all of the analysis that we did, we kind of constructed this algorithm that, I mean, it was very, very crude, right? Very, very proof of concept. But the idea was that you could look at it if something like this happened again in future so that police could bring about a swifter resolution to unrest.

54:08Anyway, a few years later, we published this paper. The academic community were like, great, you know, this is like 2013, something like this. A couple of years later, I went off to go and give a talk about this in Berlin. And I was standing on stage at like this audience, which involved lots of the public. and I was giving this really enthusiastic presentation about how great it was that we now had all of these tools, right? Now we were in a situation, I think I was very naive at the time, Michael, to be honest with you, but we were now in a situation where, you know, we could support the police, control a city's worth of people, essentially, right?

54:44I mean, I was very young, I'm very naive. And I think it just genuinely didn't occur to me that if there's one city in the world where people are a bit scared about the police having too much power and control, it's probably going to be Berlin. Yeah. Yeah. So what was their reaction? I mean, they were not happy. There was like, they tore me apart in the Q &A, which I mean, actually, it was a really important moment for me, like a really, really significant moment for me in my career. Because the thing is, up until that point, when you are a mathematician, right, When you are a physicist or whatever, and you're coming up with these mathematical ideas, you don't have to worry about the sort of, Turing wasn't worried about the ethics of changing molecules in mice's skin.

55:28He wasn't sort of thinking about the moral implications of testing the fingerprints of twins. He was just having fun with his equations. Right. Yeah. And I think that that was the moment when I really realized that a lot of the people who are designing our collective future, a lot of people who are working in artificial intelligence, who are working with algorithms, who are working with data, they've gone through with a very technical training that hasn't said to them, you need to be careful in what you're doing. You need to think about the wider implications of it. You need to not see this stuff as though it's just like a cute little mathematical model that kind of sits on a shelf and you can stand back and like admire it as though it exists in isolation of the world around it.

56:15You have to think very deeply about the way your stuff can be used and the impact that it will have on the world. And so that really was the moment when I switched course. I started writing about the ethics of algorithms, an incredible amount of work ever since. I think it really accelerated my work in public communication of science and talking about human issues as well as just technical ones. That's so cool to hear because it's so true, isn't it? I was thinking as we talked about Turing that he wound up being punished by being injected with all these chemicals because he was homosexual. And yet to prove his work right, we had to pump a bunch of rats full of changed chemicals in order to figure out how their hair would grow, not because they were homosexual, but because they were not humans.

57:10that was their punishment for just being a mouse, that we could test on them the way that, in a way, Turing was tested on. But of course, what's the alternative? That we don't test any of this at all? Because from this research, so much good can come too. So, so much hinges upon the responsibility of those who pay attention to the ethics of what they're doing. We need the knowledge, and we shouldn't stop gaining the knowledge. but there's a different thing called wisdom that we need even more. And that's how you use the knowledge. Yeah. Because I'm also sitting here thinking, well, gosh, now that computing power is just more and more democratized every day, I should start doing this predpoll stuff, but in reverse.

57:56Where should I be committing my crimes? Where should I hide a body based on the expected intuitions and search patterns of the authorities, right? I could turn the tables right on them. It's a game of cat and mouse. Is this body going to be a head stitched to a butt, Michael? Because I think that's going to be a giveaway telltale that it was you, all right, when they do finally find it. Hannah, I'm not going to snitch on myself, but the point is that math and science can help us do good and it can also help us do anti-good. It absolutely can. it's it's still such a such a living conversation absolutely absolutely and i don't think it's going to be one with a finish line i don't think this is a finish line we get to cross no no it isn't it's a moving finish line let's see how far can we push this analogy like the muscles we're running with our knowledge but the the path we take is wisdom and the finish line doesn't exist.

58:58Is it a loop? Is it a spiral? Wow. See, we'll do an episode. Is it a human stitched into a Taurus? Is it a human stitched into a Taurus creating its own individual human centipede? Why do we always come back to human centipede? It's the ultimate circle, Michael. It's the ultimate circle. It's the circle of life, the circle of one life. I think we should leave it there to you. I think we should leave it there. Yes. Thank you all for listening, Hannah. And thank you. I loved hearing all of this. If you're out there and you've got some questions you want us to answer from yourself, we do that every Thursday on Field Notes.

59:36And you can send your questions to the rest is science at goal hanger dot com. Thank you so much for watching, listening to us. If you are following us on YouTube, please do write comments below. We read all of them, maybe not all of them, sometimes quite a lot, but a lot of them, especially the ones that say first.

59:56Likewise on Spotify, if you want to leave us any comments or send us any emails, we'd love to hear from you. Thank you very much. See you next time. See you next time.

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