Alan Turing’s Final Theory Was About Leopards

Summary

The Rest Is Science – Episode Summary

Episode Title: Alan Turing’s Final Theory Was About Leopards
Date: April 27, 2026
Hosts: Professor Hannah Fry and Michael Stevens (Vsauce)

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Episode Overview

This episode explores Alan Turing’s lesser-known but groundbreaking work on biological pattern formation—the mathematical explanation for how structures like stripes, spots, and even fingerprints evolve in living organisms. Professor Fry and Michael Stevens dive deep into the scientific, historical, and cultural implications of Turing’s theory, its initial dismissal, subsequent vindication, and surprising applications in fields as far afield as urban planning and predictive policing.

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Key Discussion Points & Insights

1. The Biological Puzzle of Structure Formation

• [00:05] Hannah poses a deceptively simple yet profound question: "How does an embryo—just a perfectly symmetric sphere—know where to place the head or form structure?"
• Diffusion, as seen in physics (like ink in water), typically destroys structure, not creates it. So why doesn’t biology collapse into uniformity?

2. Alan Turing’s Genius Insight: Reaction-Diffusion Systems

• [04:38] Fry introduces Alan Turing not just as the father of computing and codebreaking, but as the mind behind a new understanding of biological patterns.
• Turing’s key idea: Not one, but two chemicals interacting and diffusing—an activator (which spurs its own production) and an inhibitor (which suppresses the activator and spreads faster) can, under the right conditions, create stable patterns:
  • Petri dish analogy: “This is special ink… as it goes, it makes more ink, but it also spits out some eraser, which can delete the extra ink. The ink is slow, but the eraser is thin and diffuses quickly… Could the ink build its own cage, essentially?” (B, [07:38])
  • Forest fire analogy: Fires spread, but helicopters (water) quash the fire perimeter, creating dynamic patches.
  • Foxes and rabbits analogy: Rabbits (slow) reproduce and attract foxes (fast), creating stable populations—“local love and long-distance hate.” (B, [13:21])

Quote: “It’s sort of like a mathematical version of local love and long-distance hate.”
— Hannah Fry, [13:21]

3. Turing's Simulations and the Pattern of Life

• [14:13] Using primitive computers, Turing simulated patterns. “What the computer was spitting out was, I mean, qualitatively identical to the spots that you get on the leopard... or stripes of a zebra.” (B, [14:13])
• The size and shape of the "container" matters: big areas yield spots, narrow ones yield stripes, very small ones have no pattern.

Quote: “If you zoomed in, the ink’s still dividing into more ink and more eraser, still reacting... but it’s found this dynamic equilibrium now.”
— Hannah Fry, [09:19]

4. Rejection, Tragedy, and Turing's Legacy

• [16:25] Turing’s 1952 biological paper, published at the cusp of DNA’s discovery, was dismissed by biologists as a ‘maths parlor trick.’
• Biologists favored DNA—a top-down blueprint—over emerging chaotic, bottom-up explanations.
• The episode recounts Turing’s tragic persecution due to his homosexuality, leading to his forced chemical castration and eventual suicide.

Quote: “Such a tragedy. It’s like gigantic, gigantic titan of computing and mathematical history that the state just treated so unbelievably badly.”
— Hannah Fry, [24:00]

5. Vindication: Turing Patterns in Modern Biology

• [24:50–29:27] Decades after Turing’s death, advances reveal real “ink and eraser” chemicals in biological systems (e.g., WNT and DKK proteins dictate hair follicle patterns in mammals).
  • Turing patterns shape leopard spots, mouse hair, the ridges on the roof of the mouth, even fingerprints.
  • Experiments modifying these chemicals in mice produced patterns that exactly matched Turing’s mathematical predictions.

Quote: “They did all the little math. They create the things, and then the mouse is printed out, and it matches the equations that they have seeded.”
— Michael Stevens, [30:11]

6. Societal Patterns: Turing Equations Beyond Biology

• [38:32] Fry explains how Turing’s framework describes not just biological, but also social patchiness:
  • Urban planning (Slum formation in global south cities: emergent “spots” of poverty amid broader affluence—see Peter Pelz, 2019)
  • Crime patterns: Reaction-diffusion models used to predict crime hotspots (burglars vs. police as activator/inhibitor).

7. Predictive Policing – Promise and Peril

• [43:01–52:11] The concept powers software (e.g., PredPol) that guides real-world policing in cities, which successfully predicted street crime clusters in some studies.
• But application quickly reveals ethical hazards: Feedback loops reinforce policing in marginalized areas, leading to algorithmic bias and potential “automated harassment” of vulnerable communities.

Quote: “If they are expecting to find crime in a particular area of the city, they’re gonna find crime.”
— Michael Stevens, [47:46]

8. Ethics and Wisdom: Lessons for Data Science

• [54:35–56:37] Fry shares a turning point from her own career: presenting riot modeling work to a Berlin audience, she’s confronted with the real-world anxieties about algorithmic power.
• Both hosts emphasize the vital importance—and lack—of ethical education for those developing powerful algorithms and predictive models.

Quote: “A lot of the people who are designing our collective future... have gone through with a very technical training that hasn’t said to them, you need to be careful in what you’re doing...”
— Hannah Fry, [56:37]

9. Broader Reflection: Math, Science, and Human Responsibility

• Science can be used for good or ill; wisdom is knowing how and when to apply knowledge responsibly.

Quote: “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.”
— Michael Stevens, [58:04]

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Notable Quotes & Memorable Moments

• On Turing's tragic timing:
  “If he’d lived to be 100, this guy could have seen his invention become what it was by 2012... he could have watched Vsauce videos.”
  — Michael Stevens, [17:15]
• On the inefficiency of Turing's vindication:
  “Only really recently...have people found, for real, the ink and the eraser, okay?...They tweaked these two proteins...and as a result, these mice grew these merged clusters of hair follicles exactly where the math said they would.”
  — Hannah Fry, [28:00]
• On the dangers of predictive policing:
  “What ended up happening essentially is that this opened the door to what was automated harassment of particular communities.”
  — Hannah Fry, [48:50]
• On ethical awakening:
  “You have to think very deeply about the way your stuff can be used and the impact that it will have on the world.”
  — Hannah Fry, [57:15]
• On the science-wisdom divide:
  “There’s a different thing called wisdom that we need even more. And that’s how you use the knowledge.”
  — Michael Stevens, [58:04]

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Key Timestamps

| Timestamp | Topic/Highlight | |---------------|------------------------------------------------------------------| | 00:05 | The mystery: how embryos self-organize | | 04:38 | Introducing Turing’s reaction-diffusion theory | | 09:37 | Forest fire, rabbits & foxes as analogies | | 14:13 | Turing simulates spots and stripes using early computers | | 16:25 | Turing’s insights rejected by mainstream biology | | 24:50 | Modern confirmation: real proteins, real patterns | | 38:32 | Turing patterns in urban planning and human systems | | 43:01 | Mathematical models of crime, rise of predictive policing | | 47:46 | Feedback loops and bias in predictive policing | | 54:35 | Hannah Fry’s ethical awakening after Berlin talk | | 56:37 | Need for ethical education in mathematical/data sciences | | 58:04 | Science vs. wisdom; power and responsibility |

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Tone and Style

• The episode blends genuine scientific wonder (“It’s wild to imagine...”) with irreverent humor (“Could you make a donut human?”), using analogies and pop culture references to explain complex ideas.
• The tone is conversational but rigorous, with a recurring thread of humility and historical awareness about the limits and dangers of knowledge.

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Concluding Thoughts

This episode serves as both a celebration of Turing’s underappreciated genius and a cautionary tale about the application of abstract science in the real world. The journey from embryo stripes to crime prediction highlights how beautiful, powerful mathematics can drive both awe-inspiring breakthroughs and unintended social harms—depending on the wisdom with which they’re used.

Final Reflective Quote:
"Math and science can help us do good and they can also help us do anti-good... There’s a different thing called wisdom that we need even more."
— Michael Stevens, [59:29; 58:04]

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For Further Exploration

• Turing’s 1952 paper: “The Chemical Basis of Morphogenesis”
• WNT and DKK proteins in modern developmental biology
• Predictive policing ethics and mathematics (e.g., PredPol)
• Hannah Fry’s writings on mathematics and algorithms ethics

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End of summary.

Transcript

[A]

Hello, and welcome to the Rest Is Science. I'm Michael Stevens.

[B]

And I'm Hannah Fry. And I've got a question for you, Michael.

[A]

Okay.

[B]

It's a bit of a. It's a bit of a weird one.

[A]

Right, Good.

[B]

It's sort of a philosophical question in a way, but maybe also. Maybe also deeply scientific. If you start out with an embryo and it's just this perfectly symmetrical sphere of cells, how does it ever decide where the head goes? Right, right. Why doesn't it just. How does it ever end up with any structure?

[A]

I mean, when it. When there's already a little bit of structure there, like, I get it, maybe there's hormones that come from the head, cells that let the neck start forming, but when you're a blastocyst, like just a ball, a symmetric ball of cells, how does it decide? All right, guys, final positions. You're the butt. You guys are the toes. You're going to be the brain. Get to work.

[B]

Ready, steady, go.

[A]

Which ways up and down? Does it have to do with, like, local gravity or the parent's body? I don't know. How does it see those?

[B]

How does it see those? Exactly. But then also, I mean. I mean, you said if there's a little bit of structure there, maybe it makes sense. But at the same time, when you look at physics, if you take, I don't know, like a glass of water, for instance, and you put a little drop of ink in it, the process that happens there is diffusion. And diffusion is like the destroyer of patterns. You don't get structure from physical processes. So what is it about biology that means that you end up with structure? It's sort of. It's a bit of a puzzle. It's a bit of a puzzle.

[A]

Well, yeah, it's really blowing my mind because I've seen a blastocyst, a human one, through a microscope when it's like eight cells, and you're like, wow, cool. But how in the world does it start assigning roles to each cell? And how do you make sure that the two cells on opposite ends don't both decide that they're going to start forming the brain tube?

[B]

Right.

[A]

Am I going to find out today?

[B]

I think you are. I think you are. There's going to be an answer. There's going to be an answer. Maybe not to your. I mean, look, I'm going to come up with slightly simpler animals. I'm not going to. I don't need to get too excited. I've only got the answer for slightly different animals. But I am going to talk about how you possibly end up with structure in biology. And the answer to to this incredibly difficult question, which, I mean, was given by a really extraordinary person. This episode is brought to you by Cancer Research uk.

[A]

Here's something strange. Your DNA contains more ancient viral fragments than genes. The genes that build our cells make up only 2% of our DNA. And for years that is what scientists focused on. They treat the rest, the ancient viruses and stuff, as junk.

[B]

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[A]

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[B]

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[A]

my car in Carvana last night.

[B]

Well, that's cool.

[A]

No, you don't understand. It went perfectly. Real offer down to the penny. They're picking it up tomorrow. Nothing went wrong. So what's the problem? That is the problem. Nothing in my life goes to smoothly. I'm waiting for the catch.

[B]

Maybe there's no catch.

[A]

That's exactly what a catch would want me to think. Wow.

[B]

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[A]

I need to knock on wood. Do we have wood? Is this table wood? I think it's laminate. Okay. Yeah, that's good. That's close enough. Car selling without a catch Sell your car today on Carvana.

[B]

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[A]

No one goes to Hank's for spreadsheets. They go for a darn good pizza. Lately though, the shop's been quiet. So Hank decides to bring back the $1 slice. He asks co pilot in Microsoft Excel to look at his sales and costs. Help him see if he can afford it. Copilot shows Hank where the money's going and which little extras make the dollar slice work. Now Hanks has a line out the door. Hank makes the pizza. Copilot handles the spreadsheets. Learn [e-mail] work.

[B]

There's a kind of strange hero who enters this story who ends up actually explaining a lot of the structure in biology. It's not somebody I think you would immediately expect. It's Alan Turing, who is best known for inventing the computer for cracking Nazi cryptography. He's the guy who came up with a lot of this stuff.

[A]

No kidding. Because I associate him with, like, steampunky computers, metal circuits. Definitely not flesh and. And. And love,

[B]

Deputy. Not flesh and love. Your lucky, lucky wife, Michael.

[A]

Well, flesh, flesh, and even procreation I don't associate much with.

[B]

No, totally. No, totally. But he was struggling with exactly this question. This is like 1952, okay? So at this point in time, he is this absolute, you know, godlike figure in the. In the cryptography community, in the sort of secret services. But the rest of the world doesn't know who he is. They just think he's this, like, crackpot old professor up in Manchester. And he's thinking about exactly this question. It's like, how is it possible that diffusion, which is the process that sort of governs how liquidy, like, things move. How is it possible that when you put that in a biological being, suddenly there's. It's not following the same rules? You get these patterns rather than being destroyed by. Destroyed by diffusion. And he was looking in particular at, like, the skin of animals, so leopards and spots, cows and their patches. So how does biology end up with these. These kind of. These features, right? Not just a head and a tail, but also front and back and left and right, and these complex patterns. You get like zebra stripes and leopard spots. And then he had this genius idea. He was like, okay, well, what if it's not just one thing that's diffusing? Okay, what if you have two things that are diffusing simultaneously that are fighting against one another? What. What would happen then? Okay, so I'm going to give you. I'm going to give you a description. I'm going to give you an analogy of what he was describing. You kind of have to go with me a little bit on this analogy. I've got. I've got a couple ready for you, but just go with me on this analogy. So, all right, imagine you've got this petri dish of water, and I'm going to put a drop of ink in there. Normal diffusion. It would just spread out and it would all be grey, okay?

[A]

And it would be random, and it

[B]

would be random and. Exactly. But this is special ink. This is like a biological ink, okay? And so it can copy itself. So ink makes more ink, all right? But the ink is like, it's slow, it's thick, it's gloopy, it diffuses. But it's really going to take its time about it. But the key thing is that it's like it's making more ink as it goes, right? Like A bacteria would, for instance, right now, if it was in there on its own, if it was just that ink making more ink, then the whole thing would be black very quickly. But what if instead of just making new bits of ink, this ink also spits out some eraser?

[A]

Eraser.

[B]

Eraser. Like ink. Eraser. Okay. This is, like, a very strange type of ink. As I said, you'd have to go with me. All right. It's very strange to have ink, but it spit out right with me. Okay. It spits out both versions of itself and the thing that can kill itself.

[A]

So does this mean, like, it, like, cell division, like, it splits into more ink and eraser in the same localized point?

[B]

Yes, think of it that way. Yes, exactly. In the same localized point, it gets more ink so that the amount of total ink increases, but you also get eraser, which can delete the extra ink. Okay, this is, like, super theoretical. So if it didn't diffuse, if it just sat there and it was spitting out ink and eraser ink and eraser ink and eraser, the two would cancel each other out, and you would just have this completely clear petri dish. Yeah, but what Turing was thinking was, okay, well, what if the ink is really thick and gloopy and slow, but the eraser is thin and slippy and can diffuse really quickly? Is there a way that this eraser could diffuse faster than the ink, spread out across the petri dish, and then end up creating this little moat around the ink as it forms effectively? Like, could the ink build its own cage, essentially?

[A]

Right.

[B]

If that were the case, and he did this all mathematically. Right. And he kind of demonstrated that you can have this where there is a moment where there's an equilibrium where the amount of ink being made exactly matches the rate at which the eraser is wiping out the borders. Okay, so it's not like a stalemate. It's not like, oh, it just stops if you zoomed in the ink still having, like, still dividing into more ink and more eraser, still kind of reacting with what's going on around it. But it's, like, found this dynamic equilibrium now. Okay, I accept that is the most mathematically accurate version of what Turing was thinking of, but I accept it's a bit abstract. So I've got a slightly more human example for you, if you like.

[A]

Okay, well, first of all, tell me, when. When in history was Turing having these thoughts?

[B]

This is 52. 1952. 1952.

[A]

So not even that long ago. Because I'm imagining these great ideas that someone can just have. In an armchair. And I'm thinking of how Einstein did that, too. He was like, what would it be like if I was riding on light? And he's just sitting, you know, in his chair thinking. And here's tiering going, hmm, zebra stripes. Let me think about this. Let me think about, like, an eraser diffusing around in a petri dish. All right, so very cool. But tell me this, like, more human

[B]

version, or this is more biological, all right, because you. You do actually get stuff quite often that can make its own. Make copies of itself, make versions of its own self. And also the thing that kills itself.

[A]

I was going to say there's gotta be chemical reactions that are similar.

[B]

Well, okay. At the time, nobody thought that there were. Everyone thought that. You don't get. You know, it doesn't make sense with. In. In terms of entropy. But there are analogies. So forest fires is a really good analogy of this, because if you think about it, when you get a little bit of fire, actually fire makes more fire. But also, if it's in a forest setting, the more fire there is, the more likely that you are to get the thing that kills the fire, which is helicopters carrying water. Okay, right. So the existence of something creates more of that same thing, and also the thing that kills it.

[A]

That's right.

[B]

So the Incan eraser analogy, if you imagine that you are looking at a forest, you're sort of top down on a forest. Okay. For some reason, you get a little bit of fire here and there, and it starts spreading and spreading and spreading. And then you get in the helicopters who can move much faster. The forest fire is spreading, but it's spreading quite slowly. And the helicopters can move much quicker. And they can encircle this forest fire and basically create a moat around it. So that in the end, you have patches of fire that are burning that are being controlled from the outside by these helicopters while the fire is burning inside.

[A]

Yeah, okay. Which is literally what happens.

[B]

Which is literally what happens.

[A]

Fire perimeters are set up. Yeah, exactly.

[B]

So.

[A]

But Tyrion didn't have this more concrete analogy because there were helicopters. Well, I don't know. When was the helicopter invented?

[B]

Well, Da Vinci came up with a version of the helicopter, if you want to go all the way back.

[A]

Well, when were they used for firefighting?

[B]

I guess. Yeah, good point.

[A]

Okay. There were helicopters in Turing's time, but

[B]

I don't think he was that. I don't think that was. I don't think that was going to him.

[A]

I think he was happy to stay abstract. Yeah.

[B]

One analogy that people did use around the time, around the 50s, was of rabbits and foxes. I sort of. I don't. I object to this analogy for other reasons, but. But mainly because the ink is thick and gloopy and slow and rabbits are quite fast. Okay? Yeah, but imagine for a moment the rabbits are not fast.

[A]

Okay, I'm doing that. I'm imagining that rabbits are slow, okay?

[B]

And rabbits sort of stay near the burrow. Okay? Rabbits make more rabbits, but they also allow foxes to exist. Okay? So the more rabbits you get, the more rabbits you get, but also the more foxes you get. So rabbits are effectively, in a sense, creating both more of themselves and the thing that kills them. Right, okay, here is the idea. If you can accept that the rabbits might be slow and sort of stay near the burrow, but that the foxes can move around much faster, then what you end up with. What you can end up with is this dynamic equilibrium where you have the. The number of rabbits that are having more rabbit babies. The rabbit babies are sort of creating this balance with the amount of foxes that are eating them. So you end up with these stable populations, these little pockets of rabbits that are kind of surrounded by foxes. So a little pocket over there and a little pocket over there and a little pocket over there, okay? And essentially what you need for this system is called a reaction diffusion system. So diffusion, because you've got the foxes or the helicopters or the eraser that's kind of diffusing through the system and the reaction, because, you know, the rabbits and the foxes are reacting together, the fire and the water, whatever it might be. But it relies on these two things. The activator, something that makes itself, but it's also slow to diffuse and makes the thing that also kills itself. So Turing was like, oh, you know what? I reckon let's just play around with this. Let's just see what happens with this. Let's just write some equations, see what happens. He came up with this mathematical system and it was like. It's sort of like a mathematical version of local love and long distance hate. Okay? So, like, the inhibitor spreads really fast and quickly, but in a local setting you can get a cluster where things are quite happy. And he didn't just scribble this on a chalkboard, by the way. He was like, because he had invented the computer, you can't really save very many people. He had access at Manchester University to this really, really crude computer. It's called the Ferranti Mark 1. And so he wrote all of these computer programs to simulate what would happen in this environment, Right? If you've got these two different chemicals or two different processes that are fighting against each other in space. And what he would do is he would start off with, like, a soup of kind of random noise, like little fluctuations here and there. And then he would watch as the computer spat out what looked like the perfect image of the spots that you get on the leopard or on the. Or the stripes of a zebra. What the computer was spitting out was, I mean, qualitatively identical to what you end up seeing in animal skin. Wow. Which is funny, right? That it's like he's literally just having this in his brain. So even if you start with this perfectly uniform soup, a kind of gray embryo where nothing interesting is happening, if you just get a tiny little variation and you've got this process that's sitting there waiting to kick in. Turing basically proved mathematically that you can get this order from chaos just purely through the laws of physics and mathematics. Okay.

[A]

Wow.

[B]

He also showed that the geometry makes a difference. So he demonstrated that if you have, like, a big space, that you've got this process going on in a big space, like the belly of a leopard, for example, then you end up with spots. And if you make it a much narrower space, then you end up with stripes, like on the tail of a leopard. And if it's too small altogether, you don't get any patterns at all. Right? So like on mice, for example, they're usually solid coloured, whereas when you have, like, larger cats, they have, like, much.

[A]

Gosh, that's true. Yeah.

[B]

Okay, so this is all, like, you know, this is all, like, nice and. Nice and theoretical. Would you like to know how the biologists reacted to it?

[A]

I would love to.

[B]

I mean, poor Turing, right? Like, he's got this unbelievable glory from World War II, and he can't tell anyone about it.

[A]

Poor Turing. There's a lot of reasons we could say poor Turing. The guy died in 1954. Born in 1912, right? So I'm listening to this story. This guy uses a computer to simulate the formation of what turns out to look just like the spots and stripes on animals. Why? Because he invented the computer. If he hadn't have died in the 50s, if he lived to be a hundred, this guy could have been watching chocolate rain on YouTube completely in his one lifetime. He could have seen his invention become what it was by, like, 2012.

[B]

Yeah, yeah, absolutely.

[A]

He could have been, like, cracking codes during World War II with his newfangled computer and then lived long Enough to have shared a Coney 2012 meme.

[B]

What year was he born? 1912.

[A]

1912.

[B]

I mean, if he'd lived to be 100, he would have seen that really famous moment in artificial intelligence where they started categorizing pictures of dogs and cats, which was really, I think, that the beginning of the breakthrough of what we've seen, what we've seen happen over the last 15 years. I mean, he could, he could have been alive to see that.

[A]

Imagine that. And here's the most unbelievable one. If he'd lived to be a hundred, he could have watched Vsauce videos.

[B]

And we all know he would have. We all know he would have.

[A]

Life is full of tragedy.

[B]

He would have been there on YouTube saying, first, first.

[A]

And he was first.

[B]

And he was first. He really. Well, Lovelace, but he really was first. We'll maybe do one on that another day. Thing is, right? So meanwhile, Turing, no one knows any of this stuff. No one thinks he's a big deal. He publishes this paper and the biologists are like, yeah, whatever. Are you joking? Well done you with your little maths parlor trick. This is not serious science. You're.

[A]

Why, why did they not think it was serious science? Because it was too mathematical.

[B]

Partly because it was too mathematical. But I think also partly, this is about the same time that people are discovering the structure of DNA. You know, this is like the point where biology is, is, is absolutely obsessed with this idea that there is a genetic blueprint that, you know, for a leopard to have spots, there must be a spot gene that's telling, telling the skin when to turn black and when to turn orange. It's like, it must be this. Everyone was obsessed with this very mechanical kind of top down view of life. And Turing here is this mathematician, he's got no background, but he's never dissected a frog in his life. You know what I mean? And he barges in and he says, oh, no, it's just a puddle of chemicals. And then fluid dynamics does the rest. You know, it's just diffusion. There's nothing going on. So they were like, you know, this is not proper science. This is like looking at a cloud and saying, it looks like a dog.

[A]

Yeah. Oh, for sure. You know what? Like he was saying all of this at the time when, yeah, people, the paradigm was very much about we're actually more robotic than we think. Not just genetically, but even in the mind. Right. The behavioralists were the key psychological field that we just learned things and then were conditioned to behave in certain ways. And that's it. There was no room for creativity. There was no room for even free will, let alone chaos to bring about order.

[B]

I agree.

[A]

Was way ahead of his time.

[B]

This is also in the shadow of World War II. Right. And I think that actually, I mean, we should definitely do some episodes at some point about the kind of the darkness of eugenics and how people were seeing genetic differences to distinguish between us. But in this aftermath of all of the horror that had happened in the Second World War, scientists were actually sort of very keen to notice the universality of humans. Right. That actually we're all the same. This is sort of a really big trend at the time. Top down stuff, right. This like this one rule that binds us all. The rule of your genes. This, rather than there being chaos and messiness that can bubble up from the bottom in Turing's life as well. I mean, you mentioned that he died in 1954 and this paper he released in 1952 to a thud. But what also happened in 1952 was the sequence of events that would lead to his lead to his death. So in January that year, he was kind of finalizing this exact paper, this exact like ink dots paper. He has this little relationship with a 19 year old working class man called Arnold Murray. And shortly after their relationship, Turing's house gets burgled. And Turing reports the crime to the police. And during the investigation he just casually mentions that the burglar was an acquaintance of Murray of, of the guy that he'd been seeing. And he admits to the police that he had been having a sexual relationship with Murray. So he's going to the police for help.

[A]

Right.

[B]

And the police take this information and turn it against him because yeah, homosexuality was illegal in the UK at this point.

[A]

Literally illegal.

[B]

Yeah, literally illegal. So is that the law was gross indecency. And Turing didn't deny it. He doesn't apologize. He is like, I haven't done anything wrong. But he gets convicted in March 1952. His paper is published in August, by the way, the one I'm describing. Wow. And the state give him a choice. They say, okay, well, you can go to prison or you can just have a year on probation, but on the condition that you undergo hormonal treatment to reduce your libido. But essentially it's a chemical castration. So he is injected with synthetic estrogen. And there's this real horrible irony to what happens to him, considering the work that he's doing. You know, he's thinking about biology, he's thinking about how you get chemicals that dictate the physical shape and boundaries and the features of a living creature. And he's spending his evenings watching his own physical shape, his own body be you know, forcibly rewritten by the chemicals that are injected by the state. Because this. This synthetic estrogen that he's given, it fundamentally changes him. You know, he develops breasts, his weight changes, he gets loads of brain fog. He suffers really severe depression by 1953. So a year later, his, his, you know, homosexuals are considered a security risk that are susceptible to Soviet blackmail. So his security clearance is revoked. So the thing that, you know, the world in which he is lauded as a hero has rejected him. The scientific community aren't interested in any of his new ideas. He's, you know, isolated. He's, like, surveilled. He's, like, physically altered. And, you know, less than two years after publishing this paper that I'm describing, while he was working on a second paper on, On. On these biological ideas, he took his own life at the age of 41 by eating an apple that was laced with cyanide. It was such a tragedy. It's like gigantic, gigantic titan of computing and mathematical history that the state just treated so unbelievably badly.

[A]

And we lost so much because of that. So much of his life and what he could have done and what he was actively working on. Completely imagine what he could have done in another 40 years.

[B]

Yeah. I mean, not least on. On the computing stuff. Right. The computing stuff that he was right there at the beginning at that people still refer back to, not in some passe way, but that he formed the absolute foundation. You know, universal Turing machines is still the absolute pinnacle of. Of what we are looking for, how we consider intelligence to be. That's right.

[A]

It wasn't just the foundation, but it was. It was also like the beams that were still hanging on, curing tests. And I mean, I knew all of that. I didn't know he'd done work that was so relevant to biology, though.

[B]

Right. And this is the thing, because while the scientific community just dismissed it as absolute junk, it turns out he wasn't just, like, onto something. He was absolutely phenomenally precise. He. He, in his own mind, managed to absolutely nail the precise mechanism that is going on behind the scenes in biological systems. So it wasn't until 1995, this is like 40 years after his death, okay. That a biologist was looking at the stripes on an angelfish and noticed that they, they. They matched Turing's equations. Okay. But this is like a little bit of a hint, but only really recently okay. In 2006, have people found, for real, the ink and the eraser, okay? We now know that this is, like, genuinely, genuinely legit. So it's called WNT and dkk. And one of the most famous confirmations of Turing's theory, it's about how mammals grow hair. Okay? So if you look really closely at skin hair follicles, they're not. They're not randomly placed. They're spaced out in this. In this dotted pattern. Okay? Like, imagine looking down at the forest. You've got these little patches of fire. Okay? Imagine looking at, like, you know, this landscape of foxes and rabbits. You've got these little pockets of rabbits, you know, and foxes roaming in between them. The activator is this protein called wnt, and it's. It tells skin cells. Okay, we'll start building a hair follicle here. And WNT is this really heavy. It's this really sticky protein. It doesn't travel very far. It stays really local, and it creates this buildup of itself, right? It's got. The more you get, the more you get. The inhibitor is this protein called dkk. And wnt, by the way, triggers the production of dkk. So it's like it's making its own enemy.

[A]

Yeah.

[B]

And DKK tells the skin, do not grow hair. Right? It sort of says, like, no more hair follicles. And dkk, exactly, as Turing said, is this much smaller, much more mobile protein that diffuses out really quickly, rushes out into the surrounding tissue much faster than WNT can spread. And so thus, you end up with hair follicles being in these little islands dotted around the landscape. I just have to tell you, right, To. To prove that this wasn't just a coincidence, biologists, they decided to, like, tweak things. They got some mice, and they decided to, like, tweak these two proteins just to see if it would make a difference. And Turing's equations predicted that if you weaken dkk, the inhibitor, then the hair follicles should kind of spread further. The one that creates the hair follicle should spread further before being stopped. And you should get these hair spots that are much bigger, much more close together, much, much more kind of merged. And so they weakened the. The inhibitor, and as a result, these mice, they grew these kind of huge, merged clusters of hair follicles exactly where the mouth said that they would. Wow. And then when they did the flip side, when they engineered mice with, like, stronger inhibitor, then these were, like, basically bald mice, right? The hair follicles were shrunk. They were spread much Further apart. Like, he nailed it. We now know for a fact, okay, and this is, like, studies that are going on in 2021, that leopard spots is exactly this mechanism. You've got the WNT DKK4. In the case of. Of. Of leopards. It happens during fetal development. You get a darker hair density and then sort of a light hair density. It's happening as the leopard is sort of growing in the womb. We know that on the roof of your mouth, the. The shape of the ridges and the roof of your mouth is a chewing pattern.

[A]

Really? I'm feeling them right now with my tongue.

[B]

You're feeling them right now. That is chewing. Chewing. A chewing 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. That follow the Turing patterns.

[A]

I was thinking of fingerprints the whole time, by the way.

[B]

Were you?

[A]

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 Turing discovered. That's much more of an order out of chaos.

[B]

Exactly. Exactly.

[A]

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

[B]

their blood, you know, even fingers and toes. Right. You see, if you think of your. Your hand as, like. It's almost like a wave of like, yes, no, yes, no, yes, no, yes, no. It's during baton. 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 inhibitor. So it's like activator, finger, inhibitor, gap. It's like. It's all Turing. It's all Turing. They came up with this in his mind. You know, he didn't even do biology.

[A]

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, like, math. They create the things, and then the mouse is printed out, and it matches the equations that they have seeded.

[B]

Exactly. Exactly. Which is. It's wild to imagine. And it's sort of like. It's sort of which way around is it. Is 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. Turing had one. If he had only done this, he would have been one of the most important scientists of the last of the last century.

[A]

Well, 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.

[B]

Exactly. So this, this idea then of. Of. 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. And 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, that the axis of head to butt, essentially.

[A]

Wow.

[B]

So there is a worm. We know this humors is 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. I should tell you, the sperm enters and that's where the butt is, in case you're interested.

[A]

Really?

[B]

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. 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.

[A]

Not quite that, no.

[B]

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.

[A]

Oh, I guess that's true. Yeah.

[B]

They still have the potential.

[A]

You couldn't take a human egg, rip it in half, reverse it, and make a butt head person.

[B]

I think that's the plot of Human Caterpillar, isn't it?

[A]

Well.

[B]

Oh, no. Centipede. Damn it.

[A]

I've got the wrong. Yeah. And also, it's not the plot of Human Centipede, but it could be the plot of human centipede 4.

[B]

I have to confess, I've never watched a single one of them. Not even a trailer.

[A]

I watched the first one.

[B]

Did you?

[A]

Well, yeah. With the premise that it has. How could you not, you know, I

[B]

didn't need that mental image in my life, frankly.

[A]

Hey, there hasn't been a four. I nailed it, you guys.

[B]

Two and three, quick.

[A]

Just kidding. I'm a huge Human Centipede fan, and I've watched them all.

[B]

We own. We own that now. If anyone. If anyone does make that blood, it's

[A]

got to be about in. In utero. Like, cell changing in order to create a. Like a donut human. Yeah, a human centipede with one person.

[B]

We all do Your own, but like perpetual motion. But.

[A]

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 Turing patterns and interact with each other in these ways that produce order.

[B]

Yeah. I mean, I think that the. In humans are just so much more complicated. Right. There's a lot going on when it comes to the sort of complexity of a. Of. Of a human.

[A]

Not for the scientists in our movie. They're going to figure it out.

[B]

Could you have. Instead of Human Centipede, could you have. Would Worm Centipede be interesting or not really? What?

[A]

Oh, how about. How about this? How about Centipede? Centipede?

[B]

I. I would rather watch that, frankly. I would rather watch that.

[A]

What is. What is the snake that eats itself? The Ouroboros. That's what I'm imagining.

[B]

If you do happen to be in charge of mega Hollywood budgets, give us a call. You know, we've got. We've got many more ideas where this came from.

[A]

Okay.

[B]

I think that's gonna. That's a good place for us to take a break. But when we come back, I'M gonna 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.

[A]

Oh, my goodness. All right, I'll be there.

[B]

You better be. I'm not doing it on my own. You better be.

[A]

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.

[B]

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[A]

Welcome 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, this idea has taken us. And Hannah, I want to hear it.

[B]

If you go back to that Analogy of the forest, right? And just seeing that you get these patches of, of fires, this little like, hot spot, as it were, of fires for a really long time. You know, probably since the 1970s, people have looked at the, 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, in urban settings. And probably, I think the clearest example of this, there was a. There was a paper that actually wasn't that long ago, 2019, and there was an urban planner called Peter Pelz, 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. 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. Why do you end up getting slums in the global stuff 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. So 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 stores, they 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. You 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 rises 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.

[A]

That's so fascinating. So the spots on a leopard, the rosettes on a leopard for those pedants out there, they're arranged in the same way that poverty spots the earth.

[B]

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.

[A]

But instead of, instead of atoms, it's people. Where does it possibly fall apart, though? This, this Similarity.

[B]

Wow. 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 localized 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 Turing patterns. Because, 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. And we've known, criminologists have known this for a really long time. By the way, the point at which you are most likely to be burgled is when you've just been burgled. Okay. 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 houses that are similar structure to one another. So burglars will repeatedly target the same area until people. People kind of notice. So 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 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. It'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. You end up seeing in real life.

[A]

Yeah.

[B]

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, et cetera. And so people were wondering, well, maybe, 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 moving.

[A]

Sure, yeah. Model them as a bottled gas.

[B]

Exactly.

[A]

See what happens.

[B]

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 maths can tell us where these hotspots are going to be next. You know, not just where they are.

[A]

Now we're predicting crime.

[B]

Now we're predicting crime, future crime. Future crime. Or at the very least, how are these hotspots moving and changing and how might they move in future? So one of the people on that original paper, this guy called Jeffrey Brantingham, he took 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 had 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.

[A]

Right.

[B]

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, the kind of the nugget of the idea is the same thing that you have these two systems that are diffusing across the space. And 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

[A]

it didn't work in Los Angeles.

[B]

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.

[A]

Okay.

[B]

But in Kent, they did. They published a proper. A proper trial of it. So they had. And it was double blind as well. So they had two teams, they two sets of patrol maps, one of them was like professional crime analysts who were doing it, humans, using their intuition. And the other was the. Was this program, this software. And what they did, the algorithm won. Basically, the algorithm was 10 times more accurate at predicting the exact 500 square

[A]

foot box where crime would occur.

[B]

And Kent, they reported an 8.5% drop in street crime during this trial period.

[A]

I'm assuming what you do is you. You look at this grid that the software has filled in with potential hotspots tonight and you put officers there.

[B]

Exactly.

[A]

Or cameras. You know, here in the States we've got these big like 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.

[B]

You 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 pred poly 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 gonna find crime.

[A]

They're gonna find it. Yeah.

[B]

And 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.

[A]

Right.

[B]

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. And so what ended up happening essentially is that this opened the door to what was automated harassment of particular communities.

[A]

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, 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 and that's changing up not only what they think will happen, but how they're treating everyone who lives in this whole city.

[B]

Right. And this. 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've published that initial paper. You know, I said that I've, like used it for my students. I've, 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. I 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, right. The algorithms work. The problem is what on earth do you do with that information?

[A]

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?

[B]

You're dealing with probabilities here, you know.

[A]

Yeah.

[B]

I mean, the same in biology, the same in forest fires, the same in all of this. You'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, 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?

[A]

Right.

[B]

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 burgled have increased, they will put a leaf through it, through your door, saying there is a, an increased likelihood that, that your house may be targeted. Make sure. That you keep your security up and that does that again, has had a randomized control trial and that again, has ended up with like, 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 to trying. Trying to apply mathematics to areas of policing. And really there when the backlash and the knotty repercussions of it came through. I mean, I should also say one of the things about Pred Poll, the reason why it was dropped in particular by the LAPD was that actually internal reports said it just didn't work. It just didn't work very well.

[A]

Well, part of it could be the name too. Predpol. It sounds like a dark organization in a movie.

[B]

Mm.

[A]

You know, it's a little bit too powerful sounding.

[B]

It does a little bit. It's a little bit Minority Report, isn't it?

[A]

Yes.

[B]

Yeah.

[A]

Not that the name was the biggest problem, but hindsight being 20 20, you can go, you know what? If I was a screenplay writer, I would call the organization that Doesn't Quite Work. Right? Predpol.

[B]

Yeah. I mean, absolutely.

[A]

Has anyone thought of calling it Bread Bowl? Because that sounds delicious. See, this is. This is the kind of commentary I am here to add, Hannah.

[B]

And I appreciate every moment.

[A]

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. And worked with the mathematicians. So what's the state of this field now?

[B]

I have to be honest with you. I mean, 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 burgery one is not alone. The one piece of work that I particularly did actually 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. Things. There was. There was looting, there was arson, there was all kinds of assaults. It was. 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. In the States. Right. They're sort of more. You get, there's a more of a kind of feeling of community.

[A]

Well, yours are called bobbies.

[B]

They're called bobbies, yeah. They're not without flaws. It's important for me to say that. But. 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. This algorithm that. I mean, it was very, very crude, right? Very, very proof of concept. But the idea was that you could. You could look at it if something like this happened again in future, so that police could bring about a swifter resolution to unrest. Anyway, 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, 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 the situation where, you know, we could support the police, control a city's worth of people, essentially. Right. I mean, I was very young and 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, right?

[A]

Yeah, yeah. So what was their reaction?

[B]

I mean, they were not happy. There was like they. They tore me apart in the Q and 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, like, 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, you know? No, he wasn't sort of, like, thinking about the moral implications of, like, testing the fingerprints of twins, you know, he was just, like, having fun with his equations. Right?

[A]

Yeah.

[B]

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 the 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. You 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. I've 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.

[A]

That's so cool to hear because it's so. 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. To figure out how their hair would grow, not because they were homosexual, but because they were not human. That 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 predpol stuff, but in reverse. Where 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.

[B]

Is this. Is this body gonna be a head stitched. Stitched to a butt, Michael. Because I think that's gonna be a giveaway telltale that it was you. All right, when they do finally find

[A]

it, Hannah, I'm not gonna snitch on myself, but the point is that math and science can help us do good and they can also help us do anti good.

[B]

Yeah, absolutely.

[A]

It's still such a living conversation.

[B]

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.

[A]

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 path we take is wisdom and the finish line doesn't exist. It's. Is it a loop? Is it a spiral? Wow. See, we'll do. We'll do an episode.

[B]

Is it a human stitched into a Taurus?

[A]

Is it a human stitched into a Taurus, creating its own individual human centipede? Why do we always come back to human centipede?

[B]

It's the ultimate circle, Michael.

[A]

It's the ultimate circle. It's the circle of life. The circle of one life.

[B]

I think we should leave it there to you.

[A]

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. And you can send your questions to. The rest is scienceolehanger.com thank you so

[B]

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 it's quite a lot, but a lot of them. Especially the ones that say first and likewise on Spotify. If you want to leave us any comments or send us any meals, we'd love to hear from you. Thank you very much. See you next time.

[A]

See you next time.

[B]

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[A]

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