B (1:56)

No, because that one only cost one pound, so you gotta also spend more. You're more likely to win. So give me that 20,000amonth for five years.

A (2:07)

Cause it's £5. I agree. This is the best one, by the way. I agree. That's the best one.

B (2:10)

Can I scratch this now or should we do it later?

A (2:13)

Let's wait. Let's wait. Right, Because I want to know what are the chances of both of us?

B (2:17)

I've got three games on mine.

A (2:19)

Yeah, I don't think it works like that.

B (2:22)

All right, we'll have to find out.

A (2:24)

We'll have to find out.

B (2:25)

Funny enough, randomness might lead us there.

A (2:28)

Look. Hey, that's what I call a hook and tease, you know?

B (2:31)

Yeah.

A (2:32)

If we're talking about randomness, we should probably define it, Right?

B (2:34)

Yeah. I'll throw one out there. I like to just say randomness is a property something has that makes it unpredictable and lacking in identifiable, recognizable patterns.

A (2:46)

Yeah. Where you don't know the outcomes in advance.

B (2:49)

When you don't know the outcomes in advance, you cannot predict them and you can't even find them. When you look at past data, you say, this seems quite random.

A (2:58)

Although I also think that, to add to that, I don't think that that means that something being random is necessarily interesting. I mean, statisticians love the idea of a weighted coin, Right. But, like, let's say that somehow I managed to trick this coin so that it landed heads 99% of the time. And. And then do 100 throws, the results would technically be random, but it would be like, heads, heads, heads, heads, heads, heads, heads, heads, heads, heads, heads. There'd probably be one, maybe a couple of tails in there somewhere.

B (3:26)

Yeah. But where they appear, we wouldn't be able to predict.

A (3:29)

Agreed.

B (3:30)

I think that's a really great difference. To point out that randomness doesn't mean equal probability.

A (3:35)

Absolutely. You do have the other end of the spectrum. Right. Like, if you have a completely fair coin, then you can't tell where any heads or tails will be. Like, it'll be kind of all over the place. And I think that when people talk about purely random, that's sort of what they mean. But really they're talking about where every possibility is equally likely to come up.

B (3:52)

That's right. That's really what they mean. Because they will say, oh, well, that doesn't look very random. And it's like, well, that'll happen sometimes randomly, because a weighted coin that's heads 99.9% of the time, it's still random which one it lands on. But what do they call it when there's an equal chance of all the.

A (4:14)

Options, like PI, for example. So the digits after the decimal place in PI. Well, we think that there's an equal chance of every digit to come up. And if it does, it's called a normal number. If you're just as likely to get a two as a four, as a, you know, as a six, as an eight, whatever. And every combination. Every two combinations. So a 22 is as likely as a 83 or whatever. And as far as we've checked, which, by the way, is very, very, very, very far after the decimal point, I would say, frankly, 10 too far. I know people Worship PI.

B (4:47)

Too far. We haven't even started. It's so long. There will never be far into PI. No, but we've checked. And what have we found?

A (4:56)

We've found so far that it looks like it's normal.

B (4:58)

It's normal. So you're.

A (4:59)

I mean, at the moment, we have checked so far into PI that basically, if you wanted to measure the radius of the entire universe, you could do it, you know, to the level of accuracy that was way beyond the width of an atom.

B (5:11)

Oh, yeah. To measure the universe, you only need PI to, like, seven digits max.

A (5:17)

Hardly any. Hardly any.

B (5:18)

You need hardly any. I think seven might even be too many. But we know it to 7 trillion at least.

A (5:24)

Yeah, I'm still going.

B (5:26)

How many digits of PI do you know? Can we do a competition?

A (5:29)

Yes, we can. I think you'll win. I think I can.

B (5:32)

So do you want to go first?

A (5:33)

Go on. Then we'll take it in turn, shall we?

B (5:35)

Okay.

A (5:35)

Okay.

C (5:36)

Okay.

B (5:36)

I like that. All right. 3.1415926.

A (5:48)

I'm out.

B (5:49)

I'm out. 5. 3141-5926-5358-9793.

A (5:56)

Once you get to. I think it's like the 762nd digits from there, it goes 99999.

B (6:03)

How many nines is that?

A (6:04)

I think it's six. In a row.

B (6:06)

In a row.

A (6:06)

Right. Which is, like, what? That feels unlike. That feels pretty random.

B (6:09)

Oh, this is the Feynman pyramid.

A (6:11)

This is the Feynman.

B (6:11)

I heard about this.

A (6:12)

Yeah. Feynman would always say that what he wanted to do was to memorize the digits of PI all the way up to 762 and then just say 999. 999, and so on and so on. Wasn't he a clever guy?

B (6:23)

He was.

A (6:24)

Okay, so this is the other end of the spectrum. Right. The digits of PI are the other end of the spectrum in terms of the random sequence. It's not random.

B (6:30)

They're random and potentially normal.

A (6:33)

Yes. Well, okay. We should be careful, though, because they're not actually random because PI is a particular number. That's true.

B (6:39)

I can pred what it'll be. It'll be the ratio between a circumference and a diameter of a circle.

A (6:43)

Exactly. But they have all the characteristics of a random sequence. So, you know, this infinite sequence in PI where the digits are distributed uniformly. Right.

B (6:52)

First of all, I want to throw in another definition of randomness that I really like. But then I got a question about it, and the definition Is that when something is random, it takes longer to describe to someone over the phone. So imagine that I flipped a coin a hundred times and it landed heads every time. I could quickly tell my friend, dude, I rolled a coin 100 times, I got heads every time. Bye.

A (7:19)

Completely predictable. Completely ordered.

B (7:22)

Exactly. The results of those flips were random. Let's say it was just weird luck. However, that string of digits is easy to communicate. Easy to communicate. So is it still random?

A (7:33)

So, okay, there's two slightly different things going on here, right?

B (7:36)

Yeah, there are, aren't there?

A (7:37)

Yeah, two subtly different things. So one of them is about whether or not the next thing that's going to happen can be predetermined. Whether it's predictable, essentially. Right. Whether this, this outcome is determined in advance, that's randomness. But then there's also the other thing here, which is like the opportunity for surprise, as it were. Right. Because you could have. It's kind of going back to that spectrum of like a very ordered sequence, easy to communicate, a very unordered sequence where, you know, like, taking a chunk of the digits of PI is really difficult to communicate. I mean, that if you were saying that down the phone, you would have to literally read them out one by one by one.

B (8:15)

Yeah, right. If I flipped a coin and I got a more typical distribution, I couldn't just call my friend and say, oh, they were all heads. I'd have to be like, oh, dude, okay, there were two heads. Tails. Heads. I'd basically have to just read the whole thing and it would take a long time. That string of digits is more random, it's less predictable, and there aren't a lot of patterns in it.

A (8:37)

Well, I don't know if it's more random. Right. But I think it has. Well, it's what the information theorists call more entropy. It's more chaotic.

B (8:45)

Okay, so then there's a difference between being random and being high entropy.

A (8:49)

Absolutely. So being random is about whether or not you can tell the next thing that's going to happen in advance. And having high entropy is about like the number of opportunities for surprise. So like in an Alphabet, you know, if I, if I had some Scrabble tiles, something that was low entropy would be aaaaaaa. Right. Perfectly ordered. Right. Nothing interesting going on. Something that was high entropy would be like, you know, T, G, D, P, L, W. Like no. No discernible patterns for you to latch onto. There's two things that are going on here, right? So one of them is randomness, and that's whether or not the next thing in the sequence is predictable. And it doesn't matter whether it's very, very likely to be heads or equally likely to be heads and tails. It's still whether you or not, you can absolutely say for certain what it's going to be in advance. But then there's this other thing that's going on, which is that we sort of have a spectrum of chaos here. So at one end you've got something that's perfectly ordered. Head, heads, heads, heads, heads, heads, heads, heads, heads. Really boring. And then the other end, you've got something that is perfectly messy. Right. There's no structure or pattern that you can latch onto. And at the one end, you're gonna be able to communicate that down the phone extremely quickly and easily. It's just all A's. And at the other end, you're gonna have to read out the entire thing. There's no way that you can compress that message.

B (10:05)

Yeah. That's a very important distinction to make the difference between randomness, meaning can't predict it, and how disordered is the result that we got from a random unpredictable process. You can throw Scrabble tiles on the ground and it is possible that they'll land in a way that spells a sentence that would be highly ordered. But if they just look like a mess, it's very unordered. And I think it's important to point out, and this will keep us in the topic of randomness, but it's important to point out that this is starting to feel a little bit subjective.

A (10:39)

Yeah.

B (10:39)

Because what do you mean it looks messy? What I mean is there's a lot of ways Scrabble tiles thrown on the floor can look messy. There's a lot of ways, but there's, like, only one way. They can fall on the ground and spell out how I was born.

A (10:55)

Yeah.

B (10:56)

All right. And so the fewer different ways something can be arranged to look the way it does, the higher the entropy.

A (11:04)

Yeah, absolutely.

B (11:05)

Richard Feynman had a quote that I actually wrote in my notes. We measure disorder by the number of ways the insides can be arranged so that from the outside it looks the same.

A (11:14)

Oh, that's so good.

B (11:17)

Yeah.

A (11:17)

He really was smart, wasn't he?

B (11:19)

Flip a coin a hundred times and get heads the whole time. There's only one way you could get heads every time. But how many ways are there to get something that's really hard to communicate on the phone? Most of them.

A (11:31)

A bunch. A lot. A lot.

B (11:32)

A lot that has low entropy.

A (11:34)

Yeah. I like this idea of the distinction between randomness and disorder, which is what we're really ultimately talking about here. You know, the other thing about the throwing those Scrabble tiles on the floor and getting a sentence that spells out the way that you were born is that that's you imparting meaning onto what is essentially a random sequence. Because if you carried on going forever, if you chucked, you know, Scrabble tiles on the floor over and over and over and over again, eventually you would end up with something that. That told the story of your birth and your death.

B (12:06)

Well, right, but like, let's just imagine that the universe is really huge, like infinite, and that there are intelligent civilizations that number almost infinite as well. Then the chances that I could throw Scrabble tiles on the ground and some alien somewhere could look and go, that's how I was born. Using its language becomes higher and higher. So whether something is disordered really depends on the context that they're looking from.

A (12:38)

Have you read the Library of Babel by Jorge Borges?

B (12:42)

Yeah, I have.

A (12:43)

Oh, my God, I love it so much. I love it so much. But he had this. It's a really short story. It's only like three pages or something. But he had this idea that there was a fictional library, a kind of infinite library where every single possible combination of letters on a page existed within the books within this library. So that in theory, you could go in. I mean, spaces are included, right? You could go in and you could go to some shelf in this library, pull a book off, flick to a page, and it just say nothing on the page at all apart from your name, right, written in the middle. But there would also be a page somewhere in that library where your name would be written vertically, right? And another one diagonally, and another one where it was just your name over and over and over and over again. Every possible combination of a way to write your name must appear within that library. But the sort of twist on this story is that even though that might be true, because, as you said, the number of ways to order letters on a page, the number of ways to order Scrabble tiles is so gigantically massive, what this means is your experience of going into the library is that you pull out a book, you open a page, it's junk. Another page, it's junk. Another page, it's junk. So he has these librarians wandering around this infinite library looking for meaning and essentially finding nothing. There's one person who has spent 10 years and found half a sentence, right? Someone found the word. It's a dog or Whatever.

B (14:14)

Even that's hard to believe.

A (14:15)

Even that's hard to believe.

B (14:16)

Even though every single sentence possible is in that library, your likelihood of finding one of them in your lifetime, it's gotta be close to zero. It's not zero.

A (14:27)

It's not zero. Do you know that someone made this? Someone made a digital version?

B (14:31)

Yeah. I talked about it in my video messages for the future, and I got to speak to the guy who made it, and he told me how he coded the site to work, because, yes, it contains every combination of letters, including spaces of up to a certain number of characters.

A (14:46)

He only did every possible page, right, Rather than every possible book.

B (14:50)

Yeah, it's not every possible book, but it also doesn't exist on a server, like every combination. Instead, it's coded within numbers.

A (15:00)

He did a mathematical trick, basically.

B (15:02)

That's right. You can search, and it will find anything you search in the Library of.

A (15:07)

Babel, because we already know they're out there. In theory, they already exist. But what he managed to do was to create a way that he can order them and allow you to search them.

B (15:17)

Right. So if I looked up your name, I would find it on a certain page, in a certain book, in a certain volume in a certain wing. But then if anyone else looked up your name, it would be in that same spot. So he's cataloged everything that's ever been said and everything that still hasn't been said. It is a little bit scary.

A (15:35)

It's really scary. But I also think that what this demonstrates is just how, like, how difficult we find it to conceive of these worlds, Right? Of randomness, of combinations. I think that we are just really, really bad at having intuition for what randomness looks like.

B (15:50)

We really are. You said something that I want to get back to, which is that the librarians in the Library of Babel are looking for meaning. What is meaning?

A (16:03)

Is it meaning something that we effectively create because the universe doesn't provide it?

B (16:09)

Yeah, I might not totally agree with that yet, but I think I will say this. That to me, meaning is what happens when information is discarded but can be put back in. Meaning is what happens when information is discarded but not lost.

A (16:27)

Go on.

B (16:28)

Okay, so let's just talk about someone's name, right? Like, the name Hannah Fry means something. It means you. It means your life story. It means a lot, and it means a lot to different people. But I don't have to refer to you. By describing everything about you and everything that you've done and where you are right now, I can just say Your name. If someone goes, oh, who'd you see today? I don't have to be like, I saw the woman who was born in this town. And it'd go on and on. I can just say, hannah, a lot of information has been discarded. I'm not mentioning a lot of stuff, and yet I am.

A (17:05)

It's like, you know, it kind of goes back to that idea of, like, how quickly can you communicate something around the phone? Exactly.

B (17:10)

I'm compressing everything you represent into a single name. When we ask what something means, we're asking what information has been discarded.

A (17:20)

Right? Okay. You're asking for the invisible thing that you're no longer directly communicating, but that is common, shared agreement between. Right.

B (17:28)

I heard what you said, but what did you not say? Because that's what you meant.

A (17:32)

But then here's. I'm gonna go back to what I said before. That meaning is something that we create, right? Because the universe doesn't provide it. And I actually, I want to double down on that. Because the thing is, the words Hannah Fry, they might mean something now, right? They might have a shared meaning between people who are my family or whatever right now. But this is something that only has existed for the briefest flicker of time and will quite soon cease to exist. Right? Like in a hundred years, maybe less, it will have no longer any meaning whatsoever. In the same way as, like, Ignatius Spelling, you know, or whatever, it doesn't have any meaning to it.

B (18:11)

That means a lot to me. That was my father's name. How dare you.

A (18:16)

And so, yeah, I think that actually meaning is something that we are creating, that we are putting on top of the randomness that already exists.

B (18:24)

Not only are we creating meaning, it is what we do.

A (18:28)

Yeah.

B (18:28)

I think our niche as a species is that we create meaning. We discard information to save time to solve problems. It's all what cognition really is. And I think we make meaning just like bees make honey. Okay? We take stuff. We take information from the universe, just like bees take nectar and we go, eh, this could be sweeter. And we discard a bunch of stuff. The bees dry out that nectar until they've just got this really, really sweet honey. And we take in all this information and we discard stuff until we've just got this meaningful thing.

A (19:08)

I really like that idea. I really like that idea. I think we can go further with it. So I'm going to pause for a break, and when we come back, we're going to see if there are other ways to make meaning from randomness and disorder.

B (19:28)

This Episode is brought to you by Cancer Research UK, who over the past 50 years have helped double cancer survival in the UK.

A (19:35)

You might have heard of BRCA genes. These are the ones that made headlines when Angelina Jolie revealed that she carried a faulty version.

B (19:42)

Yeah. BRCA genes are part of our DNA. They help to repair cells and keep them healthy. The risk comes when BRCA genes are faulty and about 1 in 400 people inherit a faulty version, increasing the risk of some cancers.

A (19:55)

Yeah. Now, this discovery came From Cancer Research UK scientists who came across the BRCA1 and BRCA2 genes. A breakthrough that changed how doctors prevent, diagnose and treat canc. And now we've got genetic testing that means that people who have faulty BRCA genes can take steps to prevent cancer or to receive Taylor treatment.

B (20:15)

Yeah. The discovery also revealed a weakness in cancer. By turning that flaw against the disease, researchers developed PARP inhibitors, targeted drugs that are now helping thousands of people.

A (20:26)

And all of this really points to a future where medicine is no longer just one size fits all. It's something that's, that's informed by your own DNA. So for more information about Cancer Research uk, their research breakthroughs and how you can support them, visit cancerresearchuk.org restiscience. This episode is brought to you by Thriver. Every January, we make ambitious health decisions, usually with surprisingly little real information. We change things and often just hope for the best.

B (21:01)

Which is strange because we'll track things like our steps and our sleep obsessively. But what's actually going on inside our bodies, it's often total guesswork.

A (21:10)

Thriver is built to fix that. It's a proactive health platform built around regular at home blood tests so you can understand what's really going on inside your body.

B (21:20)

You can track over 50 biomarkers across things like heart health, metabolic health, hormones and nutrition. And Thriva doesn't just hand you numbers. It explains what they mean, how they're changing over time and what you might want to do next. Grounded in evidence, not guesswork.

A (21:36)

And then the app pulls everything together for you. Your blood results, your trends over time, and wearable insights like sleep, movement and cardio fitness. So the bigger picture actually makes sense.

B (21:46)

Go to Thriva Co to get started. That's T H R I v a co use code tris for 20% off your first blood test.

C (22:00)

The world moves fast. Your workday even faster. Pitching products, drafting reports, analyzing data. Microsoft 365 Copilot is your AI assistant for work built into Word, Excel, PowerPoint, and other Microsoft 365 apps you use, helping you quickly write, analyze, create, and summarize so you can cut through clutter and clear a path to your best work. Learn more@Microsoft.com M365 copilot.

A (22:35)

Welcome back. We are talking randomness. We are talking meaning. We're talking disorder and chaos. All of the sweet, easy subjects for today.

B (22:44)

It's been so random. I think I was thinking about that during the break. I went to the bathroom and I was thinking. I was like, yep, I think people use the word random nowadays in the right way. They're saying, look, I couldn't have predicted it. I didn't know that guy would be at the party. So he was a rando.

A (23:03)

Yeah. That one I don't mind is when people say, oh, I just crashed my car. I'm so random. That's the one that bothers me. It's like, no, no, no, no, no. You can't. You can't retrospectively apply.

B (23:15)

A person who talks like that is someone that I would predict would be in a car accident. It's not random.

A (23:21)

That's actually my natural voice.

B (23:23)

Oh, so you're just, like, putting on this.

A (23:25)

This one is the fake one. I'm from Essex, you know. Come on. Okay, we agree then, that the. There's a lot of disorder, there's a lot of uncertainty. But the thing is, I think science is really, really very good at taking that uncertainty and using it for our own advantage.

B (23:41)

Give me an example.

A (23:42)

All right. One of my favorite stories is back in, like, the 1700s, there was a lot of woo woo in medicine, right? Even, like, official hospitals had a lot of crazy, crazy stuff going on.

B (23:55)

What wasn't woo woo back then?

A (23:57)

I mean, mathematics. Thank you. There's a lot of good, good mathematics in the 1700s. But anyway, one of the most amazing bits of woo woo was this thing called Perkins metallic tractors. Have you come across these?

B (24:07)

No.

A (24:08)

Basically, there were these little rods, and they had pointy ends. And what you would do is you would go around to somebody who was, like, not feeling very well, and you would point the rods over the bit of their body that was hurting. And then technically, if you stood there for about 20 minutes, they could draw out the noxious electrical fluids. I think people were quite into electricity at the time, and they sold for an absolute fortune. Old Georgie Washington, he had a set. Everyone was, like, fully bought into them. And then there was this guy called John Haygarth, and he was like, I think this is not. Something's not right. Here. So what he does, he went away and he made some little wooden versions of these tractor scenes, and he painted them to look like the original. And then he went into a hospital and he was like, okay, there's lots of people here. Right. If I try this on a number of different people and I won't tell them, sometimes I'm going to use the real tractors, sometimes I'm going to randomly assign the other ones, the wooden ones, and I'll see who ends up getting better. And he went around and he collected all the data, wiggling these little sticks in front of people and demonstrated that the real ones had made no difference whatsoever. Whether he was wiggling the metal sticks, the really expensive tractors, or these little wooden toothpicks that he mocked up at home didn't make any difference. But what he was effectively doing there was he was using the kind of randomness, the disorder of the universe to his own advantage to kind of prove that there was nothing special about these tractors.

B (25:35)

That's right. He found their meaning, or lack thereof, by randomly applying them. Was it like double blind? Did the users not even know whether they had the real things or not?

A (25:47)

The. No, he. Cause he was the user, so. No, he knew, he knew, he knew. But it was a bit later on. I mean, it was like the 1940s when control trials, Random control trials.

B (25:58)

So he was like one of the first on record to be doing this kind of a randomly random trial.

A (26:04)

Exactly.

B (26:05)

To find whether there's meaning or not.

A (26:07)

Exactly.

B (26:08)

Did he look at the outcomes of people that he didn't do any of the treatments to?

A (26:12)

No. This is like early doors. Right. So for that stuff, for it to be properly the point at which it became the new gold standard, that was the treatment of TB in the 1940s. And then there was a particular drug, it was called streptomycin. And people weren't sure whether it was actually making a difference or not, because, of course, you give a drug to some people with TB and they get better, you give it to other people with TB and they don't get better. And it's really difficult to kind of hone in on precisely the role of the drug in all of that. So that was when they did it absolutely properly. They deliberately withheld the drug from a randomly selected group of individuals who had TB in order to work out whether it was the drug that was making the difference or it was just randomness.

B (26:52)

Yeah, right. Cause there could even be a placebo effect where, whether it's real medicine or not, just the fact that A doctor cares enough to give you attention can affect how your body heals, how it reacts, what symptoms.

A (27:06)

And it's only by harnessing randomness and disorder, by doing it on multiple people, that you can start to separate out.

B (27:12)

Some of these, to separate out the meaning.

A (27:14)

The meaning, exactly.

B (27:15)

Meaning can even emerge from randomness. I think my favorite example is Zipf's Law. Yeah, Zipf's Law is this really bizarre phenomenon that we've observed in every known language. We've observed this in craters on the moon. I'll describe it using language. So in languages, there are words that are used more than others, and you can rank how often a word is used. So in English, it's the word. The. The second most used word, as it turns out, will be used about half as often as the most used word.

A (27:52)

Really?

B (27:53)

I'm not done yet.

A (27:53)

Okay.

B (27:54)

The third most used word will show up in texts a third as often as the number one, the fourth, a quarter as much, the fifth, a fifth as much, and so on, all the way down. I did a video about this and I just. I said, well, Vsauce is my YouTube channel. I'll check the word sauce. And I don't remember the exact number, but sauce was like the 5000th most used word in English, and it showed up 1 5000th as often as the word the in the Gutenberg corpus and in Google's corpus. I was like, this is incredible. And so what's going on? Right.

A (28:30)

Yeah. I mean, that sort of feels like it's not random. That feels like it's like an imparted pattern.

B (28:35)

Maybe it's put in there by human minds. No, because we see this in craters as well. Crater size and location. It follows Zipf's Law. The most common size shows up a certain amount of time. The next most common shows up half as often as the most. And this. It's incredible. But then later it was found that Zipfs law is also obeyed by random typing on a keyboard. No. Yep. That just a monkey slapping a keyboard is going to also create a language that follows Zipf's law.

A (29:06)

Wait, so hold on, hold on, hold on, though. What are you counting? What counts as the most common thing there? Like, combination of letters.

B (29:12)

You just treat it like they're words and you say, what's the most common word? Which means letters that are separated by spaces. What's the most common word that the monkey's typing? And then you look and they're going to follow the same pattern. And that is because the space bar is what defines A word ending and beginning. The space bar is one of the keys. So imagine a keyboard that just had letters and a space bar. It's got 27 buttons. Eventually you're going to hit the space bar, and that makes shorter words more likely than longer ones. A longer word means that you've gone a long time without hitting the space bar. It's much less likely. And how much less likely, you can look at the math behind it, and it just forms that exact same shape. Wow.

A (29:59)

So actually, what Zip's law is doing there is that it's like this inherent pattern that appears when things are generated.

B (30:08)

Effectively at random and when you stand far enough away. Which brings us back to that subjective quality of randomness.

A (30:15)

Like, if you zoom out, if you zoom out, you have a different view.

B (30:18)

Yeah. If I look at a bottle of gas really close up, those molecules are going everywhere. They're bouncing off of each other. It's incredibly chaotic, maximum disruption. But I go far enough back and I go, oh, it's cooling down. Or, oh, the hot air at the ground floor is rising. It becomes incredibly predictable the further away I look. Even though at the microscopic level, its nature is randomness.

A (30:43)

And that's it. Right? That idea of, like, sliding along from order to chaos, from, like, clean everything being the same to messy disorder, actually, that's a function of scale as well. Like, you zoom in, zoom out. You know, you see this in, in patterns of human behavior as well. One of my favorite examples of this is, is burglaries. So your chance of being burgled is actually at its highest when you've just been burgled. Right. So in, in a lot of ways, burglaries follow a similar pattern to earthquakes. Right. The very first shock is very difficult to predict. But once something has happened, then the aftershocks in inverted commas have this kind of very clear pattern. They kind of, they, they, they get smaller and smaller and smaller as you go further away from, from the epicenter, and they also decay away in time. A few people notice this with burglaries, right? That it's like if you look across a city, it's really hard to say, here's where a burglary is going to be for the first one. But once a group starts targeting a particular area, particularly in cities where houses are structured in the same way all the way across the street, you know, people get to know the layout of the house. They get to know where you keep your valuables. Also, people replace their valuables. I mean, that's another thing, right? Or it might be that they had been and spotted something and wanted to come back for it because they couldn't get it the first time. So a little while ago, this is maybe about 10 years ago or so, a group of mathematicians and criminologists, they noticed that there were these patterns that actually, even though they were random and when you were at the scale of the individual, when it's your house being burgled, right. It feels like there is no order anywhere here. But when you zoom out to the scale of the city, actually there's a pattern that appears in the randomness. So what they did is they created these tools that could be used by police so that if a set of burglary started, these tools could say, okay, this is where we predict they're likely to happen next. And Kent police in particular, they were one of the early adopters of this. And in the first few weeks, I mean, they had this amazing drop in the number of crimes, like 8 and a half percent drop, right? Really kind of demonstrated this, this proof of concept that there is some predictability. There are these patterns you can latch onto. There was one story in particular of where this model that had been created told Kent police, you need to go to this particular square on the map. This is where crimes are likely to happen. Tonight. The police officer turned up, pulled up in a car, and as they arrived in the street, they saw someone climbing through a window, burgling house. Right? Now, okay, that story sounds really positive, right? And everyone got very excited about this at the time. The problem is, if I tell you, here's where there's likely to be events happening tonight, here's where burglaries are likely to happen, and you're the police, right? Your options are quite limited because what you can't do is suddenly change all of your officers to like flood that area. Because then what you're doing is you're disproportionately policing particular neighborhoods over others. And then of course you're going to capture more crimes, which then just makes the model think that area is a worse area. And so. And you get to the point where you're actually using statistics to kind of harass particular neighborhoods, right? Which is very bad. So what they do now is that when there has been a burglary, they still have the models running, saying this is where they're likely to be. They'll post a leaflet through your door and they'll say your chances of being burgled have increased. Make sure that you lock your windows, make sure that you Lock your doors. That does have a genuine quantifiable decrease in the probability of burglary across an entire city. Wow.

B (34:14)

Yeah. It's a very chaotic system, isn't it, that you've got the behavior of the burglars, but then also the behavior of the police. And so if they're catching more criminals, because that's where they all are, then that feeds back into the system. And it. It's a double pendulum of crime.

A (34:28)

It just. Just gets very messy very quickly. I mean, I would sort of say that a double pendulum in the original sense is a bit of a crime to watch it.

B (34:37)

But let me tell you, this triple pendulum. Oh, God, just imagine.

A (34:41)

No, I can't. It's too, too, too chaotic now.

B (34:43)

When, when scientists need random numbers, where do they get them? Because as. As we've been dancing around, like, the more knowledge you have, the less random something becomes. And so we have to look at probably the most confusing things. Lava lamps. Their behavior is very chaotic. And you can train a camera at a lava lamp and just ask, is a blob going to cover this pixel or not? And that can generate the numbers that you need that have, like, no patterns, completely unpredictable, just like the lava lamp. Random.org uses static electricity in the atmosphere to generate random numbers. You can go there right now and tell me, give me a bunch of random numbers between 1 and 100. And it does a pretty good job. I mean, good in the sense that no one's gonna be able to predict what it produces. However, to a certain extent, it should be predictable. If you knew a lot about the atmosphere right now, you would have a better chance of predicting what numbers it's gonna produce. But since most of us don't, it's random. In order to find meaning, we have to. And we're advantaged by using randomness, random trials. Right. Which means that to find meaning, we need those things that have none. What a wonderful yin and yang.

A (36:01)

Wait, let me just sit with it for a second. So in order to find meaning from things that look random, we need to use things that have no meaning at all.

B (36:11)

Yeah.

A (36:12)

To be able to separate out the two.

B (36:13)

Yeah.

A (36:14)

That's nice, isn't it?

B (36:15)

You need both bright and burning.

A (36:17)

Yeah, I like that a lot. You know the Enigma machine during World War II? Basically, it's like a typewriter where you type in your message and it has a series of cogs and dials that changes. If you hit, like a. The letter G, for instance, it will. Behind the scenes, this mechanical thing will change it into I don't know, a letter R, for instance. And then when you hit G again, those wheels will have turned around and it will generate a different letter T, for instance. Okay. So the idea from the Germans perspective was what they wanted was it to just look like a random jumble of letters. But what the British realized, even though when you originally look at these, these. These encoded messages, it's really hard to grab onto anything, what the British realized was that there was little bits of meaning in there that they could latch onto. And one of them in particular was that messages that were sent would often end with the message Heil Hitler. So they knew that the last few letters of any communication were likely to have been that. And so they managed to, like, grab onto that little bit of meaning hidden within the randomness in order to decode the entire thing. Wow. But then there's also another level of meaning here, because what you would do is you would set the dials in a particular way. You would have, like, a particular code of two letters or three letters that would allow you to set the dials in a particular way for that day. And they worked out that what the Germans were doing is they were choosing the letters of their girlfriends. Their girlfriend's initials. Right. Or their wives. So they had this little book which was just all of the German military personnel that they were targeting that they knew were sending these messages, and all of their lovers and mistresses and girlfriends.

B (38:04)

And now you've got a really short list of initial settings to decode to.

A (38:09)

Try to kind of, like, shortcut you.

B (38:11)

So just by learning who the Germans were dating, the randomness became a lot less random because we can't help it.

A (38:19)

Right. Humans can't help but impart meaning.

B (38:21)

We put this meaning in, and then it all unravels.

A (38:24)

Yeah.

B (38:25)

So is this related to the question of I'm going to flip this coin, I'm going to catch it.

A (38:30)

Yeah.

B (38:30)

And I'm going to put it down on the table.

A (38:32)

Yeah.

B (38:33)

What's the probability that it's heads or tails?

A (38:39)

50. 50.

B (38:39)

50. 50. Okay. You just looked at it, and now what's the probability for you or me? For me, it's 100% tails because I saw it.

A (38:52)

Oh, you're lying.

B (38:53)

But for you, it's still 50. 50.

A (38:55)

Right.

B (38:55)

Probability is a measure of our ignorance. It's not a measure of something that's objectively out there in the universe that God would know.

A (39:03)

No, no, I totally agree.

B (39:04)

A supremely omniscient creature would have to go the probability. Hmm. For who? How much do they not know? As the police learn more or as the love lives of the Germans become more known, Meaning emerges.

A (39:18)

Meaning emerges. You know how you were talking earlier about how humans are these basically meaning machines, right, that like we live in that space between perfect order and perfect chaos and that is where you can find meaning. I think that's true beyond just like human experience. I think that's also true for like the whole reason we are here in the first place. So in the 1960s there were these two astronomers who were putting telescopes in the sky like recording, you know, loads of information. And there was basically this constant hiss in their antenna, right, that they just couldn't get rid of it.

B (39:52)

Now I haven't checked the news for decades, but it was just birds, right?

A (39:56)

That was I think one of the original hypotheses. But then they tried it to non bird places.

B (40:00)

Oh, so they fixed that and so the noise went away?

A (40:03)

No, the noise did not go away. Literally wherever they went. They tried it at day, they tried it at night. They cleaned out pigeon droppings from the inside. They thought that might be the cause. Nothing, nothing would get rid of this hiss. It felt like it was very random, right? It's like static on your tv, this kind of no discernible source. This just like total cosmic mystery. And what people eventually worked out that actually this random noise, it wasn't an error, it was what's become known as the cosmic microwave background. Essentially they're like the oldest light in the universe, right? The residual heat from the Big bang, this like afterglow that has been traveling through space for billions of years, that now appears on Earth as this electromagnetic bit of radiation, a kind of crackle on your TV sets if you were born pre 1995. But anyway, since then what people have done is they have used, you know, satellites to like map out this noise in inverted common in like this exquisite detail. And what you see is that at the moment of the Big bang, we were so, so, so close to having aaaaaaaaa. Right? Or heads, heads, heads, heads, heads, heads, heads. It was unbelievably close to being completely ordered. The entire universe had almost no fluctuations in it whatsoever. So the estimates are that it was variations of less than one part in 100,000 in terms of temperature, right? It was almost completely blanket uniform, but it wasn't totally uniform. It was somewhere on that spectrum between perfect order and perfect chaos. Just nudged ever so slightly to the right of perfect order. There were these little quantum jitters, right? These like tiny little variations in temperature and what that meant was that over time, gravity started to form around those really tiny variations. And that then became the seeds of every planet, of every galaxy, of every solar system that we have. Every. Every star that we have across the entire universe is because of those tiny little moments of non order in an otherwise ordered state.

B (42:06)

So if this early universe, which is. That's what the cosmic microwave background radiation is. It's like the furthest away light. It is us literally looking at what the universe looked like when it was 300,000 years old or something. At the moment it became transparent.

A (42:20)

Absolutely.

B (42:21)

Okay. A long time ago.

A (42:22)

Yeah.

B (42:22)

If that had been more disordered, what would the universe look like today?

A (42:28)

Yeah. If it had been a complete mess, it would have been black holes everywhere.

B (42:31)

Okay. And what if it had been super ordered?

A (42:34)

If it had been perfectly ordered? Death. Nothing would have. Nothing would have formed.

B (42:38)

But because of these imperfections, gravity is eventually able to make not black holes, but galaxies, planets, koalas.

A (42:49)

Exactly. Exactly. On the hierarchy of things that we care about. Yes. And that's it. Right. This is the sweet spot in between. It's where there is the potential to surprise, but the potential to create meaning by which. Which in this case, I literally mean planets. You know, the physical matter on which life can form. The only reason why exist is because there was randomness.

B (43:14)

So mountains and scratch cards and gingivitis only exist because of randomness.

A (43:22)

Only exist because we live in the sweet spot between order and chaos.

B (43:26)

The sweet spot. Just random enough to be interesting.

A (43:29)

Yeah.

B (43:30)

I'm gonna go one further and say it's not just the physical matter. It's not just living organisms. But I'm working on a theory that consciousness itself comes about because the universe has no meaning.

A (43:43)

Go on.

B (43:44)

So that's the short version. The elevator pitch is that you exist because life has no meaning and you are alone. Which sounds sad, but, like, if our universe was completely uniform, discarding information didn't matter because there was just one thing, then there would be no reason for a creature to evolve that incited and excited information that created meaning because there would only be one meaning. But we don't live in a universe with meaning. We live in one that is filled with lots of different meanings. I can make anything mean anything. So we'll do an episode on consciousness later. But I think that we need to have a certain amount of complexity, which is the name for this region in between chaos and order. We need a bit of complexity for a creature like ourselves to exist. And a creature who needs to figure things out in its own head, pack up meaning, and unpack the meaning. That all happens up here. And I think, long story short, we wind up kind of living in here a little bit. And that's where the concept of a self emerges. That I am a conscious being who lives in my interior. All of this only happens because the universe has no single meaning. It's got a lot that we get to make.

A (44:58)

Hey, look, I think that there's no more positive way to end an episode than to say life exists because the universe has no meaning. That's the, you know, perfect possible wrap up, apart from actually these scratch cards, which we still haven't done.

B (45:10)

Oh, my gosh. Let's do it. Let's do it. All right. All right, guys, this is it. So the symbols that we want to match are either a plant, a window, a house, a chest, or. Or a bulb. The winning image is a stack of coins.

A (45:31)

So basically none of us won anything.

B (45:33)

The delicious taste of defeat.

A (45:35)

Next time. Next time. Well, that is a wrap on this episode, but there is a lot more where that came from. So please do make sure that you are following. The rest is Science on YouTube or wherever you get your podcasts. Make sure that you like and subscribe. Okay? That's my message to you.

B (45:49)

Do it. And you can always reach out to us at the restiscience at goalhanger. Com.

A (45:55)

See you next time.