Prediction The Code


Prediction

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For as long as human beings have walked upon earth,

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we've tried to make sense of our world

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and predict what the future will bring.

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Yet today,

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our lives seem more complicated and unpredictable than ever.

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And half the population of the planet

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now live in busy, sprawling cities.

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Every day throws up thousands of different encounters.

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A mass of interactions and forces that seem beyond our control.

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WOMAN LAUGHS

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It's hard to see how any of this could be connected.

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BABY CRIES

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Yet when we start to look closely at all this complexity,

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surprising patterns begin to emerge.

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It's these patterns that I believe point to an underlying Code

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at the very heart of existence

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that controls not only our world and everything in it, but even us.

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As a mathematician, I'm fascinated by the patterns we see all around us.

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Patterns that reflect the hidden connections between everything.

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From the movement of rush hour crowds...

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..to the shifting shape of a flock of starlings.

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The cacophony of a billion Internet searches...

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and the vagaries of the weather.

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THUNDER ROLLS

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CHEERING

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Together, these patterns and connections make up the Code.

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A model of our world that describes not only how it works,

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but can also predict what our future holds.

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Around 500 years ago, a ship was caught in a terrible storm.

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As rain lashed the decks

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and gale force winds tore through the rigging,

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the ship began to take on water.

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The captain had no choice but to run his ship aground and wait for help.

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But help never arrived, and the natives were hostile.

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After eight long months, and with his crew facing certain starvation,

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the captain came up with an ingenious plan.

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He summoned the local chief and told him his God was angry.

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So angry, in fact, that if they didn't bring supplies within three days,

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God would swallow the moon.

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And sure enough, as the moon rose on the third night, it had already begun to disappear.

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Terrified, the locals ran from all directions towards the ship,

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laden with provisions.

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The year was 1504, and the captain?

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Christopher Columbus.

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And the reason he was apparently able to command the heavens

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was because he had something like this.

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It's a set of lunar tables.

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And each one of these numbers represents a lunar eclipse.

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Today's date is June 15th, and it says that in about five hours' time

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the same thing is going to happen to the moon here in Cyprus.

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During a lunar eclipse, the earth passes between the sun and the moon,

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casting its shadow across the lunar surface.

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And there it goes.

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The moon has been swallowed up by the shadow of the Earth.

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But the amazing thing is actually the moon doesn't completely disappear, cos...

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there's a kind of... red, ghostly moon up there.

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And that's because the light from the sun is being refracted around the Earth.

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Really quite spooky.

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I can imagine how terrified the islanders would have been when they saw that 500 years ago.

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And the only explanation for them would have been that the gods really were angry with them.

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We now know that the movement of the planets is incredibly predictable.

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By understanding the Code, we can model their orbits far back into the past.

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And see thousands of years into the future.

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It's thanks to the Code that we're no longer frightened by an eclipse.

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In fact, the Code is such a powerful thing

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that I'm even prepared to entrust my life to it.

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This strange contraption is five and a half metres high.

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Using the force of gravity,

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a 30-kilogram ball will hurtle down the ramp and fire off the end.

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And when it does, I will be sitting directly in its path.

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If I get my sums wrong, I'll be killed outright.

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To calculate how far the ball's going to go,

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I need some key measurements about the ramp.

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Little h is 0.98 metres.

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The angle is 49.1 degree.

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So gravity, I know, on the Earth...

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is 9.8 metres per second squared.

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Interestingly, you don't have to know the weight of the ball, the mass of the ball.

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That's not relevant to how far the thing's going to go.

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Two times gravity, times the height, 5.5,

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multiplied by the speed, divided by 49.1,

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take the cosine...

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That will give me a distance of 9.95 metres.

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But we've got air resistance, there's friction on the... the ramp as well.

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What about the wind today? 9.16.

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OK, so the predicted distance is going to be 5.6 metres.

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That's where I think the ball is going to land.

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Which means if I set up my deckchair here, I should be able to watch the whole thing in complete safety.

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OK, release the ball.

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And that is the power of the Code.

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We can do this again and again and again...

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..and the numbers mean the ball is going to land in the same place each time.

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If everything in the world behaved according to equations that give definite answers,

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we'd be able to predict the future with absolute certainty.

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But unfortunately things aren't quite that simple.

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The natural world often appears so complex

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it's hard to imagine we could write equations to describe it.

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Even though we might glimpse what we think are patterns,

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they seem almost impossible to understand.

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I've come to witness a mysterious phenomenon

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that happens here in Denmark for a few short weeks every year.

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WINGS FLUTTER

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BIRDS TWITTER

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WINGS FLUTTER

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First few appearing, I think.

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These are starlings,

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making their annual migration between southern Europe and Scandinavia.

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A single flock can contain a million birds or more.

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Their dance obscures the fading evening light,

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giving the formation its eerie name -

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The Black Sun.

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There's another massive group coming in.

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Oh!

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There are thousands of them up there.

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It's not really clear why they do this.

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It's maybe like, kind of, safety in numbers.

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The whole shape looks quite intimidating.

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It looks like one large, black beast,

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frightening off any predators

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that might be looking for a bit of dinner before sunset.

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Look at that.

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Ah.

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It's almost hypnotic.

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It's amazing. There are so many of them,

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it's a wonder they don't smash into each other

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and sort of knock some out of the sky. But they don't seem to.

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Incredible synchronisation.

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Oh!

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You're never quite sure what it's going to do next.

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'It's an almost impossible achievement.

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'How can each bird predict the movements of thousands of others?'

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That's extraordinary?

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As strange as it seems, by reducing each starling to numbers,

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we can model what's happening on a computer.

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We start with a flock of virtual starlings,

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all flying at different speeds and in different directions.

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And then we give them some simple rules.

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The first is for each bird to fly at the same speed.

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The second rule is to stay close to your neighbours.

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And finally, if you see a predator nearby, get out of the way.

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Three simple rules are all it takes to create something

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that looks uncannily like the movement of a real flock of starlings.

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Oh, here they come.

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Oh!

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HE LAUGHS

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In fact, a recent study has shown

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that even in a flock of hundreds of thousands of birds,

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each starling only has to keep track of its seven nearest neighbours.

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And then...they've all gone.

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The sky's clear again.

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Who'd have thought that something so extraordinarily complex

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as a constantly shifting flock of thousands of birds in flight

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can have at its heart such a simple and elegant Code?

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WOMAN LAUGHS

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CHILD LAUGHS

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BABY CRIES

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It seems inconceivable that human beings

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could ever be reduced to a mathematical model like starlings.

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CLOCK TICKS

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But Iain Couzin studies how animals behave in groups,

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and his research has revealed some surprising parallels.

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How can you possibly begin to understand something like this huge mass of people?

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Even when you look at the crowd for a few seconds,

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you realise there's so many complicated factors at play.

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I started my research looking at simple organisms,

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organisms like ant swarms, schooling fish.

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And remarkably, our insights from studying those systems

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led to new insights in studying human crowds.

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But people are much more complicated than a...a fish or an ant.

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Exactly, but that's almost the beauty of this,

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is we're thinking about more interesting things

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when we're walking through crowds than, "How do I avoid that person and that obstacle?"

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You know, we're thinking about what we're going to cook for dinner or what our friends are doing.

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And so, in actual fact, we're almost on auto-pilot,

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and we're actually using very simple rules of interaction

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just like the schooling fish and the swarming ants.

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So can we learn things from the ants?

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We could learn an huge amount from the ants.

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Ants don't suffer from problems such as congestion.

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Because they're not selfish. And I'm afraid to say we are.

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We want to minimise our own travel time,

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but we don't necessarily care whether we do so at the expense of other individuals.

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Of all the animals Iain has studied,

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human beings are, in some ways, the most predictable.

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We walk at an optimum speed of 1.3 metres per second,

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and prefer to walk in straight lines to get to our destination.

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What happens is you will naturally fall

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into the slipstream of someone moving in the same direction as you.

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And so without you even knowing it, you're forming a lane.

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Similarly, pedestrians moving in the other direction will also form lanes,

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very much like the ants do.

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These lanes help us to avoid collisions.

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However, in a large open space,

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like the concourse at Grand Central Station,

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the lanes inevitably cross each other,

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which could lead to congestion.

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But when you put an obstacle - like this information desk -

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in the middle of the crowd, rather than getting in the way,

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it acts like a roundabout

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and increases the flow through the station by as much as 13%.

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These rules are so effective at predicting what we'll do,

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they can even be used to simulate crowds of people.

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Each individual is actually described by a set of numbers

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as they move through an environment.

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Exactly. We're capturing the average type of behaviour of pedestrians.

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We're capturing these simple and local rules that people use within crowds

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to then make predictions as to how the whole crowd

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is going to flow through different environments.

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We can use this underlying Code of the crowd

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to design buildings that are more efficient and safer.

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Simulations like these are able to accurately predict

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how quickly a building can be evacuated,

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even before it has been built.

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As a crowd, people are incredibly predictable.

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There are simple rules that we follow without being aware of it.

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But most of the time, we don't live on autopilot.

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And when the crowd disperses, so too do the rules of group behaviour.

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SIREN BLARES

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As individuals with our own free will, we're much harder to predict.

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Or so we think.

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Before we gets started, I would like to mention the rules.

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They are very simple.

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There are three throws and there are only three throws.

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We use a three-prime shoot, which means you go one, two, three

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and you release your throw on four.

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A throw of rock is a closed fist.

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You can throw it any way you want as long as it is a closed fist.

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Your paper must be horizontal.

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Your scissors must be vertical.

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That will be foul.

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The game of rock, paper, scissors is known all over the world.

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And some people take it very seriously.

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For those of you who don't know, and there should be very few,

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the throw of paper covers the throw of rock.

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The throw of scissors cuts the throw of paper,

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and the throw of rock crushes the throw of scissors.

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In Philadelphia, the Rock, Paper, Scissors League

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competes four times a week.

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The people in this room are fighting

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to go to the world championship in Las Vegas

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and the chance to win 10,000.

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Sweetji in the lead. Rock versus scissors for Sweetji.

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You're on the verge of elimination, Drew Bag.

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Third and final set, winner moves on.

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THE CROWD CHANTS AND CLAPS

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Rock versus scissors.

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And what a match, to take us down to the final four.

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The intriguing thing about this game is that it should be impossible

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to predict what your opponent's going to do next.

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In rock, paper, scissors, they're all pretty much equivalent.

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So each throw beats one and loses to another,

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so essentially it's a game of even odds.

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A bit like a flip of a coin.

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But if the game is entirely random,

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every player would be evenly matched.

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And yet some people win time and time again.

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It is match point, Sweetji.

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B-Pac has no points here in round number two.

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He will need two straight throws.

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Can he get through number one?

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No. Sweetji!

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So now our final match of the night.

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Sweetji, you're going to play dOGulas.

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The more we play, the more we're influenced by our past throws.

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Begin.

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And that creates patterns that can be exploited to win the game.

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Sweetji came fifth in the league last year,

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and this season looks set to do even better.

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dOGulas!

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Rock crushes scissors.

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Sweetji still has point...

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Rock crushes scissors!

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SHE SCREAMS

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Sweetji, Philadelphia Rock, Paper, Scissors City League Champion here at the Raven Lounge.

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-Congratulations.

-Thank you.

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So that was five consecutive wins.

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-What was the key to your success, do you think?

-I try to read people.

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-Yeah, you do, yeah?

-Or at least try to think what they're thinking.

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-You're looking for their patterns then?

-Yeah, a little bit like...

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Their patterns, and they'll be trying to learn mine and go against that.

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Rock, paper, scissors reveals a fundamental truth

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about human nature.

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We are so addicted to patterns

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that we let them seep into almost everything we do.

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And these patterns are the key

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to predicting many aspects of our behaviour.

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Even the darkest parts of our nature.

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SCREAMS

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Deceased. Female, five foot two.

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Complexion, dark. Eyes, brown. Hair, brown.

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When you see this much activity in such a small geographic area

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in such a tight time frame,

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that's a warning bell that something's going on,

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we have a predator operating.

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Kim Rossmo has 20 years' experience as a Detective Inspector.

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He specialises in hunting down serial killers.

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The victim's body was found here in the corner

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by a police officer that came in shortly after the crime had occurred.

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The prime crime scene would be...

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But Rossmo is no ordinary cop,

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because he's got a PhD

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and uses mathematics to understand the patterns criminals leave behind.

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There's a logic in how the offender hunted for the victim

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and the location where he committed the crime.

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If we can decode that and if we can understand that pattern,

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we can use that information to help us focus a criminal investigation.

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The reason it's so hard to catch serial killers is because there's often no link to their crimes.

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They kill random strangers

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in locations they have no obvious connection to.

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It's very common in the investigation of a serial murder case

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to have hundreds, thousands, even tens of thousands of suspects.

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It's a needle-in-a-haystack problem.

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Where do you start?

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In 1888, the most notorious serial killer of all, Jack the Ripper,

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killed five women in London's East End.

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Since then, countless people have tried to solve the mystery of the Ripper's identity.

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But Rossmo thinks he could have tracked him down

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without seeing a scrap of evidence.

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Because he's worked out where Jack the Ripper most likely lived.

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Based only on the location of the crimes.

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Flower and Dean Street should have been the epicentre of their search.

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And all he used to do it is an equation.

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Inherently, we're all lazy,

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and criminals just as much as anyone else.

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They want to accomplish their goals close to home rather than further away,

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because it involves too much effort, too much time, too much travel.

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The first half of Rossmo's equation

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models what's known as the least-effort principle.

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It means that the crime locations

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are statistically more likely the nearer they are to where the offender lives.

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If you have a choice of going to the corner store for a loaf of bread

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or one that's seven miles down the road, you'll pick the corner store.

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It seems a bit gruesome to apply the same thing to a serial killer

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as to going and buying a loaf of bread or milk.

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Well, actually, if we can get over the horrible nature of these crimes

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and recognise that these are human beings like the rest of us,

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we can, because we understand ourselves,

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maybe bet some understanding of these individuals.

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The second half of the equation

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describes something called the buffer zone.

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Criminals avoid committing crimes too close to home,

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for fear of drawing attention to themselves.

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It's the interaction of these two behaviours that allows Rossmo

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to calculate the most probable location of the criminal.

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These individuals have to not only obtain their target -

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or capture a victim -

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but avoid apprehension by the police and identification by witnesses.

0:26:100:26:15

The technique, known as geographic profiling,

0:26:180:26:21

is now used by police all over the world.

0:26:210:26:25

Police are examining the possibility that a small explosion

0:26:300:26:34

near a branch of Barclays Bank in West London

0:26:340:26:36

was the work of an extortionist.

0:26:360:26:38

Police believe the demand

0:26:380:26:40

came from the blackmailer known as Mardi Gra.

0:26:400:26:42

In the late '90s, Rossmo was called in by Scotland Yard

0:26:440:26:48

to help catch the notorious Mardi Gra bomber,

0:26:480:26:52

who for three years waged a campaign of terror

0:26:520:26:55

against banks and supermarkets.

0:26:550:26:58

A 17-year-old man is recovering in hospital after being injured

0:26:580:27:01

in an explosion at a Sainsbury's store in South London.

0:27:010:27:04

'Police are advising the public to be vigilant.

0:27:040:27:06

'In truth, they can only wait to see what Mardi Gra does next.'

0:27:060:27:10

How many bombs did he let off during that time?

0:27:160:27:19

Total, 36 known linked offences.

0:27:190:27:23

So you can see, they range from the north of Cambridge,

0:27:230:27:26

all the way down to the strait of Dover.

0:27:260:27:29

But most of them are in Greater London.

0:27:290:27:31

So this is a map showing the locations of all the bombs that were set off?

0:27:310:27:35

-That's right.

-There's certainly a concentration on London,

0:27:350:27:39

but it looks pretty randomly scattered.

0:27:390:27:42

So now you're feeding those locations into the equation?

0:27:420:27:45

Right. And what we have here now is the geo-profile.

0:27:450:27:49

And that's going to show us the most likely location

0:27:490:27:52

where the offender lived.

0:27:520:27:54

With dark orange being the most likely or the most probable.

0:27:540:27:58

So we can see that the major focus is around the Chiswick area.

0:27:580:28:02

In fact, in the report we prepared for Scotland Yard,

0:28:020:28:05

we even prioritised postcodes for that.

0:28:050:28:07

And how successful was it in this case?

0:28:070:28:09

Well, let me show you the locations...

0:28:090:28:12

of the two brothers, Edgar and Ronald Pearce.

0:28:120:28:15

-Right, that is really in the hot zone, isn't it?

-Yes.

0:28:150:28:19

Edgar's home is in the top 0.8%

0:28:190:28:21

of the area of the crimes in Greater London.

0:28:210:28:24

-So less than 1%.

-That's extraordinary.

0:28:240:28:27

Edgar Pearce had demanded £10,000 a day from Barclays.

0:28:310:28:35

And when he and his brother tried to collect it from a cash point in Chiswick,

0:28:350:28:39

the police were waiting.

0:28:390:28:41

Two bothers in their 60s were remanded in custody by magistrates

0:28:410:28:44

in connection with the so-called Mardi Gra bombings.

0:28:440:28:47

Ronald and Edgar Pearce, both from Chiswick in West London,

0:28:470:28:51

each face three conspiracy charges.

0:28:510:28:54

Based on the apparently random location of 36 bombs,

0:28:540:28:58

Rossmo's geographic profile narrowed the location of the Mardi Gra bomber

0:28:580:29:04

from 300 square miles to a postcode in Chiswick.

0:29:040:29:07

Although his bother, Ronald, was acquitted,

0:29:100:29:13

Edgar Pearce pleaded guilty and was jailed for 21 years.

0:29:130:29:18

So do you think the bomber was aware that he was creating these patterns?

0:29:180:29:22

No, he wasn't. But it's very difficult for humans

0:29:220:29:25

to engage in completely random behaviour.

0:29:250:29:28

Very few of us are aware of the patterns we leave behind.

0:29:340:29:38

WOMAN LAUGHS

0:29:380:29:40

From the way we move in a crowd...

0:29:400:29:43

..to the choices we make in a game...

0:29:460:29:48

Paper covers rock!

0:29:480:29:50

The victim's body was found here...

0:29:500:29:53

..or even how we commit murder.

0:29:530:29:55

In reality, these crimes are not random...

0:29:550:29:59

None of it is random.

0:29:590:30:01

It's all part of the Code.

0:30:010:30:05

There are always tell-tale patterns.

0:30:050:30:08

And if we're able to decode them,

0:30:080:30:11

we can use those patterns to model our behaviour.

0:30:110:30:15

And this leads to the intriguing possibility

0:30:150:30:19

that if we can reduce human beings to numbers, we might be able to predict our future

0:30:190:30:25

in the same way as we can predict the movement of the planets or the trajectory of a ball.

0:30:250:30:30

But the course of our lives never seems to run entirely smoothly,

0:30:390:30:44

and the future rarely turns out exactly as we'd planned.

0:30:440:30:49

I may have a good idea what I'm going to be doing tomorrow, or even next week,

0:30:490:30:53

but as the weeks turn into months and months to years, our future becomes less certain.

0:30:530:30:58

Every decision we make, every situation we encounter,

0:31:020:31:06

every person we meet, sends our life down a different path.

0:31:060:31:11

As you watch each stick floating off downstream, there's no sure way of predicting their fate.

0:31:120:31:17

I might be able to hazard a guess where a stick will be in two minutes.

0:31:170:31:22

But what about two hours? Two days?

0:31:220:31:25

'..Turn into years, our future becomes far less certain.'

0:31:250:31:29

Life sometimes seems so unpredictable that we think of it as being random.

0:31:290:31:35

But in fact it isn't random at all.

0:31:350:31:38

Simply a sequence of cause and effect.

0:31:380:31:40

A freak accident.

0:31:400:31:44

I'm so sorry.

0:31:440:31:45

A slight delay.

0:31:450:31:47

A missed bus.

0:31:470:31:49

A broken promise.

0:31:490:31:51

There are millions of factors that intervene to affect our journey through life,

0:31:510:31:58

and the tiniest shift in any one of these can completely change its future course.

0:31:580:32:04

The white one's caught in a dam, but the red one's fast.

0:32:050:32:07

I think this'd be a good finishing line.

0:32:190:32:23

And here comes the white. It's way ahead of the red.

0:32:230:32:27

And white's the winner.

0:32:280:32:32

Right, let's give it another go.

0:32:320:32:33

The truth is, our lives are controlled by the strangest code of all...

0:32:330:32:40

the code of chaos.

0:32:400:32:42

Our lives aren't random, they're chaotic,

0:32:460:32:49

a tangled web of cause and effect in which insignificant moments

0:32:490:32:54

can escalate into events that change our lives forever.

0:32:540:32:59

Any difference, no matter how small, can have a huge effect on the outcome.

0:32:590:33:05

It's this incredible sensitivity to even the slightest change

0:33:050:33:09

which is one of the defining features of chaos.

0:33:090:33:12

Because chaotic systems appear so random, it's often difficult to see a pattern.

0:33:180:33:23

And that has led us to sometimes misinterpret our world in a spectacular manner.

0:33:250:33:31

'In this land of many mysteries, it's a strange fact

0:33:370:33:40

'that large legends seem to collect around the smallest creatures.

0:33:400:33:44

'One of these is a mousy little rodent called the lemming.

0:33:440:33:49

'Here's an actual living legend, for it's said of this tiny animal

0:33:490:33:53

''that it commits mass suicide by rushing into the sea in droves.

0:33:530:33:57

This film from 1958

0:33:570:34:00

set out to explain the wildly fluctuating population

0:34:000:34:03

of these tiny rodents.

0:34:030:34:05

'Ahead lies the Arctic shore, and beyond, the sea. And still the little animals surge forward.

0:34:110:34:19

'Their frenzy takes them tumbling down the terraced cliffs,

0:34:210:34:25

'creating tiny avalanches of sliding soil and rocks.'

0:34:250:34:29

The legend of suicidal lemmings

0:34:330:34:35

was the accepted explanation for why the Arctic can be overrun with them one year

0:34:350:34:39

and completely empty the next.

0:34:390:34:41

'They reach the final precipice.

0:34:410:34:45

'This is the last chance to turn back.

0:34:450:34:48

'Yet over they go, casting themselves bodily out into space.'

0:34:520:34:56

This film popularised the belief that lemmings are stupid, reckless and suicidal.

0:35:010:35:07

The very word "lemming" has come to mean as much.

0:35:070:35:10

The trouble is, though, it isn't true.

0:35:150:35:18

In fact, it's been claimed that the whole thing was faked.

0:35:180:35:22

The film-makers apparently flew in hundreds of captive-bred lemmings

0:35:260:35:30

and drove them over the cliffs and out to sea.

0:35:300:35:33

'Soon the Arctic Sea is dotted with tiny bobbing bodies.

0:35:370:35:40

'And so is acted out the legend of mass suicide.'

0:35:430:35:48

Now, as appalling as this sounds, the reason for the alleged lemming abuse stems not so much

0:35:480:35:53

from ignoring the moral code,

0:35:530:35:55

but rather an ignorance of the mathematical one.

0:35:550:35:58

What no-one knew at the time was that the incredible fluctuation

0:35:580:36:04

in lemming numbers has nothing to do with mass suicide.

0:36:040:36:08

It's all because of chaos.

0:36:090:36:12

And there's a simple equation at its heart.

0:36:120:36:15

So, if I want to know how many lemmings there'll be next year,

0:36:170:36:22

what I need to do is take this year's population, "P",

0:36:220:36:26

and multiply that by the growth rate "R".

0:36:260:36:29

But not all lemmings will survive,

0:36:290:36:31

so there's a bit of the equation which tells me how many lemmings will die during the year.

0:36:310:36:36

So that's R times P times P.

0:36:360:36:39

So we can rewrite this equation

0:36:390:36:42

as the growth rate R times P times one minus P.

0:36:420:36:47

Now, this equation isn't specific to lemmings,

0:36:470:36:50

it actually applies to any animal population.

0:36:500:36:52

And the interesting part of the equation is this number R, the growth rate.

0:36:520:36:57

Because when we choose different values for R,

0:36:570:37:00

we get a very different behaviour for the population growth.

0:37:000:37:03

The growth rate determines how quickly a population expands.

0:37:040:37:08

For most species of mammal, this is usually below 2.

0:37:080:37:12

With a growth rate in this range, the equation predicts

0:37:120:37:16

that a population will rise until it stabilises at a fixed value.

0:37:160:37:20

But it turns out lemmings are one of the fastest-reproducing mammals on the planet.

0:37:200:37:26

Let's take R equals 3.1.

0:37:260:37:30

The lemmings don't stabilise now, but ping-pong between two different values.

0:37:300:37:35

So the population is high, then low, and back to high again, low again.

0:37:350:37:40

But when the growth rate reaches a value just over 3.57

0:37:400:37:44

then something incredibly unexpected happens.

0:37:440:37:49

Rather than levelling off at a fixed number,

0:37:490:37:52

or fluctuating between two values, their population erupts into chaos.

0:37:520:37:58

A plague of almost biblical proportions one year can plummet to near extinction the next.

0:37:580:38:04

It's almost impossible to predict how many lemmings you're going to have.

0:38:040:38:09

In fact, there doesn't seem to be any pattern to this at all.

0:38:090:38:12

And of course, this is exactly what's seen in reality.

0:38:120:38:16

Unpredictable boom-and-bust lemming populations.

0:38:160:38:20

Lemmings are one of the few creatures on Earth that breed so quickly

0:38:210:38:25

their growth rate can sometimes exceed this tipping point.

0:38:250:38:29

It's such an odd phenomenon that mass suicide seems like a plausible answer.

0:38:320:38:37

But the real explanation comes from the Code.

0:38:370:38:41

From this equation.

0:38:410:38:42

The problem is we can never know exactly how many lemmings are born or how many die.

0:38:480:38:53

And just the smallest difference in the growth rate R, produces a totally different answer.

0:38:530:39:00

And this is true of all equations that model chaos.

0:39:000:39:04

Although they can explain how something happens,

0:39:040:39:08

they're almost useless at predicting the future.

0:39:080:39:11

I can use an equation to calculate where this ball will land,

0:39:190:39:22

because even if I'm slightly out in any of my measurements,

0:39:220:39:25

it will only make a small difference to the final result.

0:39:250:39:28

The ball will be released from the ramp at 49.1 degrees.

0:39:280:39:34

But if this ball behaved according to the laws of chaos,

0:39:340:39:37

the tiniest shift in the ball's position

0:39:370:39:40

or the angle of release could dramatically alter its trajectory.

0:39:400:39:45

I'd have no idea whether it would just simply fall harmlessly

0:39:470:39:50

off the end of the ramp.

0:39:500:39:52

Or be sent into orbit.

0:39:550:39:58

I'd have no idea where to put my deckchair.

0:40:020:40:06

It turns out that much of the world is chaotic,

0:40:080:40:12

making it almost impossible to predict.

0:40:120:40:15

But that doesn't stop us trying.

0:40:170:40:20

Knowing whether the sun is going to shine

0:40:240:40:27

or the heavens are going to open, is a British obsession.

0:40:270:40:30

But trying to plan our lives around the vagaries of the weather

0:40:300:40:34

seems almost futile.

0:40:340:40:36

Even though we have precise equations that can describe how clashing air masses interact

0:40:420:40:48

to create clouds, wind and rainfall,

0:40:480:40:50

it doesn't really help us very much with our predictions.

0:40:500:40:56

THUNDER CRASHES

0:40:560:40:59

That's because we can never know the exact speed of every air particle.

0:40:590:41:05

The precise temperature at every point in space,

0:41:050:41:08

or the pressure across the whole planet.

0:41:080:41:11

And just a small variation in any one of these

0:41:110:41:14

can produce a vastly different forecast.

0:41:140:41:16

This is a map of how the weather looks right now.

0:41:220:41:26

The blue lines represent cold fronts and the red lines represent warm fronts.

0:41:260:41:31

In order to make a prediction

0:41:310:41:33

what we do is to take the mathematical equations for the weather

0:41:330:41:36

and create a model.

0:41:360:41:38

Now the trouble is, I can't know the precise atmospheric conditions,

0:41:380:41:43

so I take as much data as possible.

0:41:430:41:45

Then I make small variations in the data and run the model again

0:41:450:41:50

and again and again and what I get is different predictions according to those slight variations.

0:41:500:41:56

So for the weather tomorrow, the predictions are petty similar.

0:41:560:42:00

We've got a lot of blue lines together predicting a cold front.

0:42:000:42:05

A lot of red lines together predicting a warm front.

0:42:050:42:08

But look what happens when I look a little bit further ahead.

0:42:080:42:11

So two days, three days ahead...

0:42:110:42:15

so you can see these different predictions are beginning to spread out.

0:42:150:42:20

You can still see some sort of pattern in the weather

0:42:200:42:23

but if I move a week ahead...

0:42:230:42:26

..and I couldn't hazard a guess as to what the weather's going to be.

0:42:270:42:31

There are red and blue lines all over the place.

0:42:310:42:33

One prediction says it's going to be hot, the other says cold,

0:42:330:42:37

and if I go ten days ahead,

0:42:370:42:40

it just looks like a scrambled mess of spaghetti.

0:42:400:42:44

There's absolutely no way to make any prediction that far in advance.

0:42:440:42:49

And that's why beyond just a few days,

0:42:490:42:52

the weather forecast can be so spectacularly wrong.

0:42:520:42:55

Once we understand that the atmosphere is chaotic,

0:43:000:43:03

we can appreciate that the smallest change in the initial conditions

0:43:030:43:08

can dramatically alter what will happen.

0:43:080:43:11

The movement of just one molecule of air can be magnified over time

0:43:140:43:18

to have a huge effect on the weather as a whole.

0:43:180:43:21

We refer to this phenomenon as the "butterfly effect".

0:43:240:43:28

The idea that something as small as the flap of a butterfly's wings

0:43:280:43:33

might create changes in the atmosphere

0:43:330:43:36

that could ultimately lead to a tornado on the other side of the world.

0:43:360:43:40

CRASHING THUNDER

0:43:400:43:43

As a crowd, the patterns we make are incredibly predictable.

0:43:580:44:03

Even as individuals our actions are controlled by the Code.

0:44:040:44:09

And by untangling chaotic systems like the weather,

0:44:130:44:16

we've uncovered evidence of the Code in what we once thought of

0:44:160:44:21

as impossibly complex.

0:44:210:44:24

When we look at things from a different angle,

0:44:260:44:28

surprising patterns emerge.

0:44:280:44:31

Patterns that can reveal defining truths about ourselves and our future.

0:44:330:44:39

In 1906, an unfortunate cow laid down its life

0:44:470:44:51

for a place in mathematical history.

0:44:510:44:54

One.

0:44:540:44:55

Ten.

0:44:550:44:57

264.

0:44:570:44:59

417.

0:44:590:45:01

'The cow was the subject of a guess-the-weight competition at a village fare.

0:45:010:45:06

'The lucky person who came closest

0:45:060:45:09

'would win the slaughtered animal's meat.'

0:45:090:45:12

1,020.

0:45:130:45:14

2,137.

0:45:160:45:17

'The amazing thing was nobody guessed correctly.'

0:45:170:45:20

..570.

0:45:200:45:21

'And yet everybody got it right.'

0:45:210:45:24

4,510.

0:45:260:45:28

To show you how they did it,

0:45:300:45:32

I'm not going to use a cow, I'm going to use a jar of jelly beans.

0:45:320:45:36

450?

0:45:400:45:41

800?

0:45:410:45:43

12,000.

0:45:430:45:44

7,000.

0:45:440:45:45

How many jellybeans do you think there are in this jar?

0:45:450:45:48

Um,

0:45:480:45:50

50...

0:45:500:45:52

80 thousand.

0:45:520:45:54

80 thousand?

0:45:540:45:55

No, actually 50,000.

0:45:550:45:58

50,000. OK, yeah.

0:45:580:46:00

It's incredibly difficult for anyone to guess how many jellybeans there are.

0:46:040:46:09

I asked 160 people and most were way off the mark.

0:46:100:46:15

Everything from 400 right up to 50,000 beans.

0:46:150:46:20

In fact only four people got anywhere near the correct answer of 4,510.

0:46:200:46:27

Plus 1,500, plus 3,217, plus 83... .

0:46:270:46:36

If I add all the answers together and take the average, I get the combined guess of the entire group.

0:46:360:46:43

Plus, 4,000, plus 5,000,

0:46:430:46:46

463,

0:46:460:46:48

Plus 853, plus 1,000,

0:46:480:46:52

plus 5,000...

0:46:520:46:55

Which gives a grand total

0:46:550:46:58

of 722,383.5.

0:46:580:47:04

Somebody thought there was half a bean in there.

0:47:040:47:06

Now there are 160 guesses made, so let's see how close they are collectively.

0:47:060:47:13

Wow, that's extraordinary.

0:47:130:47:16

You remember there were 4,510.

0:47:160:47:20

The average guess to the nearest bean is 4,515.

0:47:200:47:26

I thought it would be close, but I didn't think it would be THAT close.

0:47:260:47:30

That is ridiculous.

0:47:300:47:31

Though we had guesses that were all over the place, up in 30,000s right down in the 400s,

0:47:310:47:36

collectively we get something which is just 0.1% away

0:47:360:47:40

from the real number of beans in there.

0:47:400:47:43

So as individuals the guesses are just that, guesses.

0:47:430:47:47

But when you take them collectively they become something else entirely.

0:47:470:47:52

-5,000.

-1,450.

-9,200.

0:47:520:47:57

What tends to happen is that more or less as many people

0:47:570:48:01

will underestimate the number of jellybeans as overestimate it.

0:48:010:48:05

-1,763...

-6,000.

0:48:050:48:08

A few people will be way off the mark either way, but that doesn't matter.

0:48:080:48:13

Provided you ask enough people, the errors should cancel each other out.

0:48:130:48:19

-1,000.

-1,275.

-700?

0:48:190:48:24

The accuracy of the group is far greater than the individual.

0:48:240:48:28

We call it "the wisdom of the crowd".

0:48:280:48:31

160 people is a powerful tool for working out how many jellybeans there are in the jar.

0:48:310:48:40

But imagine what you could do with a crowd of millions.

0:48:400:48:44

That's exactly what they use here at Google.

0:48:470:48:50

With access to over two billion web searches a day,

0:48:520:48:55

Google have found a way of tapping into the wisdom of the biggest crowd on Earth.

0:48:550:49:00

And by doing so,

0:49:000:49:02

they've been able to reveal the forces that control our lives,

0:49:020:49:07

and harness them to make predictions about us.

0:49:070:49:11

'Think of the things that people search for on a daily basis.

0:49:110:49:14

'Think of the things that YOU search for on a daily basis.'

0:49:140:49:17

I searched for cities in Mexico and films in Hackney today.

0:49:170:49:23

Lots of people may be searching for the similar...

0:49:230:49:26

a similar thing, movies in Hackney, for example.

0:49:260:49:29

And if you look at that query over the past three years,

0:49:290:49:33

um, what the pattern of searches for that term looks like.

0:49:330:49:36

Google had a hunch they could use all our searches

0:49:400:49:43

to make predictions about our lives.

0:49:430:49:46

They wanted to see if they could match the pattern of certain searches

0:49:470:49:51

with events in the real world.

0:49:510:49:53

Google began by seeing if they could predict outbreaks of flu.

0:49:550:49:59

So flu has a nice seasonal pattern

0:50:010:50:04

and because it has that pattern every year over many years,

0:50:040:50:09

we're able to...to take that trend

0:50:090:50:12

and say which search queries match that pattern.

0:50:120:50:16

So we built a database that included over 50 million different search terms.

0:50:160:50:21

50 million?

0:50:210:50:22

-Yes.

-Wow, yeah.

0:50:220:50:24

We didn't only include things that may be related to flu.

0:50:240:50:27

We included things like Britney Spears or...

0:50:270:50:29

Everything people search for would be included.

0:50:290:50:32

When Google looked back over the past five years of data,

0:50:350:50:38

there were certain search terms whose popularity exactly matched

0:50:380:50:43

the pattern of flu cases.

0:50:430:50:45

So people were searching for things like "symptoms"

0:50:450:50:49

or "medications" or "sore throat".

0:50:490:50:52

There are other things like complications.

0:50:520:50:56

So you're saying that the sort of number of search terms for flu-related things

0:50:560:51:01

-almost exactly mirrors the actual cases of flu that we see in the population?

-That's true.

0:51:010:51:07

It is an indicator of flu activity just based on lots of people searching for these terms.

0:51:070:51:12

We were amazed by this finding.

0:51:120:51:14

As soon as they see this pattern of search terms

0:51:190:51:22

Google can predict there will be an outbreak of flu.

0:51:220:51:26

Often before people had even gone to the doctor.

0:51:260:51:29

This is the extraordinary power of the Code.

0:51:310:51:36

But it's just the tip of the iceberg.

0:51:380:51:42

The searches we make can be used to predict

0:51:420:51:44

where we'll go on holiday.

0:51:440:51:46

What model of car we're going to buy.

0:51:460:51:48

Or how we're going to vote,

0:51:480:51:50

often before we know ourselves.

0:51:500:51:53

It's even been possible to forecast the movement of the Stock Market

0:51:550:51:59

from the number of negative words used on Twitter.

0:51:590:52:02

Analysing such vast amounts of data, doesn't just allow us to make predictions.

0:52:060:52:12

It can also tell us something fundamental about ourselves.

0:52:120:52:17

You look out at a city like this and it looks like, you know, some arbitrary jumbled mess.

0:52:200:52:26

Yet the city IS people.

0:52:260:52:30

It's not the buildings and the streets.

0:52:300:52:32

They're the stage upon which the real actors

0:52:320:52:36

are playing out the story of civilisation.

0:52:360:52:39

Geoffrey West is a physicist who's spent his life trying to see meaningful patterns in the universe.

0:52:420:52:48

And how he's turned his attention to the dynamics of human life in cities.

0:52:480:52:55

So you can see there's all kinds of infrastructure here.

0:52:590:53:03

There's the obvious, the roads, the electrical lines, the sewer lines.

0:53:030:53:09

They're an extraordinary network that is sustaining New York City.

0:53:090:53:13

You know, coming at it as a physicist,

0:53:130:53:16

I had this hunch that there is an underlying code to all this.

0:53:160:53:21

West amassed data about cities all over the world.

0:53:240:53:29

And the patterns he found mean that for any given population size,

0:53:290:53:33

he can predict the amount of roads,

0:53:330:53:35

electrical wiring or office space that city has.

0:53:350:53:40

But he also discovered something much more surprising.

0:53:430:53:46

One of the most interesting results we discovered was that, um...

0:53:500:53:56

Wages scale in a very systematic way

0:53:560:54:00

and the rule that came out was that if you doubled the size of the city,

0:54:000:54:04

you get this marvellous 15% increase in the wages.

0:54:040:54:08

-If you live in a large city, you're going to earn more?

-Yes.

0:54:080:54:13

So what, if there are two mathematicians

0:54:130:54:15

in two different cities - one twice the size - doing the same job,

0:54:150:54:19

-one will have a bigger income?

-On the average, that is what the data say.

0:54:190:54:24

-Was that a surprise to you to see that?

-A huge surprise.

0:54:240:54:26

I thought there was something wrong with the data.

0:54:260:54:29

And then it was like, "Of course! That's why cities exist."

0:54:290:54:35

Incredibly, it's not just people's salaries that increase.

0:54:400:54:43

When a city doubles in size, every measure of social and economic activity

0:54:430:54:49

goes up by 15% per person.

0:54:490:54:52

That's 15% more restaurants to choose from.

0:54:520:54:56

15% more art galleries to visit. 15% more shops to go to.

0:54:560:55:00

In short, life gets 15% better.

0:55:010:55:05

You know it looks like it's a magic formula

0:55:110:55:13

that we as social human beings have discovered...

0:55:130:55:17

..this 15% bonus, so to speak, is, I believe, the reason

0:55:200:55:25

that people are attracted to cities

0:55:250:55:28

and why there has been this continuous migration

0:55:280:55:32

from the countryside and into the cities.

0:55:320:55:35

And at some deeper level, actually drive our civilisation.

0:55:350:55:40

According to Geoffrey West, humankind has an ultimate number.

0:55:430:55:48

It's this extra 15%

0:55:480:55:51

or 1.15.

0:55:510:55:53

He believes it's the most important driving force in humanity.

0:55:530:55:58

This single number, 1.15,

0:56:000:56:03

predicts our future.

0:56:030:56:05

It will bring us together in ever expanding cities

0:56:060:56:10

and shape our destiny for as long as human beings exist.

0:56:100:56:14

Five hundred years ago, when faced with an eclipse,

0:56:250:56:29

many of us would have believed it was the work of an angry god.

0:56:290:56:33

But as we've unearthed the language of the Code,

0:56:330:56:35

we've discovered that the apparent mysteries of our world

0:56:350:56:39

can be understood without invoking the supernatural.

0:56:390:56:43

And this for me is what's so remarkable.

0:56:430:56:46

That despite the incredible complexity of the world we live in,

0:56:460:56:50

it can all, ultimately, be explained by numbers.

0:56:500:56:54

Just like the orbit of the planets, life too follows a pattern.

0:56:570:57:02

And it can all be reduced to cause and effect.

0:57:040:57:07

In the end, even the flip of a coin

0:57:110:57:13

is determined by how fast it's spinning

0:57:130:57:16

and how long it takes to hit the ground.

0:57:160:57:18

The ultimate symbol of chance isn't random at all.

0:57:180:57:23

It only appears that way.

0:57:230:57:26

When we don't understand the Code,

0:57:280:57:31

the only way we can make sense of our world is to make up stories.

0:57:310:57:36

But the truth is far more extraordinary.

0:57:370:57:40

Everything has mathematics at its heart.

0:57:420:57:46

When everything is stripped away all that remains is the Code.

0:57:470:57:52

Find clues to help you solve the Code's treasure hunt at...

0:57:570:58:05

Plus get a free set of mathematical puzzles and a treasure hunt clue

0:58:050:58:10

when you follow the links to the Open University.

0:58:100:58:14

Or call 0845 366 8026.

0:58:140:58:19

Subtitles by Red Bee Media Ltd

0:58:320:58:35

E-mail [email protected]

0:58:350:58:38

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