Brian Klaas

So he starts to invent a weather computer and tries his best to improve forecasting of how he might be able to predict the weather. So he has this really rudimentary computer that can only handle a few variables. I think it was about 12 variables of the weather. So maybe you've got temperature and wind speed and so on.

And he plugs them into this computer, this sort of early computer, and runs a simulation. And one day, he decides to rerun the simulation starting from halfway through. But he sort of groans and doesn't really want to start all the way at the beginning. It will take too long. So he figures, I'll just start halfway.

And he plugs them into this computer, this sort of early computer, and runs a simulation. And one day, he decides to rerun the simulation starting from halfway through. But he sort of groans and doesn't really want to start all the way at the beginning. It will take too long. So he figures, I'll just start halfway.

And he plugs them into this computer, this sort of early computer, and runs a simulation. And one day, he decides to rerun the simulation starting from halfway through. But he sort of groans and doesn't really want to start all the way at the beginning. It will take too long. So he figures, I'll just start halfway.

I'll look at the computer printout for the data for all of the variables in the model. I'll type them in exactly, and I'll rerun the simulation starting from halfway through. And what he sees, we can only imagine he's got this sort of befuddled look as he looks at the screen because the data is completely different.

I'll look at the computer printout for the data for all of the variables in the model. I'll type them in exactly, and I'll rerun the simulation starting from halfway through. And what he sees, we can only imagine he's got this sort of befuddled look as he looks at the screen because the data is completely different.

I'll look at the computer printout for the data for all of the variables in the model. I'll type them in exactly, and I'll rerun the simulation starting from halfway through. And what he sees, we can only imagine he's got this sort of befuddled look as he looks at the screen because the data is completely different.

The weather patterns have changed radically from simulation one to simulation two, even though they're using the same data. And what he finds after a lot of sort of chin scratching is that the numbers that were printed out ended after three decimal places. So imagine that you've got a number like 1.234. It would be printed as 1.234, but the actual number might be 1.23456789.

The weather patterns have changed radically from simulation one to simulation two, even though they're using the same data. And what he finds after a lot of sort of chin scratching is that the numbers that were printed out ended after three decimal places. So imagine that you've got a number like 1.234. It would be printed as 1.234, but the actual number might be 1.23456789.

The weather patterns have changed radically from simulation one to simulation two, even though they're using the same data. And what he finds after a lot of sort of chin scratching is that the numbers that were printed out ended after three decimal places. So imagine that you've got a number like 1.234. It would be printed as 1.234, but the actual number might be 1.23456789.

And so in losing those little tiny numbers after the third decimal place, what Lorenz realized was that that was where the weather was diverging. And this is the origin story of a realm of science called chaos theory, where you realize that these tiny changes over time can have profound consequences.

And so in losing those little tiny numbers after the third decimal place, what Lorenz realized was that that was where the weather was diverging. And this is the origin story of a realm of science called chaos theory, where you realize that these tiny changes over time can have profound consequences.

And so in losing those little tiny numbers after the third decimal place, what Lorenz realized was that that was where the weather was diverging. And this is the origin story of a realm of science called chaos theory, where you realize that these tiny changes over time can have profound consequences.

And it's, by the way, the reason why today, even with the best supercomputers, we cannot forecast the weather reliably beyond seven to 10 days. And that's because we can't measure everything absolutely perfectly. There's always going to be these little variations. And that's the difference between a forecast being correct two weeks down the road and being wildly off.

And it's, by the way, the reason why today, even with the best supercomputers, we cannot forecast the weather reliably beyond seven to 10 days. And that's because we can't measure everything absolutely perfectly. There's always going to be these little variations. And that's the difference between a forecast being correct two weeks down the road and being wildly off.

And it's, by the way, the reason why today, even with the best supercomputers, we cannot forecast the weather reliably beyond seven to 10 days. And that's because we can't measure everything absolutely perfectly. There's always going to be these little variations. And that's the difference between a forecast being correct two weeks down the road and being wildly off.

So imagine that you are a sort of pre-modern hunter-gatherer, and you see this little rustling in the grass, or you hear this rustling in the grass. Now, your brain could either decide it's probably nothing and just carry on with your day, or your brain could say, okay, hold on, this might be a saber-toothed tiger, this might be a predator.

So imagine that you are a sort of pre-modern hunter-gatherer, and you see this little rustling in the grass, or you hear this rustling in the grass. Now, your brain could either decide it's probably nothing and just carry on with your day, or your brain could say, okay, hold on, this might be a saber-toothed tiger, this might be a predator.

So imagine that you are a sort of pre-modern hunter-gatherer, and you see this little rustling in the grass, or you hear this rustling in the grass. Now, your brain could either decide it's probably nothing and just carry on with your day, or your brain could say, okay, hold on, this might be a saber-toothed tiger, this might be a predator.

Now, if you happen to make a mistake and you think it is a saber tooth tiger when it's not, you will survive and maybe waste a little bit of energy by running away. But if you make the other kind of mistake, if you think it's nothing and it turns out to be a saber tooth tiger, you will die. So our brains have evolved to overemphasize patterns.