D (1:26)

America is under attack. Come down to the common room and watch. Right.

A (1:30)

It came out of the clear blue sky on a mild fall morning in Manhattan.

C (1:35)

Oh, it's 2001.

A (1:37)

Yes, 2001. Sunil's standing there in horror. Smoke appeared everywhere, as though a mist had suddenly settled. And I mean, he has a million questions like, who did this? Why is America at war? So he goes online to get any.

D (1:52)

Sort of new information.

A (1:53)

But he just.

D (1:56)

A lot of the websites were just not responding.

A (1:58)

Some websites had just crashed. Others would, like, just keep loading but then never fully load.

D (2:04)

At one point, they, you know, it was such overwhelming demand for, you know, news that they resorted to serving only.

A (2:11)

Text, plain text, because there were so.

D (2:13)

Many people trying to get to those.

C (2:15)

Websites because there was just so many people trying to get access.

A (2:18)

So much hunger of people trying to figure out, like, what the hell is going on here?

D (2:21)

Correct.

A (2:22)

And. And he's like, why is it that at the very moment when I and the world want to access something the most, that's when I can't access it? And this used to happen all the time as millions of people flooded the system. Last week, a picture of a dress was posted on Tumblr, some website, healthcare.gov or picture that broke the Internet or video suddenly become popular. 15 minutes of Pokemon Go launch, and you get this crash of people.

C (2:51)

Traffic had already passed initial prediction, this.

A (2:53)

Flash flood, and it breaks the Internet and this situation, a demand that is.

D (2:59)

Beyond what they planned for.

A (3:00)

Sunil became kind of obsessed with it.

D (3:02)

And so I ended up looking at websites, how they architect some of these infrastructures.

A (3:06)

And he's like, there's gotta be a way to fix this.

D (3:09)

Yeah, like, what can I apply to solve that problem?

A (3:12)

Meanwhile, Sunil's wife is working in Atlanta.

D (3:15)

We were doing back and forth between Oxford and Atlanta.

A (3:18)

And at a certain point, he's happy, having kind of a hard time with this Internet problem, because how do you.

D (3:23)

Design a system for the future when you don't know what the future is?

A (3:25)

And one day he just thinks to himself, georgia Tech is down the street. Maybe someone there could help me point.

D (3:31)

Me to some direction that I could take.

A (3:33)

So he just emailed some people in the engineering school.

D (3:35)

I said, oh, I'm a PhD student looking to, you know, discuss some ideas. Can I come and see you? And that was basically it. I didn't describe the problem in my email. I didn't really expect anything. Anything substantial.

A (3:52)

But within 30 minutes, he gets a response from a guy named Craig Tovey.

D (3:57)

Saying, come by my office.

E (3:59)

And then a very tall guy knocks on my door and says, I'm looking for Craig.

A (4:09)

This, of course, is Craig Tovey.

E (4:10)

Hi. Good to meet you.

A (4:12)

Craig's a systems engineer, operations research. His job is to make huge operations run smoothly. Factories, shipping routes, that kind of thing.

D (4:20)

So anyway, I walked into his office, we sat down, we started talking. I started describing the problem.

A (4:26)

So Sunil is like, look, I'm trying to fix the Internet. I'm trying to stop it from breaking every time one of these Internet flash floods happen.

D (4:36)

He didn't say a lot, actually, at the beginning. He just kept listening. And then 25, 30 minutes later, you know, suddenly Craig Tovey said, oh, oh, oh, wait.

A (4:46)

Craig stands up and then went back.

D (4:48)

To his desk and pulled out a.

A (4:49)

Paper, and he plops it down in front of Sunil. It is called the Pattern and effectiveness of Forager allocation among Flower Patches by honeybee colonies.

D (4:59)

So at the time, I thought, oh, why are we talking about honeybees?

C (5:04)

Yeah, why are we talking about honeybees?

A (5:06)

Well, Craig had this hunch that bees had something to teach Sunil and all of us, really, because it turns out bees are sort of a model of how to thrive in an uncertain world.

C (5:18)

Hmm. Okay.

A (5:22)

So that's the story I want to talk about today. The story of how a multi billion dollar tech industry used a trick they learned from millions of years of honeybee evolution to build the Internet as we know it.

C (5:37)

Okay, giddy up.

A (5:41)

Okay, so Craig and his B study. It really began with his collaborator.

F (5:46)

I'd like you to refer to me as Tom.

A (5:47)

Dr. Tom Seeley.

F (5:48)

You could introduce me as doctor or professor, but let's quickly switch to Tom.

A (5:54)

He's a retired professor of neurobiology at Cornell and one of the world's top experts on honeybees.

F (5:59)

I did pretty much pioneer the study of how honeybees live in the wild on their own.

A (6:05)

And he started doing that work more than 60 years ago when he was a kid.

F (6:09)

Yep. A swarm of bees moved into a. A large black walnut not far from my parents house.

A (6:15)

And Tom says he would watch those bees flying in and out of this knot hole in the tree like shooting.

F (6:21)

Stars zooming off in all directions.

A (6:24)

And he'd see them and he'd be like, where are they? Where are they going? Like he kind of knew where they were going.

F (6:29)

There were fields of a dairy farm.

A (6:31)

Up on the hillside with lots of flowers full of nectar and pollen for them to eat.

F (6:34)

And they're probably going up to that.

A (6:36)

Like, I know where they're going, but how do they know where they're going? Like, what. What is going on here? It.

C (6:45)

Are they.

G (6:45)

They're not.

C (6:46)

Are they. Aren't they just like flying and if they see some pollen or nectar, they, they bring it home?

A (6:52)

No, no. It's way, way harder and more complicated than that.

G (6:56)

Why?

C (6:57)

How so?

A (6:58)

There's many different reasons. And this is all stuff Tom would eventually learn in college and grad school. Okay, so like first flower patches are not evenly or clearly distributed. They're not just everywhere.

C (7:11)

Okay, fair.

A (7:11)

They're also not all blooming at the same time. So there might be flowers that are blooming at certain times of the year or even certain times of the day. Also, you need to find the flowers in bloom that still have nectar because you're in competition with all these other pollinators and you have to do all your food gathering before winter comes because, you know, then all the flowers go away. Huh. And. And you have to get something like for guess how many flowers. If you're a bee, guess how many flowers you have to hit to get like a little bottle of honey that you would find in a grocery store.

C (7:49)

Oh, oh, okay. Like one of those bare little squeezy bottles.

A (7:54)

Yeah, one of those little bear Squeezy bottles.

C (7:55)

How many flowers went into that? That's such a nice question. I. I don't know. Like, 10,000?

A (8:03)

2 million.

C (8:05)

2 million.

A (8:06)

2 million. And the hive needs, like, the equivalent of 200 squeezy bottles of honey to survive through the winter.

C (8:15)

Wow.

A (8:16)

All of this is an efficiency game. Like, you only have so much time when the sun is shining, when these flowers are blooming, that you can. You can hit them.

C (8:26)

Okay, okay.

A (8:27)

So they have to do all of this. It's really hard. And they have, like, nobody. They have no boss. Nobody is in charge.

C (8:36)

Queen bee isn't in charge.

A (8:38)

So that's the thing. So back in Aristotle's time, that was.

F (8:42)

The idea for many, you know, centuries. People thought the queen was a ruler. But no, that's not correct.

A (8:47)

The queen's whole job is to make babies.

C (8:49)

Yeah.

F (8:50)

She's not telling anybody else what to do.

A (8:53)

Nobody is.

E (8:53)

The colony is intelligent in some way that the individual bees are not.

A (9:00)

And when Craig found out about this.

E (9:03)

It started with NPR from a radio story. Tom Seely talking about honeybees.

A (9:07)

He was just like, what?

E (9:10)

I was just in awe because at.

A (9:12)

The time, Craig was working on robots, he was trying to get a group of them to build a car.

E (9:16)

It was an unsolved problem, and he.

A (9:18)

Was kind of stumped.

E (9:19)

I have no idea how to get these robots to work together, to be as a group, like, more intelligent than the individual stupid robots. But here were these honeybees doing exactly that, and I'm thinking, wow, let's understand how the bees are doing that, and then we can copy that and apply it to robots.

A (9:42)

And Craig thought this would be pretty straightforward.

E (9:44)

I thought that biologists had figured out.

A (9:47)

Everything, but turns out they hadn't.

E (9:49)

That's right.

F (9:50)

If somebody asked me how much we know about how a honeybee colony works, I'd say 50%.

A (9:56)

And, you know, 50%'s not nothing. They knew, for example, about the waggle dance, which you might have heard of.

C (10:02)

Yes, I've heard that bees will waggle. And what is the waggle, though?

A (10:06)

I mean, it's quite sophisticated. It's a dance that bees do to sort of show other bees where they just came from.

C (10:14)

Oh, so it's like a. It's a choreographed map. It's like a map dance.

A (10:19)

It's like a map dance. And this is like a piece of the puzzle.

E (10:22)

Oh, yeah. Von Frisch won the Nobel Prize. But what the biologists had not figured.

A (10:27)

Out is the bigger picture, what Tom.

F (10:30)

Would call the wisdom of the hive.

A (10:33)

And that is what Tom was working on when Craig called him.

E (10:37)

And Tom said, well, you know, come help me run these experiments.

A (10:42)

And the two of them would end up doing an experiment together that would give us a little peek into that wisdom of the hive and would eventually become the paper that many years later, Craig would slap down in front of Sunil in his office.

E (10:56)

It's still vivid in my memory. I mean, in fact, this is one of the best weeks of my life.

A (11:02)

It's July 1991.

E (11:04)

Okay. Now this is in the Adirondacks, Upstate New York, like way upstate, close to the Canadian border.

A (11:11)

Craig had to drive on these dark.

E (11:13)

Back roads for an hour and a half, two hours, looking for a little.

A (11:17)

Sign for this research station.

E (11:18)

Cranberry Biological Station.

A (11:20)

Cranberry Biological Station. Okay.

E (11:22)

Cranberry Lake. I'm sorry? Cranberry Lake.

A (11:24)

Cranberry Lake.

F (11:25)

Yeah. I always amazed that he pulled it off, that he found this biological station up in the middle of the woods, deep at night.

A (11:33)

And the reason Tom studies bees at this place, way out in the middle of nowhere, is because there are no.

E (11:40)

Naturally occurring honeybee colonies.

A (11:42)

What?

C (11:43)

Wait, why would you study bees in a place where there are no bees?

A (11:47)

Right. Because the absence of bees lets you set up kind of controlled experiments without any other bees messing it up.

F (11:55)

That's right.

E (11:56)

It's a byob. Bring your own bees set up. So Tom brought an experimental, you know, a little colony of about 4,000 bees.

A (12:07)

Okay.

E (12:08)

And you know, after breakfast, we go out to where the hive is that Tom has set up.

A (12:13)

A wooden box two feet high, kind of like a beekeeper would have, but with one big difference.

E (12:18)

The front is all glass.

A (12:19)

Basically a transparent hive.

E (12:21)

Yeah.

A (12:21)

So you can watch the bees inside.

E (12:23)

And each bee had to be individually recognizable.

A (12:26)

How do you do that?

E (12:28)

So you put a little two digit number on their thorax and a little dab of paint on their abdomen.

F (12:37)

Until they're painted, they're anonymous members of a colony. But once they're painted, you get to know them. Some are nervous Nellies, others come.

A (12:44)

Really?

F (12:45)

Yeah.

A (12:45)

They have little personalities.

F (12:47)

Oh, definitely, definitely. Some get up in the morning and get things going. Others hang out till 11 o'.

G (12:54)

Clock.

A (12:56)

Anyway, when the sun comes out on that first morning, a big wave of painted bees whooshes out.

C (13:02)

Except for those slackers who are sleeping in.

A (13:04)

Fine. Except for the lazy ones who sleep again. But pretty quickly, the ones who have gone to work, they start flying out to these feeders, these fake flower patches that Tom has put out for them.

E (13:13)

Imagine a glass petri dish where we have high sugar content water.

A (13:18)

It's a little buffet for them.

E (13:19)

Yeah, that's right.

A (13:21)

And so the way the experiment works is that there are two. Two fake flower patches, but they're. They're not equal. One is better than the other. And what they want to see is like, how do the bees suss that out? And how do they make it kind of a collective decision to send more bees to one rather than the other? Whoa.

C (13:45)

Okay. Oh. So this is, this is it. This is where you see, maybe it's. It's not just random. Like, they might get to glimpse hive intelligence.

A (13:54)

Like, this is because, remember, this is the whole thing, right? Like, winter's coming, they're on a clock. And let's see how they. They prioritize how they make that decision.

C (14:02)

Huh? Wait, and then how. How do they make one flat, like, fake flower patch better than the other?

A (14:08)

Well, there's lots of ways.

E (14:09)

You know, we can put 1.5 molar sugar.

A (14:11)

One feeder might have sweeter sugar water than the other.

E (14:14)

We can Change it from 1 1/2 to 2 and see how the behavior of the bees changes.

A (14:19)

Or one might be bigger and one smaller.

E (14:21)

If you had a lot of bees coming to the same feeder, some of them would have to wait.

A (14:25)

But Craig says, for the purposes of explaining this, just imagine this situation.

E (14:29)

One feeder which is close about five.

A (14:32)

Minutes from the hive, and another one.

E (14:34)

That'S 10 minutes to fly out to the flower patch, fill up her stomach with nectar, and fly back a little further away. Okay, so Tom is at the hive, recording each bee as it leaves the hive and each bee as it comes back.

A (14:49)

Also, are you afraid? Have you ever. I mean, probably not, but like, have you ever been stung by a bee? Are you afraid of bees at all?

E (14:56)

I am somewhat allergic to bees.

A (14:59)

Oh, I did not know that.

E (15:02)

So I am scared of them. But ordinarily, honeybees are not all that aggressive.

A (15:07)

Anyway. Right away, the guys notice there are.

E (15:10)

A lot of bees going to this five minute feeder.

A (15:13)

The feeder close to the hive is blowing up. And when they follow those bees back.

E (15:17)

To the hive, to my eyes it just looks like chaos. But if you're Tom Seely, you'll spot the waggle dance. A waggle dance.

A (15:27)

The bee effectively saying, hey, go that way. And pretty soon they see another bee from that close by feeder come in and dance.

C (15:34)

Hey, go that way.

A (15:35)

And another, hey, go that way. And another, hey, go that way. And each time those bees dance, they're bringing more bees back to the five minute path. And as all that's happening, every now.

C (15:49)

And then, hey, go this way.

A (15:51)

A bee comes in and dances for the 10 minute patch.

C (15:55)

Hey, go this way. But go that way, go that way, go that way.

E (16:01)

There will be more bees going to that five minute patch and there will be fewer bees going to the 10 minute patch.

C (16:09)

Hey, go this way.

E (16:10)

Just because the bee's coming back twice as often.

C (16:14)

Hey, go this way, go that way. Hey, go that way. Oh, okay. So the closer flower patch gets more bees saying, go that way, that way, that way. And so more bees go that way instead of this way.

A (16:30)

Right.

E (16:31)

So now here comes one of the beautiful parts of it and this is.

A (16:34)

Where it gets really interesting.

E (16:35)

If there are a lot of bees going to this five minute patch, eventually Craig says there will be more and more depleted flowers.

A (16:43)

This patch, it starts to run out of nectar.

E (16:48)

That means that a honeybee, she's going to take longer to fill her stomach.

C (16:53)

Hey, go that way.

D (16:54)

Right.

E (16:54)

So a five minute patch, if it's crowded, is no longer a five minute patch. Go that way, it might become a seven minute patch.

A (17:03)

And as the patch gets more and more picked over, now it's an eight.

E (17:06)

Minute patch and then a ten minute patch.

A (17:09)

And hey, at that point, go this way. It's taking the bees the same amount of time go that way to go to the close by patch as it is to go to the one that's further away.

C (17:18)

Go this way.

A (17:19)

And because of the dancing, the hive is sort of evening out the number of bees it's sending to each patch.

C (17:25)

Go that way, go this way, go that way, go this way.

E (17:29)

Then you're in equilibrium. Even though the bees don't have stopwatches, they equalize the round trip time.

A (17:41)

The hive has taken into account distance and crowdedness and figured out the way to get the most nectar in the least amount of time. And in the real world, the bees.

E (17:54)

May very well be going to five different patches, or a dozen, not just two.

A (17:59)

And dealing with actual nature, which means taking into account different types of flowers and weather and predators. But no matter how many other variables the bees have to deal with, the allocation of bees amongst the flower patches is astonishingly efficient. It looks like there's an air traffic controller or like the hive is thinking.

F (18:28)

Yeah, that's right. I think, though, when we use the words like thinking, we're thinking in human terms. But if you say thinking is just a matter of taking in information, processing it to make decisions, then I think that definition of thinking applies.

A (18:42)

And the way that they were processing this incredibly complex set of information was by following one simple rule.

E (18:54)

If one flower patch has a smaller round trip time than the others, send more bees there.

A (18:59)

Like whichever patch bees are coming back most quickly from. That's where you send more bees. And that's it.

E (19:11)

It's gorgeous, isn't it? A friend of mine once said it's very Z.

A (19:20)

But it's also, weirdly, it's very bottom line. Like, it's like, I don't care where you're going. I don't care what you're like. It's like what really matters is when you show up with the goods. Like, show up with the goods and then we'll talk and then we'll negotiate. So this system Craig and Tom observed at Cranberry Lake, Craig wrote it up as math.

F (19:44)

He called it the honeybee algorithm.

A (19:47)

What does it look like on paper?

E (19:50)

Equations. I wouldn't want to show a 4th grader F sub n of X sub n F sub n of X sub n divided by X sub n is equal to F sub M. And when.

A (20:03)

Craig tried to use this algorithm on his car building robot problem, it didn't apply. It was completely unhelpful.

E (20:10)

It was such a different problem, which.

A (20:12)

Is why it was so exciting to him when Sunil walked into his office more than a decade later with this other problem, the Internet problem, which to Craig anyway, looked basically the same as the one the bees were facing.

E (20:27)

And so after 15 minutes, I said to Sunil, let's imitate the bees.

A (20:36)

How could imitating the bees help Sunil stop the Internet from breaking?

C (20:40)

I am desperate to. To finally understand this. But first, we need to take a quick break.

A (20:44)

All right, we'll be right back.

G (20:51)

Hey, I'm Molly Webster, and this episode is sponsored by Better Help. We get it. February, it's flowers, it's candy, it's stuffed animals, and of course, it's lots of talk about relationships and dating. And it can seem like all those people, they have love figured out, but no one does. Love is confusing. Sometimes it's hard to know. How do you state your needs in a way that doesn't sound like an attack? Or when should we all just give each other a hug and move on? It would be great to have someone kind of in the corner helping you through these situations. Therapy can be that someone Better Help is the perfect place to find a therapist who can help you navigate your relationships. Just fill out a short questionnaire that helps identify your needs and preferences, and BetterHelp will match you with a therapist. If you don't like your match, you just switch to a different therapist at any time. Sign up and get 10% off@betterhelp.com Radiolab and see if talking to someone about your struggles in finding that special someone and keeping them is just the thing you need. That's betterhelp.com Radiolab Radiolab is supported by.

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A (24:33)

Lulu Latif, Radiolab back with be.

C (24:39)

And you were going to tell us how they're like, in our Internet or something?

A (24:44)

Yeah, basically, pretty much. But to do that, I needed an example. So I found something in my own life, which I apologize in advance for how annoying this is going to be.

C (24:55)

Okay.

A (24:55)

Do you remember the Hamster Dance?

C (25:00)

No, I don't think I do. Is this like a website?

A (25:02)

Yeah, yeah, yeah, yeah. Okay. I'm kind of surprised you don't remember it. It was a very early web kind of thing. It was. But all it was, it was just a song with a web page and the webpage was just a bunch of gifs of cartoon hamsters dancing. It's the most annoying song that you will ever.

C (25:18)

Can you still sing it?

A (25:19)

I can. Would you like to hear it?

C (25:20)

Yes, please.

A (25:20)

It was, Anyway, something like that. It's very, very annoying.

D (25:33)

Okay.

A (25:35)

And I remember when I was like, in high school, I thought it was so funny. And one day I tried to show it to my cousin because I was like, oh, this is so funny. You have to see this. And I remember it, like, taking a really long time to load. And I was like, oh. Because there's so many people who are.

C (25:49)

Need to get their eyes on this.

A (25:50)

Right. And so what was happening behind the scenes was. Well, okay, so like basic Internet 101.

G (25:55)

Yeah.

A (25:56)

Every website on the Internet. Like, for example, what was at the time hamsterdance.com exists geographically somewhere, like in.

C (26:07)

In the cloud or.

A (26:09)

No, no, like a single earthbound computer. The owner of the website paid for it to live there. And that computer, which is called a server, is not in a house or an office. It is in a special building dedicated to servers. The server farm. Right. Or a data center.

C (26:29)

Okay, so it's at a data center in, like, Ohio.

A (26:31)

Yeah, wherever it is.

C (26:32)

Okay.

A (26:33)

These server farms are all over the world and connected together, they more or less make up the Internet.

C (26:37)

Oh.

A (26:38)

And so when you. When the request comes in, when, you know, young me types in hamsterdance.com and presses enter.

D (26:46)

And that server, the actual infrastructure that's.

A (26:49)

Holding it, who is just sitting around in Utah or whatever on the server farm, gets a little notification. Oh, someone wants to see that hamsterdance.com website. All right, not too busy with anything else. So it does a little computation. Go ahead and get that for you.

D (27:05)

Some execution of some Java code or something.

A (27:08)

Let me see here. I think it's around here somewhere.

D (27:10)

And then push that content back to you via Internet.

C (27:13)

Here you go.

A (27:14)

Where it pops up on my family's desktop computer back in 1999. And then you can enjoy it. Then the server just goes back to hanging out on the server farm. But then, oh, another person wants to see that website. All right, let me just go ahead and get that for you. There you go. All right. Where was I? Man, I just love this country. Oh, oh, oh, no. And as it starts to go viral.

D (27:47)

Number of people are all bombarding with.

A (27:49)

Requests till that one server is like, oh, my God, somebody help. Can no longer serve up this website in a timely manner.

D (27:57)

And so now people have to wait in a queue. Right.

A (28:00)

And this would happen all the time because back then the way servers were allocated was like you'd get how many servers you paid for.

D (28:07)

Right.

A (28:07)

Like the owner of the Amsterdam website probably would have been like, hey, I'll just pay for the one server.

D (28:12)

Because I don't expect my website to experience a lot of demand.

A (28:15)

And how wrong they were.

D (28:17)

Right.

A (28:17)

Do you remember what that was like, though? Like how I do the, like, slow.

C (28:21)

Loading and the like if something took.

D (28:25)

Longer than five seconds. Generally the human psychology was that people give up on the website.

A (28:29)

So as Sunil saw it, the problem was relatively simple. There are a bunch of servers and.

D (28:33)

Sometimes they can be not very busy.

A (28:36)

Sometimes they're just sitting around doing nothing, but other times they could be overly busy. And so how in a system that is changing so quickly, do you get those servers who are doing nothing to help those servers who are doing too.

D (28:48)

Much start moving these servers around where they need it?

A (28:51)

And this is the problem that Sunil brought to Craig.

D (28:54)

Yeah, hi, I'm Sunil.

E (28:56)

And I mean, heck, within 20 seconds I saw that the problem was similar to the honeybee problem.

C (29:02)

Like immediately, like that fast.

A (29:05)

Yeah, because to Craig, it was like he had been holding onto this rusty old key that he was holding for more than a decade. And Sunil showed up with something that looked like it might be the exactly matching lock.

E (29:17)

Yeah, yeah.

D (29:18)

Like evolution has solved this problem in some way. Right. And now you're saying, okay, can it do it in the artificial domain?

A (29:25)

And so they got to Work.

E (29:28)

Right. F sub N of X sub N.

A (29:30)

Dusted off that old math equation, mapping.

D (29:32)

The two structure of the. You know what the bees are doing.

A (29:35)

And sitting there, they started to connect all of these dots.

D (29:38)

Like, collection of bees make up the beehive. Collection of servers make up the Internet server farm.

E (29:43)

Yes, exactly.

A (29:44)

And wait, so that would mean. Oh, my God, the stuff on the Internet are flower patches. Oh, bingo.

D (29:50)

At that point, things got really exciting.

C (29:52)

Wait, I'm sorry. Just please stop that.

A (29:55)

Sorry.

C (29:56)

How is what the bees are doing anything like what the server's doing? Like, lay out the parallel.

A (30:02)

Okay, why don't we. Okay, let's. Why don't we cranberry lake this thing? Okay.

C (30:07)

Okay.

A (30:08)

So instead of a meadow filled with wildflowers.

C (30:12)

Yeah.

A (30:12)

Okay. Picture the entire Internet. Got it. It's the beginning of the day. You're just signing onto your computer, and the first thing you decide to do on there.

C (30:23)

Charlie. Charlie bit me.

A (30:26)

Is watch a video of a baby biting his older brother's finger naturally. Right?

C (30:31)

Charlie bit. Yes. Okay. How I love to start my day.

A (30:35)

Of course. Okay, so you watching this video is like a flower opening in the meadow, right? The meadow of the Internet.

C (30:44)

Why is me watching analogous to a flower opening?

A (30:48)

You watching is like the flower filling up with nectar. It's your desire, which they can capitalize on, basically.

C (30:55)

It's like me watching is money for it's.

A (30:58)

Oh, it's money. It's money for them. My eyes on the video, your eyeballs and your attention. That's like the server company's nectar.

C (31:04)

Oh, okay. I get it. I get it. We are the flowers.

A (31:08)

Yeah. And as more and more people watch and then share this video with their friends who watch it too, and share it with their friends who watch it too. That's like more and more flowers opening in the flower patch. And the server hosting that video is like a single bee being, like, holy motherly bonanza of nectar over here. I'm gonna need some backup.

C (31:30)

Oh, so now it has to recruit more bees, AKA more servers.

A (31:35)

Yeah. So it does like a computer version of a waggle dance this server to server digital nudge.

E (31:42)

Okay.

A (31:42)

Let's call it a ping. That means, hey, I need some help. Come get some of this stuff.

B (31:47)

Okay.

A (31:47)

And so servers buzz over to the Charlie bit me video and start servicing all of these flowers, Right? Helping all these people to watch this video. And then it kind of keeps going, right? The video popularity grows. Flowers keep opening, more bees needed, more servers needed. And very quickly, all these servers are just servicing the Charlie bit Me video.

C (32:07)

Okay?

A (32:08)

But then. Wait a second, now there's a ping. But it's not for the Charlie bit me video. Giant container ship wedged from bank to bank. Now everybody wants to see pictures of this huge boat on the BBC website in one of the world's most important shipping lanes that just got stuck in the Suez Canal.

C (32:25)

I remember that I was a flower, right?

A (32:28)

Right. So, like you, all these flowers opening up all over the world, all of a sudden, the servers that are serving up these boat pictures that now they're the ones who are saying, hey, we need backup. And again, there is this server to server, peer to peer, B2B, waggle dance, recruitment, ping, calling out to the idle servers to come help. And those servers come and they ping out. And those servers who are no longer needed with the Charlie bit me thing are now like, oh, man, I gotta go help with this Suez Canal thing.

C (32:58)

Okay, got it. But then in the actual Internet, this is happening times like a gazillion, right? All the time. Literally billions of people, billions of flowers. So could this algorithm, this little fix, actually work?

A (33:16)

Well, they had to figure that out initially.

D (33:19)

Excitement kind of sets in. But then you go to prove what you thought would be a good solution. Right.

A (33:26)

To test this idea, Sunil and Craig decided to compare it to another algorithm.

D (33:32)

They invented that algorithm. We named it Omniscient. Where, if it could see the future, what would it do?

A (33:39)

Like, if you knew ahead of time everything that everybody would want to see on the Internet, how would you organize your servers to meet that demand? It's basically God mode.

D (33:48)

This is the best you can do? You can't do better than that.

A (33:50)

So they compared this perfect model to the way humans had been allocating servers already.

C (33:56)

Like the guessing.

A (33:57)

Right. And they compared it as well to the BEES algorithm version. And what they found was that the.

D (34:04)

Bees, even without knowing the future, they were coming within, like 20, 15% of the optimal behavior.

A (34:11)

Wow. In a bunch of their tests, the human algorithms didn't even come close.

C (34:15)

Whoa.

E (34:16)

Yeah.

D (34:17)

Turns out that at least theoretically, it worked really well.

A (34:20)

So based off these results, they publish a little paper, and then Sunil goes back to Oxford to defend his thesis.

D (34:26)

I presented the results to the committee.

A (34:29)

He tells them about the problem he's seen with the Internet and how the bees could help solve it, talking for.

D (34:33)

Like 45 minutes to an hour.

A (34:35)

And when he's finally done, the first question that he gets is, have you.

D (34:40)

Patented this idea or not?

C (34:44)

Oh, wow, that's a good question to get.

A (34:48)

But he was like, well, I just published it.

D (34:51)

You can't patent your own thing if you published.

A (34:53)

Oh, no. And so he did not patent it.

C (34:57)

Oh, he gave it away.

A (34:59)

He gave it away for free to everybody. And over the following months and years, server farms all over the world worked this bee algorithm into the Internet.

C (35:08)

Wow.

E (35:08)

You know, the honey bee algorithm made it 10, 20% more efficient, which means.

A (35:13)

We have the bees to thank for the Internet being this place where US.

C (35:17)

Climber Alex Hunold stunned crowds inside.

A (35:20)

You can get whatever you want. Video from outside, outside the Capitol shows the beginning of the storm right now. The hurricane pushed Lake Pontchartrain deep. It's peanut butter jelly time.

C (35:33)

I mean, that is just a mo. Like, just a moment for nature that. That could outperform so many thousands of human brains working on solving all kinds of problems. And just like evolution had figured this out through its own longer timeline of trial and error.

A (35:54)

Yeah. And not only that, this honeybee algorithm, or variations of it have been lifted into so many other industries. I have found people researching how to use it to forecast exchange rates, design electric cars, detect defects in wood before using that wood for construction, even for sharpening MRI images to better detect breast cancer, tumors.

C (36:17)

Bless that little bumblebee. I mean, I guess what's so I think about humans, one of our gifts and our curses, is that we can jump to the future, you know, or the past. We can jump out of the present, but we can worry. And that's like entire industries, prediction, forecasting. Whether it's weather or money or, you know, whatever, like, but we spend. Spend time worrying about the future, but it feels like what this. The elegance, it's like, not only to me is it beautiful that it came from bees, because I'm a nature lover and I love it when nature outsmarts us or has more elegance in its design, but it's also like one of the insights, if you wipe away all the technicalities, is like it's throwing away the future. It's not basing its decision of where to move based on a guess about the future. It is only responding to the present.

A (37:17)

Mm. Right. Right. It's like, it's kind of at this very subtle, like, it's like right at the line between responding to the clues of the moment and predicting the future.

C (37:36)

It's on the edge of the present.

A (37:37)

It's at the edge of the present. And it's like, all we gotta do is, like, pay attention to what's happening now and then and, like, build it into these feedback loops so that we can. Yeah. So that we can address just the next moment, like just the next moment and just the next moment after that and just the next moment after that and just the next moment after that.

C (37:54)

I mean that's if anything that might be a thing I like take around with me.

A (38:01)

Sam. This episode was reported by me, Latif Nasser with reporting help from Maria Paz Gutierrez, production by Maria Paz Gutierrez, Andy McKeown and Pat Walters, edited by Pat Walters and facts checked by Diane Kelly.

C (38:41)

We also got a lot of help for this episode from Radiolab and Terrestrial's resident bug correspondent Sammy Ramsey. And we couldn't have done this one without his help. Big thank you to you, Sammy. And if you want to hear more about bees, and here's Sammy talking about them, we have a terrestrials episode called the Crystal Ball. Honeybees who predict the future. You can go listen to him over there.

A (39:02)

Other major thank yous to John Bartholdi, John Van de Vate and James Marshall as well. We want to thank the folks at AAAS who administer the one and only Golden Goose Award. If you remember, the award goes to government funded science that sounds kind of silly or bizarre, but then goes on to change the world. This research won a Golden Goose award back in 2016, which is how I first heard about it. So thank you to all our friends there. Erin Heath, Gwendolyn Bogart, Valeria Sabate, Joanne Padrone Carney and Meredith Asbury.

C (39:34)

And bees. We barely scratched the surface of how amazing they are. If you want to learn more, read any of Tom Seeley's books. His most recent one is the incredibly titled memoir, Piping Hot Bees and Boisterous Buzz Runners.

A (39:49)

I think that should be it. But if you are still here, let me leave you with this bit of tape that has been haunting me. Sunil. Like dude, over the last 20 years I have used the Internet a lot. If, if the Internet today took the same amount of time as the Internet in the 90s, like, like you, I can easily imagine. Not just minutes, I mean hours, full days of my life, a second at a time that this could have saved.

D (40:27)

Yeah, you're right. But also what most Internet service company wants is stickability for you to keep using the service. Right?

A (40:35)

Okay, so you're saying the opposite. You have made the Internet so enjoyable that you have cost me days and hours, potentially even years of my life. So I should be. I should be mad at you.

D (40:45)

Yeah.

A (40:52)

And that's it.

C (40:54)

See you next week.

B (40:58)

Hi, I'm Gabby, I'm from San Francisco and here are the staff credits. Radiolab is hosted by Lulu Miller and Latif Nasser. Soren Wheeler is our Executive Editor. Sarah Sandbach is our Executive Director. Our Managing editor is Pat Walters. Dylan Keefe is our Director of Sound Design. Our staff includes Jeremy Bloom, W. Harry Fortuna, David Gable, Maria Paz Gutierrez, Sindhu Nainasambandan, Matt Kielty, Mona McGacher, Annie McKeown, Alex Neeson, Sara Khari, Rebecca Rand, Anisa Vitce, Arian Wack, Molly Webster, and Jessica Young, with help from Gabby Santis. Our fact checkers are Diane Kelly, Emily Krieger, Natalie Middleton, Angeli Mercado, and Sophie Semey. Leadership support for Radiolab's science programming is provided by the Simons foundation and the John Templeton Foundation. Foundational support for Radiolab was provided by the Alfred P. Sloan Foundation.

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