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  • I study ants

  • in the desert, in the tropical forest

  • and in my kitchen,

  • and in the hills around Silicon Valley where I live.

  • I've recently realized that ants

  • are using interactions differently

  • in different environments,

  • and that got me thinking that we could learn from this

  • about other systems,

  • like brains and data networks that we engineer,

  • and even cancer.

  • So what all these systems have in common

  • is that there's no central control.

  • An ant colony consists of sterile female workers --

  • those are the ants you see walking around

  • and then one or more reproductive females

  • who just lay the eggs.

  • They don't give any instructions.

  • Even though they're called queens,

  • they don't tell anybody what to do.

  • So in an ant colony, there's no one in charge,

  • and all systems like this without central control

  • are regulated using very simple interactions.

  • Ants interact using smell.

  • They smell with their antennae,

  • and they interact with their antennae,

  • so when one ant touches another with its antennae,

  • it can tell, for example, if the other ant

  • is a nestmate

  • and what task that other ant has been doing.

  • So here you see a lot of ants moving around

  • and interacting in a lab arena

  • that's connected by tubes to two other arenas.

  • So when one ant meets another,

  • it doesn't matter which ant it meets,

  • and they're actually not transmitting

  • any kind of complicated signal or message.

  • All that matters to the ant is the rate

  • at which it meets other ants.

  • And all of these interactions, taken together,

  • produce a network.

  • So this is the network of the ants

  • that you just saw moving around in the arena,

  • and it's this constantly shifting network

  • that produces the behavior of the colony,

  • like whether all the ants are hiding inside the nest,

  • or how many are going out to forage.

  • A brain actually works in the same way,

  • but what's great about ants is

  • that you can see the whole network as it happens.

  • There are more than 12,000 species of ants,

  • in every conceivable environment,

  • and they're using interactions differently

  • to meet different environmental challenges.

  • So one important environmental challenge

  • that every system has to deal with

  • is operating costs, just what it takes

  • to run the system.

  • And another environmental challenge is resources,

  • finding them and collecting them.

  • In the desert, operating costs are high

  • because water is scarce,

  • and the seed-eating ants that I study in the desert

  • have to spend water to get water.

  • So an ant outside foraging,

  • searching for seeds in the hot sun,

  • just loses water into the air.

  • But the colony gets its water

  • by metabolizing the fats out of the seeds

  • that they eat.

  • So in this environment, interactions are used

  • to activate foraging.

  • An outgoing forager doesn't go out unless

  • it gets enough interactions with returning foragers,

  • and what you see are the returning foragers

  • going into the tunnel, into the nest,

  • and meeting outgoing foragers on their way out.

  • This makes sense for the ant colony,

  • because the more food there is out there,

  • the more quickly the foragers find it,

  • the faster they come back,

  • and the more foragers they send out.

  • The system works to stay stopped,

  • unless something positive happens.

  • So interactions function to activate foragers.

  • And we've been studying the evolution of this system.

  • First of all, there's variation.

  • It turns out that colonies are different.

  • On dry days, some colonies forage less,

  • so colonies are different in how

  • they manage this trade-off

  • between spending water to search for seeds

  • and getting water back in the form of seeds.

  • And we're trying to understand why

  • some colonies forage less than others

  • by thinking about ants as neurons,

  • using models from neuroscience.

  • So just as a neuron adds up its stimulation

  • from other neurons to decide whether to fire,

  • an ant adds up its stimulation from other ants

  • to decide whether to forage.

  • And what we're looking for is whether there might be

  • small differences among colonies

  • in how many interactions each ant needs

  • before it's willing to go out and forage,

  • because a colony like that would forage less.

  • And this raises an analogous question about brains.

  • We talk about the brain,

  • but of course every brain is slightly different,

  • and maybe there are some individuals

  • or some conditions

  • in which the electrical properties of neurons are such

  • that they require more stimulus to fire,

  • and that would lead to differences in brain function.

  • So in order to ask evolutionary questions,

  • we need to know about reproductive success.

  • This is a map of the study site

  • where I have been tracking this population

  • of harvester ant colonies for 28 years,

  • which is about as long as a colony lives.

  • Each symbol is a colony,

  • and the size of the symbol is how many offspring it had,

  • because we were able to use genetic variation

  • to match up parent and offspring colonies,

  • that is, to figure out which colonies

  • were founded by a daughter queen

  • produced by which parent colony.

  • And this was amazing for me, after all these years,

  • to find out, for example, that colony 154,

  • whom I've known well for many years,

  • is a great-grandmother.

  • Here's her daughter colony,

  • here's her granddaughter colony,

  • and these are her great-granddaughter colonies.

  • And by doing this, I was able to learn

  • that offspring colonies resemble parent colonies

  • in their decisions about which days are so hot

  • that they don't forage,

  • and the offspring of parent colonies

  • live so far from each other that the ants never meet,

  • so the ants of the offspring colony

  • can't be learning this from the parent colony.

  • And so our next step is to look

  • for the genetic variation underlying this resemblance.

  • So then I was able to ask, okay, who's doing better?

  • Over the time of the study,

  • and especially in the past 10 years,

  • there's been a very severe and deepening drought

  • in the Southwestern U.S.,

  • and it turns out that the colonies that conserve water,

  • that stay in when it's really hot outside,

  • and thus sacrifice getting as much food as possible,

  • are the ones more likely to have offspring colonies.

  • So all this time, I thought that colony 154

  • was a loser, because on really dry days,

  • there'd be just this trickle of foraging,

  • while the other colonies were out

  • foraging, getting lots of food,

  • but in fact, colony 154 is a huge success.

  • She's a matriarch.

  • She's one of the rare great-grandmothers on the site.

  • To my knowledge, this is the first time

  • that we've been able to track

  • the ongoing evolution of collective behavior

  • in a natural population of animals

  • and find out what's actually working best.

  • Now, the Internet uses an algorithm

  • to regulate the flow of data

  • that's very similar to the one

  • that the harvester ants are using to regulate

  • the flow of foragers.

  • And guess what we call this analogy?

  • The anternet is coming.

  • (Applause)

  • So data doesn't leave the source computer

  • unless it gets a signal that there's enough bandwidth

  • for it to travel on.

  • In the early days of the Internet,

  • when operating costs were really high

  • and it was really important not to lose any data,

  • then the system was set up for interactions

  • to activate the flow of data.

  • It's interesting that the ants are using an algorithm

  • that's so similar to the one that we recently invented,

  • but this is only one of a handful of ant algorithms

  • that we know about,

  • and ants have had 130 million years

  • to evolve a lot of good ones,

  • and I think it's very likely

  • that some of the other 12,000 species

  • are going to have interesting algorithms

  • for data networks

  • that we haven't even thought of yet.

  • So what happens when operating costs are low?

  • Operating costs are low in the tropics,

  • because it's very humid, and it's easy for the ants

  • to be outside walking around.

  • But the ants are so abundant

  • and diverse in the tropics

  • that there's a lot of competition.

  • Whatever resource one species is using,

  • another species is likely to be using that

  • at the same time.

  • So in this environment, interactions are used

  • in the opposite way.

  • The system keeps going

  • unless something negative happens,

  • and one species that I study makes circuits

  • in the trees of foraging ants

  • going from the nest to a food source and back,

  • just round and round,

  • unless something negative happens,

  • like an interaction

  • with ants of another species.

  • So here's an example of ant security.

  • In the middle, there's an ant

  • plugging the nest entrance with its head

  • in response to interactions with another species.

  • Those are the little ones running around

  • with their abdomens up in the air.

  • But as soon as the threat is passed,

  • the entrance is open again,

  • and maybe there are situations

  • in computer security

  • where operating costs are low enough

  • that we could just block access temporarily

  • in response to an immediate threat,

  • and then open it again,

  • instead of trying to build

  • a permanent firewall or fortress.

  • So another environmental challenge

  • that all systems have to deal with

  • is resources, finding and collecting them.

  • And to do this, ants solve the problem

  • of collective search,

  • and this is a problem that's of great interest

  • right now in robotics,

  • because we've understood that,

  • rather than sending a single,

  • sophisticated, expensive robot out

  • to explore another planet

  • or to search a burning building,

  • that instead, it may be more effective

  • to get a group of cheaper robots

  • exchanging only minimal information,

  • and that's the way that ants do it.

  • So the invasive Argentine ant

  • makes expandable search networks.

  • They're good at dealing with the main problem

  • of collective search,

  • which is the trade-off between

  • searching very thoroughly

  • and covering a lot of ground.