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.
Ja proučavam mrave u pustinji, u tropskoj šumi i u mojoj kuhinji, i na brežuljcima Silicijske Doline gdje živim. Nedavno sam shvatila da mravi drugačije koriste interakciju u različitim okruženjima, i to me navelo na razmišljanje da bismo iz toga mogli naučiti o drugim sustavima, kao što su mozak ili podatkovne mreže koje proizvodimo, pa čak i rak.
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.
Ono što je svim tim sustavima zajedničko jest da ne posjeduju središnju kontrolu. Kolonija mrava sastoji se od sterilnih ženki radilica -- to su mravi koje vidite da hodaju uokolo -- i onda jedna ili više ženki za parenje koje samo polažu jajašca. One ne daju nikakve upute. Iako se nazivaju kraljicama, one nikome ne govore što da radi. Dakle u mravljoj koloniji, nitko nije glavni, i svi slični sustavi bez središnje kontrole su podešeni koristeći vrlo jednostavne interakcije. Mravi međusobno komuniciraju koristeći njuh. Njuše pomoću svojih antena, i komuniciraju pomoću svojih antena, stoga kad jedan mrav dotakne drugog svojom antenom, zna, na primjer, je li drugi mrav iz istog gnijezda i što taj drugi mrav radi. Ovdje vidite mnogo mrava koji idu uokolo i komuniciraju u lab-areni koja je povezana cijevima s druge dvije arene. Kad jedan mrav sretne drugoga, nije bitno kojeg mrava sretne, i oni zapravo ne emitiraju nikakvu vrstu kompliciranih signala ili poruka. Sve što je mravu bitno jest stopa kojom susreće drugog mrava. I sve te interakcije, zajedno, tvore mrežu. To je mreža mrava koje ste upravo vidjeli da se kreću arenom, ta se mreža stalno mijenja i određuje vrste ponašanja kolonije, kao što je npr. hoće li se svi mravi sakriti u gnijezdo, ili koliko će ih ići van u potragu za hranom. Mozak zapravo radi na istom principu, ali ono što je kod mrava super jest to što se cijela mreža može vidjeti u tijeku događanja.
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.
Postoji više od 12 tisuća mrava, u svakom zamislivom okruženju, i koriste interakciju drugačije kako bi svladali preprjeke iz okruženja. Jedan značajan izazov koji svaki sustav mora riješiti jesu operativni troškovi, tj. ono što je potrebno da bi sustav funkcionirao. Drugi značajan izazov su resursi, pronalazak i skupljanje resursa. U pustinji su operativni troškovi visoki jer vlada oskudica vode, i mravi sjemenojedi koje proučavam u pustinji moraju potrošiti vodu da bi je dobili. Stoga mrav koji traga za hranom, tražeći sjemenje po žarkom suncu, gubi vodu u zrak. Ali kolonija dobiva svoju vodu metabolizirajući masti iz sjemenja koje jedu. Stoga u tom okruženju, interakcija se koristi kako bi se aktivirala potraga za hranom. Tragač za hranom ne ide van ukoliko ne dobije dovoljan broj informacija od povratnika, ono što vidite su mravi koji se vraćaju iz potrage za hranom ulaze u tunel, u gnijezdo, susrećući se s onima što kreću u potragu za hranom. Za mravlju koloniju to ima smisla, jer što je više hrane tamo vani, to je brže tragači nalaze, brže se vraćaju, stoga više tragača opet šalju van, Sustav radi da ostane zaustavljen, osim ako se ne dogodi nešto dobro.
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.
Dakle interakcija funkcionira na način da pokrene tragače. Proučavali smo evoluciju ovog sustava. Prije svega, postoji varijacija. Ispada da su kolonije različite. Za sušnih dana, neke kolonije tragaju za hranom manje, stoga se kolonije razlikuju po tome kako upravljaju tim kompenziranjem između trošenja vode za traženje sjemenja i dobivanja vode natrag kroz sjemenje. I pokušavamo shvatiti zašto neke kolonije tragaju manje od ostalih razmišljajući o mravima kao neuronima, koristeći modele iz neuroznanosti. Stoga baš kao što neuron uključuje stimulaciju od drugog neurona kako bi odlučio treba li odaslati signal tako i mrav dobiva stimulaciju od drugih mrava kako bi odlučio treba li ići tragati za hranom. Ono što tražimo jest postoji li možda mala razlika između kolonija u tome koliko interakcije svaki mrav treba kako bi ga se potaklo na to da krene u potragu za hranom, jer bi kolonija tad to radila manje.
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.
I to otvara još jedno analogno pitanje o mozgu. Razgovaramo o mozgu, ali naravno da je svaki mozak ponešto drugačiji, i možda postoje pojedinci ili neka stanja u kojem su električna svojstva neurona takva da im je potrebno više stimulansa da se pokrenu, i to bi vodilo razlikama u funkcijama mozga.
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.
Stoga ako ćemo postavljati evolucijska pitanja, trebamo znati više o reproduktivnom uspjehu. Ovo je mapa stranice studije gdje sam pratila populaciju mravlje kolonije sakupljača punih 28 godina, što je otprilike jednako vremenu trajanja kolonije. Svaki simbol je kolonija, a veličina simbola predstavlja koliko potomaka ima, jer možemo upotrijebiti genetske varijacije kako bismo uparili roditelja i potomka kolonije, što će reći, da shvatimo koje od kolonija su se razvile od kćeri kraljice proizvedene od kojih roditeljskih kolonija. I to je meni zadivljujuće, nakon svih ovih godina, saznati, na primjer, da kolonija 154, koju poznajem dobro već godinama, je pra-prabaka. Ovo je njena kći kolonija, ovdje vidimo unuku koloniju, a ovo su njene praunuke. I čineći to, naučila sam da potomci kolonija liče svojim roditeljskim kolonijama u smislu odluka o tome koji su dani toliko vreli da neće ići u potragu za hranom, i potomci roditeljske kolonije žive toliko daleko jedni od drugih da se mravi nikad ne sretnu, stoga kolonija potomaka nije to mogla naučiti od svoje roditeljske kolonije. Pa je naš sljedeći korak istražiti genetske varijacije koje stoje iza tih sličnosti.
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.
Tada sam mogla pitati, u redu, tko to radi bolje? Tijekom vremena proučavanja, a posebice unazad deset godina, bilo je vrlo ozbiljnih suša u jugozapadnom SAD-u, i ispada da kolonije koje konzerviraju vodu, koje ostaju unutra kad je jako vruće vani, žrtvujući da skupe što je više moguće hrane su one koje kasnije imaju više kolonija potomaka. Stoga sam cijelo ovo vrijeme mislila da je kolonija 154 gubitnička, jer za vrlo sušnih dana, jedva bi nekolicina mravi išla u potragu za hranom dok bi ostale kolonije bile vani tražeći i nabavljajući mnogo hrane, međutim, kolonija 154 je vrlo uspješna. Ona je matrijarhat. Ona je jedna od rijetkih prabaka ovdje. Koliko ja znam, ovo je prvi put da smo bili u mogućnosti pratiti evoluciju kolektivnog ponašanja u prirodnoj populaciji životinja i saznati što je zapravo najefikasnije.
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.
Sada, internet koristi jedan algoritam kako bi regulirao protok podataka koji je vrlo sličan onome koji koristi mrav sakupljač kako bi regulirao protok tragača za hranom. I pogodite kako nazivamo tu analogiju? Anternet dolazi. (ant=mrav) (Pljesak) Stoga podaci ne odlaze s računala ukoliko ne dobiju signal da postoji dovoljna propusnost za nesmetano kolanje. U začecima interneta, kad su operativni troškovi bili vrlo visoki i kad je bilo vrlo važno ne izgubiti nikakav podatak, sustav je bio podešen za interakcije na način da aktivira protok podataka. Zanimljivo je da mravi koriste algoritam koji je toliko sličan onome koji smo mi nedavno izumili, a to je tek jedan od mnoštva mravljih algoritama koje poznajemo, a mravi su imali 130 milijuna godina da razviju mnogo dobrih, i mislim da je vrlo izgledno da će neki od ostalih 12 tisuća vrsta imati vrlo zanimljive algoritme za podatkovne mreže kojih se mi još nismo ni sjetili.
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.
Dakle, što se događa kad su operativni troškovi niski? Operativni troškovi su niski u tropskim područjima, jer je tamo vrlo vlažno, pa je mravima lako hodati uokolo. Ali u tropskim područjima mrava je toliko puno i velika je raznolikost da postoji velika konkurencija. Koji god resurs koristi jedna vrsta, vjerojatno je da će isti taj resurs koristiti druga vrsta u isto vrijeme. Stoga u tom okruženju , interakcija se koristi u obrnutom smjeru. Sustav radi dok se ne dogodi nešto loše i jedna vrsta koju proučavam kruži u drveću mrava tragača idući od gnijezda do resursa hrane i natrag, i tako u krug, dok se nešto loše ne dogodi, kao interakcija s mravima druge vrste. Evo primjera mravljeg osiguranja U sredini je mrav koji zatvara ulaz u gnijezdo svojom glavom kao odgovor na interakciju s drugom vrstom To su ovi maleni što trčkaraju sa svojim trbuhom podignutim u zrak. No čim prijetnja prođe, ulaz se ponovno otvara, i možda postoje situacije u računalnoj sigurnosti gdje su operativni troškovi dovoljno niski pa bismo privremeno zablokirali prilaz kao odgovor na trenutnu prijetnju, i onda ga opet otvorili, umjesto da gradimo trajni vatrozid ili utvrdu.
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. And what they do is, when there are many ants in a small space, then each one can search very thoroughly because there will be another ant nearby searching over there, but when there are a few ants in a large space, then they need to stretch out their paths to cover more ground. I think they use interactions to assess density, so when they're really crowded, they meet more often, and they search more thoroughly. Different ant species must use different algorithms, because they've evolved to deal with different resources, and it could be really useful to know about this, and so we recently asked ants to solve the collective search problem in the extreme environment of microgravity in the International Space Station. When I first saw this picture, I thought, Oh no, they've mounted the habitat vertically, but then I realized that, of course, it doesn't matter. So the idea here is that the ants are working so hard to hang on to the wall or the floor or whatever you call it that they're less likely to interact, and so the relationship between how crowded they are and how often they meet would be messed up. We're still analyzing the data. I don't have the results yet. But it would be interesting to know how other species solve this problem in different environments on Earth, and so we're setting up a program to encourage kids around the world to try this experiment with different species. It's very simple. It can be done with cheap materials. And that way, we could make a global map of ant collective search algorithms. And I think it's pretty likely that the invasive species, the ones that come into our buildings, are going to be really good at this, because they're in your kitchen because they're really good at finding food and water.
Stoga još jedan okolišni izazov s kojim se svi sustavi moraju pozabaviti su resursi – tj. njihovo pronalaženje i skupljanje. I kako bi to učinili, mravi rješavaju problem kolektivne pretrage, a to je problem od našeg najvećeg zanimanja upravo sada u robotici, jer razumijemo da, umjesto da šaljemo pojedinačnog, sofisticiranog, skupog robota da istražuje drugi planet ili provjerava zgradu u plamenu, bilo bi mnogo učinkovitije nabaviti skupinu jeftinih robota koji izmjenjuju samo minimum informacija, a to je način na koji to rade mravi. Invazivni argentinski mrav radi proširive mreže za istraživanje. Oni su dobri u rješavanju glavnog problema kolektivne pretrage, što je zapravo razmjena između vrlo temeljite pretrage i one koja obuhvaća veća područja. Ono što oni rade jest, kad ima puno mravi na malom mjestu, oni mogu tražiti vrlo temeljito jer će uvijek biti drugi mrav u blizini koji će također tražiti, međutim kad ima malo mrava na velikoj površini, moraju proširiti svoje putanje kako bi pokrili veće područje. Mislim da koriste interakciju kako bi procijenili gustoću, pa kad je zaista gužva susreću se češće, i traže mnogo temeljitije. Razne vrste moraju koristiti različite algoritme, jer su evoluirali kako bi se nosili sa sakupljanjem različitih resursa, i moglo bi biti vrlo korisno znati ovo, pa smo nedavno pitali mrave da riješe problem kolektivnog pretraživanja u ekstremnim uvijetima mikrogravitacije u Međunarodnoj Svemirskoj Stanici. Kad sam prvi put vidjela ovu sliku, pomislila sam, o ne, postavili su stanište vertikalno, a tada sam shvatila da to, naravno, nije ni bitno. Dakle, ovdje je zamisao da mravi rade tako naporno da se drže uz zid ili pod ili kakogod ćete to nazvati pa je manja vjerojatnost za interakciju, pa je veza između koliko su skučeni i koliko se često susreću pobrkana. Još uvijek proučavamo podatke. Nemam još rezultate. Ali bilo bi zanimljivo saznati kako ostale vrste rješavaju ovaj problem u različitim uvjetima na Zemlji, stoga postavljamo program kako bismo potaknuli djecu širom svijeta da isprobaju ovaj eksperiment s različitim vrstama. Vrlo je jednostavan. Vrlo je jeftin. Na taj način mogli bismo napraviti globalnu kartu algoritama koji koriste mravi za kolektivno pretraživanje. Držim da je vrlo vjerojatno da će invazivne vrste, one koje dolaze u naše zgrade, biti vrlo uspješne u ovome, jer su oni u vašoj kuhinji stoga što su vrsni u pronalasku hrane i vode.
So the most familiar resource for ants is a picnic, and this is a clustered resource. When there's one piece of fruit, there's likely to be another piece of fruit nearby, and the ants that specialize on clustered resources use interactions for recruitment. So when one ant meets another, or when it meets a chemical deposited on the ground by another, then it changes direction to follow in the direction of the interaction, and that's how you get the trail of ants sharing your picnic.
Resurs s kojim su mravi najbolje upoznati jest piknik, i to je grupirani resurs. Gdje postoji jedan komad voća, velike su šanse da će postojati i drugi komad blizu, i mravi koji su specijalizirani za grupirane resurse koriste interakciju za regrutaciju. Kad jedan mrav sretne drugoga, ili kad naiđe na kemikaliju na tlu koju je nanio drugi mrav, mijenja smjer kako bi pratio smjer interakcije, i tako se dobiva trag mravi koji dijele hranu s vašeg izleta.
Now this is a place where I think we might be able to learn something from ants about cancer. I mean, first, it's obvious that we could do a lot to prevent cancer by not allowing people to spread around or sell the toxins that promote the evolution of cancer in our bodies, but I don't think the ants can help us much with this because ants never poison their own colonies. But we might be able to learn something from ants about treating cancer. There are many different kinds of cancer. Each one originates in a particular part of the body, and then some kinds of cancer will spread or metastasize to particular other tissues where they must be getting resources that they need. So if you think from the perspective of early metastatic cancer cells as they're out searching around for the resources that they need, if those resources are clustered, they're likely to use interactions for recruitment, and if we can figure out how cancer cells are recruiting, then maybe we could set traps to catch them before they become established.
Mislim da je ovo mjesto gdje bismo mogli naučiti nešto od mravi u vezi s rakom. Mislim, očigledno je da bismo mogli učiniti mnogo da spriječimo rak ne dopuštajući ljudima da šire uokolo ili prodaju toksine koji promiču evoluciju raka u našim tijelima, ali ne vjerujem da nam tu mravi mogu puno pomoći jer mravi nikad ne truju svoju vlastitu koloniju. Ali mogli bismo naučiti nešto od mrava u vezi liječenja raka. Postoji mnogo vrsta raka. Svaki potječe iz posebnog dijela našeg tijela, pa će se neki dijelovi raka raširiti ili metastazirati na određeno drugo tkivo gdje će moći skupiti resurse koji su im potrebni. Stoga ako razmislite iz perspektive stanice raka u ranoj fazi metastaze dok uokolo tragaju za resursima koji su im potrebni, ako su ti resursi grupirani, vjerojatnije je da će koristiti interakcije za regrutaciju, i ako uspijemo shvatiti kako se ćelije raka regrutiraju, možda bismo im mogli postaviti zamke kako bismo ih ulovili prije nego se smjeste.
So ants are using interactions in different ways in a huge variety of environments, and we could learn from this about other systems that operate without central control. Using only simple interactions, ant colonies have been performing amazing feats for more than 130 million years. We have a lot to learn from them.
Mravi koriste interakciju na različite načine u ogromnoj raznolikosti okoliša, a mi bismo mogli učiti iz toga o ostalim sustavima koji rade bez središnje kontrole. Koristeći se jednostavnim interakcijama, kolonije mrava izvode zapanjujuće podvige već više od 130 milijuna godina. Možemo mnogo naučiti od njih.
Thank you.
Hvala.
(Applause)
(Pljesak)