00:12

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.

00:41

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 femaleswho 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.

02:23

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.

03:39

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 stimulationfrom 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.

04:32

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.

04:53

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.

06:07

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 outforaging, 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.

07:04

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 bandwidthfor 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.

08:10

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.

09:33

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 interestright 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 thoroughlybecause 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.

12:09

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.

12:36

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 thisbecause 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.

13:37

So ants are using interactions in different ways in a huge variety of environments, and we could learn from thisabout 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.

13:58

Thank you.

14:01

(Applause)

00:12

我研究各種螞蟻 沙漠中的脉幢、熱帶雨林里的歪沃、 我廚房里的螞蟻， 以及我硅谷的家周邊山上的螞蟻嫌松。 最近我注意到螞蟻在不同的環(huán)境下 交互方式也是不同的沪曙， 這讓我想到或許我們能從中學(xué)到些什么 用到其它系統(tǒng)上。 例如大腦結(jié)構(gòu)或者我們的數(shù)據(jù)網(wǎng)絡(luò) 甚至是癌癥萎羔。 這些系統(tǒng)的共同點(diǎn)在于 沒有一個(gè)中央控制結(jié)構(gòu)液走。 蟻群的工蟻由不育的雌性構(gòu)成— 工蟻就是你能看到的螞蟻— 而能夠生育的雌性螞蟻（蟻后） 只負(fù)責(zé)產(chǎn)卵。 蟻后不會(huì)發(fā)號(hào)指令贾陷。 雖然它們叫做蟻后缘眶， 但是它們不會(huì)指揮其它工蟻。 所以任何蟻群都沒有一個(gè)最高負(fù)責(zé)人髓废， 所有這些系統(tǒng)都是沒有中央控制的巷懈， 僅僅通過簡(jiǎn)單的交互方式進(jìn)行運(yùn)作. 螞蟻的交互是通過嗅覺進(jìn)行的. 它們用觸角（antennae）去嗅. 用觸角來交流。 所以當(dāng)一只螞蟻的觸角碰到另一只螞蟻的觸角 它就知道另一個(gè)螞蟻 是不是同一個(gè)巢穴的 以及這只螞蟻正要做什么事情. 現(xiàn)在你看到的這個(gè)螞蟻的活動(dòng)場(chǎng)所 通過玻璃管子跟另外兩個(gè)場(chǎng)所連接著 螞蟻在這些活動(dòng)場(chǎng)所里走來走去. 當(dāng)一只螞蟻遇到了另外一支螞蟻, 遇見的是哪只螞蟻并不重要, 它們也沒通過觸角傳遞 任何復(fù)雜的信號(hào)或消息. 唯一傳遞的是兩只螞蟻 相互遇見的頻率. 這些交互信息匯總起來后, 我們就得到了一個(gè)網(wǎng)絡(luò). 這就是剛才你看到的螞蟻 四處移動(dòng)之后生成的網(wǎng)絡(luò)圖, 正是這張不斷變化中的網(wǎng)絡(luò), 塑造了這個(gè)蟻群的行為, 像是有多少螞蟻躲在巢穴里, 多少螞蟻出去尋食之類的信息. 大腦差不多也是這么工作的, 相比起來觀察螞蟻吸引人的地方之一, 是你可以看到整個(gè)網(wǎng)路是如何運(yùn)作的. 螞蟻的種類超過一萬兩千種, 你能想象到的環(huán)境里都有螞蟻存在, 而且不同環(huán)境下的蟻群會(huì)使用 不同的交流方式以適應(yīng)環(huán)境特點(diǎn). 例如不同環(huán)境下不同蟻群 普遍面臨的問題之一 是如何控制"運(yùn)營(yíng)開支", 即需要花多大成本 才能生存下來. 另一個(gè)環(huán)境帶來的挑戰(zhàn)是, 如何去搜尋和收集資源. 在沙漠中, 水非常的稀少, 所以運(yùn)營(yíng)開支很大, 我研究的一種生活在沙漠中以植物種子為食的螞蟻 尋找水源的同時(shí)需要消耗水. 所以當(dāng)一只螞蟻外出覓食的時(shí)候, 在火辣辣的太陽底下找種子的時(shí)候, 它體內(nèi)的水分會(huì)被蒸發(fā). 而蟻群 可以通過消化種子富含的脂肪 產(chǎn)生需要的水. 所以在這種環(huán)境下, 螞蟻之間的交互 主要用來決定是否外出覓食. 一個(gè)準(zhǔn)備外出的覓食者不會(huì)輕易外出, 除非得到了足夠的歸巢的覓食者的反饋, 你現(xiàn)在看到的是回來的覓食者, 在通過蟻巢的管道進(jìn)入蟻穴時(shí), 跟沿路準(zhǔn)備外外出的螞蟻進(jìn)行交流. 這對(duì)蟻群來說很重要, 因?yàn)橥饷娴氖澄镌蕉? 覓食的螞蟻找到食物的速度越快, 它們回來的就更快, 那么就會(huì)有更多的螞蟻出去覓食. 這個(gè)系統(tǒng)默認(rèn)的行為是按兵不動(dòng), 除非看到了足夠的好處. 所以在這里交互是為了決定是否出去覓食. 我們已經(jīng)研究這種系統(tǒng)演化有一段時(shí)間了. 首先, 這種演化各不相同. 不同的蟻群的行為是不一樣的. 在旱季, 有些蟻群覓食的少, 不同蟻群之間的差異 就體現(xiàn)在它們?nèi)绾巫鰴?quán)衡 如何在消耗更多水分去尋找食物 以及獲得更多食物和水之間權(quán)衡 我們嘗試將蟻群 類比成神經(jīng)細(xì)胞組織基于腦神經(jīng)科學(xué)的相關(guān)理論 來理解蟻群覓食行為的差異慌洪。 所以就像是一個(gè)神經(jīng)元是否觸發(fā)顶燕， 取決于相連的神經(jīng)元觸發(fā)強(qiáng)度之和， 螞蟻的行為也由其它螞蟻決定冈爹， 是否要出去覓食涌攻。 于是我們就希望能夠找到 覓食行為存在差異的蟻群之間 是否螞蟻在覓食前交互的其它螞蟻數(shù)量 也是存在對(duì)應(yīng)差異的振愿。 因?yàn)橄衲菢拥南伻簳?huì)更少外出覓食乌企。 這個(gè)問題也可以用大腦來進(jìn)行類比。 我們提到的大腦 當(dāng)然也是每個(gè)大腦都有些許不同的 肯定有一些個(gè)體在某些環(huán)境下 他們的神經(jīng)元的電特性決定了 需要接受更多的刺激才會(huì)激發(fā)渠啤。 而這會(huì)導(dǎo)致腦的功能差異剂买。 而為了解答之前系統(tǒng)演化的問題惠爽， 我們首先需要研究下后代繁殖率。 這張圖顯示的是我的研究站附近的蟻群圖 我在這個(gè)地方研究收獲螞蟻（一種西方蟻） 種群演化已經(jīng)超過28年了瞬哼。 這大概也是一個(gè)種群能夠延續(xù)的時(shí)間婚肆。 每一個(gè)圓圈都表示一個(gè)種群， 圓圈的大小表示后代的規(guī)模坐慰， 我們可以通過基因變化分析(genetic variation) 來確認(rèn)種群之間的父子關(guān)系较性， 也就是能夠確認(rèn)每個(gè)蟻群 里面的蟻后來自于 哪個(gè)父代蟻群。 研究這么多年之后我有了一些迷人的發(fā)現(xiàn)结胀，例如赞咙，154號(hào)種群， 我研究很多年的這個(gè)糟港， 算是祖母級(jí)別的攀操。 這是她的女兒種群， 這是她的孫女種群秸抚， 這是重孫女種群速和。 分析這些種群使我能夠 發(fā)現(xiàn)后代種群（的多少）體現(xiàn)了 父代種群在炎熱天氣下 覓食的策略差異， 而且考慮到父代種群 與后代種群之間距離很遠(yuǎn)剥汤，不可能遇見颠放， 所以后代種群中的螞蟻 不會(huì)從父代種群那里學(xué)習(xí)到什么。 于是第二步就是看看 這種相似性的基因?qū)W變異根源吭敢。 然后我就可以提出這個(gè)問題：哪群螞蟻的策略更好碰凶？ 在研究進(jìn)行中的那些年里， 尤其是最近的十年鹿驼， 實(shí)驗(yàn)所在的美國(guó)西南部 經(jīng)歷了非常嚴(yán)重和持久的干旱欲低， 結(jié)果是那些更注重保持水分的蟻群， 那些大熱天不出門的蟻群蠢沿， 也就是那些失去了更多覓食機(jī)會(huì)的蟻群伸头， 反而是更有可能有后代蟻群的。 我曾經(jīng)一度認(rèn)為154號(hào)種群 是進(jìn)化的失敗者舷蟀， 因?yàn)樵诤导荆?它們很少出去覓食恤磷， 反之其它的種群 會(huì)出去尋找更多的食物， 但是結(jié)果是野宜，154號(hào)種群非常的成功扫步。 她是事實(shí)上的統(tǒng)領(lǐng)。她是這個(gè)研究點(diǎn)非常少見的有重孫后代的蟻群 就我所知匈子，這還是第一次 我們?nèi)祟惸軌蜃粉櫟?自然界中野生生物群體的 集體行為進(jìn)化 以及找到最適合環(huán)境的生存方式. 現(xiàn)在, 互聯(lián)網(wǎng)使用的算法 用來分配數(shù)據(jù)流動(dòng)的算法 與這些螞蟻使用的算法 即如何安排工蟻外出覓食的算法 非常相似. 你們猜我們?nèi)绾畏Q呼這種相似性? 蟻群互聯(lián)網(wǎng)(Anternet)的到來. (掌聲) 所以發(fā)送數(shù)據(jù)的電腦 在得到信號(hào)確認(rèn)帶寬足夠之前 不會(huì)將數(shù)據(jù)發(fā)送出去. 在互聯(lián)網(wǎng)的早期, 發(fā)送和接收數(shù)據(jù)的成本非常高, 所以任何形式的數(shù)據(jù)丟失都是不可以接受的, 所以網(wǎng)絡(luò)系統(tǒng)被設(shè)計(jì)利用相互之間的交互 來決定何時(shí)發(fā)送數(shù)據(jù). 發(fā)現(xiàn)螞蟻跟我們?nèi)祟愖罱虐l(fā)明的算法 有這么大的相似性是很叫人驚喜的, 而且現(xiàn)在我們只發(fā)現(xiàn)了螞蟻使用的算法中 一小部分的算法, 螞蟻已經(jīng)有了1.3億年的歷史 已經(jīng)演化出很多好的算法, 因此我相信有可能 另外尚未研究的1.2萬螞蟻種類中 也有很多有意思的算法, 可以用于數(shù)據(jù)網(wǎng)絡(luò) 這些算法甚至超過了我們的想象. 例如, 當(dāng)運(yùn)營(yíng)成本很低的時(shí)候呢? 熱帶雨林里, 蟻群覓食的成本很低, 因?yàn)槟抢锓浅５臐駶?rùn), 對(duì)于蟻群來說 外出覓食也非常容易. 但是螞蟻的種類是如此的繁多 數(shù)量也非常龐大 因此螞蟻之間的競(jìng)爭(zhēng)非常激烈. 一個(gè)蟻群需要用到的任何資源 基本上都有競(jìng)爭(zhēng)者 與之爭(zhēng)奪. 所以在這樣的環(huán)境下, 相互接觸的用途 完全反了過來. 蟻群的系統(tǒng)不斷的擴(kuò)張, 直到一些不好的事情發(fā)生, 我研究的一種蟻群會(huì)在叢林里 構(gòu)建自己的覓食網(wǎng)絡(luò), 在蟻穴和食物時(shí)間不斷的來回, 一圈一圈的覓食, 直到一些不好的事情發(fā)生, 例如遇到了 別的種類的螞蟻. 這是螞蟻安防的一個(gè)例子. 中間的位置, 一只螞蟻 在跟另外的種群的螞蟻觸碰了觸角之后 將蟻穴的入口用自己的頭擋住了. 這些小的河胎、腹部朝上的螞蟻 正在這周圍走動(dòng). 但是一旦危險(xiǎn)解除, 入口就會(huì)重新開啟, 或許我們也可以聯(lián)想到 在計(jì)算機(jī)安全領(lǐng)域 這個(gè)領(lǐng)域的運(yùn)營(yíng)成本也低到 我們可以臨時(shí)的中斷網(wǎng)絡(luò)訪問 以應(yīng)對(duì)臨時(shí)的威脅, 稍后繼續(xù)開放, 而不是現(xiàn)在的做法 嘗試構(gòu)造一個(gè)永久的防火墻. 另一個(gè)環(huán)境帶來的挑戰(zhàn) 所有的蟻群系統(tǒng)都需要面對(duì)的 是如何尋找和搜集資源. 蟻群為了解決這個(gè)問題, 采用了 集體搜索(collective search)的方法, 而這個(gè)問題現(xiàn)在已經(jīng)引起了 機(jī)器人研究人員的極大興趣, 因?yàn)槲覀兌贾? 與其用一個(gè)單一的 復(fù)雜且昂貴的機(jī)器人 去探索另外的星球 或去火場(chǎng)搜救, 或許有更好的方式 就是造一堆便宜的機(jī)器人 相互之間僅僅交換簡(jiǎn)單的信息, 就像是螞蟻所做的那樣. 這種外來的阿根廷螞蟻 很擅長(zhǎng)擴(kuò)大自己的搜索網(wǎng)絡(luò). 它們非常善于解決集體搜索中的 主要問題, 即如何在兩個(gè)不同的目標(biāo)之間權(quán)衡 既要能夠搜索的徹底 又要搜索的范圍廣. 它們是這么做的, 當(dāng)搜索空間小而螞蟻很多時(shí), 它們會(huì)搜尋的非常徹底因?yàn)樗鼈冎琅R近的區(qū)域 有別的螞蟻在搜索, 但是當(dāng)搜索面積很大 且螞蟻很少時(shí), 它們會(huì)擴(kuò)張自己的搜索路徑 去覆蓋更大的面積. 我想它們之間的接觸主要交換的是螞蟻的密度信息, 當(dāng)它們的密度很大時(shí), 它們碰見的就越多, 搜尋的也就越仔細(xì). 不同種類的螞蟻使用的算法應(yīng)該是不同的, 因?yàn)殡S著一代代的演化 它們需要的資源不同. 知道這些差異真的很有用. 所以最近我們把螞蟻 放在微重力的極端環(huán)境中 希望能夠幫助 國(guó)際空間站 解決集體搜索的難題. 當(dāng)我第一次看到這張照片, 我想, 呀, 他們把蟻穴豎起來放著了, 但是馬上意識(shí)到, 其實(shí)橫豎都一樣的. 這個(gè)實(shí)驗(yàn)的想法是 螞蟻要花大力氣把自己掛在墻上 或者也可以說是地板上, 你怎么看都行 這樣它們就沒有精力去交互了,所以關(guān)于螞蟻密度的信息 以及它們相互遇見的頻率 都會(huì)亂掉. 我們還在分析這些數(shù)據(jù). 我還沒有結(jié)論. 但如果我們能夠知道地球上的 其它物種如何解決此類問題 這一定非常的有意思, 所以我們創(chuàng)建了一個(gè)活動(dòng) 鼓勵(lì)全世界的小朋友們 用不同的螞蟻種類重復(fù)我們的實(shí)驗(yàn). 非常簡(jiǎn)單. 做起來也不需要多少成本. 這樣, 我們就能夠繪制一張 螞蟻集體搜索算法的"世界地圖". 我想那些外來的螞蟻種類, 那些混進(jìn)我們大樓的螞蟻, 對(duì)于集體搜索非常在行, 因?yàn)樗鼈円呀?jīng)跑到你的廚房 非常地善于找到食物和水. 對(duì)于螞蟻而言最為相似的資源 是野餐的地方, 是一個(gè)集中的資源.當(dāng)一塊水果掉在地上, 周圍很可能還有更多的水果渣, 因此生活在集中資源多的地方的螞蟻 通過相互接觸來召集伙伴. 所以當(dāng)一只螞蟻遇見另一只螞蟻, 或是另一只螞蟻沿路留下的 化學(xué)氣味, 然后它就會(huì)改變自己的方向 沖著接觸方提供的方向去搜尋 這就是為什么能夠有一只螞蟻大軍 與你分享野餐的原因. 現(xiàn)在, 我覺得我們或許可以 從螞蟻身上獲得治療癌癥的一些啟發(fā). 我是說, 首先, 我們可以做很多事情 來阻止癌癥 例如禁止有人向其他人銷售 可能增加我們身體患癌癥風(fēng)險(xiǎn)的 有毒有害商品, 但是我不認(rèn)為在這點(diǎn)上螞蟻能夠幫助我們什么, 因?yàn)樗鼈儚膩聿粫?huì)毒害同類. 但是我們或許可以從螞蟻那里學(xué)到一些方法 來治療癌癥. 癌癥有很多不同的種類. 每一種癌癥一開始都附著在身體的特定部位. 然后一些類型的癌癥(癌細(xì)胞) 會(huì)擴(kuò)散或傳播到其它特定的組織結(jié)構(gòu)中 它們需要在那里獲得自己需要的資源. 現(xiàn)在如果你從這個(gè)角度 去看待早期癌細(xì)胞 它們也是在體內(nèi)搜尋 尋找他們需要的資源, 如果這些資源是集中的, 那么它們很可能通過相互接觸來召喚更多的癌細(xì)胞, 那么如果我們能夠破解癌細(xì)胞相互召喚的機(jī)制 我們或許就能夠設(shè)置陷阱 在癌細(xì)胞聚集之前捕獲它們. 所以螞蟻在不同的環(huán)境下 使用了完全不同的交互算法.我們能夠從中學(xué)習(xí) 并將結(jié)果用于那些沒有 中央控制的系統(tǒng). 僅僅通過簡(jiǎn)單的接觸, 螞蟻已經(jīng)創(chuàng)造了 長(zhǎng)達(dá)1.3億年的偉大歷史. 我們還有很多需要向它們學(xué)習(xí). 感謝大家. (掌聲)