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.
我研究螞蟻, 在沙漠中,在熱帶雨林中, 還有我家的廚房, 以及我住的矽谷四周的山丘上。 我最近才領悟到原來螞蟻 使用不同的互動法 在不同的環境中, 而這使我不禁去想, 我們或許能從這件事 了解其他系統, 像大腦及我們設計出的數據網路, 甚至癌症。
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.
所以這些系統的共通點 在於沒有中央控制。 蟻群是由不孕的雌性工蟻—— 就是你看到四處走動的那些螞蟻—— 還有一隻或多隻能生育的雌蟻組成, 而這種雌蟻只下蛋。 牠們不下任何指令。 即使牠們被稱為蟻后, 牠們也不會告訴任何螞蟻要做什麼。 所以在蟻群內,沒有負責人。 所有像這樣沒有中央控制的系統, 要以非常簡單的互動來規範。 螞蟻以嗅覺互動。 牠們以觸角聞味道, 而且牠們也以觸角互動, 所以當一隻螞蟻以觸角 碰觸另外一隻螞蟻, 牠就能分辨,譬如說這一隻 是不是同窩蟻, 還有這隻螞蟻正在做什麼任務。 所以這裡你看到很多螞蟻 在這個實驗場所四處走動及互動, 這個場所與另外兩個以管子相連。 所以當某隻螞蟻碰到另一隻, 碰到的是哪一隻螞蟻並不重要, 而且牠們其實並沒有傳送 任何複雜的信號或訊息。 對螞蟻而言最重要的是 牠們碰到其他螞蟻的頻率。 而所有的互動,全部一起看, 會產生一種網路。 那麼這就是螞蟻的網路, 由你剛剛看到的那個場所 裡面的移動所形成。 就是這個不斷移位的網路, 產生蟻群的行為, 像是不是所有的螞蟻都躲在窩裡, 或有多少在外面覓食。 大腦其實也以同樣的方法運作, 但研究螞蟻最棒的地方, 在於你能親眼目睹整個網路形成。
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.
有超過一萬兩千種螞蟻, 存在於每一種可想到的環境中, 而牠們互動的方法也不同, 以因應不同的環境挑戰。 所以有一種很重要的環境挑戰, 是每一種系統都必須面對的, 就是營業成本,就是到底要花多少 來經營系統。 而另一種環境挑戰則是資源, 要找尋及收集資源。 在沙漠裡,營業成本很高, 因為水很稀少, 而且我在沙漠中研究的 一種吃種子的螞蟻, 必須先用掉水才能得到水。 所以一隻螞蟻在外面覓食, 在烈日下尋找種子, 就會失去水分,釋放到空氣中。 但這個蟻群會因此得到水份, 也就是從代謝牠們所吃的種子 所含的脂肪以得到水份, 所以在這樣的環境下,互動是要 動員覓食行為。 指派為覓食者的螞蟻, 在與回來的覓食蟻 得到足夠的互動前不會出去, 而你現在看到的是回來的覓食蟻, 進入隧道中,進入窩裡, 與正要出去的覓食蟻互動。 這種方法對蟻群而言很有道理, 因為外面的食物愈多, 覓食蟻就能愈快找到食物, 牠們也就愈快回來, 所以就會送更多的覓食蟻出去。 這個系統的原理是保持不動, 直到有好事發生。
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.
所以互動的功用 在促使覓食蟻開始活動。 我們一直在研究這個系統的演化。 首先,這系統裡有變數。 結果證明每一種蟻群都不一樣。 在乾燥的日子, 某些蟻群的覓食行為會少一點, 所以每個蟻群的不同點在於 牠們如何權衡 是要花掉水分以尋找種子, 還是要找種子回來以取得裡面的水。 我們試圖瞭解為什麼 某些蟻群的覓食行為較少, 透過將螞蟻視為神經元的方法, 並使用神經科學的模式。 所以就像神經元會累積 從別的神經元送來的刺激, 以決定是否發射, 螞蟻也會累積 從別的螞蟻傳來的刺激, 以決定是否出去覓食。 我們在找的是,是否在 各個蟻群間有微小的差異, 也就是螞蟻需要多少互動 牠才願意出去覓食, 因為像那樣的蟻群可能較少覓食。
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.
這也引發大家對大腦 產生類似的問題。 我們談論大腦, 但當然每個大腦都有少許不同, 而或許有某些人, 或在某些情況下, 神經元的電性質也像這樣, 需要更多的刺激才能發射, 而那會導致大腦功能產生差異。
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.
所以為了要問進化的問題, 我們必須瞭解生殖成功率。 這是研究地點的地圖, 我已經在那裡追蹤這種 收割蟻群 28 年了, 這大約就是一個蟻群的壽命。 每一個符號代表一個蟻群, 而符號的大小代表 這個蟻群有多少後代, 因為我們能用遺傳變異 去匹配親子蟻群, 也就是說,去找出哪個蟻群 是哪一隻蟻后的女兒 所產生的。 這對我而言很奇妙,在這些年之後, 能發現到,舉個例,蟻群 154, 多年來我非常瞭解的一個蟻群, 居然是曾祖母。 這是牠的女兒的蟻群, 這是牠的孫女的蟻群, 而這些是牠的曾孫女的蟻群。 經由這麼做,我學到 後代蟻群與親代蟻群, 在決定哪一天太熱, 不出去覓食這方面很相似, 而親代蟻群的後代, 兩者住得很遠, 這些螞蟻從來沒有見過面, 所以後代蟻群的螞蟻, 不可能從牠們的親代蟻群學到這個。 因此我們下一步要去找 產生這種相似度的遺傳變異。
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.
所以我才能問,好,誰做得比較好? 在做研究的這段時間, 特別是過去十年, 曾有非常嚴重且日益加深的乾旱, 發生在美國西南部, 結果是這些節約用水的蟻群, 就是在外面真的 很熱的時候還留在窩裡, 從而犧牲盡可能找最多食物的蟻群, 愈有可能產生後代蟻群。 所以這些時間,我以為蟻群 154 是輸家,因為在很乾燥的日子, 牠們只有稀稀落落的覓食行為, 而其他的蟻群都跑出去 覓食,獲得大量的食物, 但其實,蟻群 154 非常成功。 她是女族長。 她是當地少有的曾祖母輩之一。 據我所知,這也是第一次 我們能追蹤 在自然的動物族群中 持續進行的集體行為演化, 並找出什麼是最佳的運作方式。
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.
那麼,網際網路使用一種演算法 以管理資料流, 與這個非常相似, 就是收獲蟻在使用以管理 覓食蟻的流程。 你猜我們怎麼叫這種類比? 蟻際網路來了! (掌聲) 所以資料不會從源計算機輸出, 直到它得到信號,有足夠的頻寬 讓資料傳出去。 在網際網路發展早期, 當營業成本還很高, 而且很重要 不能失去任何資料的時期, 系統被設成互動 以啟動資料流。 有趣的是螞蟻會使用一種演算法, 與我們最近發明的非常相似, 但這只是我們所知的 螞蟻演算法之一罷了, 螞蟻有一億三千萬年的時間 發展很多很好的演算法, 我想非常有可能 在其他一萬二千種螞蟻中, 能找到有趣的演算法 給資料網路使用, 是我們想都沒想過的。
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.
所以,營業成本低的時候 會發生什麼? 熱帶地方的營業成本低, 因為那裡很濕,螞蟻很容易 在外面走來走去。 但在熱帶地方, 螞蟻量很大,種類極多, 因此競爭也很激烈。 某種螞蟻在使用的資源, 別的螞蟻可能在同時間 也需要使用。 所以在這種環境,互動的使用法 恰好相反。 系統要持續運轉, 直到負面事件發生。 而我在研究的某種螞蟻 還會形成迴路, 覓食蟻在蟻窩到食物源 往來的樹上 不斷繞圈圈, 直到某件負面事件發生為止, 像是與別種螞蟻 有了互動。 所以這是螞蟻的保全案例。 在中間有一隻螞蟻 正以牠的頭堵住窩的入口, 回應牠與另一種螞蟻互動的結果。 這些小東西跑來跑去 腹部朝上。 一但威脅消失, 入口就又打開了, 也許在某些情況下, 電腦的安全性 在營運成本夠低時, 我們只要暫時把存取擋住, 以回應立即性的威脅, 然後再把它打開就好, 而不用試著建立 一個永久性的防火牆或要塞。
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.
那麼另一種環境挑戰, 所有系統都要面對的, 是資源,要找尋及收集資源。 要做這個,螞蟻解決了 集體搜尋的問題, 而這在機器人科學 是目前極感興趣的問題, 因為我們已經瞭解, 與其送出一個單獨操作、 複雜又很貴的機器人出去 探險另一個星球, 或去搜索一棟燃燒的建築物, 還不如這樣可能更有效, 就是找一組便宜的機器人 只交換最少的資訊, 而那正是螞蟻做的方式。 所以入侵種阿根廷蟻 建造可擴充的搜尋網路。 牠們對解決集體搜尋的 主要問題很在行, 那是權衡了 要完全徹底的搜尋, 還是要涵蓋大片土地的結果。 牠們所做的是, 如果在一個小空間裡有很多螞蟻, 那麼每一隻都可以徹底搜尋, 因為附近一定會有另一隻螞蟻 在另一邊搜尋, 但當在一塊很大的空間裡, 只有少數的螞蟻的時候, 那麼牠們需要延伸牠們的路徑 以涵蓋更多的地面。 我想牠們使用互動以評估密度, 所以當牠們真的很擠的時候, 牠們就更常碰到彼此, 牠們就更徹底搜尋。 不同的螞蟻物種 必須使用不同的演算法, 因為牠們已進化以面對 不同的資源, 而知道這一點可能非常有用, 所以我們最近要求螞蟻 解決集體搜尋問題, 在極端環境下, 即微重力狀態, 在國際太空站裡。 當我第一次看到這張照片,我在想, 不會吧,他們居然把棲地裝成垂直的, 但隨後我意識到,當然,那無所謂。 所以這個想法就是,螞蟻 要非常努力地緊緊抓住 牆壁或地板,隨便你怎麼叫, 所以牠們就不太可能互動, 如此一來這兩者之間的關係, 就是有多擠及多常碰到 就會被搞亂。 我們仍在分析那些數據。 我還沒拿到結果。 但這應該滿有趣的,如果你知道 其他物種在地球上不同的環境裡, 如何解決這個問題, 所以我們辦了一個計劃, 鼓勵全世界各地的孩子, 以不同的物種試做這個實驗。 這很簡單。 用很便宜的材料就能做。 這樣一來,我們或許可以 做一張全球地圖, 畫著螞蟻的集體搜尋演算法。 而我想入侵物種很有可能, 就是跑進我們房屋裡的那些, 在這方面的表現會非常好, 因為牠們在你的廚房裡, 因為牠們真的很會找食物及水。
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.
所以對螞蟻最熟悉的資源 就是野餐。 而且這還是叢集資源。 有一片水果出現在這裡, 有另一片在附近的可能性就很大。 而那些專做叢集資源的螞蟻 就會用互動招募螞蟻大軍。 所以當某隻螞蟻碰到另一隻螞蟻, 或當這隻螞蟻碰到另一隻螞蟻 留在地面的某種化學物品, 那牠就會改變方向, 依照互動的指示, 而這就是你如何得到一條螞蟻線 來分享你的野餐。
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.
那這是我認為我們或許可以 從螞蟻身上學到關於癌症的地方。 我是說第一,很明顯我們能做很多事 以避免癌症, 藉著不讓人們傳播 或販賣會在我們 體內致癌的毒素, 但我不認為螞蟻在這方面 能幫我們多少, 因為螞蟻從不毒化自己的蟻群。 但我們或許能從螞蟻學到 與治療癌症有關的事。 癌症有許多不同的類型。 每一種都源自身體的特定部位, 然後某些癌症會擴散 或轉移到某些特定的組織, 這些癌細胞一定是在那裡 得到它們所需的資源。 所以如果你從 癌細胞早期轉移的角度想, 好像它們是去外面四處尋找 它們所需的資源, 假如這些資源是叢集的, 它們就很有可能使用互動 來招募其它細胞, 如果我們能找出癌細胞 如何招兵買馬, 那也許我們可以設下陷阱, 在它們變得穩定前捕捉它們。
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.
所以螞蟻在大不同的各種環境下, 以不同的方法互動, 而我們可以從中學到 其他系統如何運作, 不靠中央控制。 僅僅使用簡單的互動, 蟻群已經執行 驚人壯舉超過一億三千萬年。 我們還有很多要向牠們學習。
Thank you.
謝謝。
(Applause)
(掌聲)