In my early days as a graduate student, I went on a snorkeling trip off the coast of the Bahamas. I'd actually never swum in the ocean before, so it was a bit terrifying. What I remember the most is, as I put my head in the water and I was trying really hard to breathe through the snorkel, this huge group of striped yellow and black fish came straight at me ... and I just froze. And then, as if it had suddenly changed its mind, came towards me and then swerved to the right and went right around me. It was absolutely mesmerizing. Maybe many of you have had this experience. Of course, there's the color and the beauty of it, but there was also just the sheer oneness of it, as if it wasn't hundreds of fish but a single entity with a single collective mind that was making decisions. When I look back, I think that experience really ended up determining what I've worked on for most of my career.
在我剛開始成為研究生的時候, 我到巴哈馬海岸去浮潛。 我其實從未在海洋中游泳過, 所以我有點害怕。 我最難忘的是,當我把頭沉入水中, 並竭力透過呼吸管呼吸, 有一大群黃黑條紋的魚 筆直朝我遊來…… 我呆住了。 然後,牠們好像突然轉念了一樣, 朝我過來之後就向右急轉彎, 從我身邊繞過。 那實在非常迷人。 也許在座有許多人有過這種體驗。 當然,魚群的顏色及美麗都很難忘, 但牠們還有著一種純粹的一體感, 彷彿牠們並不是數百條魚, 而是一個整體,包含著 一個做出決策的集體思維。 回想起來,我認為那段經歷 使我最終下定決心 去做這份佔據我大半生涯的工作。
I'm a computer scientist, and the field that I work in is artificial intelligence. And a key theme in AI is being able to understand intelligence by creating our own computational systems that display intelligence the way we see it in nature. Now, most popular views of AI, of course, come from science fiction and the movies, and I'm personally a big Star Wars fan. But that tends to be a very human-centric view of intelligence. When you think of a fish school, or when I think of a flock of starlings, that feels like a really different kind of intelligence. For starters, any one fish is just so tiny compared to the sheer size of the collective, so it seems that any one individual would have a really limited and myopic view of what's going on, and intelligence isn't really about the individual but somehow a property of the group itself.
我是個計算機科學家, 我研究的領域是人工智慧。 人工智慧的關鍵主題 是要能理解「智慧」的本質, 做法是創建自己的計算系統 (computational system) 來展現類似於自然生物的智慧。 當然,目前最熱門的人工智慧觀點 來自科幻小說和電影, 我個人是《星際大戰》的忠實粉絲。 但那往往是個非常 以人為中心的智慧觀。 當你思考魚群 或想像一群椋鳥, 那感覺是一種完全 不同的智慧形式。 首先,和整體魚群的大小相比較, 一條魚真的是太小了, 所以,似乎其中任何一個個體 對正在發生的事應該 眼光短淺、缺乏遠見。 而且「智慧」並不體現在個體身上, 而是團體本身的一種特性。
Secondly, and the thing that I still find most remarkable, is that we know that there are no leaders supervising this fish school. Instead, this incredible collective mind behavior is emerging purely from the interactions of one fish and another. Somehow, there are these interactions or rules of engagement between neighboring fish that make it all work out.
第二,我仍然認為是最了不起的事, 就是我們知道在這魚群中 並不存在管理著群體的領導者。 反而,這個集體思維 所做出的非凡行為 單純來自魚與魚間的互動。 不知何故,相鄰近的魚之間 會存在著這些互動, 或者說是約定好的行為規則, 從而產生這集體行為。
So the question for AI then becomes, what are those rules of engagement that lead to this kind of intelligence, and of course, can we create our own?
所以,對人工智慧的問題變成是: 是什麼約定規則產生這種智慧的? 當然還有,我們能否自己創造一個?
And that's the primary thing that I work on with my team in my lab. We work on it through theory, looking at abstract rule systems and thinking about the mathematics behind it. We also do it through biology, working closely with experimentalists. But mostly, we do it through robotics, where we try to create our own collective systems that can do the kinds of things that we see in nature, or at least try to.
這是我與團隊的實驗研究主題。 我們透過理論來研究, 探究抽象的規則系統, 思考其背後的數學原理。 我們也透過生物學來研究, 與實驗者密切合作。 但最主要是通過機器人研究, 嘗試創造我們自己的集體系統, 讓系統能做出,或至少試著做出 自然界中的智慧行為。
One of our first robotic quests along this line was to create our very own colony of a thousand robots. So very simple robots, but they could be programmed to exhibit collective intelligence, and that's what we were able to do. So this is what a single robot looks like. It's quite small, about the size of a quarter, and you can program how it moves, but it can also wirelessly communicate with other robots, and it can measure distances from them. And so now we can start to program exactly an interaction, a rule of engagement between neighbors. And once we have this system, we can start to program many different kinds of rules of engagement that you would see in nature.
我們最初以這種方式 在機器人方面的探索之一, 是創造我們自己的千人機器人群體。 機器人非常簡單, 但能通過程式設計讓它們 展現出集體智慧, 這是我們能夠做到的。 單個的機器人看起來是這樣的。 它很小,約 25 分硬幣的大小, 你可以設計程式來規範它如何移動, 它也能以無線的方式 和其他機器人溝通, 能測量與其他機器人的距離。 我們就可以開始 針對一套互動規則來設計程式, 指定鄰近機器人之間的行為規則。 一旦有了這個系統, 我們就可針對自然界中的 各類約定規則來編寫程式。
So for example, spontaneous synchronization, how audiences are clapping and suddenly start all clapping together, the fireflies flashing together. We can program rules for pattern formation, how cells in a tissue determine what role they're going to take on and set the patterns of our bodies. We can program rules for migration, and in this way, we're really learning from nature's rules.
比如「自發性同步」, 一旦有觀眾開始拍手, 全部都驟然跟著拍手, 螢火蟲也會一起發光。 我們可以編寫圖案形成的規則, (pattern formation) 組織中的細胞 如何決定它們將扮演什麼角色 並設定我們身體的模式。 我們可編寫遷移的規則, 以這種方式,我們能真正地 向自然界的規則學習。
But we can also take it a step further. We can actually take these rules that we've learned from nature and combine them and create entirely new collective behaviors of our very own.
但,我們也可以再進一步。 我們可以組合這些 向自然界學來的規則, 創造出我們自己的、 全新的集體行為。
So for example, imagine that you had two different kinds of rules. So your first rule is a motion rule where a moving robot can move around other stationary robots. And your second rule is a pattern rule where a robot takes on a color based on its two nearest neighbors. So if I start with a blob of robots in a little pattern seed, it turns out that these two rules are sufficient for the group to be able to self-assemble a simple line pattern. And if I have more complicated pattern rules, and I design error correction rules, we can actually create really, really complicated self assemblies, and here's what that looks like.
比如, 想像你有兩種不同的規則。 第一種是動作規則, 讓移動中的機器人 可以繞著靜止的機器人轉動。 第二種是模式規則, 機器人會根據旁邊 兩名同伴的顔色來呈現顏色。 所以,最開始我只需一小群機器人, 就能埋下一顆「模式種子」, 結果,對這個群體而言, 有這兩種規則就足以自我組裝出 一個簡單的線條樣式。 如果我有更複雜的模式規則 且設計出修正錯誤的規則, 我們就能實際造出 非常複雜的自我組裝樣式, 看起來就會像是這樣。
So here, you're going to see a thousand robots that are working together to self-assemble the letter K. The K is on its side. And the important thing is that no one is in charge. So any single robot is only talking to a small number of robots nearby it, and it's using its motion rule to move around the half-built structure just looking for a place to fit in based on its pattern rules. And even though no robot is doing anything perfectly, the rules are such that we can get the collective to do its goal robustly together. And the illusion becomes almost so perfect, you know -- you just start to not even notice that they're individual robots at all, and it becomes a single entity, kind of like the school of fish.
所以,各位將會在這裡 看到一千個機器人, 它們正在合作並自我組裝出 英文字母「K」。 這是一個側過來的 K 。 重要的是,沒有人在主導。 所以任何一個機器人都只是在 和它附近的少數幾個機器人交談, 它會用它的動作規則, 在這個半成品周圍移動, 根據它的模式規則, 找個適合的位置插進去。 雖然沒有任一機器人 完美地做好一件事, 規則是這樣的, 我們可以讓集體一起 穩健地完成目標。 這個幻覺幾乎完美, 你甚至會忘了它們各自是個機器人, 合起來成了單一的實體, 就像一群魚。
So these are robots and rules in two dimensions, but we can also think about robots and rules in three dimensions. So what if we could create robots that could build together? And here, we can take inspiration from social insects. So if you think about mound-building termites or you think about army ants, they create incredible, complex nest structures out of mud and even out of their own bodies. And like the system I showed you before, these insects actually also have pattern rules that help them determine what to build, but the pattern can be made out of other insects, or it could be made out of mud. And we can use that same idea to create rules for robots.
上面這些是二維世界中的 機器人及規則, 但我們也可以思考 三維世界中的機器人及規則。 如果我們造出能 共同建設的機器人會如何呢? 這裡,我們的靈感來自於群居昆蟲。 如果你想到建立土墩的白蟻 或是行軍蟻, 牠們造出很了不起、 很複雜的巢穴結構, 用泥巴,甚至用自己的身體。 就像我先前給各位看的系統, 這些昆蟲其實也有模式規則 來協助牠們決定要建造什麼, 做模型的材料可以是其他昆蟲 甚至是泥巴。 我們可以把同樣的想法 用來為機器人創造規則。
So here, you're going to see some simulated robots. So the simulated robot has a motion rule, which is how it traverses through the structure, looking for a place to fit in, and it has pattern rules where it looks at groups of blocks to decide whether to place a block. And with the right motion rules and the right pattern rules, we can actually get the robots to build whatever we want. And of course, everybody wants their own tower.
在這裡你將看到的 是一些模擬的機器人。 這模擬機器人有一條動作規則: 以何種方式在結構中來回移動, 並尋找一個適合插入的地方。 同樣它也有一套模式規則, 使它在看到一堆積木時 決定是否放下手中的積木。 有正確的動作規則 和正確的模式規則, 我們就能夠讓機器人建造出 任何我們想要的東西。 當然,每個人都想擁有 屬於自己的一座塔。
(Laughter)
(笑聲)
So once we have these rules, we can start to create the robot bodies that go with these rules. So here, you see a robot that can climb over blocks, but it can also lift and move these blocks and it can start to edit the very structure that it's on. But with these rules, this is really only one kind of robot body that you could imagine. You could imagine many different kinds of robot bodies. So if you think about robots that maybe could move sandbags and could help build levees, or we could think of robots that built out of soft materials and worked together to shore up a collapsed building -- so just the same kind of rules in different kinds of bodies. Or if, like my group, you are completely obsessed with army ants, then maybe one day we can make robots that can climb over literally anything including other members of their tribe, and self-assemble things out of their own bodies. Once you understand the rules, just many different kinds of robot visions become possible.
一旦我們有了這些規則, 我們就可以配合這些規則 開始打造機器人的身體。 在這裡,各位可以看到, 機器人能爬過積木, 它也可以舉起和搬動這些積木, 它可以自己開始修建這個結構。 但是配合這些規則, 這其實只是所有你能想到的 機器人身體構造情況中的一種。 你還可想像出多種 不同的機器人身體構造。 所以,你也許可以想像出 會搬移沙袋的機器人, 它們能協助築堤, 我們或許也可用軟材料做機器人, 共同撐起倒塌的建築物。 這只是把同樣的規則 放到不同類的身體中。 或者,和我的團隊一樣, 你可能對行軍蟻很著迷, 那麼也許有一天 我們做出能爬過任何東西的機器人, 包括爬過它們自己的夥伴成員, 用它們自己的身體組裝出東西。 一旦你瞭解了規則, 多種不同類型的 機器人遠景都變為可能。
And coming back to the snorkeling trip, we actually understand a great deal about the rules that fish schools use. So if we can invent the bodies to go with that, then maybe there is a future where I and my group will get to snorkel with a fish school of our own creation.
回到我的浮潛之旅, 其實我們瞭解很多魚群的規則。 所以,若我們能發明出 配合這些規則的身體, 那麼也許在未來, 我和團隊會和我們創造出的 魚群一起浮潛。
Each of these systems that I showed you brings us closer to having the mathematical and the conceptual tools to create our own versions of collective power, and this can enable many different kinds of future applications, whether you think about robots that build flood barriers or you think about robotic bee colonies that could pollinate crops or underwater schools of robots that monitor coral reefs, or if we reach for the stars and we thinking about programming constellations of satellites. In each of these systems, being able to understand how to design the rules of engagement and being able to create good collective behavior becomes a key to realizing these visions.
每一個我展現給你們的系統 讓我們更進一步邁向 這些數學和概念性工具 來創造我們自己的集體力量, 這就能讓許多種 未來技術都成為可能, 你可考慮用機器人來建立防洪設施, 用機器蜜蜂群來授粉, 或用水底機器人群體來監看珊瑚礁; 或是我們雄心萬丈, 可以考慮為一群衛星設計程式。 在所有這些系統中, 能夠瞭解如何設計出約定規則, 以及能夠創造出好的集體行為, 是實現這些遠景的關鍵。
So, so far I've talked about rules for insects and for fish and for robots, but what about the rules that apply to our own human collective? And the last thought that I'd like to leave you with is that science is of course itself an incredible manifestation of collective intelligence, but unlike the beautiful fish schools that I study, I feel we still have a much longer evolutionary path to walk. So in addition to working on improving the science of robot collectives, I also work on creating robots and thinking about rules that will improve our own scientific collective. There's this saying that I love: who does science determines what science gets done. Imagine a society where we had rules of engagement where every child grew up believing that they could stand here and be a technologist of the future, or where every adult believed that they had the ability not just to understand but to change how science and technology impacts their everyday lives. What would that society look like? I believe that we can do that. I believe that we can choose our rules, and we engineer not just robots but we can engineer our own human collective, and if we do and when we do, it will be beautiful.
目前,我已經談過了昆蟲、魚 和機器人之間的規則, 那麼用在我們自己 人類群體上的規則呢? 最後我想留給各位 去思考的一件事是 當然科學本身是 集體智慧的一種偉大表現形式, 但不像我研究的美麗魚群, 我覺得我們還有 非常長的演化之路要走。 所以除了致力於發展機器人 群體的科學研究之外, 我也從事創造機器人的工作, 並且思考一些規則, 它將對我們自己的 科學研究群體大有裨益。 分享一句我喜歡的話: 做科學的人,決定了科學能做什麽。 想像一個這樣的社會: 我們有個約定規則: 每個孩子在成長的過程中都相信 他們能站在這個講臺上 成為未來的科技專家; 或每個成年人都相信他們有能力 不僅理解而且改變 科技對日常生活的影響。 那樣的社會會是怎樣的? 我相信我們能讓它成真。 我相信我們能選擇我們的規則, 除了機器人之外, 我們也能設計我們自己的人類群體, 如果我們做到了, 世界會變得無比美好。
Thank you.
謝謝。
(Applause)
(掌聲)