Freeman: 我們正處於一個潛在的變革之中， Freeman: We are in the midst of a revolution so insidious,
而我們卻看不到變化。 we can't even see it.
機器人存在於我們身邊並為我們工作， Robots live and work beside us.
現在我們要讓它們學會思考自我。 And now we're designing them to think for themselves.
我們正在賦予它們憑借自己學習進步的力量。 We're giving them the power to learn to move on their own.
這些新的生命形式會變得 Will these new life-forms evolve
比我們更聰明更能幹嗎？ to be smarter and more capable than us?
還是我們會和機械融為一體？ Or will we choose to merge with the machines?
機器人是人類進化的未來嗎？ Are robots the future of human evolution?
與摩根弗裏曼穿越蟲洞 Through The Wormhole
空間、時間、生命本身， Space, time, life itself.
宇宙的奧秘盡在穿越蟲洞。 The secrets of the cosmos lie through the wormhole.
-- Captions by vitac -- www.Vitac.Com
Captions Paid For By Discovery Communications
Translated by Allegro1756 Sync & Corrected: iscol.
我們人類喜歡認為自己 We humans like to think of ourselves
處於進化的頂端。 as the pinnacle of evolution.
我們確實是地球上最聰明的、最具適應力的生命形式。 We are the smartest, most adaptable form of life on earth.
我們已經重塑這個世界來滿足我們的需要。 We have reshaped the world to suit our needs.
就像智人替代了直立猿人， But just as homo sapiens replaced homo erectus,
必然會有什麼將會替代我們。 it's inevitable something will replace us.
但假若我們能夠自己制造繼承人呢？ What if we're building our own successors?
就像我們學習走動、思考、感受自我一樣， Just as we learn to move, think, and feel for ourselves,
我們現在正在給予機器人同樣的能力。 we're now giving robots those same powers.
這將會把我們帶到什麼樣的境地？ Where will this lead?
這是人類將會轉變的形式嗎？ Is this what humanity will become?
少年時，我用一些備用部件造了一輛自行車。 When I was a teenager, I built a bicycle from spare parts.
我造的自行車平衡性非常好， My bicycle was so well-balanced,
我可以推動它就保持平衡而不需要騎在上面。 I could jog alongside it without holding onto it.
沒人認為這怎麼厲害，但卻使我思考 Nobody was that impressed, but it made me think.
我們有一天會擁有可以 Would we one day have machines
完全憑借自身行動的機器嗎？ that truly could move on their own?
那個時候它們還會需要我們嗎？ Would they even need us anymore?
劍橋大學的Daniel Wolpert Daniel Wolpert of the university of Cambridge
認為如果機器人 believes that if robots
是人類進化的未來， are to be the future of human evolution,
它們就必須像我們一樣學習行動... they're going to have to learn to move as well as we do...
因為行動是我們強大智慧的 Because movement is the supreme achievement
最高成就。 of our powerful intellect.
我認為我們能夠提出的最基本的問題是， The most fundamental question I think we can ever ask is,
為什麼以及何時動物進化出了腦？ why and when have animals ever evolved a brain?
現在，當我問我的學生這個問題時， Now, when I ask my students this question,
他們會告訴我， they'll tell me,
我們用大腦來思考或感知世界， we have ones to think or to perceive the world,
但這是完全錯誤的。 and that's completely wrong.
我們只因為一個原因而具有大腦， We have a brain for one reason and one reason only,
那就是要產生能夠適應環境的複雜行為， and that's to produce adaptable and complex movement,
因為行為才是我們具有的 because movement is the only way we have
唯一可以影響世界的方式。 of affecting the world around us.
Freeman: 我們大腦的各種智力 Freeman: All of our brains' intellectual capacity
都源於一個原始的動機-- grew from one primal motivation --
學得如何更加擅長行動， to learn how to move better.
這包括我們用兩條腿行走的能力， It was our ability to walk on two legs,
使用複雜的面部表情交談和表露情感， to speak and emote with complex facial movements,
並靈巧的控制四肢 and to manipulate our dexterous limbs
使人類站在食物鏈頂端。 that put humans on top of the food chain.
如果沒有行動的能力， There can be no value
感知、情感和思考 to perception or emotions or thinking
都沒有意義。 without the ability to act.
所有的其他特征， All those other features,
比如記憶、認知、愛、恐懼都要發展為行為， like memory, cognition, love, fear play into movement,
這些都是大腦輸出的最終結果。 which is the final output of the brain.
Freeman: 沒有任何機器可以做出 Freeman: No machine could handle
像我們每天做的多種複雜行為。 the huge variety of complex movements we perform every day.
想象一個機器人 Just imagine a robot
想要玩一場英格蘭最流行的消遣遊戲。 trying to play one of England's most famous pastimes.
盡管這次擊球看起來很簡單， So, although that shot looks simple
對於我來說毫不費力， and it felt effortless to me,
而我腦中進行的則是非常複雜的 the complexity of what's going on in my brain
肢體協調。 is really quite remarkable.
我必須注意投球手投出的球， I have to follow the ball as the bowler bowls it
並預測它會彈到哪裏， and predict where it's going to bounce
以及它如何從地面彈起。 and how it's gonna rise from the ground.
然後我需要作出決定 I then have to make a decision
我要如何去擊球， as to what type of shot I'm going to make.
最終，我需要協調600塊肌肉 And finally, I have to contract my 600 muscles
通過特定的順序來進行擊球動作。 in a particular sequence to execute the shot.
此時，每一塊肌肉的運動 Now, each of those components
都經過了複雜的數學運算， has real mathematical complexity,
而這種能力現今遠超所有的機器人。 which is currently beyond the ability of any robotic device.
Freeman: 讓機器人擁有 Freeman: One of the greatest challenges
我們這樣行為的最大挑戰之一 in getting robots to move like we do
就是要讓它們學會處理不確定因素-- is teaching them to deal with uncertainty --
而對於我們的大腦來說這是本能。 something our brains do intrinsically.
球不會以兩次相同的情況飛向你， A ball will never come at you the same way twice.
每次你都必須要迅速調整動作。 You must instantly adjust your swing each time.
問題是... The question is...
人腦如何處理這些不確定因素？ How does the human brain deal with all this uncertainty?
Daniel認為這需要一種概率估計理論 Daniel thinks it uses a theory of probability estimation
稱作貝葉斯推理， called bayesian inference 【貝葉斯推理是由英國牧師貝葉斯發現的一種歸納推理方法， 與經典的統計歸納推理方法相比， 貝葉斯推理在得出結論時不僅要根據當前所觀察到的樣本信息， 而且還要根據推理者過去有關的經驗和知識。】
才能進行運算。 to figure it out. 【貝葉斯推理是由英國牧師貝葉斯發現的一種歸納推理方法， 與經典的統計歸納推理方法相比， 貝葉斯推理在得出結論時不僅要根據當前所觀察到的樣本信息， 而且還要根據推理者過去有關的經驗和知識。】
Wolpert: 擊球手現在要做的很重要的事情 Wolpert: So, a critical thing the batsman now has to do
就是這個球會在哪裏彈起， is decide where this ball is going to bounce,
只要他們能夠准確擊球， so as they can prepare the correct shot,
就需要貝葉斯推理。 and for that, they need bayesian inference.
貝葉斯推理可以做的是 What bayesian inference is all about
從兩種不同的信息來源中 is deciding, optimally, the bounce location of the ball
決定球會彈起的最准確的位置。 from two different sources of information.
Freeman: 一個信息來源是很明顯的， Freeman: One source of information is obvious.
就是你盯著球。 You look at the ball.
Wolpert: 你可以通過觀察球飛來的 Wolpert: So, you can use vision of the trajectory of the ball
運動軌跡 as it comes in
來嘗試估計它會在哪裏彈起。 to try and estimate where it's going to bounce.
但是視覺並不完美， But vision is not perfect,
因為我們對視覺的加工存在變動， in that we have variability in our visual processors,
所以至少落地點的變動範圍就是紅色圓圈顯示的， so at least where distribution's shown in red here
作為球會落在某一點的概率分布。 as the probable bounce locations.
但貝葉斯規則說還有一個信息來源， But bayes' rule says there's another source of information.
就是關於落地點的先驗知識。 It's the prior knowledge about possible bounce locations. 【先驗知識 是先於經驗的知識】
如果你是個好擊球手， If you're a good batter,
你就能有效地看著投球手 then you can effectively look at the bowler
就能通過他特殊的投球方式或暗示了解到球的運行方式-- and maybe know by his particular bowling style or small cues --
這裏用藍色底紋表示-- and that's shown by the blue shading --
這是一個不同的區域。 which is a different area.
那麼貝葉斯推理就整合了 So, bayesian inference is a way of combining
紅色範圍和藍色範圍， this red distribution with the blue distribution,
你通過兩種信息的交集 and you do that by multiplying the numbers together in each
產生了這個黃色分布， to generate this yellow distribution,
這個分布稱為可信度。 which is termed the belief.
通過使用這樣的信息， And using that information,
擊球手現在就可以准備擊球了。 the batsman can now prepare his shot.
好吧，我應該躲開一點。 Okay, I should probably get out of the way.
Freeman: 擊球手的大腦，就像我們所有人， Freeman: The batsman's brain, like all of ours,
自動進行了這些計算。 is doing this math automatically.
我們這個物種錘煉出了對於行為的預測， We are a species that's honed for movement prediction.
這就使我們成為了 It's what has made us
這顆星球上最好的獵人和工具匠。 the planet's best hunters and toolmakers.
我們已經有了比我們行動 We already have robots that are faster and more accurate
更快更准確的機器人。 than we are.
但是我們要為它們的每個行為編程， But we have to program their every move.
機器人只能沿著我們已經走過的 For robots to walk down the evolutionary road
進化道路前進， we've already traveled,
它們要自己學會行動。 they're going to have to learn to move on their own.
然後會發生什麼呢？ What happens then?
它們會像我們這樣進化出複雜的大腦嗎？ Will they evolve complex brains like ours?
佛蒙特大學的機器人設計師Josh Bongard， Robot builders Josh Bongard of the university of Vermont
和康奈爾大學的Hod Lipson and Hod Lipson of Cornell university
正在嘗試回答這個問題。 are trying to answer that question.
我們與世界之間的聯系， Increasingly, we see that interaction with the world,
和物質世界之間的聯系 對於智力的發生發展很重要。 with the physical world, is important for intelligence.
你不能在一個罐子裏造一個大腦。 You can't just build a brain in a jar.
Freeman: Hod和Josh的目標就是制造一個機器 Freeman: Hod and Josh's goal is to build a machine
足夠聰明來學習自己如何移動。 that's smart enough to learn how to move around all by itself.
他們創造了一群形態奇怪的 They've created a menagerie of strange robotic forms
機器人。 along the way.
而他們的工作開始於一個可以 But their work starts with the computer program
使機器人身體進化的計算機程序。 designed to evolve robot bodies.
它會模擬不同的身體結構， It simulates various body plans
然後用多種方法讓這個身體運動。 and then tries various strategies to get them to move.
好的，那麼我們來經曆一次-- Okay, so, let's walk our way through --
不是雙關語--真實的進化模擬。 no pun intended -- an actual evolutionary simulation.
這個例子中， So, in this case,
我們告訴計算機我們想要一個兩條腿的機器人， we've told the computer that we want a robot that has two legs,
而計算機就需要指出 but we want the computer to figure out
這個兩條腿的機器人 how to get the robot
如何協調移動雙腿。 to orchestrate the movement of the robot's legs.
這裏，我們能看到有點令人驚訝的事， And here, we see something a little bit surprising,
這種進化並沒有找到我們使用兩條腿的行動方式， that evolution hasn't discovered the solution that we use.
有時這個進化進程 Sometimes when we run this evolutionary process,
會產生類似步行的行為， it produces something familiar like walking,
而其他例子中， 它產生的行為對於我們都很陌生， and in other cases, it produces something that's not familiar,
這些行為都不會發生在我們身上。 something we wouldn't have come up with on our own.
Freeman: 適者生存 Freeman: It's survival of the fittest
或至少不會那麼笨拙， or perhaps the least awkward.
就像大自然基於每一代的 Just as mother nature selects generations
生存能力選擇生物， based on their ability to survive,
這個模擬程序也是如此。 so does the simulation.
計算機刪除了那些 The computer deletes the robots
做的不好的機器人， that aren't doing a very good job,
並保留下了做的 and the computer then takes the robots
稍微好一點的機器人， that are doing a slightly better job
對它們進行改進， and makes modified copies of them
一次次重複這個過程。 and repeats this process over and over again.
這樣過了一會兒， 計算機開始發現一些 And after a while, the computer starts to discover robots
在這個例子中， 能夠從屏幕的左側 that, in this case, are able to walk
行走到屏幕右側的機器人。 from the left side of the screen to the right side of the screen.
Freeman: 這是一個加速的進化過程， Freeman: This is evolution on steroids.
大自然花去了數百萬年的時間， What took mother nature millions of years
而在計算機中只進行了幾個小時。 takes the computer just a few hours.
經過一夜，計算機測試了上千代機器人， Overnight, the computer tests thousands of generations,
最終產生了一個達到目標的機器人。 and eventually it produces a robot that meets the goal.
當這個模擬創造出了 When the simulation makes something
看起來非常有趣的東西時， that looks particularly interesting,
Hod和Josh就采用這種形體方案 並將其建造出來。 Hod and Josh take that body plan and build it.
現在他們能夠測試 Now they can test whether
計算機模擬中成功的行走方案 the strategies for moving learned in simulation
能否應用於現實世界中。 work as well in the real world.
這個機器人叫做quadratot， (非要翻譯的話可以理解為「正方形四腳機器人」) So, this robot is called the quadratot,
它基本上算是一個正在通過機器人進化技術 and it's basically a robot that learns how to walk
學習如何行走的機器人。 using evolutionary robotic techniques.
我們在這裏看到的 And so, what we can see here
就是這個機器人如何學習的例子。 is a particular example of how this robot learns.
這是它做出的最開始的步伐， This is one of the earliest gaits that it did,
我們能發現它移動的不是很遠很快。 and we can see that it's not moving very far or very fast.
有點像小孩， It's kind of like a child
很小的時候只會爬的動作。 during its very early behaviors of crawling.
它正在嘗試不同的方法， It's trying out different things.
一些更有利行走的， 或是不利於行走的。 Some things work better. Some things work less well.
它正在獲得經驗， 從中學習， And it's taking the experiences and learning from them
並逐漸改善自己的步伐。 and gradually improving its gait.
Freeman: 已經存在許多機器人可以通過 Freeman: There are many robots that can move well
預先編寫的特定程序完美運動， while executing a specific predesigned task.
但是Hod和Josh的機器人 But Hod and Josh's robots
則需要在新環境中 must start to learn by themselves from scratch
全靠自己開始學習。 in an unknown environment.
它能夠感覺到自己的進步， It can sense its own progress,
像嬰兒從爬行開始學習一樣， and like a baby learning to crawl,
通過每走一步以及每次跌倒， it becomes more aware of its body
它越來越能意識到自己的身體。 with every step and every tumble.
Hod和Josh認為這種自我認知 Hod and Josh believe this self-awareness
逐漸會構成意識的基礎形式。 gradually builds into a basic form of consciousness.
通常我們都會問到， Often, we've raised the question,
什麼是有意識的，什麼沒有？ is something conscious, or is it not?
但它的確不是一個黑白分明的東西， But it's really not a black-and-white thing.
它更像是關於 It's more about
一個實體能多大程度具有自我這一概念， to what degree an entity's able to conceive of itself,
多大程度的模擬自己、考慮自己 、 to simulate itself, to think about itself,
具有自我意識。 to be self-aware.
就像機器人通過多種複雜方式學習移動， As robots learn to move in more complex ways,
它們就有可能發展出和我們同等的意識， it's possible they will develop levels of consciousness
甚至超越於我們的意識。 equal to ours and maybe beyond.
不過根據一位科學家的說法， But according to one scientist,
一個機器人若真的擁有意識... for a robot to become truly conscious...
...它就必須具有情感。 ...it must develop feelings.
意識是什麼？ What is consciousness?
這要看你問誰了， The answer depends on who you talk to.
一位醫生對意識的定義可能會不同於牧師， A doctor's definition would be different from a priest's.
但我們都認為我們的高層次意識 But we all agree that our high-level consciousness
將我們與其他生物體區分開， is what separates us from other organisms
當然也與機器人區分開。 and, of course, from robots.
要用什麼辦法才能讓機器人擁有意識呢？ What would it take for robots to become conscious?
它們能通過單純的邏輯發展出意識嗎？ Can they get there on logic alone?
還是必須要學會感受？ Or must they also learn to feel?
來自伊利諾伊大學的Pentti Haikonen教授認為 Professor Pentti Haikonen from the university of Illinois
機器人只能在它們可以體驗感情後 believes machines will only become conscious
才能發展出意識。 when they can experience emotions.
他從很小的時候開始就有著這種觀點， It's a belief he has held since he was very young
也是他第一次思考意識的意義時。 when he first contemplated what it meant to be conscious.
還是4、5歲的樣子， 我站在家裏的廚房中， When I was 4 or 5 years old, I was standing in our kitchen,
突然被一種神秘的存在感驚呆了... and suddenly I was struck by the mystery of existence...
...為什麼我會是我， ...how and why I was me
而不是我姐姐或哥哥， and why I was not my sister or my brother.
我是如何進到我的身體中的？ How did I get inside myself?
Freeman: 隨著他的長大， Freeman: As he got older,
Pentti意識到使他感受到意識的 Pentti realized that what made him feel conscious
就是他腦中 of being inside his head
對於身邊世界中人和物的 were his emotional reactions to the people and objects
各種情緒反應。 in the world around him.
我們意識背後的神經活動 The neural processes that are behind our consciousness
發生於我們的腦內， take place inside our brain,
但是我們並不以這種方式看世界。 but we don't see things that way.
舉個例子，當你紮破你的手指時， For instance, when you cut your finger,
手指的疼痛感就這麼出現了， the pain is in the finger or so it appears,
但實際上疼痛感在這裏。 but, actually, the pain is in here.
有了感受才意味著意識。 To feel is to be conscious.
Freeman: 我們的大腦對於周圍世界的 Freeman: Our brain's raw experience of the world around us
原始感受只是通過我們的感覺產生的 is just a series of electrical impulses
一系列的電脈沖， generated by our senses.
只是我們把這些電脈沖與相關的情感 However, we translate these impulses into mental images
轉譯成了心理表象。 by making emotional associations with them.
令人愉悅的聲音， A sound is pleasing.
平靜安詳的景象。 A view is peaceful.
根據Pentti的理解，意識只是 Consciousness, according to Pentti,
承載著多種心理表象的馬賽克。 is just a rich mosaic of emotionally laden mental images.
他認為要使得一個機器人真正具有意識， He believes that to have a truly conscious machine,
你就必須賦予它們 you must give it the power
將感官數據與情緒聯系起來的能力。 to associate sensory data with emotions.
而這個機器人，它已經具有了這種能力。 And in this robot, he has begun that process.
這是第一個機器人 This is the first robot
可以使用這種神經網絡聯系， that utilizes associative neural networks.
這和我們人類的學習方式有些類似。 It is the same kind of learning that we humans use.
當我們看到或聽到一些東西時， When we see and hear something,
我們會把這兩種感覺聯系起來， we make a connection between those things,
之後的日子中如果我們 看到或者聽到同樣的事情， and later on when we see or hear the other thing,
我們就會感受到相對應的另一種感覺。 the other thing comes to our mind.
Freeman: xrc-1機器人像我們一樣 Freeman: The xcr-1 experiences the world
直接感受這個世界。 directly through its senses like we do.
它有一些基本的觸覺、視覺和聽覺。 On board are the basics of touch, sight, and sound.
Pentti已經開始賦予其情感 Pentti has begun the process of giving it emotional associations
與特定感知數據的聯系， to specific sensory data,
比如綠色。 like the color green.
Pentti在這個機器人前面放置一個綠色物體， Pentti places a green object in front of the robot,
它能夠識別。 which it recognizes.
綠色。 Green.
然後他給它一個不好的聯系-- Then he gives green a bad association --
從後側猛擊它。 a smack on the backside.
這種對於聯系的學習就像小孩子一樣。 The associative learning is similar to little children.
疼。 Hurt.
你可以告訴孩子這個不好或這是好的， And you say that this is not good or this is good,
或者揍孩子。 or you may also smack the little child.
疼。 Hurt.
當然我並不建議這麼做。 I don't recommend that.
綠色，不好。 Green bad.
機器人對於綠色物體的心理意象 Freeman: The robot's mental image of the green object
現在聯系上了不好的情緒。 is now associated with the emotion bad.
從現在開始，它會一直避著綠色瓶子。 And from now on, it will avoid the green bottle.
但這並不是xrc-1所有的學習部分， But it's not all pain for the xcr-1.
就像我們教機器人 Just like we teach the robot
把疼痛聯系上了綠色物體， to associate pain with the green object,
我們也能夠教機器人 we can teach the robot
把愉快和其他物體聯系起來， to associate, also, pleasure with objects,
這個例子中是一個藍色物體。 in this case with the blue object, like this.
藍色。 Blue.
Freeman: 為了讓它把藍色聯系上好的感覺， Freeman: To give blue a good association,
Pentti輕輕撫摸機器人頂部。 Pentti gently strokes the top of the robot.
藍色，好。 Blue good.
這個簡單的實驗證明了 This simple experiment demonstrates
這個機器人具有對物體的心理意象 that this robot has mental images of objects
和心理內容。 and mental content.
Freeman: 這還是它發展成長的早期， Freeman: It's still early in its development,
但xcr-1已經學會了基本的情緒反應， but the xcr-1 has learned the basics of emotional reaction
從害怕... from fear...
綠色，不好。 Green bad.
到渴望。 ...to desire.
藍色，好。 Blue good.
U+266A Now's my time for love U+266A
U+266A lonely moments seem to... U+266A
隨著xrc-1更高級的版本中 As a more advanced version of the xcr-1
存儲著心理意象的記憶... fills its memory with mental images...
牙醫，不好。 Dentist bad.
...它就開始能夠 ...it will start to be able
憑借自己與新環境互動， to react to new situations on its own
最終會像所有具有情緒波動的生物一樣 and eventually experience the world
體驗這個世界。 much like any emotionally-driven being.
我最大的夢想就是 It is my great dream
我建造的機器人有一天能問 to build robot that is one day able to ask,
「我是如何成為自己的呢？」 "How did I get inside myself?"
Freeman: 當機器人到達這種程度時， Freeman: Once robots reach this point,
什麼還能阻止它們進步， what's to stop them from moving on
變成與我們不同的卻有意識的東西？ and becoming conscious of things we're not?
這個人認為機器人是人類的未來， This man thinks robots will become the future of humanity
因為它們具有我們缺乏的東西。 because they'll have something we lack.
在最後一個說出"Eureka"的人類之後 Their brains will have the capacity for genius
它們仍然會表現得非常天才。 long after the last human ever says "Eureka."
阿基米德第一次在浴缸中喊出了Eureka， （因為發現浮力原理） for Archimedes, Eureka happened in the bathtub.
愛因斯坦在乘坐電車時想到了相對論， Einstein was riding a streetcar when relativity dawned on him.
這些傑出的思維都曾閃現出靈感， These brilliant minds had a flash of inspiration
推動了全人類的進步。 and drove all of humanity forward.
但是今天的科學問題， But the scientific questions of today,
比如探索亞原子粒子的真相， probing shoals of subatomic particles
以及我們大量的遺傳密碼， and our vast genetic code,
這些問題如此複雜， have become so complex
需要數以千計的研究者團隊 that they take teams of thousands of researchers
來解決這些問題。 to solve.
單一科學天才的時代結束了嗎？ Is the age of the single scientific genius over?
但如果機器人能夠解決問題呢？ Not if machines have their way.
數據科學家Michael Schmidt Data scientist Michael Schmidt
認為這個世界充滿著複雜的美-- sees the world filled with intricate beauty --
玫瑰花開， the flowering of a rose,
葉脈伸展， the veins branching on a leaf,
黃蜂飛舞。 the flight of a Bumblebee.
在這些大自然奇跡的背後 But below the surface of nature's wonders,
Michael看到了埋藏的 Michael also sees a treasure trove
人們尚未知曉的數學複雜性。 of uncharted mathematical complexity.
Schmidt: 我很喜歡來這裏，大自然是很美妙的。 Schmidt: Well, I love coming out here. Nature is beautiful.
這裏的每株植物、每只蜜蜂、以及這個花園的 There are equations hidden in every plant and every bee
生態系統中都隱藏著許多數學方程式。 and the ecosystems involved in this garden.
有一些科學指出了 And part of science is figuring out
這些事情是如何進行的。 what causes those things to happen.
Freeman: 科學是我們理解大自然的努力， Freeman: Science is our effort to make sense of nature,
這樣的追求造就了許多非常著名的發現。 and this quest has given us some very famous discoveries.
在牛頓時期， In Newton's time,
他發現了非常重要的物理定律， he was able to figure out a very important rule in physics,
即萬有引力定律， which is the law of gravity.
它可以預測這個蘋果如何墜落， It predicts how this apple falls
以及施加到這個蘋果上的各種力。 and the forces that act upon this apple.
時至今日，我們仍對類似的問題感興趣， Today in science, we're interested in similar problems
不僅僅是蘋果如何掉落， but not just about how the apple falls
還有在我們身邊世界中遵從 but the massive complexity that follows
這種簡單動態的大量複雜性。 from this very simple dynamic to the world around us.
舉個例子，當我扔掉這個蘋果， For example, when I drop this apple,
蘋果砸在地上會激起灰塵， the apple stirs up dust.
灰塵會砸在花上， This dust could hit a flower,
蜜蜂受驚，這朵花通過其授粉的可能性就低。 and a bee may be less likely to pollinate that flower.
這個花園中的整個生態系統 And the entire ecosystem in this garden
可能因為單一事件而發生急劇變化。 could change dramatically from that single event.
Freeman: 科學家已經知道了四種基本自然力， Freeman: Scientists understand the basic forces of nature,
但是在真實世界中 but making precise predictions
通過這種驚人的複雜性 about what will happen in the real world
來准確預測會發生什麼 with its staggering complexity
足以使人類大腦崩潰。 is overwhelming to the human mind.
人類想要理解並計算出 So, one of the reasons why it's extremely difficult
自然定律以及公式是極為困難的， for humans to understand and figure out
原因之一就是 the equations and the laws of nature
參與其中的變量的數量。 is literally the number of variables that are at play.
影響一個系統的變量 There could be thousands of variables
會有數千個， that influence a system
而我們才剛開始梳理它們。 that we're only just beginning to tease apart.
實際中，這些公式數量很多， In fact, there are so many of these equations,
我們永遠不可能將它們全部分析， we'll never be able to finish analyzing them
如果用手算的話。 if we do it by hand.
Freeman: 2006年， Freeman: In 2006,
Michael著手開發智能計算機軟件， Michael began developing intelligent computer software
這個軟件可以觀察複雜的自然系統 that could observe complex natural systems
從看起來像是混亂的事件中得到意義。 and derive meaning from what seems like chaos.
這是一個雙擺， So, what I have here is a double pendulum.
你可以看到它只有兩個臂， If you look at it, it consists of two arms.
一個臂的頂部可繞軸運動， One arm swings along the top axis,
第二個臂的一端連接在第一個臂的尾部， and the second arm is attached to the bottom of the first arm,
這兩個擺臂掛在一起， and it's two pendulums that are hooked together,
一個位於另一個的末端。 one pendulum at the end of the other.
現在這個雙擺就是複雜性的很好的例子， Now, the pendulum is a great example of complexity
因為它能夠表現出一些最複雜的行為， because it exhibits some of the most complex behavior
我們稱之為混沌。 that we're aware of, which is called chaos.
所以當你從這類設備中收集數據時， So, when you collect data from this sort of device,
它的行為看起來幾乎是完全隨機的， it looks almost completely random,
不會出現任何類型的固定模式。 and there doesn't appear to be any sort of pattern.
但由於這是一個確定的物理系統， But because this is a physical deterministic system,
就存在一個運動規則。 a pattern does exist.
Freeman: 在雙擺的混沌運動中尋找固定模式 Freeman: Finding a pattern amidst the chaos of the double pendulum
幾十年來一直困擾著科學家。 has stumped scientists for decades.
但這時Michael突現靈感， But then Michael had a flash of inspiration.
為什麼不以大自然創造我們的方式來想辦法呢， Why not grow new ideas the same way nature created us,
通過進化？ using evolution?
他稱自己的程序為Eureka， He called his program Eureka.
Eureka開始於充斥著隨機公式的原始湯， Eureka starts with a primordial soup of random equations
然後檢查這些公式看哪些 and checks how closely they fit
更接近於描述雙擺的運動方式。 the behavior of the double pendulum.
不合理的公式就會被電腦「殺掉」， If they don't fit, the computer kills them.
如果接近，計算機就允許它們進入下一代， If they do, the computer moves them into the next generation,
在這一代中它們發生突變， 並試圖變得更符合對雙擺運動的描述。 where they mutate and try to get an even closer fit.
最終，就會產生一個公式贏家， Eventually, a winning equation emerges,
阿基米德也會引以為傲。 one that Archimedes would be proud of.
Eureka！ Eureka!
Schmidt: 現在我正在進行著我們的算法， Schmidt: And I'm running our algorithm now.
左邊窗口中的是公式列表， On the left pane are the lists of the equations
都是Eureka想出的解決雙擺運動的公式。 that Eureka has thought up for this double pendulum.
再往前，我們能看到公式變得複雜了， Walking up, we can see we increase the complexity,
同時我們也增加了與收集到的數據的一致性， and we're also increasing the agreement with the data.
隨著進化，我們往上看， And eventually, as you go up,
你就能得到與數據極為一致的公式， you start to get an extremely close agreement with the data,
最終你就能得到一個真相， and eventually you snap on to a truth
它在描述雙擺運動的准確性上有了很大的提升。 where you get a large improvement in the accuracy.
我們就能看到這裏產生的一個公式， And we can actually look in here and see exactly what pops out.
比方說在這裏你能看到一個9.8， For example here, you might notice we have a 9.8,
如果你還記得你的物理課的內容， and if you remember from physics courses,
這個數字正是地球的重力系數(重力加速度)。 that is the coefficient of gravity on earth.
重要的是這裏的 What's very important is the difference
雙擺的兩個不同的擺動角度， between the two angles of the double pendulum.
這樣就產生了。 This pops out.
實質上，我們通過這個軟件 Essentially, we've used this software
以及為了模擬混沌來收集的數據， and the data we've collected to model chaos,
直接從這些數據中梳理得到了解答。 and we've teased out the solution directly from the data.
Freeman: Eureka不僅發現了 Freeman: Eureka has not only discovered
描述雙擺運動的公式， a single equation to explain how a double pendulum moves.
它還為看起來像是混沌的事件賦予了含義-- It has found meaning in what looks like chaos --
在這之前從未有人類或機器人成功過。 something no human or machine has done before.
Schmidt: 我們可以收集整個數據集， Schmidt: So, we could collect an entirely new data set,
再次運行這個程序， run this process again,
盡管這些數據完全不同-- and even though the data is completely different --
我們看到的運動方式是不同的-- we could have different observations --
我們仍然可以辨認出隱藏的真相， we can still identify the underlying truth,
隱藏的模式，就是這個公式。 the underlying pattern, which is this equation.
Freeman: 對於Michael來說，未來的科學探索 Freeman: To Michael, the future of scientific exploration
並不在於我們的頭腦中， isn't inside our heads.
而在於機器中。 It's inside machines.
它們能否看著一些數據 Whether they're looking at patterns of data
來自於遺傳學、粒子物理以及氣象學， from genetics, particle physics, or meteorology,
像Eureka這樣的程序就能按需要發展出靈感， programs like Eureka can evolve inspiration on demand,
讓它們發現大自然的基本真理， finding basic truths about nature
而這些人類都沒能做到。 that no human ever could.
我們即將到達這種， We're gonna reach a point
我們可以決定我們想要發現什麼的水平， where we decide what we want to discover
我們讓機器幫我們搞清事實。 and we let the machines figure this out for us.
Eureka能夠取得這些發現 Eureka can find these relationships
而不會帶有人類的偏見與限制。 without human bias and without human limitations.
我們創造了為我們服務的機器人， We created robots to serve us.
隨著它們學著自己的運動、感受和思考方式， As the machines learn their own ways to move, feel, and think,
它們最終會突破這種角色。 they will eventually grow out of that role.
那麼機器人能否共同工作呢？ What if they start working together?
它們能否建立一個社會， Could they build their own society,
機器人為機器人自己建立起來的社會？ one made by the robots for the robots?
地球上沒有比我們更成功的物種， There's no species on earth more successful than us.
這是由於 We owe that success
我們頭腦中有台強大的「計算機」， to the powerful computer inside our heads.
但征服地球用到的不僅是一個個體的腦， But it takes more than one brain to conquer a planet.
人類能夠繁榮是因為我們學會了 Homo sapiens thrive because we have learned
將這些「計算機」組成「社會」共同工作。 to make those computers work together as a society.
如果機器人也共同思考會什麼樣呢？ What will happen when robots put their heads together?
白天他是機器人學家， 晚上則是美食大廚， Roboticist by day and gourmet chef by night,
弗吉尼亞理工學院的Dennis Hong教授 Professor Dennis Hong of Virginia tech
是制造可合作機器人的專家， is a specialist in building cooperative robots.
但他也在實驗室外搜尋合作的例子。 But he also sees cooperation outside the lab.
我們通常不會專門考慮這些， So, we don't really think about it,
但日常生活中處處都涉及了合作。 but everything in our daily lives involves cooperation.
比方說，烹飪，通常認為是個體行為， For example, cooking oftentimes is thought of as a solo act,
但你是否考慮過這涉及了很多人， but if you think about it, a lot of people are involved
而且需要大量的細心協調 and a lot of careful coordination is required
才能做出美食。 to make it happen.
哦，Charli，謝謝你。 Oh, thank you, Charli.
拿個西紅柿作為例子， Take this tomato as an example.
這個西紅柿的產生開始於一粒種子， This tomato most likely started its life as a seed,
這需要育種人員 where a group of breeders
選擇攜帶優勢基因的種子， need to choose the right sequence of genes
這樣的西紅柿滾圓多汁美味。 for a plump, juicy, tasty tomato.
然後這些種子會被種植，自身生長，最後收獲， The seeds needed to be planted, grown, harvested,
接著西紅柿就要送去市場。 then the tomatoes needed to get to the market.
Freeman: 糧食生產是一個複雜的合作網絡， Freeman: Food production is a complex web of coordination.
雖然這樣已經很好了， But as good as it is,
但人類的合作是有限度的。 human cooperation has its limits.
Hong: 哎呀。 Hong: Oops.
Freeman: 每天，就像大多數人一樣， Freeman: Every day, like most of us,
Dennis就要面對人類 Dennis has to contend with the prime example
錯誤合作的第一個例子-- of human cooperation gone wrong --
交通。 traffic.
問題在於，我們作為人類， The problem is, us being human,
我們都需要、想要盡快到達我們的目的地， we all need to, want to get to our destination
這樣就造成了交通堵塞。 as quick as possible, thus we have traffic jams.
Freeman: 如果沒有交通燈， Freeman: If it wasn't for traffic lights,
實際生活中它們也是很簡單的機器人， which are, in reality, very simple robots,
我們就幾乎不可能到達任何地方。 it will be almost impossible to get anywhere.
這些交通燈之間互相交談， These traffic lights, they talk to each other.
它們與其他信號交叉口的 They communicate with other traffic lights
交通燈交流， at other intersections.
另外它們有攝像頭， And they have cameras,
這樣就能看到交通模式， so they actually see the traffic patterns
並為我們人類做決定。 and make decisions for us, for humans.
哦，是這樣啊。 Oh, there you go.
謝謝你，交通燈。 Thank you, traffic light.
Freeman: 交通是一個煩人的事， Freeman: Traffic is a nuisance.
不過人類其他的失敗合作 But other failures of human cooperation
後果更為嚴重... are much more serious...
通常都是致命的。 And often deadly.
[機槍射擊] [ Machine-gun fire ]
Dennis認為一個機器人社會 Dennis believes a society of robots
會比我們的社會更好的合作。 can be much better collaborators than we are.
Hong與賓夕法尼亞大學的 So, in collaboration with Daniel Lee
Daniel Lee合作， at the university of Pennsylvania,
設計出了一組可以進行 機器人世界杯的機器人， he designed a group of robots to compete in the robocup,
也就是機器人足球錦標賽。 an international robotic soccer championship.
機器人世界杯是一項自主式機器人進行的足球比賽， Robocup is an autonomous robot soccer competition,
這就是說你有一個機器人足球隊， which means that you have a team of robots,
然後你按下「開始」， 接著人們就不需要再管它們了， you press "Start," And then nobody touches anything.
這些機器人就要查看四周，尋找足球在哪裏， And the robots need to look around, see where the ball is,
它們需要互相配合，這樣進行一場足球比賽。 need to coordinate and actually play a game of soccer.
Freeman: Dennis的稱作Darwin-op的 機器人足球運動員， Freeman: Dennis' soccer robots, called Darwin-op,
它們是完全自主的。 are fully autonomous.
它們通過複雜的傳感器和軟件 They use complex sensors and software
遊走於賽場之上， to navigate the playing field.
此外它們比人類的足球運動員 And they have a serious competitive edge
更具有競爭優勢， over their human counterparts.
機器人隊員之間可以互相讀取思維， Teammates can read each other's minds.
這樣，如果你看人類足球運動員的比賽， So, if you look at human soccer players,
很明顯他們很擅長做自己的事， obviously they're great at what they do.
他們有時通過大聲喊叫互相交流， They communicate sometimes by shouting,
有時通過小手勢， sometimes by a subtle gesture,
但是，這仍不是非常精確， but, again, it's not really accurate,
無法同時或及時 and they cannot share all the information together
共享信息， at the same time in real time,
但機器人就可以做到。 but robots can do that.
Freeman: 每個機器人都即時知道所有機器人的 Freeman: Each robot knows the exact location and destination
確切的位置和將會到達的目的地， of the other robots at all times.
它們可以調整自己的策略， They can adjust their strategy
必要的話甚至是它們的角色。 and even their roles as necessary.
Hong：由於球在哪裏，哪裏就有機會， Hong: Depending on where the ball is, where the opponents are,
它們指導性的變換角色， they didactically switch their roles.
守門員可以成為前鋒， So the goalie becomes a striker,
前鋒又可以變成守門員或後衛。 a striker becomes a goalie or defense.
Freeman: 它們可能不會像貝利那樣敏捷， 也可能不像貝克漢姆那樣踢球， Freeman: They may not be as agile as PelU+00E9 or bend it like Beckham,
但它們能夠帶球過人， but they are able to dribble past their opponents,
傳球，得分... pass the ball, score a goal...
甚至歡呼。 And even celebrate.
Dennis認為機器人世界杯 Dennis believes robocup
正是機器人社會的開始， is just the beginning of robot societies.
Dennis想象了一個思考機器相互聯系的團體， Dennis imagines a connected community of thinking machines
它會遠比人類社會 that would be far more sophisticated
更為精密複雜。 than human communities.
他稱之為雲機器人。 He calls it cloud robotics.
Hong: 雲機器人是智能的共享網絡， Hong: Cloud robotics is a shared network of intelligence.
有些類似於我們人類的「常識」， It's similar to what we call common sense in humans.
在這些小機器人進行的 So, just like those smaller robots
機器人世界杯足球比賽中， that play soccer for robocup,
它們共享公用數據、團隊數據來實現目標， they share a common data, team data, to achieve the goal,
這個例子中的目標就是贏得足球比賽。 in this case, winning the soccer game.
而對於雲機器人， For cloud robotics,
就算世界上最遠的角落裏的機器人 robots from the furthest corners in the world,
都可以連接上雲服務器， they can all connect to the cloud
共享信息和智能來做自己的工作。 and share information and intelligence to do their job.
Freeman: 人類花去了一生的時間來掌握知識， Freeman: Humans spend a lifetime mastering knowledge,
機器人卻可以在幾微秒之內完成學習。 but future robots could learn it all in microseconds.
它們可以用基於人類社會的 They could create their own hyper-connected network
同樣的合作精神創建 using the same spirit of cooperation
屬於自己的超連接網絡， that built human society
卻不會產生像阻礙人類發展的自私和貪婪。 without the selfishness and greed that hold us back.
機器人的運作受限於明確的規則， Robots operate by a very well-defined set of rules.
不會像人類這樣總是違背規則。 The human impetus to break them is just not there.
Freeman: 機器人已經知道了如何互相交談， Freeman: Robots already know how to talk to one another.
但現在柏林的一位科學家， But now a scientist in Berlin
已經將機器人之間的交流發展得更加深入。 has taken robotic communication a step further.
他的機器人正用著他也不了解的語言交談。 His machines are speaking a language he doesn't understand.
Motakay tokima.
你知道我剛才說了什麼嗎？ Did you understand what I just said?
你當然不知道， Of course you didn't
因為我說的不是任何一種已知的人類語言， because I wasn't speaking any known human language.
但它不是無意義的， But it wasn't nonsense.
這是一種機器人語言。 It was a robot language.
我們人類花了上千年 We humans took tens of thousands of years
才發展出了我們交流的複雜的方法。 to develop our complex means of communication.
現在機器人跟隨者人類的指引， Now robots are following our lead,
以光速發展出語言。 and they're doing it at light speed.
很快有一天， Someday soon,
機器人也許會把我們人類排斥於交談之外。 robots may decide to exclude us from their conversation.
機器人: Tokima， Robot: Tokima.
Lucabo.
Miyoto.
Motakay.
Tokima, kymamu.
Tokima.
Simeta.
Tokima.
Motakay.
Steels: 如果沒有語言， Steels: Without language,
我們人類就無法成就今天， our species would never be where it is today.
語言是人類創造出來的 It's the most magnificent thing
最傑出的東西。 that has ever been created by humanity.
如果你審視我們自己， If you look at ourselves,
很明顯，沒有語言， then it's pretty clear that without language,
我們就不可能 we would not be able
成就今天的一切。 to do the kinds of things that we're doing.
Freeman: Luc Steels是一位研究人工智能的教授， Freeman: Luc Steels, a Professor of artificial intelligence,
他認為語言是機器人 sees language as the key
發展出真正智能的關鍵。 to developing true robot intelligence.
Steels: 我一直在嘗試理解的是 Steels: What I'm trying to understand is,
我們應該如何生成這種 how can we synthesize this process
能夠在一個機器人或一群機器人中 so that we can start up a kind of evolution in a robot
開始的一種進化過程， or in a population of robots
而這個過程可以產生 that will also lead to the growth
類似於我們的溝通系統。 of a rich communication system like we have.
Freeman: 機器人已經可以互相交流， Freeman: Machines already communicate with each other,
但這要基於 but these are based
人類預先給定的代碼語言。 on predetermined, human-coded languages.
Luc想要知道 Luc wants to know
如果給機器人用自己創造的語言交流的機會， how future robot societies might communicate
那未來的機器人社會會是什麼樣的。 given the chance to make a language on their own.
Luc賦予了他的機器人基本的語言成分， Luc gives his robots the basic ingredients of language,
比如可能會用到的聲音， like potential sounds to use,
以及將它們連接起來的可能的方式， and possible ways to join them together.
但機器人說什麼還要取決於它們自己。 But what the robots say is up to them.
我們加入了學習機制， We put in learning mechanisms,
還有發明機制， we put in invention mechanisms,
不同機制的加入可以允許它們整理出自己的語言。 mechanisms so that they can coordinate their language.
它們可以協商如何說話， They can kind of negotiate how they're gonna speak,
但我們不會加入我們的語言或概念。 but we don't put in our language or our concepts.
Freeman: 這還不足以讓機器人知道如何說話， Freeman: It's not enough for the robots to know how to speak.
它們還需要一些談論的東西。 They need to have something to speak about.
luc的下一步 Luc's next step
就是要教機器人如何認識自己的身體。 is to teach the robots how to recognize their own bodies.
Steels: 為了學習語言， Steels: In order to learn language,
你就需要學習自己的身體結構， you actually have to learn about your own body
以及自己身體的行動方式。 and the movements of your own body.
這裏你看到的是 So, what you see here is an internal model
機器人的內部模型， that the robot is building of itself.
這個機器人正在學習 This robot is trying to learn here,
一種聯系， is the relationship
介於這些不同的感覺通道 between all these different sensory channels
與自身運動指令間的關系。 and its own motor commands.
Freeman: 隨著機器人看著鏡子中運動的自己， Freeman: As a robot watches itself move in the mirror,
它形成了自身四肢和關鍵的三維模型。 it forms a 3-d model of its limbs and joints.
它存儲了這種感覺信息的記憶， It stores this information in sense memory
現在已經准備和另一個機器人談論行為了。 and is now ready to talk to another robot about movement.
Tokima.
那麼現在這個機器人正在講話， So, now, this robot is talking.
他正在讓對方做一個行為， He's asking an action.
這個機器人正在做的就是--伸展手臂， This robot is doing -- you know, stretching the arm.
可這不是他要求的動作， No, this is not what was requested,
他再次展示什麼才是正確的動作。 and he's showing again what the right action is.
Freeman: 她的第一次嘗試並沒有成功， Freeman: She's unsuccessful in her first attempt,
但最終這個機器人學會了 but eventually the robot learns
「Tokima」意思是「舉起兩只手臂」， that "Tokima" Means "Raise two arms."
在嘗試不同的詞彙重複進行學習過程後， after repeating this process with different words,
他們又試了一次。 they try again.
Homakey.
另一個請求， Another request.
他正在做這個詞代表的行為。 He's doing the action.
是的，這是正確的動作。 Yes, this is the right kind of action.
那麼換句話說， So, in other words,
這個機器人已經從對方那裏學會了這個詞， this robot has learned the word from the other one
反過來也一樣。 and vice versa.
他們現在有一種方法來談論動作了。 They now have a way to talk about actions.
Freeman: 機器人語言已經發展的很完善了， Freeman: The robots' language is already so well-developed,
它們甚至可以教Luc機器人語言。 they can teach it to Luc.
我們來看看他要讓我做什麼。 Let's see what, you know, what he asks me to do.
Motakay.
好的，Motakay。 Okay, motakay.
不對，這不正確， 所以他開始向我展示正確動作。 No, this is not right, so he's showing it to me.
好吧，我現在正在學習這個動作。 Okay, I'm learning this gesture now.
Motakay.
這就是Motakay， Motakay is this.
好的，這次我做對了。 Okay, I got it right.
現在我就要他一起擺姿勢。 So, now I'm going to use the gesture with him.
Motakay。 Motakay.
好的，你正在做的是正確的。 Okay, yes, you're doing the right thing.
謝謝。 Thank you.
隨著機器人重複這一過程， As the robots repeat this process,
他們產生了對於其他機器人 they generate words and actions
具有真實意義的詞彙和行為， that have real meanings for one another.
這樣機器人的詞彙量就開始增加。 And so the robots' vocabulary grows.
他們創造出的每一個新詞 Every new word they create
都是我們無法理解的。 is one more that we can't understand.
機器人把我們 Is it only a matter of time
徹底排除在談話之外只是時間問題嗎？ before they lock us out of the conversation completely?
Steels: 我認為這是完全有可能的， Steels: And I think it's actually totally possible,
社會需要尋找一個平衡點， but society will kind of have to find the balance
介於我們想要機器人去做什麼 between what it is that we want robots for
與我們願意給它們多少自由之間。 and how much autonomy are we willing to give them.
Freeman: 如果我們允許機器人任意行動， Freeman: If we're giving robots autonomy to move,
去感受，去創造自己的語言， to feel, to make their own language,
這就足以超越我們嗎? could that be enough for them to surpass us?
畢竟，什麼才會使機器人「滅絕」呢？ After all, what's robot for "Exterminate"?
但這位日本科學家 But one Japanese scientist
不認為未來人類會與機器人對抗， doesn't see the future as robots versus humans.
實際上，他正在有意構建人類與機器人的交叉點。 In fact, he is purposefully engineering their intersection.
我們知道智人並不是人類進化的結局， We know that homo sapiens cannot be the end of evolution.
但我們的後代會仍以生物形式進化， 還是會成為機械人？ But will our descendents be biological or mechanical?
一些人認為具有智慧的機器人 Some believe that intelligent machines
最終會成為主宰地球的生物， will eventually become the dominant creatures on earth.
但下一步的進化， But the next evolutionary step
可能不是機器人取代了人類， may not be robot replacing human.
而是一種融合人類與機器的新型生命形式。 It could be a life-form that fuses man and machine.
他就是山海嘉之。 This is Yoshiyuki Sankai.
受到像艾薩克·阿西莫夫這樣作者的影響， Inspired by authors like Isaac Asimov,
他一直夢想著將人類和機器人這兩種生命形式融合， he has always dreamed of fusing human and robotic life-forms
成為他稱為的「混合輔助肢體系統」... into something he calls the hybrid assistive limb system...
或簡稱H.A.L. Or H.A.L.
山海：這是我的夢想之一， Sankai: That one is one of my dreams.
我們能夠開發出這類設備， We could develop such kind of devices,
就像H.A.L系統機器服， like a robot suit h.A.L. System,
來協助人類以及人類肢體活動。 for supporting the humans and human's physical movements.
Freeman: 現在，經過20年的研究， Freeman: And now, after 20 years of research,
他成功了。 he has succeeded.
H.A.L.可以通過閱讀大腦意圖來協助身體運動， H.A.L. Assists the human body by reading the brain's intentions
並為穿戴者的運動提供輔助力量。 and providing assistive power to support the wearer's movement.
如果她想要按照自己腦中的意圖運動， If she wish to or try to move as her brain generates intentions,
機器服就能夠檢測到這些意圖信號， and the robot detects these intention signals
就可以輔助她的運動。 and wants to assist her movements.
Freeman: 當大腦向肌肉發送一個運動信號時， Freeman: When the brain signals a muscle to move,
信號脈沖會先傳入脊髓， it transmits a pulse through the spinal cord
然後傳入運動區域。 and into the area of movement.
這樣的生物電信號 This bioelectric signal
可以從皮膚表面檢測到。 is detectable on the surface of the skin.
山海嘉之設計的H.A.L.機器服可以捕獲這些信號， Yoshiyuki designed the h.A.L. Suit to pick up these impulses
然後恰當激活電機 and then activate the appropriate motors
來協助使用者身體的運動。 in order to assist the body in its movement.
人類大腦可以直接控制機器服， The human brain is directly controlling the robotic suit.
這不僅僅是一項技術突破， It's not just a technological breakthrough.
山海嘉之已經將H.A.L.機器服 Yoshiyuki already has h.A.L. Suits at work
應用於日本的康複診所。 in rehabilitation clinics in Japan.
如果人們身體的某些部分出了問題， 比如癱瘓... So, if some of the body has some problems, like a paralyzed...
這就可以幫助這些病人， So this would help patients, who are such kind,
包括殘疾人也可以使用。 and a handicapped person can use it.
Freeman: 多年來不能行走的人 Freeman: People who haven't walked in years
現在就可以重新行動， are now on the move again
這都多虧了腦控機器人腿。 thanks to these brain-powered robot legs.
山海嘉之同樣也開發了軀幹和手臂型號的機器服， Yoshiyuki has also developed a model for the torso and arm
可以提供 that can provide
額外200公斤的升舉力量， up to 200 kilograms of extra lifting power,
讓一般人變得非常強壯。 turning regular humans into strongmen.
但這並不僅僅關於力量有多大， But it's not all about strength.
他認為 He believes
將機器人機械與人類的融合 the merging of robotic machinery and human biology
允許我們持續創造行為上的成就。 will allow us to preserve great achievements in movement.
像老虎伍茲和羅傑費德勒一樣， Athletes like Tiger Woods or Roger federer
將獨特的技巧手藝帶到他們的運動中。 bring unique skill and artistry to their sports.
但是，他們死後， 這些技巧也會消失， However, when they die, so does their movement.
不過H.A.L機器服 But since the h.A.L. Suit
可以檢測並記憶其穿戴者的運動方式， can detect and memorize the movements of its wearer,
這些技巧就不會消失了。 that knowledge doesn't have to disappear.
如果像老虎伍茲這樣的運動員 If some of these athletes like Tiger Woods,
也穿戴著這樣的機器服運動， if they wear it and they swing it,
所有的數據--動作數據和生理數據-- every data -- motion data and physiological data --
都會集中在電腦裏。 also gather in the computers.
Freeman: 我們曾經建立了許多大型圖書館來保存知識， Freeman: We once built great libraries to preserve knowledge
通過書寫將知識傳給後代， expressed through writing for future generations.
而山海嘉之想要創造一個行為圖書館， Yoshiyuki wants to create a great library of movement.
通過整合我們的身體與機械外骨骼， By merging our bodies with robotic exoskeletons,
我們不僅會更強壯， we will not only be stronger,
還能全都像 We will all move as well
天才運動員和藝術家一樣行動。 as the most talented athletes and artists.
上個世紀的大眾文化都聚焦於 The last century of popular culture
機器人暴亂的末日場景， has focused on apocalyptic scenarios of robotic mutiny.
但H.A.L.機器服開辟了一個不同的未來。 But the h.A.L. Suit opens up a different future.
我們傾向於將機器人認為是另一種生命形式， We tend to think about robotics as an alternative life-form
它們也許某一天會替代我們或與我們競爭。 that may someday replace or compete with humans.
但我認為現實的則是 But I think the reality of the matter is
我們和機器人合作的日漸增加， that, increasingly, we'll see humans and robots cooperate
最終從生理上和心理上 and actually become one kind of species
變成同一個物種。 both physically and mentally.
Schmidt: 我認為機器人才是未來， Schmidt: Absolutely, I think robots are the future.
我們必須依賴於它們， I think we need to rely on them.
否則我們就會停滯不前。 Otherwise, we will stagnate and make no more progress.
生活在地球上的生命都會終結， Eventually, life on the earth will come to an end.
那我們遺留下來的是什麼？ What is our legacy?
除了意識我們什麼都無法留下， We will leave nothing unless we leave consciousness.
所以我們就需要大量的意識機器人。 We need conscious robots everywhere.
意識才是我們的遺產， That will be our legacy.
這將會是全人類每個人的遺產。 That will be the legacy of mankind.
機器人會迅速變得更聰明、更敏捷， Robots are rapidly becoming smarter, more agile,
並發展出人類的眾多特征， and are developing human traits
比如意識、情感和靈感。 like consciousness, emotions, and inspiration.
它們會在進化的道路上把我們遠遠地甩在後面， Will they leave us behind on the evolutionary highway,
還是人類和機器融合，進入一個新的紀元？ or will humans join the machines in a new age?
進化是無法預測的... Evolution is unpredictable...
而且注定要讓我們感到驚訝。 And is bound to surprise us.