Freeman: 我们正处于一个潜在的变革之中， Freeman: We are in the midst of a revolution so insidious,
而我们却看不到变化。 we can't even see it.
机器人存在于我们身边并为我们工作， Robots live and work beside us.
现在我们要让它们学会思考自我。 And now we're designing them to think for themselves.
我们正在赋予它们凭借自己学习进步的力量。 We're giving them the power to learn to move on their own.
这些新的生命形式会变得 Will these new life-forms evolve
比我们更聪明更能干吗？ to be smarter and more capable than us?
还是我们会和机械融为一体？ Or will we choose to merge with the machines?
机器人是人类进化的未来吗？ Are robots the future of human evolution?
与摩根弗里曼穿越虫洞 Through The Wormhole
空间、时间、生命本身， Space, time, life itself.
宇宙的奥秘尽在穿越虫洞。 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.
我们人类喜欢认为自己 We humans like to think of ourselves
处于进化的顶端。 as the pinnacle of evolution.
我们确实是地球上最聪明的、最具适应力的生命形式。 We are the smartest, most adaptable form of life on earth.
我们已经重塑这个世界来满足我们的需要。 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,
必然会有什么将会替代我们。 it's inevitable something will replace us.
但假若我们能够自己制造继承人呢？ What if we're building our own successors?
就像我们学习走动、思考、感受自我一样， Just as we learn to move, think, and feel for ourselves,
我们现在正在给予机器人同样的能力。 we're now giving robots those same powers.
这将会把我们带到什么样的境地？ Where will this lead?
这是人类将会转变的形式吗？ Is this what humanity will become?
少年时，我用一些备用部件造了一辆自行车。 When I was a teenager, I built a bicycle from spare parts.
我造的自行车平衡性非常好， My bicycle was so well-balanced,
我可以推动它就保持平衡而不需要骑在上面。 I could jog alongside it without holding onto it.
没人认为这怎么厉害，但却使我思考 Nobody was that impressed, but it made me think.
我们有一天会拥有可以 Would we one day have machines
完全凭借自身行动的机器吗？ that truly could move on their own?
那个时候它们还会需要我们吗？ Would they even need us anymore?
剑桥大学的Daniel Wolpert Daniel Wolpert of the university of Cambridge
认为如果机器人 believes that if robots
是人类进化的未来， are to be the future of human evolution,
它们就必须像我们一样学习行动... they're going to have to learn to move as well as we do...
因为行动是我们强大智慧的 Because movement is the supreme achievement
最高成就。 of our powerful intellect.
我认为我们能够提出的最基本的问题是， The most fundamental question I think we can ever ask is,
为什么以及何时动物进化出了脑？ why and when have animals ever evolved a brain?
现在，当我问我的学生这个问题时， Now, when I ask my students this question,
他们会告诉我， they'll tell me,
我们用大脑来思考或感知世界， we have ones to think or to perceive the world,
但这是完全错误的。 and that's completely wrong.
我们只因为一个原因而具有大脑， We have a brain for one reason and one reason only,
那就是要产生能够适应环境的复杂行为， and that's to produce adaptable and complex movement,
因为行为才是我们具有的 because movement is the only way we have
唯一可以影响世界的方式。 of affecting the world around us.
Freeman: 我们大脑的各种智力 Freeman: All of our brains' intellectual capacity
都源于一个原始的动机-- grew from one primal motivation --
学得如何更加擅长行动， to learn how to move better.
这包括我们用两条腿行走的能力， It was our ability to walk on two legs,
使用复杂的面部表情交谈和表露情感， to speak and emote with complex facial movements,
并灵巧的控制四肢 and to manipulate our dexterous limbs
使人类站在食物链顶端。 that put humans on top of the food chain.
如果没有行动的能力， There can be no value
感知、情感和思考 to perception or emotions or thinking
都没有意义。 without the ability to act.
所有的其他特征， All those other features,
比如记忆、认知、爱、恐惧都要发展为行为， like memory, cognition, love, fear play into movement,
这些都是大脑输出的最终结果。 which is the final output of the brain.
Freeman: 没有任何机器可以做出 Freeman: No machine could handle
像我们每天做的多种复杂行为。 the huge variety of complex movements we perform every day.
想象一个机器人 Just imagine a robot
想要玩一场英格兰最流行的消遣游戏。 trying to play one of England's most famous pastimes.
尽管这次击球看起来很简单， So, although that shot looks simple
对于我来说毫不费力， and it felt effortless to me,
而我脑中进行的则是非常复杂的 the complexity of what's going on in my brain
肢体协调。 is really quite remarkable.
我必须注意投球手投出的球， I have to follow the ball as the bowler bowls it
并预测它会弹到哪里， and predict where it's going to bounce
以及它如何从地面弹起。 and how it's gonna rise from the ground.
然后我需要作出决定 I then have to make a decision
我要如何去击球， as to what type of shot I'm going to make.
最终，我需要协调600块肌肉 And finally, I have to contract my 600 muscles
通过特定的顺序来进行击球动作。 in a particular sequence to execute the shot.
此时，每一块肌肉的运动 Now, each of those components
都经过了复杂的数学运算， has real mathematical complexity,
而这种能力现今远超所有的机器人。 which is currently beyond the ability of any robotic device.
Freeman: 让机器人拥有 Freeman: One of the greatest challenges
我们这样行为的最大挑战之一 in getting robots to move like we do
就是要让它们学会处理不确定因素-- is teaching them to deal with uncertainty --
而对于我们的大脑来说这是本能。 something our brains do intrinsically.
球不会以两次相同的情况飞向你， A ball will never come at you the same way twice.
每次你都必须要迅速调整动作。 You must instantly adjust your swing each time.
问题是... The question is...
人脑如何处理这些不确定因素？ How does the human brain deal with all this uncertainty?
Daniel认为这需要一种概率估计理论 Daniel thinks it uses a theory of probability estimation
称作贝叶斯推理， called bayesian inference 【贝叶斯推理是由英国牧师贝叶斯发现的一种归纳推理方法， 与经典的统计归纳推理方法相比， 贝叶斯推理在得出结论时不仅要根据当前所观察到的样本信息， 而且还要根据推理者过去有关的经验和知识。】
才能进行运算。 to figure it out. 【贝叶斯推理是由英国牧师贝叶斯发现的一种归纳推理方法， 与经典的统计归纳推理方法相比， 贝叶斯推理在得出结论时不仅要根据当前所观察到的样本信息， 而且还要根据推理者过去有关的经验和知识。】
Wolpert: 击球手现在要做的很重要的事情 Wolpert: So, a critical thing the batsman now has to do
就是这个球会在哪里弹起， is decide where this ball is going to bounce,
只要他们能够准确击球， so as they can prepare the correct shot,
就需要贝叶斯推理。 and for that, they need bayesian inference.
贝叶斯推理可以做的是 What bayesian inference is all about
从两种不同的信息来源中 is deciding, optimally, the bounce location of the ball
决定球会弹起的最准确的位置。 from two different sources of information.
Freeman: 一个信息来源是很明显的， Freeman: One source of information is obvious.
就是你盯着球。 You look at the ball.
Wolpert: 你可以通过观察球飞来的 Wolpert: So, you can use vision of the trajectory of the ball
运动轨迹 as it comes in
来尝试估计它会在哪里弹起。 to try and estimate where it's going to bounce.
但是视觉并不完美， But vision is not perfect,
因为我们对视觉的加工存在变动， in that we have variability in our visual processors,
所以至少落地点的变动范围就是红色圆圈显示的， so at least where distribution's shown in red here
作为球会落在某一点的概率分布。 as the probable bounce locations.
但贝叶斯规则说还有一个信息来源， But bayes' rule says there's another source of information.
就是关于落地点的先验知识。 It's the prior knowledge about possible bounce locations. 【先验知识 是先于经验的知识】
如果你是个好击球手， If you're a good batter,
你就能有效地看着投球手 then you can effectively look at the bowler
就能通过他特殊的投球方式或暗示了解到球的运行方式-- and maybe know by his particular bowling style or small cues --
这里用蓝色底纹表示-- and that's shown by the blue shading --
这是一个不同的区域。 which is a different area.
那么贝叶斯推理就整合了 So, bayesian inference is a way of combining
红色范围和蓝色范围， this red distribution with the blue distribution,
你通过两种信息的交集 and you do that by multiplying the numbers together in each
产生了这个黄色分布， to generate this yellow distribution,
这个分布称为可信度。 which is termed the belief.
通过使用这样的信息， And using that information,
击球手现在就可以准备击球了。 the batsman can now prepare his shot.
好吧，我应该躲开一点。 Okay, I should probably get out of the way.
Freeman: 击球手的大脑，就像我们所有人， Freeman: The batsman's brain, like all of ours,
自动进行了这些计算。 is doing this math automatically.
我们这个物种锤炼出了对于行为的预测， We are a species that's honed for movement prediction.
这就使我们成为了 It's what has made us
这颗星球上最好的猎人和工具匠。 the planet's best hunters and toolmakers.
我们已经有了比我们行动 We already have robots that are faster and more accurate
更快更准确的机器人。 than we are.
但是我们要为它们的每个行为编程， But we have to program their every move.
机器人只能沿着我们已经走过的 For robots to walk down the evolutionary road
进化道路前进， we've already traveled,
它们要自己学会行动。 they're going to have to learn to move on their own.
然后会发生什么呢？ What happens then?
它们会像我们这样进化出复杂的大脑吗？ Will they evolve complex brains like ours?
佛蒙特大学的机器人设计师Josh Bongard， Robot builders Josh Bongard of the university of Vermont
和康奈尔大学的Hod Lipson and Hod Lipson of Cornell university
正在尝试回答这个问题。 are trying to answer that question.
我们与世界之间的联系， Increasingly, we see that interaction with the world,
和物质世界之间的联系 对于智力的发生发展很重要。 with the physical world, is important for intelligence.
你不能在一个罐子里造一个大脑。 You can't just build a brain in a jar.
Freeman: Hod和Josh的目标就是制造一个机器 Freeman: Hod and Josh's goal is to build a machine
足够聪明来学习自己如何移动。 that's smart enough to learn how to move around all by itself.
他们创造了一群形态奇怪的 They've created a menagerie of strange robotic forms
机器人。 along the way.
而他们的工作开始于一个可以 But their work starts with the computer program
使机器人身体进化的计算机程序。 designed to evolve robot bodies.
它会模拟不同的身体结构， It simulates various body plans
然后用多种方法让这个身体运动。 and then tries various strategies to get them to move.
好的，那么我们来经历一次-- Okay, so, let's walk our way through --
不是双关语--真实的进化模拟。 no pun intended -- an actual evolutionary simulation.
这个例子中， So, in this case,
我们告诉计算机我们想要一个两条腿的机器人， we've told the computer that we want a robot that has two legs,
而计算机就需要指出 but we want the computer to figure out
这个两条腿的机器人 how to get the robot
如何协调移动双腿。 to orchestrate the movement of the robot's legs.
这里，我们能看到有点令人惊讶的事， And here, we see something a little bit surprising,
这种进化并没有找到我们使用两条腿的行动方式， that evolution hasn't discovered the solution that we use.
有时这个进化进程 Sometimes when we run this evolutionary process,
会产生类似步行的行为， it produces something familiar like walking,
而其他例子中， 它产生的行为对于我们都很陌生， and in other cases, it produces something that's not familiar,
这些行为都不会发生在我们身上。 something we wouldn't have come up with on our own.
Freeman: 适者生存 Freeman: It's survival of the fittest
或至少不会那么笨拙， or perhaps the least awkward.
就像大自然基于每一代的 Just as mother nature selects generations
生存能力选择生物， based on their ability to survive,
这个模拟程序也是如此。 so does the simulation.
计算机删除了那些 The computer deletes the robots
做的不好的机器人， that aren't doing a very good job,
并保留下了做的 and the computer then takes the robots
稍微好一点的机器人， that are doing a slightly better job
对它们进行改进， and makes modified copies of them
一次次重复这个过程。 and repeats this process over and over again.
这样过了一会儿， 计算机开始发现一些 And after a while, the computer starts to discover robots
在这个例子中， 能够从屏幕的左侧 that, in this case, are able to walk
行走到屏幕右侧的机器人。 from the left side of the screen to the right side of the screen.
Freeman: 这是一个加速的进化过程， Freeman: This is evolution on steroids.
大自然花去了数百万年的时间， What took mother nature millions of years
而在计算机中只进行了几个小时。 takes the computer just a few hours.
经过一夜，计算机测试了上千代机器人， Overnight, the computer tests thousands of generations,
最终产生了一个达到目标的机器人。 and eventually it produces a robot that meets the goal.
当这个模拟创造出了 When the simulation makes something
看起来非常有趣的东西时， that looks particularly interesting,
Hod和Josh就采用这种形体方案 并将其建造出来。 Hod and Josh take that body plan and build it.
现在他们能够测试 Now they can test whether
计算机模拟中成功的行走方案 the strategies for moving learned in simulation
能否应用于现实世界中。 work as well in the real world.
这个机器人叫做quadratot， (非要翻译的话可以理解为“正方形四脚机器人”) So, this robot is called the quadratot,
它基本上算是一个正在通过机器人进化技术 and it's basically a robot that learns how to walk
学习如何行走的机器人。 using evolutionary robotic techniques.
我们在这里看到的 And so, what we can see here
就是这个机器人如何学习的例子。 is a particular example of how this robot learns.
这是它做出的最开始的步伐， This is one of the earliest gaits that it did,
我们能发现它移动的不是很远很快。 and we can see that it's not moving very far or very fast.
有点像小孩， It's kind of like a child
很小的时候只会爬的动作。 during its very early behaviors of crawling.
它正在尝试不同的方法， It's trying out different things.
一些更有利行走的， 或是不利于行走的。 Some things work better. Some things work less well.
它正在获得经验， 从中学习， And it's taking the experiences and learning from them
并逐渐改善自己的步伐。 and gradually improving its gait.
Freeman: 已经存在许多机器人可以通过 Freeman: There are many robots that can move well
预先编写的特定程序完美运动， while executing a specific predesigned task.
但是Hod和Josh的机器人 But Hod and Josh's robots
则需要在新环境中 must start to learn by themselves from scratch
全靠自己开始学习。 in an unknown environment.
它能够感觉到自己的进步， It can sense its own progress,
像婴儿从爬行开始学习一样， and like a baby learning to crawl,
通过每走一步以及每次跌倒， it becomes more aware of its body
它越来越能意识到自己的身体。 with every step and every tumble.
Hod和Josh认为这种自我认知 Hod and Josh believe this self-awareness
逐渐会构成意识的基础形式。 gradually builds into a basic form of consciousness.
通常我们都会问到， Often, we've raised the question,
什么是有意识的，什么没有？ is something conscious, or is it not?
但它的确不是一个黑白分明的东西， But it's really not a black-and-white thing.
它更像是关于 It's more about
一个实体能多大程度具有自我这一概念， to what degree an entity's able to conceive of itself,
多大程度的模拟自己、考虑自己 、 to simulate itself, to think about itself,
具有自我意识。 to be self-aware.
就像机器人通过多种复杂方式学习移动， As robots learn to move in more complex ways,
它们就有可能发展出和我们同等的意识， it's possible they will develop levels of consciousness
甚至超越于我们的意识。 equal to ours and maybe beyond.
不过根据一位科学家的说法， But according to one scientist,
一个机器人若真的拥有意识... for a robot to become truly conscious...
...它就必须具有情感。 ...it must develop feelings.
意识是什么？ What is consciousness?
这要看你问谁了， The answer depends on who you talk to.
一位医生对意识的定义可能会不同于牧师， A doctor's definition would be different from a priest's.
但我们都认为我们的高层次意识 But we all agree that our high-level consciousness
将我们与其他生物体区分开， is what separates us from other organisms
当然也与机器人区分开。 and, of course, from robots.
要用什么办法才能让机器人拥有意识呢？ What would it take for robots to become conscious?
它们能通过单纯的逻辑发展出意识吗？ Can they get there on logic alone?
还是必须要学会感受？ Or must they also learn to feel?
来自伊利诺伊大学的Pentti Haikonen教授认为 Professor Pentti Haikonen from the university of Illinois
机器人只能在它们可以体验感情后 believes machines will only become conscious
才能发展出意识。 when they can experience emotions.
他从很小的时候开始就有着这种观点， It's a belief he has held since he was very young
也是他第一次思考意识的意义时。 when he first contemplated what it meant to be conscious.
还是4、5岁的样子， 我站在家里的厨房中， When I was 4 or 5 years old, I was standing in our kitchen,
突然被一种神秘的存在感惊呆了... and suddenly I was struck by the mystery of existence...
...为什么我会是我， ...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.
我是如何进到我的身体中的？ How did I get inside myself?
Freeman: 随着他的长大， Freeman: As he got older,
Pentti意识到使他感受到意识的 Pentti realized that what made him feel conscious
就是他脑中 of being inside his head
对于身边世界中人和物的 were his emotional reactions to the people and objects
各种情绪反应。 in the world around him.
我们意识背后的神经活动 The neural processes that are behind our consciousness
发生于我们的脑内， take place inside our brain,
但是我们并不以这种方式看世界。 but we don't see things that way.
举个例子，当你扎破你的手指时， For instance, when you cut your finger,
手指的疼痛感就这么出现了， the pain is in the finger or so it appears,
但实际上疼痛感在这里。 but, actually, the pain is in here.
有了感受才意味着意识。 To feel is to be conscious.
Freeman: 我们的大脑对于周围世界的 Freeman: Our brain's raw experience of the world around us
原始感受只是通过我们的感觉产生的 is just a series of electrical impulses
一系列的电脉冲， generated by our senses.
只是我们把这些电脉冲与相关的情感 However, we translate these impulses into mental images
转译成了心理表象。 by making emotional associations with them.
令人愉悦的声音， A sound is pleasing.
平静安详的景象。 A view is peaceful.
根据Pentti的理解，意识只是 Consciousness, according to Pentti,
承载着多种心理表象的马赛克。 is just a rich mosaic of emotionally laden mental images.
他认为要使得一个机器人真正具有意识， He believes that to have a truly conscious machine,
你就必须赋予它们 you must give it the power
将感官数据与情绪联系起来的能力。 to associate sensory data with emotions.
而这个机器人，它已经具有了这种能力。 And in this robot, he has begun that process.
这是第一个机器人 This is the first robot
可以使用这种神经网络联系， that utilizes associative neural networks.
这和我们人类的学习方式有些类似。 It is the same kind of learning that we humans use.
当我们看到或听到一些东西时， When we see and hear something,
我们会把这两种感觉联系起来， we make a connection between those things,
之后的日子中如果我们 看到或者听到同样的事情， and later on when we see or hear the other thing,
我们就会感受到相对应的另一种感觉。 the other thing comes to our mind.
Freeman: xrc-1机器人像我们一样 Freeman: The xcr-1 experiences the world
直接感受这个世界。 directly through its senses like we do.
它有一些基本的触觉、视觉和听觉。 On board are the basics of touch, sight, and sound.
Pentti已经开始赋予其情感 Pentti has begun the process of giving it emotional associations
与特定感知数据的联系， to specific sensory data,
比如绿色。 like the color green.
Pentti在这个机器人前面放置一个绿色物体， Pentti places a green object in front of the robot,
它能够识别。 which it recognizes.
绿色。 Green.
然后他给它一个不好的联系-- Then he gives green a bad association --
从后侧猛击它。 a smack on the backside.
这种对于联系的学习就像小孩子一样。 The associative learning is similar to little children.
疼。 Hurt.
你可以告诉孩子这个不好或这是好的， 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.
当然我并不建议这么做。 I don't recommend that.
绿色，不好。 Green bad.
机器人对于绿色物体的心理意象 Freeman: The robot's mental image of the green object
现在联系上了不好的情绪。 is now associated with the emotion bad.
从现在开始，它会一直避着绿色瓶子。 And from now on, it will avoid the green bottle.
但这并不是xrc-1所有的学习部分， But it's not all pain for the xcr-1.
就像我们教机器人 Just like we teach the robot
把疼痛联系上了绿色物体， to associate pain with the green object,
我们也能够教机器人 we can teach the robot
把愉快和其他物体联系起来， to associate, also, pleasure with objects,
这个例子中是一个蓝色物体。 in this case with the blue object, like this.
蓝色。 Blue.
Freeman: 为了让它把蓝色联系上好的感觉， Freeman: To give blue a good association,
Pentti轻轻抚摸机器人顶部。 Pentti gently strokes the top of the robot.
蓝色，好。 Blue good.
这个简单的实验证明了 This simple experiment demonstrates
这个机器人具有对物体的心理意象 that this robot has mental images of objects
和心理内容。 and mental content.
Freeman: 这还是它发展成长的早期， Freeman: It's still early in its development,
但xcr-1已经学会了基本的情绪反应， but the xcr-1 has learned the basics of emotional reaction
从害怕... from fear...
绿色，不好。 Green bad.
到渴望。 ...to desire.
蓝色，好。 Blue good.
♪ Now's my time for love ♪
♪ lonely moments seem to... ♪
随着xrc-1更高级的版本中 As a more advanced version of the xcr-1
存储着心理意象的记忆... fills its memory with mental images...
牙医，不好。 Dentist bad.
...它就开始能够 ...it will start to be able
凭借自己与新环境互动， to react to new situations on its own
最终会像所有具有情绪波动的生物一样 and eventually experience the world
体验这个世界。 much like any emotionally-driven being.
我最大的梦想就是 It is my great dream
我建造的机器人有一天能问 to build robot that is one day able to ask,
“我是如何成为自己的呢？” "How did I get inside myself?"
Freeman: 当机器人到达这种程度时， Freeman: Once robots reach this point,
什么还能阻止它们进步， what's to stop them from moving on
变成与我们不同的却有意识的东西？ and becoming conscious of things we're not?
这个人认为机器人是人类的未来， This man thinks robots will become the future of humanity
因为它们具有我们缺乏的东西。 because they'll have something we lack.
在最后一个说出"Eureka"的人类之后 Their brains will have the capacity for genius
它们仍然会表现得非常天才。 long after the last human ever says "Eureka."
阿基米德第一次在浴缸中喊出了Eureka， （因为发现浮力原理） for Archimedes, Eureka happened in the bathtub.
爱因斯坦在乘坐电车时想到了相对论， Einstein was riding a streetcar when relativity dawned on him.
这些杰出的思维都曾闪现出灵感， These brilliant minds had a flash of inspiration
推动了全人类的进步。 and drove all of humanity forward.
但是今天的科学问题， But the scientific questions of today,
比如探索亚原子粒子的真相， probing shoals of subatomic particles
以及我们大量的遗传密码， and our vast genetic code,
这些问题如此复杂， have become so complex
需要数以千计的研究者团队 that they take teams of thousands of researchers
来解决这些问题。 to solve.
单一科学天才的时代结束了吗？ Is the age of the single scientific genius over?
但如果机器人能够解决问题呢？ Not if machines have their way.
数据科学家Michael Schmidt Data scientist Michael Schmidt
认为这个世界充满着复杂的美-- sees the world filled with intricate beauty --
玫瑰花开， the flowering of a rose,
叶脉伸展， the veins branching on a leaf,
黄蜂飞舞。 the flight of a Bumblebee.
在这些大自然奇迹的背后 But below the surface of nature's wonders,
Michael看到了埋藏的 Michael also sees a treasure trove
人们尚未知晓的数学复杂性。 of uncharted mathematical complexity.
Schmidt: 我很喜欢来这里，大自然是很美妙的。 Schmidt: Well, I love coming out here. Nature is beautiful.
这里的每株植物、每只蜜蜂、以及这个花园的 There are equations hidden in every plant and every bee
生态系统中都隐藏着许多数学方程式。 and the ecosystems involved in this garden.
有一些科学指出了 And part of science is figuring out
这些事情是如何进行的。 what causes those things to happen.
Freeman: 科学是我们理解大自然的努力， Freeman: Science is our effort to make sense of nature,
这样的追求造就了许多非常著名的发现。 and this quest has given us some very famous discoveries.
在牛顿时期， In Newton's time,
他发现了非常重要的物理定律， he was able to figure out a very important rule in physics,
即万有引力定律， which is the law of gravity.
它可以预测这个苹果如何坠落， It predicts how this apple falls
以及施加到这个苹果上的各种力。 and the forces that act upon this apple.
时至今日，我们仍对类似的问题感兴趣， Today in science, we're interested in similar problems
不仅仅是苹果如何掉落， but not just about how the apple falls
还有在我们身边世界中遵从 but the massive complexity that follows
这种简单动态的大量复杂性。 from this very simple dynamic to the world around us.
举个例子，当我扔掉这个苹果， For example, when I drop this apple,
苹果砸在地上会激起灰尘， the apple stirs up dust.
灰尘会砸在花上， This dust could hit a flower,
蜜蜂受惊，这朵花通过其授粉的可能性就低。 and a bee may be less likely to pollinate that flower.
这个花园中的整个生态系统 And the entire ecosystem in this garden
可能因为单一事件而发生急剧变化。 could change dramatically from that single event.
Freeman: 科学家已经知道了四种基本自然力， Freeman: Scientists understand the basic forces of nature,
但是在真实世界中 but making precise predictions
通过这种惊人的复杂性 about what will happen in the real world
来准确预测会发生什么 with its staggering complexity
足以使人类大脑崩溃。 is overwhelming to the human mind.
人类想要理解并计算出 So, one of the reasons why it's extremely difficult
自然定律以及公式是极为困难的， for humans to understand and figure out
原因之一就是 the equations and the laws of nature
参与其中的变量的数量。 is literally the number of variables that are at play.
影响一个系统的变量 There could be thousands of variables
会有数千个， that influence a system
而我们才刚开始梳理它们。 that we're only just beginning to tease apart.
实际中，这些公式数量很多， In fact, there are so many of these equations,
我们永远不可能将它们全部分析， we'll never be able to finish analyzing them
如果用手算的话。 if we do it by hand.
Freeman: 2006年， Freeman: In 2006,
Michael着手开发智能计算机软件， Michael began developing intelligent computer software
这个软件可以观察复杂的自然系统 that could observe complex natural systems
从看起来像是混乱的事件中得到意义。 and derive meaning from what seems like chaos.
这是一个双摆， So, what I have here is a double pendulum.
你可以看到它只有两个臂， If you look at it, it consists of two arms.
一个臂的顶部可绕轴运动， One arm swings along the top axis,
第二个臂的一端连接在第一个臂的尾部， and the second arm is attached to the bottom of the first arm,
这两个摆臂挂在一起， and it's two pendulums that are hooked together,
一个位于另一个的末端。 one pendulum at the end of the other.
现在这个双摆就是复杂性的很好的例子， Now, the pendulum is a great example of complexity
因为它能够表现出一些最复杂的行为， because it exhibits some of the most complex behavior
我们称之为混沌。 that we're aware of, which is called chaos.
所以当你从这类设备中收集数据时， So, when you collect data from this sort of device,
它的行为看起来几乎是完全随机的， it looks almost completely random,
不会出现任何类型的固定模式。 and there doesn't appear to be any sort of pattern.
但由于这是一个确定的物理系统， But because this is a physical deterministic system,
就存在一个运动规则。 a pattern does exist.
Freeman: 在双摆的混沌运动中寻找固定模式 Freeman: Finding a pattern amidst the chaos of the double pendulum
几十年来一直困扰着科学家。 has stumped scientists for decades.
但这时Michael突现灵感， But then Michael had a flash of inspiration.
为什么不以大自然创造我们的方式来想办法呢， Why not grow new ideas the same way nature created us,
通过进化？ using evolution?
他称自己的程序为Eureka， He called his program Eureka.
Eureka开始于充斥着随机公式的原始汤， Eureka starts with a primordial soup of random equations
然后检查这些公式看哪些 and checks how closely they fit
更接近于描述双摆的运动方式。 the behavior of the double pendulum.
不合理的公式就会被电脑“杀掉”， If they don't fit, the computer kills them.
如果接近，计算机就允许它们进入下一代， If they do, the computer moves them into the next generation,
在这一代中它们发生突变， 并试图变得更符合对双摆运动的描述。 where they mutate and try to get an even closer fit.
最终，就会产生一个公式赢家， Eventually, a winning equation emerges,
阿基米德也会引以为傲。 one that Archimedes would be proud of.
Eureka！ Eureka!
Schmidt: 现在我正在进行着我们的算法， Schmidt: And I'm running our algorithm now.
左边窗口中的是公式列表， On the left pane are the lists of the equations
都是Eureka想出的解决双摆运动的公式。 that Eureka has thought up for this double pendulum.
再往前，我们能看到公式变得复杂了， Walking up, we can see we increase the complexity,
同时我们也增加了与收集到的数据的一致性， and we're also increasing the agreement with the data.
随着进化，我们往上看， And eventually, as you go up,
你就能得到与数据极为一致的公式， you start to get an extremely close agreement with the data,
最终你就能得到一个真相， and eventually you snap on to a truth
它在描述双摆运动的准确性上有了很大的提升。 where you get a large improvement in the accuracy.
我们就能看到这里产生的一个公式， And we can actually look in here and see exactly what pops out.
比方说在这里你能看到一个9.8， For example here, you might notice we have a 9.8,
如果你还记得你的物理课的内容， and if you remember from physics courses,
这个数字正是地球的重力系数(重力加速度)。 that is the coefficient of gravity on earth.
重要的是这里的 What's very important is the difference
双摆的两个不同的摆动角度， between the two angles of the double pendulum.
这样就产生了。 This pops out.
实质上，我们通过这个软件 Essentially, we've used this software
以及为了模拟混沌来收集的数据， and the data we've collected to model chaos,
直接从这些数据中梳理得到了解答。 and we've teased out the solution directly from the data.
Freeman: Eureka不仅发现了 Freeman: Eureka has not only discovered
描述双摆运动的公式， a single equation to explain how a double pendulum moves.
它还为看起来像是混沌的事件赋予了含义-- It has found meaning in what looks like chaos --
在这之前从未有人类或机器人成功过。 something no human or machine has done before.
Schmidt: 我们可以收集整个数据集， Schmidt: So, we could collect an entirely new data set,
再次运行这个程序， run this process again,
尽管这些数据完全不同-- and even though the data is completely different --
我们看到的运动方式是不同的-- we could have different observations --
我们仍然可以辨认出隐藏的真相， we can still identify the underlying truth,
隐藏的模式，就是这个公式。 the underlying pattern, which is this equation.
Freeman: 对于Michael来说，未来的科学探索 Freeman: To Michael, the future of scientific exploration
并不在于我们的头脑中， isn't inside our heads.
而在于机器中。 It's inside machines.
它们能否看着一些数据 Whether they're looking at patterns of data
来自于遗传学、粒子物理以及气象学， from genetics, particle physics, or meteorology,
像Eureka这样的程序就能按需要发展出灵感， programs like Eureka can evolve inspiration on demand,
让它们发现大自然的基本真理， finding basic truths about nature
而这些人类都没能做到。 that no human ever could.
我们即将到达这种， We're gonna reach a point
我们可以决定我们想要发现什么的水平， where we decide what we want to discover
我们让机器帮我们搞清事实。 and we let the machines figure this out for us.
Eureka能够取得这些发现 Eureka can find these relationships
而不会带有人类的偏见与限制。 without human bias and without human limitations.
我们创造了为我们服务的机器人， We created robots to serve us.
随着它们学着自己的运动、感受和思考方式， As the machines learn their own ways to move, feel, and think,
它们最终会突破这种角色。 they will eventually grow out of that role.
那么机器人能否共同工作呢？ What if they start working together?
它们能否建立一个社会， Could they build their own society,
机器人为机器人自己建立起来的社会？ one made by the robots for the robots?
地球上没有比我们更成功的物种， There's no species on earth more successful than us.
这是由于 We owe that success
我们头脑中有台强大的“计算机”， to the powerful computer inside our heads.
但征服地球用到的不仅是一个个体的脑， But it takes more than one brain to conquer a planet.
人类能够繁荣是因为我们学会了 Homo sapiens thrive because we have learned
将这些“计算机”组成“社会”共同工作。 to make those computers work together as a society.
如果机器人也共同思考会什么样呢？ What will happen when robots put their heads together?
白天他是机器人学家， 晚上则是美食大厨， Roboticist by day and gourmet chef by night,
弗吉尼亚理工学院的Dennis Hong教授 Professor Dennis Hong of Virginia tech
是制造可合作机器人的专家， is a specialist in building cooperative robots.
但他也在实验室外搜寻合作的例子。 But he also sees cooperation outside the lab.
我们通常不会专门考虑这些， So, we don't really think about it,
但日常生活中处处都涉及了合作。 but everything in our daily lives involves cooperation.
比方说，烹饪，通常认为是个体行为， For example, cooking oftentimes is thought of as a solo act,
但你是否考虑过这涉及了很多人， but if you think about it, a lot of people are involved
而且需要大量的细心协调 and a lot of careful coordination is required
才能做出美食。 to make it happen.
哦，Charli，谢谢你。 Oh, thank you, Charli.
拿个西红柿作为例子， Take this tomato as an example.
这个西红柿的产生开始于一粒种子， This tomato most likely started its life as a seed,
这需要育种人员 where a group of breeders
选择携带优势基因的种子， need to choose the right sequence of genes
这样的西红柿滚圆多汁美味。 for a plump, juicy, tasty tomato.
然后这些种子会被种植，自身生长，最后收获， The seeds needed to be planted, grown, harvested,
接着西红柿就要送去市场。 then the tomatoes needed to get to the market.
Freeman: 粮食生产是一个复杂的合作网络， Freeman: Food production is a complex web of coordination.
虽然这样已经很好了， But as good as it is,
但人类的合作是有限度的。 human cooperation has its limits.
Hong: 哎呀。 Hong: Oops.
Freeman: 每天，就像大多数人一样， Freeman: Every day, like most of us,
Dennis就要面对人类 Dennis has to contend with the prime example
错误合作的第一个例子-- of human cooperation gone wrong --
交通。 traffic.
问题在于，我们作为人类， The problem is, us being human,
我们都需要、想要尽快到达我们的目的地， we all need to, want to get to our destination
这样就造成了交通堵塞。 as quick as possible, thus we have traffic jams.
Freeman: 如果没有交通灯， Freeman: If it wasn't for traffic lights,
实际生活中它们也是很简单的机器人， which are, in reality, very simple robots,
我们就几乎不可能到达任何地方。 it will be almost impossible to get anywhere.
这些交通灯之间互相交谈， These traffic lights, they talk to each other.
它们与其他信号交叉口的 They communicate with other traffic lights
交通灯交流， at other intersections.
另外它们有摄像头， And they have cameras,
这样就能看到交通模式， so they actually see the traffic patterns
并为我们人类做决定。 and make decisions for us, for humans.
哦，是这样啊。 Oh, there you go.
谢谢你，交通灯。 Thank you, traffic light.
Freeman: 交通是一个烦人的事， Freeman: Traffic is a nuisance.
不过人类其他的失败合作 But other failures of human cooperation
后果更为严重... are much more serious...
通常都是致命的。 And often deadly.
[机枪射击] [ Machine-gun fire ]
Dennis认为一个机器人社会 Dennis believes a society of robots
会比我们的社会更好的合作。 can be much better collaborators than we are.
Hong与宾夕法尼亚大学的 So, in collaboration with Daniel Lee
Daniel Lee合作， at the university of Pennsylvania,
设计出了一组可以进行 机器人世界杯的机器人， he designed a group of robots to compete in the robocup,
也就是机器人足球锦标赛。 an international robotic soccer championship.
机器人世界杯是一项自主式机器人进行的足球比赛， Robocup is an autonomous robot soccer competition,
这就是说你有一个机器人足球队， which means that you have a team of robots,
然后你按下“开始”， 接着人们就不需要再管它们了， you press "Start," And then nobody touches anything.
这些机器人就要查看四周，寻找足球在哪里， And the robots need to look around, see where the ball is,
它们需要互相配合，这样进行一场足球比赛。 need to coordinate and actually play a game of soccer.
Freeman: Dennis的称作Darwin-op的 机器人足球运动员， Freeman: Dennis' soccer robots, called Darwin-op,
它们是完全自主的。 are fully autonomous.
它们通过复杂的传感器和软件 They use complex sensors and software
游走于赛场之上， to navigate the playing field.
此外它们比人类的足球运动员 And they have a serious competitive edge
更具有竞争优势， over their human counterparts.
机器人队员之间可以互相读取思维， Teammates can read each other's minds.
这样，如果你看人类足球运动员的比赛， So, if you look at human soccer players,
很明显他们很擅长做自己的事， obviously they're great at what they do.
他们有时通过大声喊叫互相交流， They communicate sometimes by shouting,
有时通过小手势， sometimes by a subtle gesture,
但是，这仍不是非常精确， but, again, it's not really accurate,
无法同时或及时 and they cannot share all the information together
共享信息， at the same time in real time,
但机器人就可以做到。 but robots can do that.
Freeman: 每个机器人都即时知道所有机器人的 Freeman: Each robot knows the exact location and destination
确切的位置和将会到达的目的地， of the other robots at all times.
它们可以调整自己的策略， They can adjust their strategy
必要的话甚至是它们的角色。 and even their roles as necessary.
Hong：由于球在哪里，哪里就有机会， Hong: Depending on where the ball is, where the opponents are,
它们指导性的变换角色， they didactically switch their roles.
守门员可以成为前锋， So the goalie becomes a striker,
前锋又可以变成守门员或后卫。 a striker becomes a goalie or defense.
Freeman: 它们可能不会像贝利那样敏捷， 也可能不像贝克汉姆那样踢球， Freeman: They may not be as agile as Pelé or bend it like Beckham,
但它们能够带球过人， but they are able to dribble past their opponents,
传球，得分... pass the ball, score a goal...
甚至欢呼。 And even celebrate.
Dennis认为机器人世界杯 Dennis believes robocup
正是机器人社会的开始， is just the beginning of robot societies.
Dennis想象了一个思考机器相互联系的团体， Dennis imagines a connected community of thinking machines
它会远比人类社会 that would be far more sophisticated
更为精密复杂。 than human communities.
他称之为云机器人。 He calls it cloud robotics.
Hong: 云机器人是智能的共享网络， Hong: Cloud robotics is a shared network of intelligence.
有些类似于我们人类的“常识”， It's similar to what we call common sense in humans.
在这些小机器人进行的 So, just like those smaller robots
机器人世界杯足球比赛中， that play soccer for robocup,
它们共享公用数据、团队数据来实现目标， they share a common data, team data, to achieve the goal,
这个例子中的目标就是赢得足球比赛。 in this case, winning the soccer game.
而对于云机器人， For cloud robotics,
就算世界上最远的角落里的机器人 robots from the furthest corners in the world,
都可以连接上云服务器， they can all connect to the cloud
共享信息和智能来做自己的工作。 and share information and intelligence to do their job.
Freeman: 人类花去了一生的时间来掌握知识， Freeman: Humans spend a lifetime mastering knowledge,
机器人却可以在几微秒之内完成学习。 but future robots could learn it all in microseconds.
它们可以用基于人类社会的 They could create their own hyper-connected network
同样的合作精神创建 using the same spirit of cooperation
属于自己的超连接网络， that built human society
却不会产生像阻碍人类发展的自私和贪婪。 without the selfishness and greed that hold us back.
机器人的运作受限于明确的规则， Robots operate by a very well-defined set of rules.
不会像人类这样总是违背规则。 The human impetus to break them is just not there.
Freeman: 机器人已经知道了如何互相交谈， Freeman: Robots already know how to talk to one another.
但现在柏林的一位科学家， But now a scientist in Berlin
已经将机器人之间的交流发展得更加深入。 has taken robotic communication a step further.
他的机器人正用着他也不了解的语言交谈。 His machines are speaking a language he doesn't understand.
Motakay tokima.
你知道我刚才说了什么吗？ Did you understand what I just said?
你当然不知道， Of course you didn't
因为我说的不是任何一种已知的人类语言， because I wasn't speaking any known human language.
但它不是无意义的， But it wasn't nonsense.
这是一种机器人语言。 It was a robot language.
我们人类花了上千年 We humans took tens of thousands of years
才发展出了我们交流的复杂的方法。 to develop our complex means of communication.
现在机器人跟随者人类的指引， Now robots are following our lead,
以光速发展出语言。 and they're doing it at light speed.
很快有一天， Someday soon,
机器人也许会把我们人类排斥于交谈之外。 robots may decide to exclude us from their conversation.
机器人: Tokima， Robot: Tokima.
Lucabo.
Miyoto.
Motakay.
Tokima, kymamu.
Tokima.
Simeta.
Tokima.
Motakay.
Steels: 如果没有语言， Steels: Without language,
我们人类就无法成就今天， our species would never be where it is today.
语言是人类创造出来的 It's the most magnificent thing
最杰出的东西。 that has ever been created by humanity.
如果你审视我们自己， If you look at ourselves,
很明显，没有语言， then it's pretty clear that without language,
我们就不可能 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,
他认为语言是机器人 sees language as the key
发展出真正智能的关键。 to developing true robot intelligence.
Steels: 我一直在尝试理解的是 Steels: What I'm trying to understand is,
我们应该如何生成这种 how can we synthesize this process
能够在一个机器人或一群机器人中 so that we can start up a kind of evolution in a robot
开始的一种进化过程， or in a population of robots
而这个过程可以产生 that will also lead to the growth
类似于我们的沟通系统。 of a rich communication system like we have.
Freeman: 机器人已经可以互相交流， Freeman: Machines already communicate with each other,
但这要基于 but these are based
人类预先给定的代码语言。 on predetermined, human-coded languages.
Luc想要知道 Luc wants to know
如果给机器人用自己创造的语言交流的机会， how future robot societies might communicate
那未来的机器人社会会是什么样的。 given the chance to make a language on their own.
Luc赋予了他的机器人基本的语言成分， Luc gives his robots the basic ingredients of language,
比如可能会用到的声音， like potential sounds to use,
以及将它们连接起来的可能的方式， and possible ways to join them together.
但机器人说什么还要取决于它们自己。 But what the robots say is up to them.
我们加入了学习机制， We put in learning mechanisms,
还有发明机制， we put in invention mechanisms,
不同机制的加入可以允许它们整理出自己的语言。 mechanisms so that they can coordinate their language.
它们可以协商如何说话， They can kind of negotiate how they're gonna speak,
但我们不会加入我们的语言或概念。 but we don't put in our language or our concepts.
Freeman: 这还不足以让机器人知道如何说话， Freeman: It's not enough for the robots to know how to speak.
它们还需要一些谈论的东西。 They need to have something to speak about.
luc的下一步 Luc's next step
就是要教机器人如何认识自己的身体。 is to teach the robots how to recognize their own bodies.
Steels: 为了学习语言， Steels: In order to learn language,
你就需要学习自己的身体结构， you actually have to learn about your own body
以及自己身体的行动方式。 and the movements of your own body.
这里你看到的是 So, what you see here is an internal model
机器人的内部模型， that the robot is building of itself.
这个机器人正在学习 This robot is trying to learn here,
一种联系， is the relationship
介于这些不同的感觉通道 between all these different sensory channels
与自身运动指令间的关系。 and its own motor commands.
Freeman: 随着机器人看着镜子中运动的自己， Freeman: As a robot watches itself move in the mirror,
它形成了自身四肢和关键的三维模型。 it forms a 3-d model of its limbs and joints.
它存储了这种感觉信息的记忆， It stores this information in sense memory
现在已经准备和另一个机器人谈论行为了。 and is now ready to talk to another robot about movement.
Tokima.
那么现在这个机器人正在讲话， So, now, this robot is talking.
他正在让对方做一个行为， He's asking an action.
这个机器人正在做的就是--伸展手臂， This robot is doing -- you know, stretching the arm.
可这不是他要求的动作， No, this is not what was requested,
他再次展示什么才是正确的动作。 and he's showing again what the right action is.
Freeman: 她的第一次尝试并没有成功， Freeman: She's unsuccessful in her first attempt,
但最终这个机器人学会了 but eventually the robot learns
“Tokima”意思是“举起两只手臂”， that "Tokima" Means "Raise two arms."
在尝试不同的词汇重复进行学习过程后， after repeating this process with different words,
他们又试了一次。 they try again.
Homakey.
另一个请求， Another request.
他正在做这个词代表的行为。 He's doing the action.
是的，这是正确的动作。 Yes, this is the right kind of action.
那么换句话说， So, in other words,
这个机器人已经从对方那里学会了这个词， this robot has learned the word from the other one
反过来也一样。 and vice versa.
他们现在有一种方法来谈论动作了。 They now have a way to talk about actions.
Freeman: 机器人语言已经发展的很完善了， Freeman: The robots' language is already so well-developed,
它们甚至可以教Luc机器人语言。 they can teach it to Luc.
我们来看看他要让我做什么。 Let's see what, you know, what he asks me to do.
Motakay.
好的，Motakay。 Okay, motakay.
不对，这不正确， 所以他开始向我展示正确动作。 No, this is not right, so he's showing it to me.
好吧，我现在正在学习这个动作。 Okay, I'm learning this gesture now.
Motakay.
这就是Motakay， Motakay is this.
好的，这次我做对了。 Okay, I got it right.
现在我就要他一起摆姿势。 So, now I'm going to use the gesture with him.
Motakay。 Motakay.
好的，你正在做的是正确的。 Okay, yes, you're doing the right thing.
谢谢。 Thank you.
随着机器人重复这一过程， As the robots repeat this process,
他们产生了对于其他机器人 they generate words and actions
具有真实意义的词汇和行为， that have real meanings for one another.
这样机器人的词汇量就开始增加。 And so the robots' vocabulary grows.
他们创造出的每一个新词 Every new word they create
都是我们无法理解的。 is one more that we can't understand.
机器人把我们 Is it only a matter of time
彻底排除在谈话之外只是时间问题吗？ before they lock us out of the conversation completely?
Steels: 我认为这是完全有可能的， Steels: And I think it's actually totally possible,
社会需要寻找一个平衡点， but society will kind of have to find the balance
介于我们想要机器人去做什么 between what it is that we want robots for
与我们愿意给它们多少自由之间。 and how much autonomy are we willing to give them.
Freeman: 如果我们允许机器人任意行动， Freeman: If we're giving robots autonomy to move,
去感受，去创造自己的语言， to feel, to make their own language,
这就足以超越我们吗? could that be enough for them to surpass us?
毕竟，什么才会使机器人“灭绝”呢？ After all, what's robot for "Exterminate"?
但这位日本科学家 But one Japanese scientist
不认为未来人类会与机器人对抗， doesn't see the future as robots versus humans.
实际上，他正在有意构建人类与机器人的交叉点。 In fact, he is purposefully engineering their intersection.
我们知道智人并不是人类进化的结局， We know that homo sapiens cannot be the end of evolution.
但我们的后代会仍以生物形式进化， 还是会成为机械人？ But will our descendents be biological or mechanical?
一些人认为具有智慧的机器人 Some believe that intelligent machines
最终会成为主宰地球的生物， will eventually become the dominant creatures on earth.
但下一步的进化， But the next evolutionary step
可能不是机器人取代了人类， may not be robot replacing human.
而是一种融合人类与机器的新型生命形式。 It could be a life-form that fuses man and machine.
他就是山海嘉之。 This is Yoshiyuki Sankai.
受到像艾萨克·阿西莫夫这样作者的影响， Inspired by authors like Isaac Asimov,
他一直梦想着将人类和机器人这两种生命形式融合， he has always dreamed of fusing human and robotic life-forms
成为他称为的“混合辅助肢体系统”... into something he calls the hybrid assistive limb system...
或简称H.A.L. Or H.A.L.
山海：这是我的梦想之一， Sankai: That one is one of my dreams.
我们能够开发出这类设备， We could develop such kind of devices,
就像H.A.L系统机器服， like a robot suit h.A.L. System,
来协助人类以及人类肢体活动。 for supporting the humans and human's physical movements.
Freeman: 现在，经过20年的研究， Freeman: And now, after 20 years of research,
他成功了。 he has succeeded.
H.A.L.可以通过阅读大脑意图来协助身体运动， H.A.L. Assists the human body by reading the brain's intentions
并为穿戴者的运动提供辅助力量。 and providing assistive power to support the wearer's movement.
如果她想要按照自己脑中的意图运动， If she wish to or try to move as her brain generates intentions,
机器服就能够检测到这些意图信号， and the robot detects these intention signals
就可以辅助她的运动。 and wants to assist her movements.
Freeman: 当大脑向肌肉发送一个运动信号时， Freeman: When the brain signals a muscle to move,
信号脉冲会先传入脊髓， it transmits a pulse through the spinal cord
然后传入运动区域。 and into the area of movement.
这样的生物电信号 This bioelectric signal
可以从皮肤表面检测到。 is detectable on the surface of the skin.
山海嘉之设计的H.A.L.机器服可以捕获这些信号， Yoshiyuki designed the h.A.L. Suit to pick up these impulses
然后恰当激活电机 and then activate the appropriate motors
来协助使用者身体的运动。 in order to assist the body in its movement.
人类大脑可以直接控制机器服， The human brain is directly controlling the robotic suit.
这不仅仅是一项技术突破， It's not just a technological breakthrough.
山海嘉之已经将H.A.L.机器服 Yoshiyuki already has h.A.L. Suits at work
应用于日本的康复诊所。 in rehabilitation clinics in Japan.
如果人们身体的某些部分出了问题， 比如瘫痪... So, if some of the body has some problems, like a paralyzed...
这就可以帮助这些病人， So this would help patients, who are such kind,
包括残疾人也可以使用。 and a handicapped person can use it.
Freeman: 多年来不能行走的人 Freeman: People who haven't walked in years
现在就可以重新行动， are now on the move again
这都多亏了脑控机器人腿。 thanks to these brain-powered robot legs.
山海嘉之同样也开发了躯干和手臂型号的机器服， Yoshiyuki has also developed a model for the torso and arm
可以提供 that can provide
额外200公斤的升举力量， up to 200 kilograms of extra lifting power,
让一般人变得非常强壮。 turning regular humans into strongmen.
但这并不仅仅关于力量有多大， But it's not all about strength.
他认为 He believes
将机器人机械与人类的融合 the merging of robotic machinery and human biology
允许我们持续创造行为上的成就。 will allow us to preserve great achievements in movement.
像老虎伍兹和罗杰费德勒一样， Athletes like Tiger Woods or Roger federer
将独特的技巧手艺带到他们的运动中。 bring unique skill and artistry to their sports.
但是，他们死后， 这些技巧也会消失， However, when they die, so does their movement.
不过H.A.L机器服 But since the h.A.L. Suit
可以检测并记忆其穿戴者的运动方式， can detect and memorize the movements of its wearer,
这些技巧就不会消失了。 that knowledge doesn't have to disappear.
如果像老虎伍兹这样的运动员 If some of these athletes like Tiger Woods,
也穿戴着这样的机器服运动， if they wear it and they swing it,
所有的数据--动作数据和生理数据-- every data -- motion data and physiological data --
都会集中在电脑里。 also gather in the computers.
Freeman: 我们曾经建立了许多大型图书馆来保存知识， Freeman: We once built great libraries to preserve knowledge
通过书写将知识传给后代， expressed through writing for future generations.
而山海嘉之想要创造一个行为图书馆， Yoshiyuki wants to create a great library of movement.
通过整合我们的身体与机械外骨骼， By merging our bodies with robotic exoskeletons,
我们不仅会更强壮， we will not only be stronger,
还能全都像 We will all move as well
天才运动员和艺术家一样行动。 as the most talented athletes and artists.
上个世纪的大众文化都聚焦于 The last century of popular culture
机器人暴乱的末日场景， has focused on apocalyptic scenarios of robotic mutiny.
但H.A.L.机器服开辟了一个不同的未来。 But the h.A.L. Suit opens up a different future.
我们倾向于将机器人认为是另一种生命形式， We tend to think about robotics as an alternative life-form
它们也许某一天会替代我们或与我们竞争。 that may someday replace or compete with humans.
但我认为现实的则是 But I think the reality of the matter is
我们和机器人合作的日渐增加， that, increasingly, we'll see humans and robots cooperate
最终从生理上和心理上 and actually become one kind of species
变成同一个物种。 both physically and mentally.
Schmidt: 我认为机器人才是未来， Schmidt: Absolutely, I think robots are the future.
我们必须依赖于它们， I think we need to rely on them.
否则我们就会停滞不前。 Otherwise, we will stagnate and make no more progress.
生活在地球上的生命都会终结， Eventually, life on the earth will come to an end.
那我们遗留下来的是什么？ What is our legacy?
除了意识我们什么都无法留下， We will leave nothing unless we leave consciousness.
所以我们就需要大量的意识机器人。 We need conscious robots everywhere.
意识才是我们的遗产， That will be our legacy.
这将会是全人类每个人的遗产。 That will be the legacy of mankind.
机器人会迅速变得更聪明、更敏捷， Robots are rapidly becoming smarter, more agile,
并发展出人类的众多特征， and are developing human traits
比如意识、情感和灵感。 like consciousness, emotions, and inspiration.
它们会在进化的道路上把我们远远地甩在后面， Will they leave us behind on the evolutionary highway,
还是人类和机器融合，进入一个新的纪元？ or will humans join the machines in a new age?
进化是无法预测的... Evolution is unpredictable...
而且注定要让我们感到惊讶。 And is bound to surprise us.