TED2011
Skylar Tibbits: Can we make things that make themselves?
斯凯拉·蒂比茨：我们能制作可以制作自己的东西么？
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MIT研究人员斯凯拉·蒂比茨从事自我装配的研究 —— 一个取代建造东西（一把椅子，一幢摩天大楼）的主意，我们能创建制作自己的材料，很像DNA链打包的方式。目前还是一个巨大概念的早期阶段；蒂比茨向我们展示了三个还在实验中的项目，我们可以一窥一个自我装配的未来可能的样子。
Skylar Tibbits - Inventor
Skylar Tibbits, a TED Fellow, is an artist and computational architect working on "smart" components that can assemble themselves. Full bio
Skylar Tibbits, a TED Fellow, is an artist and computational architect working on "smart" components that can assemble themselves. Full bio
Double-click the English transcript below to play the video.
00:15
Today I'd like to show you
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今天我想向各位展示
00:17
the future of the way we make things.
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未来我们制作东西的方式。
00:19
I believe that soon our buildings and machines
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我相信很快我们的建筑和机器
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will be self-assembling,
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将能自我组装，
00:23
replicating and repairing themselves.
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自我复制和自我修复。
00:25
So I'm going to show you
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因此我要向各位展示
00:27
what I believe is the current state of manufacturing,
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我所认为的制造业的当前状况，
00:29
and then compare that to some natural systems.
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接着再将其与一些自然系统比较。
00:32
So in the current state of manufacturing, we have skyscrapers --
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那么在当前的制造业中，我们有摩天大楼 ——
00:35
two and a half years [of assembly time],
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两年半的时间，
00:37
500,000 to a million parts,
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50万至上百万个部分，
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fairly complex,
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非常复杂，
00:41
new, exciting technologies in steel, concrete, glass.
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使用了在钢铁，混凝土和玻璃方面的新技术。
00:44
We have exciting machines
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我们有令人激动的机器，
00:46
that can take us into space --
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可以带我们进入太空——
00:48
five years [of assembly time], 2.5 million parts.
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五年时间，两百五十万个部分。
00:51
But on the other side, if you look at the natural systems,
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但另一方面，如果看看自然系统，
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we have proteins
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我们有拥有两百万种类型的
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that have two million types,
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蛋白质，
00:58
can fold in 10,000 nanoseconds,
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能在一万纳秒内折叠起来，
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or DNA with three billion base pairs
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我们能在大约一小时内
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we can replicate in roughly an hour.
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对带有三十亿碱基对的DNA进行复制。
01:05
So there's all of this complexity
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这就是我们
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in our natural systems,
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自然系统的复杂性，
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but they're extremely efficient,
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但它们非常高效，
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far more efficient than anything we can build,
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比我们建造的任何东西都要高效，
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far more complex than anything we can build.
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比我们能建造的任何东西都要复杂。
01:15
They're far more efficient in terms of energy.
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它们在能源方面更加高效。
01:17
They hardly ever make mistakes.
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它们很少犯错。
01:20
And they can repair themselves for longevity.
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他们能自我修复保持长寿。
01:22
So there's something super interesting about natural systems.
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关于自然系统有件超级有意思的事情。
01:25
And if we can translate that
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如果我们能将其
01:27
into our built environment,
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转换为我们的建筑环境，
01:29
then there's some exciting potential for the way that we build things.
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那么我们构建事物的方式就会有很大的潜力。
01:31
And I think the key to that is self-assembly.
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我认为关键是自我组装。
01:34
So if we want to utilize self-assembly in our physical environment,
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如果我们想要在自身的身体环境中利用自我组装，
01:37
I think there's four key factors.
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我认为有四个关键因素。
01:39
The first is that we need to decode
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第一个是，我们需要解码
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all of the complexity of what we want to build --
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我们所要建造的东西的所有的复杂度 ——
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so our buildings and machines.
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也就是我们的建筑和机器。
01:45
And we need to decode that into simple sequences --
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我们需要把它们解码成简单的序列 ——
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basically the DNA of how our buildings work.
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基本上就是我们的建筑运作的DNA。
01:49
Then we need programmable parts
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接着我们需要可编程的部分
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that can take that sequence
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这部分能接受这一序列
01:53
and use that to fold up, or reconfigure.
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并用于折叠或是重塑。
01:56
We need some energy that's going to allow that to activate,
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我们需要一些能量来进行激活，
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allow our parts to be able to fold up from the program.
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使我们的这些部分能够依照程序折叠起来。
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And we need some type of error correction redundancy
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我们需要一些类型的纠错冗余
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to guarantee that we have successfully built what we want.
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以保证我们成功建造的就是我们想要的。
02:07
So I'm going to show you a number of projects
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因此，我要向各位展示一些
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that my colleagues and I at MIT are working on
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我和我的同事正在进行的
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to achieve this self-assembling future.
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要实现这种自我组装的未来的项目。
02:13
The first two are the MacroBot and DeciBot.
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头两个项目是MacroBot和DeciBot。
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So these projects are large-scale reconfigurable robots --
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这些项目都是大规模可重构机器人 ——
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8 ft., 12 ft. long proteins.
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8英尺，12英尺长的蛋白质。
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They're embedded with mechanical electrical devices, sensors.
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它们嵌入机电设备，传感器。
02:26
You decode what you want to fold up into,
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你需要把想要折叠的方式解码成，
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into a sequence of angles --
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解码成一系列角度 ——
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so negative 120, negative 120, 0, 0,
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负120度，负120度，0度，0度，
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120, negative 120 -- something like that;
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120度，负120度，——这类的东西；
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so a sequence of angles, or turns,
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一系列角度，或转向，
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and you send that sequence through the string.
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可以用电线把这个次序传过去。
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Each unit takes its message -- so negative 120 --
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每个单元获取自己的消息 —— 比如负120.
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it rotates to that, checks if it got there
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它进行旋转，检查是否旋转到位
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and then passes it to its neighbor.
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然后把序列传给它的邻居。
02:48
So these are the brilliant scientists,
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有许多杰出的科学家，
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engineers, designers that worked on this project.
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工程师，设计师为这个项目工作。
02:52
And I think it really brings to light:
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我认为这一项目真的揭示出：
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Is this really scalable?
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这真的可扩展么？
02:56
I mean, thousands of dollars, lots of man hours
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我是说，花费数千美元许多人时
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made to make this eight-foot robot.
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来制造这个八英尺的机器人。
03:01
Can we really scale this up? Can we really embed robotics into every part?
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我们真的能扩大它么？我们真的能在每个部分中都嵌入机器人么？
03:04
The next one questions that
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下一个问题是
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and looks at passive nature,
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看看被动性，
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or passively trying to have reconfiguration programmability.
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或被动地尝试让重组具有可编程性。
03:11
But it goes a step further,
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但它更进了一步，
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and it tries to have actual computation.
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它尝试进行实际计算。
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It basically embeds the most fundamental building block of computing,
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基本上嵌入了多数计算的基础构建模块，
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the digital logic gate,
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数字逻辑门，
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directly into your parts.
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直接进入各个部分。
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So this is a NAND gate.
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这是与非门。
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You have one tetrahedron which is the gate
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每个要用于计算的门上
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that's going to do your computing,
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都有一个四面体，
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and you have two input tetrahedrons.
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有两个输入四面体。
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One of them is the input from the user, as you're building your bricks.
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其中一个是来自用户的输入，就像你在构建砖块。
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The other one is from the previous brick that was placed.
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另一个是来之前前放好的一块砖的输入。
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And then it gives you an output in 3D space.
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接着它会给出三维空间的输出。
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So what this means
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这意味着
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is that the user can start plugging in what they want the bricks to do.
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用户以他们想要的方式堆砌砖块。
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It computes on what it was doing before
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它依据之前所做的
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and what you said you wanted it to do.
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和你的指令进行计算。
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And now it starts moving in three-dimensional space --
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现在它开始在三维空间内移动 ——
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so up or down.
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上或者下。
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So on the left-hand side, [1,1] input equals 0 output, which goes down.
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看左面，[1,1] 的输入等于0输出，表示向下。
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On the right-hand side,
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在右边，
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[0,0] input is a 1 output, which goes up.
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[0,0] 的输入是1输出，表示向上。
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And so what that really means
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因此这真正的的意味是
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is that our structures now contain the blueprints
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我们的结构中现在包含了
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of what we want to build.
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我们想要构建的蓝图。
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So they have all of the information embedded in them of what was constructed.
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因此关于想要构建的事物的信息已经全部嵌入其中。
04:08
So that means that we can have some form of self-replication.
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这意味着我们有了某种形式的自我复制。
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In this case I call it self-guided replication,
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对这种情况，我称之为自我导向复制，
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because your structure contains the exact blueprints.
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因为你的结构中包含了精确的蓝图。
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If you have errors, you can replace a part.
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如果遇到错误，你可以替换一个部分。
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All the local information is embedded to tell you how to fix it.
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所有的本地信息都嵌入其中，告诉你如何修复它。
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So you could have something that climbs along and reads it
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因此你有个可以攀爬的东西，能读出它
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and can output at one to one.
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并一个一个的输出。
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It's directly embedded; there's no external instructions.
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它是直接嵌入的；没有外部指令输入。
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So the last project I'll show is called Biased Chains,
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我要展示的最后一个项目名为偏心链条，
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and it's probably the most exciting example that we have right now
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它可能是我们现在看到的被动自我装配系统中
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of passive self-assembly systems.
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最令人激动的例子。
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So it takes the reconfigurability
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它具有可重构性
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and programmability
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和可编程性
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and makes it a completely passive system.
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使它成了为了一个完全地被动系统。
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So basically you have a chain of elements.
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基本上就是你有了一连串的元素。
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Each element is completely identical,
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每个元素都是完全相同的，
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and they're biased.
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且它们是偏心的。
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So each chain, or each element, wants to turn right or left.
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每个链条，或每个元素想要向右转或是向左转。
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So as you assemble the chain, you're basically programming it.
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如果你要装配链条，需要为它编程。
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You're telling each unit if it should turn right or left.
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要告诉每个单元是要左转还是右转。
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So when you shake the chain,
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当你摇动这个链条时，
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it then folds up
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它就折叠起来
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into any configuration that you've programmed in --
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编程你所为它编码的任何结构 ——
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so in this case, a spiral,
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因此这种情况下，一个螺旋体，
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or in this case,
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火这种情况，
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two cubes next to each other.
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两个相连的立方体。
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So you can basically program
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基本上你可以在
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any three-dimensional shape --
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三维空间内编程 ——
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or one-dimensional, two-dimensional -- up into this chain completely passively.
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或是一维、二维 —— 这链条是完全被动的。
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So what does this tell us about the future?
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这向我们预示了怎样的未来呢？
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I think that it's telling us
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我认为这告诉我们
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that there's new possibilities for self-assembly, replication, repair
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这些在我们的身体结构、我们的建筑和机器中
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in our physical structures, our buildings, machines.
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这种自我装配、自我复制和自我修复的可能性。
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There's new programmability in these parts.
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在这些部分中有新的可编程性。
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And from that you have new possibilities for computing.
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从中你能获得计算的新可能性。
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We'll have spatial computing.
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我们将有空间计算。
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Imagine if our buildings, our bridges, machines,
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想象一下我们的建筑、桥梁、机器，
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all of our bricks could actually compute.
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所有的砖块都能进行实际计算。
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That's amazing parallel and distributed computing power,
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多么令人惊奇的并行计算和分布式计算能力和
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new design possibilities.
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新的设计可能性啊。
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So it's exciting potential for this.
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这是项令人激动的潜力。
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So I think these projects I've showed here
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我认为这些我向各位展示的项目
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are just a tiny step towards this future,
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仅仅是迈向未来的一小步，
05:51
if we implement these new technologies
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如果我们为一个新的自我组装的世界
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for a new self-assembling world.
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实现了这些新技术的话。
05:55
Thank you.
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谢谢。
05:57
(Applause)
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（掌声）
ABOUT THE SPEAKER
Skylar Tibbits - InventorSkylar Tibbits, a TED Fellow, is an artist and computational architect working on "smart" components that can assemble themselves.
Why you should listen
Can we create objects that assemble themselves -- that zip together like a strand of DNA or that have the ability for transformation embedded into them? These are the questions that Skylar Tibbits investigates in his Self-Assembly Lab at MIT, a cross-disciplinary research space where designers, scientists and engineers come together to find ways for disordered parts to become ordered structures.
A trained architect, designer and computer scientist, Tibbits teaches design studios at MIT’s Department of Architecture and co-teaches the seminar “How to Make (Almost) Anything” at MIT’s Media Lab. Before that, he worked at a number of design offices including Zaha Hadid Architects, Asymptote Architecture, SKIII Space Variations and Point b Design. His work has been shown at the Guggenheim Museum and the Beijing Biennale.
Tibbits has collaborated with a number of influential people over the years, including Neil Gershenfeld and The Center for Bits and Atoms, Erik and Marty Demaine at MIT, Adam Bly at SEED Media Group and Marc Fornes of THEVERYMANY. In 2007, he and Marc Fornes co-curated Scriptedbypurpose, the first exhibition focused exclusively on scripted processes within design. Also in 2007, he founded SJET, a multifaceted practice and research platform for experimental computation and design. SJET crosses disciplines from architecture and design, fabrication, computer science and robotics.
Skylar Tibbits | Speaker | TED.com