ABOUT THE SPEAKER
Skylar Tibbits - Inventor
Skylar 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.

More profile about the speaker
Skylar Tibbits | Speaker | TED.com
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?

Filmed:
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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。
- Inventor
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今天 I'd like to show显示 you
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今天我想向各位展示
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the future未来 of the way we make things.
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未来我们制作东西的方式。
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I believe that soon不久 our buildings房屋 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自组装,
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将能自我组装,
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replicating复制 and repairing修复 themselves他们自己.
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自我复制和自我修复。
00:25
So I'm going to show显示 you
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因此我要向各位展示
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what I believe is the current当前 state of manufacturing制造业,
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我所认为的制造业的当前状况,
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and then compare比较 that to some natural自然 systems系统.
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接着再将其与一些自然系统比较。
00:32
So in the current当前 state of manufacturing制造业, we have skyscrapers摩天大楼 --
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那么在当前的制造业中,我们有摩天大楼 ——
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two and a half years年份 [of assembly部件 time],
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两年半的时间,
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500,000 to a million百万 parts部分,
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50万至上百万个部分,
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fairly相当 complex复杂,
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非常复杂,
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new, exciting扣人心弦 technologies技术 in steel, concrete具体, glass玻璃.
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使用了在钢铁,混凝土和玻璃方面的新技术。
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We have exciting扣人心弦 machines
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我们有令人激动的机器,
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that can take us into space空间 --
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可以带我们进入太空——
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five years年份 [of assembly部件 time], 2.5 million百万 parts部分.
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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自然 systems系统,
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但另一方面,如果看看自然系统,
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we have proteins蛋白质
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我们有拥有两百万种类型的
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that have two million百万 types类型,
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蛋白质,
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can fold in 10,000 nanoseconds纳秒,
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能在一万纳秒内折叠起来,
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or DNA脱氧核糖核酸 with three billion十亿 base基础 pairs
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我们能在大约一小时内
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we can replicate复制 in roughly大致 an hour小时.
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对带有三十亿碱基对的DNA进行复制。
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So there's all of this complexity复杂
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这就是我们
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in our natural自然 systems系统,
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自然系统的复杂性,
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but they're extremely非常 efficient高效,
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但它们非常高效,
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far more efficient高效 than anything we can build建立,
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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复杂 than anything we can build建立.
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比我们能建造的任何东西都要复杂。
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They're far more efficient高效 in terms条款 of energy能源.
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它们在能源方面更加高效。
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They hardly几乎不 ever make mistakes错误.
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它们很少犯错。
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And they can repair修理 themselves他们自己 for longevity长寿.
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他们能自我修复保持长寿。
01:22
So there's something super interesting有趣 about natural自然 systems系统.
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关于自然系统有件超级有意思的事情。
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And if we can translate翻译 that
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如果我们能将其
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into our built内置 environment环境,
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转换为我们的建筑环境,
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then there's some exciting扣人心弦 potential潜在 for the way that we build建立 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自组装.
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我认为关键是自我组装。
01:34
So if we want to utilize利用 self-assembly自组装 in our physical物理 environment环境,
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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,
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I think there's four key factors因素.
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我认为有四个关键因素。
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The first is that we need to decode解码
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第一个是,我们需要解码
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all of the complexity复杂 of what we want to build建立 --
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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房屋 and machines.
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也就是我们的建筑和机器。
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And we need to decode解码 that into simple简单 sequences序列 --
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我们需要把它们解码成简单的序列 ——
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basically基本上 the DNA脱氧核糖核酸 of how our buildings房屋 work.
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基本上就是我们的建筑运作的DNA。
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Then we need programmable可编程的 parts部分
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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序列
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这部分能接受这一序列
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and use that to fold up, or reconfigure重新配置.
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并用于折叠或是重塑。
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We need some energy能源 that's going to allow允许 that to activate启用,
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我们需要一些能量来进行激活,
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allow允许 our parts部分 to be able能够 to fold up from the program程序.
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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类型 of error错误 correction更正 redundancy冗余
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我们需要一些类型的纠错冗余
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to guarantee保证 that we have successfully顺利 built内置 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显示 you a number of projects项目
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因此,我要向各位展示一些
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that my colleagues同事 and I at MITMIT are working加工 on
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我和我的同事正在进行的
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to achieve实现 this self-assembling自组装 future未来.
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要实现这种自我组装的未来的项目。
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The first two are the MacroBotMacroBot and DeciBotDeciBot.
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头两个项目是MacroBot和DeciBot。
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So these projects项目 are large-scale大规模 reconfigurable可重构 robots机器人 --
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这些项目都是大规模可重构机器人 ——
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8 ftFT., 12 ftFT. long proteins蛋白质.
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8英尺,12英尺长的蛋白质。
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They're embedded嵌入式 with mechanical机械 electrical电动 devices设备, sensors传感器.
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它们嵌入机电设备,传感器。
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You decode解码 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序列 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序列 of angles, or turns,
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一系列角度,或转向,
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and you send发送 that sequence序列 through通过 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单元 takes its message信息 -- 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旋转 to that, checks检查 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通行证 it to its neighbor邻居.
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然后把序列传给它的邻居。
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So these are the brilliant辉煌 scientists科学家们,
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有许多杰出的科学家,
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engineers工程师, designers设计师 that worked工作 on this project项目.
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工程师,设计师为这个项目工作。
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And I think it really brings带来 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可扩展性?
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这真的可扩展么?
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I mean, thousands数千 of dollars美元, lots of man hours小时
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我是说,花费数千美元许多人时
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made制作 to make this eight-foot八脚 robot机器人.
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来制造这个八英尺的机器人。
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Can we really scale规模 this up? Can we really embed robotics机器人 into every一切 part部分?
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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?
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The next下一个 one questions问题 that
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下一个问题是
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and looks容貌 at passive被动 nature性质,
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看看被动性,
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or passively被动 trying to have reconfiguration重构 programmability可编程.
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或被动地尝试让重组具有可编程性。
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But it goes a step further进一步,
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但它更进了一步,
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and it tries尝试 to have actual实际 computation计算.
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它尝试进行实际计算。
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It basically基本上 embeds嵌入视频 the most fundamental基本的 building建造 block of computing计算,
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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数字 logic逻辑 gate,
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数字逻辑门,
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directly into your parts部分.
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直接进入各个部分。
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So this is a NANDNAND gate.
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这是与非门。
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You have one tetrahedron四面体 which哪一个 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计算,
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都有一个四面体,
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and you have two input输入 tetrahedrons四面体.
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有两个输入四面体。
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One of them is the input输入 from the user用户, as you're building建造 your bricks砖块.
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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以前 brick that was placed放置.
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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产量 in 3D space空间.
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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手段
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这意味着
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is that the user用户 can start开始 plugging堵漏 in what they want the bricks砖块 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单位计算 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启动 moving移动 in three-dimensional三维 space空间 --
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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左手 side, [1,1] input输入 equals等于 0 output产量, which哪一个 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右手 side,
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在右边,
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[0,0] input输入 is a 1 output产量, which哪一个 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手段
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因此这真正的的意味是
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is that our structures结构 now contain包含 the blueprints蓝图
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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建立.
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我们想要构建的蓝图。
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So they have all of the information信息 embedded嵌入式 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。
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So that means手段 that we can have some form形成 of self-replication自我复制.
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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案件 I call it self-guided自导 replication复制,
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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结构体 contains包含 the exact精确 blueprints蓝图.
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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错误, you can replace更换 a part部分.
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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本地 information信息 is embedded嵌入式 to tell you how to fix固定 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攀登 along沿 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产量 at one to one.
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并一个一个的输出。
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It's directly embedded嵌入式; there's no external外部 instructions说明.
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它是直接嵌入的;没有外部指令输入。
04:27
So the last project项目 I'll show显示 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大概 the most exciting扣人心弦 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被动 self-assembly自组装 systems系统.
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最令人激动的例子。
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So it takes the reconfigurability可重构
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它具有可重构性
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and programmability可编程
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和可编程性
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and makes品牌 it a completely全然 passive被动 system系统.
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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基本上 you have a chain of elements分子.
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基本上就是你有了一连串的元素。
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Each element元件 is completely全然 identical相同,
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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元件, 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集合 the chain, you're basically基本上 programming程序设计 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告诉 each unit单元 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褶皱 up
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它就折叠起来
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into any configuration组态 that you've programmed程序 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案件, a spiral螺旋,
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因此这种情况下,一个螺旋体,
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or in this case案件,
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火这种情况,
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two cubes立方体 next下一个 to each other.
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两个相连的立方体。
05:14
So you can basically基本上 program程序
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基本上你可以在
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any three-dimensional三维 shape形状 --
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三维空间内编程 ——
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or one-dimensional一维, two-dimensional二维 -- up into this chain completely全然 passively被动.
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或是一维、二维 —— 这链条是完全被动的。
05:21
So what does this tell us about the future未来?
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这向我们预示了怎样的未来呢?
05:23
I think that it's telling告诉 us
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我认为这告诉我们
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that there's new possibilities可能性 for self-assembly自组装, replication复制, repair修理
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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物理 structures结构, our buildings房屋, machines.
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这种自我装配、自我复制和自我修复的可能性。
05:31
There's new programmability可编程 in these parts部分.
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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可能性 for computing计算.
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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空间的 computing计算.
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我们将有空间计算。
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Imagine想像 if our buildings房屋, our bridges桥梁, machines,
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想象一下我们的建筑、桥梁、机器,
05:39
all of our bricks砖块 could actually其实 compute计算.
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所有的砖块都能进行实际计算。
05:41
That's amazing惊人 parallel平行 and distributed分散式 computing计算 power功率,
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多么令人惊奇的并行计算和分布式计算能力和
05:43
new design设计 possibilities可能性.
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新的设计可能性啊。
05:45
So it's exciting扣人心弦 potential潜在 for this.
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这是项令人激动的潜力。
05:47
So I think these projects项目 I've showed显示 here
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我认为这些我向各位展示的项目
05:49
are just a tiny step towards this future未来,
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仅仅是迈向未来的一小步,
05:51
if we implement实行 these new technologies技术
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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自组装 world世界.
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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掌声)
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(掌声)
Translated by Felix Chen
Reviewed by Chunxiang Qian

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ABOUT THE SPEAKER
Skylar Tibbits - Inventor
Skylar 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.

More profile about the speaker
Skylar Tibbits | Speaker | TED.com