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