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Skylar Tibbits 談我們能製造出可自我建構的物體嗎?

Skylar Tibbits: Can we make things that make themselves?

 

Photo of three lions hunting on the Serengeti.

講者:Skylar Tibbits

2011年2月演講,2011年9月在TED2011上線

 

翻譯:洪曉慧

編輯:朱學恆

簡繁轉換:洪曉慧

後製:洪曉慧

字幕影片後制:謝旻均

 

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閱讀中文字幕純文字版本

 

關於這場演講

麻省理工學院研究員Skylar Tibbits致力於自組裝研究-這取代了一般建造物體(椅子或摩天大樓)的觀念,創造出可自我建構的材料,使用相當類似於DNA自我鏈合的方式。這是個遠大想法的初期階段;Tibbits向大家展示三個實驗中的計劃,預示自組裝的未來可能是什麼模樣。

 

關於Skylar Tibbits

Skylar Tibbits是TED會員、藝術家及計算建築師,致力於研究可進行自組裝的「智慧」元件。

 

為什麼要聽他演講

Skylar Tibbits是一個訓練有素的建築師、設計師和電腦科學家,他的研究著重於為我們物理環境中的大規模結構進行自組裝技術開發。在麻省理工學院建築系,他指導一個研究生設計工作室,也在麻省理工學院媒體實驗室研討課程擔任共同指導教師。在此之前,他曾任職於一些設計事務所,包括Zaha Hadid建築事務所,Asymptote建築事務所,SKIII Space Variations及Point b Design。他的作品曾在古根漢博物館及北京雙年展展出。

 

這些年來,Tibbits與一些有影響力的人合作,包括Neil Gershenfeld和麻省理工學院電位與原子中心,麻省理工學院的Erik和Marty Demaine,種子傳媒集團的Adam Bly和THEVERYMANY的Marc Fornes。2007年,他和Marc Fornes共同策劃了Scriptedbypurpose,這是第一個專門著重於設計過程的展覽。同樣在2007年,他創立了SJET,這是一個在實驗計算與設計領域進行多方面實作及研究的平台。SJET平台跨越多個學科,包括建築與設計、製造、計算機科學和機器人。

 

Skylar Tibbits的英語網上資料

Home: sjet.us

 

[TED科技‧娛樂‧設計]

已有中譯字幕的TED影片目錄(繁體)(簡體)。請注意繁簡目錄是不一樣的。

 

Skylar Tibbits 談我們能製造出可自我建構的物體嗎?

今天,我想向大家說明未來物品的製造方式。我相信不久後,我們的建築物和機器都將擁有自組裝、複製和自我修復的能力,所以我將要告訴你們我所認為的物品製造現況,並將其與一些自然系統做比較。

 

因此,以製造的現況來說,我們有摩天大樓,建造組裝耗時兩年半,其中有五十萬到一百萬個材料元件,相當複雜;並擁有鋼鐵、水泥玻璃這些令人振奮的新科技。我們有能使人類進入太空的神奇機器,建造組裝耗時五年,含有250萬個材料元件。

 

但另一方面,如果你觀察自然系統,人類有200萬種蛋白質,能在十萬分之一秒內完成折疊;或擁有30億個鹼基對的DNA,可在大約一小時內完成複製。自然系統中存在著這所有的複雜性,但它們非常有效率,遠比任何我們可以建造的東西更有效率、更複雜。以能源方面來說,它們節能得多;它們幾乎不會犯錯,並可自我修復以增加使用壽命。

 

因此,自然系統中具有相當令人感興趣的部份,如果我們能將其轉換到我們的建築環境中,就能使我們建造物體的方式擁有令人興奮的潛力。我認為其中關鍵在於自組裝。

 

因此,如果我們想將自組裝用於物理環境中,我認為其中有四個關鍵因素。首先,我們必須將想要建設之物的複雜性解碼,就是我們的建築物和機器。我們必須將其解碼成簡單序列-基本上就是建構我們建築的DNA,然後,我們必須擁有可編程的元件,以使用這些序列進行折疊或重新配置。我們需要一些能使其活化的能源,使我們的元件可經由程式進行折疊;我們需要某些類型的錯誤校正冗餘資料,確保我們能成功構建出我們想要的物體。

 

所以我想讓大家觀賞一些計劃,這是我同事和我正於麻省理工學院研究的,以實現自組裝未來這個想法。頭兩個是MacroBot和DeciBot機器人,這些計劃研究的是大型變形機器人-8英尺寬,12英尺長的模擬蛋白質,它們被嵌入機電設備及感測器中。你將想折疊的形態解碼成角度序列-負120度,負120度,0度,0度,120度,負120度-像這樣的角度。所以一個角度序列或轉動序列,藉由傳輸線傳送,每個單位元件都接收到各自的訊息-這是負120度。它轉向這個角度,檢查是否形成正確角度,然後將其傳遞給相鄰元件。

 

這些是研究這個計劃的傑出科學家、工程師、設計師,我認為這確實讓我們思考一個事實:它的功能真的可以擴展嗎?我的意思是,耗費數千美元、大量工時,成功製造出這個八腳機器人,我們真的能擴展它的功能嗎?我們真的能將這個機器人技術嵌入所有元件嗎?下個問題是,著眼於它的被動性質,或是說被動地擁有重新建構之編程能力。但我們更進一步試著使它擁有實際運算的能力,它基本上嵌入了可進行運算的最基本建構模塊,即數位邏輯閘,直接嵌入元件當中。

 

所以,這是一個反及閘(NAND gate)。你看到的這個四面體就是能進行運算的邏輯閘,共有兩個用於輸入的四面體,其中一個輸入值來自於使用者,就是由你控制磚塊的建構;另一個輸入值來自於之前已放置的磚塊。然後,它會產生一個三維空間的輸出,因此,這意味著,使用者可以開始輸入他們希望磚塊放置的位置。它的計算有賴於本身之前的位置,以及你希望它放置的位置。現在它開始做三維空間的移動-上升或下降,因此,在左側輸入[1,1]時會輸出0,這代表下降;在右側輸入[0,0]時會輸出1,這代表上升。其中真正的含義是,現在我們的結構包含了我們想要建設的藍圖。

 

因此,它已嵌入我們建構好的所有訊息。這意味著我們能使它進行某種形式的自我複製。在這個例子中,我稱之為自我導向複製。因為你的結構中包含了精確的藍圖,如果其中有錯誤,你可以更換一個元件,所有原本嵌入的訊息可以告訴你如何進行修正,所以,你可以讓某種東西沿著它爬上並讀取資料,也可使其逐一輸出。這是直接嵌入的訊息,沒有任何外部指令。

 

我展示最後一個計劃,稱為偏向鏈,這或許是我們目前在被動自組裝系統方面所擁有的最令人興奮的例子。因此,它具有重組性及可編程性,這使其成為一個完全被動的系統。所以基本上這是一條由元件組成的鏈條,每個元件完全相同且具偏向性,因此,每條鏈子或每個元件,可向左或向右轉動,所以,當你組裝這個鏈條時,基本上是將其編程,告訴每個元件單位該向右或向左轉,因此,當你搖晃這條鏈子時,它會折疊成任何你所編入程式的結構。在這個例子中是螺旋形,或在這個例子中是兩個彼此相鄰的立方體,所以你基本上可以編入任何三維形狀的程式,或一維、二維形狀-讓這個鏈條形成完全被動的組態。

 

那麼,這讓我們對未來有什麼想法呢?我認為這告訴我們一些關於自組裝、複製、修復功能的新可能性,存在於我們的物理結構、建築物及機器當中。這些元件中存在新的可編程性,我們亦可從中得到新的運算可能性。我們將可擁有空間上的運算。試想,如果我們的建築物、橋樑、機器、所有一磚一瓦都可進行計算,這是驚人的平行及分佈式計算能力,新的設計可能性,因此它擁有令人興奮的潛力。因此,我認為,我向大家展示的這些計劃,只是向未來邁出的小小一步,如果我們能將這個新技術實施在一個自組裝的新世界。

 

謝謝

 

(掌聲)

 

以下為系統擷取之英文原文

About this Talk

MIT researcher Skylar Tibbits works on self-assembly -- the idea that instead of building something (a chair, a skyscraper), we can create materials that build themselves, much the way a strand of DNA zips itself together. It's a big concept at early stages; Tibbits shows us three in-the-lab projects that hint at what a self-assembling future might look like.

About the Speaker

Skylar Tibbits, a TED Fellow, is an artist and computational architect working on "smart" components that can assemble themselves. Full bio and more links

Transcript

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.

So in the current state of manufacturing, we have skyscrapers -- two and a half years, 500,000 to a million parts, fairly complex, new and exciting technologies in steel, concrete, glass. We have exciting machines that can take us into space -- five years, 2.5 million parts.

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.

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.

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.

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.

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.

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 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.

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.

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.

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 you are just a tiny step towards this future, if we implement these new technologies for a new self-assembling world.

Thank you.

(Applause)


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