Many of us here use technology in our day-to-day. And some of us rely on technology to do our jobs. For a while, I thought of machines and the technologies that drive them as perfect tools that could make my work more efficient and more productive.
在座的各位大多在日常中使用科技, 有些人的工作離不開科技。 有一陣子,我認為機器、科技 只是實現工作高產、高效的工具。
But with the rise of automation across so many different industries, it led me to wonder: If machines are starting to be able to do the work traditionally done by humans, what will become of the human hand? How does our desire for perfection, precision and automation affect our ability to be creative?
但隨着自動化技術滲透各產業, 我不禁思考, 如果機器能夠做人類的傳統工作, 那人類的手用來做什麼? 對完美、精確和自動化的追求 如何影響我們的創造力?
In my work as an artist and researcher, I explore AI and robotics to develop new processes for human creativity. For the past few years, I've made work alongside machines, data and emerging technologies. It's part of a lifelong fascination about the dynamics of individuals and systems and all the messiness that that entails. It's how I'm exploring questions about where AI ends and we begin and where I'm developing processes that investigate potential sensory mixes of the future. I think it's where philosophy and technology intersect.
作為藝術家和研究者, 我研究運用人工智慧和機器人 來開發人類的創造力。 過去幾年裡, 我運用機器、數據 和新型技術進行創作。 其中永恆的魅力 在於人與技術間奇妙的動力學, 還有其中不可避免的混亂。 我借此來探索 AI 與人類的邊界 以及探索未來感官融合的可能。 我覺得這是哲學與技術的交匯。
Doing this work has taught me a few things. It's taught me how embracing imperfection can actually teach us something about ourselves. It's taught me that exploring art can actually help shape the technology that shapes us. And it's taught me that combining AI and robotics with traditional forms of creativity -- visual arts in my case -- can help us think a little bit more deeply about what is human and what is the machine. And it's led me to the realization that collaboration is the key to creating the space for both as we move forward.
這項工作教會了我一些道理, 它教會我,坦然接受不完美 有助於更認識自己。 它教會我,探索藝術, 能夠更好地構建科技,然後構建生活。 它教會我,將 AI 和機器人 結合到傳統創作中, 能幫助我們更深入理解 何為人類,何為機器。 它讓我意識到, 在前行路上, 合作是創造人機生存空間的關機。
It all started with a simple experiment with machines, called "Drawing Operations Unit: Generation 1." I call the machine "D.O.U.G." for short. Before I built D.O.U.G, I didn't know anything about building robots. I took some open-source robotic arm designs, I hacked together a system where the robot would match my gestures and follow [them] in real time. The premise was simple: I would lead, and it would follow. I would draw a line, and it would mimic my line.
這一切都緣起於 一個簡單的機器實驗, 那個機器叫「第一代繪畫器」 (Drawing Operations Unit: Generation 1) 我叫它「道格」(D.O.U.G.)。 在「道格」之前, 我對製造機器人一無所知。 我參照了一些開源的機械臂設計, 編成了一個系統,來實現匹配手勢, 並實時模仿。 方式很簡單: 我畫,它模仿。 我畫一條線,它也畫一條線。
So back in 2015, there we were, drawing for the first time, in front of a small audience in New York City. The process was pretty sparse -- no lights, no sounds, nothing to hide behind. Just my palms sweating and the robot's new servos heating up. (Laughs) Clearly, we were not built for this. But something interesting happened, something I didn't anticipate.
2015 年,我們第一次 在紐約市的一小群觀衆前作畫。 整個過程很冷清, 沒有燈光,沒有音樂,什麼都沒有, 只有手掌冒出的汗, 和機械臂升高的溫度。 (笑)顯然這不是最理想的效果。 但我不曾預料到, 一些有趣的事情發生了。
See, D.O.U.G., in its primitive form, wasn't tracking my line perfectly. While in the simulation that happened onscreen it was pixel-perfect, in physical reality, it was a different story. It would slip and slide and punctuate and falter, and I would be forced to respond. There was nothing pristine about it. And yet, somehow, the mistakes made the work more interesting. The machine was interpreting my line but not perfectly. And I was forced to respond. We were adapting to each other in real time.
初代的「道格」並沒有 完美地模仿我的線條, 在計算機模擬中 它的模仿是精準完美的, 但到了現實世界, 就是另一番景象了。 它會滑動,會卡頓,會晃動, 於是我不得不應和它的線條。 它的狀態並不完美, 然而這些失誤讓作品更加有趣, 機器模仿我的線條,但並不完美, 於是我必須應和它, 我們不斷實時地熟悉彼此。
And seeing this taught me a few things. It showed me that our mistakes actually made the work more interesting. And I realized that, you know, through the imperfection of the machine, our imperfections became what was beautiful about the interaction. And I was excited, because it led me to the realization that maybe part of the beauty of human and machine systems is their shared inherent fallibility. For the second generation of D.O.U.G., I knew I wanted to explore this idea. But instead of an accident produced by pushing a robotic arm to its limits, I wanted to design a system that would respond to my drawings in ways that I didn't expect.
我領悟到了一些事情, 我們的失誤實際上讓創作更加有趣, 透過機器的不完美, 我們的不完美成就了人機交流之美。 我激動地意識到, 或許人機系統的美妙之處, 有一部分來自共同的、固有的失誤。 到了「道格」第二代, 我知道我要探索這個想法。 我並不打算放大機器的失誤, 而是設計能夠以意料之外的方式 回應我筆畫的系統。
So, I used a visual algorithm to extract visual information from decades of my digital and analog drawings. I trained a neural net on these drawings in order to generate recurring patterns in the work that were then fed through custom software back into the machine. I painstakingly collected as many of my drawings as I could find -- finished works, unfinished experiments and random sketches -- and tagged them for the AI system. And since I'm an artist, I've been making work for over 20 years. Collecting that many drawings took months, it was a whole thing.
於是,我運用機器視覺算法 來提取我幾十年來的數字繪畫。 我以此訓練了一個神經網路, 優化機器的遞歸模式 需要大量的樣本, 這些樣本經過專門軟件 處理後導入機器。 於是我使盡渾身解數 彙集我的畫作, 成品、未完成的實驗品、隨筆畫—— 把它們標記給 AI 系統。 作為藝術家,我作畫超過二十年, 所以彙集這些畫作花了幾個月的時間, 這是個大工程。
And here's the thing about training AI systems: it's actually a lot of hard work. A lot of work goes on behind the scenes. But in doing the work, I realized a little bit more about how the architecture of an AI is constructed. And I realized it's not just made of models and classifiers for the neural network. But it's a fundamentally malleable and shapable system, one in which the human hand is always present. It's far from the omnipotent AI we've been told to believe in.
說到訓練人工智慧, 這其實要費一番功夫, 背後有很多工作要做。 但過程中,我對人工智慧的結構 瞭解得更深入了一點。 我意識到這不僅是 神經網路的模型和分類器, 更是可延展、可塑的系統, 人類的手始終參與其中。 它不再是我們認為 無所不能的人工智慧。
So I collected these drawings for the neural net. And we realized something that wasn't previously possible. My robot D.O.U.G. became a real-time interactive reflection of the work I'd done through the course of my life. The data was personal, but the results were powerful. And I got really excited, because I started thinking maybe machines don't need to be just tools, but they can function as nonhuman collaborators. And even more than that, I thought maybe the future of human creativity isn't in what it makes but how it comes together to explore new ways of making.
用畫作訓練神經網路後, 前所未有的事情發生了—— 我的機器人道格 在實時交互的創作中, 呼應了我過去人生幾十年的作品。 輸入的數據僅來源於我, 輸出的結果卻遠超於我。 我感到非常興奮, 或許機器不該只是工具, 它還可以是非人的合作者。 更進一步想, 也許未來的人類創作 不在於作品本身, 而在於人機共同探索藝術的方式。
So if D.O.U.G._1 was the muscle, and D.O.U.G._2 was the brain, then I like to think of D.O.U.G._3 as the family. I knew I wanted to explore this idea of human-nonhuman collaboration at scale. So over the past few months, I worked with my team to develop 20 custom robots that could work with me as a collective. They would work as a group, and together, we would collaborate with all of New York City.
如果說一代「道格」是肌肉, 二代「道格」是大腦, 三代「道格」便是家人。 我想要將人機合作的想法放大。 於是在過去幾個月裡, 我和團隊造出了 20 個定製的機器人 與我集體創作。 它們會像團隊一樣協作, 我和它們一起, 與整個紐約市攜手合作。
I was really inspired by Stanford researcher Fei-Fei Li, who said, "if we want to teach machines how to think, we need to first teach them how to see." It made me think of the past decade of my life in New York, and how I'd been all watched over by these surveillance cameras around the city. And I thought it would be really interesting if I could use them to teach my robots to see. So with this project, I thought about the gaze of the machine, and I began to think about vision as multidimensional, as views from somewhere. We collected video from publicly available camera feeds on the internet of people walking on the sidewalks, cars and taxis on the road, all kinds of urban movement. We trained a vision algorithm on those feeds based on a technique called "optical flow," to analyze the collective density, direction, dwell and velocity states of urban movement. Our system extracted those states from the feeds as positional data and became pads for my robotic units to draw on. Instead of a collaboration of one-to-one, we made a collaboration of many-to-many. By combining the vision of human and machine in the city, we reimagined what a landscape painting could be.
史丹佛大學的李飛飛教授 激勵了我的靈感,她說: 「要想教機器如何思考, 先要教它如何看見。」 這讓我想起了 過去幾十年的紐約生活, 城市上空的攝像頭一直俯視著我。 如果我用它們來訓練機器視覺, 那一定很有趣。 在這個專案中, 我思考著機器對我們的凝視。 於是我開始將視覺看成多元的, 看成某處來的觀點。 我們從各處收集影片, 網路上的公眾攝影機拍的影片, 人行道上的行人, 車道上的轎車、計程車…… 城市中的各類運動軌跡。 基於一種叫「光流法」的技術, 我們訓練了一個視覺算法, 來分析收集到的人流密度, 都市中軌跡的方向、速度, 以及生活方式。 系統從海量的位置數據中 提取出這些參數, 我的機器人依靠這些數據來作畫。 與之前的一對一合作不同, 我們實現了多對多的合作。 透過結合城市中 人類與機器的視界, 我們重構了景觀繪畫。
Throughout all of my experiments with D.O.U.G., no two performances have ever been the same. And through collaboration, we create something that neither of us could have done alone: we explore the boundaries of our creativity, human and nonhuman working in parallel.
在與「道格」共同作畫的經歷中, 沒有哪兩次是完全相同的。 透過合作, 我們完成了無法獨自做到的事, 我們共同探索了創作的邊界、 人類與非人類的平行工作。
I think this is just the beginning. This year, I've launched Scilicet, my new lab exploring human and interhuman collaboration. We're really interested in the feedback loop between individual, artificial and ecological systems. We're connecting human and machine output to biometrics and other kinds of environmental data. We're inviting anyone who's interested in the future of work, systems and interhuman collaboration to explore with us. We know it's not just technologists that have to do this work and that we all have a role to play.
我想這才剛剛開始。 今年, 我創辦了 Scilicet 實驗室, 以探索人類和人類間的合作。 我們對人類、AI 與生態系統之間的 反饋關係很感興趣。 我們將人類和 AI 與生物特徵識別數據 和其他環境數據相聯繫, 我們邀請所有 對未來的作品、系統、 人類合作感興趣的人 加入我們,一同探索。 這項事業不僅屬於科技工作者, 每個人都能作出貢獻。
We believe that by teaching machines how to do the work traditionally done by humans, we can explore and evolve our criteria of what's made possible by the human hand. And part of that journey is embracing the imperfections and recognizing the fallibility of both human and machine, in order to expand the potential of both.
我們相信 透過教授機器 完成人類的傳統工作, 我們就能探索和更新 對人類創造可能性的認知。 這段旅程的一部分是悅納不完美, 發現人機共有的缺陷, 以此更好地發掘兩者的潛能。
Today, I'm still in pursuit of finding the beauty in human and nonhuman creativity. In the future, I have no idea what that will look like, but I'm pretty curious to find out.
今天,我仍追求著人機創作的美妙。 我還不知道未來這會變得怎樣, 但我滿懷好奇,探索不止。
Thank you.
謝謝大家。
(Applause)
(掌聲)