TED@BCG Mumbai
Sougwen Chung: Why I draw with robots
鍾愫君: 與機器人共同作畫
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當人類和機器人共同創造藝術時，會發生什麼？在這場令人歎為觀止的演講中，藝術家鍾愫君（Sougwen Chung）展示了她如何將自己的藝術風格「傳授」給機器，並在意外發現機器人也犯錯誤後，分享了他們的合作成果。她說：「人機系統的優點之一就是他們固有的、共同的失誤。」
Sougwen Chung - Artist, researcher
Sougwen 愫君 Chung is an artist and researcher whose work explores the dynamics between humans and systems. Full bio
Sougwen 愫君 Chung is an artist and researcher whose work explores the dynamics between humans and systems. Full bio
Double-click the English transcript below to play the video.
00:12
Many of us here use technology
in our day-to-day.
in our day-to-day.
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在座的各位大多在日常中使用科技，
00:16
And some of us rely
on technology to do our jobs.
on technology to do our jobs.
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有些人的工作離不開科技。
00:19
For a while, I thought of machines
and the technologies that drive them
and the technologies that drive them
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有一陣子，我認為機器、科技
00:23
as perfect tools that could make my work
more efficient and more productive.
more efficient and more productive.
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只是實現工作高產、高效的工具。
00:28
But with the rise of automation
across so many different industries,
across so many different industries,
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但隨着自動化技術滲透各產業，
00:31
it led me to wonder:
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我不禁思考，
00:33
If machines are starting
to be able to do the work
to be able to do the work
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如果機器能夠做人類的傳統工作，
00:35
traditionally done by humans,
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00:37
what will become of the human hand?
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那人類的手用來做什麼？
00:40
How does our desire for perfection,
precision and automation
precision and automation
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對完美、精確和自動化的追求
00:44
affect our ability to be creative?
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如何影響我們的創造力？
00:46
In my work as an artist and researcher,
I explore AI and robotics
I explore AI and robotics
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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。
來開發人類的創造力。
00:50
to develop new processes
for human creativity.
for human creativity.
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00:54
For the past few years,
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過去幾年裡，
00:55
I've made work alongside machines,
data and emerging technologies.
data and emerging technologies.
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我運用機器、數據
和新型技術進行創作。
和新型技術進行創作。
01:00
It's part of a lifelong fascination
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其中永恆的魅力
01:02
about the dynamics
of individuals and systems
of individuals and systems
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在於人與技術間奇妙的動力學，
01:04
and all the messiness that that entails.
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還有其中不可避免的混亂。
01:07
It's how I'm exploring questions about
where AI ends and we begin
where AI ends and we begin
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我借此來探索 AI 與人類的邊界
01:12
and where I'm developing processes
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以及探索未來感官融合的可能。
01:13
that investigate potential
sensory mixes of the future.
sensory mixes of the future.
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01:17
I think it's where philosophy
and technology intersect.
and technology intersect.
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我覺得這是哲學與技術的交匯。
01:20
Doing this work
has taught me a few things.
has taught me a few things.
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這項工作教會了我一些道理，
01:23
It's taught me how embracing imperfection
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它教會我，坦然接受不完美
01:26
can actually teach us
something about ourselves.
something about ourselves.
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有助於更認識自己。
01:29
It's taught me that exploring art
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它教會我，探索藝術，
01:31
can actually help shape
the technology that shapes us.
the technology that shapes us.
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能夠更好地構建科技，然後構建生活。
01:35
And it's taught me
that combining AI and robotics
that combining AI and robotics
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它教會我，將 AI 和機器人
01:38
with traditional forms of creativity --
visual arts in my case --
visual arts in my case --
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結合到傳統創作中，
01:41
can help us think a little bit more deeply
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能幫助我們更深入理解
何為人類，何為機器。
何為人類，何為機器。
01:44
about what is human
and what is the machine.
and what is the machine.
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01:47
And it's led me to the realization
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它讓我意識到，
在前行路上，
01:49
that collaboration is the key
to creating the space for both
to creating the space for both
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合作是創造人機生存空間的關機。
01:52
as we move forward.
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01:54
It all started with a simple
experiment with machines,
experiment with machines,
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這一切都緣起於
一個簡單的機器實驗，
一個簡單的機器實驗，
01:57
called "Drawing Operations
Unit: Generation 1."
Unit: Generation 1."
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那個機器叫「第一代繪畫器」
（Drawing Operations Unit: Generation 1）
（Drawing Operations Unit: Generation 1）
02:00
I call the machine "D.O.U.G." for short.
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我叫它「道格」（D.O.U.G.）。
02:02
Before I built D.O.U.G,
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在「道格」之前，
02:04
I didn't know anything
about building robots.
about building robots.
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我對製造機器人一無所知。
02:07
I took some open-source
robotic arm designs,
robotic arm designs,
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我參照了一些開源的機械臂設計，
02:10
I hacked together a system
where the robot would match my gestures
where the robot would match my gestures
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編成了一個系統，來實現匹配手勢，
02:13
and follow [them] in real time.
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並實時模仿。
02:15
The premise was simple:
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方式很簡單：
02:16
I would lead, and it would follow.
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我畫，它模仿。
02:19
I would draw a line,
and it would mimic my line.
and it would mimic my line.
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我畫一條線，它也畫一條線。
02:22
So back in 2015, there we were,
drawing for the first time,
drawing for the first time,
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2015 年，我們第一次
02:26
in front of a small audience
in New York City.
in New York City.
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在紐約市的一小群觀衆前作畫。
02:28
The process was pretty sparse --
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整個過程很冷清，
02:31
no lights, no sounds,
nothing to hide behind.
nothing to hide behind.
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沒有燈光，沒有音樂，什麼都沒有，
02:35
Just my palms sweating
and the robot's new servos heating up.
and the robot's new servos heating up.
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只有手掌冒出的汗，
和機械臂升高的溫度。
和機械臂升高的溫度。
02:38
(Laughs) Clearly, we were
not built for this.
not built for this.
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（笑）顯然這不是最理想的效果。
02:41
But something interesting happened,
something I didn't anticipate.
something I didn't anticipate.
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但我不曾預料到，
一些有趣的事情發生了。
一些有趣的事情發生了。
02:45
See, D.O.U.G., in its primitive form,
wasn't tracking my line perfectly.
wasn't tracking my line perfectly.
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初代的「道格」並沒有
完美地模仿我的線條，
完美地模仿我的線條，
02:49
While in the simulation
that happened onscreen
that happened onscreen
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在計算機模擬中
02:52
it was pixel-perfect,
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它的模仿是精準完美的，
02:53
in physical reality,
it was a different story.
it was a different story.
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但到了現實世界，
就是另一番景象了。
就是另一番景象了。
02:56
It would slip and slide
and punctuate and falter,
and punctuate and falter,
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它會滑動，會卡頓，會晃動，
02:59
and I would be forced to respond.
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於是我不得不應和它的線條。
03:01
There was nothing pristine about it.
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它的狀態並不完美，
03:03
And yet, somehow, the mistakes
made the work more interesting.
made the work more interesting.
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然而這些失誤讓作品更加有趣，
03:06
The machine was interpreting
my line but not perfectly.
my line but not perfectly.
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機器模仿我的線條，但並不完美，
03:09
And I was forced to respond.
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於是我必須應和它，
03:10
We were adapting
to each other in real time.
to each other in real time.
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我們不斷實時地熟悉彼此。
03:13
And seeing this taught me a few things.
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我領悟到了一些事情，
03:15
It showed me that our mistakes
actually made the work more interesting.
actually made the work more interesting.
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我們的失誤實際上讓創作更加有趣，
03:20
And I realized that, you know,
through the imperfection of the machine,
through the imperfection of the machine,
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透過機器的不完美，
03:24
our imperfections became
what was beautiful about the interaction.
what was beautiful about the interaction.
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我們的不完美成就了人機交流之美。
03:29
And I was excited,
because it led me to the realization
because it led me to the realization
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我激動地意識到，
03:32
that maybe part of the beauty
of human and machine systems
of human and machine systems
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或許人機系統的美妙之處，
03:36
is their shared inherent fallibility.
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有一部分來自共同的、固有的失誤。
03:39
For the second generation of D.O.U.G.,
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到了「道格」第二代，
03:41
I knew I wanted to explore this idea.
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我知道我要探索這個想法。
03:43
But instead of an accident produced
by pushing a robotic arm to its limits,
by pushing a robotic arm to its limits,
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我並不打算放大機器的失誤，
03:47
I wanted to design a system
that would respond to my drawings
that would respond to my drawings
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而是設計能夠以意料之外的方式
03:50
in ways that I didn't expect.
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回應我筆畫的系統。
03:52
So, I used a visual algorithm
to extract visual information
to extract visual information
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於是，我運用機器視覺算法
03:56
from decades of my digital
and analog drawings.
and analog drawings.
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來提取我幾十年來的數字繪畫。
03:59
I trained a neural net on these drawings
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我以此訓練了一個神經網路，
04:01
in order to generate
recurring patterns in the work
recurring patterns in the work
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優化機器的遞歸模式
需要大量的樣本，
需要大量的樣本，
04:04
that were then fed through custom software
back into the machine.
back into the machine.
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這些樣本經過專門軟件
處理後導入機器。
處理後導入機器。
04:07
I painstakingly collected
as many of my drawings as I could find --
as many of my drawings as I could find --
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於是我使盡渾身解數
彙集我的畫作，
彙集我的畫作，
04:12
finished works, unfinished experiments
and random sketches --
and random sketches --
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成品、未完成的實驗品、隨筆畫——
04:16
and tagged them for the AI system.
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把它們標記給 AI 系統。
04:18
And since I'm an artist,
I've been making work for over 20 years.
I've been making work for over 20 years.
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作為藝術家，我作畫超過二十年，
04:22
Collecting that many drawings took months,
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所以彙集這些畫作花了幾個月的時間，
04:24
it was a whole thing.
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這是個大工程。
04:25
And here's the thing
about training AI systems:
about training AI systems:
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說到訓練人工智慧，
04:28
it's actually a lot of hard work.
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這其實要費一番功夫，
04:31
A lot of work goes on behind the scenes.
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背後有很多工作要做。
04:33
But in doing the work,
I realized a little bit more
I realized a little bit more
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但過程中，我對人工智慧的結構
04:35
about how the architecture
of an AI is constructed.
of an AI is constructed.
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瞭解得更深入了一點。
04:39
And I realized it's not just made
of models and classifiers
of models and classifiers
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我意識到這不僅是
神經網路的模型和分類器，
神經網路的模型和分類器，
04:42
for the neural network.
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04:43
But it's a fundamentally
malleable and shapable system,
malleable and shapable system,
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更是可延展、可塑的系統，
04:47
one in which the human hand
is always present.
is always present.
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人類的手始終參與其中。
04:50
It's far from the omnipotent AI
we've been told to believe in.
we've been told to believe in.
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它不再是我們認為
無所不能的人工智慧。
無所不能的人工智慧。
04:54
So I collected these drawings
for the neural net.
for the neural net.
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用畫作訓練神經網路後，
04:56
And we realized something
that wasn't previously possible.
that wasn't previously possible.
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前所未有的事情發生了——
05:00
My robot D.O.U.G. became
a real-time interactive reflection
a real-time interactive reflection
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我的機器人道格
在實時交互的創作中，
在實時交互的創作中，
呼應了我過去人生幾十年的作品。
05:05
of the work I'd done
through the course of my life.
through the course of my life.
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05:07
The data was personal,
but the results were powerful.
but the results were powerful.
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輸入的數據僅來源於我，
輸出的結果卻遠超於我。
輸出的結果卻遠超於我。
05:11
And I got really excited,
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我感到非常興奮，
05:13
because I started thinking maybe
machines don't need to be just tools,
machines don't need to be just tools,
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或許機器不該只是工具，
05:17
but they can function
as nonhuman collaborators.
as nonhuman collaborators.
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它還可以是非人的合作者。
05:21
And even more than that,
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更進一步想，
05:23
I thought maybe
the future of human creativity
the future of human creativity
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也許未來的人類創作
不在於作品本身，
不在於作品本身，
05:25
isn't in what it makes
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05:27
but how it comes together
to explore new ways of making.
to explore new ways of making.
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而在於人機共同探索藝術的方式。
05:31
So if D.O.U.G._1 was the muscle,
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如果說一代「道格」是肌肉，
05:33
and D.O.U.G._2 was the brain,
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二代「道格」是大腦，
05:35
then I like to think
of D.O.U.G._3 as the family.
of D.O.U.G._3 as the family.
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三代「道格」便是家人。
05:38
I knew I wanted to explore this idea
of human-nonhuman collaboration at scale.
of human-nonhuman collaboration at scale.
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我想要將人機合作的想法放大。
05:43
So over the past few months,
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於是在過去幾個月裡，
05:44
I worked with my team
to develop 20 custom robots
to develop 20 custom robots
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我和團隊造出了 20 個定製的機器人
05:47
that could work with me as a collective.
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與我集體創作。
05:49
They would work as a group,
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它們會像團隊一樣協作，
05:51
and together, we would collaborate
with all of New York City.
with all of New York City.
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我和它們一起，
與整個紐約市攜手合作。
與整個紐約市攜手合作。
05:54
I was really inspired
by Stanford researcher Fei-Fei Li,
by Stanford researcher Fei-Fei Li,
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史丹佛大學的李飛飛教授
激勵了我的靈感，她說：
激勵了我的靈感，她說：
05:57
who said, "if we want to teach
machines how to think,
machines how to think,
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「要想教機器如何思考，
05:59
we need to first teach them how to see."
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先要教它如何看見。」
06:01
It made me think of the past decade
of my life in New York,
of my life in New York,
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這讓我想起了
過去幾十年的紐約生活，
過去幾十年的紐約生活，
06:04
and how I'd been all watched over by these
surveillance cameras around the city.
surveillance cameras around the city.
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城市上空的攝像頭一直俯視著我。
06:08
And I thought it would be
really interesting
really interesting
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如果我用它們來訓練機器視覺，
06:10
if I could use them
to teach my robots to see.
to teach my robots to see.
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那一定很有趣。
06:12
So with this project,
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在這個專案中，
06:14
I thought about the gaze of the machine,
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我思考著機器對我們的凝視。
06:16
and I began to think about vision
as multidimensional,
as multidimensional,
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於是我開始將視覺看成多元的，
06:20
as views from somewhere.
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看成某處來的觀點。
06:22
We collected video
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我們從各處收集影片，
06:24
from publicly available
camera feeds on the internet
camera feeds on the internet
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網路上的公眾攝影機拍的影片，
06:27
of people walking on the sidewalks,
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人行道上的行人，
車道上的轎車、計程車……
06:28
cars and taxis on the road,
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城市中的各類運動軌跡。
06:30
all kinds of urban movement.
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06:33
We trained a vision algorithm
on those feeds
on those feeds
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基於一種叫「光流法」的技術，
06:35
based on a technique
called "optical flow,"
called "optical flow,"
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我們訓練了一個視覺算法，
06:38
to analyze the collective density,
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來分析收集到的人流密度，
06:40
direction, dwell and velocity states
of urban movement.
of urban movement.
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都市中軌跡的方向、速度，
以及生活方式。
以及生活方式。
06:44
Our system extracted those states
from the feeds as positional data
from the feeds as positional data
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系統從海量的位置數據中
提取出這些參數，
提取出這些參數，
06:48
and became pads for my
robotic units to draw on.
robotic units to draw on.
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我的機器人依靠這些數據來作畫。
06:51
Instead of a collaboration of one-to-one,
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與之前的一對一合作不同，
06:54
we made a collaboration of many-to-many.
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我們實現了多對多的合作。
06:57
By combining the vision of human
and machine in the city,
and machine in the city,
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透過結合城市中
人類與機器的視界，
人類與機器的視界，
07:01
we reimagined what
a landscape painting could be.
a landscape painting could be.
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我們重構了景觀繪畫。
07:03
Throughout all of my
experiments with D.O.U.G.,
experiments with D.O.U.G.,
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在與「道格」共同作畫的經歷中，
07:06
no two performances
have ever been the same.
have ever been the same.
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沒有哪兩次是完全相同的。
07:08
And through collaboration,
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透過合作，
07:10
we create something that neither of us
could have done alone:
could have done alone:
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我們完成了無法獨自做到的事，
07:13
we explore the boundaries
of our creativity,
of our creativity,
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我們共同探索了創作的邊界、
07:15
human and nonhuman working in parallel.
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人類與非人類的平行工作。
07:19
I think this is just the beginning.
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我想這才剛剛開始。
07:22
This year, I've launched Scilicet,
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今年， 我創辦了 Scilicet 實驗室，
07:24
my new lab exploring human
and interhuman collaboration.
and interhuman collaboration.
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以探索人類和人類間的合作。
07:29
We're really interested
in the feedback loop
in the feedback loop
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我們對人類、AI 與生態系統之間的
07:31
between individual, artificial
and ecological systems.
and ecological systems.
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反饋關係很感興趣。
07:36
We're connecting human and machine output
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我們將人類和 AI
07:38
to biometrics and other kinds
of environmental data.
of environmental data.
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與生物特徵識別數據
和其他環境數據相聯繫，
和其他環境數據相聯繫，
07:41
We're inviting anyone who's interested
in the future of work, systems
in the future of work, systems
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我們邀請所有
對未來的作品、系統、
對未來的作品、系統、
07:45
and interhuman collaboration
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人類合作感興趣的人
07:47
to explore with us.
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加入我們，一同探索。
07:48
We know it's not just technologists
that have to do this work
that have to do this work
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這項事業不僅屬於科技工作者，
07:52
and that we all have a role to play.
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每個人都能作出貢獻。
07:54
We believe that by teaching machines
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我們相信
透過教授機器
完成人類的傳統工作，
完成人類的傳統工作，
07:56
how to do the work
traditionally done by humans,
traditionally done by humans,
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07:59
we can explore and evolve our criteria
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我們就能探索和更新
08:02
of what's made possible by the human hand.
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對人類創造可能性的認知。
08:04
And part of that journey
is embracing the imperfections
is embracing the imperfections
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這段旅程的一部分是悅納不完美，
08:08
and recognizing the fallibility
of both human and machine,
of both human and machine,
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發現人機共有的缺陷，
08:12
in order to expand the potential of both.
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以此更好地發掘兩者的潛能。
08:14
Today, I'm still in pursuit
of finding the beauty
of finding the beauty
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今天，我仍追求著人機創作的美妙。
08:17
in human and nonhuman creativity.
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08:19
In the future, I have no idea
what that will look like,
what that will look like,
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我還不知道未來這會變得怎樣，
08:23
but I'm pretty curious to find out.
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但我滿懷好奇，探索不止。
08:25
Thank you.
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謝謝大家。
08:26
(Applause)
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（掌聲）
ABOUT THE SPEAKER
Sougwen Chung - Artist, researcherSougwen 愫君 Chung is an artist and researcher whose work explores the dynamics between humans and systems.
Why you should listen
Sougwen Chung's work explores the mark-made-by-hand and the mark-made-by-machine as an approach to understanding the dynamics of humans and systems. Chung is a former research fellow at MIT’s Media Lab and a pioneer in the field of human-machine collaboration. In 2019, she was selected as the Woman of the Year in Monaco for achievement in the Arts & Sciences.
In 2018 she was an inaugural E.A.T. Artist in Resident in partnership with New Museum and Bell Labs, and was awarded a commission for her project Omnia per Omnia. In 2016, Chung received Japan Media Art’s Excellence Award in for her project, Drawing Operations. She is a former research fellow at MIT’s Media Lab. She has been awarded Artist in Residence positions at Google, Eyebeam, Japan Media Arts and Pier 9 Autodesk. Her speculative critical practice spans performance, installation and drawings which have been featured in numerous exhibitions at museums and galleries around the world.
Sougwen Chung | Speaker | TED.com