TED@BCG Mumbai
Sougwen Chung: Why I draw with robots
钟愫君: 我为何与机器人共同作画
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当人类和机器人一起创造艺术时会发生什么？在这场令人叹为观止的演讲中，艺术家钟愫君（Sougwen Chung）展示了她如何将自己的艺术风格“传授”给一台机器——并在意外发现机器人也会犯错后，分享了他们合作的成果，她说：“人类和机器系统的美妙之一正是它们固有的、共同的不完美。 ”
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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在座的各位在日常生活中
都会使用科技，
都会使用科技，
00:16
And some of us rely
on technology to do our jobs.
on technology to do our jobs.
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许多人依赖科技来
进行他们的工作。
进行他们的工作。
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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有一段时间，我认为机器和科技
00:23
as perfect tools that could make my work
more efficient and more productive.
more efficient and more productive.
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只是让我的工作更高效、高产的
完美工具。
完美工具。
00:28
But with the rise of automation
across so many different industries,
across so many different industries,
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但随着自动化技术
在各行各业的崛起，
在各行各业的崛起，
00:31
it led me to wonder:
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让我不禁试想：
00:33
If machines are starting
to be able to do the work
to be able to do the work
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如果机器能够完成
00:35
traditionally done by humans,
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原本由人类做的工作，
00:37
what will become of the human hand?
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那我们人类之手又能做些什么呢？
00:40
How does our desire for perfection,
precision and automation
precision and automation
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对完美，精确和自动化的追求
00:44
affect our ability to be creative?
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是如何影响我的创造力？
00:46
In my work as an artist and researcher,
I explore AI and robotics
I explore AI and robotics
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作为艺术家和研究者，
我研究人工智能和机器人，
我研究人工智能和机器人，
00:50
to develop new processes
for human creativity.
for human creativity.
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以此来开发人类创造的新途径。
00:54
For the past few years,
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过去几年里，
00:55
I've made work alongside machines,
data and emerging technologies.
data and emerging technologies.
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我运用机器，数据
和新兴科技进行创作。
和新兴科技进行创作。
01:00
It's part of a lifelong fascination
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其中一部分永恒的魅力
01:02
about the dynamics
of individuals and systems
of individuals and systems
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在于人与技术间奇妙的动态，
01:04
and all the messiness that that entails.
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还有其中不可避免的混乱。
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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我借此来探索人工智能与我们的界限，
01:12
and where I'm developing processes
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以及探索发展
01:13
that investigate potential
sensory mixes of the future.
sensory mixes of the future.
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未来感官融合的可能。
01:17
I think it's where philosophy
and technology intersect.
and technology intersect.
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我想这是哲学与技术的交汇点。
01:20
Doing this work
has taught me a few things.
has taught me a few things.
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这项工作教会了我一些事。
01:23
It's taught me how embracing imperfection
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它教会我拥抱不完美
01:26
can actually teach us
something about ourselves.
something about ourselves.
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可以帮助我们认识自我。
01:29
It's taught me that exploring art
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它教会我探索艺术
01:31
can actually help shape
the technology that shapes us.
the technology that shapes us.
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能够更好的构建科技，
从而塑造自我。
从而塑造自我。
01:35
And it's taught me
that combining AI and robotics
that combining AI and robotics
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它教会我将人工智能和机器人
01:38
with traditional forms of creativity --
visual arts in my case --
visual arts in my case --
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结合到传统的创作中——
以我创作的视觉艺术为例——
以我创作的视觉艺术为例——
01:41
can help us think a little bit more deeply
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能够帮助我们更深入理解
01:44
about what is human
and what is the machine.
and what is the machine.
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何为人类，何为机器。
01:47
And it's led me to the realization
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它让我意识到
01:49
that collaboration is the key
to creating the space for both
to creating the space for both
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在我们进步的路上，
合作是创造人与机器共存空间的关键。
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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这一切都始于
一个简单的机器实验，
一个简单的机器实验，
01:57
called "Drawing Operations
Unit: Generation 1."
Unit: Generation 1."
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实验机器叫“绘图机器：初代”
（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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我把它简称为道格（D.O.U.G.），
02:02
Before I built D.O.U.G,
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在我建造道格之前，
02:04
I didn't know anything
about building robots.
about building robots.
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我对造机器人一无所知，
02:07
I took some open-source
robotic arm designs,
robotic arm designs,
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我参考了一些开源的机器臂设计，
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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编成了一个系统来实现匹配手势，
02:13
and follow [them] in real time.
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并实时模仿它们。
02:15
The premise was simple:
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前提很简单：
02:16
I would lead, and it would follow.
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我画，而它会学我。
02:19
I would draw a line,
and it would mimic my line.
and it would mimic my line.
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我画一条线，
它也会跟着我画一条线。
它也会跟着我画一条线。
02:22
So back in 2015, there we were,
drawing for the first time,
drawing for the first time,
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回到 2015 年，那是我们第一次
02:26
in front of a small audience
in New York City.
in New York City.
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在纽约的一小群观众前作画。
02:28
The process was pretty sparse --
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整个过程非常冷清——
02:31
no lights, no sounds,
nothing to hide behind.
nothing to hide behind.
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没有灯光，没有音效，
也没有什么悬念。
也没有什么悬念。
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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只有手掌冒出的冷汗
和机器臂不断升高的温度。
和机器臂不断升高的温度。
02:38
(Laughs) Clearly, we were
not built for this.
not built for this.
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（笑声）显然，
这不是我们想要的效果。
这不是我们想要的效果。
02:41
But something interesting happened,
something I didn't anticipate.
something I didn't anticipate.
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但有趣的事发生了，
完全出乎意料。
完全出乎意料。
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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初代的道格并没有
完美地模仿我画的线条，
完美地模仿我画的线条，
02:49
While in the simulation
that happened onscreen
that happened onscreen
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在计算器模拟中显示
02:52
it was pixel-perfect,
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它的模仿事精确完美的，
02:53
in physical reality,
it was a different story.
it was a different story.
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但到了现实中，却并非如此。
02:56
It would slip and slide
and punctuate and falter,
and punctuate and falter,
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它会滑动，会卡顿，会晃动，
02:59
and I would be forced to respond.
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于是我不得不附和它的线条。
03:01
There was nothing pristine about it.
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它的状态不完美，
03:03
And yet, somehow, the mistakes
made the work more interesting.
made the work more interesting.
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而这些失误让作品更加有趣，
03:06
The machine was interpreting
my line but not perfectly.
my line but not perfectly.
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机器在模仿我的线条，
但是并不完美，
但是并不完美，
03:09
And I was forced to respond.
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于是变成我在附和机器。
03:10
We were adapting
to each other in real time.
to each other in real time.
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我们不断地实时熟悉彼此。
03:13
And seeing this taught me a few things.
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看到这些，教会了我一些事，
03:15
It showed me that our mistakes
actually made the work more interesting.
actually made the work more interesting.
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我们的失误，实际上
让我们的作品更加有趣，
让我们的作品更加有趣，
03:20
And I realized that, you know,
through the imperfection of the machine,
through the imperfection of the machine,
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我从机器的不完美中意识到，
03:24
our imperfections became
what was beautiful about the interaction.
what was beautiful about the interaction.
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我们的不完美成就了这互动之美。
03:29
And I was excited,
because it led me to the realization
because it led me to the realization
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而我很兴奋，因为它让我意识到
03:32
that maybe part of the beauty
of human and machine systems
of human and machine systems
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或许人类和机器系统的美妙之一
03:36
is their shared inherent fallibility.
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正是他们共同的、固有的不完美。
03:39
For the second generation of D.O.U.G.,
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对于第二代的道格，
03:41
I knew I wanted to explore this idea.
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我知道我要探索这个想法，
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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我并非打算通过放大机器臂的失误，
03:47
I wanted to design a system
that would respond to my drawings
that would respond to my drawings
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而是想要设计一个系统
能够以出其不意的方式
能够以出其不意的方式
03:50
in ways that I didn't expect.
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回应我的画作。
03:52
So, I used a visual algorithm
to extract visual information
to extract visual information
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所以，我运用一个视觉算法
来提取我几十年来的
来提取我几十年来的
03:56
from decades of my digital
and analog drawings.
and analog drawings.
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数字和实体绘图中的视觉样本信息，
03:59
I trained a neural net on these drawings
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以此我训练了一个神经网络
04:01
in order to generate
recurring patterns in the work
recurring patterns in the work
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优化机器的循环模式，
04:04
that were then fed through custom software
back into the machine.
back into the machine.
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视觉样本由经专门的
软件处理导入机器。
软件处理导入机器。
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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于是我煞费苦心地
收集我的所有的画作——
收集我的所有的画作——
04:12
finished works, unfinished experiments
and random sketches --
and random sketches --
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成品，半成品，随手简笔画——
04:16
and tagged them for the AI system.
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把它们标记给人工智能系统。
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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作为一位艺术家，
我作画超过了 20 年，
我作画超过了 20 年，
04:22
Collecting that many drawings took months,
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所以收集这些画作花了好多个月，
04:24
it was a whole thing.
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这是个大工程。
04:25
And here's the thing
about training AI systems:
about training AI systems:
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说到训练人工智能：
04:28
it's actually a lot of hard work.
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这其实大费功夫。
04:31
A lot of work goes on behind the scenes.
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幕后的工作很多很多，
04:33
But in doing the work,
I realized a little bit more
I realized a little bit more
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但在其中，我对人工智能的构造
04:35
about how the architecture
of an AI is constructed.
of an AI is constructed.
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更深入了解了一些。
04:39
And I realized it's not just made
of models and classifiers
of models and classifiers
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我意识到它不仅是
神经网络的
神经网络的
04:42
for the neural network.
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模型和分屏器。
04:43
But it's a fundamentally
malleable and shapable system,
malleable and shapable system,
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它是一个可延展的、可塑的系统，
04:47
one in which the human hand
is always present.
is always present.
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人类的手始终参与其中。
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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它不再是我们认为的
无所不能的人工智能。
无所不能的人工智能。
04:54
So I collected these drawings
for the neural net.
for the neural net.
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所以，我收集画作以训练神经网络，
04:56
And we realized something
that wasn't previously possible.
that wasn't previously possible.
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而且我们意识到
前所未有的事情发生了，
前所未有的事情发生了，
05:00
My robot D.O.U.G. became
a real-time interactive reflection
a real-time interactive reflection
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我对机器人道格
在实时交互创作中，
在实时交互创作中，
05:05
of the work I'd done
through the course of my life.
through the course of my life.
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对我过去人生几十年的作品做出回应。
05:07
The data was personal,
but the results were powerful.
but the results were powerful.
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数据源于我个人，
但结果却很有力。
但结果却很有力。
05:11
And I got really excited,
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我感到非常兴奋，
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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因为我开始想或许机器不该只是工具，
05:17
but they can function
as nonhuman collaborators.
as nonhuman collaborators.
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它还可以是非人的合作者。
05:21
And even more than that,
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再进一步想，
05:23
I thought maybe
the future of human creativity
the future of human creativity
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也许未来的人类创作
05:25
isn't in what it makes
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不在于作品本身，
05:27
but how it comes together
to explore new ways of making.
to explore new ways of making.
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而在于对艺术诞生新方式的探索。
05:31
So if D.O.U.G._1 was the muscle,
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所以，如果道格初代是肌肉，
05:33
and D.O.U.G._2 was the brain,
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那么道格二代就是大脑，
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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然后我想道格三代就是家人。
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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我知道我想要将对
人类和非人类合作的想法放大。
人类和非人类合作的想法放大。
05:43
So over the past few months,
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于是再过去的几个月里，
05:44
I worked with my team
to develop 20 custom robots
to develop 20 custom robots
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我和团队造出了 20 个定制的机器人
05:47
that could work with me as a collective.
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与我集体创作。
05:49
They would work as a group,
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它们像团队一样工作，
05:51
and together, we would collaborate
with all of New York City.
with all of New York City.
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我们共同与整个纽约市携手合作，
05:54
I was really inspired
by Stanford researcher Fei-Fei Li,
by Stanford researcher Fei-Fei Li,
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斯坦福大学的研究员李飞飞
激发了我对灵感，
激发了我对灵感，
05:57
who said, "if we want to teach
machines how to think,
machines how to think,
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她说，"若像教机器如何思考，
05:59
we need to first teach them how to see."
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先要教它们如何看见。"
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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这让我想起了过去
十年的纽约生活，
十年的纽约生活，
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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城市上空的监控摄像头监视着我，
06:08
And I thought it would be
really interesting
really interesting
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如果我用它们来训练
06:10
if I could use them
to teach my robots to see.
to teach my robots to see.
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我的机器人的视觉，
那会非常有趣。
那会非常有趣。
06:12
So with this project,
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所以在这个项目中，
06:14
I thought about the gaze of the machine,
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我思考机器对我们的凝视，
06:16
and I began to think about vision
as multidimensional,
as multidimensional,
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于是我开始将视觉看成多元化的，
06:20
as views from somewhere.
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视作来自某处的视点。
06:22
We collected video
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我们收集视频，
06:24
from publicly available
camera feeds on the internet
camera feeds on the internet
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从网络上公共摄像头的影片
06:27
of people walking on the sidewalks,
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到行人在路上走的片段，
06:28
cars and taxis on the road,
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道路上的汽车和出租，
06:30
all kinds of urban movement.
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城市中各种车水马龙的片段。
06:33
We trained a vision algorithm
on those feeds
on those feeds
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基于一种“光流技术”，
06:35
based on a technique
called "optical flow,"
called "optical flow,"
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我们训练了一种视觉算法，
06:38
to analyze the collective density,
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来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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城市流动的方向，
速度状态以及居住方式。
速度状态以及居住方式。
06:44
Our system extracted those states
from the feeds as positional data
from the feeds as positional data
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我们的系统从海量的
位置数据中提取这些信息，
位置数据中提取这些信息，
06:48
and became pads for my
robotic units to draw on.
robotic units to draw on.
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我们的机器人依靠这些信息来作画，
06:51
Instead of a collaboration of one-to-one,
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与之前的一对一合作不同，
06:54
we made a collaboration of many-to-many.
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我们实现了多对多的合作。
06:57
By combining the vision of human
and machine in the city,
and machine in the city,
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通过结合城市中人类与机器的视角，
07:01
we reimagined what
a landscape painting could be.
a landscape painting could be.
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我们重构了一个景观绘图可能的样子。
07:03
Throughout all of my
experiments with D.O.U.G.,
experiments with D.O.U.G.,
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在我和道格所有的实验中，
07:06
no two performances
have ever been the same.
have ever been the same.
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没有哪两次的呈现是相同的，
07:08
And through collaboration,
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而且通过合作，
07:10
we create something that neither of us
could have done alone:
could have done alone:
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我们创作了我们
无法独自实现的事情，
无法独自实现的事情，
07:13
we explore the boundaries
of our creativity,
of our creativity,
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我们共同探索了创造力的边界，
07:15
human and nonhuman working in parallel.
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人类和非人类并肩工作。
07:19
I think this is just the beginning.
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我想这才是开始，
07:22
This year, I've launched Scilicet,
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今年，我创办了 Scilicet，
07:24
my new lab exploring human
and interhuman collaboration.
and interhuman collaboration.
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这个新实验室旨在探索
人类和非人类间的合作，
人类和非人类间的合作，
07:29
We're really interested
in the feedback loop
in the feedback loop
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我们对个体，人工和生态系统
07:31
between individual, artificial
and ecological systems.
and ecological systems.
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之间的反馈关系非常感兴趣。
07:36
We're connecting human and machine output
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我们将人类和机器与
07:38
to biometrics and other kinds
of environmental data.
of environmental data.
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生物特征识别和其他环境数据相结合。
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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我们邀请任何对未来的作品、系统
07:45
and interhuman collaboration
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和人际间合作感兴趣的人
07:47
to explore with us.
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和我们共同探索。
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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我们知道不仅是科技工作者肩负使命，
07:52
and that we all have a role to play.
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所有人都可以参与其中。
07:54
We believe that by teaching machines
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我们坚信通过教授机器
07:56
how to do the work
traditionally done by humans,
traditionally done by humans,
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如何去完成人类的传统工作，
07:59
we can explore and evolve our criteria
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我们就能不断探索和创新
08:02
of what's made possible by the human hand.
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超越人类之手所能达到的可能。
08:04
And part of that journey
is embracing the imperfections
is embracing the imperfections
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这段旅程之一便是拥抱不完美，
08:08
and recognizing the fallibility
of both human and machine,
of both human and machine,
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发现人类和机器共有的缺憾，
08:12
in order to expand the potential of both.
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才能更好的拓展我们共同的潜能。
08:14
Today, I'm still in pursuit
of finding the beauty
of finding the beauty
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今天，我仍在追寻人类和
08:17
in human and nonhuman creativity.
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非人类协作的美妙之处。
08:19
In the future, I have no idea
what that will look like,
what that will look like,
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在未来，我不知道会怎样，
08:23
but I'm pretty curious to find out.
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但是我满怀好奇去寻找答案。
08:25
Thank you.
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谢谢。
08:26
(Applause)
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（掌声）
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