ABOUT THE SPEAKER
Sougwen Chung - Artist, researcher
Sougwen 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.
More profile about the speaker
Sougwen Chung | Speaker | TED.com
TED@BCG Mumbai

Sougwen Chung: Why I draw with robots

Sougwen Chung: Por que desenho com robôs?

Filmed:
160,983 views

O que acontece quando robôs e humanos criam arte juntos? Nesta palestra inspiradora, a artista Sougwen Chung mostra como ela “ensinou” seu estilo artístico para uma máquina, e compartilha os resultados dessa colaboração depois de descobrir algo surpreendente: robôs também erram. Ela afirma que “Parte da beleza dos sistemas humanos e das máquinas vem de sua falibilidade herdada e partilhada”.
- Artist, researcher
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.
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Muitos de nós usamos
a tecnologia no dia a dia.
00:16
And some of us rely
on technology to do our jobs.
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E alguns de nós dependem
da tecnologia para trabalhar.
Por um tempo,
00:19
For a while, I thought of machines
and the technologies that drive them
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acreditei que as máquinas
e a tecnologia por trás delas
00:23
as perfect tools that could make my work
more efficient and more productive.
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eram ferramentas perfeitas que tornavam
meu trabalho mais eficiente e produtivo.
Mas com o surgimento da automação
em tantos ramos industriais diferentes,
00:28
But with the rise of automation
across so many different industries,
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00:31
it led me to wonder:
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comecei a pensar: "Se as máquinas
estão começando a fazer o trabalho
00:33
If machines are starting
to be able to do the work
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tradicionalmente feito pelo homem,
qual será a parte humana na produção?"
00:35
traditionally done by humans,
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00:37
what will become of the human hand?
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00:40
How does our desire for perfection,
precision and automation
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Como nossa busca pela perfeição,
precisão e automação
afeta nossa capacidade
de sermos criativos?
00:44
affect our ability to be creative?
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Em meu trabalho como artista
e pesquisadora, exploro IA e robótica
00:46
In my work as an artist and researcher,
I explore AI and robotics
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00:50
to develop new processes
for human creativity.
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para desenvolver novos processos
da criatividade humana.
00:54
For the past few years,
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Durante os últimos anos,
00:55
I've made work alongside machines,
data and emerging technologies.
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tenho trabalhado com máquinas,
dados e tecnologias emergentes.
01:00
It's part of a lifelong fascination
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É parte da minha fascinação
01:02
about the dynamics
of individuals and systems
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sobre a dinâmica de indivíduos e máquinas
01:04
and all the messiness that that entails.
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e toda a bagunça envolvida.
01:07
It's how I'm exploring questions about
where AI ends and we begin
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É assim que estou explorando
onde a IA termina e nós entramos
e onde estou desenvolvendo processos
01:12
and where I'm developing processes
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01:13
that investigate potential
sensory mixes of the future.
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que investigam potenciais
combinações sensoriais do futuro.
01:17
I think it's where philosophy
and technology intersect.
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Acho que é onde a filosofia
e a tecnologia se encontram.
Ao realizar esse trabalho,
tenho aprendido algumas coisas:
01:20
Doing this work
has taught me a few things.
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01:23
It's taught me how embracing imperfection
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que aceitar a imperfeição
01:26
can actually teach us
something about ourselves.
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pode nos ensinar algumas coisas
sobre nós mesmos;
01:29
It's taught me that exploring art
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que explorar a arte
01:31
can actually help shape
the technology that shapes us.
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pode ajudar a aperfeiçoar
a tecnologia que nos aperfeiçoa;
01:35
And it's taught me
that combining AI and robotics
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e que combinar IA e robótica
01:38
with traditional forms of creativity --
visual arts in my case --
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com meios criativos tradicionais,
artes visuais no meu caso,
01:41
can help us think a little bit more deeply
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pode nos ajudar a pensar
mais profundamente
01:44
about what is human
and what is the machine.
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sobre o que é humano e o que é máquina.
E tenho aprendido que a colaboração
é a chave para criar espaço para ambos
01:47
And it's led me to the realization
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01:49
that collaboration is the key
to creating the space for both
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01:52
as we move forward.
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conforme progredimos.
01:54
It all started with a simple
experiment with machines,
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Tudo começou com uma simples
experiência com máquinas,
01:57
called "Drawing Operations
Unit: Generation 1."
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a "Drawing Operations Unit: Generation 1".
02:00
I call the machine "D.O.U.G." for short.
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Eu a apelidei de "D.O.U.G.".
Antes de construir o D.O.U.G.,
02:02
Before I built D.O.U.G,
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02:04
I didn't know anything
about building robots.
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eu não sabia nada sobre criação de robôs.
02:07
I took some open-source
robotic arm designs,
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Peguei designs de braços robóticos
de código aberto
02:10
I hacked together a system
where the robot would match my gestures
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e os juntei a um sistema
no qual o robô copiava meus gestos
02:13
and follow [them] in real time.
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e os seguia em tempo real.
02:15
The premise was simple:
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A premissa era simples:
02:16
I would lead, and it would follow.
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eu guiaria, e ele seguiria;
02:19
I would draw a line,
and it would mimic my line.
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eu desenharia uma linha, e ele a imitaria.
02:22
So back in 2015, there we were,
drawing for the first time,
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Em 2015, estávamos desenhando
pela primeira vez
02:26
in front of a small audience
in New York City.
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para um pequeno público em Nova York.
02:28
The process was pretty sparse --
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O processo era bem simples,
02:31
no lights, no sounds,
nothing to hide behind.
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sem luzes, sons, nada escondido.
02:35
Just my palms sweating
and the robot's new servos heating up.
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Somente minhas mãos suando
e meu robô esquentando.
02:38
(Laughs) Clearly, we were
not built for this.
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Obviamente, não fomos feitos para isso.
02:41
But something interesting happened,
something I didn't anticipate.
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Mas algo interessante aconteceu,
algo que eu não tinha previsto.
O D.O.U.G., em sua forma original,
não copiava minhas linhas perfeitamente.
02:45
See, D.O.U.G., in its primitive form,
wasn't tracking my line perfectly.
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02:49
While in the simulation
that happened onscreen
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Apesar de na simulação exibida na tela
02:52
it was pixel-perfect,
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ele parecer perfeito,
02:53
in physical reality,
it was a different story.
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na prática era outra história.
02:56
It would slip and slide
and punctuate and falter,
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Ele escorregava, deslizava,
pontuava e vacilava,
02:59
and I would be forced to respond.
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e eu era forçada a reagir.
Não era perfeito, mesmo assim,
de certa forma,
03:01
There was nothing pristine about it.
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03:03
And yet, somehow, the mistakes
made the work more interesting.
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os erros tornaram o trabalho
mais interessante.
03:06
The machine was interpreting
my line but not perfectly.
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A máquina interpretava minhas linhas,
mas não perfeitamente
03:09
And I was forced to respond.
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e eu era forçada a reagir.
03:10
We were adapting
to each other in real time.
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Nos adaptávamos um ao outro
simultaneamente
03:13
And seeing this taught me a few things.
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e isso me ensinou algumas coisas.
03:15
It showed me that our mistakes
actually made the work more interesting.
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Nossos erros tornaram
o trabalho mais interessante
03:20
And I realized that, you know,
through the imperfection of the machine,
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e descobri que, por meio
da imperfeição da máquina,
03:24
our imperfections became
what was beautiful about the interaction.
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nossas imperfeições tornaram-se
a beleza da interação.
03:29
And I was excited,
because it led me to the realization
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Fiquei muito animada
porque me fez perceber
03:32
that maybe part of the beauty
of human and machine systems
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que talvez, parte da beleza
de sistemas homem-máquina,
03:36
is their shared inherent fallibility.
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seja o compartilhamento
de suas falhas inerentes.
03:39
For the second generation of D.O.U.G.,
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Para a segunda geração do D.O.U.G.,
03:41
I knew I wanted to explore this idea.
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eu sabia que queria explorar essa ideia.
03:43
But instead of an accident produced
by pushing a robotic arm to its limits,
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Mas em vez de ser algo acidental produzido
por levar um braço robótico ao seu limite,
03:47
I wanted to design a system
that would respond to my drawings
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queria um sistema que reagisse
aos meus desenhos de forma imprevista.
03:50
in ways that I didn't expect.
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03:52
So, I used a visual algorithm
to extract visual information
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Então usei um algoritmo de visão
para coletar informações
03:56
from decades of my digital
and analog drawings.
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de meus desenhos digitais e analógicos
produzidos em décadas.
Treinei uma rede neural com esses desenhos
03:59
I trained a neural net on these drawings
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04:01
in order to generate
recurring patterns in the work
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para gerar padrões recorrentes no trabalho
04:04
that were then fed through custom software
back into the machine.
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que, então, alimentaram a máquina
através de um software.
04:07
I painstakingly collected
as many of my drawings as I could find --
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Coletei meticulosamente
todos os desenhos que encontrei,
04:12
finished works, unfinished experiments
and random sketches --
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trabalhos concluídos e incompletos,
rascunhos aleatórios
04:16
and tagged them for the AI system.
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e os identifiquei no sistema de IA.
04:18
And since I'm an artist,
I've been making work for over 20 years.
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Como artista, venho desenhando
há mais de 20 anos.
04:22
Collecting that many drawings took months,
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Juntar tantos desenhos levou meses;
04:24
it was a whole thing.
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foi muito complicado.
04:25
And here's the thing
about training AI systems:
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E o problema de treinar sistemas de IA
é que, na verdade, é um trabalho árduo.
04:28
it's actually a lot of hard work.
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04:31
A lot of work goes on behind the scenes.
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Muito trabalho acontece nos bastidores.
Mas trabalhando com isso,
04:33
But in doing the work,
I realized a little bit more
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aprendi mais sobre a estrutura
da arquitetura de uma IA,
04:35
about how the architecture
of an AI is constructed.
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e percebi que não é feita somente
de modelos e classificadores
04:39
And I realized it's not just made
of models and classifiers
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04:42
for the neural network.
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para a rede neural.
04:43
But it's a fundamentally
malleable and shapable system,
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É basicamente um sistema
maleável e moldável,
04:47
one in which the human hand
is always present.
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no qual o toque humano
está sempre presente.
04:50
It's far from the omnipotent AI
we've been told to believe in.
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Está longe da onipotente IA
na qual nos fizeram acreditar.
04:54
So I collected these drawings
for the neural net.
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Depois de coletar os desenhos
para a rede neural,
04:56
And we realized something
that wasn't previously possible.
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descobrimos algo que antes era impossível.
05:00
My robot D.O.U.G. became
a real-time interactive reflection
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Meu robô D.O.U.G. tornou-se
um reflexo interativo em tempo real
05:05
of the work I'd done
through the course of my life.
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do trabalho que fiz durante minha vida.
05:07
The data was personal,
but the results were powerful.
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Os dados eram pessoais,
mas os resultados, poderosos.
Fiquei bem animada, pois comecei a pensar
05:11
And I got really excited,
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05:13
because I started thinking maybe
machines don't need to be just tools,
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que talvez as máquinas não precisassem
ser somente ferramentas;
elas podiam funcionar
como colaboradores não humanos.
05:17
but they can function
as nonhuman collaborators.
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05:21
And even more than that,
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Mais do que isso,
05:23
I thought maybe
the future of human creativity
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pensei que talvez o futuro
da criatividade humana
05:25
isn't in what it makes
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não estivesse na criação,
05:27
but how it comes together
to explore new ways of making.
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mas na exploração
de novos métodos da criação.
05:31
So if D.O.U.G._1 was the muscle,
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Então se D.O.U.G._1 era o braço,
05:33
and D.O.U.G._2 was the brain,
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e D.O.U.G._2 o cérebro,
05:35
then I like to think
of D.O.U.G._3 as the family.
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então gosto de pensar
no D.O.U.G._3 como a família.
05:38
I knew I wanted to explore this idea
of human-nonhuman collaboration at scale.
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Queria explorar a ideia de colaboração
entre humano e máquina em escala,
então, durante os últimos meses,
05:43
So over the past few months,
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05:44
I worked with my team
to develop 20 custom robots
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tenho trabalhado com minha equipe
para desenvolver 20 robôs
que trabalhariam comigo coletivamente.
05:47
that could work with me as a collective.
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Trabalhariam como um grupo,
05:49
They would work as a group,
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e juntos, colaboraríamos
com toda a cidade de Nova York.
05:51
and together, we would collaborate
with all of New York City.
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Fui inspirada pela pesquisadora
Fei-Fei Li da Stanford, que disse:
05:54
I was really inspired
by Stanford researcher Fei-Fei Li,
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05:57
who said, "if we want to teach
machines how to think,
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"Se queremos ensinar as máquinas a pensar,
05:59
we need to first teach them how to see."
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precisamos primeiro
ensiná-las a enxergar".
06:01
It made me think of the past decade
of my life in New York,
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Isso me fez pensar na última década
que passei em Nova York,
06:04
and how I'd been all watched over by these
surveillance cameras around the city.
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e em como fui observada pelas câmeras
de segurança espalhadas pela cidade.
E pensei que seria interessante
06:08
And I thought it would be
really interesting
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se pudesse usá-las para ensinar
meus robôs a enxergar.
06:10
if I could use them
to teach my robots to see.
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06:12
So with this project,
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Então, com esse projeto,
06:14
I thought about the gaze of the machine,
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pensei sobre a perspectiva da máquina
06:16
and I began to think about vision
as multidimensional,
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e comecei a considerar a visão
como multidimensional,
06:20
as views from somewhere.
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como pontos de vista.
06:22
We collected video
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Coletamos vídeos
06:24
from publicly available
camera feeds on the internet
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transmitidos publicamente na internet
de pessoas andando nas calçadas,
06:27
of people walking on the sidewalks,
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carros e táxis nas ruas,
06:28
cars and taxis on the road,
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06:30
all kinds of urban movement.
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todo tipo de movimento urbano.
06:33
We trained a vision algorithm
on those feeds
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Com esses dados, treinamos
um algoritmo de visão
06:35
based on a technique
called "optical flow,"
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baseado em uma técnica
chamada "fluxo ótico"
06:38
to analyze the collective density,
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para analisar a densidade coletiva,
06:40
direction, dwell and velocity states
of urban movement.
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direção, repouso e velocidade
dos movimentos urbanos.
06:44
Our system extracted those states
from the feeds as positional data
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Nosso sistema extraiu esses estados
das fontes como dados de posicionamento
06:48
and became pads for my
robotic units to draw on.
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e se tornou a base
para meus robôs desenharem.
06:51
Instead of a collaboration of one-to-one,
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Em vez de uma colaboração um para um,
06:54
we made a collaboration of many-to-many.
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criamos uma colaboração
de muitos para muitos.
06:57
By combining the vision of human
and machine in the city,
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Combinando a visão do ser humano
e da máquina na cidade,
repensamos o que a pintura
de uma paisagem poderia ser.
07:01
we reimagined what
a landscape painting could be.
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Em todos os meus experimentos
com o D.O.U.G.,
07:03
Throughout all of my
experiments with D.O.U.G.,
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nunca tivemos performances idênticas,
07:06
no two performances
have ever been the same.
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e através da colaboração
07:08
And through collaboration,
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07:10
we create something that neither of us
could have done alone:
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criamos algo que nenhum de nós
poderia ter feito sozinho:
07:13
we explore the boundaries
of our creativity,
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exploramos os limites
de nossa criatividade,
07:15
human and nonhuman working in parallel.
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com humano e não humano
trabalhando paralelamente.
07:19
I think this is just the beginning.
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Acho que estamos só começando.
07:22
This year, I've launched Scilicet,
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Este ano inaugurei o Scilicet,
07:24
my new lab exploring human
and interhuman collaboration.
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meu novo laboratório, onde exploro
a colaboração humana e inter-humana.
Estamos muito interessados
no ciclo de feedback
07:29
We're really interested
in the feedback loop
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07:31
between individual, artificial
and ecological systems.
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entre sistemas individuais,
artificiais e ecológicos,
Estamos conectando a produção
de humanos e máquinas
07:36
We're connecting human and machine output
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07:38
to biometrics and other kinds
of environmental data.
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à biometria e a outros tipos
de dados ambientais.
07:41
We're inviting anyone who's interested
in the future of work, systems
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Convidamos todos que têm interesse
no futuro do trabalho, dos sistemas
07:45
and interhuman collaboration
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e na colaboração inter-humana
a explorarem conosco.
07:47
to explore with us.
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07:48
We know it's not just technologists
that have to do this work
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Sabemos que não são só tecnólogos
que devem fazer esse trabalho
07:52
and that we all have a role to play.
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e que todos nós temos um papel a cumprir.
Acreditamos que ao ensinar máquinas
07:54
We believe that by teaching machines
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a como fazer o trabalho
tradicionalmente feito por humanos,
07:56
how to do the work
traditionally done by humans,
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07:59
we can explore and evolve our criteria
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podemos explorar e expandir nosso critério
08:02
of what's made possible by the human hand.
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do que é possível para a humanidade.
Parte dessa jornada
é aceitar as imperfeições
08:04
And part of that journey
is embracing the imperfections
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08:08
and recognizing the fallibility
of both human and machine,
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e reconhecer a falibilidade tanto
de humanos como de máquinas
08:12
in order to expand the potential of both.
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para expandir o potencial de ambos.
08:14
Today, I'm still in pursuit
of finding the beauty
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Atualmente ainda estou buscando a beleza
na criatividade humana e não humana.
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,
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Não sei como ela será no futuro
08:23
but I'm pretty curious to find out.
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mas estou muito curiosa em descobrir.
08:25
Thank you.
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Obrigada.
08:26
(Applause)
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(Aplausos)
Translated by Letícia Barbosa
Reviewed by Julia Yada

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ABOUT THE SPEAKER
Sougwen Chung - Artist, researcher
Sougwen 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.
More profile about the speaker
Sougwen Chung | Speaker | TED.com