ABOUT THE SPEAKER
Oscar Schwartz - Writer and poet
Oscar Schwartz's research and writing concerns the influence of digital technology on culture and human interaction.

Why you should listen

Oscar Schwartz is an Australian writer and poet undertaking a PhD that asks whether a computer can write poetry. His research led to the development of a Turing test for poetry, which is available on a website he cofounded called bot or not.

More profile about the speaker
Oscar Schwartz | Speaker | TED.com
TEDxYouth@Sydney

Oscar Schwartz: Can a computer write poetry?

Oscar Schawartz: Pode un ordenador escribir poesía?

Filmed:
875,724 views

Se les un poema e te sintes conmovido por él, pero logo descubres que en realidade foi escrito por un ordenador, sentiríaste de distinta maneira ante a experiencia? Pensarías que o ordenador se expresou por sí mesmo e foi creativo, ou sentiríaste enganado por un sucio truco? Nesta charla, o escritor Oscar Schwartz examina porqué reaccionamos tan contundentemente á idea dun ordenador escribindo poesía-- e como esta reacción nos axuda a entender que significa ser humano.
- Writer and poet
Oscar Schwartz's research and writing concerns the influence of digital technology on culture and human interaction. Full bio

Double-click the English transcript below to play the video.

00:12
I have a question.
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Teño unha pregunta.
00:15
Can a computer write poetry?
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Pode un ordenador escribir poesía?
00:18
This is a provocative question.
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Esta é unha pregunta provocadora.
00:21
You think about it for a minute,
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Pensas neso por un minuto,
00:23
and you suddenly have a bunch
of other questions like:
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e de golpe unha morea
de preguntas
como:
00:26
What is a computer?
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Que é un ordenador?
00:28
What is poetry?
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Que é poesía?
00:30
What is creativity?
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Que é creatividade?
Pero estas son preguntas
00:33
But these are questions
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que a xente pasa toda a súa vida
tratando de responder,
00:34
that people spend their entire
lifetime trying to answer,
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00:37
not in a single TED Talk.
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non nunha única charla TED
Polo que intentaremos un enfoque diferente
00:40
So we're going to have to try
a different approach.
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Velaquí arriba temos dous poemas.
00:42
So up here, we have two poems.
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00:45
One of them is written by a human,
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Un deles está escrito por un humano,
e o outro por un ordenador.
00:48
and the other one's written by a computer.
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Vouvos a pedir que me digades cal é cal
00:50
I'm going to ask you to tell me
which one's which.
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Intentádeo:
00:53
Have a go:
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Poema1:
Pequena mosca/ Os teus xogos de estío,/
00:55
Poem 1: Little Fly / Thy summer's play, /
My thoughtless hand / Has brush'd away.
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A miña atolondrada man/ Levaron
00:59
Am I not / A fly like thee? /
Or art not thou / A man like me?
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Non son eu / Unha mosca coma ti?/
Non es ti / Un home coma min?
Poema 2:
Podémonos sentir / Activista ao longo / das mañás da túa vida/
01:02
Poem 2: We can feel / Activist
through your life's / morning /
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Para ao ver, Papa odio o / non toda a noite
para comezar unha gran outra cousa (...)
01:05
Pauses to see, pope I hate the / Non
all the night to start a / great otherwise (...)
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Ben, rematouse o tempo.
01:10
Alright, time's up.
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01:11
Hands up if you think Poem 1
was written by a human.
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Mans arriba quen crea que o Poema 1
foi escrito por un humano.
Vale, a maioría de vós.
01:17
OK, most of you.
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Mans arriba quen crea que o Poema 2
foi escrito por un humano.
01:19
Hands up if you think Poem 2
was written by a human.
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01:23
Very brave of you,
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Moi valentes,
01:24
because the first one was written
by the human poet William Blake.
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porque o primeiro foi escrito
polo poeta William Blake.
O segundo foi escrito por un algoritmo
01:29
The second one was written by an algorithm
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01:32
that took all the language
from my Facebook feed on one day
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que colleu toda a linguaxe da miña conta
de Facebook nun só día
01:36
and then regenerated it algorithmically,
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e logo rexenerouno algoritmicamente,
01:39
according to methods that I'll describe
a little bit later on.
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seguindo métodos que describirei
brevemente máis adiante
01:43
So let's try another test.
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Entón vamos a intentar outra proba.
01:46
Again, you haven't got ages to read this,
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De novo, non tedes toda a vida
para ler isto,
01:48
so just trust your gut.
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confiade no voso instinto.
01:50
Poem 1: A lion roars and a dog barks.
It is interesting / and fascinating
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Poema 1: Un león ruxe e o can ladra.
É interesante / é fascinante
que o paxaro voará e non /
ruxirá ou ladrará.
01:54
that a bird will fly and not / roar
or bark. Enthralling stories about animals
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Cautivadoras historias sobre animais
están nos meus soños
e cantareinos todos se eu/
01:58
are in my dreams and I will sing them all
if I / am not exhausted or weary.
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non estou exhausto ou agotado.
02:02
Poem 2: Oh! kangaroos, sequins, chocolate
sodas! / You are really beautiful!
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Poema 2: Oh! canguros, abelorios,
sodas de chocolate!/
Sodes realmente belos!
Perlas, / harmónicas, lambetadas,
aspirinas!
02:06
Pearls, / harmonicas, jujubes, aspirins!
All / the stuff they've always talked about (...)
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Todas / as cousas
das que sempre se falou (...)
Ben, rematouse o tempo.
02:11
Alright, time's up.
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02:12
So if you think the first poem
was written by a human,
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Se pensedes que o primeiro poema,
foi escrito por un humano
02:15
put your hand up.
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levantade a man.
02:17
OK.
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Vale.
02:18
And if you think the second poem
was written by a human,
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E se pensades que o segundo poema
foi escrito por un humano,
02:21
put your hand up.
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levantade a man.
02:23
We have, more or less, a 50/50 split here.
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Temos, máis ou menos,
unha división aquí de 50/50.
02:28
It was much harder.
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Foi moito máis difícil.
02:29
The answer is,
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Velaquí a resposta,
02:31
the first poem was generated
by an algorithm called Racter,
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o primeiro poema foi xerado
por un algoritmo chamado Racter,
02:34
that was created back in the 1970s,
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que foi creado no 1970,
02:37
and the second poem was written
by a guy called Frank O'Hara,
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e o segundo poema foi escrito
por un tipo chamado Frank O'Hara,
02:41
who happens to be
one of my favorite human poets.
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quen resulta ser
un dos meus poetas favoritos.
02:44
(Laughter)
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(Risas)
02:48
So what we've just done now
is a Turing test for poetry.
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Así que o que acabamos de facer agora
é un test Turing de poesía.
02:52
The Turing test was first proposed
by this guy, Alan Turing, in 1950,
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O test Turing foi proposto
por primeira vez por este tipo,
Alan Turing, no 1950.
02:56
in order to answer the question,
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co fin de responder á pregunta,
02:58
can computers think?
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poden os ordenadores pensar?
Alan Turing
cría que se o ordenador fose capaz
03:00
Alan Turing believed that if
a computer was able
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03:03
to have a to have a text-based
conversation with a human,
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de ter un texto basaedo na conversación
cun humano,
03:06
with such proficiency
such that the human couldn't tell
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con tal dominio que o humano
non puidese dicir
03:08
whether they are talking
to a computer or a human,
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se están falando cun ordenador
ou cun humano,
03:11
then the computer can be said
to have intelligence.
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logo poderíase dicir do ordenador
que ten intelixencia.
03:15
So in 2013, my friend
Benjamin Laird and I,
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Así que no 2013,
o meu amigo Benjamin Laird e eu,
03:18
we created a Turing test
for poetry online.
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creamos un test Turing online
para a poesía.
03:21
It's called bot or not,
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Chámase robot ou non,
03:22
and you can go and play it for yourselves.
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e podedes probalo por vós mesmos.
03:24
But basically, it's the game
we just played.
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Pero basicamente,
é un xogo ao que xa xogamos.
03:27
You're presented with a poem,
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Preséntasevos un poema,
03:28
you don't know whether it was written
by a human or a computer
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e non sabedes se foi
escrito por un humano
ou un ordenador
e tedes que adivinar.
03:31
and you have to guess.
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Miles e miles de persoas
xa fixeron esta proba online,
03:33
So thousands and thousands
of people have taken this test online,
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polo que temos os resultados.
03:36
so we have results.
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03:37
And what are the results?
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E cales son eses resultados?
03:39
Well, Turing said that if a computer
could fool a human
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Turing dixo que se un ordenador
poidese enganar a un humano
03:42
30 percent of the time
that it was a human,
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o 30% do tempo que iso sería un humano,
03:45
then it passes the Turing test
for intelligence.
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entón pasaría o test Turing por intelixencia.
03:48
We have poems on the bot or not database
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Temos poemas na base de datos de robot ou non
03:51
that have fooled 65 percent
of human readers into thinking
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que enganaron ao 65% dos lectores humanos
ao creer
03:54
it was written by a human.
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que foi escrito por un humano.
03:55
So, I think we have an answer
to our question.
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Porén, penso que temos unha resposta a nosa pregunta.
03:59
According to the logic of the Turing test,
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De acordo coa lóxica o test Turing,
pode un ordenador escribir poesía?
04:01
can a computer write poetry?
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04:03
Well, yes, absolutely it can.
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Claro, por suposto que pode.
04:07
But if you're feeling
a little bit uncomfortable
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Pero se vos sentides un pouco molestos
coa resposta, non pasa nada.
04:10
with this answer, that's OK.
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04:12
If you're having a bunch
of gut reactions to it,
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se tedes un montón
de reaccións instintivas,
iso tamén está ben
porque isto non é o final da historia.
04:14
that's also OK because
this isn't the end of the story.
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04:18
Let's play our third and final test.
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Vamos a xogar o noso terceiro e derradeiro test.
04:22
Again, you're going to have to read
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De novo, vades a ter que ler
04:23
and tell me which you think is human.
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e dicirme cal pensades que é humano.
04:25
Poem 1: Reg flags the reason
for pretty flags. / And ribbons.
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Poema 1: Bandeiras vermellas son a razón
para fermosas bandeiras. / e cenefas.
Cenefas de bandeiras /E materiais pesados /
Razóns para levar materiais pesados. (...)
04:29
Ribbons of flags / And wearing material /
Reasons for wearing material. (...)
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04:33
Poem 2: A wounded deer leaps
highest, / I've heard the daffodil
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Poema 2: Un cervo salta ao máis alto, /
xa escoitei ao narciso
Xa escoitei á bandeira hoxe /
Xa escoitei ao cazador contar;/
04:37
I've heard the flag to-day /
I've heard the hunter tell; /
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Isto non é máis que o éxtase da morte, /
E despois a pausa está case rematada (...)
04:41
'Tis but the ecstasy of death, /
And then the brake is almost done (...)
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Ok, rematouse o tempo.
04:44
OK, time is up.
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Mans arriba os que pensedes
que o Poema 1
04:46
So hands up if you think Poem 1
was written by a human.
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foi escrito por un humano.
Mans arriba os que pensedes
que o Poema 2
04:51
Hands up if you think Poem 2
was written by a human.
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foi escrito por un humano.
04:55
Whoa, that's a lot more people.
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Guau!, iso é máis xente do esperado.
Estaredes sorprendidos
de descubrir que o Poema 1
04:58
So you'd be surprised to find that Poem 1
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05:01
was written by the very
human poet Gertrude Stein.
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foi escrito pola gran poetisa
Gertrude Stein.
05:06
And Poem 2 was generated
by an algorithm called RKCP.
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E o Poema 2 foi xerado
por un algoritmo chamado RKCP.
05:11
Now before we go on, let me describe
very quickly and simply,
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Agora, antes de que sigamos,
deixádeme describir moi rápida e brevemente,
05:14
how RKCP works.
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cómo funciona RKCP.
05:16
So RKCP is an algorithm
designed by Ray Kurzweil,
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Pois ben...
RKP é un algoritmo deseñado
por Ray Kurzweill,
05:20
who's a director of engineering at Google
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director de enxeñería en Google
e un firme crente
da intelixencia artificial
05:22
and a firm believer
in artificial intelligence.
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05:25
So, you give RKCP a source text,
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Entón,
vós dades a RKP un texto de orixe,
05:29
it analyzes the source text in order
to find out how it uses language,
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e él analíza o texto de orixe
co fin de descubrir
como emprega a linguaxe,
05:34
and then it regenerates language
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e logo rexenera a linguaxe
05:36
that emulates that first text.
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que imita ese primeiro texto.
05:38
So in the poem we just saw before,
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Polo que no poema que acabamos de ver,
05:40
Poem 2, the one that you all
thought was human,
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Poema 2,
o que todos pensastes que era humano,
05:43
it was fed a bunch of poems
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foi creado
a través dun montón de poemas
05:45
by a poet called Emily Dickinson
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dunha poetisa chamada Emily Dickinson
observando a maneira na que ela
empregaba a linguaxe,
05:47
it looked at the way she used language,
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aprendiu o modelo,
05:49
learned the model,
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e logo xerou un modelo
de acordo a esa mesma estrutura.
05:50
and then it regenerated a model
according to that same structure.
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Pero o máis importante
que hai que saber de RKCP
05:56
But the important thing to know about RKCP
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05:58
is that it doesn't know the meaning
of the words it's using.
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é que non sabe o significado
das palabras que está a usar.
A linguaxe é tan só materia prima,
06:02
The language is just raw material,
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06:04
it could be Chinese,
it could be in Swedish,
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podería ser chino, podería ser Sueco,
06:06
it could be the collected language
from your Facebook feed for one day.
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podería ser a linguaxe recompilada
no día en Facebook
06:11
It's just raw material.
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é só materia prima.
06:13
And nevertheless, it's able
to create a poem
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E aínda así, é capaz de crear un poema
06:16
that seems more human
than Gertrude Stein's poem,
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que parece máis humano
que o poema de Gertrude Stein,
06:19
and Gertrude Stein is a human.
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e Gertrude Stein é humana.
06:22
So what we've done here is,
more or less, a reverse Turing test.
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Así que o que fixemos aquí é, máis ou menos,
o contrario o test Turing.
06:27
So Gertrude Stein, who's a human,
is able to write a poem
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Polo que, Gertrude Stein, humana,
é capaz de escribir un poema
06:33
that fools a majority
of human judges into thinking
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que leva á maioría
de xuíces humanos a pensar
06:36
that it was written by a computer.
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que foi escrito por un ordenador.
06:39
Therefore, according to the logic
of the reverse Turing test,
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Polo tanto, segundo a lóxica revertida
do test de Turing,
06:43
Gertrude Stein is a computer.
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Gertrude Stein é un ordenador.
06:45
(Laughter)
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(Risas)
06:47
Feeling confused?
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Confundidos?
06:49
I think that's fair enough.
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Bastante xusto.
06:51
So far we've had humans
that write like humans,
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Ata agora tivemos humanos
que escribiron como humanos,
06:55
we have computers that write
like computers,
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temos ordenadores
que escriben como ordenadores,
06:58
we have computers that write like humans,
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temos ordenadores
que escriben como humanos,
07:01
but we also have,
perhaps most confusingly,
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pero tamén temos,
quizáis máis confusamente,
07:05
humans that write like computers.
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humanos que escriben como ordenadores.
07:08
So what do we take from all of this?
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Así que...que sacamos de todo isto?
07:11
Do we take that William Blake
is somehow more of a human
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Damos por sentado
que William Blake
é dalguna maneira máis humano
07:14
than Gertrude Stein?
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que Gertrude Stein?
07:16
Or that Gertrude Stein is more
of a computer than William Blake?
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Ou que Gertrude Stein
é máis ordenador que William Blake?
07:19
(Laughter)
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(Risas)
07:20
These are questions
I've been asking myself
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Estas son preguntas
que me preguntei a min mesmo
07:23
for around two years now,
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durante arredor de dous anos,
07:24
and I don't have any answers.
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e aínda non teño respostas,
07:26
But what I do have are a bunch of insights
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Pero o que sí teño
son unha morea de percepcións
07:29
about our relationship with technology.
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sobre a nosa relación coa tecnoloxía.
07:32
So my first insight is that,
for some reason,
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Así que a miña primeira percepción
é que,
por algunha razón,
07:36
we associate poetry with being human.
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asociamos a poesía co ser humano.
07:40
So that when we ask,
"Can a computer write poetry?"
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Porén cando preguntamos,
"Pode un ordenador escribir poesía?"
07:43
we're also asking,
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tamén estamos a preguntar,
07:45
"What does it mean to be human
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" Que siginifica ser humano
07:46
and how do we put boundaries
around this category?
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e como poñemos límites a esta categoría?
07:50
How do we say who or what
can be part of this category?"
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Como dicimos
quen ou que pode ser parte desta categoría?
07:54
This is an essentially
philosophical question, I believe,
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Esta é unha pregunta filosófica esencial, creo,
07:57
and it can't be answered
with a yes or no test,
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e non pode ser respondida
con un test de sí ou non
08:00
like the Turing test.
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como o test Turing.
08:01
I also believe that Alan Turing
understood this,
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Tamén creo que Alan Turing
comprendiu isto,
08:04
and that when he devised
his test back in 1950,
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e cando ideou o seu test no 1950,
08:08
he was doing it
as a philosophical provocation.
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el fíxoo como unha provocación filosófica.
08:13
So my second insight is that,
when we take the Turing test for poetry,
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Polo que a miña segunda percepción
é que, cando realizamos
o test Turing para poesía,
08:18
we're not really testing
the capacity of the computers
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non estamos realmente probando
a capacidade dos ordenadores
08:22
because poetry-generating algorithms,
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porque a poesía xerada
mediante algoritmos,
08:25
they're pretty simple and have existed,
more or less, since the 1950s.
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é bastante simple e xa existiu,
máis ou menos, dende o 1950.
08:31
What we are doing with the Turing
test for poetry, rather,
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O que estamos a facer
co test Turing de poesía, preferentemente,
08:34
is collecting opinions about what
constitutes humanness.
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é recolectar opinións
sobre que consitúe a raza humana.
08:40
So, what I've figured out,
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Así, o que descubrín,
08:43
we've seen this when earlier today,
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o que xa vimos antes,
08:46
we say that William Blake
is more of a human
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dixemos que William Blake é máis humano
08:48
than Gertrude Stein.
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que Gertrude Stein.
08:50
Of course, this doesn't mean
that William Blake
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Por suposto, isto non significa
que William Blake
08:52
was actually more human
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fose realmente máis humano
08:54
or that Gertrude Stein
was more of a computer.
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ou que Gertrude Stein
fose máis un ordenador.
08:57
It simply means that the category
of the human is unstable.
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Simplemente quere dicir
que a categoría de humano é inestable.
09:03
This has led me to understand
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Isto levoume a entender
09:05
that the human is not a cold, hard fact.
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que o ser humano
non é un obxecto frío e duro.
09:08
Rather, it is something
that's constructed with our opinions
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Senón que é máis ben algo construído
a través das nosas opinións
09:11
and something that changes over time.
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e que cambia co tempo.
09:16
So my final insight is that
the computer, more or less,
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Polo que a miña percepción final
é que o ordenador, máis ou menos,
09:21
works like a mirror
that reflects any idea of a human
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funciona como un espello
que reflicte calquer idea do ser humano
09:25
that we show it.
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que lle mostremos.
09:26
We show it Emily Dickinson,
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Mostrámoslle a Emily Dickinson,
09:28
it gives Emily Dickinson back to us.
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e devólvenos a Emily Dickinson.
09:31
We show it William Blake,
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Mostrámoslle a William Blake,
09:33
that's what it reflects back to us.
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e iso é o que nos reflicte a nós.
09:35
We show it Gertrude Stein,
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Mostrámoslle Gertrude Stein.
09:37
what we get back is Gertrude Stein.
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o que nos mostra de volta
é a Gertrude Stein.
09:41
More than any other bit of technology,
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Máis que calquera outra tecnoloxía,
09:43
the computer is a mirror that reflects
any idea of the human we teach it.
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o ordenador é un espello
que reflicte calquera idea
do ser humano que lle ensinemos.
09:50
So I'm sure a lot of you have been hearing
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Porén, estou seguro
de que moitos de vós oístes
09:52
a lot about artificial
intelligence recently.
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un montón sobre intelixencia artifical
ultimamente.
09:56
And much of the conversation is,
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E a maior parte da conversación é sobre,
10:00
can we build it?
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podemos creala?
10:02
Can we build an intelligent computer?
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Podemos crear un ordenador intelixente?
10:05
Can we build a creative computer?
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Podemos construír un ordenador creativo?
10:08
What we seem to be asking over and over
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O que parece que nos
preguntamos continuamente
10:10
is can we build a human-like computer?
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é se podemos crear un humano
como un ordenador?
10:13
But what we've seen just now
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Pero o que vimos ata agora
10:15
is that the human
is not a scientific fact,
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é que o ser humano
non é un feito científico,
10:18
that it's an ever-shifting,
concatenating idea
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iso é unha idea concatenante
e en constante evolución
10:22
and one that changes over time.
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e unha que cambia co tempo.
10:24
So that when we begin
to grapple with the ideas
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Polo que cando comezamos a pelear
con estas ideas
10:27
of artificial intelligence in the future,
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de intelixencia artifical no futuro,
10:30
we shouldn't only be asking ourselves,
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non deberíamos de preguntarnos,
10:32
"Can we build it?"
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"Podemos construíla?"
10:33
But we should also be asking ourselves,
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Pero tamén deberiamos plantearnos,
10:35
"What idea of the human
do we want to have reflected back to us?"
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"Que idea do humano
queremos reflectir en nós mesmos?"
10:39
This is an essentially philosophical idea,
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Esta é unha idea filosófica esencial,
10:42
and it's one that can't be answered
with software alone,
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e é unha que non pode ser respondida
por un software solo,
10:45
but I think requires a moment
of species-wide, existential reflection.
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pero creo que require
un momento de reflexión existencial
sobre a amplitude da especie.
10:51
Thank you.
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Graciñas.
10:52
(Applause)
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(Aplausos)

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ABOUT THE SPEAKER
Oscar Schwartz - Writer and poet
Oscar Schwartz's research and writing concerns the influence of digital technology on culture and human interaction.

Why you should listen

Oscar Schwartz is an Australian writer and poet undertaking a PhD that asks whether a computer can write poetry. His research led to the development of a Turing test for poetry, which is available on a website he cofounded called bot or not.

More profile about the speaker
Oscar Schwartz | Speaker | TED.com