Return to Video

Can a computer write poetry? | Oscar Schwartz | TEDxYouth@Sydney

  • 0:17 - 0:19
    I have a question:
  • 0:19 - 0:21
    Can a computer write poetry?
  • 0:22 - 0:24
    This is a provocative question.
  • 0:25 - 0:27
    You think about it for a minute,
  • 0:27 - 0:30
    and you suddenly have a bunch
    of other questions like:
  • 0:30 - 0:32
    What is a computer?
  • 0:32 - 0:33
    What is poetry?
  • 0:34 - 0:36
    What is creativity?
  • 0:36 - 0:38
    But these are questions
  • 0:38 - 0:41
    that people spend their entire
    lifetime trying to answer,
  • 0:41 - 0:43
    not in a single TED Talk.
  • 0:43 - 0:45
    So we're going to have to try
    a different approach.
  • 0:45 - 0:48
    So up here, we have two poems.
  • 0:48 - 0:50
    One of them is written by a human,
  • 0:50 - 0:53
    and the other one's written by a computer.
  • 0:53 - 0:55
    I'm going to ask you to tell me
    which one's which.
  • 0:56 - 0:58
    You're not going to have long to read
  • 0:58 - 1:00
    because we haven't got long
    to do this speech.
  • 1:00 - 1:02
    Have a go, start reading.
  • 1:03 - 1:07
    Poem 1: Little Fly / Thy summer's play, /
    My thoughtless hand / Has brush'd away.
  • 1:07 - 1:10
    A I not / A fly like thee? /
    Or art not thou / A man like me?
  • 1:10 - 1:13
    Poem 2: We can feel / Activist
    through your life's / morning /
  • 1:13 - 1:17
    Pauses to see, pope I hate the / Non
    all the night to start a great otherwise
  • 1:17 - 1:18
    Alright, time's up.
  • 1:18 - 1:23
    Hands up if you think Poem 1
    was written by a human.
  • 1:24 - 1:26
    OK, most of you.
  • 1:26 - 1:29
    Hands up if you think Poem 2
    was written by a human.
  • 1:30 - 1:31
    Very brave of you,
  • 1:31 - 1:36
    because the first one was written
    by the human poet William Blake.
  • 1:36 - 1:39
    The second one was written by an algorithm
  • 1:39 - 1:43
    that took all the language
    from my Facebook feed on one day
  • 1:43 - 1:45
    and then regenerated it algorithmically,
  • 1:45 - 1:49
    according to methods that I'll describe
    a little bit later on.
  • 1:49 - 1:52
    But most of you got that right,
    it's probably a little bit easy.
  • 1:52 - 1:54
    So let's try another test.
  • 1:56 - 1:58
    Again, you haven't got ages to read this,
  • 1:58 - 2:00
    so just trust your gut.
  • 2:00 - 2:03
    Poem 1: A lion roars and a dog barks.
    It is interesting / and fascinating
  • 2:03 - 2:07
    that a bird will fly and not / roar
    or bark. Enthralling stories about animals
  • 2:07 - 2:11
    are in my dreams and I will sing them all
    if I / am not exhausted or weary.
  • 2:11 - 2:14
    Poem 2: Oh! kangaroos, sequins, chocolate
    sodas! / You are really beautiful!
  • 2:14 - 2:18
    Pearls, / harmonicas, jujubes, aspirins!
    All / the stuff they've always talked about
  • 2:22 - 2:24
    Alright, time's up.
  • 2:24 - 2:26
    So if you think the first poem
    was written by a human,
  • 2:26 - 2:28
    put your hand up.
  • 2:29 - 2:30
    OK.
  • 2:30 - 2:32
    And if you think the second poem
    was written by a human,
  • 2:32 - 2:34
    put your hand up.
  • 2:35 - 2:38
    We have, more or less, a 50/50 split here.
  • 2:39 - 2:40
    It was much harder.
  • 2:40 - 2:41
    The answer is,
  • 2:41 - 2:45
    the first poem was generated
    by an algorithm called Racter,
  • 2:45 - 2:48
    that was created back in the 1970s,
  • 2:48 - 2:51
    and the second poem was written
    by a guy called Frank O'Hara,
  • 2:51 - 2:54
    who happens to be one
    of my favorite human poets.
  • 2:54 - 2:57
    (Laughter)
  • 2:58 - 3:01
    So what we've just done now
    is a Turing test for poetry.
  • 3:01 - 3:06
    The Turing test was first proposed
    by this guy, Alan Turing, in 1950,
  • 3:06 - 3:07
    in order to answer the question,
  • 3:07 - 3:10
    can computers think?
  • 3:10 - 3:12
    Alan Turing believed that if
    a computer was able
  • 3:12 - 3:15
    to have a to have a text-based
    conversation with a human,
  • 3:15 - 3:18
    with such proficiency
    such that the human couldn't tell
  • 3:18 - 3:21
    whether they are talking
    to a computer or a human,
  • 3:21 - 3:24
    then the computer can be said
    to have intelligence.
  • 3:24 - 3:27
    So in 2013, my friend
    Benjamin Laird and I,
  • 3:27 - 3:30
    we created a Turing test
    for poetry online.
  • 3:30 - 3:31
    It's called bot or not,
  • 3:31 - 3:33
    and you can go and play it for yourselves.
  • 3:33 - 3:36
    But basically, it's the game
    we just played.
  • 3:36 - 3:37
    You're presented with a poem,
  • 3:37 - 3:40
    you don't know whether it was written
    by a human or a computer
  • 3:40 - 3:41
    and you have to guess.
  • 3:41 - 3:45
    So thousands and thousands
    of people have taken this test online,
  • 3:45 - 3:46
    so we have results.
  • 3:46 - 3:48
    And what are the results?
  • 3:48 - 3:50
    Well, Turing said that if a computer
    could fool a human
  • 3:50 - 3:53
    30 percent of the time
    that it was a human,
  • 3:53 - 3:55
    then it passes the Turing test
    for intelligence.
  • 3:56 - 3:58
    We have poems on the bot or not database
  • 3:58 - 4:01
    that have fooled 65 percent
    of human readers into thinking
  • 4:01 - 4:03
    it was written by a human.
  • 4:03 - 4:05
    So, I think we have an answer
    to our question.
  • 4:07 - 4:09
    According to the logic of the Turing test,
  • 4:09 - 4:10
    can a computer write poetry?
  • 4:10 - 4:13
    Well, yes, absolutely it can.
  • 4:14 - 4:16
    But if you're feeling
    a little bit uncomfortable
  • 4:16 - 4:19
    with this answer, that's OK.
  • 4:19 - 4:21
    If you're having a bunch
    of gut reactions to it,
  • 4:21 - 4:24
    that's also okay because
    this isn't the end of the story.
  • 4:25 - 4:27
    Let's play our third and final test.
  • 4:28 - 4:30
    Again, you're going to have to read
  • 4:30 - 4:32
    and tell me which you think is human.
  • 4:32 - 4:35
    Poem 1: Red flags the reason
    for pretty flags. / And ribbons.
  • 4:35 - 4:39
    And wearing material / Reasons
    for wearing material. / Give pleasure.
  • 4:39 - 4:43
    Poem 2: A wounded deer leaps
    highest, / I've heard the daffodil
  • 4:43 - 4:46
    I've heard the flag to-day /
    I've heard the hunter tell; /
  • 4:46 - 4:50
    'Tis but the ecstasy of death, /
    And then the brake is almost done
  • 4:50 - 4:53
    And sunrise grows so near /
    sunrise grows so near
  • 4:53 - 4:56
    That we can touch the despair and /
    frenzied hope of all the ages
  • 4:56 - 4:58
    OK, time is up.
  • 4:58 - 5:02
    So hands up if you think Poem 1
    was written by a human.
  • 5:03 - 5:06
    Hands up if you think Poem 2
    was written by a human.
  • 5:06 - 5:08
    Whoa, that's a lot more people.
  • 5:09 - 5:12
    So you'd be surprised to find that Poem 1
  • 5:12 - 5:15
    was written by the very
    human poet Gertrude Stein.
  • 5:17 - 5:22
    And Poem 2 was generated
    by an algorithm called RKCP.
  • 5:22 - 5:25
    Now before we go on, let me describe
    very quickly and simply,
  • 5:25 - 5:26
    how RKCP works.
  • 5:27 - 5:31
    So RKCP is an algorithm
    designed by Ray Kurzweil,
  • 5:31 - 5:33
    who's a director of engineering at Google
  • 5:33 - 5:35
    and a firm believer
    in artificial intelligence.
  • 5:36 - 5:39
    So, you give RKCP a source text,
  • 5:39 - 5:44
    it analyzes the source text in order
    to find out how it uses language,
  • 5:44 - 5:46
    and then it regenerates language
  • 5:46 - 5:48
    that emulates that first text.
  • 5:48 - 5:51
    So in the poem we just saw before,
  • 5:51 - 5:53
    Poem 2, the one that you all
    thought was human,
  • 5:53 - 5:54
    it was fed a bunch of poems
  • 5:54 - 5:56
    by a poet called Emily Dickinson
  • 5:56 - 5:59
    and looked at the way she used language,
  • 5:59 - 6:00
    learned the model,
  • 6:00 - 6:04
    and then it regenerated a model
    according to that same structure.
  • 6:06 - 6:08
    But the important thing to know about RKCP
  • 6:08 - 6:11
    is that it doesn't know the meaning
    of the words it's using.
  • 6:12 - 6:13
    The language is just raw material,
  • 6:13 - 6:15
    it could be Chinese,
    it could be in Swedish,
  • 6:15 - 6:20
    it could be the collected language
    from your Facebook feed for one day.
  • 6:20 - 6:22
    It's just raw material.
  • 6:22 - 6:25
    And nevertheless, it's able
    to create a poem
  • 6:25 - 6:28
    that seems more human
    than Gertrude Stein's poem,
  • 6:28 - 6:30
    and Gertrude Stein is a human.
  • 6:31 - 6:35
    So what we've done here is,
    more or less, a reverse Turing test.
  • 6:36 - 6:41
    So Gertrude Stein, who's a human,
    is able to write a poem
  • 6:41 - 6:44
    that fools a majority
    of human judges into thinking
  • 6:44 - 6:47
    that it was written by a computer.
  • 6:47 - 6:50
    Therefore, according to the logic
    of the reverse Turing test,
  • 6:50 - 6:52
    Gertrude Stein is a computer.
  • 6:52 - 6:54
    (Laughter)
  • 6:55 - 6:56
    Feeling confused?
  • 6:56 - 6:58
    I think that's fair enough.
  • 6:58 - 7:02
    So far we've had humans
    that write like humans,
  • 7:02 - 7:05
    we have computers that write
    like computers,
  • 7:05 - 7:08
    we have computers that write like humans,
  • 7:08 - 7:11
    but we also have,
    perhaps most confusingly,
  • 7:13 - 7:15
    humans that write like computers.
  • 7:16 - 7:18
    So what do we take from all of this?
  • 7:19 - 7:21
    Do we take that William Blake
    is somehow more of a human
  • 7:21 - 7:23
    than Gertrude Stein?
  • 7:23 - 7:26
    Or that Gertrude Stein is more
    of a computer than William Blake?
  • 7:26 - 7:27
    (Laughter)
  • 7:27 - 7:29
    These are questions
    I've been asking myself
  • 7:29 - 7:31
    for around two years now,
  • 7:31 - 7:33
    and I don't have any answers.
  • 7:33 - 7:35
    But what I do have are a bunch of insights
  • 7:35 - 7:38
    about our relationship with technology.
  • 7:39 - 7:42
    So my first insight is that,
    for some reason,
  • 7:43 - 7:46
    we associate poetry with being human.
  • 7:46 - 7:49
    So that when we ask,
    "Can a computer write poetry?"
  • 7:49 - 7:51
    we're also asking,
  • 7:51 - 7:53
    "What does it mean to be human
  • 7:53 - 7:55
    and how do we put boundaries
    around this category?
  • 7:56 - 7:59
    How do we say who or what
    can be part of this category?"
  • 7:59 - 8:03
    This is an essentially
    philosophical question, I believe,
  • 8:03 - 8:05
    and it can't be answered
    with a yes or no test,
  • 8:05 - 8:06
    like the Turing test.
  • 8:07 - 8:10
    I also believe that Alan Turing
    understood this,
  • 8:10 - 8:13
    and that when he devised
    his test back in 1950,
  • 8:13 - 8:16
    he was doing it
    as a philosophical provocation.
  • 8:18 - 8:23
    So my second insight is that,
    when we take the Turing test for poetry,
  • 8:23 - 8:26
    we're not really testing
    the capacity of the computers
  • 8:26 - 8:29
    because poetry-generating algorithms,
  • 8:29 - 8:34
    they're pretty simple and have existed,
    more or less, since the 1950s.
  • 8:35 - 8:38
    What we are doing with the Turing
    test for poetry, rather,
  • 8:38 - 8:43
    is collecting opinions about what
    constitutes humanness.
  • 8:44 - 8:46
    So, what I've figured out,
  • 8:46 - 8:49
    we've seen this when earlier today,
  • 8:49 - 8:52
    we saw that William Blake
    is more of a human
  • 8:52 - 8:53
    than Gertrude Stein.
  • 8:53 - 8:56
    Of course, this doesn't mean
    that William Blake
  • 8:56 - 8:57
    was actually more human
  • 8:57 - 9:00
    or that Gertrude Stein
    was more of a computer.
  • 9:00 - 9:05
    It simply means that the category
    of the human is unstable.
  • 9:06 - 9:08
    This has led me to understand
  • 9:08 - 9:11
    that the human is not a cold, hard fact.
  • 9:11 - 9:14
    Rather, it is something
    that's constructed with our opinions
  • 9:14 - 9:17
    and something that changes over time.
  • 9:17 - 9:22
    That is to say, the category of the human
    is unstable.
  • 9:23 - 9:27
    So my final insight is that
    the computer, more or less,
  • 9:27 - 9:31
    works like a mirror
    that reflects any idea of a human
  • 9:31 - 9:32
    that we show it.
  • 9:33 - 9:34
    We show it Emily Dickinson,
  • 9:34 - 9:37
    it gives Emily Dickinson back to us.
  • 9:37 - 9:39
    We show it William Blake,
  • 9:39 - 9:41
    that's what it reflects back to us.
  • 9:41 - 9:43
    We show it Gertrude Stein,
  • 9:43 - 9:45
    what we get back is Gertrude Stein.
  • 9:46 - 9:48
    More than any other bit of technology,
  • 9:48 - 9:54
    the computer is a mirror that reflects
    any idea of the human we teach it.
  • 9:55 - 9:57
    So I'm sure a lot of you have been hearing
  • 9:57 - 10:00
    a lot about artificial
    intelligence recently.
  • 10:01 - 10:04
    And much of the conversation is kind of,
  • 10:05 - 10:06
    Can we build it?
  • 10:07 - 10:10
    Can we build an intelligent computer?
  • 10:10 - 10:12
    Can we build a creative computer?
  • 10:12 - 10:14
    What we seem to be asking over and over
  • 10:14 - 10:17
    is can we build a human-like computer?
  • 10:18 - 10:19
    But what we've seen just now
  • 10:19 - 10:22
    is that the human
    is not a scientific fact,
  • 10:22 - 10:25
    that it's an ever-shifting,
    concatenating idea
  • 10:25 - 10:28
    and one that changes over time.
  • 10:28 - 10:31
    So that when we begin
    to grapple with the ideas
  • 10:31 - 10:33
    of artificial intelligence in the future,
  • 10:33 - 10:35
    we shouldn't only be asking ourselves,
  • 10:35 - 10:37
    "Can we build it?"
  • 10:37 - 10:39
    But we should also be asking ourselves,
  • 10:39 - 10:42
    "What idea of the human
    do we want to have reflected back to us?"
  • 10:42 - 10:45
    This is an essentially philosophical idea,
  • 10:45 - 10:48
    and it's one that can't be answered
    with software alone,
  • 10:48 - 10:53
    but I think requires a moment
    of species-wide, existential reflection.
  • 10:53 - 10:54
    Thank you.
  • 10:54 - 10:57
    (Applause)
Title:
Can a computer write poetry? | Oscar Schwartz | TEDxYouth@Sydney
Description:

If you read a poem and feel moved by it, but then find out it was actually written by a computer, would you feel differently about the experience? Would you think that the computer had expressed itself and been creative, or would you feel like you had fallen for a cheap trick? In this talk, writer Oscar Schwartz examines why we react so strongly to the idea of a computer writing poetry -- and how this reaction helps us understand what it means to be human.

--

Poetry test #1

Poem 1
Little Fly
Thy summer’s play,
My thoughtless hand
Has bush’d away.

Am not I
A fly like thee?
Or art not thou
A man like me?

Poem 2
We can feel
Activist through your life’s
Morning
Pauses to see, pope I hate the
Non all the night to start a
great otherwise

I’ll snake swirling
Vastness guess
Totally mental hamsters if I
Know I put on a year a crucial
Absolutely.

Poetry test #2

Poem 1
A lion roars and a dog barks. It is interesting
and fascinating that a bird will fly and not
roar or bark. Enthralling stories about animals
are in my dreams and I will sing them all if I
am not exhausted and weary.

Poem 2
Oh! kangaroos, sequins, chocolate sodas!
You really are beautiful! Pearls,
harmonicas, jujubes, aspirins! All
the stuff they’ve always talked about

still makes a poem a surprise!
These things are with us every day
even on beachheads and biers. They
do have meaning. They’re strong as rocks.

Poetry test #3

Poem 1
Red flags the reason for pretty flags.
And ribbons.
Ribbons of flags
And wearing material
Reason for wearing material.
Give pleasure.
Can you give me the regions.
The regions and the land.
The regions and wheels.
All wheels are perfect.
Enthusiasm.

Poem 2
A wounded deer leaps highest,
I’ve heard the daffodil
I’ve heard the flag to-day
I’ve heard the hunter tell;
‘Tis but the ecstasy of death,
And then the brake is almost done,
And sunrise grows so near
sunrise grows so near
That we can touch the despair and
frenzied hope of all the ages.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx

more » « less
Video Language:
English
Team:
closed TED
Project:
TEDxTalks
Duration:
11:04
  • 3:12.06
    to have a to have a text-based → to have a text-based

    Thank you!

English subtitles

Revisions Compare revisions