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【英语语言学习】电脑可以写诗吗?

时间:2016-10-20 06:23来源:互联网 提供网友:yajing   字体: [ ]
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I have a question. Can a computer write poetry? This is a provocative1 question. You think about it for a minute, and you suddenly have a bunch of other questions like: What is a computer? What is poetry? What is creativity? But these are questions that people spend their entire lifetime trying to answer, not in a single TED2 Talk. So we're going to have to try a different approach.
So up here, we have two poems. One of them is written by a human, and the other one's written by a computer. I'm going to ask you to tell me which one's which. Have a go:
Poem 1: Little Fly / Thy summer's play, / My thoughtless hand / Has brush'd away. Am I not / A fly like thee? / Or art not thou / A man like me?
Poem 2: We can feel / Activist3 through your life's / morning / Pauses to see, pope I hate the / Non all the night to start a / great otherwise (...)
Alright, time's up. Hands up if you think Poem 1 was written by a human. OK, most of you. Hands up if you think Poem 2 was written by a human. Very brave of you, because the first one was written by the human poet William Blake. The second one was written by an algorithm that took all the language from my Facebook feed on one day and then regenerated4 it algorithmically, according to methods that I'll describe a little bit later on. So let's try another test. Again, you haven't got ages to read this, so just trust your gut5.
Poem 1: A lion roars and a dog barks. It is interesting / and fascinating that a bird will fly and not / roar or bark. Enthralling6 stories about animals are in my dreams and I will sing them all if I / am not exhausted7 or weary.
Poem 2: Oh! kangaroos, sequins, chocolate sodas8! / You are really beautiful! Pearls, / harmonicas, jujubes, aspirins! All / the stuff they've always talked about (...)
Alright, time's up. So if you think the first poem was written by a human, put your hand up. OK. And if you think the second poem was written by a human, put your hand up. We have, more or less, a 50/50 split here. It was much harder.
The answer is, the first poem was generated by an algorithm called Racter, that was created back in the 1970s, and the second poem was written by a guy called Frank O'Hara, who happens to be one of my favorite human poets.
So what we've just done now is a Turing test for poetry. The Turing test was first proposed by this guy, Alan Turing, in 1950, in order to answer the question, can computers think? Alan Turing believed that if a computer was able to have a to have a text-based conversation with a human, with such proficiency9 such that the human couldn't tell whether they are talking to a computer or a human, then the computer can be said to have intelligence.
So in 2013, my friend Benjamin Laird and I, we created a Turing test for poetry online. It's called bot or not, and you can go and play it for yourselves. But basically, it's the game we just played. You're presented with a poem, you don't know whether it was written by a human or a computer and you have to guess. So thousands and thousands of people have taken this test online, so we have results.
And what are the results? Well, Turing said that if a computer could fool a human 30 percent of the time that it was a human, then it passes the Turing test for intelligence. We have poems on the bot or not database that have fooled 65 percent of human readers into thinking it was written by a human. So, I think we have an answer to our question. According to the logic10 of the Turing test, can a computer write poetry? Well, yes, absolutely it can. But if you're feeling a little bit uncomfortable with this answer, that's OK. If you're having a bunch of gut reactions to it, that's also OK because this isn't the end of the story.
Let's play our third and final test. Again, you're going to have to read and tell me which you think is human.
Poem 1: Reg flags the reason for pretty flags. / And ribbons. Ribbons of flags / And wearing material / Reasons for wearing material. (...)
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 ecstasy11 of death, / And then the brake is almost done (...)
OK, time is up. So hands up if you think Poem 1 was written by a human. Hands up if you think Poem 2 was written by a human. Whoa, that's a lot more people. So you'd be surprised to find that Poem 1 was written by the very human poet Gertrude Stein. And Poem 2 was generated by an algorithm called RKCP. Now before we go on, let me describe very quickly and simply, how RKCP works. So RKCP is an algorithm designed by Ray Kurzweil, who's a director of engineering at Google and a firm believer in artificial intelligence. So, you give RKCP a source text, it analyzes12 the source text in order to find out how it uses language, and then it regenerates13 language that emulates14 that first text.
So in the poem we just saw before, Poem 2, the one that you all thought was human, it was fed a bunch of poems by a poet called Emily Dickinson it looked at the way she used language, learned the model, and then it regenerated a model according to that same structure. But the important thing to know about RKCP is that it doesn't know the meaning of the words it's using. The language is just raw material, it could be Chinese, it could be in Swedish, it could be the collected language from your Facebook feed for one day. It's just raw material. And nevertheless, it's able to create a poem that seems more human than Gertrude Stein's poem, and Gertrude Stein is a human.
So what we've done here is, more or less, a reverse Turing test. So Gertrude Stein, who's a human, is able to write a poem that fools a majority of human judges into thinking that it was written by a computer. Therefore, according to the logic of the reverse Turing test, Gertrude Stein is a computer.
Feeling confused? I think that's fair enough.
So far we've had humans that write like humans, we have computers that write like computers, we have computers that write like humans, but we also have, perhaps most confusingly, humans that write like computers.
So what do we take from all of this? Do we take that William Blake is somehow more of a human than Gertrude Stein? Or that Gertrude Stein is more of a computer than William Blake?
These are questions I've been asking myself for around two years now, and I don't have any answers. But what I do have are a bunch of insights about our relationship with technology.
So my first insight is that, for some reason, we associate poetry with being human. So that when we ask, "Can a computer write poetry?" we're also asking, "What does it mean to be human and how do we put boundaries around this category? How do we say who or what can be part of this category?" This is an essentially15 philosophical16 question, I believe, and it can't be answered with a yes or no test, like the Turing test. I also believe that Alan Turing understood this, and that when he devised his test back in 1950, he was doing it as a philosophical provocation17.
So my second insight is that, when we take the Turing test for poetry, we're not really testing the capacity of the computers because poetry-generating algorithms, they're pretty simple and have existed, more or less, since the 1950s. What we are doing with the Turing test for poetry, rather, is collecting opinions about what constitutes humanness. So, what I've figured out, we've seen this when earlier today, we say that William Blake is more of a human than Gertrude Stein. Of course, this doesn't mean that William Blake was actually more human or that Gertrude Stein was more of a computer. It simply means that the category of the human is unstable18. This has led me to understand that the human is not a cold, hard fact. Rather, it is something that's constructed with our opinions and something that changes over time.
So my final insight is that the computer, more or less, works like a mirror that reflects any idea of a human that we show it. We show it Emily Dickinson, it gives Emily Dickinson back to us. We show it William Blake, that's what it reflects back to us. We show it Gertrude Stein, what we get back is Gertrude Stein. More than any other bit of technology, the computer is a mirror that reflects any idea of the human we teach it.
So I'm sure a lot of you have been hearing a lot about artificial intelligence recently. And much of the conversation is, can we build it? Can we build an intelligent computer? Can we build a creative computer? What we seem to be asking over and over is can we build a human-like computer?
But what we've seen just now is that the human is not a scientific fact, that it's an ever-shifting, concatenating19 idea and one that changes over time. So that when we begin to grapple with the ideas of artificial intelligence in the future, we shouldn't only be asking ourselves, "Can we build it?" But we should also be asking ourselves, "What idea of the human do we want to have reflected back to us?" This is an essentially philosophical idea, and it's one that can't be answered with software alone, but I think requires a moment of species-wide, existential reflection.
Thank you.

点击收听单词发音收听单词发音  

1 provocative e0Jzj     
adj.挑衅的,煽动的,刺激的,挑逗的
参考例句:
  • She wore a very provocative dress.她穿了一件非常性感的裙子。
  • His provocative words only fueled the argument further.他的挑衅性讲话只能使争论进一步激化。
2 ted 9gazhs     
vt.翻晒,撒,撒开
参考例句:
  • The invaders gut ted the village.侵略者把村中财物洗劫一空。
  • She often teds the corn when it's sunny.天好的时候她就翻晒玉米。
3 activist gyAzO     
n.活动分子,积极分子
参考例句:
  • He's been a trade union activist for many years.多年来他一直是工会的积极分子。
  • He is a social activist in our factory.他是我厂的社会活动积极分子。
4 regenerated 67df9da7e5af2af5acd8771deef0296f     
v.新生,再生( regenerate的过去式和过去分词 )
参考例句:
  • They are regarded as being enveloped in regenerated gneisses. 它们被认为包围在再生的片麻岩之中。 来自辞典例句
  • The party soon regenerated under her leadership. 该党在她的领导下很快焕然一新。 来自辞典例句
5 gut MezzP     
n.[pl.]胆量;内脏;adj.本能的;vt.取出内脏
参考例句:
  • It is not always necessary to gut the fish prior to freezing.冷冻鱼之前并不总是需要先把内脏掏空。
  • My immediate gut feeling was to refuse.我本能的直接反应是拒绝。
6 enthralling b491b0cfdbf95ce2c84d3fe85b18f2cb     
迷人的
参考例句:
  • There will be an enthralling race tomorrow. 明天会有场吸引人的比赛。
  • There was something terribly enthralling in the exercise of influence. 在这样地施加影响时,令人感到销魂夺魄。
7 exhausted 7taz4r     
adj.极其疲惫的,精疲力尽的
参考例句:
  • It was a long haul home and we arrived exhausted.搬运回家的这段路程特别长,到家时我们已筋疲力尽。
  • Jenny was exhausted by the hustle of city life.珍妮被城市生活的忙乱弄得筋疲力尽。
8 sodas c10ddd4eedc33e2ce63fa8dfafd61880     
n.苏打( soda的名词复数 );碱;苏打水;汽水
参考例句:
  • There are plenty of sodas in the refrigerator. 冰箱里有很多碳酸饮料。 来自辞典例句
  • Two whisky and sodas, please. 请来两杯威士忌苏打。 来自辞典例句
9 proficiency m1LzU     
n.精通,熟练,精练
参考例句:
  • He plied his trade and gained proficiency in it.他勤习手艺,技术渐渐达到了十分娴熟的地步。
  • How do you think of your proficiency in written and spoken English?你认为你的书面英语和口语熟练程度如何?
10 logic j0HxI     
n.逻辑(学);逻辑性
参考例句:
  • What sort of logic is that?这是什么逻辑?
  • I don't follow the logic of your argument.我不明白你的论点逻辑性何在。
11 ecstasy 9kJzY     
n.狂喜,心醉神怡,入迷
参考例句:
  • He listened to the music with ecstasy.他听音乐听得入了神。
  • Speechless with ecstasy,the little boys gazed at the toys.小孩注视着那些玩具,高兴得说不出话来。
12 analyzes e2e80b8320e1dd8d4c035d41fd7e44e5     
v.分析( analyze的第三人称单数 );分解;解释;对…进行心理分析
参考例句:
  • This approach analyzes management by studying experience usually through cases. 这个学派通常从实例获得经验,用以分析管理。 来自辞典例句
  • The econometrician analyzes statistical data. 经济计量学者要分析统计材料。 来自辞典例句
13 regenerates db5c9d3239d5ae9968cc931d6dd84c2f     
n.新生,再生( regenerate的名词复数 )v.新生,再生( regenerate的第三人称单数 )
参考例句:
  • This activity regenerates some of the ATP lost in proton reduction. 这一反应可以使在质子还原过程中丢失的某些ATP再生。 来自辞典例句
  • Level 2-Heals all allied Heroes for 300 HP. Fully Regenerates converted creeps. 二级-治疗地图上所有的友方英雄300点的生命,完全恢复皈依你的单位。 来自互联网
14 emulates 026e018a5e95a5861f3ddda5945b9eaf     
v.与…竞争( emulate的第三人称单数 );努力赶上;计算机程序等仿真;模仿
参考例句:
  • A device or computer program that emulates. 一种可以进行仿真的设备或计算机程序。 来自辞典例句
  • Sleep Timer Emulates the time found in most TVs. 睡眠计时器模拟时发现,大多数电视。 来自互联网
15 essentially nntxw     
adv.本质上,实质上,基本上
参考例句:
  • Really great men are essentially modest.真正的伟人大都很谦虚。
  • She is an essentially selfish person.她本质上是个自私自利的人。
16 philosophical rN5xh     
adj.哲学家的,哲学上的,达观的
参考例句:
  • The teacher couldn't answer the philosophical problem.老师不能解答这个哲学问题。
  • She is very philosophical about her bad luck.她对自己的不幸看得很开。
17 provocation QB9yV     
n.激怒,刺激,挑拨,挑衅的事物,激怒的原因
参考例句:
  • He's got a fiery temper and flares up at the slightest provocation.他是火爆性子,一点就着。
  • They did not react to this provocation.他们对这一挑衅未作反应。
18 unstable Ijgwa     
adj.不稳定的,易变的
参考例句:
  • This bookcase is too unstable to hold so many books.这书橱很不结实,装不了这么多书。
  • The patient's condition was unstable.那患者的病情不稳定。
19 concatenating 4f85445b86b45b81472632f844d98b1b     
v.把 (一系列事件、事情等)联系起来( concatenate的现在分词 )
参考例句:
  • I've done some categorizing, concatenating, and taking a guess at gender in my use of pronouns. 我进行了分类、合并,并通过用代词来猜测了一下人们的性别。 来自互联网
  • Calling this function creates a single string by concatenating the array elements you specify. 调用此函数将串联所指定的数组元素以创建一个字符串。 来自互联网
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TAG标签:   英语听力  听力教程  英语学习
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