劍橋雅思13Test1閱讀passage3真題+翻譯(2)
2024-04-05 15:32:29 來源：中國教育在線
劍橋雅思13Test1閱讀passage3真題+翻譯(2) 關(guān)于這個問題下面小編就來為各個考生解答下。
劍橋雅思13Test1閱讀passage3真題+翻譯
READING PASSAGE 3
You should spend about 20 minutes on Questions 27-40, which are based on Reading Passage 3 below.
Artificial artists
人工智能藝術(shù)家
Can computers really create works of art?
電腦真的能創(chuàng)作藝術(shù)嗎?
Researchers like Colton don't believe it is right to measure machine creativity directly to that of humans who ‘have had millennia to develop our skills’. Others, though, are fascinated by the prospect that a computer might create something as original and subtle as our best artists. So far, only one has come close. Composer David Cope invented a program called Experiments in Musical Intelligence, or EMI. Not only did EMI create compositions in Cope’s style, but also that of the most revered classical composers, including Bach, Chopin and Mozart. Audiences were moved to tears, and EMI even fooled classical music experts into thinking they were hearing genuine Bach. Not everyone was impressed however. Some, such as Wiggins, have blasted Cope's work as pseudoscience, and condemned him for his deliberately vague explanation of how the software worked. Meanwhile, Douglas Hofstadter of Indiana University said EMI created replicas which still rely completely on the original artist’s creative impulses. When audiences found out the truth they were often outraged with Cope, and one music lover even tried to punch him. Amid such controversy, Cope destroyed EMI's vital databases.
像Colton這樣的研究者并不認(rèn)為我們應(yīng)當(dāng)直接用人類藝術(shù)的標(biāo)準(zhǔn)來衡量機(jī)器創(chuàng)作,畢竟人類“有著數(shù)千年的時光來發(fā)展藝術(shù)造詣”,雖然有些人著迷于電腦將來可能創(chuàng)作出新穎而又精妙的作品,正如人類的藝術(shù)家一樣。到目前為止,只有一臺機(jī)器幾乎做到了。作曲家David Cope寫出了一個叫音樂智能實(shí)驗(yàn)(EMI)的程序。它不僅僅能仿作Cope的曲風(fēng),連最負(fù)盛名的古典音樂大師如巴赫、肖邦、莫扎特的曲風(fēng)也能模仿。聽眾們聽著它的作品感動淚流,連古典音樂都沒能發(fā)覺所聽曲目并不是真正的巴赫作品。當(dāng)然,也不是所有人都買賬。有些人比如Wiggins也曾攻擊過他的作品是偽科學(xué),并譴責(zé)他刻意隱瞞自己軟件的運(yùn)作方式。同時,印第安納大學(xué)的Douglas Hofstadter說EMI的模仿完全有賴于原作者的創(chuàng)作靈感。而當(dāng)聽眾們發(fā)現(xiàn)真相時,他們通常會對Cope惱羞成怒,有個音樂愛好者甚至要沖上去揍Cope。在這樣的爭議中,Cope銷毀了EMI的關(guān)鍵數(shù)據(jù)。
But why did so many people love the music, yet recoil when they discovered how it was composed? A study by computer scientist David Moffat of Glasgow Caledonian University provides a clue. He asked both expert musicians and non-experts to assess six compositions. The participants weren’t told beforehand whether the tunes were composed by humans or computers, but were asked to guess, and then rate how much they liked each one. People who thought the composer was a computer tended to dislike the piece more than those who believed it was human. This was true even among the experts, who might have been expected to be more objective in their analyses.
但是為什么人們明明喜歡那些音樂,卻又在發(fā)現(xiàn)它們的創(chuàng)作方式后感到厭惡呢?格拉斯哥卡里多尼亞大學(xué)的電腦科學(xué)家David Moffat所做的一個實(shí)驗(yàn)為我們提供了一些線索。實(shí)驗(yàn)中他請專業(yè)的音樂家和非專業(yè)人士來評判6部音樂作品。參與者事先都不知道所聽曲目是人類創(chuàng)作還是電腦產(chǎn)物,但他們會被要求去揣測,然后給這些作品按喜愛程度評級。如果人們認(rèn)為某個曲目是電腦產(chǎn)物,他們的喜愛程度就會低于人類創(chuàng)作的曲目。甚至在當(dāng)中也是如此,我們可能本來會指望他們在分析評價時能夠更客觀一些。
Where does this prejudice come from? Paul Bloom of Yale University has a suggestion: he reckons part of the pleasure we get from art stems from the creative process behind the work. This can give it an ‘irresistible essence', says Bloom. Meanwhile, experiments by Justin Kruger of New York University have shown that people's enjoyment of an artwork increases if they think more time and effort was needed to create it. Similarly, Colton thinks that when people experience art, they wonder what the artist might have been thinking or what the artist is trying to tell them. It seems obvious, therefore, that with computers producing art, this speculation is cut short - there's nothing to explore. But as technology becomes increasingly complex, finding those greater depths in computer art could become possible. This is precisely why Colton asks the Painting Fool to tap into online social networks for its inspiration: hopefully this way it will choose themes that will already be meaningful to us.
那這種偏見從何而來呢?耶魯大學(xué)的Paul bloom有這樣的說法:他認(rèn)為我們欣賞藝術(shù)作品的愉悅有一部分來自于作品背后的創(chuàng)作過程。他說,這能為藝術(shù)作品帶來一種“不可抵擋的精妙”。同時,紐約大學(xué)的 Justin Kruger的實(shí)驗(yàn)表明,人們認(rèn)為如果作者創(chuàng)作該作品時花的時間精力越多,他們就會越享受這個作品。Colton也有相同的看法,他認(rèn)為當(dāng)人們欣賞藝術(shù)時,他們會思考作者在創(chuàng)作時在想些什么、他們想要傳達(dá)什么。因此,顯而易見的是,當(dāng)電腦在創(chuàng)作藝術(shù)品時,這個思索過程是被腰斬了的—這里沒什么可發(fā)掘的。但是隨著科技變得愈發(fā)復(fù)雜,也有可能在電腦藝術(shù)中找到深層的意義。這正是Colton會讓傻瓜繪圖一頭扎進(jìn)社交網(wǎng)絡(luò)里搜尋靈感的原因:希望這樣它能找到一些對我們來說已經(jīng)很有意義的主題。
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