Artificial Intelligence Challenges What It Means to Be Creative人工智能挑戰(zhàn)創(chuàng)造力定義

理查德·莫斯　劉斯杭/譯

When British artist Harold Cohen met his first computer in 1968， he wondered if the machine might help solve a mystery that had long puzzled him： How can we look at a drawing， a few little scribbles， and see a face？ Five years later， he devised a robotic artist called AARON to explore this idea. He equipped it with basic rules for painting and for how body parts are represented in portraiture—and then set it loose making art.

當(dāng)英國(guó)藝術(shù)家哈羅德·科恩于1968年遇到他的第一臺(tái)計(jì)算機(jī)時(shí)，他便開始思考這臺(tái)機(jī)器能否解答一個(gè)令他困惑已久的疑問(wèn)：我們?nèi)绾文芡ㄟ^(guò)看一幅素描，僅僅幾筆涂鴉，便看出一張臉？五年后，他設(shè)計(jì)出名為AARON的機(jī)器人藝術(shù)家，來(lái)探索這一想法。他為它設(shè)置了作畫和肖像中身體部位呈現(xiàn)方式的基本規(guī)則，然后讓其自由地開始藝術(shù)創(chuàng)作。

Not far behind was the composer David Cope， who coined the phrase “musical intelligence” to describe his experiments with artificial intelligence-powered composition. Cope once told me that as early as the 1960s， it seemed to him “perfectly logical to do creative things with algorithms” rather than to painstakingly draw by hand every word of a story， note of a musical composition or brush stroke of a painting. He initially tinkered with algorithms on paper， then in 1981 moved to computers to help solve a case of composers block.

不久之后，作曲家戴維·科普創(chuàng)造了“音樂智能”一詞來(lái)描述他的人工智能驅(qū)動(dòng)作曲實(shí)驗(yàn)?？破赵嬖V我，他早在1960年代就認(rèn)為，“利用算法進(jìn)行創(chuàng)造性活動(dòng)完全合乎邏輯”，而無(wú)需煞費(fèi)苦心地親筆手寫故事中的每個(gè)詞、親自設(shè)計(jì)樂曲中的每個(gè)音符、親手描繪畫作中的每一筆。他起初在紙上擺弄算法，后于1981年轉(zhuǎn)而使用計(jì)算機(jī)解決作曲瓶頸問(wèn)題。

Cohen and Cope were among a handful of eccentrics pushing computers to go against their nature as cold， calculating things. The still-nascent1 field of AI had its focus set squarely on solid concepts like reasoning and planning， or on tasks like playing chess and checkers or solving mathematical problems. Most AI researchers balked2 at the notion of creative machines.

當(dāng)時(shí)科恩和科普屬于少數(shù)幾位推動(dòng)計(jì)算機(jī)違背其冷冰冰計(jì)算本質(zhì)的“怪人”。那時(shí)人工智能這一領(lǐng)域尚處于萌芽階段，重點(diǎn)完全放在推理和規(guī)劃等較為實(shí)際的概念上，或者下國(guó)際象棋和跳棋或解數(shù)學(xué)題上。大多數(shù)人工智能研究者都對(duì)機(jī)器擁有創(chuàng)造力這一想法望而卻步。

Slowly， however， as Cohen and Cope cranked out a stream of academic papers and books about their work， a field emerged around them： computational creativity. It included the study and development of autonomous creative systems， interactive tools that support human creativity and mathematical approaches to modeling human creativity. In the late 1990s， computational creativity became a formalized area of study with a growing cohort of researchers and eventually its own journal and annual event.

然而，隨著科恩和科普接連不斷地發(fā)布一系列與他們的工作相關(guān)的學(xué)術(shù)文章和書籍，一個(gè)新興領(lǐng)域在他們周圍應(yīng)運(yùn)而生：計(jì)算創(chuàng)意學(xué)。這包括研究與開發(fā)自動(dòng)創(chuàng)作系統(tǒng)、支持人類創(chuàng)造活動(dòng)的互動(dòng)工具和以人類創(chuàng)造力建模的數(shù)學(xué)方法。1990年代末，計(jì)算創(chuàng)意學(xué)成為正式的研究領(lǐng)域，研究者隊(duì)伍日益壯大，最終還創(chuàng)辦了相關(guān)期刊和年度活動(dòng)。

Soon enough—thanks to new techniques rooted in machine learning and artificial neural networks， in which connected computing nodes attempt to mirror the workings of the brain—creative AIs could absorb and internalize real-world data and identify patterns and rules that they could apply to their creations.

很快，具有創(chuàng)造力的人工智能就能夠吸收及內(nèi)化現(xiàn)實(shí)世界的數(shù)據(jù)，并識(shí)別可應(yīng)用于其創(chuàng)作的模式與規(guī)則——這要?dú)w功于建立在機(jī)器學(xué)習(xí)和人工神經(jīng)網(wǎng)絡(luò)基礎(chǔ)上的新技術(shù)，其中相互連接的計(jì)算節(jié)點(diǎn)會(huì)試圖模擬大腦運(yùn)作。

Computer scientist Simon Colton， then at Imperial College London and now at Queen Mary University of London and Monash University in Melbourne， Australia， spent much of the 2000s building the Painting Fool. The computer program analyzed the text of news articles and other written works to determine the sentiment and extract keywords. It then combined that analysis with an automated search of the photography website Flickr to help it generate painterly collages in the mood of the original article. Later the Painting Fool learned to paint portraits in real time of people it met through an attached camera， again applying its “mood” to the style of the portrait （or in some cases refusing to paint anything because it was in a bad mood）.

計(jì)算機(jī)科學(xué)家西蒙·科爾頓曾在倫敦帝國(guó)理工學(xué)院工作，現(xiàn)任職于倫敦瑪麗女王大學(xué)和澳大利亞莫納什大學(xué)。2000年代的大部分時(shí)間里，他都在打造名為“繪畫傻瓜”的電腦程序。這一程序通過(guò)分析新聞和其他書面作品的文本，判斷情緒傾向并提取關(guān)鍵詞；接著將分析結(jié)果和攝影網(wǎng)站Flickr的自動(dòng)搜索功能結(jié)合，生成反映原始文本情緒特征的拼貼畫。后來(lái)，“繪畫傻瓜”學(xué)會(huì)了通過(guò)連接相機(jī)為遇到的人實(shí)時(shí)繪制肖像，再次將它的“情緒”應(yīng)用到肖像的風(fēng)格中（或者在某些情況下，它因心情不佳而拒絕作畫）。

Similarly， in the early 2010s， computational creativity turned to gaming. AI researcher and game designer Michael Cook dedicated his Ph.D. thesis and early research associate work at Goldsmiths， University of London to creating ANGELINA—which made simple games based on news articles from The Guardian3， combining current affairs text analysis with hard-coded design and programming techniques.

2010年代初，計(jì)算創(chuàng)意學(xué)同樣也在游戲領(lǐng)域得以應(yīng)用。人工智能研究者兼游戲設(shè)計(jì)師邁克爾·庫(kù)克將自己的博士論文和倫敦大學(xué)戈德史密斯學(xué)院的早期研究助理工作都傾注于打造 “安杰利娜”：它可以根據(jù)《衛(wèi)報(bào)》的新聞文章制作簡(jiǎn)單的游戲，將時(shí)事文本分析、硬編碼設(shè)計(jì)和編程技術(shù)相結(jié)合。

During this era， Colton says， AIs began to look like creative artists in their own right—incorporating elements of creativity such as intentionality， skill， appreciation and imagination. But what followed was a focus on mimicry， along with controversy over what it means to be creative.

科爾頓說(shuō)，人工智能在這個(gè)時(shí)代開始變得如同自成一格的創(chuàng)意藝術(shù)家——融合了諸如意圖、技巧、鑒賞力及想象力等具有創(chuàng)造性的元素。但隨之出現(xiàn)了對(duì)模仿的關(guān)注，以及對(duì)何為創(chuàng)造性的爭(zhēng)議。

New techniques that excelled at classifying data to high degrees of precision through repeated analysis helped AI master existing creative styles. AI could now create works like those of classical composers， famous painters， novelists and more.

善于通過(guò)重復(fù)分析將數(shù)據(jù)高度精確分類的新技術(shù)，幫助人工智能掌握了現(xiàn)有的創(chuàng)作風(fēng)格。它目前可以創(chuàng)作類似出自古典作曲家、著名畫家、小說(shuō)家等人之手的作品。

One AI-authored painting modeled on thousands of portraits painted between the 14th and 20th centuries sold for $432，500 at auction. In another case， study participants struggled to differentiate the musical phrases of Johann Sebastian Bach4 from those created by a computer program called Kulitta that had been trained on Bachs compositions. Even IBM5 got in on the fun， tasking its Watson AI system with analyzing 9，000 recipes to devise its own cuisine ideas.

一幅以14世紀(jì)至20世紀(jì)數(shù)千幅肖像畫為藍(lán)本的人工智能畫作在拍賣會(huì)上以432，500美元的價(jià)格成交。另有一例研究顯示，參與者難以分辨約翰·塞巴斯蒂安·巴赫的音樂樂句和據(jù)其曲目訓(xùn)練的電腦程序Kulitta的作品。就連國(guó)際商業(yè)機(jī)器公司也加入其中，指令旗下的沃森人工智能系統(tǒng)分析9000份食譜，自主開發(fā)創(chuàng)意菜式。

But many in the field， as well as onlookers， wondered if these AIs really showed creativity. Though sophisticated in their mimicry， these creative AIs seemed incapable of true innovation because they lacked the capacity to incorporate new influences from their environment. Colton and a colleague described them as requiring “much human intervention， supervision， and highly technical knowledge” in producing creative results. Overall， as composer and computer music researcher Palle Dahl-stedt puts it， these AIs converged toward the mean， creating something typical of what is already out there， whereas creativity is supposed to diverge away from the typical.

但許多業(yè)內(nèi)人士和旁觀者都對(duì)這些人工智能是否真正展現(xiàn)出創(chuàng)造力持懷疑態(tài)度。盡管這些具有“創(chuàng)造力”的人工智能在模仿方面已然爐火純青，但因缺乏從環(huán)境吸收新影響因素的能力，似乎無(wú)法進(jìn)行真正的創(chuàng)新?？茽栴D和一位同事將其描述為，需要“大量的人為干預(yù)、監(jiān)督和高度技術(shù)性的知識(shí)”才能產(chǎn)出具有創(chuàng)造性的結(jié)果。總的來(lái)說(shuō)，正如作曲家兼計(jì)算機(jī)音樂研究者帕勒·達(dá)爾斯泰特所言，這些人工智能往往表現(xiàn)平平，創(chuàng)作出來(lái)的東西具有已有事物的典型特征，但創(chuàng)意是應(yīng)該不同凡響的。

In order to make the step to true creativity， Dahlstedt suggested， AI “would have to model the causes of the music， the conditions for its coming into being—not the results.” True creativity is a quest for originality. It is a recombination of disparate ideas in new ways. It is unexpected solutions. It might be music or painting or dance， but also the flash of inspiration that helps lead to advances on the order of light bulbs and airplanes and the periodic table. In the view of many in the computational creativity field， it is not yet attainable by machines.

達(dá)爾斯泰特認(rèn)為，若要進(jìn)一步掌握真正的創(chuàng)造力，人工智能“必須模擬音樂產(chǎn)生的原因，即其創(chuàng)作情形——而非結(jié)果”。真正的創(chuàng)造力是對(duì)原創(chuàng)性的追求，是對(duì)迥然不同的各種觀點(diǎn)的重新排列組合，是意料之外的解決方案。它可能以音樂、繪畫或舞蹈的形式呈現(xiàn)，亦可能是靈感閃現(xiàn)，幫助推動(dòng)燈泡、飛機(jī)和元素周期表的發(fā)展。計(jì)算創(chuàng)意學(xué)領(lǐng)域的大部分人認(rèn)為，這是機(jī)器尚無(wú)法企及的。

In just the past few years， creative AIs have expanded into style invention—into authorship that is individualized rather than imitative and that pro-jects meaning and intentionality， even if none exists. For Colton， this element of intentionality—a focus on the process， more so than the final output—is key to achieving creativity. But he wonders whether meaning and authenticity are also essential， as the same poem could lead to vastly different interpretations if the reader knows it was written by a man versus a woman versus a machine. If an AI lacks the self-awareness to reflect on its actions and experiences， and to communicate its creative intent， then is it truly creative？ Or is the creativity still with the author who fed it data and directed it to act？

近幾年，創(chuàng)造性人工智能的應(yīng)用已經(jīng)擴(kuò)展至風(fēng)格開創(chuàng)的層面，即個(gè)性化的創(chuàng)作者而非模仿者，甚至能展現(xiàn)本不存在的意義與意圖?？茽栴D認(rèn)為，意圖這一元素——即對(duì)過(guò)程的關(guān)注超出最終結(jié)果——是實(shí)現(xiàn)創(chuàng)造力的關(guān)鍵。但他也在思考意義和真實(shí)性是否同樣至關(guān)重要，因?yàn)樽x者在知道作者為男性、女性或機(jī)器的情況下，可能會(huì)對(duì)同一首詩(shī)作出截然不同的解讀。如果人工智能缺乏自我意識(shí)，無(wú)法反省自身行為和經(jīng)歷，也無(wú)法表述自己的創(chuàng)作意圖，它真的能算具有創(chuàng)造力嗎？還是說(shuō)，創(chuàng)造力仍屬于為它提供數(shù)據(jù)并向它下達(dá)指令的作者？

Ultimately， moving from an attempt at thinking machines to an attempt at creative machines may transform our understanding of ourselves. Seventy years ago Alan Turing—sometimes described as the father of artificial intelligence—devised a test he called “the imitation game” to measure a machines intelligence against our own. “Turings greatest insight，” writes philosopher of technology Joel Parthemore of the University of Sk?vde in Sweden， “l(fā)ie in seeing digital computers as a mirror by which the human mind could consider itself in ways that previously were not possible.”

最終來(lái)看，嘗試將計(jì)算機(jī)從思考機(jī)器轉(zhuǎn)而打造為創(chuàng)造性機(jī)器可能會(huì)改變我們對(duì)自身的認(rèn)識(shí)。70年前，被譽(yù)為人工智能之父的艾倫·圖靈設(shè)計(jì)了一種他稱為“模仿游戲”的測(cè)試來(lái)衡量對(duì)比機(jī)器與人類的智力。瑞典舍夫德大學(xué)的技術(shù)哲學(xué)家約埃爾·帕特莫爾如是寫道：“圖靈最偉大的見解就是將數(shù)字計(jì)算機(jī)視為一面鏡子，讓人類經(jīng)由它以前所未有的方式自我反思。”

（譯者為“《英語(yǔ)世界》杯”翻譯大賽獲獎(jiǎng)?wù)撸?/p>