Artificial Intelligence Challenges What It Means to Be Creative人工智能挑戰創造力定義

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.

當英國藝術家哈羅德·科恩于1968年遇到他的第一臺計算機時，他便開始思考這臺機器能否解答一個令他困惑已久的疑問：我們如何能通過看一幅素描，僅僅幾筆涂鴉，便看出一張臉？五年后，他設計出名為AARON的機器人藝術家，來探索這一想法。他為它設置了作畫和肖像中身體部位呈現方式的基本規則，然后讓其自由地開始藝術創作。

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·科普創造了“音樂智能”一詞來描述他的人工智能驅動作曲實驗。科普曾告訴我，他早在1960年代就認為，“利用算法進行創造性活動完全合乎邏輯”，而無需煞費苦心地親筆手寫故事中的每個詞、親自設計樂曲中的每個音符、親手描繪畫作中的每一筆。他起初在紙上擺弄算法，后于1981年轉而使用計算機解決作曲瓶頸問題。

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.

當時科恩和科普屬于少數幾位推動計算機違背其冷冰冰計算本質的“怪人”。那時人工智能這一領域尚處于萌芽階段，重點完全放在推理和規劃等較為實際的概念上，或者下國際象棋和跳棋或解數學題上。大多數人工智能研究者都對機器擁有創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布一系列與他們的工作相關的學術文章和書籍，一個新興領域在他們周圍應運而生：計算創意學。這包括研究與開發自動創作系統、支持人類創造活動的互動工具和以人類創造力建模的數學方法。1990年代末，計算創意學成為正式的研究領域，研究者隊伍日益壯大，最終還創辦了相關期刊和年度活動。

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，具有創造力的人工智能就能夠吸收及內化現實世界的數據，并識別可應用于其創作的模式與規則——這要歸功于建立在機器學習和人工神經網絡基礎上的新技術，其中相互連接的計算節點會試圖模擬大腦運作。

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）.

計算機科學家西蒙·科爾頓曾在倫敦帝國理工學院工作，現任職于倫敦瑪麗女王大學和澳大利亞莫納什大學。2000年代的大部分時間里，他都在打造名為“繪畫傻瓜”的電腦程序。這一程序通過分析新聞和其他書面作品的文本，判斷情緒傾向并提取關鍵詞；接著將分析結果和攝影網站Flickr的自動搜索功能結合，生成反映原始文本情緒特征的拼貼畫。后來，“繪畫傻瓜”學會了通過連接相機為遇到的人實時繪制肖像，再次將它的“情緒”應用到肖像的風格中（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，計算創意學同樣也在游戲領域得以應用。人工智能研究者兼游戲設計師邁克爾·庫克將自己的博士論文和倫敦大學戈德史密斯學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《衛報》的新聞文章制作簡單的游戲，將時事文本分析、硬編碼設計和編程技術相結合。

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，人工智能在這個時代開始變得如同自成一格的創意藝術家——融合了諸如意圖、技巧、鑒賞力及想象力等具有創造性的元素。但隨之出現了對模仿的關注，以及對何為創造性的爭議。

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.

善于通過重復分析將數據高度精確分類的新技術，幫助人工智能掌握了現有的創作風格。它目前可以創作類似出自古典作曲家、著名畫家、小說家等人之手的作品。

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世紀至20世紀數千幅肖像畫為藍本的人工智能畫作在拍賣會上以432，500美元的價格成交。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其曲目訓練的電腦程序Kulitta的作品。就連國際商業機器公司也加入其中，指令旗下的沃森人工智能系統分析9000份食譜，自主開發創意菜式。

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.

但許多業內人士和旁觀者都對這些人工智能是否真正展現出創造力持懷疑態度。盡管這些具有“創造力”的人工智能在模仿方面已然爐火純青，但因缺乏從環境吸收新影響因素的能力，似乎無法進行真正的創新。科爾頓和一位同事將其描述為，需要“大量的人為干預、監督和高度技術性的知識”才能產出具有創造性的結果。總的來說，正如作曲家兼計算機音樂研究者帕勒·達爾斯泰特所言，這些人工智能往往表現平平，創作出來的東西具有已有事物的典型特征，但創意是應該不同凡響的。

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.

達爾斯泰特認為，若要進一步掌握真正的創造力，人工智能“必須模擬音樂產生的原因，即其創作情形——而非結果”。真正的創造力是對原創性的追求，是對迥然不同的各種觀點的重新排列組合，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，亦可能是靈感閃現，幫助推動燈泡、飛機和元素周期表的發展。計算創意學領域的大部分人認為，這是機器尚無法企及的。

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，創造性人工智能的應用已經擴展至風格開創的層面，即個性化的創作者而非模仿者，甚至能展現本不存在的意義與意圖。科爾頓認為，意圖這一元素——即對過程的關注超出最終結果——是實現創造力的關鍵。但他也在思考意義和真實性是否同樣至關重要，因為讀者在知道作者為男性、女性或機器的情況下，可能會對同一首詩作出截然不同的解讀。如果人工智能缺乏自我意識，無法反省自身行為和經歷，也無法表述自己的創作意圖，它真的能算具有創造力嗎？還是說，創造力仍屬于為它提供數據并向它下達指令的作者？

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， “lie in seeing digital computers as a mirror by which the human mind could consider itself in ways that previously were not possible.”

最終來看，嘗試將計算機從思考機器轉而打造為創造性機器可能會改變我們對自身的認識。70年前，被譽為人工智能之父的艾倫·圖靈設計了一種他稱為“模仿游戲”的測試來衡量對比機器與人類的智力。瑞典舍夫德大學的技術哲學家約埃爾·帕特莫爾如是寫道：“圖靈最偉大的見解就是將數字計算機視為一面鏡子，讓人類經由它以前所未有的方式自我反思。”

（譯者為“《英語世界》杯”翻譯大賽獲獎者）