Artificial Intelligence Challenges What It Means to Be Creative人工智能挑战创造力定义

理查德·莫斯　刘斯杭/译

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.

当英国艺术家哈罗德·科恩于1968年遇到他的第一台计算机时，他便开始思考这台机器能否解答一个令他困惑已久的疑问：我们如何能通过看一幅素描，仅仅几笔涂鸦，便看出一张脸？五年后，他设计出名为AARON的机器人艺术家，来探索这一想法。他为它设置了作画和肖像中身体部位呈现方式的基本规则，然后让其自由地开始艺术创作。

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·科普创造了“音乐智能”一词来描述他的人工智能驱动作曲实验。科普曾告诉我，他早在1960年代就认为，“利用算法进行创造性活动完全合乎逻辑”，而无需煞费苦心地亲笔手写故事中的每个词、亲自设计乐曲中的每个音符、亲手描绘画作中的每一笔。他起初在纸上摆弄算法，后于1981年转而使用计算机解决作曲瓶颈问题。

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.

当时科恩和科普属于少数几位推动计算机违背其冷冰冰计算本质的“怪人”。那时人工智能这一领域尚处于萌芽阶段，重点完全放在推理和规划等较为实际的概念上，或者下国际象棋和跳棋或解数学题上。大多数人工智能研究者都对机器拥有创造力这一想法望而却步。

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，随着科恩和科普接连不断地发布一系列与他们的工作相关的学术文章和书籍，一个新兴领域在他们周围应运而生：计算创意学。这包括研究与开发自动创作系统、支持人类创造活动的互动工具和以人类创造力建模的数学方法。1990年代末，计算创意学成为正式的研究领域，研究者队伍日益壮大，最终还创办了相关期刊和年度活动。

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，具有创造力的人工智能就能够吸收及内化现实世界的数据，并识别可应用于其创作的模式与规则——这要归功于建立在机器学习和人工神经网络基础上的新技术，其中相互连接的计算节点会试图模拟大脑运作。

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）.

計算机科学家西蒙·科尔顿曾在伦敦帝国理工学院工作，现任职于伦敦玛丽女王大学和澳大利亚莫纳什大学。2000年代的大部分时间里，他都在打造名为“绘画傻瓜”的电脑程序。这一程序通过分析新闻和其他书面作品的文本，判断情绪倾向并提取关键词；接着将分析结果和摄影网站Flickr的自动搜索功能结合，生成反映原始文本情绪特征的拼贴画。后来，“绘画傻瓜”学会了通过连接相机为遇到的人实时绘制肖像，再次将它的“情绪”应用到肖像的风格中（或者在某些情况下，它因心情不佳而拒绝作画）。

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，计算创意学同样也在游戏领域得以应用。人工智能研究者兼游戏设计师迈克尔·库克将自己的博士论文和伦敦大学戈德史密斯学院的早期研究助理工作都倾注于打造 “安杰利娜”：它可以根据《卫报》的新闻文章制作简单的游戏，将时事文本分析、硬编码设计和编程技术相结合。

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.

科尔顿说，人工智能在这个时代开始变得如同自成一格的创意艺术家——融合了诸如意图、技巧、鉴赏力及想象力等具有创造性的元素。但随之出现了对模仿的关注，以及对何为创造性的争议。

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.

善于通过重复分析将数据高度精确分类的新技术，帮助人工智能掌握了现有的创作风格。它目前可以创作类似出自古典作曲家、著名画家、小说家等人之手的作品。

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世纪至20世纪数千幅肖像画为蓝本的人工智能画作在拍卖会上以432，500美元的价格成交。另有一例研究显示，参与者难以分辨约翰·塞巴斯蒂安·巴赫的音乐乐句和据其曲目训练的电脑程序Kulitta的作品。就连国际商业机器公司也加入其中，指令旗下的沃森人工智能系统分析9000份食谱，自主开发创意菜式。

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.

但许多业内人士和旁观者都对这些人工智能是否真正展现出创造力持怀疑态度。尽管这些具有“创造力”的人工智能在模仿方面已然炉火纯青，但因缺乏从环境吸收新影响因素的能力，似乎无法进行真正的创新。科尔顿和一位同事将其描述为，需要“大量的人为干预、监督和高度技术性的知识”才能产出具有创造性的结果。总的来说，正如作曲家兼计算机音乐研究者帕勒·达尔斯泰特所言，这些人工智能往往表现平平，创作出来的东西具有已有事物的典型特征，但创意是应该不同凡响的。

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.

达尔斯泰特认为，若要进一步掌握真正的創造力，人工智能“必须模拟音乐产生的原因，即其创作情形——而非结果”。真正的创造力是对原创性的追求，是对迥然不同的各种观点的重新排列组合，是意料之外的解决方案。它可能以音乐、绘画或舞蹈的形式呈现，亦可能是灵感闪现，帮助推动灯泡、飞机和元素周期表的发展。计算创意学领域的大部分人认为，这是机器尚无法企及的。

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？

近几年，创造性人工智能的应用已经扩展至风格开创的层面，即个性化的创作者而非模仿者，甚至能展现本不存在的意义与意图。科尔顿认为，意图这一元素——即对过程的关注超出最终结果——是实现创造力的关键。但他也在思考意义和真实性是否同样至关重要，因为读者在知道作者为男性、女性或机器的情况下，可能会对同一首诗作出截然不同的解读。如果人工智能缺乏自我意识，无法反省自身行为和经历，也无法表述自己的创作意图，它真的能算具有创造力吗？还是说，创造力仍属于为它提供数据并向它下达指令的作者？

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.”

最終来看，尝试将计算机从思考机器转而打造为创造性机器可能会改变我们对自身的认识。70年前，被誉为人工智能之父的艾伦·图灵设计了一种他称为“模仿游戏”的测试来衡量对比机器与人类的智力。瑞典舍夫德大学的技术哲学家约埃尔·帕特莫尔如是写道：“图灵最伟大的见解就是将数字计算机视为一面镜子，让人类经由它以前所未有的方式自我反思。”

（译者为“《英语世界》杯”翻译大赛获奖者）