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

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 composer’s block.

不久之后，作曲家戴维·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）.

计算机科学家西蒙·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 Bach’s compositions. Even IBM5 got in on the fun， tasking its Watson AI system with analyzing 9，000 recipes to devise its own cuisine ideas.