Teresa Carey: This is Scientific American’s 60-Second Science. I'm Teresa Carey.
Teresa Carey：这里是《科学美国人》的 60 秒科学。我是Teresa Carey。
Every morning at five o’clock, composer Walter Werzowa would sit down at his computer to anticipate a particular daily e-mail. It came from six time zones away, where a team had been working all night (or day, rather) to draft Beethoven’s unfinished 10th Symphony—almost two centuries after his death.
每天清晨5点，作曲家沃尔特·维尔佐瓦(Walter Werzowa)都会坐在他的电脑前，等待着一封特别的邮件，它来自6个时区之外，在那里，有支团队用一整夜的时间(准确来说是一整个白天)来续写贝多芬未完成的《第十交响曲》——在贝多芬逝世近两百年之后。
The e-mail contained hundreds of variations, and Werzowa listened to them all.
这封邮件包含数百种曲谱，维尔佐瓦全部听完了。
Werzowa: So by nine, 10 o’clock in the morning, it’s like I’m already in heaven.
Werzowa：我会一直听到九十点钟，那感觉仿佛身处天堂。
Carey: Werzowa was listening for the perfect tune—a sound that was unmistakably Beethoven.
Carey：维尔佐瓦所听的曲调堪称完美——毫无疑问就是贝多芬的音乐。
But the phrases he was listening to weren’t composed by Beethoven. They were created by artificial intelligence—a computer simulation of Beethoven’s creative process.
但这些乐句并非出自贝多芬，它们是由人工智能(AI)创造的——是对贝多芬创作过程的计算机模拟。
Werzowa: There were hundreds of options, and some are better than others. But then there is that one which grabs you, and that was just a beautiful process.
Werzowa：AI会给我上百种选项，其中不乏优秀的乐章，但会有那么一段令人折服，发现它的过程十分美妙。
Carey: Ludwig van Beethoven was one of the most renowned composers in Western music history. When he died in 1827, he left behind musical sketches and notes that hinted at a masterpiece. There was barely enough to make out a phrase, let alone a whole symphony. But that didn’t stop people from trying.
路德维希·范·贝多芬(Ludwig van Beethoven)是西方音乐史上最著名的作曲家之一，在1827年去世之际，他未能完成《第十交响曲》这部杰作，只留下了一些音乐草稿和片段。用这些残片谱写出一个乐句都很难，更不用说整部交响乐了，但这并未阻止人们的尝试。
In 1988 musicologist Barry Cooper attempted. But he didn’t get beyond the first movement. Beethoven’s handwritten notes on the second and third movements are meager—not enough to compose a symphony.
1988年，音乐学家巴里·库珀(Barry Cooper)进行了一次尝试，但最终止步于第一乐章——贝多芬留下的关于第二和第三乐章的草稿寥寥无几，难以谱出一章交响乐。
Werzowa: A movement of a symphony can have up to 40,000 notes. And some of his themes were three bars, like 20 notes. It’s very little information.
Werzowa：交响乐的一个乐章可以包含多达4万个音符。贝多芬留下的一些主题旋律却只有三个小节，大约20个音符。我们掌握的信息太少了。
Carey: Werzowa and a group of music experts and computer scientists teamed up to use machine learning to create the symphony. Ahmed Elgammal, the director of the Art and Artificial Intelligence Laboratory at Rutgers University, led the AI side of the team.
Carey： 维尔佐瓦与一群音乐专家和计算机科学家组成了团队，使用机器学习来创作交响乐，AI的部分由美国罗格斯大学(Rutgers University)的艺术与AI实验室主任艾哈迈德·贾迈勒(Ahmed Elgammal)所领导。
Elgammal: When you listen to music read by AI to continue a theme of music, usually it’s a very short few seconds, and then they start diverging and becoming boring and not interesting. They cannot really take that and compose a full movement of a symphony.
Elgammal：AI生成的音乐旋律往往在几秒之后就会开始发散、变得无趣乏味，它无法很好地延续乐曲的主题，续写出完整的交响乐章。
Carey: The team’s first task was to teach the AI to think like Beethoven. To do that, they gave it Beethoven’s complete works, his sketches and notes. They taught it Beethoven's process—like how he went from those iconic four notes to his entire Fifth Symphony.
Carey：团队的第一项任务是教会AI像贝多芬一样思考。为此，他们向AI输入了贝多芬的完成作品、草稿和片段，还教给它贝多芬的创作过程，比如贝多芬如何从4个标志性的音符构建出完整的《第五交响曲》(即c小调第五交响曲《命运》)
[CLIP: Notes from Symphony no. 5]
[剪辑：来自第 3 号交响乐的音符。5]
Carey: Then they taught it to harmonize with a melody, compose a bridge between two sections and assign instrumentation. With all that knowledge, the AI came as close to thinking like Beethoven as possible. But it still wasn’t enough.
Carey：然后，他们教AI为旋律作和声、连接乐段并进行器乐谱写，有了这些知识，AI已经出落得尽可能接近贝多芬的思考模式了，但这还是不够。
Elgammal: The way music generation using AI works is very similar to the way, when you write an e-mail, you find that the e-mail thread predicts what’s the next word for you or what the rest of the sentence is for you.
Elgammal：用AI来生成音乐的过程和写电子邮件很像，邮件程序可以预测你想要输入的下一个单词，或是预测句子的剩余部分。
Carey: But let the computer predict your words long enough, and eventually, the text will sound like gibberish.
Carey：但如果一直按照程序自动联想的内容写下去，文本最终会变成胡言乱语。
Elgammal: It doesn’t really generate something that can continue for a long time and be consistent. So that was the main challenge in dealing with this project: How can you take a motif or a short phrase of music that Beethoven wrote in his sketch and continue it into a segment of music?
Elgammal：程序无法长时间持续地产出一致而稳定的内容，这就是这个项目面对的主要挑战：怎样才能把贝多芬草稿中的主题或短句延伸成一整段音乐?
Carey: That’s where Werzowa’s daily e-mails came in. On those early mornings, he was selecting what he thought was Beethoven’s best. And, piece by piece, the team built a symphony.
Carey：维尔佐瓦收到的每日邮件应运而生：每天清晨，他都会选出他认为最接近贝多芬的片段，然后一小段一小段地，团队构建出了一整部交响乐。
Matthew Guzdial researches creativity and machine learning at the University of Alberta. He didn’t work on the Beethoven project, but he says that AI is overhyped.
Matthew Guzdial在阿尔伯塔大学研究创造力和机器学习。他没有参与贝多芬项目，但他说人工智能被夸大了。
Guzdial: Modern AI, modern machine learning, is all about just taking small local patterns and replicating them. And it’s up to a human to then take what the AI outputs and find the genius. The genius wasn’t there. The genius wasn’t in the AI. The genius was in the human who was doing the selection.
Guzdial：现代人工智能、现代机器学习所做的，只是选取较小的局部模式然后将其复制。对AI输出结果的解读和择优则完全取决于人类，音乐创作的天才之处并不在于复制。拥有音乐天才的不是AI，而是做选择的人。
Carey: Elgammal wants to make the AI tool available to help other artists overcome writer’s block or boost their performance. But both Elgammal and Werzowa say that the AI shouldn’t replace the role of an artist. Instead it should enhance their work and process.
Carey：贾迈勒希望让这个AI变成一种能实际应用的工具，帮助其他艺术家克服作曲障碍、提高艺术表现。不过他和维尔佐瓦都表示，AI不该取代艺术家的角色，而应该帮助艺术家优化他们的作品和创作过程。
Werzowa: Like every tool, you can use a knife to kill somebody or to save somebody’s life, like with a scalpel in a surgery. So it can go any way. If you look at the kids, like kids are born creative. It’s like everything is about being creative, creative and having fun. And somehow we’re losing this. I think if we could sit back on a Saturday afternoon in our kitchen, and because maybe we’re a little bit scared to make mistakes, ask the AI to help us to write us a sonata, song or whatever in teamwork, life will be so much more beautiful.
Werzowa：许多工具都像是双刃剑，既可能成为杀人的凶器，也可能成为救命的手术刀，用法取决于你。孩子们天生拥有创造力，仿佛一切皆可创造，一切皆有乐趣;年岁的增长似乎让我们逐渐丧失创造力，或许是因为害怕犯错。我想，我们可以在某个周六午后钻进厨房，在AI的协助下一起写点东西——奏鸣曲、歌谣或者别的什么，生活也许会更加美好。
Carey: The team released the 10th Symphony over the weekend. When asked who gets credit for writing it— Beethoven, the AI or the team behind it—Werzowa insists it is a collaborative effort. But, suspending disbelief for a moment, it isn’t hard to imagine that we’re listening to Beethoven once again.
Carey：团队最近发布了这部《第十交响曲》。当被问到这部作品应当归功于谁——贝多芬、AI还是背后的团队时。维尔佐瓦坚定地回答，这是多方协作的成果。不论如何，我们又一次听到了贝多芬。
Werzowa: I dare to say that nobody knows Beethoven as well as the AI, did—as well as the algorithm. I think music, when you hear it, when you feel it, when you close your eyes, it does something to your body. Close your eyes, sit back and be open for it, and I would love to hear what you felt after.
Werzowa：我敢肯定，没有人能像这个AI算法一样了解贝多芬。闭上双眼用心聆听，你的身体会对音乐产生反应。所以，闭上双眼，放松下来，沉浸到这部《第十交响曲》中吧，我很想知道你听完后的感受。
Carey: Thanks for listening. For Scientific American’s 60-Second Science, I’m Teresa Carey.
Carey：感谢您的收听。这里是《科学美国人》的 60 秒科学，我是 Teresa Carey。
Teresa Carey: This is Scientific American’s 60-Second Science. I'm Teresa Carey.
Every morning at five o’clock, composer Walter Werzowa would sit down at his computer to anticipate a particular daily e-mail. It came from six time zones away, where a team had been working all night (or day, rather) to draft Beethoven’s unfinished 10th Symphony—almost two centuries after his death.
The e-mail contained hundreds of variations, and Werzowa listened to them all.
Werzowa: So by nine, 10 o’clock in the morning, it’s like I’m already in heaven.
Carey: Werzowa was listening for the perfect tune—a sound that was unmistakably Beethoven.
But the phrases he was listening to weren’t composed by Beethoven. They were created by artificial intelligence—a computer simulation of Beethoven’s creative process.
Werzowa: There were hundreds of options, and some are better than others. But then there is that one which grabs you, and that was just a beautiful process.
Carey: Ludwig van Beethoven was one of the most renowned composers in Western music history. When he died in 1827, he left behind musical sketches and notes that hinted at a masterpiece. There was barely enough to make out a phrase, let alone a whole symphony. But that didn’t stop people from trying.
In 1988 musicologist Barry Cooper attempted. But he didn’t get beyond the first movement. Beethoven’s handwritten notes on the second and third movements are meager—not enough to compose a symphony.
Werzowa: A movement of a symphony can have up to 40,000 notes. And some of his themes were three bars, like 20 notes. It’s very little information.
Carey: Werzowa and a group of music experts and computer scientists teamed up to use machine learning to create the symphony. Ahmed Elgammal, the director of the Art and Artificial Intelligence Laboratory at Rutgers University, led the AI side of the team.
Elgammal: When you listen to music read by AI to continue a theme of music, usually it’s a very short few seconds, and then they start diverging and becoming boring and not interesting. They cannot really take that and compose a full movement of a symphony.
Carey: The team’s first task was to teach the AI to think like Beethoven. To do that, they gave it Beethoven’s complete works, his sketches and notes. They taught it Beethoven's process—like how he went from those iconic four notes to his entire Fifth Symphony.
[CLIP: Notes from Symphony no. 5]
Carey: Then they taught it to harmonize with a melody, compose a bridge between two sections and assign instrumentation. With all that knowledge, the AI came as close to thinking like Beethoven as possible. But it still wasn’t enough.
Elgammal: The way music generation using AI works is very similar to the way, when you write an e-mail, you find that the e-mail thread predicts what’s the next word for you or what the rest of the sentence is for you.
Carey: But let the computer predict your words long enough, and eventually, the text will sound like gibberish.
Elgammal: It doesn’t really generate something that can continue for a long time and be consistent. So that was the main challenge in dealing with this project: How can you take a motif or a short phrase of music that Beethoven wrote in his sketch and continue it into a segment of music?
Carey: That’s where Werzowa’s daily e-mails came in. On those early mornings, he was selecting what he thought was Beethoven’s best. And, piece by piece, the team built a symphony.
Matthew Guzdial researches creativity and machine learning at the University of Alberta. He didn’t work on the Beethoven project, but he says that AI is overhyped.
Guzdial: Modern AI, modern machine learning, is all about just taking small local patterns and replicating them. And it’s up to a human to then take what the AI outputs and find the genius. The genius wasn’t there. The genius wasn’t in the AI. The genius was in the human who was doing the selection.
Carey: Elgammal wants to make the AI tool available to help other artists overcome writer’s block or boost their performance. But both Elgammal and Werzowa say that the AI shouldn’t replace the role of an artist. Instead it should enhance their work and process.
Werzowa: Like every tool, you can use a knife to kill somebody or to save somebody’s life, like with a scalpel in a surgery. So it can go any way. If you look at the kids, like kids are born creative. It’s like everything is about being creative, creative and having fun. And somehow we’re losing this. I think if we could sit back on a Saturday afternoon in our kitchen, and because maybe we’re a little bit scared to make mistakes, ask the AI to help us to write us a sonata, song or whatever in teamwork, life will be so much more beautiful.
Carey: The team released the 10th Symphony over the weekend. When asked who gets credit for writing it— Beethoven, the AI or the team behind it—Werzowa insists it is a collaborative effort. But, suspending disbelief for a moment, it isn’t hard to imagine that we’re listening to Beethoven once again.
Werzowa: I dare to say that nobody knows Beethoven as well as the AI, did—as well as the algorithm. I think music, when you hear it, when you feel it, when you close your eyes, it does something to your body. Close your eyes, sit back and be open for it, and I would love to hear what you felt after.
Carey: Thanks for listening. For Scientific American’s 60-Second Science, I’m Teresa Carey.
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