未来智库 > 人工智能论文 > 人工智能莫扎特:机器与艺术的火花


发布时间:2018-05-09 10:36:00 文章来源:未来智库    
    Sometime in the coming decades, an external system that collects and analyzes endless streams of biometric data will probably be able to understand what’s going on in my body and in my brain much better than me. Such a system will transform politics and economics by allowing governments and corporations to predict and manipulate human desires. What will it do to art? Will art remain humanity’s last line of defense against the rise of the all-knowing algorithms?1
    In the modern world art is usually associated with human emotions. We tend to think that artists are channeling internal psychological forces, and that the whole purpose of art is to connect us with our emotions or to inspire in us some new feeling. Consequently, when we come to evaluate art, we tend to judge it by its emotional impact and to believe that beauty is in the eye of the beholder2.
    This view of art developed during the Romantic period in the 19th century, and came to maturity exactly a century ago, when in 1917 Marcel Duchamp purchased an ordinary mass-produced urinal, declared it a work of art, named it “Fountain,”3 signed it, and submitted it to an art exhibition. In countless classrooms across the world, first-year art students are shown an image of Duchamp’s“Fountain,” and at a sign from the teacher all hell breaks loose4. It is art! No it isn’t! Yes it is! No way!
    After letting the students release some steam, the teacher focuses the discussion by asking “What exactly is art? And how do we determine whether something is a work of art or not?” After a few more minutes of back and forth the teacher steers the class in the right direction: “Art is anything people think is art, and beauty is in the eye of the beholder.” If people think that a urinal is a beautiful work of art―then it is.
    In 1952, the composer John Cage5 outdid Duchamp by creating “4’33”.” This piece, originally composed for a piano but today also played by full symphonic orchestras6, consists of 4 minutes and 33 seconds during which no instrument plays anything. The piece encourages the audience to observe their inner experiences in order to examine what music is, what we expect of it, and how music differs from the random noises of everyday life. The message is that it is our own expectations and emotions that define music and that separate art from noise.
         If art is defined by human emotions, what might happen once external algorithms are able to understand and manipulate human emotions better than Shakespeare, Picasso or Lennon?7 After all, emotions are not some mystical phenomenon―they are a biochemical process. Hence, given enough biometric data and enough computing power, it might be possible to hack love, hate, boredom and joy.
    In the not-too-distant future, a machine-learning8 algorithm could analyze the biometric data streaming from sensors on and inside your body, determine your personality type and your changing moods, and calculate the emotional impact that a particular song―or even a particular musical key―is likely to have on you.
    Of all forms of art, music is probably the most susceptible to Big Data9 analysis, because both inputs and outputs lend themselves to mathematical depiction. The inputs are the mathematical patterns of soundwaves, and the outputs are the electrochemical patterns of neural storms. Allow a learning machine to go over millions of musical experiences, and it will learn how particular inputs result in particular outputs.
    The idea of computers composing music is hardly new. David Cope, a musicology professor at the University of California in Santa Cruz, created a computer program called EMI (Experiments in Musical Intelligence), which specialized in imitating the style of Johann Sebastian Bach.10 In a public showdown at the University of Oregon, an audience of university students and professors listened to three pieces―one a genuine Bach, another produced by EMI and a third composed by a local musicology professor, Steve Larson. The audience was then asked to vote on who composed which piece. The result? The audience thought that EMI’s piece was genuine Bach, that Bach’s piece was composed by Larson, and that Larson’s piece was produced by a computer.
    Hence in the long run, algorithms may learn how to compose entire tunes, playing on11 human emotions as if they were a piano keyboard. Using your personal biometric data the algorithms could even produce personalized melodies, which you alone in the entire world would appreciate.
    It is often said that people connect with art because they find themselves in it. If art is really about inspiring(or manipulating) human emotions, few if any human musicians will have a chance of competing with such an algorithm, because they cannot match it in understanding the chief instrument they are playing on: the human biochemical system.
         Will this result in great art? That depends on the definition of art. If beauty is indeed in the ears of the listener, then biometric algorithms stand a chance of producing the best art in history. If art is about something deeper than human emotions, and should express a truth beyond our biochemical vibrations, biometric algorithms might not make very good artists. But nor would most humans. In order to enter the art market, algorithms won’t have to begin by straight away surpassing Beethoven. It is enough if they outperform Justin Bieber12.