edited by Detlef Diederichsen and Arno Raffeiner (Music 781.76 Art)
The notion of artificial intelligence (AI) and machine learning has been in the news a ton lately. First we had all of those Deep Dream images a few years back, then the latest image generators like DALL-E and Midjourney have made the news for the last year or so. Most recently, it seems like practically every news outlet has multiple articles about potential impacts of the new AI chatbot ChatGPT on society, from education to employment to disinformation propaganda. Those involved with the arts have their own concerns about AI, as evidenced by many visual artists complaining about AI-generated art intentionally done in their unique personal styles.
Of course music is already being made with AI, too. There have been experiments with forms of computer-based generative music back to the 1960s, although those predate our modern conceptions of AI’s machine learning capabilities. Some artists are exploring using AI in their own music, and there are already tons of AI-based music apps out there to play with exhibiting various stages of sophistication. While we’re not yet seeing pop songs generated entirely by AI hit the charts, the possibility already exists. And there are some areas of the music market that will likely become dominated by AI quickly: Billboard reported in March that the “functional music” market (music that’s generally of an ambient nature and marketed to folks as background music for things like concentration, relaxation or sleep) is already seeing lots of AI-generated music. It’s easy to produce with AI apps, and it’s a profitable market for copyright holders that could and probably will be easily leveraged by some entity who uploads millions of AI-generated ambient tracks.
All of this is developing faster than the pace of most book publication schedules, but on our recent arrivals shelf, you’ll find collection of essays called Artificial Music. This little publication is kind of hard to categorize: it’s essentially a little book, but it’s part of what will ultimately become a 25-volume series called “The New Alphabet,” published by HKW in Berlin and Spector Books in Leipzig, Germany. All of the little books in this series are focusing on the variety of new kinds of technologies and philosophies affecting contemporary life, and likely to contribute to changes we can barely even anticipate in the coming years. A few volumes relate to music: we also have Volume 2, “Listen to Lists,” which discusses primarily how new music streaming technologies are changing patterns of music consumption worldwide. Each volume is made up of a series of essays from specialists familiar with the topic at hand. In the case of this “Artificial Music” volume, contributors include George Lewis, whose “Voyager” piece is one of the earliest improvisational interactive pieces between live performers and software, journalist Laura Aha, Professor of Cognitive Science at Indiana University Douglas Hofstadter, and even a pair of AI apps that were used to supply some images and text for the book!
The book starts with an introduction and essay from editor Detlef Diederichsen. The introduction defines the scope of AI activities in our era as those driven by “machine learning and neural networks interacting with big data,” which is a pretty succinct but accurate way of describing the technology as it stands. And the relationship between this volume and the “Listen to Lists” volume is quickly mentioned as well: “In the music industry, algorithms are already the norm, using feedback functions to provide consumers with a range of music increasingly tailored to their specific needs.” Indeed, AI is likely going to play a significant role on the listeners’ side of music consumption as time goes on, but for this volume, the focus will instead be on the creation of music, and how the capabilities of AI may intersect with the activities of composers and musicians.
The essay that follows acts as an introduction to the work of composer David Cope, who started using machine-learning concepts in the 1980s with his Emmy program, providing the music of various composers to the software so that it could learn to compose in the same style as those composers. Even in the 1990s, his system was already robust enough that skilled listeners could only guess between works made by the original composers or generated by Emmy between 40 and 60 percent of the time. It also introduces us to cognitive scientist Douglas R. Hofstadter, who published a Pulitzer prize-winning book about limitations of artificial intelligence in 1980. He had predicted that computers would never beat humans at chess, which of course happened in 1997 with Deep Blue. He had also predicted that computers wouldn’t be able to create emotionally-charged music, because they simply don’t have the kinds of life experience that leads to quality composing. But then he experienced some music composed in the style of Chopin by Emmy, which he found emotionally moving. His response to this was poetry, some of which is reproduced in this book under the title “Staring Emmy Straight in the Eye—And Doing My Best Not to Flinch,” in which he somewhat humorously acknowledges that experiencing music from Emmy has provoked him to reconsider the very essence of what it might mean to compose beautiful, meaningful music.
Laura Aha’s essay discusses the nature of music, and how AI developments relate to it. It’s a fantastic brief history of both issues that could serve as an introduction for anyone curious about where we are today, and where we might go next with all of this. To summarize, since music is relatively easy to boil down to mathematical principles and basic rules of engagement, its circumstances create a pretty optimal environment for machine learning to become very good at making music that we like. She distinguishes between AI approaches that we’ve seen so far in classical music and pop music circles: classical composers have mostly used AI concepts to create self-generative works that sound unusual or surprising, while pop music composers have focused on reproducing the conditions one finds among most hit songs. But issues of ownership begin to arise here: Holly Herndon’s work with creating an AI version of her own voice on her album Proto, for example, point to us living in a time where machine learning can be “trained” on a particular artist’s voice and then used to create new music that sounds just like them. Who owns this music? Who made it? This reminds me of the lawsuit that’s happening right now between stock photography company Getty Images and Stability AI, a company that makes the Stable Diffusion app which produces visual art after being trained on massive amounts of pre-existing images. In a similar situation in February, the US Copyright Office declared that art used in the comic book “Zarya of the Dawn” can’t be copyrighted, as it was produced using the Midjourney image generator, and as such, the images aren’t made by a particular human. We are certainly living in interesting times!
An essay by indigenous artist Tiara Roxanne raises fascinating issues around colonization and AI. She thinks these new technologies, which of course must in some way be extensions of the dominant culture producing them, have the potential to create and further sustain forms of “data colonialism” when they are initially programmed in such a way that certain voices such as those of indigenous people are marginalized. She raises a number of important points here that I haven’t heard in discussions about AI before, and this is probably my favorite takeaway from this book.
Composer George Lewis discusses his work with using computers as improvisation participants, an art that he’s worked on since the 1980s with his “Rainbow Family” and “Voyager” pieces. He reflects on a number of his motivations for working toward having non-human improvising partners that can generate ideas and adapt them in the moment, and he further breaks down the act of improvisation into five aspects (Indeterminacy, agency, analysis, judgement and choice) that can be broadly applicable among humans improvising amongst ourselves as well. And Zola Jesus ends the book with a powerful poem that questions the provenance and nature of art, artificial or otherwise.
As a short book with a lot of important ideas, I’d highly recommend Artificial Music for anyone who is pondering the interesting moment we’re living through, and how it might affect our music.
(If you enjoy this, you may also wish to try Listen to Lists edited by Lina Brion or Audio Culture: Readings in Modern Music edited by Christopher Cox.)
( publisher’s official Artificial Music web site )
Recommended
by Scott S.
Polley Music Library
Have you read or listened to this one? What did you think? Did you find this review helpful?
New reviews appear every month on the Staff Recommendations page of the BookGuide website. You can visit that page to see them all, or watch them appear here in the BookGuide Blog individually over the course of the entire month. Click the tag for the reviewer's name to see more of this reviewer’s recommendations!
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