This is not the first time that I’ve written about AI creativity, and I doubt that it will be the last. It’s a question that comes up repeatedly, and that is very much in the current mind, with events like the strikes by the Writers’ Guild of America and the Screen Actors Guild, in which the use of AI to create scripts and to generate images of actors was an issue. Can an AI system be creative and, if so, what would that creativity look like?

I’m skeptical about AI creativity, though recently I hypothesized that an AI system optimized for “hallucinations” might be the start of “artificial creativity.” That’s a path that’s well worth investigating. But let’s take a step back and think more carefully about what creativity means.

It’s all too easy to say that creativity is, at its heart, combinatory. Ethan Mollick (with whom I rarely disagree) writes, “In the real world, most new ideas do not come from the ether; they are based on combinations of existing concepts, which is why innovation scholars have long pointed to the importance of recombination in generating ideas.” He’s partially right, but that statement misses the point—in part because Mollick studies business innovation which, despite the name, is all too often nothing more than recombination. Remember all the VC dollars thrown at new “social media” companies that were ultimately just reinventions of Twitter, Facebook, or one of their predecessors? Remember all the “Uber for X” startups? The thousands of alt-coins that (used to) attract lots of capital? The current wave of AI startups is no different. There’s a lot of posturing here, but very little creativity.

No, to find creativity, we’ll have to look more closely. It’s naive to say that creativity isn’t partly based on the work of predecessors. You wouldn’t get Beethoven without the works of Haydn and Mozart. At the same time, you don’t get Beethoven out of the works of Haydn and Mozart. An AI trained on the works on Haydn and Mozart wouldn’t give you Beethoven; it would give you some (probably rather dull) amalgam, lacking the creativity of either Haydn or Mozart. Nor can you derive the Beatles by mixing together Chuck Berry and Little Richard, though (again) there are obvious relationships.

At this point, we have to make some distinctions about what we mean by “creativity.” AI can write poems—not terribly well, but they certainly rhyme, and they can be prompted to convey certain sentiments. I wouldn’t mistake anything I’ve seen for the work of a great (or even good) poet, but companies like Hallmark provide a market for millions of lines of verse, and that market is probably more lucrative than the market for poets who publish in “little magazines.” And it’s been a long time since I’ve expected anything worthwhile from the music industry, which is much more about industry than music. There’s an almost unending appetite for “industrial” music.

Related work from others:  Latest from MIT Tech Review - Now we know what OpenAI’s superalignment team has been up to

So, what is creativity? Creativity certainly depends on the past: “shoulders of giants” and all of that. There are few great artists or technical innovators who don’t understand their relationship to the past. That relationship is often uncomfortable, but it’s essential. At the same time, great artists add something new, create new possibilities. Arne Eigenfeldt, writing about music, says that “it takes true creativity to produce something outside the existing paradigm,” and that the “music industry has been driven by style-replicating processes for decades.” AI that merely mixes and matches style is uninteresting. But Eigenfeldt would be the last person to say that AI has nothing to do with creativity. It’s another tool; prompting AI, and curating its output is itself a creative act. Artists working with AI can do more experiments, and potentially create more art that breaks paradigms, art that indeed makes something new.

Of all the arts, music has historically been the most amenable to borrowing, stealing, or whatever you want to call it. The history of Thelonious Monk’s Rhythm-a-ning stretches back to George Gershwin’s I’ve Got Rhythm and Duke Ellington’s Ducky Wucky, and forward (or is it sideways) to songs as unlikely as the theme song for The Flintstones. There is no question about creativity, but it’s creativity that’s based on a vocabulary that has a long history. And there’s no question that all of these expressions of creativity include elements that go beyond a simple “remixing” of that vocabulary.

What about other arts? While borrowing in literature is usually more covert than overt, T.S. Eliot famously said, “Immature poets imitate; mature poets steal; bad poets deface what they take, and good poets make it into something better, or at least something different. The good poet welds his theft into a whole of feeling which is unique, utterly different from that from which it was torn.” This is often quoted incorrectly as “Good writers borrow, great writers steal,” a quote that’s also attributed to Oscar Wilde (“Talent borrows, genius steals”) and many others. While the history of copying this quote about copying is interesting in its own right, Eliot’s version shows how “theft” becomes something new, something that wasn’t couldn’t have been predicted or anticipated. It’s worth thinking of William Blake’s reinterpretation of Milton’s Paradise Lost, in which Satan is the hero; “The reason Milton wrote in fetters when he wrote of Angels and God, and at liberty when of Devils and Hell, is that he was a true Poet and of the Devil’s party without knowing it” (The Marriage of Heaven and Hell, page 6).  But Blake’s works are far from a remixing; they’re radically different. Blake certainly understood his connection to Milton, but more than any other poet created works that are completely unlike anything that came before. (Follow the link to see images of Blake’s work.) While Blake may represent creation at its most radical, literature that is worth reading is never just a remixing; it always adds something new, if it is not to be entirely in “fetters.”

Related work from others:  Latest from MIT Tech Review - Generative AI is empowering the digital workforce

I’ve argued that what matters to us in a literary work is the fact that a human wrote it. We value a poem like Wordsworth’s “Lines Composed A Few Miles Above Tintern Abbey, on Revisiting the Banks of the Wye During a Tour” because of the texture of Wordsworth’s thought, and his thought reflecting on itself. I’ve used the long and prosaic title rather than the shorter “Tintern Abbey” to emphasize that. Whether it’s Wordsworth or Ginsburg’s Howl, what matters is that someone has thought these thoughts. But that’s certainly a post-Romantic take on creativity—one that Wordsworth would have agreed with, but that would have been very strange to Shakespeare or Chaucer. Chaucer would have thought that literature was about retelling good stories, and not necessarily original ones; the Canterbury Tales steals from many models, ranging from classical literature to Dante. So do Shakespeare’s plays. But in both cases, thinking that these works could come from recombining the original works misses the point. What makes them worth reading isn’t that they’re retellings of old material, it’s what isn’t in the original. Macbeth may be based on Holinshed’s Chronicles, but Holinshed (should you ever read it) is dull. Hamlet was almost certainly based on an earlier play (called Ur-Hamlet), probably written by one of Shakespeare’s contemporaries, about which very little is known. There’s something great imaginatively happening in all of these works: characters that we can think about and care about, something we might even call the “invention of the human.”1

As in literature, copying in painting is usually covert rather than overt. Pablo Picasso also may have said “good artists copy, great artists steal,” joining Eliot, Wilde, and others. Copying paintings by great artists is still an exercise for aspiring artists—although most of us recognize that more paintings in the style of Vermeer aren’t interesting as works of art. They’re perhaps valuable as stand-ins when the original is on tour, and the technology used to create them is certainly of interest; I’m particularly interested in an AI-created Rembrandt that used a 3D printer to mimic his brushstrokes. This technology may be useful for repairing damaged works of art. But as far as new paintings—in a very real sense, much as we may wish we had more, we have enough. Hanging a picture of your company’s founder in the style of Vermeer on your wall would be a joke—either on the institution of Art, or on you, depending on whether you understand what you’re doing.

Related work from others:  AI Trends - Best Practices for Building the AI Development Platform in Government 

The question of remixing becomes more important if we turn to recent and more commercial art. While I wouldn’t want a painting of Tim O’Reilly in the style of Vermeer on my wall, many people are using tools like Midjourney and Stable Diffusion to create their own images in the style of living, working artists; images in the style of Greg Rutkowski have been requested over 400,000 times. After his images were removed from Stable Diffusion’s training data, fans developed an alternate model that was tuned to produce images in Rutkowski’s style. While that’s certainly a strong sign of ongoing popularity, it is important to think about the consequences. Does ease of creating faux-Rutkowski compromise his ability to make a living? Fans are clearly putting faux-Rutkowski as wallpaper on their laptops, if not ordering high-resolution prints and putting them on their walls. If this is a joke, who is the butt? Would a publisher generate a faux-image as a book cover? Is Rutkowski’s style (as opposed to a specific work) protected by copyright laws? We don’t know; a number of cases are in the legal system now. Most of these cases involve the terra incognita of training data, though most of these cases involve the use of copyrighted material as training data, not the recreation of a specific style, let alone a specific work.

What about creativity? Creativity sets a high bar, and I don’t think AI meets it yet. At least one artist thinks that tools like Midjourney are being trained to favor photorealism, rather than originality. In The Curse of Recursion, a research group shows that generative AI that is trained on the output of generative AI—will produce less surprising, original output. Its output will become pedestrian, expected, mediocre, and that might be fine for many applications. With human artists such as Rutkowski or Holly Mengert (whose story is eerily similar to Rutkowski’s), creativity lies in what they put into their art, not the possibility of imitating their style. We see that clearly when we’re not blinded by AI’s presence: if a human imitated their styles, would we call that creative? Or just derivative? It’s amazing that an AI system can produce derivative works, but we have to remember that they are derivative works. And we have to recognize that AI, as a tool for artists, makes perfect sense. Just as we don’t confuse the artist’s creativity with the paintbrush, we shouldn’t confuse their creativity with the AI.

Footnotes

The title of Harold Bloom’s book on Shakespeare. Bloom is also one of a minority of scholars who believes that Shakespeare wrote the Ur-Hamlet, which was an early version of Hamlet. Given that we know next to nothing about the original play, this is at best an interesting conjecture.

Similar Posts