• by Josh Engroff , Op-Ed Contributor, June 22, 2017

One particularly arresting moment in William Gibson’s cyberpunk classic, Neuromancer, occurs toward the end when Case, the protagonist, meets the eponymous and anthropomorphized AI face-to-face: 

“You’re the other AI. You’re Rio. You’re the one who wants to stop Wintermute. What’s your name?”

The boy did a handstand in the surf, laughing. He walked on his hands, then flipped out of the water…”To call up a demon you must learn its name. Men dreamed that, once, but now it is real in another way. You know that Case.” 

“A Turing code’s not your name”

“Neuromancer,” the boy said, slitting long gray eyes against the rising sun…”Neuro from the nerves, the silver paths. Romancer. Necromancer. I call up the dead.” 

Few things in science are as weirdly uncanny as the AI with a knack for poetry. Consider the otherwise homicidal character of Roy Batty from Bladerunner. Roy’s murderous drive, it turns out, isn’t just another dystopian archetype (see Skynet) but a sincere commitment to Dylan Thomas’ exhortation to “burn and rave at the dying of the light.” 

Which is why Roy’s death soliloquy — deftly evoking a mixture of pride, wonder, and heartbreak in just three lines — comes as such a shock: “I've seen things you people wouldn't believe. Attack ships on fire off the shoulder of Orion. I watched C-beams glitter in the dark near the Tannhäuser Gate. All those moments will be lost in time, like tears in rain. Time to die.” Roy, we hardly knew ya. 

Of all the amazing capabilities that AI has now unlocked for marketers — video analysis, image recognition, natural language understanding, conversational commerce, programmatic bid optimization, predictive analytics — artistic creation is not high on the list. The job of emotional storytelling, whether through text, video, audio (or some combination), still seems a uniquely human endeavor and thus, one assumes, immune to the disruptions of AI. 

This may prove to be an old-fashioned assumption. 

Last year in Japan, a short story co-written by an AI was shortlisted for the The Nikkei Hoshi Shinichi Literary Award; judges for the prize were not told which pieces were written by humans, and which by human / AI teams. 

In the realm of music, the company Jukedeck has advanced its technology to the point that most listeners assume that the short musical pieces created by its AI are, in fact, created the old-fashioned way. As Ed Rex, the CEO of Jukedeck, put it: “We’re already at the stage where you can ask your phone to write you a novel soundtrack. It’s not some futuristic idea — you can actually do it right now."

Three months ago, Google’s AI research division announced a deep-learning tool to create music and art alongside humans. Among other things, this could be particularly useful for the short-form types of creative usually produced by agencies, like ad copy and jingles. 

Any discussion of computational creativity or creative AI has to, at some point, define what it means by “creativity.” And defining creativity in objective terms turns out to be quite difficult. As an expedient oversimplification, we can speak of two types: generative creativity, and combinatorial creativity. Both types include the concept of originality, the idea that the new thing created is a novel thing in the world, not a replication of some already existing thing. 

Generative creativity takes it a step further, in that the artwork thus created is, to the mind of its creator, wholly without precedent. It is “original” in the pure sense of originating from the artist’s mind, psyche, and sensory interactions with the world. This is often what people think of when they think of “creativity.”

It turns out, however, that the vast majority of creative output falls under the other category. Combinatorial Creativity is the novel combination of pre-existing ideas or objects. Picasso put a fine point on this artistic reality when he said “good artists borrow; great artists steal.”

If there’s nothing new under the sun, then there’s all the more reason to think that neural networks — accessing large data sets and trained through reinforcement learning — will not only significantly disrupt the creative economy, but completely redefine our understanding of artistic creativity.