Creativity is sometimes taken to be an inexplicable aspect of human activity. By summarizing a considerable body of literature on creativity, I hope to show how to turn some of the best ideas about creativity into programs that are demonstrably more creative than any we have seen to date. I believe the key to building more creative programs is to give them the ability to reflect on and modify their own frameworks and criteria. That is, I believe that the key to creativity is at the metalevel.
What's the purpose of humanity if machines can learn ingenuity? The value placed on creativity in modern times has led to a range of writers and thinkers trying to articulate what it is, how to stimulate it, and why it is important. It was while sitting on a committee at the Royal Society assessing what impact machine learning was likely to have on society in the coming decades that I first encountered the theories of Margaret Boden. Her ideas struck me as the most relevant when it came to addressing creativity in machines. Boden is an original thinker who has managed to fuse many disciplines: philosopher, psychologist, physician, AI expert and cognitive scientist. In her eighties now, with white hair flying like sparks and an ever active brain, she is enjoying engaging enthusiastically with the prospect of what these "tin cans", as she likes to call computers, might be capable of.
Such creative software can be used for autonomous creative tasks, such as inventing mathematical theories, writing poems, painting pictures, and composing music. However, computational creativity studies also enable us to understand human creativity and to produce programs for creative people to use, where the software acts as a creative collaborator rather than a mere tool. Historically, it's been difficult for society to come to terms with machines that purport to be intelligent and even more difficult to admit that they might be creative. For instance, in 1934, some professors at the University of Manchester in the United Kingdom built meccano models that were able to solve some mathematical equations. Groundbreaking for its time, this project was written up in a piece in Meccano Magazine. The article was titled "Are Thinking Machines Possible" and was very upbeat, but surprisingly ends by stating that "Truly creative thinking of course will always remain beyond the power of any machine." Surely, though, this attitude has changed in light of the amazing advances in hardware and software technology that followed those meccano models?
The game of Go played between a DeepMind computer program and a human champion created an existential crisis of sorts for Marcus du Sautoy, a mathematician and professor at Oxford University. "I've always compared doing mathematics to playing the game of Go," he says, and Go is not supposed to be a game that a computer can easily play because it requires intuition and creativity. So when du Sautoy saw DeepMind's AlphaGo beat Lee Sedol, he thought that there had been a sea change in artificial intelligence that would impact other creative realms. He set out to investigate the role that AI can play in helping us understand creativity, and ended up writing The Creativity Code: Art and Innovation in the Age of AI (Harvard University Press). The Verge spoke to du Sautoy about different types of creativity, AI helping humans become more creative (instead of replacing them), and the creative fields where artificial intelligence struggles most.
Just as manufacturing automation cuts into human jobs, the prospect of creative artificial intelligence raises the specter of robot writers, robot artists and robot musicians who never sleep and always agree with their patron. Robert and Christian discuss some possibilities in this episode of the Stuff to Blow Your Mind podcast.