Researchers Propose 'Neuro-Symbolic' Approach for Generative Art

On the topic of creating art, Spanish surrealist painter Joan Miro once said "the works must be conceived with fire in the soul, but executed with clinical coolness." No matter how much cool compute they may pack, how can today's AI models hope to access that essential "fire in the soul" when generating their artworks? In a new paper, researchers from Adobe, Georgia Tech, and Facebook AI Research propose a neuro-symbolic hybrid approach to address the challenge of creativity in generative art. Generative art refers to the creation of artworks using autonomous processes with no direct human control. There are two general classes of generative art: "neural," where a deep neural network is trained to generate samples from a data distribution; and "symbolic" or "algorithmic," where a human artist designs the primary parameters and an autonomous system then works within these constraints to generate samples.