Recently, a computer-generated work of art titled "Edmond de Belamy, from La Famille de Belamy" was sold by the auction house Christie's for $350,000 to an anonymous bidder. Purported to be the first auctioned portrait generated by artificial intelligence (AI), the work, produced by the French art collective Obvious, fetched hundreds of thousands of dollars more than works by Andy Warhol and Roy Lichtenstein offered in the same auction. Almost immediately, authorship of the painting was contested. An artist and programmer named Robbie Barrat claimed on Twitter that Obvious produced the painting using an algorithm he had created and shared online. The issues raised by this dispute may be the first of many profound questions of ownership, attribution, and intellectual property rights for the burgeoning field of computer-generated artwork.
As AI makes seemingly inexorable progress into every field of human endeavor, it may be inevitable that smart machines will become central to creativity and innovation, activities often considered to represent the highest form of human intelligence. In fact, computational creativity--an emerging field concerned with algorithms that can produce creative or inventive artifacts--has already made significant inroads in many real-world applications. In endeavors as varied as cooking, literature, fashion, circuit design, and drug discovery, AI systems are now able to produce ideas and artifacts that meet the criteria of novelty, surprise, and usefulness that lead to them being judged as creative by human experts.
These AI technologies challenge the fundamental building blocks of existing intellectual property (IP) laws and institutions, which are misaligned with Al-driven innovation on multiple fronts. IP rights are intended to provide incentives for innovators to engage in creative endeavors and to bring the fruits of these activities to society, while simultaneously balancing the need for market competition and dissemination of new knowledge. IP laws put human inventors and creators at the center of the creative process, reflecting deep-rooted assumptions about the inherent humanness of creativity. These assumptions have now been overturned by advances in computational creativity.
Psychologists define creativity as the generation of a product or service that is novel and judged to be appropriate, useful, or valuable by a knowledgeable social group, and simultaneously generates a measure of surprise, beauty, or amazement. To produce innovative songs, paintings, writings, or other artifacts that meet these standards, computational creativity systems use a variety of algorithmic techniques, including genetic algorithms, simulated annealing, stochastic sampling and filtering, and deep neural networks. The particulars of how these systems function is not as important as understanding that they are producing work that appears to be the product of creative thinking.
Tests analogous to the Turing test for assessing intelligent behavior by machines have been recently proposed by AI researchers such as Mark Riedl, whereby algorithms can be judged "creative" if they exhibit behaviors that human observers consider to be creative. A number of systems have now created artifacts in specific domains that pass this test. Examples that have captured public attention include Google's Magenta system, which composes novel and pleasing...