Computational creativity is the study of building software that exhibits behavior that would be deemed creative in humans. 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 has 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. Even within Computer Science, people are still skeptical about the creative potential of software. A typical statement of detractors of computational creativity is that “simulating artistic techniques means also simulating human thinking and reasoning, especially creative thinking. This is impossible to do using algorithms or information processing systems.” We could not disagree more. As is hopefully evident from the examples in this paper, creativity is not some mystical gift that is beyond scientific study but rather something that can be investigated, simulated, and harnessed for the good of society. And while society might still be catching up, computational creativity as a discipline has come of age. This maturity is evident in the amount of activity related to computational creativity in recent years; in the sophistication of the creative software we are building; in the cultural value of the artifacts being produced by our software; and most importantly, in the consensus we are finding on general issues of computational creativity.
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| Martial Raysse, America, America (1964) Neon, painted metal, 2.4 x 1.65 x 0.45 m, Centre Pompidou–Musée national d’art moderne–Centre de création industrielle, Paris, France. |
Computational creativity is a very lively subject area, with many issues still open to debate. For instance, many people still turn to the Turing test (Turing, 1950) to approximate the value of the artifacts produced by their software. That is, if a certain number of people cannot determine which artifacts were computer generated and which were human generated, then the software is doing well. Other people believe that the Turing test is inappropriate for creative software. One has to ask the question: “Under full disclosure, would people value the artifacts produced by a computer as highly as they would the human produced ones?” In some domains, the answer could be yes: for instance, a joke is still funny whether or not it is produced by a computer. In other domains, such as the visual arts, however, the answer is very likely to be no. This highlights the fact that the production process, and not just the outcome of it, is taken into account when assessing artworks. Hence, one could argue that such Turing-style tests are essentially setting the computers up for a fall.
Building creative software provides both a technical challenge and a social one. To proceed further, we need to embrace the fact that computers are not human. We should be loud and proud about the artifacts being produced by our software. We should celebrate the sophistication of the artificial intelligence (AI) techniques we have employed to endow the software with creative behavior. And we should help the general public to appreciate the value of these computer creations by describing the methods employed by the software to create them.
Creativity seems mysterious because when we have creative ideas it is very difficult to explain how we got them and we often talk about vague notions like “inspiration” and “intuition” when we try to explain creativity. The fact that we are not conscious of how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. As a matter of fact, we are not aware of how we perform other activities such as language understanding, pattern recognition, and so on, but we have better and better AI techniques able to replicate such activities.
Since nothing can arise from the emptiness, we must understand that every creative work or creative idea is always preceded by a historical-cultural scheme; it is a fruit of the cultural inheritance and the lived experiences. As Margaret Boden states in her book Artificial Intelligence and Natural Man (Boden, 1987):
Probably the new thoughts that originate in the mind are not completely new, because have their seeds in representations that already are in the mind. To put it differently, the germ of our culture, all our knowledge and our experience, is behind each creative idea. The greater the knowledge and the experience, the greater the possibility of finding an unthinkable relation that leads to a creative idea. If we understand creativity like the result of establishing new relations between pieces of knowledge that we already have, then the more previous knowledge one has the more capacity to be creative.
With this understanding in mind, an operational, and widely accepted, definition of creativity is: “A creative idea is a novel and valuable combination of known ideas.” In other words, physical laws, theorems, musical pieces can be generated from a finite set of existing elements and, therefore, creativity is an advanced form of problem solving that involves memory, analogy, learning, and reasoning under constraints, among others, and is therefore possible to replicate by means of computers.
This article addresses the question of the possibility of achieving computational creativity through some examples of computer programs capable of replicating some aspects of creative behavior. Due to space limitations we could not include other interesting areas of application such as: storytelling (Gervás, 2009), poetry (Montfort et al., 2014), science (Langley et al., 1987), or even humor (Ritchie, 2009). Therefore, the paper addresses, with different levels of detail, representative results of some achievements in the fields of music and visual arts. The reason for focusing on these artistic fields is that they are by far the ones in which there is more activity and where the results obtained are most impressive. The paper ends with some reflections on the recent trend of democratization of creativity by means of assisting and augmenting human creativity.
For further reading regarding computational creativity in general, I recommend the AI Magazine special issue on Computational Creativity (Colton et al., 2009), as well as the books by Boden (1991, 1994, 2009), Dartnall (1994), Partridge & Rowe (1994), Bentley & Corne (2002), and McCormack & d’Inverno (2012).
