Computational Creativity is a new academic field devoted to the study and modeling of computational processes that achieve creative tasks.
Its main duties are the study of computational models of (human) creativity, designing artificially creative systems, and also to deploy computational systems for supporting creativity. Understanding brain processes behind creativity and modeling them using computational means is one of the grand challenges for systems biology therefore Computational Creativity, inspired by cognitive psychology and neuroscience, is an interesting research field.
We may sympathize with polish professor Włodzisław Duch who writes that human-level intelligence is far beyond what Artificial Intelligence can provide now, especially in regard to the high-level functions like: reasoning, planning and the use of language. But undoubtedly computational models show great promise both in elucidating mechanisms behind such high-level mental functions.
But we should not forget, as professor Margaret Boden states in the following video, that the computational units that current Artificial Intelligence (AI) can cope with are hugely simple compared with even a single neuron, so to say that current computational cognitive systems are inspired by our brain doesn’t mean that are equal to the most complex organ in the known universe.
Computational Creativity as a subfield of Artificial Intelligence research (the science of having machines solve problems that require intelligence when solved by humans according to pioneer social scientist Herbert Simon) aims is to create artifacts and ideas which are historically associated with creative people in fields like: mathematics, poetry, musical composition, videogames, design, visual arts, gastronomy… According to the definition by british professors Simon Colton and Geraint A. Wiggins this emerging academic field is a mix of “philosophy, science and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviours that unbiased observers would deem to be creative”.
But wait, what is creativity anyway? According to the definition by the Oxford Dictionary it is the use of imagination or original ideas to create something. If we agree with this definition, and understand that creative tasks are those where there is no clear “best” outcome, we shall proceed to proclaim that Computational Creativity departs from mainstream Artificial Intelligence because the notion of optimality is ill-defined for the conception of innovative products and ideas.
We can find an example in the works made by artist Robbie Barrat using Generative Adversarial Networks. This revolutionary class of artificial intelligence algorithms used in unsupervised machine learning were introduced by Ian Goodfellow, a scientist at Google Brain, and have been used to produce samples of photorealistic images. According to this young artist his role as a creator is like that of artist Sol Lewitt, best known for writing out instructions or rule sets for creating drawings and then having others execute the rules to create his artwork. Unlike with traditional generative art where the artist establishes the code and the computer has no room for interpretation, with this kind of AI generated artworks the artist is like giving imagination to the machines. A century ago Marcel Duchamp, pioneer of conceptual art, emphasized that the artist’s idea was the key element in the artistic creation in clear opposition to the classical aesthetic perspective. This new art created by Generative Adversarial Networks radically flips this idea, not just creating a new thought for an object, but creating an artifact capable of doing the thinking and creating for us.
So we now enter a really fertile philosophical question: Is it possible to conceive of a creativity that is in some way distinct from the human one? Researcher Anna Jordanous believes that the recent academic changes to the definition of Computational Creativity are being thought in order to make the implications of the word “creativity” more general, rather than having to be linked only to human creativity. She believes that John McCarthy, the artificial intelligence pioneer, was certain when saying that “to ascribe certain beliefs, knowledge, free will, intentions, consciousness, abilities or wants to a machine or computer program is legitimate when (…) the ascription helps us understand the structure of the machine”.
Computational systems are not human and so we should accept the creativity they exhibit as legitime creativity, but not as we know it since they will never be exactly the same as homo sapiens. Consequently we should apply to “our” machines the ethics of Neurodiversity, asserting that neurological differences should be recognized and respected as a social category on par with gender, ethnicity, sexual orientation, disability or biological/artificial status. Just now that smart machines are starting to generate their own designs and pieces of art we should think and debate around this themes, because how will we as human beings be able to understand their own original results when they free themselves from our limitations and aesthetics? Are we ready to adapt and fall in love with the ways of non-anthropocentric creativity?
Maybe, if we keep moving towards a posthumanistic new understanding of our biological substrate we will be a little closer to accept that this new era of cognitive digital machines is a very special moment to become open minded with the possibilites that lie before us. Since probably pretty soon the machine will have the idea that makes the art…
*Picture: (artificialintelligence-news.com & landscape by Robbie Barrat's AI system).