Computational creativity

Computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts.

The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends:

The field of computational creativity concerns itself with theoretical and practical issues in the study of creativity. Theoretical work on the nature and proper definition of creativity is performed in parallel with practical work on the implementation of systems that exhibit creativity, with one strand of work informing the other.

As measured by the amount of activity in the field (e.g., publications, conferences and workshops), computational creativity is a growing area of research. But the field is still hampered by a number of fundamental problems. Creativity is very difficult, perhaps even impossible, to define in objective terms. Is it a state of mind, a talent or ability, or a process? Creativity takes many forms in human activity, some eminent (sometimes referred to as "Creativity" with a capital C) and some mundane.

These are problems that complicate the study of creativity in general, but certain problems attach themselves specifically to computational creativity:

Indeed, not all computer theorists would agree with the premise that computers can only do what they are programmed to do—a key point in favor of computational creativity.

Because no single perspective or definition seems to offer a complete picture of creativity, the AI researchers Newell, Shaw and Simon developed the combination of novelty and usefulness into the cornerstone of a multi-pronged view of creativity, one that uses the following four criteria to categorize a given answer or solution as creative:

Whereas the above reflects a "top-down" approach to computational creativity, an alternative thread has developed among "bottom-up" computational psychologists involved in artificial neural network research. During the late 1980s and early 1990s, for example, such generative neural systems were driven by genetic algorithms. Experiments involving recurrent nets were successful in hybridizing simple musical melodies and predicting listener expectations.

Concurrent with such research, a number of computational psychologists took the perspective, popularized by Stephen Wolfram, that system behaviors perceived as complex, including the mind's creative output, could arise from what would be considered simple algorithms. As neuro-philosophical thinking matured, it also became evident that language actually presented an obstacle to producing a scientific model of cognition, creative or not, since it carried with it so many unscientific aggrandizements that were more uplifting than accurate. Thus questions naturally arose as to how "rich," "complex," and "wonderful" creative cognition actually was.

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