• Computational creativity

    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.

      • To construct a program or computer capable of human-level creativity.
      • To better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans.
      • To design programs that can enhance human creativity without necessarily being creative themselves.
      • Can creativity be hard-wired? In existing systems to which creativity is attributed, is the creativity that of the system or that of the system's programmer or designer?
      • How do we evaluate computational creativity? What counts as creativity in a computational system? Are natural language generation systems creative? Are machine translation systems creative? What distinguishes research in computational creativity from research in artificial intelligence generally?
      • If eminent creativity is about rule-breaking or the disavowal of convention, how is it possible for an algorithmic system to be creative? In essence, this is a variant of Ada Lovelace's objection to machine intelligence, as recapitulated by modern theorists such as Teresa Amabile. If a machine can do only what it was programmed to do, how can its behavior ever be called creative?
      • Placing a familiar object in an unfamiliar setting (e.g., Marcel Duchamp's Fountain) or an unfamiliar object in a familiar setting (e.g., a fish-out-of-water story such as The Beverly Hillbillies)
      • Blending two superficially different objects or genres (e.g., a sci-fi story set in the Wild West, with robot cowboys, as in Westworld, or the reverse, as in Firefly; Japanese haiku poems, etc.)
      • Comparing a familiar object to a superficially unrelated and semantically distant concept (e.g., "Makeup is the Western burka"; "A zoo is a gallery with living exhibits")
      • Adding a new and unexpected feature to an existing concept (e.g., adding a scalpel to a Swiss Army knife; adding a camera to a mobile phone)
      • Compressing two incongruous scenarios into the same narrative to get a joke (e.g., the Emo Philips joke "Women are always using me to advance their careers. Damned anthropologists!")
      • Using an iconic image from one domain in a domain for an unrelated or incongruous idea or product (e.g., using the Marlboro Man image to sell cars, or to advertise the dangers of smoking-related impotence).
      • A first input space (contains one conceptual structure or mental space)
      • A second input space (to be blended with the first input)
      • A generic space of stock conventions and image-schemas that allow the input spaces to be understood from an integrated perspective
      • A blend space in which a selected projection of elements from both input spaces are combined; inferences arising from this combination also reside here, sometimes leading to emergent structures that conflict with the inputs.
      • ICCC 2016, Paris, France
      • ICCC 2015, Park City, Utah, USA. Keynote: Emily Short
      • ICCC 2014, Ljubljana, Slovenia. Keynote: Oliver Deussen
      • ICCC 2013, Sydney, Australia. Keynote: Arne Dietrich
      • ICCC 2012, Dublin, Ireland. Keynote: Steven Smith
      • ICCC 2011, Mexico City, Mexico. Keynote: George E Lewis
      • ICCC 2010, Lisbon, Portugal. Keynote/Inivited Talks: Nancy J Nersessian and Mary Lou Maher
      • IJWCC 2003, Acapulco, Mexico, as part of IJCAI'2003
      • IJWCC 2004, Madrid, Spain, as part of ECCBR'2004
      • IJWCC 2005, Edinburgh, UK, as part of IJCAI'2005
      • IJWCC 2006, Riva del Garda, Italy, as part of ECAI'2006
      • IJWCC 2007, London, UK, a stand-alone event
      • IJWCC 2008, Madrid, Spain, a stand-alone event
      • CCSMC 2016, 17–19 June, University of Huddersfield, UK. Keynotes: Geraint Wiggins and Graeme Bailey.
      • Pereira, F. C. (2007). "Creativity and Artificial Intelligence: A Conceptual Blending Approach". Applications of Cognitive Linguistics series, Mouton de Gruyter.
      • Veale, T. (2012). "Exploding the Creativity Myth: The Computational Foundations of Linguistic Creativity". Bloomsbury Academic, London.
      • McCormack, J. and d'Inverno, M. (eds.) (2012). "Computers and Creativity". Springer, Berlin.
      • Veale, T., Feyaerts, K. and Forceville, C. (2013, forthcoming). "Creativity and the Agile Mind: A Multidisciplinary study of a Multifaceted phenomenon". Mouton de Gruyter.
      • New Generation Computing, volume 24, issue 3, 2006
      • Journal of Knowledge-Based Systems, volume 19, issue 7, November 2006
      • AI Magazine, volume 30, number 3, Fall 2009
      • Minds and Machines, volume 20, number 4, November 2010
      • Cognitive Computation, volume 4, issue 3, September 2012
      • AIEDAM, volume 27, number 4, Fall 2013
      • Computers in Entertainment, two special issues on Music Meta-Creation (MuMe), Fall 2016 (forthcoming)
      • JCMS 2016, Journal of Creative Music Systems
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    • Computational creativity