Abstract
This article explores the notion of human and computational creativity as well as core challenges for computational musical creativity. It also examines the philosophical dilemma of computational creativity as being suspended between algorithmic determinism and random sampling, and suggests a resolution from a perspective that conceives of “creativity” as an essentially functional concept dependent on a problem space, a frame of reference (e.g. a standard strategy, a gatekeeper, another mind, or a community), and relevance. Second, this article proposes four challenges for artificial musical creativity and musical AI: (1) the <em>cognitive challenge</em> that musical creativity requires a model of music cognition, (2) the <em>challenge of the external world</em>, that many cases of musical creativity require references to the external world, (3) the <em>embodiment challenge</em>, that many cases of musical creativity require a model of the human body, the instrument(s) and the performative setting in various ways, (4) the <em>challenge of creativity at the meta-level</em>, that musical creativity across the board requires creativity at the meta-level. Based on these challenges it is argued that the general capacity of music and its creation fundamentally involves general (artificial) intelligence and that therefore musical creativity at large is fundamentally an AI-complete problem.
Highlights
Computational creativity and creative AI are amongst the areas of computer science that attract the most attention and interest
Refining the standard definition above, this subsection argues that human and computational creativity can be understood as producing a solution in a complex possibility space defined by a certain problem setting (Boden, 2004), which (a) is difficult to find in comparison with a given reference, (b) lies within the boundaries of the problem setting, and (c) is of use or relevant
Music is often conceived of as pure structure or “absolute music” (Hanslick, 1854; Dahlhaus, 1991), and, it has often been a prime domain for computational creativity and witnessed some of the earliest attempts at computational composition (e.g. Hiller, 1970)
Summary
Computational creativity and creative AI are amongst the areas of computer science that attract the most attention and interest. It is useful to distinguish between music generation and creative models, in which the former aims to generate instances within a given, predefined setting or style, and the latter focuses on the modeling of the phenomenon of “creativity” itself. Within the latter, attempts to explicate “creativity” commonly require properties of the outcome to go beyond mere generation and replication, such as novelty, originality, discovery, something unexpected, sometimes termed capital “C” Creativity (e.g. Cohen, 1999). While it may seem intuitive that creativity is too hard to define, this insight bears two substantial philosophical dilemmas concerning computational creativity
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