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can see no reason why not, but it must do so within the confines of its ability to
manipulate representations. Thus, its creativity will be limited, perhaps to the point
where we are insufficiently engaged with its behaviour to claim it as a rival to our
own creativity.
Human creativity is analogously confined by physical, chemical, biological and
social constraints. In keeping with both Boden's definition and our own, we find
human creativity interesting because it redefines the boundaries of what is possible
within the constraints we believe are imposed upon us. If we can build generative
systems that interact within the same constraints as ourselves, we can potentially
circumvent the limitations of the purely digital system. And this is what artists do
when they map the digital world to real textual, sonic and visual events. A program
running on a computer is not very interesting unless we can experience what it is
doing somehow. Once we can, the discrete events—changing values, production of
spatio-temporal patterns—take on new meanings for those observers that perceive
them, outside the internal semantics of the generative system that created them. For
this reason we can see no reason why machine creativity cannot in theory rival our
own.
How will we know that our creativity has been rivalled? In the usual ways:
machine-generated works will appear in the best galleries, win the most prestigious
art prizes, sell for vast sums of money, be acclaimed by critics, cited in publications
and referenced by other artists. But also, if we are to progress in any methodical
sense, because we shall be able to assess their creativity in an objective way, em-
ploying measures such as that implicit in the new definition of creativity that we
have presented.
13.10 Conclusions
We have described in this chapter a way to interpret creativity independently of in-
formal concepts such as value and appropriateness. These concepts, we feel, have
encumbered the search, production and recognition of autonomous creative soft-
ware to date. Instead, we have proposed a testable measure for creativity that can
be applied wherever it is possible to sample and measure the novelty of designs
selected from a large, even infinite, space of possibilities. In fact our approach is,
in theory, applicable to any digital computational generative system and its output.
As a proof of concept, our definition has been encoded in software and employed
to measure the creativity of images, enabling it to automatically offer results that
humans perceive to be more creative than those made using similar, but differently
assisted, algorithmic techniques.
We do not pretend to have shelled the “automated creativity nut”, but we be-
lieve we have caused a fissure from which we can pry it open. No doubt our powers
of persuasion are not so strong as to ensure that our ideas will be uncontroversial,
particularly as our suggestions run against the grain of much that has been written
