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Tony Veale

Abstract

As generative AI systems grow in sophistication, so too do our expectations of their outputs. For as automated systems acculturate themselves to ever larger sets of inspiring human examples, the more we expect them to produce human-quality outputs, and the greater our disappointment when they fall short. While our generative systems must embody some sense of what constitutes human creativity if their efforts are to be valued as creative by human judges, computers are not human, and need not go so far as to actively pretend to be human to be seen as creative. As discomfiting objects that reside at the boundary of two seemingly disjoint categories, creative machines arouse our sense of the uncanny, or what Freud memorably called the Unheimlich. Like a ventriloquist’s doll that finds its own voice, computers are free to blend the human and the non-human, to surprise us with their knowledge of our world and to discomfit with their detached, other-worldly perspectives on it. Nowhere is our embrace of the unnatural and the uncanny more evident than in the popularity of Twitterbots, automatic text generators on Twitter that are followed by humans precisely because they are non-human, and because their outputs so often seem meaningful yet unnatural. This paper evaluates a metaphor generator named @MetaphorMagnet, a Twitterbot that tempers the uncanny with aptness to yield results that are provocative but meaningful.

Open access

Tarek R. Besold, Kai-Uwe Kühnberger and Tony Veale

Abstract

Over the last decade, computational creativity as a field of scientific investigation and computational systems engineering has seen growing popularity. Still, the levels of development between projects aiming at systems for artistic production or performance and endeavours addressing creative problem-solving or models of creative cognitive capacities is diverging. While the former have already seen several great successes, the latter still remain in their infancy. This volume collects reports on work trying to close the accrued gap.