Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Artificial General Intelligence

The Journal of the Artificial General Intelligence Society

3 Issues per year

Open Access
Online
ISSN
1946-0163
See all formats and pricing
More options …

Unnatural Selection: Seeing Human Intelligence in Artificial Creations

Tony Veale
Published Online: 2015-12-30 | DOI: https://doi.org/10.1515/jagi-2015-0002

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.

Keywords: computational creativity; language; art; readymades; modernism; Twitterbots

References

  • Barnden, J. 2008. Metaphor and artificial intelligence: Why they matter to each other. In Gibbs, R. W., ed., The Cambridge Handbook of Metaphor and Thought. Cambridge, UK: Cambridge University Press. 311–338.Google Scholar

  • Brants, T., and Franz, A. 2006. Web IT 5-gram database, Version 1. The Linguistic Data Consortium.Google Scholar

  • Burroughs, W. S. 1963. The Cut-Up Method. In Jones, L., ed., The Moderns: An Anthology of New Writing in America. New York, NY: Corinth Books.Google Scholar

  • Carbonell, J. G. 1981. Metaphor: An inescapable phenomenon in natural language comprehension. Technical Report 2404, Carnegie Mellon Computer Science Department, Pittsburgh, PA.Google Scholar

  • Chamberlain, W., and Etter, T. 1983. The Policeman’s Beard is Half-Constructed: Computer Prose and Poetry. London, UK: Warner Books.Google Scholar

  • Dewey, C. 2014. What happens when @everyword ends? The Wall Street Journal (Intersect column) May 23.Google Scholar

  • Duchamp, M. 1917. The Richard Mutt Case. Blind Man 2:4–5.Google Scholar

  • Falkenhainer, B.; Forbus, K. D.; and Gentner, D. 1989. Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence 41:1–63.Google Scholar

  • Fass, D. 1991. Met*: a method for discriminating metonymy and metaphor by computer. Computational Linguistics 17(1):49–90.Google Scholar

  • Fellbaum, C., ed. 1998. WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press.Google Scholar

  • Freud, S. 1919. Das Unheimliche. In Collected Papers, volume XII. G.W. 229–268.Google Scholar

  • Gentner, D.; Falkenhainer, B.; and Skorstad, J. 1989. Metaphor: The Good, The Bad and the Ugly. In Wilks, Y., ed., Theoretical Issues in Natural Language Processing. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar

  • Gibson, W. 2005. God’s Little Toys. Wired Magazine 13.07.Google Scholar

  • Giora, R.; Fein, O.; Kronrod, A.; Elnatan, I.; Shuval, N.; and Zur, A. 2004. Weapons of Mass Distraction: Optimal Innovation and Pleasure Ratings. Metaphor and Symbol 19(2):115–141.CrossrefGoogle Scholar

  • Glucksberg, S. 1998. Understanding metaphors. Current Directions in Psychological Science 7:39–43.Google Scholar

  • Hughes, R. 1991. The Shock of the New: Art and the century of change. London, UK: Thames and Hudson.Google Scholar

  • Hutton, J. 1982. Aristotle’s Poetics. New York, NY: Norton.Google Scholar

  • Kazemi, D. 2015. TinySubversions.com. Web-site.Google Scholar

  • Kuenzli, R. E., and Naumann, F. M. 1989. Marcel Duchamp: artist of the century. Cambridge, MA: MIT Press.Google Scholar

  • Lakoff, G., and Johnson, M. 1980. Metaphors We Live By. Chicago, Illinois: University of Chicago Press.Google Scholar

  • Lydenberg, R. 1987. Word Cultures: Radical theory and practice in William S. Burroughs’ fictio. Chicago, Illinois: University of Illinois Press.Google Scholar

  • Magritte, R. 1929. Les Mots et les Images. La Révolution surréaliste (12):32–33.Google Scholar

  • Martin, J. H. 1990. A Computational Model of Metaphor Interpretation. San Diego, CA: Academic Press.Google Scholar

  • McKeganey, N. P. 1983. Cocktail party syndrome. Sociology of Health and Illness 5(1):95–103.Google Scholar

  • Milic, L. T. 1971. The possible usefulness of computer poetry. In Wisbey, R., ed., The Computer in Literary and Linguistic Research, Publications of the Literary and Linguistic Computing Centre. Cambridge, UK: University of Cambridge.Google Scholar

  • Reddy, M. J. 1979. The conduit metaphor: A case of frame conflict in our language about language. In Ortony, A., ed., Metaphor and Thought. Cambridge, UK: Cambridge University Press. 284–310.Google Scholar

  • Schwartz, E. R. 1974. Characteristics of Speech and Language Developments in the child with Myelomeningocele and Hydrocephalus. Journal of Speech and Hearing Disorders 39(4).Google Scholar

  • Searle, J. R. 1980. Mind, Brains and Programs. Behavioral and Brain Sciences 3:417–457.Google Scholar

  • Sontag, S. 1966. Against Interpretation and Other Essays. New York, NY: Farrar, Straus and Giroux.Google Scholar

  • Taylor, M. R. 2009. Marcel Duchamp: Étant donnés (Philadelphia Museum of Art). New Haven, Connecticut: Yale University Press.Google Scholar

  • Veale, T., and Alnajjar, K. 2015. Unweaving the Lexical Rainbow: Grounding Linguistic Creativity in Perceptual Semantics. In Ventura, D., ed., Proceedings of ICCC-2015, the 6th International Conference on Computational Creativity. Park City, UT: Association for Computational Creativity.Google Scholar

  • Veale, T., and Keane, M. T. 1997. The Competence of Sub-Optimal Structure Mapping on ‘Hard’ Analogies. In Proceedings of IJCAI’97, the 15th International Joint Conference on Artificial Intelligence. Nagoya, Japan. San Mateo, CA: Morgan Kaufmann.Google Scholar

  • Veale, T., and Li, G. 2011. Creative Introspection and Knowledge Acquisition. In Burgard, W., and Roth, D., eds., Proceedings of the 25th AAAI Conference on Artificial Intelligence. San Francisco, CA: AAAI Press.Google Scholar

  • Veale, T., and Li, G. 2015. Distributed Divergent Creativity: Computational Creative Agents at Web Scale. Cognitive Computation (May):1–12.Google Scholar

  • Veale, T. 2012. Exploding the Creativity Myth: The Computational Foundations of Linguistic Creativity. London, UK: Bloomsbury.Google Scholar

  • Veale, T. 2014. Running With Scissors: Cut-Ups, Boundary Friction and Creative Reuse. In Lamontagne, L., and Plaza, E., eds., Proceedings of ICCBR-2014, the 22nd International Conference on Case-Based Reasoning.Google Scholar

  • Veale, T. 2015. Game of Tropes: Exploring the Placebo Effect in Computational Creativity. In Ventura, D., ed., Proceedings of ICCC-2015, the 6th International Conference on Computational Creativity. Park City, UT: Association for Computational Creativity.Google Scholar

  • Way, E. C. 1991. Knowledge Representation and Metaphor. Studies in Cognitive systems. Amsterdam, the Netherlands: Kluwer Academic.Google Scholar

  • Wilks, Y. 1975. A preferential, pattern-seeking, semantics for natural language inference. Artificial Intelligence 6(1):53–74.Google Scholar

  • Wilks, Y. 1978. Making Preferences More Active. Artificial Intelligence 11(3):197–223.Google Scholar

About the article

Received: 2015-05-17

Accepted: 2015-11-19

Published Online: 2015-12-30

Published in Print: 2015-12-01


Citation Information: Journal of Artificial General Intelligence, ISSN (Online) 1946-0163, DOI: https://doi.org/10.1515/jagi-2015-0002.

Export Citation

© 2015 Tony Veale, published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Comments (0)

Please log in or register to comment.
Log in