Hostname: page-component-77c89778f8-fv566 Total loading time: 0 Render date: 2024-07-20T07:21:21.847Z Has data issue: false hasContentIssue false
  • English
  • Français

CREATIVE THINKING: COMPUTATIONAL TOOLS IMBUED WITH AI

Part of: Design Support Tools

Published online by Cambridge University Press:  11 June 2020

R. E. Wendrich
R. E. Wendrich*
Affiliation:
University of Twente, The Netherlands
*
*r.e.wendrich@utwente.nl

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This paper presents a test bed for AI technology on the integration of creative AI (CAI) with hybrid design tools (HDTs). The objective is to build and develop tools and programs for creative people (e.g. designers, engineers) to use, whereby the artificial intelligence (AI) software acts as a creative collaborator rather than a mere tool. The goal is to find a set of guiding principles, metaphors and ideas that inform the development of a CAI praxis imbued with computational support tools, new theories, experiments, and applications. Results and findings are presented of early-stage research.

Keywords

artificial intelligence (AI)design creativitydesign toolscyber-physical systems
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 481 - 490
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Amabile, T.M. and Pratt, M.G. (2016), “The dynamic componential model of creativity and innovation in organizations: Making progress, making meaning”, Research in Organizational Behaviour, Vol. 36, pp. 157–183.CrossRefGoogle Scholar
Brock, A. et al. (2016), “Generative and discriminative voxel modelling with convolutional neural networks”, arXiv preprint arXiv: 1608.04236.Google Scholar
Crilly, N., Maier, A. and Clarkson, P.J. (2008), “Representing artefacts as media: Modelling the relationship between designer intent and consumer experience”, International Journal of Design, Vol. 2 No. 3, pp. 15–27.Google Scholar
Dai, J., Lu, Y. and Wu, Y.N. (2016), “Generative modeling of convolutional neural networks”, Statistics and Its Interface, Vol. 9 No. 4, pp. 485–496. https://doi.org/10.4310/sii.2016.v9.n4.a8CrossRefGoogle Scholar
Dalcher, D. (2006), “Consilience for universal design: the emergence of a third culture”, Universal Access in the Information Society, Vol. 5 No. 3, pp. 253–268.CrossRefGoogle Scholar
Fuller, R.B. (1969), “Ideas and integrities: A spontaneous autobiographical disclosure”, Estate of R. Buckm. Fuller.Google Scholar
Gatys, L.A., Ecker, A.S. and Bethge, M. (2015), “A neural algorithm of artistic style”, arXiv: 1508.06576.Google Scholar
Goffman, E. (1974), Frame analysis: An essay on the organization of experience, Harvard University Press.Google Scholar
Goldschmidt, G. (2015), “Ubiquitous serendipity: Potential visual design stimuli are everywhere”, In Studying visual and spatial reasoning for design creativity, pp. 205–214. Springer Netherlands.Google Scholar
Kahneman, D., Slovic, P. and Tversky, A. (1982), Judgment and Uncertainty: Heuristic and Biases.CrossRefGoogle Scholar
Koen, P.A. et al. (2002), Fuzzy front end: Effective methods, tools, and techniques, Wiley, New York, NY.Google Scholar
Koestler, A. (1964), The act of creation.Google Scholar
Lee, H. et al. (2009), “Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations”, In Proc. of the 26th Ann. Int. Conf. on M.L. ACM.CrossRefGoogle Scholar
Mannheim, K. (1939), “Ideology and utopia: An introduction to the sociology of knowledge”, Brace and Company.Google Scholar
Maruyama, M. (1963). The second cybernetics: Deviation-amplifying mutual casual processes.Google Scholar
MacEachren, A.M. (1995, 2004), “How maps work: representation, visualization, and design”, Guilford Press. American Scientist, Vol. 51 No. 2, pp. 164–179.Google Scholar
Mlodinow, L. (2008), The Drunkard's Walk: How Randomness Affects Our Lives.Google Scholar
Nóbrega, V.A., Miyagawa, S. and Lesure, C.L. (2018), Cross-Modality Information Transfer: A Hypothesis about the Relationship among Prehistoric Cave Paintings, Symbolic Thinking, and the Emergence of Language.CrossRefGoogle Scholar
Nowotny, H. (2010), Insatiable curiosity: innovation in a fragile future, MIT Press.Google Scholar
Robertson, B. and Radcliffe, D. (2009), “Impact of CAD tools on creative problem solving in engineering design”, Computer-Aided Design, Vol. 41 No. 3, pp. 136–146.CrossRefGoogle Scholar
TensorFlow, (2015), https://www.tensorflow.org/, Last Accessed: November 11, 2019Google Scholar
Vuletic, T. et al. (2018), “The challenges in computer supported conceptual engineering design”, Computers in Industry, Vol. 95, pp. 22–37.CrossRefGoogle Scholar
Wendrich, R.E. (2010), “Raw Shaping Form Finding: Tacit Tangible CAD”, Computer-Aided Design and Applications, Vol. 7 No. 4, pp. 505–531. https://doi.org/10.3722/cadaps.2010.505-531CrossRefGoogle Scholar
Wendrich, R.E. (2015), “Integrated Creativity and Play Environments in Design and Engineering Processes”, Volume 1B: 35th Computers and Information in Engineering Conference. https://doi.org/10.1115/detc2015-47214CrossRefGoogle Scholar
Wendrich, R.E. and D’Çruz, M. (2011) in Wendrich (2016), “Hybrid design tools for conceptual design and design engineering processes: bridging the design gap: towards an intuitive design tool”.Google Scholar