-------- Forwarded Message --------
We would like to invite you to a workshop on
thinking machines and design on June 10 or 11 in Stockholm,
Sweden (exact date time TBD):
http://ecis2019.eu/programme/workshops-and-tutorials/thinking-machines-and-design-a-time-to-reconsider-1.421431
Innovators and systems designers in a wide range of areas,
such as architecture, semiconductor chip design, video game
development, industrial design, and construction,
increasingly use smarter and largely autonomous tools to
generate better designs. Such tools are increasingly
acquiring autonomy and learning capabilities, thus prompting
interactions between humans and machines to change as the
latter gain more intelligence (Seidel, Berente, Lindberg,
et al. 2018).
Design resulting from such interactions are often novel and
useful, i.e. innovative (Amabile 1996; Leonard-Barton 1998).
Autonomous tools deploy software that undergird artificial
intelligence (AI) methods such as machine learning, pattern
recognition, meta-heuristics, and evolutionary algorithms to
generate design artifacts beyond the capabilities of most—or
maybe any—humans. Such tools allow designers to
algorithmically generate a solution space based on certain
parameters, within which outcomes are generated. The
autonomous generation of design outcomes is also known as
procedural generation (Hendrikx et al. 2011), procedural
modeling (Mnih et al. 2015), and even computational
creativity (Liapis et al. 2014).
Using autonomous design tools fundamentally revamps the
process of innovation and design. Tasks that were formerly
conducted by human designers are now conducted by tools.
Designers must understand how tools can be used, and they
must be able to evaluate the outcomes that autonomous tools
generate. Moreover, tools must be developed and adjusted to
match the designers’ understanding of the overall innovation
process, and these decisions with regards to the tool become
part of the overall design endeavor (Seidel, Berente,
Martinez, et al. 2018.)
We know little about how AI-based methods will change design
practices, necessary competencies, and associated
organizational arrangements. As these developments represent
areas of broad interest to industry and society at large, it
is incumbent on us as researchers to begin to formulate
concrete research agendas. To facilitate this process, we
propose a workshop to outline the key concerns and research
directions that the application of AI methods and thinking
machines to design will entail. We will specifically
consider the implications of AI for design science research,
organizational scholarship, and IS-oriented studies of
design and innovation.
References
Amabile, T. 1996. Creativity in Context, Westview Press.
Hendrikx, M., Meijer, S., Van Der Velden, J., and Iosup, A.
2011. “Procedural Content Generation for Games: A Survey,”
ACM Transactions on Multimedia Computing, Communications and
Applications (February), pp. 1–24.
Leonard-Barton, D. 1998. Wellsprings of Knowledge : Building
and Sustaining the Sources of Innovation, Harvard Business
School Press.
Liapis, A., Yannakakis, G. N., and Togelius, J. 2014.
“Computational Game Creativity,” Proceedings of the 5th
International Conference on Computational Creativity, pp.
46–53.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness,
J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland,
A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A.,
Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg,
S., and Hassabis, D. 2015. “Human-Level Control through Deep
Reinforcement Learning,” Nature (518:7540), Nature
Publishing Group, pp. 529–533.
Seidel, S., Berente, N., Lindberg, A., Lyytinen, K., and
Nickerson, J. V. 2018. “Autonomous Tools and Design: A
Triple-Loop Approach to Human-Machine Learning,”
Communications of the ACM (62:1), pp. 50–57.
Seidel, S., Berente, N., Martinez, B., Lindberg, A.,
Lyytinen, K., and Nickerson, J. V. 2018. “Autonomous Tools
in System Design: Reflective Practice in Ubisofts Ghost
Recon Wildlands Project,” Computer (51:10), pp. 16–23.
Best,
Aron Lindberg (primary contact) (aron.lindberg@stevens.edu)
Stefan Seidel (stefan.seidel@uni.li)
Tuure Tuunanen (tuure@tuunanen.fi)
Rikard Lindgren (rikard.lindgren@ait.gu.se)
Kalle Lyytinen (kalle@case.edu)
Prof. Dr. Stefan
Seidel
Professor and Chair
of Information Systems
& Innovation
University of
Liechtenstein
Institute of Information
Systems
Fürst-Franz-Josef-Strasse,
9490 Vaduz, Liechtenstein
Phone +423 265 11 11,
Direct +423 265 13 03
stefan.seidel@uni.li,
www.uni.li