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Crowdsourcing intelligent design

  • Wei Xiang
  • Ling-yun Sun
  • Wei-tao You
  • Chang-yuan Yang
Article

Abstract

Design intelligence, namely, artificial intelligence to solve creative problems and produce creative ideas, has improved rapidly with the new generation artificial intelligence. However, existing methods are more skillful in learning from data and have limitations in creating original ideas different from the training data. Crowdsourcing offers a promising method to produce creative designs by combining human inspiration and machines’ computational ability. We propose a crowdsourcing intelligent design method called ‘flexible crowdsourcing design’. Design ideas produced through crowdsourcing design can be unreliable and inconsistent because they rely solely on selection among participants’ submissions of ideas. In contrast, the flexible crowdsourcing design method employs a cultivation procedure that integrates the ideas from crowd participants and cultivates these ideas to improve design quality at the same time. We introduce a series of studies to show how flexible crowdsourcing design can produce original design ideas consistently. Specifically, we will describe the typical procedure of flexible crowdsourcing design, the refined crowdsourcing tasks, the factors that affect the idea development process, the method for calculating idea development potential, and two applications of the flexible crowdsourcing design method. Finally, it summarizes the design capabilities enabled by crowdsourcing intelligent design. This method enhances the performance of crowdsourcing design and supports the development of design intelligence.

Keywords

Crowdsourcing Flexible crowdsourcing design Design intelligence 

CLC number

TP391 

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References

  1. Ball LJ, Ormerod TC, 1995. Structured and opportunistic processing in design: a critical discussion. Int J Hum-Comput Stud, 43(1):131–151. https://doi.org/10.1006/ijhc.1995.1038CrossRefGoogle Scholar
  2. Chan J, Dang S, Dow SP, 2016. Improving crowd innovation with expert facilitation. Proc 19th ACM Conf on Computer-Supported Cooperative Work & Social Computing, p.1223–1235. https://doi.org/10.1145/2818048.2820023Google Scholar
  3. Chang DN, Chen CH, Lee KM, 2014. A crowdsourcing development approach based on a neuro-fuzzy network for creating innovative product concepts. Neurocomputing, 142:60–72. https://doi.org/10.1016/j.neucom.2014.03.044CrossRefGoogle Scholar
  4. Cross N, 2006. Designerly Ways of Knowing. Springer, London. https://doi.org/10.1007/1-84628-301-9Google Scholar
  5. Dontcheva M, Morris RR, Brandt JR, et al., 2014. Combining crowdsourcing and learning to improve engagement and performance. Proc SIGCHI Conf on Human Factors in Computing Systems, p.3379–3388. https://doi.org/10.1145/2556288.2557217Google Scholar
  6. Flores RL, Belaud JP, le Lann JM, et al., 2015. Using the collective intelligence for inventive problem solving: a contribution for open computer aided innovation. Expert Syst Appl, 42(23):9340–9352. https://doi.org/10.1016/j.eswa.2015.08.024CrossRefGoogle Scholar
  7. Gatys LA, Ecker AS, Bethge M, 2016. Image style transfer using convolutional neural networks. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2414–2423. https://doi.org/10.1109/CVPR.2016.265Google Scholar
  8. Glickman ME, 1999. Parameter estimation in large dynamic paired comparison experiments. J Roy Stat Soc Ser C, 48(3):377–394. https://doi.org/10.1111/1467-9876.00159CrossRefzbMATHGoogle Scholar
  9. Goldschmidt G, 2015. Ubiquitous serendipity: potential visual design stimuli are everywhere. In: Gero JS (Ed.), Studying Visual and Spatial Reasoning for Design Creativity. Springer Dordrecht Netherlands, p.205–214. https://doi.org/10.1007/978-94-017-9297-4_12Google Scholar
  10. Ikeda K, Morishima A, Rahman H, et al., 2016. Collaborative crowdsourcing with crowd4U. Proc VLDB Endowm, 9(13):1497–1500. https://doi.org/10.14778/3007263.3007293CrossRefGoogle Scholar
  11. Kim J, Dontcheva M, Li W, et al., 2015. Motif: supporting novice creativity through expert patterns. Proc 33rd Annual ACM Conf on Human Factors in Computing Systems, p.1211–1220. https://doi.org/10.1145/2702123.2702507Google Scholar
  12. Lafreniere B, Grossman T, Anderson F, et al., 2016. Crowdsourced fabrication. Proc 29th Annual Symp on User Interface Software and Technology, p.15–28. https://doi.org/10.1145/2984511.2984553CrossRefGoogle Scholar
  13. Li W, Wu WJ, Wang HM, et al., 2017. Crowd intelligence in AI 2.0 era. Front Inform Technol Electron Eng, 18(1): 15–43. https://doi.org/10.1631/FITEE.1601859CrossRefGoogle Scholar
  14. Michelucci P, Dickinson JL, 2016. The power of crowds. Science, 351(6268):32–33. https://doi.org/10.1126/science.aad6499CrossRefGoogle Scholar
  15. O’Donovan P, Agarwala A, Hertzmann A, 2014. Learning layouts for single-pagegraphic designs. IEEE Trans Vis Comput Graph, 20(8):1200–1213. https://doi.org/10.1109/TVCG.2014.48CrossRefGoogle Scholar
  16. Pan YH, 2017. Special issue on artificial intelligence 2.0. Front Inform Technol Electron Eng, 18(1):1–2. https://doi.org/10.1631/FITEE.1710000CrossRefGoogle Scholar
  17. Park CH, Son KH, Lee JH, et al., 2013. Crowd vs. crowd: large-scale cooperative design through open team competition. Proc Conf on Computer Supported Cooperative Work, p.1275–1284. https://doi.org/10.1145/2441776.2441920Google Scholar
  18. Pauwels P, de Meyer R, van Campenhout J, 2013. Design thinking support: information systems versus reasoning. Des Iss, 29(2):42–59. https://doi.org/10.1162/DESI_a_00209Google Scholar
  19. Pinel F, Varshney LR, Bhattacharjya D, 2015. A culinary computational creativity system. In: Besold TR, Schorlemmer M, Smaill A (Eds.), Computational Creativity Research: Towards Creative Machines. Springer, Paris, p.327–346. https://doi.org/10.2991/978-94-6239-085-0_16Google Scholar
  20. Prats M, Earl CF, 2006. Exploration through drawings in the conceptual stage of product design. In: Gero JS (Ed.), Design Computing and Cognition. Springer Dordrecht Netherlands, p.83–102. https://doi.org/10.1007/978-1-4020-5131-9_5CrossRefGoogle Scholar
  21. Ren J, Nickerson JV, Mason W, et al., 2014. Increasing the crowd’s capacity to create: how alternative generation affects the diversity, relevance and effectiveness of generated ads. Dec Supp Syst, 65:28–39. https://doi.org/10.1016/j.dss.2014.05.009CrossRefGoogle Scholar
  22. Schneider OS, Seifi H, Kashani S, et al., 2016. HapTurk: crowdsourcing affective ratings of vibrotactile icons. Proc CHI Conf on Human Factors in Computing Systems, p.3248–3260. https://doi.org/10.1145/2858036.2858279Google Scholar
  23. Sun LY, Xiang W, Chai CL, et al., 2014a. Creative segment: a descriptive theory applied to computer-aided sketching. Des Stud, 35(1):54–79. https://doi.org/10.1016/j.destud.2013.10.003CrossRefGoogle Scholar
  24. Sun LY, Xiang W, Chai CL, et al., 2014b. Designers’ perception during sketching: an examination of creative segment theory using eye movements. Des Stud, 35(6): 593–613. https://doi.org/10.1016/j.destud.2014.04.004CrossRefGoogle Scholar
  25. Sun LY, Xiang W, Chen S, et al., 2015. Collaborative sketching in crowdsourcing design: a new method for idea generation. Int J Technol Des Educat, 25(3):409–427. https://doi.org/10.1007/s10798-014-9283-yCrossRefGoogle Scholar
  26. Suzuki R, Salehi N, Lam MS, et al., 2016. Atelier: repurposing expert crowdsourcing tasks as micro-internships. Proc CHI Conf on Human Factors in Computing Systems, p.2645–2656. https://doi.org/10.1145/2858036.2858121Google Scholar
  27. van der Maaten L, Weinberger K, 2012. Stochastic triplet embedding. IEEE Int Workshop on Machine Learning for Signal Processing, p.1–6. https://doi.org/10.1109/MLSP.2012.6349720Google Scholar
  28. Wah C, van Horn G, Branson S, et al., 2014. Similarity comparisons for interactive fine-grained categorization. IEEE Conf on Computer Vision and Pattern Recognition, p.859–866. https://doi.org/10.1109/CVPR.2014.115Google Scholar
  29. Warby SC, Wendt SL, Welinder P, et al., 2014. Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods. Nat Methods, 11(4):385–392. https://doi.org/10.1038/nmeth.2855CrossRefGoogle Scholar
  30. Wiltschnig S, Christensen BT, Ball LJ, 2013. Collaborative problem–solution co-evolution in creative design. Des Stud, 34(5):515–542. https://doi.org/10.1016/j.destud.2013.01.002CrossRefGoogle Scholar
  31. Xiang W, Sun LY, Xia SC, et al., 2017. An evolutionary computation method of crowdsourcing ideation that integrates the balanced-exploration pattern. J Mech Eng, 53(15):73–80 (in Chinese). https://doi.org/10.3901/JME.2017.15.073CrossRefGoogle Scholar
  32. Xu AB, Rao HM, Dow SP, et al., 2015. A classroom study of using crowd feedback in the iterative design process. Proc 18th ACM Conf on Computer Supported Cooperative Work & Social Computing, p.1637–1648. https://doi.org/10.1145/2675133.2675140Google Scholar
  33. Yu LX, Nickerson JV, 2011. Cooks or cobblers?: crowd creativity through combination. Proc SIGCHI Conf on Human Factors in Computing Systems, p.1393–1402. https://doi.org/10.1145/1978942.1979147Google Scholar
  34. Yu LX, Kittur A, Kraut RE, 2014. Distributed analogical idea generation: inventing with crowds. Proc SIGCHI Conf on Human Factors in Computing Systems, p.1245–1254. https://doi.org/10.1145/2556288.2557371Google Scholar
  35. Yu LX, Kraut RE, Kittur A, 2016. Distributed analogical idea generation with multiple constraints. Proc 19th ACM Conf on Computer-Supported Cooperative Work & Social Computing, p.1236–1245. https://doi.org/10.1145/2818048.2835201Google Scholar
  36. Zhao Q, Huang ZH, Harper FM, et al., 2016. Precision crowdsourcing: closing the loop to turn information consumers into information contributors. Proc 19th ACM Conf on Computer-Supported Cooperative Work & Social Computing, p.1615–1625. https://doi.org/10.1145/2818048.2819957Google Scholar
  37. Zhu JY, Krähenbühl P, Shechtman E, et al., 2016. Generative visual manipulation on the natural image manifold. Proc 14th European Conf on Computer Vision, p.597–613. https://doi.org/10.1007/978-3-319-46454-1_36Google Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Lab of CAD & CGZhejiang UniversityHangzhouChina
  2. 2.Modern Industrial Design InstituteZhejiang UniversityHangzhouChina
  3. 3.International User Experience Business Unit, Alibaba GroupHangzhouChina

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