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.
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References
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.1995.1038
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/10.1145/2818048.2820023
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.2014.03.044
Cross N, 2006. Designerly Ways of Knowing. Springer, London. https://doi.org/10.1007/1-84628-301-9
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/10.1145/2556288.2557217
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.2015.08.024
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.2016.265
Glickman ME, 1999. Parameter estimation in large dynamic paired comparison experiments. J Roy Stat Soc Ser C, 48(3):377–394. https://doi.org/10.1111/1467-9876.00159
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_12
Ikeda K, Morishima A, Rahman H, et al., 2016. Collaborative crowdsourcing with crowd4U. Proc VLDB Endowm, 9(13):1497–1500. https://doi.org/10.14778/3007263.3007293
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/10.1145/2702123.2702507
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/10.1145/2984511.2984553
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.1601859
Michelucci P, Dickinson JL, 2016. The power of crowds. Science, 351(6268):32–33. https://doi.org/10.1126/science.aad6499
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.2014.48
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.1710000
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/10.1145/2441776.2441920
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_00209
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_16
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_5
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.2014.05.009
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/10.1145/2858036.2858279
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.2013.10.003
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.2014.04.004
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-y
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/10.1145/2858036.2858121
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.2012.6349720
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.2014.115
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.2855
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.2013.01.002
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.2017.15.073
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/10.1145/2675133.2675140
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/10.1145/1978942.1979147
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/10.1145/2556288.2557371
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/10.1145/2818048.2835201
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/10.1145/2818048.2819957
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_36
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Project supported by the National Natural Science Foundation of China (No. 61672451), the National Basic Research Program (973) of China (No. 2015CB352503), and the Alibaba-Zhejiang University Joint Institute of Frontier Technologies
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Xiang, W., Sun, Ly., You, Wt. et al. Crowdsourcing intelligent design. Frontiers Inf Technol Electronic Eng 19, 126–138 (2018). https://doi.org/10.1631/FITEE.1700810
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DOI: https://doi.org/10.1631/FITEE.1700810