When happy accidents spark creativity: Bringing collaborative speculation to life with generative AI

Intech Pub Date : 2022-06-01 DOI:10.48550/arXiv.2206.00533
Ziv Epstein, Hope Schroeder, Dava Newman
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引用次数: 8

Abstract

Generative AI techniques like those that synthesize images from text (text-to-image models) offer new possibilities for creatively imagining new ideas. We investigate the capabilities of these models to help communities engage in conversations about their collective future. In particular, we design and deploy a facilitated experience where participants collaboratively speculate on utopias they want to see, and then produce AI-generated imagery from those speculations. In a series of in-depth user interviews, we invite participants to reflect on the generated images and refine their visions for the future. We synthesize findings with a bespoke community zine on the experience. We observe that participants often generated ideas for implementing their vision and drew new lateral considerations as a result of viewing the generated images. Critically, we find that the unexpected difference between the participant's imagined output and the generated image is what facilitated new insight for the participant. We hope our experimental model for co-creation, computational creativity, and community reflection inspires the use of generative models to help communities and organizations envision better futures.
当快乐的意外激发创造力时:用生成式人工智能将协作思考带入生活
生成式人工智能技术,比如那些从文本合成图像的技术(文本到图像模型),为创造性地想象新想法提供了新的可能性。我们调查了这些模型的能力,以帮助社区参与关于他们集体未来的对话。特别是,我们设计并部署了一种便利的体验,参与者可以协同推测他们想要看到的乌托邦,然后根据这些推测生成人工智能生成的图像。在一系列深入的用户访谈中,我们邀请参与者反思生成的图像并完善他们对未来的愿景。我们将研究结果与定制的社区杂志结合起来。我们观察到,参与者经常产生实现他们愿景的想法,并在观看生成的图像后得出新的横向考虑。至关重要的是,我们发现参与者想象的输出和生成的图像之间的意想不到的差异促进了参与者的新见解。我们希望我们关于共同创造、计算创造力和社区反思的实验模型能激发生成模型的使用,帮助社区和组织展望更美好的未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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