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contributor authorChen, Liuqing
contributor authorSun, Lingyun
contributor authorHan, Ji
date accessioned2023-11-29T18:58:02Z
date available2023-11-29T18:58:02Z
date copyright4/19/2023 12:00:00 AM
date issued4/19/2023 12:00:00 AM
date issued2023-04-19
identifier issn1530-9827
identifier otherjcise_23_5_051012.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4294495
description abstractCreativity is a fundamental feature of human intelligence. However, achieving creativity is often considered a challenging task, particularly in design. In recent years, using computational machines to support people in creative activities in design, such as idea generation and evaluation, has become a popular research topic. Although there exist many creativity support tools, few of them could produce creative solutions in a direct manner, but produce stimuli instead. DALL·E is currently the most advanced computational model that could generate creative ideas in pictorial formats based on textual descriptions. This study conducts a Turing test, a computational test, and an expert test to evaluate DALL·E’s capability in achieving combinational creativity comparing with human designers. The results reveal that DALL·E could achieve combinational creativity at a similar level to novice designers and indicate the differences between computer and human creativity.
publisherThe American Society of Mechanical Engineers (ASME)
titleA Comparison Study of Human and Machine-Generated Creativity
typeJournal Paper
journal volume23
journal issue5
journal titleJournal of Computing and Information Science in Engineering
identifier doi10.1115/1.4062232
journal fristpage51012-1
journal lastpage51012-10
page10
treeJournal of Computing and Information Science in Engineering:;2023:;volume( 023 ):;issue: 005
contenttypeFulltext


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