A Comparison Study of Human and Machine-Generated Creativity

IF 2.6 3区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computing and Information Science in Engineering Pub Date : 2023-03-29 DOI:10.1115/1.4062232

Liuqing Chen, Lingyun Sun, Ji Han

引用次数: 1

Abstract

Creativity 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.

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人类和机器产生的创造力的比较研究

创造力是人类智力的一个基本特征。然而，实现创造力通常被认为是一项具有挑战性的任务，特别是在设计中。近年来，利用计算机器来支持人们在设计中的创造性活动，如想法的产生和评估，已经成为一个热门的研究课题。虽然存在许多支持创造力的工具，但很少有工具能够直接产生创造性的解决方案，而是产生刺激。DALL·E是目前最先进的计算模型，它可以基于文本描述生成图形格式的创意。本研究通过图灵测试、计算测试和专家测试来评估DALL·E与人类设计师相比实现组合创造力的能力。结果表明，DALL·E可以达到与新手设计师相近的组合创造力水平，并表明了计算机与人类创造力的差异。

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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来源期刊

Journal of Computing and Information Science in Engineering 工程技术-工程：制造

CiteScore

6.30

自引率

12.90%

发文量

100

审稿时长

6 months

期刊介绍： The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications. Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping