Hughes Rowan T, Zhu Liming, Bednarz Tomasz
Expanded Perception and Interaction Centre (EPICentre), Faculty of Art and Design, University of New South Wales, Sydney, NSW, Australia.
CSIRO Data61, Australian Technology Park, Eveleigh, NSW, Astralia.
Front Artif Intell. 2021 Apr 28;4:604234. doi: 10.3389/frai.2021.604234. eCollection 2021.
The future of work and workplace is very much in flux. A vast amount has been written about artificial intelligence (AI) and its impact on work, with much of it focused on automation and its impact in terms of potential job losses. This review will address one area where AI is being added to creative and design practitioners' toolbox to enhance their creativity, productivity, and design horizons. A designer's primary purpose is to create, or generate, the most optimal artifact or prototype, given a set of constraints. We have seen AI encroaching into this space with the advent of generative networks and generative adversarial networks (GANs) in particular. This area has become one of the most active research fields in machine learning over the past number of years, and a number of these techniques, particularly those around plausible image generation, have garnered considerable media attention. We will look beyond automatic techniques and solutions and see how GANs are being incorporated into user pipelines for design practitioners. A systematic review of publications indexed on ScienceDirect, SpringerLink, Web of Science, Scopus, IEEExplore, and ACM DigitalLibrary was conducted from 2015 to 2020. Results are reported according to PRISMA statement. From 317 search results, 34 studies (including two snowball sampled) are reviewed, highlighting key trends in this area. The studies' limitations are presented, particularly a lack of user studies and the prevalence of toy-examples or implementations that are unlikely to scale. Areas for future study are also identified.
工作和工作场所的未来正处于不断变化之中。关于人工智能（AI）及其对工作的影响，已经有大量的著述，其中大部分聚焦于自动化及其在潜在失业方面的影响。本综述将探讨人工智能被纳入创意和设计从业者工具箱以提升其创造力、生产力和设计视野的一个领域。设计师的主要目的是在给定一组约束条件下，创建或生成最优的工件或原型。随着生成网络尤其是生成对抗网络（GANs）的出现，我们已经看到人工智能正在侵入这个领域。在过去几年里，这个领域已成为机器学习中最活跃的研究领域之一，其中一些技术，特别是那些围绕可信图像生成的技术，已经获得了相当多的媒体关注。我们将超越自动技术和解决方案，看看生成对抗网络是如何被纳入设计从业者的用户流程中的。对2015年至2020年在ScienceDirect、SpringerLink、Web of Science、Scopus、IEEExplore和ACM DigitalLibrary上索引的出版物进行了系统综述。结果根据PRISMA声明报告。从317个搜索结果中，对34项研究（包括两项滚雪球抽样研究）进行了综述，突出了该领域的关键趋势。介绍了这些研究的局限性，特别是缺乏用户研究以及存在不太可能扩展的玩具示例或实现方式。还确定了未来的研究领域。
