![]() A systematic review of publications indexed on ScienceDirect, SpringerLink, Web of Science, Scopus, IEEExplore, and ACM DigitalLibrary was conducted from 2015 to 2020. ![]() We will look beyond automatic techniques and solutions and see how GANs are being incorporated into user pipelines for design practitioners. 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 have seen AI encroaching into this space with the advent of generative networks and generative adversarial networks (GANs) in particular. ![]() A designer’s primary purpose is to create, or generate, the most optimal artifact or prototype, given a set of constraints. 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 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. The future of work and workplace is very much in flux.
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