This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity.
翻译:本文回顾了人工智能(AI)技术及其在创意产业应用方面的艺术现状。本文提供了人工智能(AI)技术及其在创意产业方面的应用的简要背景,特别是机器学习(ML)算法,其中包括进化神经网络(CNNs)、创新反转网络(GANs)、经常性神经网络(RNNS)和深强化学习(DRL)。我们将创造性应用分为与如何使用人工智能技术有关的五组:一)内容创建,二(信息分析)信息分析,三)内容增强和后期生产工作流程,四)信息提取和增强,以及(v)数据压缩。我们严格审查这些领域中迅速进步的技术的成功和局限性,包括进化神经网络(CNNs)、创新网络(GANs)、神经网络(RNNNS)和深强化学习(DRL)。我们预计,在近期内,机器学习的AI将被广泛采用,作为创新的工具或协作辅助工具。我们发现,在限制较少的领域,机器学习的成功之处,即AI是创新的衍生者,因此,其创造者的潜力是有限的,因此,因此,AI或研发者的潜力是获得最大的。