It has also been used to understand socially relevant problems such as homelessness and to predict natural events. At higher levels, AI has been used to analyze human and social activity by observing their convocation and actions. AI can, for example, help smart devices or computers to understand text and read it out loud, hear voices and respond, view images and recognize objects in them, and even predict what may happen next after a series of events. With recent developments in high-performance computing and increased data storage capacities, AI technologies have been empowered and are increasingly being adopted across numerous applications, ranging from simple daily tasks, intelligent assistants and finance to highly specific command, control operations and national security. AI techniques enable machines to perform tasks that typically require some degree of human-like intelligence. Over recent years, one of the most rapidly advancing scientific techniques for practical purposes has been Artificial Intelligence (AI). In some cases they also enable us to perform tasks or create things that were previously impossible. The aim of new technologies is normally to make a specific process easier, more accurate, faster or cheaper. 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. 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. In contrast, we observe that the successes of ML in domains with fewer constraints, where AI is the ‘creator’, remain modest. We foresee that, in the near future, ML-based AI will be adopted widely as a tool or collaborative assistant for creativity. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We categorize 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.

A brief background of AI, and specifically machine learning (ML) algorithms, is provided including convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs) and deep Reinforcement Learning (DRL). This paper reviews the current state of the art in artificial intelligence (AI) technologies and applications in the context of the creative industries.

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