![]() ![]() ![]() One of the limitations in the past was the readiness of the technology itself, and another was the belief that AI could attempt to replicate human creative behaviour (Rowe and Partridge 1993). There have been many studies over several decades into the possibility of applying AI in the creative sector. Basically, creatives have a lifetime of experiences to build on, enabling them to think ‘outside of the box’ and ask ‘What if’ questions that cannot readily be addressed by constrained learning systems. While AI accomplishments rely heavily on conformity of data, creativity often exploits the human imagination to drive original ideas which may not follow general rules. Processes associated with the creative sector demand significantly different levels of innovation and skill sets compared to routine behaviours. ![]() This potential, however, does not come without concerns over the wider social impact of AI technologies which must be taken into account when designing and deploying these tools. AI has been recognized by governments across the world to have potential as a major driver of economic growth and social progress (Hall and Pesenti 2018 NSTC 2016). 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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