The aim of new technologies is normally to make a specific process easier, more accurate, faster or cheaper. In some cases they also enable us to perform tasks or create things that were previously impossible. Over recent years, one of the most rapidly advancing scientific techniques for practical purposes has been Artificial Intelligence (AI). AI techniques enable machines to perform tasks that typically require some degree of human-like intelligence. 

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 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. At higher levels, AI has been used to analyze human and social activity by observing their convocation and actions.

Processes associated with the creative sector demand significantly different levels of innovation and skill sets compared to routine behaviors. 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. 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.

AI and creative industry

Applications of AI in the creative industries have dramatically increased in the last five years. Based on analysis of data and Gateway to Research, Davies et al revealed that the growth rate of research publications on AI exceeds 500% in many countries and the most of these publications relate to image-based data. Analysis on company usage from the Crunchbase database indicates that AI is used more in games and for immersive applications, advertising and marketing, than in other creative applications. AI in the current media and creative industries can be divided across three areas: creation, production and consumption. They provide details of AI/ML-based research and development, as well as emerging challenges and trends.

Fear of AI

Due to the wide impact of AI in the creative industry, there is a fear of being replaced by AI. Recently, in a statement, comic book writer David Crownson is fearful that artificial intelligence (AI) is "going to put a lot of people out of work" in his industry.

He stated that, with studios and big-name publishers looking for ways to save money and cut corners, they will no doubt use AI technology. As the growth of AI surged to prominence recently, writers and illustrators of comic books and animations continue to be particularly concerned about its potential impact on them.

With a small but fast-growing number of AI-made comics and animated TV programmes already released commercially, the technology could transform the industry.

The future of AI

In order to produce an original work, such as music or abstract art, it would be beneficial to support increased diversity and context when training AI systems. The quality of the solution in such cases is difficult to define and will inevitably depend on audience preferences and popular contemporary trends. High-dimensional datasets that can represent some of these characteristics will therefore be needed. Furthermore, the loss functions that drive the convergence of the network’s internal weights must reflect perceptions rather than simple mathematical differences. Research into such loss functions that better reflect human perception of performance or quality is therefore an area for further research.

Research into, and development of, AI-based solutions continue apace. AI is attracting major investments from governments and large international organizations alongside venture capital investments in start-up enterprises. ML algorithms will be the primary driver for most AI systems in the future and AI solutions will, in turn, impact an even wider range of sectors. The pace of AI research has been predicated, not just on innovative algorithms (the basics are not too dissimilar to those published in the 1980s), but also on our ability to generate, access and store massive amounts of data, and on advances in graphics processing architectures and parallel hardware to process these massive amounts of data. New computational solutions such as quantum computing, will likely play an increasing role in this respect.