There has been an explosion of interest in ‘creative AI’, but does this mean that artists will be replaced by machines? No, definitely not, says Anne Ploin, Oxford Internet Institute researcher and one of the team behind today’s report on the potential impact of machine learning (ML) on creative work.
The report, ‘AI and the Arts: How Machine Learning is Changing Artistic Work’, was co-authored with OII researchers Professor Rebecca Eynon and Dr Isis Hjorth as well as Professor Michael A. Osborne from Oxford’s Department of Engineering.
Their study took place in 2019, a high point for AI in art. It was also a time of high interest around the role of AI (Artificial Intelligence) in the future of work, and particularly around the idea that automation could transform non-manual professions, with a previous study by Professor Michael A. Osborne and Dr Carl Benedict Frey predicting that some 30% of jobs could, technically, be replaced in an AI revolution by 2030.
Mx Ploin says it was clear from their research that machine learning was becoming a tool for artists – but will not replace artists. She maintains, ‘The main message is that human agency in the creative process is never going away. Parts of the creative process can be automated in interesting ways using AI (generating many versions of an image, for example), but the creative decision-making which results in artworks cannot be replicated by current AI technology.’
She adds, ‘Artistic creativity is about making choices [what material to use, what to draw/paint/create, what message to carry across to an audience] and develops in the context in which an artist works. Art can be a response to a political context, to an artist’s background, to the world we inhabit. This cannot be replicated using machine learning, which is just a data-driven tool. You cannot – for now – transfer life experience into data.’
She adds, ‘AI models can extrapolate in unexpected ways, draw attention to an entirely unrecognised factor in a certain style of painting [from having been trained on hundreds of artworks]. But machine learning models aren’t autonomous.
‘They aren’t going to create new artistic movements on their own – those are PR stories. The real changes that we’re seeing are around the new skills that artists develop to ‘hack’ technical tools, such as machine learning, to make art on their own terms, and around the importance of curation in an increasingly data-driven world.’
The research paper uses a case study of the use of current machine learning techniques in artistic work, and investigates the scope of AI-enhanced creativity and whether human/algorithm synergies may help unlock human creative potential. In doing so, the report breaks down the uncertainty surrounding the application of AI in the creative arts into three key questions.
How does using generative algorithms alter the creative processes and embodied experiences of artists?,How do artists sense and reflect upon the relationship between human and machine creative intelligence? and What is the nature of human/algorithmic creative complementarity?
According to Mx Ploin, ‘We interviewed 14 experts who work in the creative arts, including media and fine artists whose work centred around generative ML techniques. We also talked to curators and researchers in this field. This allowed us to develop fuller understanding of the implications of AI – ranging from automation to complementarity – in a domain at the heart of human experience: creativity.’
They found a range of responses to the use of machine learning and AI. New activities required by using ML models involved both continuity with previous creative processes and rupture from past practices. There were major changes around the generative process, the evolving ways ML outputs were conceptualised, and artists’ embodied experiences of their practice.
And, says the researcher, there were similarities between the use of machine learning and previous periods in art history, such as the code-based and computer arts of the 1960s and 1970s. But the use of ML models was a “step change” from past tools, according to many artists.
But, she maintains, while the machine learning models could help produce ‘surprising variations of existing images’, practitioners felt the artist remained irreplaceable in terms of giving images artistic context and intention––that is, in making artworks.
Ultimately, most agreed that despite the increased affordances of ML technologies, the relationship between artists and their media remained essentially unchanged, as artists ultimately work to address human – rather than technical – questions.
Don’t let it put you off going to art school. We need more artists
The report concludes that human/ML complementarity in the arts is a rich and ongoing process, with contemporary artists continuously exploring and expanding technological capabilities to make artworks. Although ML-based processes raise challenges around skills, a common language, resources, and inclusion, what is clear is that the future of ML arts will belong to those with both technical and artistic skills. There is more to come.
But, says Mx Ploin, ‘Don’t let it put you off going to art school. We need more artists.’