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Paper short abstract:

Technology, like AI, is present in the generation and the distribution of culture. How do artists exploit neural networks for creative purposes and what impact have these algorithms on contemporary practices?

Paper long abstract:

Through practice-based research methods we have been exploring the potentials and limits of current AI technology, more precisely neural networks in the context of image, text, and form. From the proof of concept, deep learning (DL) has evolved to a tool that is applied for art production. Even more, we see a specific genre or nish emerging that specifically concentrates on art made with AI.

In terms of DL development, in relatively short time the generation of high-resolution images to 3D objects have been achieved. What is more exciting, there are models, like CLIP and text2mesh, that do not need the same kind of media input as the output. The first one is the text-to-image model. Such twist contributes towards creativity arousal, which manifests itself in art practice and feeds back to the developers’ pipeline. Yet again, we see how the artists act as catalysts for technology development.

Such novel creative scenarios and processes are enabled by not only available AI models but by hard work behind implementing these new technologies into real-time and autonomous applications with custom-made data sets and algorithms. AI does not create a ‘push the button’ masterpiece but requires quite a deep understanding of the technology behind and a creative mind to come up with high-quality work. Our previous research has shown that the most interesting and valuable results are achieved when DL tools are combined with human input. Thus, AI opens new avenues for inspiration and offers novel tool sets but fails to automate creativity.