Recently, generative art has been one of the fields where AI, especially deep learning has caught the public eye. Algorithms and online tools such as Dall-E are able to produce astounding results based on large artistic datasets. One class of algorithms that has been at the root of this success is the Generative Adversarial Network (GAN), frequently used in online art-generating tools because of their ability to produce realistic artefacts.
but, is this “””real””” art? is this “””real””” creativity?
To address this, Fay investigated current theories on art and art education and found that these imply that true human creativity can be split into three types: 1) combinational, 2) explorative and 3) transformative creativity but that it also requires real-world experiences and interactions with people and the environment. Therefore, Fay in her thesis proposes to combine the GAN with an Internet of Things (IoT) setup to make it behave more creative.
She then designed a system that extends the original GAN with an interactive IoT system (implemented in an Arduino-based prototype) to simulate a more creative process. The prototype of the design showed a successful implementation of creative behaviour that can react to the environment and gradually change the direction of the generated images.
The generated art was evaluated based on their creativity by doing task-based interviews with domain experts. The results show that the the level to which the generated images are considered to be creative depends heavily on the participant’s view of creativity.