Using a machine learning algorithm to create a computational artwork: variable

Official URL: http://dx.doi.org/10.30958/ajha.6-3-1

As computational systems have become an integral part of our daily lives, we often see that contemporary art also has adapted itself with the newborn technological changes in diversified dimensions. Machine Learning, which has recently become a remarkable development in science, has also begun to manifest itself in various artistic works. As accordingly, the artwork that has been created by the author of this article named "Variable" stands as an interactive work of art that embraces machine learning algorithms within its compositional structure. The artwork was extensively influenced by the sophisticated discourse of German philosopher Heideggerʼs book "Being and Time." Consequently, Being and Time text has been taught to a machine learning system, and thus the system has been able to automatically generate new original contents when the viewer interacts with the touch of a button. The generative system performs its Machine Learning Markov Chain operations with the implementation of a Python programming language-based library named Markovify. The work constantly redefines its own artistic title and statement with the use of a machine learning framework. In this article, the contribution of machine learning to the production of artworks is being examined while focusing on various implementations.