Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity - Wai Yie Leong (Engineering Proceedings)
Machine learning (ML) has played an increasingly pivotal role in shaping and evolving artistic expression, leading to new forms of algorithmic creativity. In this study, we explore how ML models, particularly deep learning algorithms such as generative adversarial networks (GANs), have contributed to evolving art styles by learning from vast datasets of historical and contemporary artworks. These algorithms mimic artistic techniques, generate new styles, and even create novel art forms that blend or deviate from traditional artistic boundaries. The challenges of algorithmic creativity, such as concerns about authorship, originality, and the potential loss of human touch in art are also highlighted. The role of machine learning in art raises important philosophical and ethical questions about the nature of creativity and the evolving relationship between human artists and machines. Machine learning has become a powerful tool in expanding the possibilities of artistic expression. While AI-generated art challenges traditional notions of creativity, it also opens up new horizons for collaboration and innovation in art, potentially leading to entirely new art styles in the digital age.