The Development and Impact of AI-Generated Content in Contemporary Painting

Abstract

The paper examines the evolution and impact of generative artificial intelligence (AI) technology in the field of painting, tracing its ascent from the early 21st century. It highlights pivotal developments in AI-generated content (AIGC), starting with Harold Cohen's "AARON" program in the early 1990s, progressing to Yang Zhiping's introduction of generative adversarial networks (GANs) in 2000, and culminating in Robbie Barrat's innovative applications in 2018. These milestones underscore the swift progression and vast potential of AIGC for both technical innovation and artistic expression. This analysis delves into the current landscape of AIGC in painting, identifying key technological advancements such as deep learning, GANs, style transfer, adaptive algorithms, and augmented reality. These technologies have elevated artistic creation to unprecedented levels. However, AIGC also confronts significant challenges, including issues related to copyright and intellectual property, artistic ethics and originality, technical constraints, market acceptance, and socio-ethical and legal concerns. To address these challenges, the paper proposes several solutions: clarifying legal standards, establishing authorization mechanisms, fostering hybrid creative models, enhancing the diversity of training data, promoting public education and outreach, and developing professional training and ethical guidelines.