Естетичні аспекти взаємодії штучного інтелекту та музичного мистецтва

This study examines the principal artificial intelligence (AI) algorithms applied in musical creativity. It investigates the aesthetic dimensions of AI’s interaction with musical art. Fundumental AI algorithms such as transformers, latent diffusion models, generative adversarial networks (GANs), autoencoders, and reinforcement learning neural networks employed in music creation are analyzed alongside their technical capabilities. The interplay between human creativity and algorithmic generation is traced, emphasizing the composer’s role as co-author or curator in collaboration with AI. The cultural and philosophical implications of AI implementation are described, including issues of authenticity, authorship, and audience perception of music. Challenges related to the originality and emotional depth of algorithmically generated compositions are highlighted, as well as the prospects for AI development as a tool for innovation in musical creativity. The impact of AI on aesthetic aspects of music—such as structure, harmony, rhythm, and stylistics—is explored. Challenges of ensuring originality and emotional profundity in AI-generated works are outlined, along with future perspectives on AI as an instrument for fostering innovation in music through algorithmic integration and collaboration with human artists. It is demonstrated that the interaction between human creativity and algorithmic music generation is characterized by synergy, with AI serving as a powerful tool that expands the composer’s creative potential without replacing them. Projections suggest that AI will evolve into a full-fledged co-author capable of genuine improvisation, with the integration of algorithms such as transformers and latent diffusion enhancing precision and creative freedom in music generation. The prospects for further research on the specified issue have been projected in the direction of creating music with greater emotional depth and cultural contextuality, particularly through the integration of transformers, latent diffusion, and reinforcement learning neural networks.