Generative Artificial Intelligence: Trends, Prospects, and Implications for the Creative Industry and Synthetic Data

Generative AI Generative AI has gained traction in both the creative industry and synthetic data generation. Trends and Directions in Generative Artificial Intelligence Research Aims and Research Questions This study explores the trends, possibilities and consequences of synthetic AI using both quantitative and qualitative methods. Methods We will collect information by reviewing literature, surveying 150 professionals and academic researchers, and conducting 20 semi-structured interviews with experts and industry leaders. Analysis of the reviewed literature reveals the large and growing popularity of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) as the two most prominent methods, but also the hike in popularity for transformers. Most respondents in the survey have used generative AI. The study found that 85% of respondents have experience with such AI, and eight-out-10 see it enabling increased creativity and innovation (90%), efficiency and scalability (80%) and the algorithmic provision of data as training sets for model training (75%). However, generative AI also faces challenges such as output quality (60%), complexity and computation (55%), and ethical and legal implications (70%). Interviews with experts added a deeper perspective, emphasizing the importance of transparency, accountability, and clear regulation. Quantitative and qualitative data analysis shows that generative AI has significant potential but needs improvement in technical and ethical aspects. The study's recommendations include improving output quality, computational efficiency, developing regulations, and committing to transparency