Journal of Generative Artificial Intelligence (JGAI) aims to serve as a leading platform for the dissemination of cutting-edge research and developments in the field of generative artificial intelligence. This interdisciplinary journal aims to foster innovation, collaboration, and knowledge exchange among researchers, practitioners, and enthusiasts working on generative AI, addressing a broad spectrum of topics within this domain. JGAI solicits contributions from researchers, practitioners, and experts from diverse backgrounds, including computer science, engineering, mathematics, neuroscience, and the arts. Journal publishes high-quality, peer-reviewed research articles, reviews, and surveys that contribute significantly to the advancement of generative artificial intelligence. JGAI actively address ethical concerns associated with generative AI, providing a platform for discussions, guidelines, and responsible AI practices while providing a valuable resource for the global AI community, disseminating the latest developments, methodologies, and best practices in generative AI.

Scope: The scope of the Journal of Generative Artificial Intelligence encompasses a wide range of research areas and applications related to generative artificial intelligence. This includes, but is not limited to:

Generative Models: Exploring and advancing the theory and application of generative models, such as Generative Adversarial Networks, Variational Autoencoders, and other emerging techniques.

Natural Language Processing: Investigating generative AI's role in natural language understanding, generation, and translation, as well as its impact on conversational agents and chatbots.

Computer Vision: Examining generative AI's applications in image and video synthesis, style transfer, image-to-image translation, and more.

Creative Arts: Exploring how generative AI can be leveraged for artistic creation, music composition, visual arts, and generative storytelling.

Data Augmentation: Investigating techniques for data augmentation using generative models to enhance the performance of machine learning algorithms.

Healthcare and Medicine: Addressing the role of generative AI in medical image generation, drug discovery, disease prediction, and personalized medicine.

Robotics: Exploring how generative AI contributes to the development of intelligent robots, including motion planning, object manipulation, and task execution.

Ethics and Bias: Analyzing the ethical implications, fairness, and potential biases associated with generative AI, and proposing mitigation strategies.

Applications: Showcasing practical applications of generative AI in various industries, including finance, gaming, marketing, and more.