The Journal of Generative Artificial Intelligence (JGAI) serves as a leading platform for disseminating cutting-edge research and developments in the field of generative artificial intelligence. This interdisciplinary journal fosters innovation, collaboration, and knowledge exchange among researchers, practitioners, and enthusiasts working in generative AI, addressing a broad spectrum of topics within this domain. JGAI welcomes contributions from diverse backgrounds, including computer science, engineering, mathematics, neuroscience, and the arts. The journal publishes high-quality, peer-reviewed research articles, reviews, and surveys that significantly advance the field of generative artificial intelligence. JGAI actively addresses ethical concerns associated with generative AI, providing a platform for discussions, guidelines, and responsible AI practices. It serves as 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, including but 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
Archiving
Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.
Plagiarism Policy
Plagiarism in any form constitutes a serious violation of the most basic principles of scholarship and cannot be tolerated. Examples of plagiarism include:
- Word-for-word copying of portions of another's writing without enclosing the copied passage in quotation marks and acknowledging the source in the appropriate scholarly convention.
- The use of a particularly unique term or concept that one has come across in reading without acknowledging the author or source.
- The paraphrasing or abbreviated restatement of someone else's ideas without acknowledging that another person's text has been the basis for the paraphrasing.
- False citation: material should not be attributed to a source from which it has not been obtained.
- False data: data that has been fabricated or altered in a laboratory or experiment; although not literally plagiarism, this is clearly a form of academic fraud.
- Unacknowledged multiple submission of an article for several purposes without prior approval from the parties involved.
- Unacknowledged multiple authors or collaboration: the contributions of each author or collaborator should be made clear.
- Self-plagiarism/double submission: the submission of the same or a very similar article to two or more publications at the same time.