Distributed Generative Adversarial Networks (D-GANs) are a recent addition to generative model literature, but they come with significant limitations and caveats. This paper categorizes and analyzes the existing D-GAN models and introduces a novel four-part categorization framework based on their mathematical properties. We provided generalized mathematical formulations for each D-GAN category, addressing convergence issues and potential improvements, which are essential for developing more robust and reliable D-GANs. Additionally, we conducted detailed mathematical analyses of existing D-GANs to identify their shortcomings in terms of expanse, convergence and output quality. This rigorous analysis is pivotal for advancing the field, as it highlights critical areas needing improvement and sets the stage for future innovations that will enhance the performance and reliability of distributed generative models. This work establishes a foundation for future research, and standardizes the evaluation of D-GAN models. Additionally, it guides the research community towards addressing unresolved issues and exploring new avenues, thereby enhancing the overall effectiveness and applicability of the distributed generative models in AI/ML.

Generic Large Language Models (GLLMs) have grown popularity in many professions with limited or no technical knowledge. Larger and larger GLLMs are continuously being released with enhanced capabilities, promoting the abilities of these Generative AI at the grassroots level in businesses. These tools excel in text, image, and video generation (assembling, summarizing, translating) when proper queries and prompts are given; moreover, various augmentation of up-to-date knowledge bases, making these more efficient in providing current events. Practitioners and marketers showcase the benefits of GLLMs by demonstrating various use cases. However, the reliability of GLLMs' responses is questionable in certain scenarios, particularly due to issues like hallucinations, factual inaccuracies, and inappropriate or unrelated responses. Also there remain many open questions on data collection, privacy and ethical issues that need to be addressed. This study emphasizes the reliability and security aspects of GLLMs while recognizing significant benefits in a wide variety of applications. We also provide some insides of social impacts and future directions of AI/ML applications.