The generation of videos from textual input poses a significant computational challenge within computer science. Nonetheless, recent advancements in text-to-video artificial intelligence (AI) technologies have showcased notable progress within this domain. Foreseen advancements in realistic video generation and data-driven physics simulations are poised to further propel the field forward. The emergence of text-to-video AI holds transformative potential across a plethora of creative domains, including filmmaking, advertising, graphic design, and game development, as well as within sectors such as social media, influencer marketing, and educational technology. This research study seeks to comprehensively review generative AI methodologies in text-to-video synthesis, with an emphasis on large language models and AI architectures. Multiple methods such as literature review,  technical evaluation and solution proposal were applied for this purpose. Prominent models such as OpenAI Sora, Stable Diffusion, and Lumiere are evaluated for their efficacy and architectural intricacies. However, the pursuit of Artificial General Intelligence (AGI) is accompanied by a myriad of challenges. These encompass the imperative to safeguard human rights, prevent potential misuse, and protect intellectual property rights. Ensuring the accuracy and integrity of the generated content is paramount. The computationally intensive nature of transformer models results in substantial electricity and water consumption, necessitating the formulation of strategies to mitigate environmental and computational costs to ensure long-term sustainability. This research endeavors to explore potential avenues for addressing these challenges and proposes solutions to advance environmental and computational efficiency within the context of text-to-video AI.

Challenges exist in learning and understanding religions, such as the complexity and depth of religious doctrines and teachings. Chatbots as question-answering systems can help in solving these challenges. LLM chatbots use NLP techniques to establish connections between topics and accurately respond to complex questions. These capabilities make it perfect for enlightenment on religion as a question-answering chatbot. However, LLMs also tend to generate false information, known as hallucination. Also, the chatbots' responses can include content that insults personal religious beliefs, interfaith conflicts, and controversial or sensitive topics. It must avoid such cases without promoting hate speech or offending certain groups of people or their beliefs. This study uses a vector database-based Retrieval Augmented Generation (RAG) approach to enhance the accuracy and transparency of LLMs. Our question-answering system is called “MufassirQAS''. We created a database consisting of several open-access books that include Turkish context. These books contain Turkish translations and interpretations of Islam. This database is utilized to answer religion-related questions and ensure our answers are trustworthy. The relevant part of the dataset, which LLM also uses, is presented along with the answer. We have put careful effort into creating system prompts that give instructions to prevent harmful, offensive, or disrespectful responses to respect people's values and provide reliable results. The system answers and shares additional information, such as the page number from the respective book and the articles referenced for obtaining the information. MufassirQAS and ChatGPT are also tested with sensitive questions. We got better performance with our system. Study and enhancements are still in progress. Results and future works are given.  

As the global industrial complex gears toward fulfilling the tenets of Industry 4.0 and beyond, technologies such as distributed ledger technologies, digital twins, and artificial intelligence become pivotal enablers. In the last decade, metaverse as a concept and technology found its place among crucial enablers for technology and digital advancement across several engineering domains. Metaverse has the potential to combine the elements from distributed computing platforms, the digital evolution of physical systems, and advanced learning systems to unearth a fully digitized world of comparative properties of the real world. We should ensure the privacy, integrity, and confidentiality of personal data. These requirements will lead to proper identity management in the metaverse. Given the complex nature of the metaverse, traditional centralized systems may not offer a viable identity management solution. Therefore, this study explores a decentralized identity management system called the Self-sovereign Identity (SSI) as a logical alternative to traditional centralized identity management systems. The proposed holistic framework aims to ignite new ideas and discussions related to the combined deployment of DLT, SSI, and metaverse to inspire new implementation areas within the Industry 4.0 environment. The paper also discusses various opportunities, enablers, technical \& privacy aspects, legislation requirements, and other barriers related to SSI implementation.