The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP), introducing unprecedented capabilities that are revolutionizing various domains. This paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications. Our paper contributes to providing a holistic perspective on the technical foundations, practical applications, and emerging challenges within the evolving landscape of Generative AI and LLMs. We believe that understanding the generative capabilities of AI systems and the specific context of LLMs is crucial for researchers, practitioners, and policymakers to collaboratively shape the responsible and ethical integration of these technologies into various domains. Furthermore, we identify and address main research gaps, providing valuable insights to guide future research endeavors within the AI research community.

This paper offers an insightful examination of how currently top-trending AI technologies, i.e., generative artificial intelligence (Generative AI) and large language models (LLMs), are reshaping the field of video technology, including video generation, understanding, and streaming. It highlights the innovative use of these technologies in producing highly realistic videos, a significant leap in bridging the gap between real-world dynamics and digital creation. The study also delves into the advanced capabilities of LLMs in video understanding, demonstrating their effectiveness in extracting meaningful information from visual content, thereby enhancing our interaction with videos. In the realm of video streaming, the paper discusses how LLMs contribute to more efficient and user-centric streaming experiences, adapting content delivery to individual viewer preferences. This comprehensive review navigates through the current achievements, ongoing challenges, and future possibilities of applying Generative AI and LLMs to video-related tasks, underscoring the immense potential these technologies hold for advancing the field of video technology related to multimedia, networking, and AI communities.

Spend less time creating boilerplate and repetitive code patterns, and more time on what matters: building great software. Write a comment describing the logic you want and GitHub Copilot will immediately suggest code to implement the solution. Microsoft's Github Copilot, trained by ingesting massive quantities of computer code, uses the OpenAI Codex to suggest code in real time. By all measures this will improve productivity for software professionals. Then there is Open AI's DALL-E, an AI-driven image generation tool meant to help the creative process for those in visual arts.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Generative AI and LLMs were two of the hottest topics at NeurIPS 2022, which brought the AI and ML community back in-person for the first time since 2019 and has offered "a lot of excitement," said Alice Oh, professor at the Korea Advanced Institute of Science and Technology and the conference's lead program chair. Some of that excitement may have been the sound of thousands of keyboards trying out OpenAI's ChatGPT demo, which was released on Wednesday and has been the talk of Twitter, if not NeurIPS, since then. But the Conference and Workshop on Neural Information Processing Systems, a machine learning and computational neuroscience conference held every December, this year in New Orleans, certainly had plenty of its own buzz going on. According to conference leaders, over 10,000 were in attendance in person, with another 3,000 tuning in online.