Understanding the Power and Applications of Natural Language Processing Author Dhruv Gupta ( ORCID: 0009–0004–7109–5403) Introduction We are living in the era of generative AI. In an era where you can ask AI models almost anything, they will most certainly have an answer to the query. With the increased computational power and the amount of textual data, these models are bound to improve their performance.

Incorporating Knowledge Graphs to explain reasoning processes Author Amanda Kau ( ORCID: 0009–0004–4949–9284) Introduction Large language models (LLMs) like GPT-4 possess remarkable language abilities, allowing them to function as chatbots, translators, and much more.

Exploring the Boundaries of Creativity and Responsibility in the Age of AI-Driven Media Author Vaibhav Khobragade ( ORCID: 0009–0009–8807–5982) Introduction In 2024, the discipline of Generative AI takes a big step forward with the launch of revolutionary models that convert text into dynamic films, altering the landscape of digital content creation.

Prompt Engineering — Part 2 Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering Author Dhruv Gupta (ORCID: 0009–0004–7109–5403 ) Introduction In the previous article we discussed what prompt engineering and some of the techniques used for prompt engineering.

Understanding how RNNs work and its applications Author Wenyi Pi ( ORCID : 0009–0002–2884–2771) Introduction In the ever-evolving landscape of artificial intelligence (AI), bridging the gap between humans and machines has seen remarkable progress. Researchers and enthusiasts alike have tirelessly worked across numerous aspects of this field, bringing about amazing advancements.

Solutions to Enhance LLM Performance in Long Contexts Author · Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction In the era of AI breakthroughs, large language models (LLMs) are not just advancements; they are revolutions, transforming how we interact with technology, from casual conversations with chatbots to the intricate mechanisms behind sophisticated data analysis tools.

Harvesting the Power and Knowledge of Large Language Models Author Amanda Kau (ORCID: 0009–0004–4949–9284 ) Introduction Knowledge Graphs are networks that represent data in a graphical format. The beauty of Knowledge Graphs lies in their representation of concepts, events and entities as nodes, and the relationships between them as edges.

Latest findings in multiple research directions for handling graph prediction and optimization Author · Xuzeng He ( ORCID: 0009–0005–7317–7426) A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge

An Overview of Constructing a Knowledge Graph Author · Qingqin Fang ( ORCID: 0009–0003–5348–4264) 1. Introduction 1.1 What is a Knowledge Graph Knowledge Graphs are structured semantic knowledge bases used to rapidly describe concepts and their relationships in the physical world.

Unlocking the Future of AI: The Transformative Journey of Large Language Models Author · Vaibhav Khobragade ( ORCID: 0009–0009–8807–5982) Introduction Human language development is innate and evolves throughout life. Machines lack this ability to evolve without advanced AI algorithms.

Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering Author Dhruv Gupta (ORCID: 0009–0004–7109–5403 ) Introduction Large Language Models (LLMs) have become the new normal in the field of Natural Language Processing (NLP). With their improved performance and generative power, people around the world are relying on it for