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.

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. Notably, EMU VIDEO and Genie , each pioneering a separate aspect of video production.

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. These relationships determine the context of the node, and consequently, allow for better understanding of a word’s semantics and distinguishing between its multiple possible meanings.

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. Among these domains, machine learning stands out as a pivotal area of exploration and innovation.

Introduction 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 graphs (e.g. Wikipedia). Nowadays, there is a rising trend in the research of using Machine Learning techniques on graphs to solve

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.

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

Boosting Performance for Knowledge Graphs with Neo4j APOC Library Author Wenyi Pi (ORCID: 0009–0002–2884–2771) Introduction A knowledge graph (graph database) captures information about main entities in a domain and the relationships between them. It was an augmented feature store for connected data which gave access to compute, access and operationalise structure features.

Introduction A knowledge graph (graph database) captures information about main entities in a domain and the relationships between them. It was an augmented feature store for connected data which gave access to compute, access and operationalise structure features.

Introduction Human language development is innate and evolves throughout life. Machines lack this ability to evolve without advanced artificial intelligence (AI) algorithms. Since the Turing Test was proposed in the 1950s, efforts to master machine understanding of language have led from statistical to neural language models.