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). In Instagram for instance, a graph might be used to represent users as nodes and their relationships(who follows whom) as edges.

Introduction Both knowledge graphs (KGs) and pre-trained language models (PLMs) have gained popularity due to their ability to comprehend world knowledge and their broad applicability. KGs are instrumental in applications like search engines, evident from Google’s Knowledge Graph. On the other hand, popular PLMs like BERT and GPT excel in a variety of natural language tasks.

Introduction Knowledge graphs (KGs) have proven to be an effective method of data representation that is increasingly popular. In KGs, entities and concepts are represented as nodes, while the relationships between nodes are depicted as edges. Thus, KGs can effectively capture the semantic meanings of nodes.

Definition A research collaboration network is a group of researchers, and practitioners, or both, working together on joint research activities.

Understanding Sequential Data Modelling with Keras for Time Series Prediction Author Wenyi Pi ( ORCID : 0009–0002–2884–2771) Introduction Recurrent Neural Networks (RNNs) are a special type of neural networks that are suitable for learning representations of sequential data like text in Natural Language Processing (NLP). We will walk through a complete example of using RNNs for time series prediction, covering

Introduction Recurrent Neural Networks (RNNs) are a special type of neural networks that are suitable for learning representations of sequential data like text in Natural Language Processing (NLP). We will walk through a complete example of using RNNs for time series prediction, covering data preprocessing, model building, training, evaluation, and visualisation.

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.

Introduction Large language models (LLMs) like GPT-4 possess remarkable language abilities, allowing them to function as chatbots, translators, and much more. More recent multimodal models, like Google’s Gemini, extend the capabilities of LLMs to include vision, allowing us to generate or analyse images. However, despite their increasing capabilities, LLMs are still not fully trusted by the public.

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 various kinds of problems.

Introduction The fusion of Vision-Language Models ( VLMs ), Generative Models, and Knowledge Graphs ( KGs ) is reshaping how artificial intelligence (AI) understands and interacts with the world.

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.