Atharv Gyan

Abstract Neurosymbolic AI represents a transformative approach in artificial intelligence research by uniting the powerful pattern recognition capabilities of deep learning with the clarity and logical structure of symbolic reasoning. This fusion promises to overcome the “black-box” limitations of conventional neural networks while enhancing interpretability, transparency, and decision‐making. In this article, we introduce neurosymbolic AI, trace its historical evolution, detail its key components, and explore its applications and challenges. By offering a clear, accessible explanation of this emerging field, we aim to provide readers with insights into how neurosymbolic systems can drive the next generation of explainable AI.  1. Introduction The landscape of artificial intelligence (AI) has evolved rapidly over the past few decades. Early approaches in AI predominantly relied on symbolic methods systems built on logic, rules, and human‑defined knowledge representations. In contr...

Introduction to Edge Computing and IoT Edge computing refers to the practice of processing data near the edge of the network, where the data is generated, rather than relying solely on centralized data-processing warehouses or cloud-based systems. This approach contrasts with traditional cloud computing, where data is transmitted to centralized data centers for processing and analysis. The Internet of Things (IoT) consists of a network of interconnected devices, sensors, and systems that communicate and exchange data to perform various tasks and provide valuable insights. IoT devices are deployed across various sectors, including smart homes, industrial automation, healthcare, transportation, and more. Impact of Edge Computing on IoT Improved Response Times One of the primary benefits of edge computing in IoT is the significant improvement in response times. By processing data closer to the source, edge computing reduces latency, which is the time it takes for data to travel from the...