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...

Reimagining Natural Language Processing: Tracing the Journey from Rigid Rule‐Based Systems to Dynamic AI‐Driven Innovations Introduction Natural Language Processing (NLP) stands at the fascinating crossroads of computer science, linguistics, and artificial intelligence a discipline that has undergone a remarkable evolution over the past several decades. Initially conceived through the lens of rule‐based systems, NLP was once governed by a rigid structure of hand‐crafted rules and predefined patterns. These early systems, while pioneering in their own right, were limited by their inflexibility and inability to truly capture the nuances of human language. Today, however, NLP has blossomed into a vibrant field powered by dynamic, AI-driven innovations. Modern techniques based on machine learning and deep learning have redefined what computers can understand, generate, and accomplish with language. This transformation is not merely a technical upgrade but represents a profound shift in...