Atharv Gyan

Data structures are fundamental concepts in computer science that help organize and store data efficiently. In the context of AI and machine learning, understanding data structures is crucial because these fields often deal with large volumes of data that need to be processed and analyzed quickly. Here's a starter guide to some key data structures relevant to AI and machine learning: Arrays : Arrays are one of the simplest data structures, consisting of a collection of elements stored in contiguous memory locations. In AI and machine learning, arrays are often used to represent datasets, input features, or output predictions. Lists : Lists are similar to arrays but more flexible because they can dynamically resize. In Python, for example, lists can grow or shrink as needed, making them useful for managing datasets of varying lengths. Stacks : Stacks follow the Last In, First Out (LIFO) principle, where the last element added is the first one to be removed. Stacks are commonly used ...

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...