Exploring Explainable AI: Understanding the inner workings of AI models and making their decision

Introduction: Artificial Intelligence (AI) is a multidisciplinary field that aims to create intelligent systems capable of performing tasks that typically require human intelligence. While Machine Learning (ML) is a prominent subset of AI, it is important to recognize that AI can exist and be implemented without relying solely on ML techniques. This article delves into the concept of AI without Machine Learning, exploring alternative approaches and highlighting their potential applications and limitations. Understanding AI without Machine Learning: 1. Rule-Based Systems: One approach to AI without Machine Learning involves using rule-based systems, also known as expert systems. These systems rely on a set of predefined rules and logical reasoning to make decisions or perform tasks. Rules are created by human experts in the specific domain and guide the AI system's behavior. Rule-based AI is particularly useful in areas where the knowledge and decision-making processes can be ...

  Artificial intelligence (AI) has become an integral part of our lives, influencing various aspects of society. However, one of the key challenges with AI is its inherent   lack of transparency. Traditional AI models often make decisions that seem like "black boxes," leaving users and stakeholders puzzled about  the reasoning behind those decisions. This is where Explainable AI (XAI) comes into  play. In this blog post, we will delve into the concept of Explainable AI, exploring its significance, methods, and the benefits it brings to various domains. 1.      The Need for Explainable AI: ·         Discuss the limitations of traditional AI models in terms of transparency and interoperability. ·         Highlighting real-world examples where the lack of ex-plainability has raised concerns and created challenges. ·         Explainin...