Big Data Concept

  Introduction Artificial Intelligence (AI) has transformed how we process and analyze vast datasets, with big data applications driving innovation across industries. AI systems are broadly categorized into two types: Artificial General Intelligence (AGI) and Narrow AI . While Narrow AI powers many of today’s big data tools, AGI remains a theoretical goal with profound implications. This chapter compares AGI and Narrow AI, focusing on their capabilities, limitations, and potential impacts on big data applications. Defining AGI and Narrow AI Narrow AI Narrow AI, also known as weak AI, is designed to perform specific tasks within a defined domain. It excels in areas like image recognition, natural language processing, or predictive analytics but lacks general cognitive abilities. Examples include: Recommendation systems (e.g., Netflix or Amazon algorithms). Chatbots (e.g., customer service bots). Fraud detection systems in banking. Narrow AI operates within constrained parame...

  Introduction Artificial Intelligence (AI) has evolved dramatically over the past decade, reshaping how industries handle data. Yet, not all AI systems are created equal. Most current applications rely on Narrow AI , which excels at specific tasks like image recognition, fraud detection, or recommendation systems. On the other hand, Artificial General Intelligence (AGI) —still under development—aims to replicate human-like intelligence, capable of learning, reasoning, and adapting across multiple domains. In the context of Big Data , the distinction between AGI and Narrow AI is crucial. While Narrow AI has powered much of today’s big data revolution, AGI holds the promise of transforming the landscape entirely. Narrow AI in Big Data Narrow AI systems are highly specialized, relying on predefined algorithms and training datasets. They thrive in structured environments where goals are clear. Current Contributions: Pattern Detection – Machine learning models can identif...