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Recent Research in Artificial Intelligence and the Future of AI

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Artificial intelligence (AI) research is progressing rapidly, with new advancements revolutionizing industries and everyday life. Some of the most recent and promising research areas in AI include: 1. Generative AI and Large Language Models Recent breakthroughs in generative AI, such as DeepSeek, OpenAI’s ChatGPT and Google’s Gemini, have demonstrated remarkable capabilities in natural language understanding […]

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What Is Artificial Intelligence(AI)?

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Discover the latest advancements in artificial intelligence, applications and ethical challenges of AI, read an ultimate guide for both beginners’ and professional.  Artificial Intelligence (AI) has become an integral part of modern life, influencing everything from healthcare to entertainment. But what exactly is AI, and how does it work? In this article, we will explore

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DeepSeek: A Powerful Open Source Model

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DeepSeek: The Chinese Powerhouse Challenging AI Giants with its Open-Source Reasoning Model.   The artificial intelligence landscape has been a battleground for innovation, where tech titans like OpenAI, Google DeepMind, and Anthropic dominate the scene. However, a new challenger from China, DeepSeek, is turning heads across the AI community. With the release of its revolutionary

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Revolutionizing Customer Experience with AI Agents: A New Era of Engagement

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Discover how AI agents transform customer experience with 24/7 support, personalized interactions, and real-world examples from brands like Netflix and Sephora. Learn how to balance automation with empathy for exceptional CX.     Customer experience (CX) has always been the cornerstone of successful businesses. In today’s hyper-competitive, digitally driven world, expectations have skyrocketed. Enter AI

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Trade offs between Sparse and Dense Retrievers

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  When implementing retrieval mechanisms in AI systems like Retrieval-Augmented Generation (RAG), choosing between sparse retrievers (e.g., BM25) and dense retrievers (e.g., Dense Passage Retrieval, DPR) is crucial. Both have their strengths and weaknesses, and the decision often depends on the specific use case, resources, and goals. Here’s a detailed comparison to help you understand

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How to Integrate a RAG System with a Live Database

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Integrating a Retrieval-Augmented Generation (RAG) system with a live database allows the model to generate contextually relevant responses using the most up-to-date and accurate information. This setup is especially valuable for dynamic applications like customer support, real-time analytics, or news summarization. Below is a step-by-step guide to achieving this integration.   1. Understand the Workflow

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How Does RAG Differ from Traditional Question-Answering Systems?

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Retrieval-Augmented Generation (RAG) represents a significant evolution in how question-answering (QA) systems are designed, combining the strengths of retrieval-based systems and generative models. Here’s a breakdown of the key differences between RAG and traditional QA systems:   1. Static Knowledge vs. Dynamic Knowledge Traditional QA Systems: Often rely on pre-trained models that use static knowledge.

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What is DPR, and how is it used in RAG?

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  DPR stands for Dense Passage Retrieval, a cutting-edge method for retrieving relevant documents based on their semantic content. It is a deep learning-based retrieval model introduced by Facebook AI and designed to improve the accuracy and efficiency of information retrieval tasks.   How Does DPR Work? DPR employs a dual-encoder architecture with two main

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