Reactive AI vs Proactive (Agentic) AI: Architectural Differences that Matter
Introduction: Artificial intelligence has been developing quickly, but still a lot of systems function reactively and can only respond to a situation once it has occurred. This reactive AI usage has a major drawback, as it only takes the present into account and nothing else. Moreover, it is not able to predict the future and…
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Priyadharshini
Unlocking the Power of Sentence Transformers and Text Embeddings in LLMs
Introduction: Large Language Models (LLMs) have dramatically transformed the field of natural language processing (NLP). At the heart of their effectiveness lies one powerful concept: text embeddings. These text embeddings—dense vector representations of language—enable machines to understand, compare, and generate human-like text with remarkable precision. One of the most reliable tools for generating high-quality text…
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Priyadharshini
Introduction to Data Science and Its Role in AI
Data Science is a dynamic, multidisciplinary field that plays a foundational role in today’s data-driven world. By combining statistical analysis, machine learning, and domain expertise, data science enables organizations to extract actionable insights from both structured and unstructured data. At the heart of Artificial Intelligence (AI) lies Data Science, driving the development of intelligent systems…
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