FROM KEYWORDS TO NEURAL UNDERSTANDING: THE EVOLUTION OF AI-POWERED SEARCH SYSTEMS
Keywords:
Semantic Search, Natural Language Processing, Large Language Models, Information Retrieval, Neural RankingAbstract
The integration of artificial intelligence has fundamentally transformed the landscape of information retrieval systems, marking a paradigm shift from traditional keyword-based approaches to sophisticated semantic understanding. This comprehensive article review examines the evolution of search technologies, focusing on the emergence of neural networks and large language models in modern search architectures. The article analyzes the technical foundations of AI-driven search, including vector embeddings, contextual understanding, and intent recognition systems, while addressing critical challenges in scalability, fairness, and transparency. The article explores implementations across diverse domains, including e-commerce, healthcare, and enterprise solutions, highlighting both domain-specific considerations and universal principles. Furthermore, The article investigates emerging trends in real-time personalization, multi-modal search capabilities, and the integration of search systems with augmented reality and virtual reality technologies. The article provides insights into the future trajectory of search technologies and their implications for human-information interaction, while identifying key research opportunities and potential societal impacts.
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Copyright (c) 2025 Durga Rao Manchikanti (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
