Keywords : Artificial Intelligence, Statistics, Knowledge, Tradition, Analytics, Research, Technology

Abstract : Artificial Intelligence (AI) has become one of the most influential technologies of the modern world, shaping decision-making, automation, and data-driven research across disciplines. However, the foundations of AI rely heavily on statistical reasoning, inference, and pattern recognition concepts that have deep roots in the Indian Statistical Knowledge System (ISKS). This paper explores Artificial Intelligence through the lens of the Indian Statistical Knowledge System, highlighting the relevance of indigenous Indian statistical ideas in contemporary AI development. The study examines key statistical principles found in ancient Indian knowledge traditions, such as systematic observation, classification, logical inference, estimation, and uncertainty handling. These principles closely align with modern AI techniques, including machine learning, probability-based models, and predictive analytics. By drawing conceptual parallels between traditional Indian statistical thinking and modern AI frameworks, the paper demonstrates how ancient knowledge can contribute to ethical, transparent, and explainable AI systems. The research adopts a conceptual approach, using secondary sources from classical texts, scholarly literature, and contemporary AI studies. The paper concludes that revisiting and integrating the Indian Statistical Knowledge System can provide valuable insights for building responsible and culturally grounded AI models. Such integration not only strengthens the theoretical foundation of AI but also promotes interdisciplinary research, aligning traditional wisdom with modern technological innovation.

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