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UNIFIED INTELLIGENCE FABRIC: AI-DRIVEN DATA ENGINEERING AND DEEP LEARNING FOR CROSS-DOMAIN AUTOMATION AND REAL-TIME GOVERNANCE.

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Title: UNIFIED INTELLIGENCE FABRIC: AI-DRIVEN DATA ENGINEERING AND DEEP LEARNING FOR CROSS-DOMAIN AUTOMATION AND REAL-TIME GOVERNANCE.
Authors: Nagabhyru, Kushvanth Chowdary, Garapati, Ravi Shankar, Aitha, Avinash Reddy
Source: Lex Localis: Journal of Local Self-Government; 2025 Supplement, Vol. 23 Issue S6, p3512-3532, 21p
Subject Terms: DEEP learning, AUTOMATION, DATA integration, TRANSFER of training, ARTIFICIAL intelligence, REINFORCEMENT learning
Abstract: Advances in deep learning (DL) have enormous potential to automate processes across diverse domains. Yet the deployed solutions often lack sufficient quality, traceability, and real-time responsiveness because of manual tools and static, inflexible rule systems that govern them. Greater trustworthiness, reliability, and adaptability would enable AI to take a more autonomous role as an enabler of trustworthy intelligent agents. A unified intelligence fabric integrates AI-driven data engineering with DL to fulfil these requirements and thus facilitate real-time automation with real-time governance. Unlike traditional intelligent cross-domain systems, which integrate a federation of hand-crafted ML cycles with explicit rules for decisioning and actioning, this approach enables a multi-domain intelligent system coproduced by reinforcement learning, real-time policy learning, and real-time pattern learning. The resulting model architectures can share internal representations across domains through transfer learning and continual learning. An AI-driven data-engineering pipeline creates the data required by training and inference phases, manages quality and lineage to establish data as a product, and supplies a separate feature store for real-time governance. The fabric supports interdomain use cases, including cyber, risk, and quality operations in banking; patient stratification and signal detection in healthcare; supply-chain disruptions in mining and manufacturing; and safety and pollution monitoring in smart cities. A phased deployment roadmap aligns data engineering and governance execution. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
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Abstract:Advances in deep learning (DL) have enormous potential to automate processes across diverse domains. Yet the deployed solutions often lack sufficient quality, traceability, and real-time responsiveness because of manual tools and static, inflexible rule systems that govern them. Greater trustworthiness, reliability, and adaptability would enable AI to take a more autonomous role as an enabler of trustworthy intelligent agents. A unified intelligence fabric integrates AI-driven data engineering with DL to fulfil these requirements and thus facilitate real-time automation with real-time governance. Unlike traditional intelligent cross-domain systems, which integrate a federation of hand-crafted ML cycles with explicit rules for decisioning and actioning, this approach enables a multi-domain intelligent system coproduced by reinforcement learning, real-time policy learning, and real-time pattern learning. The resulting model architectures can share internal representations across domains through transfer learning and continual learning. An AI-driven data-engineering pipeline creates the data required by training and inference phases, manages quality and lineage to establish data as a product, and supplies a separate feature store for real-time governance. The fabric supports interdomain use cases, including cyber, risk, and quality operations in banking; patient stratification and signal detection in healthcare; supply-chain disruptions in mining and manufacturing; and safety and pollution monitoring in smart cities. A phased deployment roadmap aligns data engineering and governance execution. [ABSTRACT FROM AUTHOR]
ISSN:15815374
DOI:10.52152/q5726g61