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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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Název: UNIFIED INTELLIGENCE FABRIC: AI-DRIVEN DATA ENGINEERING AND DEEP LEARNING FOR CROSS-DOMAIN AUTOMATION AND REAL-TIME GOVERNANCE.
Autoři: Nagabhyru, Kushvanth Chowdary, Garapati, Ravi Shankar, Aitha, Avinash Reddy
Zdroj: Lex Localis: Journal of Local Self-Government; 2025 Supplement, Vol. 23 Issue S6, p3512-3532, 21p
Témata: DEEP learning, AUTOMATION, DATA integration, TRANSFER of training, ARTIFICIAL intelligence, REINFORCEMENT learning
Abstrakt: 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]
Copyright of Lex Localis: Journal of Local Self-Government is the property of Institute for Local Self-Government & Public Procurement Maribor and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Lex Localis: Journal of Local Self-Government; 2025 Supplement, Vol. 23 Issue S6, p3512-3532, 21p
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  Data: 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]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Lex Localis: Journal of Local Self-Government is the property of Institute for Local Self-Government & Public Procurement Maribor and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.52152/q5726g61
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        Text: English
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        Type: general
      – SubjectFull: AUTOMATION
        Type: general
      – SubjectFull: DATA integration
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      – SubjectFull: ARTIFICIAL intelligence
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      – SubjectFull: REINFORCEMENT learning
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              Text: 2025 Supplement
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