Cognitive Digital Fabric for Autonomous Healthcare and Financial Intelligence
DOI:
https://doi.org/10.5281/zenodo.21412029Keywords:
Digital Fabric, Digital Twins, Healthcare Systems, Financial Systems, Real Time, Generative AI, Autonomous Learning, Data Processing, Cloud Native, Public Cloud, Azure Cloud, AI Lifecycle, Model Retraining, Data Aggregation, Multilingual Models, Language Models, Adaptive Systems, Decision Frameworks, Data Streams, Intelligent Systems.Abstract
Healthcare and financial ecosystems provide critical information for individuals, communities, and governments. The intelligent processing of this information is essential for improving efficiency, reducing costs, and creating value. Digital Fabric, an intelligent real-time Digital Fabric for autonomously learning and generative systems, is designed to support these ecosystems. As complex systems dealing with the virtually continuous flow of high-volume data, healthcare and financial environments are ideally suited to digital twins to develop AIs for standard decision frameworks that relate to clinical and financial conditions. The internal lifecycle of AIs requires adaptive engines which include the needs for new data, for checking, updating, retraining and deploying models, and convolution operations with other available models.
As autonomous learning manages the phase shift of the digital twins, a process to aggregate detected problems of an AI from different users over time into a generative learning request improves multilingual understanding, so increasing the number of use cases and the cohort for language models for Kolmogorov-Zasanenko complexity. Data processing capable of real-time cloud native operations on the public-cloud infrastructure of any provider is supported. Indeed, processing capability in the Azure ecosystem has been developed on the two domains Digital Fabric services. These exploits of Microsoft Copilot functions are collaborated on these services.
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