Digital Twin Active Learning for Drift-Aware AI Classification
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Solution Overview
Problem
Existing AI systems face challenges in handling scenarios where training datasets are incomplete, with discriminative AI systems being ineffective in limited state spaces and generative AI systems prone to hallucinations and requiring large parameter counts.
Innovation Solution
A hybrid distributed inference system combining discriminative and generative AI models across centralized and edge sites, utilizing local small and centralized large foundation systems, with retrieval augmented generation (RAG) and knowledge acquirers to optimize classification and reduce resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI systems are used to generalize to data not in training set, then adaptability is improved, but system complexity and resource consumption increase due to large number of parameters needed
Solution Approach 1:
The system divides the AI processing into two segments: a discriminative AI system for handling known scenarios from training data, and a generative AI system for handling novel scenarios. This segmentation allows each system to be optimized for its specific function, reducing the overall parameter complexity while maintaining generalization ability through selective use of the generative model.
Solution Approach 2:
A scenario description generator acts as an intermediary between the discriminative and generative AI systems. It creates textual descriptions of novel scenarios that can be processed by the generative model, enabling generalization without directly invoking the full complexity of a large-parameter generative system for every input.
2Device complexity
If discriminative AI systems are used in limited state spaces, then device complexity is reduced, but adaptability deteriorates when training dataset cannot describe complete scenario
Solution Approach 1:
The system merges a discriminative AI system (for handling known scenarios with low complexity) and a generative AI system (for handling novel scenarios with high adaptability) into a unified hybrid architecture. This combination allows the system to maintain simplicity for common cases while gaining adaptability for incomplete or novel scenarios through the integrated generative component.
Solution Approach 2:
The system dynamically switches between discriminative and generative AI processing modes based on the nature of the input scenario. For familiar scenarios within the training distribution, the simpler discriminative model is used; for novel or incomplete scenarios, the system transitions to the more adaptable generative model, optimizing the balance between complexity and adaptability in real-time.
3Measurement precision
If hybrid distributed inference system is used, then classification accuracy is improved, but device complexity increases due to multiple centralized and edge components
Solution Approach 1:
The hybrid AI system is segmented into distinct functional components: a discriminative AI module, a generative AI module, a scenario description generator, and a unified inference interface. This segmentation allows each component to be independently optimized and managed, reducing the operational complexity despite the multi-component architecture, while maintaining high classification accuracy through coordinated processing.
Data Source
AI summary
The disclosure includes a digital twin system. The digital twin allows for real time classification and ranking of data received from a distributed learning knowledge acquirer. The digital twin system ranks the data while the distributed learning knowledge acquirer is performing a drift evaluation. The distributed learning knowledge acquirer uses the ranked data as training data for discriminative AI models. The digital twin system offers more flexibility and precision in ranking the data. The digital twin system is a digital twin providing contextual active dynamic learning to the distributed learning knowledge acquirer's physical system. Digital twin system causes a model driven approach to allow for superior predictive capabilities by being able to examine large state spaces.