Adaptive Learning System for AI Models Using Expert Feedback and MRAC
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Solution Overview
Problem
Existing artificial intelligence models often degrade when deployed to production environments due to data and concept drift, and there is a lack of quality labeled datasets for training and validation.
Innovation Solution
A learning system approach that utilizes model reference adaptive control (MRAC) techniques to dynamically tune AI models based on feedback from subject matter experts, incorporating algorithms like parameter adaptation, model identification, adaptive laws, and online learning to adjust parameters and structures in response to changing data distributions and requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an AI model is deployed to production environment, then the model provides output to client systems, but the model performance degrades due to data and concept drift
Solution Approach 1:
The system implements continuous feedback loops where model predictions are compared against actual outcomes and expert labels. Performance metrics are monitored ongoing, and when degradation is detected, the system automatically triggers retraining with newly labeled data, creating a closed-loop control system that maintains reliability while deployed
Solution Approach 2:
The system transitions from static model deployment to dynamic adaptive modeling. The model continuously adapts to changing data distributions through online learning and periodic retraining. Data drift detection mechanisms dynamically adjust the retraining frequency, allowing the model to remain reliable in production environments despite evolving data patterns
2Measurement precision
If an AI model is trained and validated on a dataset, then the model is developed and validated, but the model significantly degrades when deployed due to lack of quality labeled data
Solution Approach 1:
The system enables automated data labeling through expert-in-the-loop approaches and semi-supervised learning techniques. The model itself generates predictions that are used to create training labels, reducing dependency on manual expert annotation while maintaining data quality. This self-service labeling capability ensures continuous availability of high-quality training data for validation and retraining
Solution Approach 2:
The system performs preliminary data preparation and labeling activities before deployment. Quality labeled datasets are created in advance through expert annotation and automated preprocessing. The system also pre-trains models on this high-quality data and performs validation to ensure they meet performance thresholds before being deployed to production environments
3Adaptability or versatility
If model reference adaptive control techniques are used to dynamically tune AI models, then the model adapts to changing data distributions, but the system complexity increases
Solution Approach 1:
The complex adaptive control system is segmented into modular functional components: drift detection module, performance monitoring module, data labeling module, model retraining module, and deployment module. Each module performs a specific function and can be independently configured and maintained. This segmentation reduces system complexity by making the adaptive control mechanism more manageable and easier to implement
Data Source
AI summary
Systems and methods for learning system approach to artificial intelligence models are disclosed. A method may include: (1) receiving, by a computer program executed by an electronic device, a current artificial intelligence (AI) model; (2) providing, by the computer program, input data from a plant to the current AI model; (3) receiving, by the computer program, current AI model predictions from the current AI model; (4) receiving, by the computer program and from subject matter expert electronic device, feedback-based labels for the current AI model predictions; (5) determining, by the computer program, a prediction error between the current AI model predictions and the feedback-based label; and (6) applying, by the computer program, a model reference adaptive control algorithm to the current AI model in response to the prediction error being greater than a threshold.