Smartphone Data Labeling for Adaptive AI Model Training
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Solution Overview
Problem
Existing AI models in deployed products struggle to identify and adapt to anomalous conditions due to unanticipated environmental factors or equipment issues, limiting their performance.
Innovation Solution
A smartphone application facilitates the identification and labeling of anomalous events by providing detailed information for technician diagnosis, allowing for on-site data labeling and model retraining, combined with edge training to update AI models incrementally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are trained with extensive labeled data sets before deployment, then the model's initial performance is improved, but the model cannot adapt to unanticipated environmental factors or equipment issues that occur after deployment
Solution Approach 1:
The system transitions from static pre-deployment training to dynamic continuous learning by enabling edge training that incrementally updates AI models with newly labeled anomalous data, allowing the model to adapt its parameters and structure over time based on real-world conditions
Solution Approach 2:
The system implements feedback loops where anomalous events detected by the AI model are collected, diagnosed by technicians using the mobile application, labeled with correct classifications, and fed back into the training process to retrain and update the model, creating a closed-loop learning system that continuously improves
2Measurement precision
If technicians manually diagnose and label each anomalous event, then the accuracy of labeled data is improved, but the time and resources required for data labeling increase significantly
Solution Approach 1:
The system enables technicians to perform self-service data labeling directly at the equipment location using mobile devices, eliminating the need to transport physical samples or data to centralized facilities, and allowing immediate labeling of anomalous events as they are diagnosed
Solution Approach 2:
The mobile application serves as an intermediary between the technician, the anomalous event, and the central server, providing a standardized interface for data collection, labeling, and transmission, which streamlines the workflow and reduces administrative overhead
3Adaptability or versatility
If the AI model is retrained with new labeled data, then the model's ability to recognize unexpected scenarios is improved, but the complexity of the training process increases
Solution Approach 1:
The training process is segmented into distributed edge training operations that can be performed independently on local devices or servers, allowing the model to be updated incrementally with small batches of new data rather than requiring complete retraining on large data sets
Solution Approach 2:
The system creates a universal training framework that can handle multiple types of data (sensor data, images, audio), multiple model architectures, and various deployment scenarios through a common interface and process, reducing the complexity associated with different training requirements
Data Source
AI summary
For deployed products containing Al models, environmental effects or failures may occur that cause the Al to detect an event that is not recognized. In these cases, it may be necessary to identify the nature of the event that triggered the Al process to output an unknown or anomalous event. This can be difficult as the product may be in operation and deployed for use in a residential or commercial setting. By identifying the nature of the event, and labeling it along with the associated data, the Al model can be retrained to allow it to properly recognize these events in the future. To facilitate this a smart phone application is disclosed that provides connectivity to critical event information, to event labeling, and to the model retraining process.