Automated Training Content Augmentation With Edge–Cloud AI Processing
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Solution Overview
Problem
Learning Management Systems face challenges in providing industry-specific training in multiple languages and closed captioning, which is costly and time-consuming, and require significant human effort to create training materials and test questions.
Innovation Solution
A system utilizing AI to process media content, generate questions, and combine them with translated audio and captioning to create multi-language training courses, reducing human interaction and network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If AI processing is performed locally at the edge device, then response time and user experience are improved, but device complexity and power consumption increase
Solution Approach 1:
The system segments the AI processing workload by deploying a lightweight edge model on the device for immediate inference and a cloud-based large model for complex tasks. This division allows fast local responses while offloading complexity to the cloud, resolving the contradiction between response time and device complexity.
Solution Approach 2:
The system adds a spatial dimension to processing by implementing a hierarchical architecture across two locations (edge device and cloud). Simple inference occurs at the edge while complex processing occurs in the cloud, enabling fast local responses without requiring the edge device to handle all computational complexity.
2Reliability
If a large language model is deployed at the edge, then data privacy is improved, but device power consumption and memory requirements increase
Solution Approach 1:
The system implements partial action by deploying only a distilled, lightweight version of the language model at the edge device. This partial deployment provides sufficient functionality for privacy-sensitive tasks while consuming acceptable power and memory resources, rather than deploying the full large model.
Solution Approach 2:
The system changes the parameters of the AI model by using knowledge distillation to create a smaller, more efficient version suitable for edge deployment. This parameter change reduces memory requirements and power consumption while maintaining adequate performance for privacy-critical operations.
3Adaptability or versatility
If training data is collected from diverse users, then model adaptability is improved, but data heterogeneity and processing complexity increase
Solution Approach 1:
The system introduces Federated Learning as an intermediary mechanism that enables diverse user data to be utilized for model training without centralizing the data. Local models are trained on device data and only model updates are shared, reducing data processing complexity while maintaining model adaptability to diverse user needs.
Data Source
AI summary
Systems, methods, and computer-readable storage media for automating training content augmentation, and more specifically to using Artificial Intelligence (AI) to analyze media content and generate questions derived from the content. Systems can receive and process media, then generate questions based on the processed media. The systems can then combine the processed media and the newly generated question, resulting in a course about the media.