AI Orchestration Layer for Cross-Platform Model Portability
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Solution Overview
Problem
Artificial intelligence (AI) models struggle to maintain accuracy when users switch from one AI platform to another, requiring retraining and resulting in less precise performance before sufficient data is collected, leading to lengthy input processes.
Innovation Solution
An AI orchestration layer that monitors interactions across different AI platforms, generates a simulated AI model based on past interactions, and uses this data to train new AI models, ensuring interoperability and portability of AI models between platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an AI model is trained on a particular platform, then prediction accuracy improves over time, but when the user switches to a new platform, the model must relearn from scratch, resulting in loss of accumulated training benefits
Solution Approach 1:
The patent creates a simulated AI model that copies the behavior and predictions of the original AI model across different platforms. This simulated model is trained on historical interaction data to replicate the original model's decision-making patterns, allowing the user to switch platforms without losing the accumulated intelligence and prediction accuracy of the original model.
2Reliability
If an AI platform requires extensive retraining when switching users, then model accuracy can be maintained, but the time required for input processes increases significantly
Solution Approach 1:
The system performs preliminary action by pre-training the simulated AI model using historical interaction data before the user actually switches platforms. This advance preparation ensures that when the user switches, the model is already trained and ready to provide accurate predictions immediately, eliminating the need for time-consuming retraining processes.
3Measurement precision
If an AI model is highly specialized to a particular user's behavior, then prediction precision improves, but the model becomes less adaptable to new platforms
Solution Approach 1:
The simulated AI model is designed with universality to function across multiple different AI platforms while maintaining user-specific prediction precision. By training the simulated model on historical data and designing it to replicate the original model's behavior patterns, it achieves both platform compatibility and user-specific accuracy, allowing it to operate effectively on different platforms without sacrificing precision.
Data Source
AI summary
There are provided systems and methods for an artificial intelligence (AI) orchestration layer to facilitate migrations between different AI platforms. A service provider may provide AI portability functions through an orchestration layer that connects different AI services and platforms. The orchestration layer may be used to monitor user interactions with a first AI platform that request AI predictive services and outputs. Using these monitored interactions, the service provider may build and train a simulated AI model that attempts to mirror or replicate the AI model trained for the user on the first AI platform. Thereafter, when the user begins use of a second AI platform that includes the same or similar functionalities to the first AI platform, the service provider may utilize the orchestration layer to assist in training an AI model on the second AI platform based on the previously trained AI model on the first AI platform.