Secure AI Engine Evaluation with Sandboxed Data Access
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Solution Overview
Problem
Advanced transformer-based AI models require significant computing resources and may operate slowly, and training them with proprietary data poses security risks, making them impractical for specialized tasks without human intervention.
Innovation Solution
A general-purpose transformer-based AI engine is used in a sandboxed testing environment to train and evaluate other AI models, restricting access to proprietary data, and a scoring engine assesses their output quality using a lighter and faster approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced transformer-based AI models are used to train specialized AI models, then the quality and accuracy of training can be improved, but the computational resources required and operation speed worsen
Solution Approach 1:
The patent extracts the training function from the heavy transformer-based AI model and implements it in a sandboxed environment. This allows the model to be trained using powerful external resources while the actual training process occurs in a controlled, lighter-weight environment that consumes fewer computational resources during operation.
Solution Approach 2:
The patent introduces a sandboxed environment as an intermediary between the proprietary data and the AI model training process. This intermediary layer enables secure access to training data without requiring the model to directly process or store sensitive information, reducing computational overhead while maintaining training quality.
2Measurement precision
If advanced transformer-based AI models are used to train specialized AI models, then the quality and accuracy of training can be improved, but the operation speed worsens
Solution Approach 1:
The training functionality is extracted from the heavy transformer model and placed in a sandboxed environment, allowing the main model to operate faster while still benefiting from high-quality training data and processes during the training phase only.
Solution Approach 2:
The system is segmented into a training phase (using sandboxed environment with full transformer capabilities) and an operation phase (using the lighter, faster specialized model). This segmentation allows each phase to be optimized independently for its specific requirements.
3Measurement precision
If proprietary data is used to train AI models, then the model performance can be improved, but security risks increase
Solution Approach 1:
A sandboxed environment serves as an intermediary layer between proprietary data and the AI model training process. This intermediary enables the model to learn from high-quality proprietary data while preventing direct access to sensitive information, thus maintaining security while improving performance.
Solution Approach 2:
The sandboxed environment implements preliminary security measures before the training process begins, establishing access controls and data protection mechanisms that prevent security breaches before they can occur during model training.
4Measurement precision
If human reviewers are used to train AI models for specialized tasks, then the quality control can be improved, but the cost and practicality worsen
Solution Approach 1:
The sandboxed environment enables the AI model to perform self-training using structured data and automated evaluation metrics. This self-service capability replaces the need for expensive human reviewers while maintaining quality control through programmatic assessment and feedback loops.
Solution Approach 2:
The training process transitions from human-based quality assessment to automated metric-based evaluation. By changing the parameters of quality control from subjective human judgment to objective computational metrics, the system maintains quality assurance while dramatically reducing costs.
Data Source
AI summary
Techniques for the secure evaluation of an artificial intelligence engine are disclosed. A computer system transmits, to a testing environment by a scoring engine, a dataset associated with an input for an artificial intelligence model to execute within the testing environment. The scoring engine comprises at least one transformer. The computer system receives, by the scoring engine and from the testing environment, a response to the dataset generated by the artificial intelligence model. The computer system determines, by the scoring engine and based on the response, a score representing a quality of the response. The computer system generates, by the scoring engine, an output based on the score.