AI Model Verification Component for Industrial Reliability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for verifying artificial intelligence models in industrial environments are inefficient and prone to errors, lacking transparency and expertise, which poses risks to operational technology and decision-making in industrial settings.
Innovation Solution
A verification component that receives an input data set comprising the AI model, associated model data, and application data, determines risk assessment scores and Key Performance Indicators, and outputs a verification score to validate the AI model's reliability and robustness, deployed as middleware between IT and OT environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional art approaches are used for verifying AI models, then domain experts can provide decision support, but the process becomes time-consuming and error-prone while lacking transparency
Solution Approach 1:
The verification component automatically executes verification suites and generates verification scores without requiring manual intervention from domain experts. The system self-performs the verification process by receiving AI models, executing predefined verification suites, and generating comprehensive verification reports, thereby eliminating time-consuming manual verification while maintaining high reliability
Solution Approach 2:
The patent replaces manual expert verification with an automated computational verification system. The verification component uses algorithmic execution of verification suites to assess AI model reliability, substituting the mechanical process of human expert review with an automated digital system that provides consistent, transparent, and rapid verification results
2Productivity
If AI models are deployed in industrial environments, then automation and efficiency improve, but uncertainty and lack of transparency increase operational risks
Solution Approach 1:
The verification component performs verification assessment before the AI model is deployed to the industrial environment. By executing verification suites and generating verification scores in advance, the system ensures that only reliable models are deployed, preventing operational risks from uncertain or unverified models while maintaining high productivity
Solution Approach 2:
The verification component acts as an intermediary between AI model development and industrial deployment. It receives AI models from development environments, performs comprehensive verification, and only releases verified models to production, thereby bridging the gap between efficiency gains from automation and the reliability requirements of industrial operations
3Productivity
If AI models are trained based on estimated data, then development speed increases, but the ability to handle changing real-world conditions decreases
Solution Approach 1:
The verification component provides feedback on model robustness by executing verification suites that test performance under varying conditions. The generated verification scores and reports indicate whether the model can handle changes in real-world conditions, allowing developers to iterate and improve model adaptability while maintaining fast development speeds through automated feedback loops
Data Source
AI summary
A verification component for verifying an artificial intelligence, AI, model is provided, the verification component configured for receiving an input data set including the AI model to be verified, associated model data and application data; the verification component configured for determining at least one risk assessment score using at least one verification suite based on the input data set; the verification component configured for determining at least one Key Performance Indicator, KPI, by monitoring the performance of the AI model based on the input data set; the verification component configured for determining a validity of the AI model as at least one verification score based on the at least one risk assessment score and/or the at least one KPI; and the verification component configured for providing the at least one verification score and/or the verified AI model as output data set.