International business machines corporation (20240330675). REVERSE DATA GENERATION AND DATA DISTRIBUTION ANALYSIS TO VALIDATE ARTIFICIAL INTELLIGENCE MODEL simplified abstract
Contents
REVERSE DATA GENERATION AND DATA DISTRIBUTION ANALYSIS TO VALIDATE ARTIFICIAL INTELLIGENCE MODEL
Organization Name
international business machines corporation
Inventor(s)
Shuang Yin Liu of Beijing (CN)
REVERSE DATA GENERATION AND DATA DISTRIBUTION ANALYSIS TO VALIDATE ARTIFICIAL INTELLIGENCE MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330675 titled 'REVERSE DATA GENERATION AND DATA DISTRIBUTION ANALYSIS TO VALIDATE ARTIFICIAL INTELLIGENCE MODEL
- Simplified Explanation:**
The validity of a trained artificial intelligence model is verified by generating a training dataset using reverse data generation. This dataset is compared with a test dataset to determine the relationship between them, and the validity of the model is confirmed.
- Key Features and Innovation:**
- Verification of the validity of a trained artificial intelligence model. - Use of reverse data generation to create a training dataset. - Comparison of the training dataset with a test dataset to establish a relationship. - Removal of data with predefined relationships to obtain a new test dataset. - Utilization of the new test dataset to verify the validity of the model.
- Potential Applications:**
- Quality assurance in artificial intelligence models. - Enhancing the reliability of AI systems. - Improving the accuracy of AI predictions.
- Problems Solved:**
- Ensuring the accuracy and reliability of trained artificial intelligence models. - Providing a systematic method to verify the validity of AI systems.
- Benefits:**
- Increased trust in AI technology. - Enhanced performance of artificial intelligence models. - Reduction of errors in AI predictions.
- Commercial Applications:**
Potential commercial applications include quality control in AI-powered systems, ensuring the accuracy of AI predictions in various industries, and enhancing the reliability of AI solutions in critical applications.
- Questions about Artificial Intelligence Model Verification:**
1. How does reverse data generation contribute to verifying the validity of trained artificial intelligence models? 2. What are the implications of using a new test dataset to confirm the validity of an AI model?
- Frequently Updated Research:**
Stay updated on the latest advancements in artificial intelligence model verification techniques to ensure the continued accuracy and reliability of AI systems.
Original Abstract Submitted
validity of a trained artificial intelligence model is verified. the verifying the validity includes generating a training dataset from the trained artificial intelligence model using reverse data generation of the trained artificial intelligence model. the training dataset generated using the reverse data generation is compared with a test dataset used to evaluate the trained artificial intelligence model. the comparing is to determine a relationship between the training dataset that was generated and the test dataset. data from the test dataset determined to have a predefined relationship with the training dataset is removed to obtain a new test dataset. the new test dataset is used to verify the validity of the trained artificial intelligence model.
