18665188. ARTIFICIAL INTELLIGENCE MODEL ACCURACY VALIDATION simplified abstract (Intel Corporation)
Contents
- 1 ARTIFICIAL INTELLIGENCE MODEL ACCURACY VALIDATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ARTIFICIAL INTELLIGENCE MODEL ACCURACY VALIDATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about AI Model Validation
- 1.13 Original Abstract Submitted
ARTIFICIAL INTELLIGENCE MODEL ACCURACY VALIDATION
Organization Name
Inventor(s)
Anjo Lucas Vahldiek-oberwagner of Berlin (DE)
Marcin Andrzej Chrapek of Zurich (CH)
Scott Constable of Portland OR (US)
ARTIFICIAL INTELLIGENCE MODEL ACCURACY VALIDATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18665188 titled 'ARTIFICIAL INTELLIGENCE MODEL ACCURACY VALIDATION
Simplified Explanation
The patent application discloses systems and methods to validate the accuracy of artificial intelligence models. An example apparatus computes accuracy statistics of an AI model, determines a signed artifact based on the accuracy statistics, software used, and input data set, and communicates the signed artifact to the user.
- The patent application focuses on validating the accuracy of artificial intelligence models.
- It involves computing accuracy statistics, determining a signed artifact, and communicating it to the user.
- The process includes using software from a trusted third party and analyzing input data sets.
Key Features and Innovation
- Validation of artificial intelligence models' accuracy.
- Use of software from a trusted third party.
- Analysis of input data sets to compute accuracy statistics.
- Communication of signed artifacts to users.
Potential Applications
The technology can be applied in various industries such as healthcare, finance, autonomous vehicles, and cybersecurity to ensure the reliability of AI models.
Problems Solved
The technology addresses the challenge of verifying the accuracy of AI models, which is crucial for their successful deployment in real-world applications.
Benefits
- Increased trust in AI models.
- Enhanced reliability of AI predictions.
- Improved decision-making based on AI insights.
Commercial Applications
Title: "Enhancing AI Model Validation for Industry Applications" The technology can be used in industries such as healthcare for diagnosing diseases, finance for predicting market trends, autonomous vehicles for safe navigation, and cybersecurity for threat detection.
Prior Art
Readers can explore prior research on AI model validation techniques, software verification methods, and data analysis tools to understand the background of this technology.
Frequently Updated Research
Researchers are constantly developing new algorithms and tools for validating AI models, improving accuracy assessment techniques, and enhancing the overall reliability of artificial intelligence systems.
Questions about AI Model Validation
How can AI model validation impact the healthcare industry?
AI model validation in healthcare can lead to more accurate disease diagnoses, personalized treatment plans, and improved patient outcomes.
What are the potential risks of using unvalidated AI models in critical applications?
Unvalidated AI models in critical applications can lead to incorrect decisions, safety hazards, and financial losses. It is essential to ensure the accuracy and reliability of AI systems before deployment.
Original Abstract Submitted
Systems, apparatus, methods, and articles of manufacture to validate the accuracy of artificial intelligence models are disclosed. An example apparatus includes machine-readable instructions; and at least one processor circuit to be programmed by the machine-readable instructions to: compute accuracy statistics of an artificial intelligence model using software applied by a trusted third party and an input data set; determine a signed artifact based on (1) the accuracy statistics indicative of the accuracy of the artificial intelligence model, (2) the software applied by the trusted third party, and (3) the input data set; and communicate the signed artifact to a user of the artificial intelligence model.
