18376660. METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
- 1 METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Cristina Cornelio of Milton Keynes (GB)
Timothy Hospedales of London (GB)
Jan Stuehmer of Heidelberg (DE)
METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18376660 titled 'METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS
Simplified Explanation
The present techniques provide a method for reducing errors in the outputs of machine learning models by resolving inconsistencies before outputting a final result that respects a set of rules or constraints.
- Machine learning models may output results that violate rules associated with the overall task, which could be dangerous or provide a poor user experience.
Potential Applications
The technology could be applied in various fields such as healthcare, finance, autonomous vehicles, and natural language processing to ensure that the outputs of machine learning models adhere to specific rules and constraints.
Problems Solved
1. Ensures that machine learning models do not output results that violate rules or constraints associated with the task. 2. Reduces the risk of errors in the outputs of machine learning models, improving the overall reliability of the models.
Benefits
1. Improved accuracy and reliability of machine learning model outputs. 2. Enhanced user experience by preventing potentially dangerous or incorrect results. 3. Increased trust in machine learning technologies.
Potential Commercial Applications
Optimizing business processes, enhancing decision-making systems, improving customer service, and developing safer autonomous systems are potential commercial applications of this technology.
Possible Prior Art
Prior art in the field of machine learning and artificial intelligence may include techniques for error detection and correction in model outputs, as well as methods for ensuring compliance with specific rules or constraints.
Unanswered Questions
How does this technology handle complex rule sets or constraints in machine learning models?
The article does not delve into the specifics of how the technology manages complex rule sets or constraints within machine learning models.
What impact does this technology have on the training process of machine learning models?
The article does not address how the implementation of this technology affects the training process of machine learning models.
Original Abstract Submitted
Broadly speaking, embodiments of the present techniques provide a method for reducing errors in the outputs of machine learning, ML, models on a potential output of the models to resolve any inconsistencies before outputting a final result from the models. The final result respects a set of rules or constraints, which may include logical constraints. Advantageously, this reduces the risk of a model outputting a result which violates some rules associated with the overall task of the model, which could be dangerous or provide a poor user experience.
