17566458. ELECTRONIC SYSTEM FOR DEVELOPING A MACHINE LEARNING MODEL simplified abstract (Samsung Electronics Co., Ltd.)
ELECTRONIC SYSTEM FOR DEVELOPING A MACHINE LEARNING MODEL
Organization Name
Inventor(s)
Tapas Kanungo of Redmond WA (US)
Dmitrii Siakov of San Jose CA (US)
Stephen Walsh of Sunnyvale CA (US)
Nehal Bengre of Mountain View CA (US)
ELECTRONIC SYSTEM FOR DEVELOPING A MACHINE LEARNING MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 17566458 titled 'ELECTRONIC SYSTEM FOR DEVELOPING A MACHINE LEARNING MODEL
Simplified Explanation
An electronic system is described in this patent application that helps a development person improve a machine learning model by providing visual information. This visual information includes logic flows, scatter plots, confusion matrices, and instances of utterances related to poor performance. The development person can use this information to identify weaknesses in the machine learning model and make improvements.
- The system provides logic flows, scatter plots, confusion matrices, and instances of utterances to help improve a machine learning model.
- The development person can trace these visual representations to identify weaknesses in the model.
- A user interface allows the development person to request additional training data, improved labels, or an improved labelling guide.
- The logic flows, scatter plots, and confusion matrices are used iteratively to continuously improve the machine learning model.
- Even a relatively unskilled person can develop a highly effective machine learning model using this system.
Potential Applications
- This technology can be applied in various fields where machine learning models are used, such as natural language processing, image recognition, and predictive analytics.
- It can be used in industries like healthcare, finance, and marketing to improve the accuracy and performance of machine learning models.
Problems Solved
- This technology solves the problem of identifying weaknesses in machine learning models and provides a systematic approach to improve them.
- It addresses the challenge of effectively utilizing visual information to enhance the development process of machine learning models.
- It helps bridge the gap between skilled and unskilled individuals in developing highly effective machine learning models.
Benefits
- The system provides visual representations that make it easier to identify weaknesses in machine learning models.
- It allows for iterative improvement of the model by providing insights and suggestions for additional training data or improved labels.
- The user interface simplifies the process of requesting and implementing changes to enhance the model's performance.
- This technology enables relatively unskilled individuals to develop highly effective machine learning models.
Original Abstract Submitted
An electronic system provides visual information to help a development person improve a machine learning model. The visual information includes logic flows, scatter plots, confusion matrices and instances of utterances which are related to poor performance. Guided by the logic flows, the development person is able trace scatter plots confusion matrices in order to identify weaknesses of the machine learning model being developed. A user interface help the development person then specify or indicate a request for additional training data, improved labels or an improved labelling guide. The logic flows, scatter plots and confusion matrices are used iteratively by the development person to repeatedly discover how the machine learning model can be improved and then getting the data to improve the machine learning model. A relatively unskilled person is able to develop a highly effective machine learning model using the logic flows, scatter plots and confusion matrices.
