18045669. RUNTIME RECOMMENDATIONS FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING simplified abstract (International Business Machines Corporation)
Contents
- 1 RUNTIME RECOMMENDATIONS FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 RUNTIME RECOMMENDATIONS FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING - 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
RUNTIME RECOMMENDATIONS FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING
Organization Name
International Business Machines Corporation
Inventor(s)
Xiaoyang Yang of San Francisco CA (US)
RUNTIME RECOMMENDATIONS FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18045669 titled 'RUNTIME RECOMMENDATIONS FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING
Simplified Explanation
The computer-implemented method described in the abstract involves accessing a user profile to determine the likelihood of the user executing different types of processing when training a new AI model. The method also involves accessing a runtime matrix that contains information about the frequency of use of different runtime environments for training AI models. Based on the user profile and the runtime matrix, one or more runtime environments are selected and suggested to the user for training the AI model.
- User profile indicates likelihood of user executing different types of processing when training AI model
- Runtime matrix contains information about frequency of use of different runtime environments for training AI models
- Selection of runtime environments based on user profile and runtime matrix
- Output of selected runtime environments to user interface along with suggestion for usage
Potential Applications
This technology could be applied in various fields such as:
- AI model training
- Machine learning development
- Data analysis
Problems Solved
This technology helps in:
- Optimizing the selection of runtime environments for training AI models
- Providing personalized suggestions based on user profiles
- Improving efficiency in AI model development
Benefits
The benefits of this technology include:
- Enhanced user experience in training AI models
- Increased accuracy in selecting runtime environments
- Time and cost savings in AI model development
Potential Commercial Applications
This technology has potential commercial applications in:
- AI software development companies
- Data analytics firms
- Research institutions
Possible Prior Art
One possible prior art for this technology could be:
- Existing AI model training platforms
- Previous research on optimizing runtime environments for AI model development
Unanswered Questions
How does this technology handle user privacy and data security concerns?
The abstract does not provide information on how user data is handled and secured within the system. It is important to understand the measures in place to protect user information.
What criteria are used to select the runtime environments for output to the user?
The abstract mentions that the selection is based on the user profile and runtime matrix, but it does not specify the exact criteria or algorithms used for this selection process. Understanding the decision-making process can provide insights into the effectiveness of the technology.
Original Abstract Submitted
According to an aspect, a computer-implemented method includes accessing a profile of a user that indicates a likelihood that the user will execute each of a plurality of types of processing when training a new AI model. A runtime matrix that includes identifiers of runtime environments is accessed. The matrix also includes, for each of the runtime environments, a frequency of use of the runtime environment to train previously trained AI models using each of the plurality of types of processing. One or more of the runtime environments is selected for output to the user based at least in part on the profile of the user and the runtime matrix. Identifiers of the selected one or more of the runtime environments are output to a user interface of the user along with a suggestion to use one of the selected one or more of the runtime environments.
