International business machines corporation (20240119350). RUNTIME RECOMMENDATIONS FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING simplified abstract

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RUNTIME RECOMMENDATIONS FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING

Organization Name

international business machines corporation

Inventor(s)

He Sheng Yang of Beijing (CN)

Mo Chi Liu of Beijing (CN)

Yun Wang of Beijing (CN)

Hong Wei Jia of Beijing (CN)

Wu Yan of San Jose CA (US)

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 20240119350 titled 'RUNTIME RECOMMENDATIONS FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING

Simplified Explanation

The computer-implemented method described in the abstract involves accessing a user's profile to determine the likelihood of executing different types of processing when training a new AI model, selecting runtime environments based on the user's profile and a runtime matrix, and providing suggestions to the user on which runtime environments to use.

  • Access user profile to determine processing preferences
  • Access runtime matrix with frequency of use data
  • Select runtime environments based on profile and matrix
  • Output selected runtime environments to user interface with suggestions

Potential Applications

This technology could be applied in various fields such as:

  • AI model training
  • Data processing
  • Machine learning research

Problems Solved

This technology helps in:

  • Optimizing AI model training processes
  • Improving user experience in selecting runtime environments
  • Enhancing efficiency in processing tasks

Benefits

The benefits of this technology include:

  • Personalized recommendations for users
  • Increased efficiency in AI model training
  • Streamlined processing workflows

Potential Commercial Applications

Optimized runtime environment selection technology can be utilized in:

  • AI software development companies
  • Cloud computing services
  • Data analytics firms

Possible Prior Art

One possible prior art could be the use of user profiles to customize software settings, but the specific application to selecting runtime environments for AI model training may be novel.

Unanswered Questions

How does this technology handle user feedback on the suggested runtime environments?

The abstract does not mention how the system incorporates user feedback to improve future suggestions.

What security measures are in place to protect user profiles and runtime environment data?

The abstract does not address the security aspects of handling sensitive user data and runtime environment information.


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.

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