17643309. ENHANCING SYNERGY BETWEEN MACHINE LEARNING MODELS AND ANNOTATORS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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ENHANCING SYNERGY BETWEEN MACHINE LEARNING MODELS AND ANNOTATORS

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Pierpaolo Tommasi of Dublin (IE)

Charles Arthur Jochim of Dublin (IE)

Stephane Deparis of Dublin (IE)

Debasis Ganguly of Dublin (IE)

ENHANCING SYNERGY BETWEEN MACHINE LEARNING MODELS AND ANNOTATORS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17643309 titled 'ENHANCING SYNERGY BETWEEN MACHINE LEARNING MODELS AND ANNOTATORS

Simplified Explanation

The patent application describes a system that improves collaboration between machine learning models and human annotators in a computing environment. Here are the key points:

  • The system coordinates annotation tasks between annotators and machine learning models.
  • It takes into account the preferences of annotators and the data annotation requirements of the machine learning models.
  • The system learns and adapts the preferences and requirements over time.
  • The goal is to enhance the synergy between annotators and machine learning models for more efficient and accurate data annotation.

Potential Applications

This technology has potential applications in various fields, including:

  • Natural language processing: Improving language understanding and translation models by leveraging human annotators' expertise.
  • Image recognition: Enhancing image classification and object detection models through collaboration with human annotators.
  • Data labeling: Streamlining the process of labeling large datasets for training machine learning models.

Problems Solved

The system addresses several challenges in the collaboration between machine learning models and human annotators:

  • Coordinating annotation tasks: It efficiently assigns tasks to annotators and machine learning models based on their preferences and requirements.
  • Learning over time: The system continuously learns and adapts to the changing preferences and requirements, improving the collaboration over time.
  • Enhancing accuracy and efficiency: By leveraging the strengths of both annotators and machine learning models, the system aims to achieve more accurate and efficient data annotation.

Benefits

The technology offers several benefits:

  • Improved accuracy: The collaboration between annotators and machine learning models can lead to more accurate data annotation.
  • Increased efficiency: By optimizing task assignments and leveraging machine learning models' capabilities, the system improves the efficiency of data annotation.
  • Adaptability: The system learns and adapts to the preferences and requirements of annotators and machine learning models, ensuring continuous improvement in collaboration.


Original Abstract Submitted

Embodiments facilitating enhanced synergy between machine learning models and annotators in a computing environment by a processor. Annotation tasks may be coordinated between one or more annotators and machine learning models based on one or more annotator preferences and data annotation requirements of a machine learning model. The one or more annotator preferences and the data annotation requirements for coordinating the annotation tasks may be learned over a period of time.

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