18596134. Method for Training Artificial Intelligence Model and Related Device simplified abstract (Huawei Technologies Co., Ltd.)
Contents
Method for Training Artificial Intelligence Model and Related Device
Organization Name
Inventor(s)
Yongzhong Wang of Hangzhou (CN)
Method for Training Artificial Intelligence Model and Related Device - A simplified explanation of the abstract
This abstract first appeared for US patent application 18596134 titled 'Method for Training Artificial Intelligence Model and Related Device
Simplified Explanation:
The method described in the patent application involves training an AI model by receiving and executing training subtasks using different training units to obtain weights for the model.
- The method involves receiving a first training subtask at a training unit.
- Executing the first training subtask using multiple subunits to obtain a weight through synchronization.
- Asynchronously receiving a second weight obtained by executing a second training subtask.
- Obtaining the final weight of the AI model based on the first and second weights.
Key Features and Innovation:
- Training an AI model using multiple training units and subunits.
- Synchronization of weights obtained from different subunits.
- Asynchronous training to improve efficiency and accuracy of the model.
Potential Applications:
This method can be applied in various fields such as image recognition, natural language processing, and autonomous driving.
Problems Solved:
This method addresses the challenge of efficiently training AI models using distributed computing resources.
Benefits:
- Improved training efficiency and accuracy.
- Scalability for training large AI models.
- Enhanced performance in complex tasks.
Commercial Applications:
Potential commercial applications include AI-powered products and services in industries such as healthcare, finance, and e-commerce.
Questions about AI:
1. How does the method described in the patent application improve the training process of AI models? 2. What are the advantages of using multiple training units and subunits in training an AI model?
Original Abstract Submitted
A method for training an artificial intelligence (AI) model includes receiving a first training subtask at a first training unit, obtaining, by executing the first training subtask using a plurality of first training subunits, a first weight that is obtained through synchronization among the plurality of first training subunits, asynchronously receiving a second weight that is obtained by executing a second training subtask by at least one second training unit, and obtaining a weight of the AI model based on the first weight and the second weight.
