Huawei technologies co., ltd. (20240211758). Method for Training Artificial Intelligence Model and Related Device simplified abstract
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 20240211758 titled 'Method for Training Artificial Intelligence Model and Related Device
The abstract describes a method for training an artificial intelligence model by receiving training subtasks at different training units, obtaining weights through synchronization and asynchronous processes, and combining these weights to update the AI model.
- The method involves executing training subtasks using multiple subunits to obtain weights.
- Synchronization among the subunits is used to obtain a first weight.
- Asynchronous processes are used to obtain a second weight from at least one other training unit.
- The weights obtained are then used to update the AI model.
Potential Applications: - This method can be applied in various industries such as healthcare, finance, and autonomous vehicles for training AI models efficiently. - It can be used in natural language processing, image recognition, and other AI applications that require complex training processes.
Problems Solved: - Efficient training of AI models by combining weights obtained from different training units. - Synchronization and asynchronous processes help in improving the accuracy and performance of the AI model.
Benefits: - Faster and more accurate training of AI models. - Improved performance and reliability of AI applications. - Cost-effective training process for large-scale AI models.
Commercial Applications: Title: Advanced AI Model Training Method for Various Industries This technology can be utilized in industries such as healthcare for medical diagnosis, finance for fraud detection, and autonomous vehicles for object recognition. The market implications include improved AI solutions, increased efficiency, and competitive advantages for businesses.
Questions about the technology: 1. How does this method improve the training process of AI models compared to traditional methods? 2. What are the potential limitations or challenges of implementing this training method in real-world applications?
Frequently Updated Research: Stay updated on recent advancements in AI model training techniques, synchronization methods, and asynchronous processes to enhance the performance and efficiency of AI applications.
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.
