Imagine a virtual assistant that not only understands your voice commands, but also anticipates your needs based on your past behavior and preferences.
Researchers at the Massachusetts Institute of Technology (MIT) have developed PockEngine, an AI model training technology. The system works on improving AI even before tasks begin to maximize efficiency and processing speed.
The ability to process information and learn in real-time without relying on cloud computing infrastructure is a step forward for portable AI. This technology can be utilized in smart devices.
The main features of PockEngine are:
1. Addresses the challenge of improving deep learning models on devices with limited computational power, such as smartphones.
2. Identifies and updates only the important segments of the neural network that contribute to accuracy, thus reducing the amount of memory and computational power required to fine-tune the AI model.
3. Enables real-time adaptation of AI on small devices, improves data privacy by enabling local computation and adaptation to individual user behavior.
4. Can run up to 15 times faster than existing methods on specific hardware without loss of accuracy.
The ability to process information and learn in real-time without relying on cloud computing infrastructure is a step forward for portable AI. This technology can be utilized in smart devices.
The main features of PockEngine are:
1. Addresses the challenge of improving deep learning models on devices with limited computational power, such as smartphones.
2. Identifies and updates only the important segments of the neural network that contribute to accuracy, thus reducing the amount of memory and computational power required to fine-tune the AI model.
3. Enables real-time adaptation of AI on small devices, improves data privacy by enabling local computation and adaptation to individual user behavior.
4. Can run up to 15 times faster than existing methods on specific hardware without loss of accuracy.