What is Edge AI?
Edge AI refers to running artificial intelligence algorithms and models directly on local devices like sensors or internet-of-things (IoT) devices. This enables real-time data processing and analysis without constant reliance on cloud infrastructure. In simpler terms, it's combining edge computing and AI to perform machine learning tasks on interconnected devices at the edge of a network.
How Does Edge AI Work?
Edge AI utilizes neural networks and deep learning to train models for recognizing, classifying, and describing objects within data. This training typically happens in a centralized data centre or the cloud due to the large amount of data involved. Once deployed, these models improve over time. If issues arise, data might be sent back to the cloud for further training, ultimately enhancing the model's performance at the edge.
Benefits of Edge AI
- Reduced Latency: On-device processing allows for much faster response times compared to relying on the cloud.
- Lower Bandwidth: Local data processing reduces the amount of data needing to be transmitted over the internet, saving bandwidth.
- Real-time Analytics: Data can be processed directly on devices without needing constant internet connection, enabling real-time insights.
- Enhanced Privacy: Data stays on the device, minimizing the risk of being intercepted during transmission.
Use Cases for Edge AI
- Healthcare: Wearable health monitors can analyze vitals and detect falls, while emergency vehicles can use edge AI for real-time patient data analysis.
- Manufacturing: Sensor data can be used for predictive maintenance, identifying potential equipment failures before they occur.
- Retail: Smart stores utilize edge AI for features like "pick-and-go" shopping and smart shopping carts for a seamless customer experience.
