AI on Edge or Edging Close to AI

AI on Edge or Edging Close to AI

Edge applications are driving the next wave of  AI computing in ways that improve lives at home, at work, in school and in transit. Today, almost every business has job functions that can benefit from the adoption of edge AI.

Edge AI is the deployment of AI applications in devices throughout the physical world. It’s called “edge AI” because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center.

This means, devices and IOT gadgets can handle AI tasks from wherever they are present. This will enable real-time data analysis and decision-making without relying on cloud infrastructure.

Edge computing traces its origins to the 1990s, when content delivery networks were created to serve web and video content from edge servers deployed close to users.

Benefits of Edge AI

• Faster processing: Edge AI is more agile than traditional cloud computing, offering faster, localized processing with less latency. 

• Reduced bandwidth usage: Running algorithms locally on edge devices reduces bandwidth usage. 

• Enhanced privacy and security: Edge AI enhances privacy and security. 

• Enhanced availability: Edge AI's independence from constant internet connectivity ensures that essential services can continue uninterrupted

Some Applications of Edge AI

• Smart home appliances: Smartphones and wearables can use Edge AI to monitor vital signs in real-time. 

• Self-driving vehicles: Use visual data and other sensors to determine road conditions. 

• Medical imaging: AI-enabled diagnostic tools can analyze X-rays, MRIs, and other medical scans at the “edge” of a hospital or clinic network. 

• Remote asset monitoring: Edge AI enables real-time analytics with processing much closer to the asset. 

• Smart traffic cameras: Can automatically adjust light timings to optimize traffic.