Artificial intelligence

Edge AI Explained: Key Benefits and Practical Examples

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By Héctor Borrás, on 25 July 2024

Edge AI is a combination of Edge Computing, an IT architecture, and artificial intelligence that processes data directly on the device or nearby local servers, eliminating the need to connect to the cloud for processing and receiving responses. In other words, it involves implementing high processing and data analysis capabilities in devices or servers so they no longer depend on cloud-based information sources, which can be helpful for digital marketing. This system is especially useful in scenarios with latency issues, where data privacy is crucial, or where connectivity errors might occur.

Edge AI operates through a series of steps. The first step involves collecting real-time data, which can come in various formats, including images and videos. This information undergoes preliminary filtering before being processed by the AI model, which has been previously trained to make decisions based on this data without needing to connect to the cloud.

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Edge AI


How Edge AI Works

It is often believed that devices using Edge AI have limited resources compared to those connected to the cloud. However, these devices can be updated to improve performance and accuracy at any time. While Edge AI primarily operates on the same device without requiring a connection, there are instances where a connection might be allowed, such as for updates or when sending or receiving essential data. Despite this, the system is designed to rely on existing information for making specific decisions.

Examples of Edge AI applications include smart surveillance cameras, autonomous vehicles, and IoT devices.


Main Benefits of Edge AI

Edge AI offers several significant advantages compared to traditional AI models that directly depend on cloud connectivity.

One advantage is the minimal response time, which can even be eliminated because there is no need to wait for the system to provide the requested information. This is crucial for applications needing real-time responses, such as security systems or autonomous vehicles and machinery.

Another advantage is energy and bandwidth savings, as the load on networks is reduced by not needing to send or download large amounts of data to the cloud. Systems can access all data without connectivity, enabling Edge AI to function under any circumstances. Since information is stored and processed on the device, data protection and security are enhanced, reducing the risk of data interception as it doesn't travel across networks.

Edge AI also allows devices to operate and perform actions autonomously, making quick decisions without latency issues or service interruptions due to connectivity problems. This ensures decisions and responses are swift and effective.


Differences Between Edge AI and Cloud AI

Edge AI is often confused with Cloud AI, but there are key differences between the two systems.

The primary difference is in data processing location and AI model execution. Edge AI processes and executes AI models on local devices or at the network edge, whereas Cloud AI does so on remote cloud servers. Unlike Edge AI, Cloud AI requires network connectivity to send data to the cloud for processing and to receive a response.

This difference results in higher latency for Cloud AI, as it must send data to the cloud and wait for processing to obtain a response. In contrast, Edge AI provides instant responses from the device, eliminating wait times. Cloud AI also requires higher bandwidth and raises privacy and security concerns since data transmission over networks is more vulnerable.

Cloud AI's hardware requirements are less demanding because cloud servers handle the processing, while Edge AI relies on the device's hardware. However, Cloud AI boasts greater analytical and processing power due to its network connectivity. Edge AI, although capable of updates, has limitations based on stored data, making its capacity and power inherently lower.

These differences mean that neither system is universally better; each has its own applications and utilities.


Examples of Edge AI Use Cases

Edge AI has numerous applications, especially in areas requiring rapid responses and where network dependency for cloud data processing is impractical.

  • Surveillance Cameras: Edge AI can quickly perform image or voice recognition without sending data to the cloud, allowing for immediate responses in emergencies and enhancing security.
  • Autonomous Vehicles: These vehicles process real-time sensor and camera data to make quick decisions, reducing accidents and hazards.
  • Robotics: Edge AI enables intelligent machines to make decisions and adapt to situations based on sensor and camera detections.
  • Portable Medical Devices: Devices using Edge AI can quickly and easily detect anomalies in breathing or heart activity.
  • Entertainment: Many games use Edge AI to enhance user experience by detecting movements or sounds.

These examples illustrate Edge AI's broad applicability, especially where quick, latency-free responses, data privacy, and real-time autonomous decision-making are crucial. However, it may be less effective in areas requiring constant access to updated information and frequent data expansion and reduction.

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Héctor Borrás