What is edge AI?

The concept of edge AI comes from edge computing and artificial intelligence. So, edge AI is a combination of both technologies, i.e., edge computing and AI. Edge AI basically refers to running AI algorithms on a local device that holds edge computing capacity, also known as on-Device AI.

In particular, for edge AI, no system is required to connect to other devices, thus allowing users to process data in real-time.

Maximum of the AI processes are conducted under cloud-based centers that need substantial computing capacity, leading to an increase in downtime. Edge AI processes parts of the edge computing device’s workflow that lets users study the data before transmitting it to different locations, thus saving time.

Working of edge AI

Some live edge AI examples will help understand the working of edge AI easily—a scenario where data processing is carried more efficiently on a local device than on a cloud.

Consider self-driving cars, autonomous drones, facial recognition, and digital assistants working on the local level devices.

1. Self-driving cars

Self-driving cars are dynamic, meaning they constantly scan the surrounding environment and analyze the situation and evaluate its trajectory. Processing real-time data is essential in these cases. Thus, the onboard Edge AI systems take charge of the data storage, analysis, and manipulation at times.

Currently, Toyota is already implementing fully automated (level 4) vehicles.

2. Automotive drones

Autonomous drones have similar requirements for working as autonomous cars as no human operator controls them. There are chances that a drone may lose control or suffer malfunctions while flying; simultaneously, it can crash or damage property or even human life.

Thus, it is essential to design drones with edge AI capabilities to fly far out of range of an internet access point. Soon, services like Amazon Prime Air that aim to provide packaging services via drones have already adopted an edge AI mechanism.

3. Facial recognition system

Another example explaining Edge AI is the facial recognition system. These systems function on computer vision algorithms after analyzing data collected by the camera.

A facial recognition app works based on security needs that operate reliably even if it is not connected to a cloud.

4. Digital assistants

Digital assistants are yet another typical example of Edge AI. Google Assistant, Alexa, and Siri are significant examples of digital assistants that work on smartphones and other digital machines, even in the absence of an internet connection.

After processing data on the device, there’s no need to deliver it to the cloud, thus helping reduce traffic and ensuring privacy.

Benefits of edge AI

1. Cost reduction

Data communication and bandwidth costs decrease as the amount of data transmitted is less than that in the cloud environment. Performing AI processes in the cloud results in cost inflation as the usage of AI hardware devices increases.

Maintaining different techniques at the local platform reduces data communication costs as data transmission is not performed. This even helps in getting faster results.

2. Focused security

There are chances of loss of data while processing data on the cloud. Edge AI prevents data loss or any chances of data leakages as the data processing takes place locally. This even helps users in controlling access to information.

Data processing takes a few milliseconds with edge AI, reducing the risk of data getting tampered with during the drastic transition.

Moreover, what makes edge AI more secure is its enhanced security features that help build trust.

3. Immediate response

Compared to a centralized IoT system, edge AI devices process data really very fast. Real-time operations like collecting data, analyzing, and processing are performed in the fastest possible way, making devices more useful for time-dependent applications.

4. Swift to handle

At times, it becomes difficult and complicated to handle AI operations. Contrarily, edge AI devices are self-contained as no data scientists or AI developers need to maintain them.

Data and insights are then automatically delivered to where they need to be visible on the spot using highly graphical interfaces or dashboards.

5. Internet bandwidth

Conducting data processing at the edge AI gateway locally helps save internet data, ultimately saving money on internet bandwidth as less data is transmitted through the internet.

For instance, Amazon AWS AI Services is an expensive one for computing in the cloud. Cloud now can be reserved as a repository for post-processed data in the future for storing analyzed data.

Future scope of edge AI

It isn’t easy to interpret and it will be too soon to map out at what scale edge AI stands in the market. Many industries have already taken revolutionary steps to implement edge AI machinery, while others have already implemented it.

Denying the fact that AI computing is slowly moving to the edge is next to impossible. The amount of data generated by IoT-capable devices continues to grow, leading to more amount of data that needs to be processed at the edge.

Also, considering privacy concerns, processing data at the local devices eliminates the need to send sensitive personal data for processing at the cloud. Thus, edge AI’s increasing demand makes it a promising technique and a brighter one indeed.

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