Humanity has always been focused on reducing the manpower for tasks and achieving the same functions with the help of computational intelligence, or what we commonly call artificial intelligence. The process by which we train machines to take decisions and perform tasks themselves is called Machine learning.

When considering the notion of machines performing vital tasks, it is important to have a real-time performance. Any delays could potentially cause huge losses.

To address this issue, we have the concept of Edge AI.

What is Edge AI and how does it work?

Let us hypothetically consider a case of autonomous self-driving cars, to understand Edge AI in a simpler format.

When a self-driving car is moving, it needs to detect objects in real-time. Any delay or glitch can prove fatal for car passengers, which is why AI must perform in real-time. Car manufacturers train their deep learning based ML models in their cloud servers. Once all the models are trained and saved in a file, it gets downloaded locally in the car itself.

In this manner, when a car needs to decide with the help of object detection, it can do it using a locally saved model.

If the model was not saved locally in the car, and the car would try to make a connection to the cloud for the decision help, there might be some delay because of a network error. This kind of delay isn’t affordable for such crucial tasks where human lives can be in danger.

This is the reason behind the concept of Edge AI.

In simple terms, performing smart AI-ML based computational tasks locally near the main device, eliminating any potential time delay is what we call Edge AI. With Edge AI, we eliminate the need for the device to contact the cloud or the internet.

Advantages of Edge AI

Let us now learn about the advantages of Edge AI, or in simpler terms advantages of applying computational intelligence locally.

Decreased latency

Data security

Real-time analytics

Greater speed

Transferring the data to the cloud and contacting the cloud takes a little time. When considering Edge AI, the data is processed locally, which saves time and improves efficiency.

Under Edge AI, the data is processed locally at the ‘edge’, and there is no need to send the data to the cloud or other locations.

The most important and distinct advantage of Edge AI has to be this one! Edge AI enables high-performance computing capabilities at the edge, providing real-time insights.

Processing data locally near or on the device improves processing speeds and eliminates problems such as network issues.

The future scope of Edge AI

The advantages mentioned in the last section make it clear, that the growth of the Edge AI market is inevitable, and it is for the good. Processing any sort of data locally is always a good idea!

As per the global Edge AI software market report, Edge AI global market requirements will grow from $346.5 million to about $1.1 billion by the end of 2024. This massive growth prediction clearly signifies the power that Edge AI possesses.

Here are a couple of future trends for Edge AI that needs attention:

5G: The next generation of cellular networks

The initial developments of 5G require the rigorous collection of fast data streams. When combined with Edge AI, these developments can prove revolutionary. It will enable data stream analysis as close as possible to the device.

IoT: Internet of Things

One industry that will be most benefited and influenced by Edge AI is IoT. This industry is device intensive, with a lot of node sensors constantly communicating with the cloud via the transfer of data. Empowering IoT with the help of edge AI will revolutionize the industry. Large amounts of IoT data generated can be analyzed and processed locally near the device.

End Note

As the world moves towards better and advanced technology practices, it becomes crucial for companies to adapt to change and use the advanced technologies to improve their businesses. Edge AI has tremendous potential to revolutionize data-intensive industries, by performing computational intelligence locally.

We, at Seaflux, are AI undefined Machine Learning enthusiasts with IoT interests, who are helping enterprises worldwide. Have a query or want to discuss AI or IoT projects where the cloud can be leveraged? Schedule a meeting with us here, we'll be happy to talk to you.

Jay Mehta - Director of Engineering
Jay Mehta

Director of Engineering