Here’s Why Edge AI is the Industry’s Future

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Edge AI is fundamentally altering numerous industries by facilitating immediate decision-making, enhancing safety protocols and optimizing operational effectiveness. This pivotal transformation has the capability to redefine our engagement with technology and usher in substantial changes across sectors such as healthcare, manufacturing, transportation and entertainment.

We’re in the middle of the artificial intelligence (AI) era, seeing the seeds of it already perform their magic — but the next development may already be arriving: Edge AI.

Edge AI, as the name suggests, is about computing on the edge of a network — you could also see it as an evolution of edge computing.

It has the potential to catapult the capabilities of AI to another level and provide us with an unprecedented and seamless experience, poised to solve problems like privacy and security limitations, bandwidth, response time, and offline operation.

In computing terminology, edge refers to devices close to computing centers or servers. There is no place for a central server. Instead, we have multiple decentralized servers with devices such as smartphones, tablets, and laptops close to them.

AI Over The Cloud — or On Your LAN?

To understand Edge AI, let’s take an example of how a doorbell might work with non-edge AI and Edge AI.

Non-Edge AI: A visitor rings the doorbell. The system sends the visitor’s details, images, and videos to a cloud server. The server processes the data and sends it to your smartphone. Your notifications depend on internet speed and how fast the server processes the data. While this next bit may not be at the top of your mind while deciding whether to open the door, there is also the case of data privacy when sending data to a third party.

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Edge AI: A visitor rings the doorbell. The doorbell has a powerful computing system that immediately takes the viewer’s details, such as images, videos, and other information, processes and sends them in a notification format to you. All of these actions happen quickly. No cloud server, no lag or delay, and a direct sending of the information.

That’s the difference between Edge AI and Non-Edge AI, from which we can extract the main characteristics.

  • Edge is within a network where sensors and smart devices such as smartphones, doorbells (as in the above example), and laptops are available.
  • Since the devices are always closer to the network, there’s zero or reduced latency or issues with bandwidth.
  • Local processing of data keeps your data secure and private, and there is no need for the Internet.

Is Edge AI the Natural Next Step for AI?

The salient benefits of Edge AI are minimal or no dependency on remote servers, localized processing of data on devices, minimal or no latency, higher data security and privacy, and real-time insights.

Let’s take the energy sector: there’s no debate about energy’s critical role in our lives. Let’s find out how Edge AI can take energy management to the next level.

  • Power grid maintenance gains a considerable advantage. Take the case of a power grid supplying power to a large city. Edge devices fitted in the power grid can optimize the power distribution and send real-time data fast to devices, enabling better maintenance of power grids and fewer breakdowns and interruptions in power supply.
  • Automated operations. Power grids and stations have many routine but critical tasks that are performed every day. Edge AI can automate these tasks with unfailing regularity and accuracy.

Meanwhile, manufacturing could also take a boost: think of a factory where production occurs on a massive scale. Modern machinery is used by the personnel to execute the whole flow of processing, producing, testing, and packaging goods.

Edge AI can automate repetitive tasks, for example, enabling intelligent machines to insert raw materials into a processor at the right slot and time, sending real-time production data to smart devices of quality assurance managers, and sending real-time alerts on the maintenance status of machines.

It can also enable better manufacturing management, for example, reducing the repair costs of machinery. Edge AI fitted on machines can send predictive maintenance alerts to stakeholders who can take timely action before the machine’s condition worsens.

Always on and without internet dependencies, Edge AI can improve production capabilities and quality because of its accuracy and tireless functioning. For example, accurate packaging of goods is important for branding. Edge AI can perform the packaging or help workers achieve accurate packaging, which helps maintain the organization’s standards and branding.

The Bottom Line

Edge AI differs from AI in terms of real-time data updates, predictive maintenance, and relentless and accurate execution of repetitive jobs — common tasks across industries.

It also stands out because of data security, speed, ease of maintenance, and low cost. There is a high possibility that industries will embrace more Edge AI in the future.

Edge AI can potentially address a lingering complaint against AI — invasion of privacy and security.

However, there is still a big challenge — how are the big corporations that thrive on your data and collect both explicitly and surreptitiously going to view Edge AI?

When you install an app on your smartphone, for example, besides your granted permissions, it collects a lot of data from background operations.

Since data is the world’s new currency, corporations are unlikely to cede ground to a technology that threatens to take control away from them.

On the flip side, having AI embedded in devices around your network can take away so many dependencies it is likely to find a foothold in many industries and possibly in your home.

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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…