What Is Edge Computing In The Age Of AI?

Edge computing isn’t a new concept – it’s been around for years – but it definitely wasn’t as prevalent as it’s becoming these days, because it simply wasn’t as relevant. But now, because of the rise of artificial technology, the Internet of Things (IoT) and a range of other types of connected devices that beginning to crop up, edge computing is suddenly back in our line of sight.

An increasing number devices are beginning to generate data, and when they do that, they’re sending all this data to the Cloud. Of course, because more data is being sent to the Cloud, there’s a lot more to deal with, thus making all the processes in the Cloud slower. Not only slower, but more expensive and less practical too.

So, naturally, people are looking for alternate ways to combat this issue. That is, how can they make AI and real-time applications faster and more efficient? Thus, introducing edge computing.

What Is Edge Computing?

Edge computing obviously offers somewhat of a solution to this. It’s a means of processing data closer to the location at which it was generated, rather than having it send it off far way to either a central cloud or a data centre. Eliminating that step in the process can be incredibly helpful in improving processes.

Now, the “edge” in “edge computing” can be one of a few things. It can be a device, a nearby server or even local infrastructure.

Ultimatley, the motivation behind using this edge is to reduce altence, improve speed, and basically just reducing the amount of data that’s having to travel across networks.

For instance, you may have a smart camera. By means of normal processes, it would need to send its footage all the way off to the cloud for analysis. But but with edge computing, it would be able to analyse the footage “on-site” and only send other really important information to a second location (and far less, of course).

Why Is Edge Computing Becoming More Important Now?

Whether you’re a computer geek or AI expert or just another person with a laptop and a smartphone, you’ll know that there’s clearly just more data being processed all around.

AI systems need more and faster responses, and devices that are connected are, as a result, generating really large volumes of data. This is partly due to the fact that there are more people in the world, and with more people come more IoT devices. And this isn’t slowing down by any means. Thus, businesses are increasingly needing real-time insights rather than waiting for cloud processing, and the demand for this is ever-growing.

What’s becoming clear now is that the AI boom we’re currently bearing witness to isn’t just creating more demand for bigger data centres; it’s also creating more demand for processing that is smarter and closer to its users.

How Edge Computing Can Solve These Issues

With edge computing, data doesn’t have to travel all the way to the cloud and back, meaning that they can save a lot of time. After all, it may not seem like much, but with this kind of technology, milliseconds within these processes count more than you may think.

This would be especially useful for industries like robotics as well as industrial systems and autononomous vehicles too.

The benefits of making this work are nothing to be scoffed at. Edge computing has the potential reduce bandwidth and storage costs, because only really important data and info undergoes the expensive process. Further to that, not only is it cheaper and better for the data centres and processes in the long run, but it can also be more reliable too.

Systems have the ability and potential to continue operating evevn when internet connectivity is poor, something that’s especially relevant in remote locations and in situations in which critical infrastructure is involved.

Another essential part of the equation when it comes to data is safety and security. Naturally, it’s better for sensitive data to remain closer to the source of its generation as it’s easier to protect. And it also means that not every single piece of information or data needs to leave the device or local environment.

The Future of Edge Computing and AI

The overarching idea here is that the future of AI may not actually be sending everything off to big data centres all over the world. In fact, it may be plausible for a lot of this work to happen on the device itself, making the process easier and reducing the need for these massive data centres. Well, at leas that’s the idea.

Ultimately, if effective and efficient, this could create some significant opportunties for new products and services, especially in industries like AI, robotics, healhtcare, manufacturing, logistics and more. It should be able to help improve user experiences by making everything quicker, better and more responsive.

Ultimatley, edge computing could become a competitive advantage for startups building AI-powered products. But, does that mean edge computing may replace the cloud?

Not necessarily. Cloud computing and edge computing are complementary, so it doesn’t mean that the cloud suddenly becomes obselete. It remains useful for storing large amounts of data, training AI models and centralised management.  The idea of edge computing simply means that not absolutely everything needs to go off to the cloud; edge computing has the potential to handle time-sensitive processing closer to users that doesn’t require massive processing capabilities and more.

Edge computing isn’t a new technology, but AI is giving it new relevance. As more devices become intelligent, processing data locally will become increasingly important. The future may involve a combination of cloud computing, AI and edge computing working together rather than competing.