What is Edge AI & How Does It Work?

Data is the new oil. Our politicians have said it, news outlets have reported on it, and analysts have speculated on just how much it’s worth. The exponential growth in technology is both the cause and effect of data being processed faster and faster. Advancements like Edge Computing revolutionised the capturing and process of data for companies across the globe, and Edge AI promises to take it one step further.

What is edge AI?

Put simply, Edge AI is the implementation of artificial intelligence (AI) in an edge computing environment. If you’re not familiar with edge computing, it essentially refers to a range of networks and devices that capture and process data at or near the ‘user’. It’s based on the principle of processing data closer to where it’s being generated, which allows for data to be understood and acted upon in milliseconds. Edge AI is simply adding AI into the Edge Computing mix.

With the rise of AI applications in recent years, companies now implementing it to complement edge computing, allowing for AI computations to happen at the edge of a localised network, where the data is created, rather than in an offsite data centre or a centralised cloud computing facility.

Edge AI promises to take data processing one step further, but it may have some drawbacks after all. Let’s start with what it promises to deliver.

Advantages of Edge AI

Safety and Security

Edge AI allows for the localised processing of data, generated by devices on-site, without the need to connect to the internet or the cloud. This makes the processing of data extremely secure, eliminating the possibility of cyber-attacks as everything is generated and processed on a closed system.

Efficiency and effectiveness

With today's technology, it’s hard to imagine data processing getting any faster. The introduction of AI into Edge Computing systems is like adding a jet engine to a Bugatti; in simple terms, Edge Ai will be able to receive, process and act upon data before its predecessors would register the data is even there in the first place.

User Experience

Enhancing the user experience is critical to every business. Edge AI will make systems and tools more responsive, reduce latency resulting in faster reaction times, and make everything connected more efficient and safe in an emergency.

Reduced Costs

Implementing Edge AI across an organisation can significantly reduce costs, especially in industries that rely on the processing of data. Firstly, having all data processed and acted upon on-site will significantly reduce latency, allowing processes to happen far quicker, increasing the efficiency of any process as a result. In addition to saving time, bandwidth can be reduced, making devices more energy efficient. Localising data storage and processing will remove the need to store large amounts of data in the cloud, significantly reducing a business's cost.

Disadvantages of Edge AI

Whilst it’s hard to believe that there are any drawbacks to implementing something like Edge AI, there are a few drawbacks, or hurdles, depending on how you look at it.

Initial Set-up Costs

Yes, implementing Edge AI will reduce your business costs in the long run, but the initial set-up cost can be massive depending on the amount of data you’re looking to process. The typical business that would benefit from Edge AI has to store and process hundreds of GB of data every day. Some companies have on-site data centres, primed and ready for Edge AI (you can find out about all the different types of Data Centres here) but those that don’t will have to purchase the hardware, and possibly hire people to maintain it.

Loss of Data

As models, AI software typically operates in a very ‘black and white’ fashion, it’s either a one or a zero, there’s no grey area. This fairly ruthless approach to analysing data can lead to data being discarded as the system deems it ‘useless’ at the time of processing. The issue is, this data could have proven to be useful, just not then and there, and should be stored accordingly. The loss of this data can lead to issues down the line. Ensuring your Edge AI system is well programmed and thoroughly scoped out can help to avoid this pitfall.

Human Error

Whilst storing and processing data in a closed network has its benefits, it also has its risks. Human error at a local level can have massive consequences for Edge AI systems. If in-house security teams aren’t able to properly protect the system from local applications and password security breaches, the risk of having the data corrupted, stolen or deleted is high.

Example of Use Cases for Edge AI

So now we know what it is, its advantages and disadvantages, it’s time to answer the most important question; where exactly will it benefit the most?

5G and 6G networks

Whilst one is only just being rolled out, and the other is just a concept, the introduction of 5G and 6G networks will have a compounding effect on the abilities of Edge AI. Once devices are connected to these networks, data transfer speeds will increase exponentially, making Edge AI far more effective.

Self-Driving Cars & Transportation Safety

IoT and sensor technologies, especially across the transportation industry, produce such enormous amounts of data that even the collection of the data can prove difficult in practice. Considering that most of the data collected by these technologies often needs to be acted upon in real-time, traditional systems are currently falling behind. With Edge AI, this data can be processed and dealt with locally, automating decisions without having to send the data to a cloud or off-site data centre.

Manufacturing

Introducing Edge AI into many manufacturing processes can revolutionise productivity, quality control and efficiency. Edge AI systems can use video analytics to spot defects that most humans wouldn’t be able to see with the naked eye, machine power usage can be monitored and optimised to reduce operational costs, and entire parts of the process can be analysed, all on-site, in real-time.

Transportation & Traffic

With self-driving cars rumoured to be commercially viable in the not-so-distant future, systems like Edge AI become extremely important. Monitoring and regulating traffic, identifying hazards and regulating speed to reduce congestion can all be achieved by implanting Edge AI.

The likelihood of other transport systems becoming fully autonomous can’t be far off either.

Passenger aircraft, for instance, have been highly automated for quite a long time, but Edge AI can facilitate the processing of data collected from their sensors in real-time, further improving flight safety.

Interested in pursuing a career in this area?

You could be considering a career in Data Centres, or you’re considering a new challenge to embark on. At MRL, our consultants are here to help you every step of the way, from taking an initial confidential call to understand your needs and manage your expectations, to interview prep, and checking in after your first day. Our team is truly committed to supporting you through the entire process. Get in touch with us today and discover how we can assist you in achieving your professional aspirations.