Edge AI is the new buzzword of the information world. In this article, I will explain what the hype is all about and present four use cases of Edge AI from different industries.
But first, let's take a short recap:
💡 In edge computing, data processing is brought to the “edge of the network”. While the definition of the edge of a network is certainly not unambiguous, edge computing simply means that the calculations are made as close to the physical location of data collection as possible. If data is collected from a device with sensors and processed on site, you can call it edge computing. Similarly, edge computation occurs when a mobile device processes image information directly on the device without sending data to the cloud.
💡 Edge AI can be thought as edge computing, which utilizes artificial intelligence methods. We are talking about Edge AI, when machine learning-based analytics or some other advanced analytics methods are applied to edge computing.
In recent years, companies have been eagerly transferring their systems and data from their own data centers to the cloud. Edge Computing and Edge AI will never replace cloud services but complement them. By definition, in Edge AI, data is collected from a very limited area. The information generated by Edge AI can be exported to the cloud as an input for broader and more in-depth analytics.
Edge AI market and trends
There is always a lot of hype associated with new technology, but there are several concrete reasons for the growth of the Edge AI market.
According to the Global Edge AI Software Market Growth report, the Edge AI software market alone will grow from $ 346.5 million to about $ 1.1 billion by 2024. Edge AI hardware and consulting market will grow at the same pace. Grand View Research estimates that the total global edge computing market will grow 37.4 percent per year and will be worth $ 43.4 billion by 2027.
The construction of 5G networks begins gradually, initially they will be set up very locally and in densely populated areas. 5G networks enable the collection of large and fast data streams. The value of Edge AI technology increases with efficient utilization and analysis of these data streams.
Massive amounts of IoT generated data
IoT and sensor technology produce such large amounts of data that even collecting the data is often tricky and sometimes even impossible in practice. For example, the latest Airbus A350 air crafts have 50,000 sensors that collect 2.5 terabytes of data every day.
The sensor data alone is just white noise. Data tells us nothing if it is detached from its place of origin and does not have metadata describing the meaning of the data. Therefore, simply retrieving data is not enough.
To summarize, you could say that only Edge AI makes it possible to fully utilize the much-hyped IoT data.
A massive amount of sensor data can be analysed locally, and operational decisions can be automated. Only the most essential data is stored in a data warehouse located in the cloud or in a data center.
People expect a smooth and seamless experience from services. Nowadays, a delay of just seconds could easily ruin the customer experience. Edge computing responds to this need by eliminating the delay caused by data transfer.
In addition, sensors, cameras, GPU processors and other hardware are constantly becoming cheaper, so both customized and highly productized Edge AI solutions are becoming available to more and more people.
4 use cases from different industries
One of the most promising Edge AI use cases is manufacturing quality control. Advanced machine vision (video analytics), an example of Edge AI, can monitor product quality tirelessly, reliably and with great precision. In our experience, video-assisted Edge AI can detect even the smallest quality deviations that are almost impossible to notice with the human eye.
Transportation and traffic
One example of Edge AI that everyone knows is a self-driving car. The car constantly collects data around it, which it then analyses in real time. There is no time to move data to the cloud and back since decisions must be made in a blink of an eye.
Passenger aircrafts have been highly automated for a long time. Real-time analysis of data collected from sensors can further improve flight safety.
While fully autonomous and fully unmanned ships may not become a reality until years from now, modern ships already have a lot of advanced data analytics.
Edge AI technology can also be used, for example, to calculate passenger numbers and to locate fast vehicles with extreme accuracy. In train traffic, more accurate positioning is the first step and a prerequisite towards autonomous rail traffic. An example of this is our pilot with Finrail, where satellite positioning was supplemented with inertia and stereo camera sensors.
A smart grid produces a huge amount of data. A truly smart grid enables demand elasticity, consumption monitoring and forecasting, renewable energy utilization and decentralized energy production. However, a smart grid requires communication between devices, and therefore transferring data through a traditional cloud service might not be the best thing to do.
Large retail chains have been doing customer analytics for a long time. The analytics is currently largely based on an analysis of completed purchases, i.e. receipt data. Although good results can be achieved with this method, the receipt data doesn’t tell you how people move around the store, how happy they are, when they stop to watch something, etc. Video analytics, which analyses fully anonymized data extracted from a video image, provides an understanding of people’s purchasing behavior that can improve customer service and the overall shopping experience.
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