The application and value of edge artificial intelligence are not “edge”

Edge AI has many applications today, including facial recognition, self-driving cars, wearable medical devices, and real-time traffic updates accessed via smartphones. Facts have shown that edge computing enables artificial intelligence devices to better predict the future and make more informed decisions without the need to transfer large amounts of data to cloud platforms for processing, which brings endless possibilities for the next generation of artificial intelligence.

Many companies are considering combining edge computing, cloud computing and artificial intelligence to cope with labor shortages, inflation and supply chain uncertainty caused by the new coronavirus epidemic. and other issues.

Artificial intelligence is usually deployed on cloud platforms, where it can process large amounts of data and consume large amounts of computing resources. However, data does not all need to be stored and processed in the cloud platform. On the contrary, edge artificial intelligence can process data on smart devices such as smartphones, laptops, wearable devices, IoT devices, vehicles, etc. more reliably, faster, and more securely, and quickly promote decision-making. This technology is undoubtedly the best option for businesses that operate in areas with little to no network connectivity.

The value of edge computing is not just about reducing latency

Today, there are billions of IoT devices (such as mobile phones, smart TVs, cars, computers, cameras) around the world ) are collecting and processing large amounts of data. While these encouraging numbers bring huge strengths, they also expose new vulnerabilities. Edge AI can quickly process data from these devices, reducing the amount of data transmitted to the cloud platform for processing. Additionally, since the data is created and processed locally, it provides better security and privacy and can effectively prevent intrusions.

Another significant benefit that edge computing brings is real-time analytics, which is evident in many use cases and is a major driver of rising adoption for many enterprises. This benefits from data being processed, analyzed and stored on local hardware or nearby servers rather than being sent to the cloud. Edge computing gateways also reduce bandwidth because edge devices only transmit the amount of data relevant to the calculation, ensuring that the bandwidth transmitted to the cloud platform is not overloaded.

The application of edge artificial intelligence computing is becoming more and more widespread

Although edge artificial intelligence is a relatively new technology, its influence in various vertical business fields is becoming more and more widespread. Come bigger. “Industry 4.0”, which has received much attention recently, is changing the way operations are done by utilizing artificial intelligence and analytics at various stages of the production line. Employing AI technology at the edge will enable machines to make informed decisions, monitor components for failure, and detect anomalies in the production process.

Edge computing is increasingly used in healthcare. It enables autonomous monitoring of wards and patient conditions by using computer vision and information from other sensors. Healthcare professionals can use artificial intelligence to detect cardiovascular abnormalities during imaging tests and detect bone misalignments, tissue damage and fractures to make treatment choices or perform surgery.

It turns out that this technology is also a boon for the automotive industry. Today, automakers are using the vast amounts of data collected by all types of vehicles to identify and detect objects on the road, thereby improving passenger safety and comfort. Real-time processing of data enabled by edge AI computing helps avoid collisions with pedestrians or other vehicles.

Technological innovation is driving business development in various fields, including intelligent forecasting of energy, future predictions in manufacturing and virtual assistants in retail. Autonomous shopping systems such as smart carts and smart checkout systems allow retailers to leverage embedded vision to improve the consumer experience. Additionally, the adoption of video analytics solutions in the construction and construction industry is increasing and mainstream market players are facing more revenue-generating opportunities.

Investment in edge artificial intelligence computing continues to grow

The only way to stay ahead of the competition is to take the initiative and invest in technology. Edge AI is so important that tech giants like Google, IBM, and Amazon are investing heavily in developing their edge computing devices.

Chinese companies are also very active. The recent number of edge computing patent applications proves China’s rapid innovation in this area. The rapid popularization of 5G and the pursuit of application scenarios such as smart grids and intelligent connected vehicles are driving innovation in this area. Many mid-level AI processor startups are raising funds to enter the cutting-edge AI hardware market.

Entrepreneurship and innovation in this area are also in full swing internationally. Dutch chipmaker Axelera AI B.V., for example, raised $27 million in an early round of financing to develop a chip that supports artificial intelligence applications outside the data center or at the edge of the network. Another company called Spot AI also recently raised $40 million to develop smarter surveillance camera technology.

All this is just the beginning. The expansion of IoT devices, the popularization of 5G technology, the improvement of parallel computing and the commercial maturity of neural networks will promote the construction of edge artificial intelligence and machine learning infrastructure.

In short, although edge artificial intelligence is still in its infancy, its future development and potential uses are unlimited. Enterprises can integrate edge artificial intelligence into various processes of operation and maintenance, and realize business value from real-time data analysis applications to reduce costs and improve quality and efficiency. At the same time, they can enhance security and privacy, reduce network delays, and reduce bandwidth costs.

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