Conceptual technology illustration of artificial intelligence and edge computing.

The Benefits of Cutting-Edge AI

Edge AI is a new computing paradigm that integrates AI into edge computing frameworks. Here are some of the benefits and use cases.

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Adoption of edge computing has grown significantly in recent years. A recent report by Research and Markets indicates that the size of the global edge computing market is expected to reach $155.90 billion by 2030.

Artificial intelligence is partly driving the growth in adoption of edge computing in industries. With the rise of IoT applications and enterprise data, there is a growing demand to develop devices that can handle information processing faster and smarter. This is where cutting-edge AI comes to life.

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The integration of AI into edge computing or edge AI enabled edge devices to use AI algorithms to process information at the edge of the device or on a nearby server of the device, reducing the time it takes for edge devices to make computing decisions. .

What is cutting-edge AI?

The concept of edge AI involves the application of AI to edge computing. Edge computing is a computing paradigm that allows data to be generated and processed at the edge of the network rather than in a central data center. Therefore, edge AI embeds AI into edge computing devices for faster and improved data processing and intelligent automation.

Benefits of advanced AI

Data Security and Privacy

With the increasing number of data accesses recorded in recent years, many companies are looking for other ways to improve data privacy. Edge AI provides a breeding ground for data privacy because data processing activities are performed at the edge of the device or closer to the device. As a result, the amount of data sent to the cloud for computation has decreased significantly. Additionally, when data is created and processed in one place, it increases data security and privacy, making it harder for hackers to gain access to your data.

Real-time analysis

Real-time data processing has become vital due to the explosive growth of data generated by mobile and IoT devices at the network edge. Therefore, one of the major benefits of cutting-edge artificial intelligence is that it facilitates real-time data processing by ensuring high-performance data computation on IoT devices.

This is possible because, with edge AI, the data needed to apply AI in edge devices is stored in the device or on a nearby server rather than in the cloud. This form of computation reduces computational latency and quickly returns processed information.

Reduced internet bandwidth

The growing amount of data generated from billions of devices around the world is driving an explosive need for internet bandwidth to process data from cloud storage centers. This practice forces companies to spend huge sums of money on bandwidth purchases and subscriptions.

However, with edge AI, there is a significant reduction in the amount of bandwidth needed to process information at the edge. Additionally, since the AI ​​calculates and processes the data locally, less data is sent to the cloud over the internet, which saves a huge amount of bandwidth.

Less energy consumption

Maintaining a round-trip connection with cloud data centers consumes a lot of energy. As a result, many businesses are looking for ways to lower their energy bills, and edge computing is one of the ways to do that.

Moreover, since AI computation requires the processing of a large amount of data, transporting this data from cloud storage centers to edge devices will increase the energy cost of any business.

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In contrast, the edge AI operating model eliminates this high cost of energy used to maintain AI processes in smart devices.

Better responsiveness

Responsiveness is one of the things that makes smart devices reliable, and cutting-edge AI ensures that. An AI solution at the edge increases the response rate of smart devices because there is no need to send data to the cloud for computation and then wait for the processed data to be sent back for decision making .

Although the process of sending data to cloud-based data centers can be completed in seconds, the Edge AI solution further reduces the time it takes for smart devices to respond to requests by generating and processing the data in the ‘device.

With a high response rate, technologies such as autonomous vehicles, robots and other smart devices can provide instant feedback to automatic and manual requests.

Edge AI use cases

Due to the increase in the use of AI to create IoT devices, software and hardware applications, smarter edge AI use cases have seen phenomenal growth. According to Allied Market Research, the global Edge AI hardware market was valued at $6.88 billion in 2020 but is expected to reach $38.87 billion in 2030. From this number, other use cases of advanced AI should emerge.

Meanwhile, some advanced AI use cases include facial recognition software, real-time traffic updates on autonomous vehicles, industrial IoT devices, healthcare, smart cameras, robots and drones. Additionally, video games, robots, smart speakers, drones, and health monitoring devices are examples of current use of advanced AI.

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