What are edge artificial intelligence and edge computing?

Edge artificial intelligence is one of the most noteworthy new areas in artificial intelligence. It is allowing people to run artificial intelligence processes without having to worry about privacy or data transmission. The speed slows down. Edge AI is making the use of artificial intelligence wider and more widespread, allowing smart devices to respond quickly to input without accessing the cloud. While this is a quick definition of edge AI, let’s take a moment to better understand edge AI by exploring some use cases. First, edge AI has widespread applications in the healthcare industry. For example, integrating edge AI on monitoring devices can more accurately monitor and analyze patients’ vital signs and respond immediately when needed. This capability can make healthcare more efficient while also reliably handling sensitive personal data. In addition, edge artificial intelligence can also be applied to smart homes. By integrating artificial intelligence into home devices, such as smart speakers and smart TVs, users can interact with smart devices more widely and conveniently. The existence of edge artificial intelligence makes these devices no longer need to rely on the cloud.

What is edge computing?

In order to truly understand edge artificial intelligence, we first need to understand edge computing and understand the most important aspects of edge computing. The best way is to compare it to cloud computing. Cloud computing is the provision of computing services over the Internet. In contrast, edge computing systems do not connect to the cloud but run on local devices. These local devices can be dedicated edge computing servers, local devices, or Internet of Things (IoT). There are many advantages to using edge computing. For example, Internet/cloud-based computing is limited by latency and bandwidth, while edge computing is not limited by these parameters.

What is edge artificial intelligence?

Now that we understand edge computing, we can look at edge artificial intelligence. Edge AI combines artificial intelligence and edge computing. Artificial intelligence algorithms run on devices with edge computing capabilities. The benefit of this is that the data can be processed in real time without the need to connect to the cloud.

Most cutting-edge artificial intelligence processes are performed in the cloud because they require large amounts of computing power. The result is that these AI processes are prone to downtime. Because edge AI systems run on edge computing devices, required data operations can be performed locally, sent when an Internet connection is established, and save time. Deep learning algorithms can run on the device itself (the point of origin of the data).

Edge AI is becoming increasingly important as more and more devices require AI without access to the cloud. Think about how many factory robots or cars are now equipped with computer vision algorithms. In this case, data transfer lag time can be fatal. Because fast response times are so important, the device itself must have an edge AI system that enables it to analyze and classify images without relying on a cloud connection.

When information processing tasks performed in the cloud are delegated to edge computers, the result is real-time latency, real-time processing. Furthermore, by limiting data transfer to the most important information, the data volume itself is reduced and communication interruptions are minimized.

Edge AI and IoT

Edge AI is combined with other digital technologies such as 5G and the Internet of Things (IoT). The IoT can generate data for use by edge AI systems, and 5G technology is critical to the continued development of edge AI and IoT.

The Internet of Things refers to various smart devices that are connected to each other through the Internet. All of these devices generate data that can be fed into an edge AI device, which also serves as a temporary storage unit for the data until synchronized with the cloud. This method of data processing allows for greater flexibility.

The fifth generation mobile network 5G is crucial to the development of edge computing intelligence and the Internet of Things. 5G can transmit data at higher speeds, up to 20Gbps, while 4G can only transmit data at 1Gbps. 5G also supports simultaneous connections (1,000,000 per square kilometer) supporting better latency speeds (1ms to 10ms). These advantages over 4G are important because as the Internet of Things develops, the amount of data will grow and transmission speeds will be affected. 5G enables more interactions between a wider range of devices, many of which can be equipped with edge computing intelligence.

USE CASES FOR EDGE AI

Use cases for edge AI include almost any situation where data processing can be done more efficiently on a local device than through the cloud. However, some of the most common use cases for edge AI include self-driving cars, autonomous drones, facial recognition, and digital assistants.

Self-driving cars are one of the most relevant use cases for edge artificial intelligence. Self-driving cars must constantly scan their surroundings and assess the situation, making corrections to their trajectory based on nearby events. Real-time data processing is critical for these situations, so its on-board edge artificial intelligence system is responsible for data storage, operation and analysis. Edge AI systems are necessary to bring Level 3 and 4 (fully autonomous) vehicles to market.

Because autonomous drones are not flown by human operators, their requirements for self-driving cars are very similar. If a drone loses control or malfunctions during flight, it could crash and cause damage to property or life. Drones may fly beyond the range of internet access points, and they must have edge AI capabilities. For services like Amazon Prime Air, which aims to deliver packages via drones, edge AI systems will be integral.

Another use case for edge AI is facial recognition systems. Facial recognition systems rely on computer vision algorithms to analyze data collected by cameras. Facial recognition applications used for tasks such as security need to run reliably even when not connected to the cloud.

Digital assistants are another common use case for edge AI. Digital assistants like Google Assistant, Alexa and Siri must be able to run on smartphones and other digital devices even when not connected to the internet. When data is processed on the device, it does not need to be transferred to the cloud, which helps reduce traffic and ensure privacy.

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