5G Paving, Edge Computing Helps AI Boost Overall Reliability

With the rapid development of artificial intelligence (AI), intelligent terminal products with perceptual and cognitive functions continue to emerge: a series of consumer-grade products such as smart assistants, smart terminals, smart wearable devices, and smart homes are changing with each passing day. On the other hand, integrated artificial intelligence products with complex systems, such as driverless cars and intelligent service robots, are also accelerating R&D and industrialization. They are gradually entering into people's production and life, bringing new industries and applications.

As a general-purpose technology, AI has also promoted the development of other technologies and industries while its own development, and the development of other related technologies and industries has in turn accelerated the progress of AI. Among them, 5G provides effective guarantee for the transmission speed and quality of AI data.

Edge calculations allow AI to enter the "fast track"

Although AI has achieved a very big breakthrough now, it also faces many challenges. The biggest thing is that AI needs to consume a lot of computing resources and storage resources when performing analysis and processing.

Especially in the age of the Internet of Things, with the explosion of intelligent terminal products, large amounts of data are transmitted through limited network connections to analysis engines located in centralized data centers for data analysis. This is counterproductive because analysis engines may appear to be under-responsive. It may also cause extra delays and waste valuable bandwidth.

At this stage, it is the edge computing that comes from one of the 5G technologies that can do these complex tasks. Edge computing is close to the edge of the network where the physical device or data source is located, and an open platform that integrates network, computing, storage, and application core capabilities provides edge intelligent services nearby to meet AI's fast connectivity, real-time services, data optimization, application intelligence, security, and Key requirements technologies such as privacy protection have greatly improved the performance and overall reliability of AI applications.

At the same time, another major advantage of edge computing is also growing in the AI field, which is real-time. For example, AR/VR, connected cars, driverless, telemedicine, and smart cities, these intelligent terminal products and solutions cannot tolerate delays exceeding several milliseconds and are extremely sensitive to jitter or delay variation. For example, connected cars require low latency, high bandwidth, and are based on proximity to user computing and content storage, all of which make the edge core a must. In many scenarios, especially when using closed automation to maintain a high availability scenario, the response time must be within tens of milliseconds. This condition cannot be achieved except for edge calculations.

Increased data security in the AI era

Unfortunately, in the era of artificial intelligence, all aspects of data collection and use are facing new risks. In the data collection process, large-scale machines automatically collect thousands of user data, including personal names, gender, phone numbers, e-mail addresses, geographic locations, and home addresses. These data are collected in large numbers. Form a comprehensive tracking of users.

In addition, in data use, big data analysis technology is widely used. Data mining can analyze deep information, not only identifying specific individuals, but also analyzing personal shopping habits, whereabouts and other information, and further expanding privacy exposure. risks of. In addition, during the entire data life cycle, personal data is always exposed to potential security risks due to hacking attacks and system security vulnerabilities.

Edge computing can bring security components closer to the attack source, start more efficient security applications, and increase the number of tiers to protect against core attacks and risks.