A preliminary study on the application of next-generation Edge AI

#AI is no longer just the subject of science fiction movies, it is being applied to every aspect of daily life at an alarming rate. From personal relationships to work projects, AI is gradually changing the way we think and behave.

Among them, a typical field is NextGEN Edge AI (next generation edge artificial intelligence) application. It provides an immersive, intuitive and fun user experience through multiple modes such as ranking, classification and design, while saving time and money.

What is NextGEN Edge AI?

NextGEN Edge AI, also known as edge intelligence or next-generation artificial intelligence, combines edge computing and Artificial intelligence technology used to track and perform machine learning tasks. It utilizes the computing power and data processing capabilities of edge devices to achieve intelligent decision-making and analysis, reducing reliance on cloud computing. By pushing intelligence to the edge, NextGEN Edge AI accelerates response times and improves data privacy and security. It will play an important role in various fields, such as smart

Edge AI workflows often involve using data from centralized data centers (such as clouds or devices) and from edge resources The data. Cloud AI is more common, and it mainly relies on cloud computing power for development and execution. Edge AI includes components such as remote devices, IoT devices, and dedicated edge servers. This architecture makes data storage and calculation more convenient, and also makes it easier for users to access data.

Because Edge AI combines AI algorithms with edge computing capabilities on local devices, it is able to process and analyze data without the need to stay connected and integrated. This enables users to access data from disparate sources, thereby reducing system downtime or latency. Therefore, Edge AI improves the efficiency of data processing by integrating edge computing and AI processes.

In addition, NextGEN Edge AI successfully integrates the AI ​​process into a basic component without interacting with physical locations, providing efficient support and convenient user data construction for user needs.

How does Edge AI work?

When it comes to AI, we often think of machines simulating humans to achieve intelligent skills such as vision, language, behavior, object recognition, autonomous driving, and language understanding. To achieve these skills, AI uses a system called a Deep Neural Network (DNN). When given various training tasks, these DNNs generate many specific types of questions and provide examples of correct answers corresponding to them.

Deep learning is a technology that requires training in a data center or cloud. To obtain accurate models, data scientists often need the support of large amounts of data and collaboration. Once trained, the model can be run through an inference engine to solve real-world problems. The inference engine runs the trained model and generates predictions based on input data. This technology has a wide range of applications, such as image recognition, natural language processing and recommendation systems. Through the combination of deep learning and inference engines, we can better understand and respond to complex real-world problems.

Typically, when Edge AI is deployed, the inference engine runs on remote computers and devices, such as factories, hospitals, cars, satellites, or homes. Once an Edge AI model is deployed, these devices continuously obtain relevant information. In order to conduct more training, edge devices often collect and send large and tedious data sets to the cloud. At the same time, once the AI ​​encounters a problem, the inference engine at the edge will be replaced, thereby greatly improving the feedback loop of performance.

Basic components of Edge AI

The following two intelligent components are usually the focus of research in the field of Edge AI:

Edge computing

By definition, edge computing is a process of locally computing and storing data at the node where the data is collected. Therefore it often involves multiple processes distributed at the edge of the network to collect, analyze and process data.

AI

AI can combine enhanced analytical capabilities with automation, allowing machines to imitate human cognitive levels to understand language and solve problems, even creating more functional edge devices.

Edge AI applications across different industries

In recent years, the use of Edge AI applications has brought new business opportunities and Innovation. Many industries, including manufacturing, healthcare, and energy, are using the core capabilities of Edge AI applications. Below, let’s discuss two typical industry applications:

Smart Energy: Intelligent Prediction

Energy industries tend to have high demand and low demand Stable characteristics. Not only will it have a direct or indirect impact on other industries, but the potential supply threats caused by it will also disrupt the health and welfare of the entire human race.

Edge AI can generate complex models based on historical data, weather characteristics and other information, and coordinate the generation, distribution, management and monitoring of energy through intelligent predictions.

Edge AI in Healthcare: AI-Enabled Solutions

Modern healthcare organizations and medical professionals can improve their patients by using Edge AI Life expectancy and living standards are the ultimate goals of this healthcare industry. At the same time, by using edge devices powered by AI, medical professionals can also perform remote surgeries and monitor patients’ daily physiological activities.

Advantages of Edge AI

Compared with our common cloud-based AI, Edge AI has the following advantages:

Higher speed/lower latency

Since each training and calculation is performed locally, there is no need to spend too much time communicating with the cloud Response waiting.

Lower bandwidth requirements and costs

With Edge AI, voice, video and high-fidelity sensor data can all be transmitted over cellular networks, is sent with less bandwidth and associated costs.

Enhanced data security

Localized processing reduces the risk of sensitive data being intercepted during transmission or stored in the cloud.

Improved reliability/autonomous technology

AI is able to run locally even if the network or cloud service fails. This has obvious advantages in application scenarios such as autonomous driving and industrial robots.

Power reduction

#In most cases, the energy consumption of performing AI tasks on the device may be lower than sending data to the cloud. Energy consumption, of course, also extends the battery life.

The future of edge technology

Nowadays, almost all Edge AI applications can be consumed on smartphones, wearable devices, and smart home appliances run on such devices. Edge AI has become an emerging field that is experiencing rapid growth. According to LF Edge’s forecast, the compound growth rate of edge devices will reach 40% by 2028. At the same time, with the expansion of cashless checkouts, smart hospitals, cities, and supply chains, AI at the edge of enterprises is expected to accelerate in the next few years.

Today, most Edge AI algorithms can perform local inferences directly on the data viewed through the device. By using data from a collection of sensors near the device, we will be able to develop more sophisticated inference tools in the future and continue to improve the corresponding Edge AI orchestration.

In addition, the related joint deep learning is also a promising technology. It can not only improve the training process by uploading the corresponding subset of the original data to the cloud, but also update the AI ​​training locally on the edge device. Note that this is not about updating the model manually, but uploading the updates to the cloud to improve the privacy and security of Edge AI.

Summary

In summary, as the next generation Edge AI application that combines edge computing and AI, it is undoubtedly the best choice for IoT devices to acquire A powerful way to obtain high-quality, actionable sensor data and save time and energy. Through continuous improvements, it improves device efficiency and network bandwidth while also improving the posture of data privacy and security. Therefore, the next generation of Edge AI can be widely applied to diverse industries.

Translator Introduction

Julian Chen, 51CTO community editor, has more than ten years of experience in IT project implementation and is good at Implement management and control of internal and external resources and risks, and focus on disseminating network and information security knowledge and experience.

Original title: The Next-Generation AI Application: What Is It and How Does It Work?, Author: Bharat P

Link: https://dzone.com/articles/the-next-generation-ai-application-what-is-it-and.

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