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Edge computing is gaining traction as new use cases emerge, notably with the arrival of 5G. According to the Linux Foundation's 2021 State of the Edge study, the worldwide market capitalization of edge computing infrastructure would be valued at more than $800 billion by 2028. At the same time, businesses are pouring money into Artificial Intelligence (AI). According to a McKinsey poll from last year, 50% of responders had implemented AI in at least one business function.

As part of their digital transformation, most businesses are investing in a wide range of technologies. Edge AI, which combines edge computing and AI, is seen as offering much more potential by progressive businesses and cloud providers among those technologies.

Let's look at some of the most current Edge AI developments and how they're influencing today's digital enterprises.

What is Edge AI, and how does it work?

AI is primarily reliant on data transfer and complex machine learning algorithms for computation. Edge computing establishes a new computing paradigm by relocating AI and machine learning to the network's edge, where data collection and processing occur. Edge AI is a new frontier result of the combination of edge computing and artificial intelligence.

Edge AI enables better computing and insights, improved data protection, and effective continuous operation control. As a result, AI-enabled apps perform better, and operational expenses are lower.

Edge AI can also assist AI in overcoming technological barriers by enabling machine learning, autonomous deep learning model application, and complex algorithms on IoT devices without the use of cloud services.

How Enterprises will Be Transformed by Edge AI

The infrastructure for edge computing is designed to manage higher AI workloads on the edge and near the edge in an effective Edge AI paradigm. Edge AI can give industry-leading speed and infinite scalability when combined with storage solutions, allowing organizations to make the most of their data.

Many organizations across the world are now experiencing the advantages of Edge AI. Edge AI can help a wide range of industries, from boosting assembly line production monitoring to operating autonomous cars. Furthermore, as more industrial applications for Edge AI develop, the recent rollout of 5G technology in several nations provides an additional push for the technology.

The following are among some of the benefits of Edge AI for businesses:

An effective predictive maintenance and asset management
Each product is inspected in less than a minute.
Lesser field difficulties
Higher levels of consumer satisfaction
Facilitate large-scale Edge AI infrastructure and edge device life-cycle management
Increase the effectiveness of city traffic control measures.
Low latency for real-time decision making.
Improved reliability in case of network failures.
Less Infrastructure is required.
Reduced power consumption.
Data is more secure.

Edge AI deployment is a smart business move, according to Insight, which predicts a 5.7% return on investment from industrial Edge AI installations over the next three years.

Technologies Influencing the development of Edge AI

With Edge AI, developments in knowledge such as data science, machine learning, and IoT development play a crucial role. However, keeping up with the rate of technical breakthroughs in computer science is the real challenge. Next-generation AI-enabled apps and gadgets, in particular, that can smoothly connect with the AI and machine learning ecosystem.

Fortunately, substantial hardware development is taking underway in the realm of edge computing, which will assist to reduce the existing limits of Edge AI. Start-up companies like Esperanto Technologies, and AIStorm are developing microchips that can manage massive AI workloads.

While major players are also involved, In August 2017, Intel paid $15.3 billion for Mobileye, a vision-safety technology business located in Tel Aviv. Baidu, a Chinese multinational technology corporation, has begun mass production of Kunlun AI chips, a second-generation ultrafast microprocessor for edge computing.

Aside from microchips, Google's Edge TPU and Nvidia's Jetson Nano, as well as Amazon, Microsoft, Intel, and Asus, have all jumped on the motherboard development bandwagon to boost edge computing's capability. The world's first deep learning enabled video camera, Amazon's AWS DeepLens, represents a significant step in this direction.

Edge AI: Some Use Cases

‍Automated Optical Inspection

In production lines, automated optical inspection is crucial. With the aid of an automated Edge AI visual analysis, it is possible to discover defective parts of built components on a production line. Without depending on massive volumes of cloud-based data transfer, automated optical inspection enables very accurate ultrafast data processing.

‍Virtual Assistants

Virtual assistants such as Amazon's Alexa and Apple's Siri benefit greatly from developments in Edge AI. It enables their machine learning algorithms to rapidly learn on the device, rather than being dependent on the cloud.

‍Autonomous Vehicles

Edge AI-enabled autonomous cars are better at identifying road traffic factors and navigating travel routes than humans because of their faster and more accurate decision-making capabilities. Therefore, transportation is quicker and safer without any human intervention.

In Conclusion

Apart from the above-mentioned use cases, Edge AI can also play a critical role in face recognition technology, industrial IoT security, and emergency medical care. Every day, the number of applications for Edge intelligence expands. Edge AI will soon become a commonplace day-to-day technology, meeting the demands of both individuals and businesses.