Between 2023 and 2032, the edge intelligence (edge AI) market size is projected to grow at a compound annual growth rate (CAGR) of 24.8%, according to Global Market Insights, highlighting the rising global adoption of cloud computing.
Over the past decade, the world has seen a technological revolution that has changed how we live, work, and communicate. The question, ‘what if?’, has led to innovations that have drastically revolutionized industries.
The development and advancement of network generations and artificial intelligence (AI) has driven various innovations that have shaped global digitalization. Since its inception, AI has encouraged humans to explore the ‘what-ifs’ of the world, providing solutions to menial problems one after the other.
With the technological progress and innovative approach of edge AI, AI capabilities will be brought closer to the source of data, transcending the abilities of traditional AI systems.
AI vs. Edge AI
AI has been at the frontier of innovation in recent years, insinuating profound impact across all industries, economies, and workforces. From responding to messages to summarizing lengthy notes, AI has assisted with daily tasks easily.
On the other hand, edge computing brings computation and data storage closer to the data source, reducing latency and bandwidth compared to traditional cloud computing. The strides made in modern technology have paved the way for the advent of edge AI—the combined power of AI and edge computing. Edge AI possesses features previously unrealized by AI.
While edge computing brings data storage closer to the needed location, AI processes the data and incorporates real-time feedback.
This powerful amalgamation facilitates the integration of AI algorithms into Internet of Things (IoT) devices, enabling real-time information processing without the need for constant cloud connectivity.
Although edge AI appears to be an enhanced version of traditional AI, differences in operations persist. Instead of running AI models at the backend of a cloud system (like the traditional AI), AI at the edge runs on connected devices operating at the network edge.
Advances in computational power have enabled AI to run at the edge, and the maturation of neural networks has enabled generalized machine learning (ML). Meanwhile, the widespread adoption of IoT devices has encouraged the rise of big data.
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Edge AI Use Cases and Challenges
The demand for more efficient operations and real-time data has prompted organizations to delve into more advanced IT solutions and automation to reduce human error and the time needed for task execution.
In recent years, edge AI has impacted various industries, including telecommunications, by optimizing network performance, enabling predictive maintenance and real-time video analytics, and enhancing customer experiences through real-time analytics and insights.
For instance, Rosenberger's Intelligent Micro Data Center Solution unlocks the potential of edge computing, offering a robust infrastructure for data processing closer to the source.
Nokia is revolutionizing IP access, aggregation, and edge networks, providing enhanced connectivity and efficiency. Additionally, Nokia has launched four third-party applications for its MX Industrial Edge (MXIE), which assist enterprises in connecting, collecting, and analyzing data from operational technology (OT) assets securely on-premises.
IoT integration is also being supported by edge AI and 5G, transforming lifestyles by enabling more efficient operations and low-latency communication.
The electronics sector has been revolutionized by the advent of innovative features such as voice recognition, personalized recommendations, and activity tracking, while smart cities have seen improvement in managing traffic flow and public safety through real-time data analysis from sensors and cameras.
Moreover, edge AI has provided enhancements and solutions to areas that need improvement, highlighting intelligence as a priority. With edge AI, applications have become more powerful and intuitive and are analyzing local data more efficiently.
Ericsson has introduced the Ericsson Local Packet Gateway, an all-in-one appliance that enables Communication Service Providers (CSPs) to explore innovative edge opportunities in hybrid private network segments and on the network edge. This solution supports high-data bandwidth and low-latency use cases, making edge applications easier to deploy and manage.
The integration of reduced bandwidth at the edge has resulted in decreased power usage and reduced costs, while enhancing privacy at the same time. Edge AI is revolutionizing the efficiency of applications by enabling data processing without the need for internet access.
A wider range of intelligent services, such as smart virtual assistants, sensor data for predictive maintenance, and energy forecasting, have been delivered by edge AI, benefitting various industries, including telecommunications.
In 2022, Etisalat UAE and Huawei launched the first 5G Edge Box in the Middle East, demonstrating a significant advancement in edge computing capabilities. Khalid Murshed, Chief Technology and Information Officer at Etisalat UAE, noted, “The success of this testing activity will support UAE industries and the government sector in achieving their digital transformational objectives as we deploy a full suite of on premise private 5G connectivity with inventive digital use cases requiring low latency and extreme reliability.”
However, innovative advancements always come with risks. Edge AI devices are susceptible to cyberattacks if they are not equipped with standardized security protocols. Given the limitations of computing resources, security measures, including firewalls or antivirus software, are needed to protect devices from any potential threats or malware intrusions. Enhanced security should be paramount to prevent any breach in the system that would put sensitive data at risk.
Notably, Cisco is planning to establish an edge data center for cloud security in Saudi Arabia, further emphasizing the importance of edge computing in safeguarding data.
Efficiency in energy use is crucial for edge AI and battery powered IoT devices. Moreover, concerns have arisen regarding the durability of edge AI, as data can be lost if the edge devices fail.
Furthermore, the interoperability of different devices and platforms appears to remain a challenge in edge AI. Devices may run on various operating systems, making seamless communication and data exchange a problem.
Also Read: Transformative IoT and Edge Computing in Saudi Arabia
The Future of Edge AI
As the adoption of 5G and 5G-Advanced increases worldwide, edge AI will, undoubtedly, aid in digitalization. The faster and more reliable connectivity of the fifth generation of networks will advance the evolution of edge AI.
Higher bandwidths and lower latency will push edge AI, ironically, to the edge, improving its features and scalability.
Like any other industry, key players in the field of edge AI have been furthering their research and development (R&D), solidifying their stance as frontrunners in edge AI innovation.
Technology giant, NVIDIA, launched the edge AI platform, Jetson, which aids developers in the creation of groundbreaking AI products. In 2019, NVIDIA’s EGX platform was introduced to provide AI capabilities with real-time feedback by enabling low-latency AI processing at the edge.
Recently, global IoT solutions provider, ASUS IoT, launched two new edge AI systems: the PE2100N series, powered by NVIDIA’s Jetson AGX Orin; and the PE1101 series, powered by NVIDIA’s Jetson Orin Nano modules.
American technology company, Intel, has released its Movidius Vision Processing Unit (VPU) for IoT. This enables intelligent cameras, edge servers, and AI appliances to leverage deep neural networks and computer-based applications.
Also Read: The Edge of Industrial Internet
American multinational technology company, Microsoft, has developed Windows Machine Learning (ML), an edge AI solution used to deploy models on any device with Windows 10. In contrast, technology conglomerate, Cisco, established the Cisco Edge Intelligence, a software designed for IoT edge devices that simplifies the secure delivery of IoT data to the right applications, at the right time, either at the edge or in the cloud.
On the other hand, Amazon’s AWS for the Edge delivers data processing, analysis, and storage close to endpoints, facilitating the deployment of APIs and tools to locations outside AWS data centers.
Chinese tech giant, Huawei, has implemented the Atlas AI computing platform. This edge-based platform offers a range of products, including accelerator modules, cards, and AI edge stations. Notably, Huawei’s Atlas 500 AI Edge Station, was designed for edge applications with high compact size capacity and edge-cloud collaboration features.
IBM also launched its PowerAI Vision, bringing models closer to the data source. The PowerAI Vision includes advanced tools, streamlined AI processes, and accelerated data labeling.
Final Thoughts
As technology evolves and impacts our businesses and lifestyles, the surge in demand for edge AI solutions has been remarkable. Today, we enjoy real-time decision-making in applications and devices driven by edge AI. This feature will continue to create ripples of impact in various industries, establishing more efficient and reliable ways for societies to thrive.
Proven to be pivotal in developing technological innovations, edge AI will encourage us to foster a world where intelligence and convenience synonymous.
Related: AI Innovation at the Edge
