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    Edge AI: The Future of Artificial Intelligence and Edge Computing

    Edge computing is witnessing a significant interest with new use cases, especially after the introduction of 5G. The 2021 State of the Edge report by the Linux Foundation predicts that the global market capitalization of edge computing infrastructure would be worth more than $800 billion by 2028. At the same time, enterprises are also heavily investing in artificial intelligence (AI). McKinsey’s survey from last year shows that 50% of the respondents have implemented AI in at least one business function.

    While most companies are making these tech investments as a part of their digital transformation journey, forward-looking organizations and cloud companies see new opportunities by fusing edge computing and AI, or Edge AI. Let’s take a closer look at the developments around Edge AI and the impact this technology is bringing on modern digital enterprises.

    What is Edge AI?

    AI relies heavily on data transmission and computation of complex machine learning algorithms. Edge computing sets up a new age computing paradigm that moves AI and machine learning to where the data generation and computation actually take place: the network’s edge. The amalgamation of both edge computing and AI gave birth to a new frontier: Edge AI.

    Edge AI allows faster computing and insights, better data security, and efficient control over continuous operation. As a result, it can enhance the performance of AI-enabled applications and keep the operating costs down. Edge AI can also assist AI in overcoming the technological challenges associated with it.

    Edge AI facilitates machine learning, autonomous application of deep learning models, and advanced algorithms on the Internet of Things (IoT) devices itself, away from cloud services.

    Also read: Data Management with AI: Making Big Data Manageable

    How Will Edge AI Transform Enterprises?

    An efficient Edge AI model has an optimized infrastructure for edge computing that can handle bulkier AI workloads on the edge and near the edge. Edge AI paired with storage solutions can provide industry-leading performance and limitless scalability that enables businesses to use their data efficiently.

    Many global businesses are already reaping the benefits of Edge AI. From improving production monitoring of an assembly line to driving autonomous vehicles, Edge AI can benefit various industries. Moreover, the recent rolling out of 5G technology in many countries gives an extra boost for Edge AI as more industrial applications for the technology continue to emerge.

    A few benefits of edge computing powered by AI on enterprises include:

    • An efficient predictive maintenance and asset management
    • Inspection span of less than one minute per product
    • Reduces field issues
    • Better customer satisfaction
    • Ensure large-scale Edge AI infrastructure and edge device life-cycle management
    • Improve traffic control measures in cities.

    Implementation of Edge AI is a wise business decision as Insight estimates an average 5.7% return on Investment (ROI) from industrial Edge AI deployments over the next three years.

    The Advantages of Applying Machine Learning on Edge

    Machine learning is the artificial simulation of the human learning process with the use of data and algorithms. Machine learning with the aid of Edge AI can lend a helping hand, particularly to businesses that rely heavily on IoT devices.

    Some of the advantages of Machine Learning on edge are mentioned below.

    Privacy: Today, information and data being the most valuable assets, consumers are cautious of the location of their data. The companies that can deliver AI-enabled personalized features in their applications can make their users understand how their data is being collected and stored. It enhances the brand loyalty of the customers.

    Reduced Latency: Most of the data processes are carried out both on network and device levels. Edge AI eliminates the requirement to send huge amounts of data across networks and devices; thus, improve the user experience.

    Minimal Bandwidth: Every single day, an enterprise with thousands of IoT devices has to transmit huge amounts of data to the cloud. Then carry out the analytics in the cloud, and retransmit the analytics results back to the device. Without a wider network bandwidth and cloud storage, this complex process would turn it into an impossible task. Not to mention the possibility of exposing sensitive information during the process.

    However, Edge AI implements cloudlet technology, which is small-scale cloud storage located at the network’s edge. Cloudlet technology enhances mobility and reduces the load of data transmission. Consequently, it can bring down the cost of data services and enhance data flow speed and reliability.

    Low-Cost Digital Infrastructure: According to Amazon, 90% of digital infrastructure costs come from Inference — a vital data generation process in machine learning. Sixty percent of organizations surveyed in a recent study conducted by RightScale agree that the holy grail of cost-saving hides in cloud computing initiatives. Edge AI, in contrast, eliminates the exorbitant expenses incurred on the AI or machine learning processes carried out on cloud-based data centers.

    Also read: Best Machine Learning Software in 2021

    Technologies Influencing Edge AI Development

    Developments in knowledge such as data science, machine learning, and IoT development have a more significant role in the sphere of Edge AI. However, the real challenge lies in strictly following the trajectory of the developments in computer science. In particular, next-generation AI-enabled applications and devices that can fit perfectly within the AI and machine learning ecosystem.

    Fortunately, the arena of edge computing is witnessing promising hardware development that will alleviate the present constraints of Edge AI. Start-ups like Sima.ai, Esperanto Technologies, and AIStorm are among the few organizations developing microchips that can handle heavy AI workloads.

    In August 2017, Intel acquired Mobileye, a Tel Aviv-based vision-safety technology company, for $15.3 billion. Recently, Baidu, a Chinese multinational technology behemoth, initiated the mass-production of second-generation Kunlun AI chips, an ultrafast microchip for edge computing.

    In addition to microchips, Google’s Edge TPU, Nvidia’s Jetson Nano, along with Amazon, Microsoft, Intel, and Asus, embarked on the motherboard development bandwagon to enhance edge computing’s prowess. Amazon’s AWS DeepLens, the world’s first deep learning enabled video camera, is a major development in this direction.

    Also read: Edge Computing Set to Explode Alongside Rise of 5G

    Challenges of Edge AI

    Poor Data Quality: Poor quality of data of major internet service providers worldwide stands as a major hindrance for the research and development in Edge AI. A recent Alation report reveals that 87% of the respondents — mostly employees of Information Technology (IT) firms — confirm poor data quality as the reason their organizations fail to implement Edge AI infrastructure.

    Vulnerable Security Feature: Some digital experts claim that the decentralized nature of edge computing increases its security features. But, in reality, locally pooled data demands security for more locations. These increased physical data points make an Edge AI infrastructure vulnerable to various cyberattacks.

    Limited Machine Learning Power: Machine learning requires greater computational power on edge computing hardware platforms. In Edge AI infrastructure, the computation performance is limited to the performance of the edge or the IoT device. In most cases, large complex Edge AI models have to be simplified prior to the deployment to the Edge AI hardware to increase its accuracy and efficiency.

    Use Cases for Edge AI

    Virtual Assistants

    Virtual assistants like Amazon’s Alexa or Apple’s Siri are great benefactors of developments in Edge AI, which enables their machine learning algorithms to deep learn at rapid speed from the data stored on the device rather than depending on the data stored in the cloud.

    Automated Optical Inspection

    Automated optical inspection plays a major role in manufacturing lines. It enables the detection of faulty parts of assembled components of a production line with the help of an automated Edge AI visual analysis. Automated optical inspection allows highly accurate ultrafast data analysis without relying on huge amounts of cloud-based data transmission.

    Autonomous Vehicles

    The quicker and accurate decision-making capability of Edge AI-enabled autonomous vehicles results in better identification of road traffic elements and easier navigation of travel routes than humans. It results in faster and safer transportation without manual interference.

    And Beyond

    Apart from all of the use cases discussed above, Edge AI can also play a crucial role in facial recognition technologies, enhancement of industrial IoT security, and emergency medical care. The list of use cases for Edge AI keeps growing every passing day. In the near future, by catering to everyone’s personal and business needs, Edge AI will turn out to be a traditional day-to-day technology.

    Read next: Detecting Vulnerabilities in Cloud-Native Architectures

    Kashyap Vyas
    Kashyap Vyas is a science and technology writer with 9+ years of experience writing about SaaS, cloud communications, data analytics, IT security, and STEM topics. In addition to IT Business Edge, he's been a contributor to publications including Interesting Engineering, Machine Design, Design World, and several other peer-reviewed journals. Kashyap is also a digital marketing enthusiast and runs his own small consulting agency.

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