The Emerging Role of AI in Edge Computing
The “things” in the Internet of Things are getting smarter, with profound implications for organizations and the way people work and live.
Internet of Things (IoT) has taken businesses today by storm and has become one of the centric strategies for businesses to emerge as the leaders in the market. Most of the businesses have laid enhanced importance towards IoT implementations into the business models for achieving enhanced levels of customer service. Harnessing the power of IoT, business models have witnessed a paradigm shift in their operations. With IoT, the analysis and processing remained limited to the central authority, however, with the advent of AI in edge computing, the analysis and processing power has been transferred to the edge devices.
Increasing penetration of machine learning and advancements in the Artificial Intelligence technologies is anticipated to be one of the major factors driving the AI edge computing market. Higher cost of implementations coupled with weak infrastructures for AI, hinders the adoptions of this technology further posing a challenge to the growth of AI edge computing market. Encouraging advancements in the sensor technology coupled with significant investments by Governments for the development of IoT to provide new opportunities to the players operating in the AI Edge Computing market.
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The advantages of AI-enhanced decision-making at the edge include the following:
- Edge-based AI is highly responsive and closer to real-time than the typical centralized IoT model deployed to date. Insights are immediately delivered and processed, most likely within the same hardware or devices.
- Edge-based AI ensures greater security. Sending data back and forth with Internet-connected devices subjects data to tampering and exposure even without anyone being aware. Processing at the edge minimizes this risk, with an additional plus: Edge-based AI-powered devices can include enhanced security features.
- Edge-based AI is highly flexible. Smart devices support the development of industry-specific or location-specific requirements, from building energy management to medical monitoring.
- Edge-based AI doesn’t require a PhD to operate. Since they can be self-contained, AI-based edge devices don’t require data scientists or AI developers to maintain. Required insights are either automatically delivered where they are needed, or visible on the spot through highly graphical interfaces or dashboards.
- Edge-based AI provides for superior customer experiences. By enabling responsiveness through location-aware services, or rerouting travel plans in the event of delays, AI helps companies build trust and rapport with their customers.
The key players influencing the industry are Cisco Systems, Inc., Huawei Technologies Co. Ltd., Nokia Networks, Hewlett Packard Enterprise, and FogHorn Systems. Also, IBM Corporation, Saguna Networks Ltd., ClearBlade, Inc., Vapor IO, and Rigado, LLC are a few other important players in the AI edge computing.
Examples of applications benefitting from AI-charged edge computing include medical devices, manufacturing systems and vehicles. Medical devices, in particular, have an acute need for at-the-edge intelligence – critical data in the operating room, for example, has to be processed in a timely fashion in order to provide the right information for physicians or doctors to act upon.
Source: The Insight Partners
