Transportable AI Brings High-Performance, High-Bandwidth Decision-Making to the Edge
June 30, 2021
The growing list of applications for artificial intelligence (AI) covers a wide area including such things as face recognition, image processing, natural language processing, military decision making, robotic control, natural language translation, data mining - the list goes on. Typically, such applications are confined to stationary data centers housing large servers and huge arrays of storage. When AI service is needed at the “edge,” close to the user or situation that will utilize AI support, it has involved communication with the AI server and its data via a network—with the inevitable latency that can compromise in-time response to real situations.
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The use of Visual Analytics is advancing rapidly as Artificial Intelligence is employed to build sophisticated tools and processes to review large data sets. By managing these large data sets from an array of sensors and video sources, the OSS solution can decipher threats in real-time with little or no human interaction. The data filtered by an AI engine is presented to aid the user in identifying patterns and actionable intelligence with increased accuracy and insights. The combination of employing AI and Visual Analytics into an actionable solution is at the core of solutions provided by OSS.
Program managers are struggling to quickly deploy AI capabilities for real-time applications in the field – outside of data centers. These leaders can speed time to market by utilizing scalable commercial compute solutions from vendors with expertise in real-time, AI-ready platforms built for rugged environments.
The mere mention of artificial intelligence, AI, brings forth images of large, dimly-lit rows of high performance power hungry equipment racks in a data center. At the same time, "the edge" evokes the idea of constrained embedded systems operating in harsh forward locations enduring all types of environmental conditions. All the while taking in data, processing it, and reacting in real-time. As this "edge" becomes ever more demanding and complex, it increases the need for AI levels of performance like that in the data centers but now to be available and useable on the spot and in systems that are transportable at the edge—a big challenge that has been addressed by One Stop Systems.
