How to Integrate Artificial Learning with Embedded Systems? – Analytics Insight

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Keywords like Embedded AI /Embedded ML /Edge AI, mean the same that encapsulate making an AI algorithm or model run seamlessly on embedded devices. However, due to a massive gap between technologies, C-Suites and even the techies don’t know where to start.
Artificial intelligence (AI) is witnessed as an imperative technology required for the growth and development of the Internet of Things (IoT), robots and autonomous vehicles. Embedding advanced AI in action in everyday life requires massive data sources to go through multiple high-speed computers in remote server farms. Besides, they are now also being proposed as a way of managing the immensely complex 5G protocols.
Embedded hardware finds its application to maintaining the health of industrial equipment operating in remote locations.
 
Systems powered with embedded sensors can be trained to spot potential problems when they are fed with real-time data. However, a neural network, applies many algorithms as an alternative solution to address the task at hand.
Embedded systems dealing with the science of integrating hardware and its related software’s at a nano scale can be used to apply/study AI and Machine Learning techniques which are just theories and concepts. For instance, a robot is an embedded system (with chips, sensors, etc) running a software which can perform/simulate AI and Machine learning tasks such as Path finding, face detection, aggregating environmental data and sending them to servers for knowledge representation, data mining, etc.
Enterprises can enjoy plenty of applications over embedded systems especially when artificial intelligence capabilities are brought into these systems. Some important upcoming application areas include:
Defect inspection, asset tracking inside and outside a connected factory, production asset inspection, will all require some combination of embedded systems and AI.
Autonomous vehicles and robotics systems will continue to rely on AI powered computer vision for safely interacting with their environment.
Object recognition, pose recognition, audio and video processing, object segmentation, facial recognition, become much easier with an AI-capable embedded system.
In a crux, credit to the broad range of hardware platforms for artificial intelligence (AI), embedded systems/IoT devices is a promising reality for the tech industry. Specialized AI applications in embedded systems are highly competitive and bring high value to the organisation.
If enterprises can take advantage of some standardized hardware and open-source, they can quickly build a powerful embedded system product and get to market quickly with a definitive competitive edge.
 
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Intel

Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.

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