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“Cognitive automation is not just a different name for intelligent automation and hyper-automation,” said Amardeep Modi, practice director at Everest Group, a technology analysis firm. “Cognitive automation refers to automation of judgment- or knowledge-based tasks or processes using AI.”

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Whether you choose native or hybrid mobile application development, one of the first hurdles you’ll need to overcome is the relatively limited resources on mobile devices. Your target mobile device will have much less processing power and memory than desktop computers or enterprise servers. These constraints might seem like a significant challenge, especially if you’re more familiar with the comparatively boundless resources for conventional software development for web apps. Put simply, RPA involves automating menial and repetitive tasks; cognitive automation adds an all-important extra layer of AI and machine learning. This is a branch of AI that addresses the interactions between humans and computers with natural language. NLP seeks to read and understand human language, but also to make sense of it in a way that is valuable.

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Those who control this information are the ultimate owners of it and can use it as they see fit. For this reason, many experts believe that symbolic AI still deserves a place in AI research, albeit in combination with more advanced AI applications like neural networks. One such project currently in the pipeline is the Neuro-Symbolic Concept Learner (NSCL). Compared with systems that only use symbolic AI, the NSCL model does not have to face the challenge of analyzing the content of images presented to them. Dr. Lili Mou is an Assistant Professor at the Department of Computing Science, University of Alberta.

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