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When a computerized model performs human tasks using machine learning then it is known as cognitive computing. Here the system uses natural learning processing, data mining and pattern recognition to perform tasks like a human. The goal of cognitive computing is creating computing frameworks that can solve complicated problems without human assistance.

The essential features for enabling cognitive computing are:

  • Adaptive: The machine should be able to learn and mimic the human tasks efficiently.

  • Interactive: Active interaction with all the elements of the system- processor, devices, cloud services and the user.

  • Iterative and stateful: The machine should be able to remember all the previous interactions and provide a suitable information for a particular problem. It should also ensure that all the necessary data is provided to a solution provided with a reliable and up-to-date input of data.

  • Contextual: The machine should recognize all the contextual elements such as user’s profile, process, task, meaning, syntax, time, location, regulations, appropriate domain, and goal.

Cognitive computing uses various artificial applications like natural language processing, machine learning, algorithms that deal with artificial intelligence, deep learning, image recognition, reasoning and decision automation, emotional intelligence, neural networks, virtual reality, and robotics.

Cognitive computing is enabled in popular companies like Google (Google deep mind), IBM (IBM Watson), Microsoft (Microsoft deep mind) etc.

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