Cognitive Computing

So what’s cognitive computing?

We humans think, so in an easy way, it can be said that cognitive computing a simulation of human thought process in a computerized model. It involves self-learning systems that make use of natural language processing, data mining and pattern recognition. The main goal is to make use of cognitive computing to create automated systems that are capable of solving problems without human assistance.

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Cognitive computing systems can harness learnings and past experiences at an immense scale to solve soft problems.

 

Cognitive computing makes use of machine learning algorithms and they continuously acquire knowledge by acquiring data by data mining techniques. The systems refine the way they look for patterns and as well as the way they process data so they become capable of anticipating new problems and modeling possible solutions. Being contextual, they understand what the data actually implicates. They draw from multiple sources to create holistic and contextual image data.

Cognitive computing is used in many Artificial Intelligence applications such as Artificial Neural Networks, Robotics, Virtual Realty.

IBM Watson makes use of cognitive computing:

 

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Watson is a question-answering computer system capable of answering questions posed in natural language, developed in IBM’s DeepQA project by a research team led by principal investigator David Ferrucci. (wiki)

 

These cognitive systems, most notably IBM Watson, rely on deep learning algorithms and neural networks to process information by comparing it to a teaching set of data. The more data the system is exposed to, the more it learns, and the more accurate it becomes over time, and the neural network is a complex “tree” of decisions the computer can make to arrive at an answer.

 

See also: Machine Learning, Artificial Neural Networks

 

 

 

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