The aim of cognitive computing is to mimic human thought processes in a computerized model. Using self-learning cognitive algorithms that use data mining, machine learning, pattern recognition, and natural language processing, the computer can imitate the way the human brain works.
Cognitive systems analyze the huge amount of data which is created by connected devices (not just the Internet Of Things) with diagnostic, predictive and prescriptive analytics tools which observe, learn and offer insights, suggestions and even automated actions.
Cognitive Computing and Machine learning addresses the challenge of passing the boundary of traditional data analytics algorithms, which spotlights the development of swift efficient cognitive algorithms.
These cognitive or machine learning algorithms enable real-time processing of huge volume of data, deliver precise predictions of various types such as recommending right products, customer segmentation, detecting fraud and risks, customer retention etc. Cognitive Computing and Machine learning supports these functions by creating a set of cognitive or machine learning algorithms that differ from the traditional statistical techniques. The emphasis is on real-time and highly scalable predictive/cognitive models, using fully automated methods that make data scientist tasks easier.
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