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What is Cognitive Computing

February 11, 2018
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Much of the excitement about cognitive computing is spurred by its enormous potential in learning, only a small fraction of which has so far been realized. The overarching goal here is to devise computational frameworks to help us learn better by exploiting data about our learning processes and activities. There are two important aspects of it—the mechanisms or insights about how we actually learn and the external manifestations of our learning activities. Cognitive computing is an emerging approach that builds upon a wealth of research and development work in Artificial Intelligence (AI).

What is Cognitive Computing?

Cognitive computing is the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The goal of cognitive computing is to create automated IT systems that are capable of solving problems without requiring human assistance.

The origins of cognitive systems work lie in cognitive science — a discipline that brings together researchers from the fields of psychology, linguistics, philosophy, computer science, and more recently, neuro-computing. We perceive “cognitive computing” as an approach that has emerged from, and attempts to subsume, the work done in AI. Cognitive informatics (CI) is the transdisciplinary enquiry of cognitive and information sciences that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, and their engineering applications via an interdisciplinary approach.

“Cognitive computing is based on self-learning systems that use machine-learning techniques to perform specific, human-like tasks in an intelligent way.”

Cognitive computing

Figure: Cognitive Computing

Cognitive computing systems use machine learning algorithms. Such systems continually acquire knowledge from the data fed into them by mining data for information. 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.

Features Required for a Cognitive System

In order to implement cognitive computing in commercial and widespread applications, a cognitive computing system must have the following features:

  • Adaptive:The system must reflect the ability to adapt (like a brain does) to any surrounding. It needs to be dynamic in data gathering and understanding goals and requirements.
  • Interactive:The cognitive system must be able to interact easily with users so that users can define their needs comfortably. Similarly, it must also interact with other processors, devices, and Cloud services.
  • Iterative & Stateful: This feature needs a careful application of the data quality and validation methodologies to ensure that the system is always provided with enough information and that the data sources it operates on deliver reliable and up-to-date input.
  • Contextual: Ability to understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task and goal. It must draw on multiple sources of information, including both structured and unstructured digital information.

Components of Cognitive Computing

Although computers are better for data processing and making calculations, they were not able to accomplish some of the most basic human tasks, like recognizing Apple or Orange from basket of fruits, till now. In today’s Digital Transformation age, various technological advancements have given machines a greater ability to understand information, to learn, to reason, and act upon it.  Cognitive Computing systems may include the following components:

  • Natural Language Processing – understand meaning and context in a language, allowing deeper, more intuitive level of discovery and even interaction with information.
  • Machine Learning with Neural Networks – algorithms that help train the system to recognize images and understand speech
  • Algorithms that learn and adapt with Artificial Intelligence
  • Deep Learning – to recognize patterns
  • Image recognition – like humans but more faster
  • Reasoning and decision automation – based on limitless data
  • Emotional Intelligence


[1] Irfan, M. T., and V. N. Gudivada. “Cognitive Computing Applications in Education and Learning.” In Handbook of Statistics, vol. 35, pp. 283-300. Elsevier, 2016.

[2] Maruti Techlabs, “Cognitive Computing and Why You Need to Know about It”, available online at:

[3] Yingxu Wang and Du Zhang, “Preface: Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)”, Fundamenta Informaticae 90 (2009), pp. i–vii

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