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Understanding Cognitive Computing

February 26, 2021

We have stepped into a radical age of tech improvements in our day-to-day functions. And as the influence of Artificial Intelligence grows, technological definitions such as deep learning, artificial learning, voice processing, text mining, semantic computing, neural networks, etc. give way to ambivalence. Let’s see if we can make them slightly simpler to grasp today.

Artificial Intelligence is known as a machine’s ability to carry out systematic responses similar to human learning and decision-making. While this should not be problematic, the concept includes multiple technologies and experiences – and as such can be confounding to business leaders and decision makers. Some refer to machine learning while talking about AI or deep learning or text mining or even automation, and the list goes on. In general anything which is automated, talks to you, plays your music is referred to as some sort of AI.

While many of us do not fully grasp its potential, we find it fashionable to express uninformed opinions. Like it’s trendy to talk about “cloud” and the “Internet of Things.”

What is Cognitive Computing?

Cognitive computing involves replicating human thought processes in a computerized model that includes self-learning programs using data mining, pattern recognition, and natural language processing to simulate the way the human brain works. This helps with problem solving, resolving dynamic scenarios marked by complexity and confusion. Machine learning, image recognition, generation of hypotheses, artificial neural networks are all used in different applications of Cognitive Computing, which is one of the Top 6 breakthrough accelerators on the AI platform.

Cognitive Computing in AI

AI and Cognitive Computing are often used synonymously by those outside the technology sector. In reality, AI and cognitive computing vary greatly and working at the conjunction of these two can reveal the distinction clearly. A conscious machine or device learns on a continuum, has a function, and communicates naturally with humans. Their algorithms gather information from human interactions and learn to reason without being programed likewise. They learn and perfect themselves on their own!

In their overlapping intersection Cognitive computing and Artificial Intelligence use related technology for cognitive power applications. Cognitive analysis can replicate human thought processes and imitate how human brains function, approaching and understanding complex scenarios. Artificial intelligence can conduct tasks similar to human learning and decision-making. Personal assistants powered by AI would understand voice commands and questions, respond with details, or operate efficiently, and reliably.

These techniques can enhance human capacities by increasing their interpretation, decision-making, perception, and reaction to other individuals in ordinary or challenging circumstances. Cognitive computing technology such as IBM Watson and artificial intelligence resources such as expert systems, intelligent personal assistant software can bolster people’s ability to understand, identify, act, study, and fix complications in much less time. The successful application of these includes Amazon Echo, Apple Siri, Google Assistant, and Microsoft Cortana.

The primary purpose of using Cognitive computing in artificial intelligence is that AI is not meant to imitate human thoughts and methods but solve a problem with the best possible algorithm. Unlike human emotions, which are often the result of uninformed judgments.

Why do we need Cognitive Computing in Artificial Intelligence?

At the heart of AI applications is the need to transform human desires and intentions into something that the machine can provide or react to external stimuli. Think about it as the pinnacle of usability. In other instances, the feature or skill is measured by more data than a person can manage, making sense from unstructured data to form informed actionable decisions or when a machine can understand natural language. To mention a few reasons:

  • Personalized response: The machine would be able to interpret various human responses and respond to them based on numerous signals. Learn and refine its knowledge about human behaviour based on previous interactions.
  • Data interpretation: It would identify correlations in broad data sets. Enhance results through iteration based on complicated parameters. Recognize and predict outcomes or actions, operate autonomously, cope with unique conditions, apply rational purpose, override questions, recognize word importance, and apply judgement.
  • Semantic learning: It can enhance operations by adding meaning to immersive semantic learning and evolve based on experiences, relevant discoveries, Big data knowledge base, and various sources. It can then use analytics into fully integrated: search, BI, analysis, simulation, polling algorithms, identification, statistics, machine learning, NLP, inferencing, content management, speech recognition, etc.

These are ambitious, extensive lists. These skills are in place for a range of commercial implementations but with minimal functional deployment. Algorithms to accomplish these outcomes perform well across narrow realms and frameworks. General-purpose AI, as in modern science fiction, is a long way away from now, of course the industrials use cases are not that far beyond the possibility, but full-fledged flying cars to intelligent robots assisting us in the kitchen would still take some time to be reality.

Summing Up

Cognitive computing and artificial intelligence help timely decision-making, provide highly relevant knowledge and refine the resources available to prevent overlooking essential innovations. Substantially reducing overload and enabling people to act more quickly and efficiently, at the same time, reducing mistakes, damages, and injury, improving health and safety.

If you want to know more on the subject of Cognitive Computing, you can reach out to the author at

About the Author

Danish Nadeem
Digital Transformation and Innovation Expert

A digital transformation consultant, Danish leads emerging technologies within Capgemini Invent for Norway. He is passionate about delivering customer success & growth strategies using the design thinking approach.

His background in B2B sales, product development & digital leadership in product manufacturing & IT services industries makes it easy for him to understand the business & technical complexities & work towards resolving user pain points.