Introduction to Cognitive Computing

Introduction to Cognitive Computing

The world of computation is evolving at an unprecedented pace, and we are witnessing numerous advances in fields such as Artificial Intelligence and others. Recently a new term, Cognitive Services, was introduced by IBM when it launched one of it’s coolest inventions, IBM Watson.

You may think of IBM Watson as a software agent, but it has much more to offer than what a conventional agent. As a matter of fact, IBM Watson is composed of diverse and fascinating technologies that make IBM Watson really smart in terms of usability. Since the advent of IBM Watson, many other software giants have started using the term Cognitive Computing.

What is Cognitive Computing?

In general, the term cognitive computing has been used to refer to hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making.

Let’s examine the above definition by considering the situation of an intelligent robot. You own this robot, and the purpose of this robot is to give you the best advice, so that you can make your decisions faster. Currently we make most of our decisions on the basis of past experiences and gut-feelings.

Now, imagine the power the above-mentioned robot can add to your decision making, provided that the robot will be able to traverse through thousands or perhaps millions of individual experiences and their results. Conducting an analysis of all this data, this robot will give you the best advice that a human can give. The decision will still be yours, but now your decisions will be solid, accurate and based on facts.

The entire concept is fascinating, with so much to offer. Cognitive Computing is just in its initial phases of development. When the technology is mature, it may be able to help many individuals, groups and enterprises to make confidently realistic and factually oriented decisions.

How Cognitive Computing Applications Work?

Cognitive Computing applications link data analysis and adaptive page displays (AUI) to adjust content for a particular type of audience.

The application and services that provide cognitive computing usually have to analyze huge amounts of data, or Big-Data if your a tech geek, that can be in thousands of Gigabytes. These applications with the use of many disruptive technologies extract the most useful information and evidences from the data.

With the help of this data, these applications learn facts and figures using various algorithms and the best available methods to solve given problems. The output of these applications is customized for the end-user, and is related to the preferences of the end-user.

Netflix’s recommendations or Google ads service are two key examples of good cognitive computing applications. Netflix tries to provide you the best recommendation for a movie that you would like to watch. On the other hand, Google ads service learns from your preferences and search history to serve you with the best related advertisements.

Composition of Cognitive Computing applications

Cognitive Computing is a blend of some of the most exciting technologies around, and it tries to make the best use of them. Some of top technologies used by cognitive computing are:

  • Machine Learning
  • Deep Learning
  • Data mining
  • Reasoning
  • Emotional Intelligence
  • Natural Language Processing
  • Speech Processing
  • Computer Vision
  • Human Computer Interaction
  • Dialog and narrative generation
  • Sentiment Analysis

Each of these technologies is in-fact a separate field of study. This makes cognitive computing applications extremely fascinating to work on.

Attributes of Cognitive Computing Systems

Some of the elementary properties of cognitive computing systems/services/applications are that they are:

  • Adaptive
    • They may learn as information changes, and as goals and requirements evolve.
  • Iterative
    • They may aid in defining a problem by asking questions or finding additional source input.
  • Stateful
    • They may “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time.
  • Contextual
    • They may understand, identify, and extract contextual elements such as meaning, syntax, time, location.
  • Interactive
    • They may interact easily with users that are either humans or machines.

Some use-cases of Cognitive Computing

Now that you have a basic understanding of Cognitive Computing, you may probably want to know the common use cases of Cognitive Computing. Some of them are:

  • Speech recognition
  • Sentiment analysis
  • Face detection
  • Risk assessment
  • Fraud detection
  • Behavioral recommendations
  • Recommender systems

We are using Speech recognition, text summarization, context analysis and sentiment analysis in Converse smartly to make the conversations really smart.

There are many use cases of Cognitive computing for enterprises also. You can understand it better with the help of the this video from IBM:

What is the difference between Cognitive Computing and Artificial Intelligence?

So aren’t these two same things? No, they are not. There are some of the most fundamental differences between the two. Although you may think of Cognitive Computing as a field derived from Artificial Intelligence, but the objective of Cognitive Computing is solely to help humans making their decisions using human-like cognition and thought process.

Artificial Intelligence does not try to mimic human thought processes. Instead, a good Artificial Intelligence system is simply a set of best possible algorithms for solving a given problem. Artificial Intelligence can replace humans effort in some areas of work with automated and intelligent systems, whereas Cognitive computing systems do not replace humans, rather they just try to create an artificial human thought process.

Also, Cognitive Computing does not make decisions for humans, but rather supplements our own decision-making, whereas a true AI may instead be making all the decisions. This could supersede human thinking capabilities in some cases.

An example of an Artificial Intelligence system can be of a computer agent that is able to diagnose any disease, provide the best possible treatment and prescribe the most appropriate medicine to the patient. On the other hand, a Cognitive computing system would recommend the set of best suited course of actions to a doctor, and those recommendations will be based on evidences and exact fact and figures from experienced doctors.

Popular Cognitive Services platforms

Some of the most popular cloud hosted cognitive services platforms are:

  • IBM Watson
  • Microsoft Cognitive Services
  • Google’s DeepMind
  • Amazon’s Cognitive Services (includes Alexa)
  • Sparkcognition
  • Numenta
  • and many others

One can use one of these or a combination to create state of art services that are able to help humans making great decisions by simulating the human thought process decorated with vast set of experiences and evidences.

Existing Expertise in Cognitive Computation

At Folio3, we have been evaluating some of the leading cognitive computing platforms for our in-house project Converse Smartly. The objective of this project is to make conversations smarter using cognitive computing services. To learn more about Converse Smartly, visit the live web app here


Cognitive Computing, as by name, simulates human cognition, allowing humans to make the best use of experiences, history, evidences, facts and figures to make confident and accurate decisions. Unlike Artificial Intelligence, which tends to replace human effort in many areas.

Cognitive Computing has a goal to help humans in their everyday tasks and decision making without actually replacing human effort. You can think of a complete cognitive computing system as a Superhuman, who is intelligent, smart, aware, understanding, accurate, helpful and emphatic. Welcome to the future of awesomeness!





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