June 19, 2016

Cognitive Computing is a significant viral topic on the world wide web these days.  Many see it as directly connected to the topic of AI or artificial intelligence.  Every vendor seems to offer it too, so in a blink of any eye, Cognitive Computing has emerged, yet how did all this come about so rapidly?

Without a doubt, Cognitive Computing is impressive and fascinating.  Yet, some people worry that artificial intelligence will make us feel inferior, but then, anybody in his right mind should have an inferiority complex every time he looks at a flower, a tree, the sky, or the smile of a small child.  By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.  Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It’s really an attempt to understand human intelligence and human cognition.  Thus, Cognitive Computing is emerging quickly and being embraced in almost every industry.  But, is it here to replace humans?  Will we all be out of work in a few years and replaced by robots or fancy computers?

For a proper definition of Cognitive Computing, we will look to the Tech Target (2016) web site that offers this break down for us.

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.

 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.

Cognitive Computing is used in numerous artificial intelligence (AI) applications, including expert systems, natural language programming, neural networks, robotics and virtual reality. The term Cognitive Computing is closely associated with IBM’s cognitive computer system, Watson.

So, out of this textbook style definition, we can conclude a few key points to consider: natural language processing, self learning systems that evolve naturally, big data mining, complex algorithms, and emulation of how the human brain works.  Yes, that does sound like AI from the Hollywood movies.  And, it does seem to reflect how human beings learn and grow.

To first appreciate Analytic and Cognitive Computing platforms, we must first consider Big Data.  We characterize Big Data with the four “Vs”, volume, velocity, variety, and veracity.  The following graphic provides some context to the four Vs.

Where did Cognitive Computing come from and how did we get here today?  Below is a graphic that maps the history of Cognitive Computing.  The journey began a very long time ago in order for us to reach this destination today, it was not a simple overnight success, it did not just burst on the scene, it took time and a lot of hard work to develop.

But, do all IT vendors really have Cognitive Computing?  No, they do not.  At best, most offer simple analytic based computing systems that they market as Cognitive Computing systems.  So, how do we define a Cognitive Computing solution?  It is:

  • Adaptive – able to learn and evolve as the situation changes
  • Flexible and insightful – able to resolve ambiguity and tolerate unpredictability
  • Make use of dynamic data in real-time or near real-time
  • Highly interactive, to both the users and the supporting platforms
  • It can make use of voice to text as an input for queries and interactions; so you can speak to it, in multiple languages too
  • Able to respond to the user in a natural language manner with text to voice interfaces, again, in multiple languages
  • Iterative and adapting with user interactions, these systems even ask questions and evolve towards a desire outcome
  • Stateful in so much as these systems concatenate previous interactions and respond in context to a point in time
  • Contextual; as they understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal
  • Integration of other forms of unstructured media, such as video cameras to incorporate facial reactions
  • Sensitive as they use sentimentality engines to make use of sentiments and emotional context

Whereas, Analytic Computing is also very capable, but it is several steps down compared to Cognitive Computing.  Analytic Computing offers many excellent features that are normally included within Cognitive Computing platforms.  These systems tend to be much more data-centric and ingress / egress data as text and numbers.  The following are some of the capabilities normally associated with Analytic Computing systems.

  • Analytic engines are used to discover and reveal meaning in data; similar to how and why we use Cognitive Computing too
  • These systems use interpretation to estimate and weight the importance found within the data sets; statistical algorithms are applied to the data
  • They look for meaningful patterns, trends, and history, within data; histograms are created that seek out deviations from the norm
  • They can sometimes use both structured and unstructured data, but mostly structured data.  When they are able to use unstructured data, they are better at analysis
  • Analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance
  • Because they are primarily textual and numerical, they report is reams of lines of data that is difficult for humans to comprehend, therefore Analytic Computing often favours data visualization to communicate insight

IBM’s Watson is perhaps the best Cognitive Computing platform in the world today, and it is years ahead of the other imitators, which are typically not cognitive at all, they are just analytic solutions made to look like cognitive systems.  It is mostly marketing hype from the competitors as they are struggling to catch up and so they place a facade in front of the customers to make it appear that they are ready to service your Cognitive Computing needs.  But, it will take them many years to match Watson’s intuition, sensibilities, and human qualities.  They cannot invent these capabilities overnight.

“When people ask how Watson is different than a search engine, I tell them to go on Google and type ‘anything that’s not an elephant’.  What do you get?  Tons of pictures of elephants.  But Watson knows those subtle differences.  It understands that when feet and noses run, those are very different things.”

In conclusion, a cognitive system is a one that performs the cognitive work of knowing, understanding, planning, deciding, problem solving, analyzing, synthesizing, assessing, and judging, as they are fully integrated with perceiving and acting.  Is it AI, maybe, maybe not.  It is more of an augmentation system then a replacement system.  In many definitions, AI completely replaces a human and in the case of Watson, we are not there yet.  But, we are getting close.  Very close.


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About the Author:

Michael Martin has more than 35 years of experience in broadband networks, optical fibre, wireless and digital communications technologies. He is a Senior Executive, Internet of Things Lead with IBM Canada’s GTS Network Services Group. Over the past 11 years with IBM, he has worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He was previously a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN:TSX). Martin currently serves on the Board of Directors for TeraGo Inc (TGO:TSX) and previously served on the Board of Directors for Avante Logixx Inc. (XX:TSX.V).  He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) and on the Board of Advisers of four different Colleges in Ontario as well as for 16 years on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section.  He holds three Masters level degrees, in business (MBA), communication (MA), and education (MEd). As well, he has diplomas and certifications in business, computer programming, internetworking, project management, media, photography, and communication technology.



Herbst, E. (2016). Cognitive Computing. Essextec. Retrieved on June 17, 2016 from, https://essextec.com/cognitive-computing

IBM. (2016). Infographic: Mapping the Path to Cognitive Computing. Retrieved on June 17, 2016 from, https://www-03.ibm.com/press/us/en/photo/41370.wss

IBM. (2016). Welcome to the era of cognitive business. Retrieved on June 19, 2016 from, http://www.ibm.com/cognitive/

Lintern, G. (2007). What is a cognitive system? Retrieved on June 19, 2016 from, http://www.cognitivesystemsdesign.net/Papers/What%20is%20a%20Cognitive%20System.pdf

Rouse, M. (2016). Cognitive Computing. Retrieved on June 17, 2016 from, http://whatis.techtarget.com/definition/cognitive-computing

Schaeffer, C. (2016). Cognitive Computing Explained. CRM Research. Retrieved on June 19, 2016 from, http://www.crmsearch.com/cognitive.php

Wikipedia. (2016). Analytic Computing. Retrieved on June 19, 2016 from, https://en.wikipedia.org/wiki/Analytics

Wikipedia. (2016). Cognitive Computing. Retrieved on June 19, 2016 from, https://en.wikipedia.org/wiki/Cognitive_computing