Understanding Cognitive Science Technology

Cognitive Computing is a highly technical and mathematical field. At a simple level, the idea of a computer that thinks like a human is appealing. At a technical level, the idea that a computer system consumes data and  information of all types and performs calculations on what it is ingesting can raise a lot of questions especially when the computer is predicting outcomes for humans to “act upon.”

The area of Cognitive Sciences is rooted in Deep Learning Artificial Intelligence (AI) and includes diverse Machine Learning (ML) methods from disciplines like: data mining, deep learning, neural networks, graph learning theory, natural language processing and decision trees or random forests.  These are some of the terms that you will see in the world of Deep Learning methods that are being used to mimic the way the human brain works. I have defined some of these in this blog to help guide our readers.

Cognitive Computing approaches can be summarized in three key words “Sense”, “Comprehend” and “Act”.  Actionable insights are key to cognitive sciences as that’s what humans do; we look at data or information and form an opinion, a judgment, and then we engage in some action based capacity.

Consumers have been experiencing the applications of machine learning in smart devices with software like Apple’s Siri and Microsoft’s Cortana, which make use of machine learning to comprehend a user’s voice and the spoken instructions to the phone via a process called natural language processing.

There are also helpful organizations to learn from such as the Cognitive Computing Consortium, a good source of information.

Market Growth Dynamics

The  Recent Tractia 2015 study forecasts the sales of enterprise AI will be a cumulative $43.5 billion between 2015 and 2024.

Deloitte Global predicts that by the end of 2016 more than 80 of the world’s 100 largest enterprise software companies will have integrated cognitive technologies into their products, a 25 percent increase on the prior year. By 2020, they expect the number will rise to about 95 of the top 100.

The use of cognitive technologies in enterprise software is only part of the overall trend toward increases use of AI in the larger enterprise market.

In 2016 Deloitte expects the cognitive technologies that will be the most important in the enterprise software market will be:

  • Machine Learning (ML) – the ability of computer systems to improve their performance by exposure to data but without the need to follow explicitly programmed instructions — is likely to be the most prevalent. It enhances a large array of applications, from classification to prediction, from anomaly detection to personalization. Machine Learning methods use Random Forests, a term that refers to an analytic technique developed by Leo Breiman and Adele Cutter. The technique improves the accuracy of predictive outcomes. Decision Trees are used in machine learning and statistics to train a system in classification, so it can predict a certain outcome based on variables that have led to that outcome in the past. With random forests, the predicted outcomes from many decision trees are combined, thus the term – “Forest.” Randomness is introduced by training each decision tree in the forest on a random sample for the entire input data set, and interjecting variables at random into each brand (decision) point of every tree in the forest, these types of methods reduce the classification errors to improve the predicted outcomes of ML methods.
  • Natural Language Processing (NLP) – whereby computers can process text in the same way as humans, for example extracting meaning from text or even generating text that is readable, stylistically natural, and grammatically correct – has multiple valuable applications when incorporated in software that analyses unstructured text.
  • Speech Recognition – the ability to automatically and accurately transcribe human speech, is useful for applications that may benefit from hands-free modes of operation.

Applications of Cognitive Technology

Because cognitive technologies extend the power of information technology to tasks traditionally performed by humans, they have the potential to enable organizations to break prevailing tradeoffs between speed, cost, and quality. Below are some examples of how different market leaders are using Cognitive Computing approaches, including our own company:

Intel is using machine learning to improve sales effectiveness and boost revenue. One approach it takes is automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns. The resulting categories can be used to prioritize sales efforts and tailor promotions. The company expects this strategy to result in $20 million in additional revenue when rolled out globally

IBM and MIT Visualization and interpretations of patterns are mental processes that humans are particularly adept at. When we see or hear something happen, we can instantly describe it: “a girl in a blue shirt caught a ball thrown by a baseball player,” or “a dog runs along the beach.” It’s a simple task for us, but an immensely hard one for computers. However, advanced research is underway in many leading companies around the world. For example, IBM and MIT are partnering up to see what they can do about making it a little easier.

The new IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension is a multi-year collaboration between the two organizations that will be looking specifically at the problem of computer vision and audition. This is a research project that we will want to watch in the Cognitive Technologies evolution. The new lab will be led by Jim DiCarlo, head of MIT’s Department for Brain & Cognitive Science; this department and CSAIL will contribute members to the new lab, as will IBM’s Watson team.

SalesChoice, our company, has primarily been researching and applying Machine Learning AI Methods in our software. We have been able to accurately develop AI methods to enable predictive forecasts and prioritize sales activities to improve sales performance at an uncannily accurate rate. With our proprietary methods, we are able to predict future sales outcomes even blind folded at over 90% predictive accuracy on whether a company will have won or lost a particular sales opportunity, on large sales data sets. This means that for our clients using our software, they can increase their win rates by 20-30% within 12 months by following our software’s recommendations, as well as reduce their Cost of Sales by 10-30%, and more importantly increase their top line revenue by 5-10%. Case Studies validate these benefits in our clients like: DigiDay, RelationEdge, and Macadamian Technologies, to name a few.

As we increasingly appreciate how to connect AI to Sales Professionals to increase their performance levels with cognitive technology approaches, we have learned that still about 40% of sales professionals will not read the signals in their CRM databases to follow the guidance that analytical ML systems are giving them. So we are now experimenting with creating iconic symbols and intelligent alert notifications on their mobile devices, giving them mental queues to THINK a little more.

Adults learn and behave in different ways – so automating B2B sales tasks with more cognitive science approaches must take advantage of diverse adult learning styles in software design (user interface) approaches.  More than three of the five senses need to be integrated into higher utility software approaches for increasing adoption of software investments. Simply inputting data and enabling data visualizations is not sufficient motivation to engage and expand a human’s mind, let alone guide them. Everyone learns differently and at different rates. Sight has been predominantly the world of software UI Experiences, Sound is increasingly important with the rise of Robo Advisors with Speech recognition capabilities, and Touch has been continually evolving since the advent of smaller form devices like mobile, and now smart watches.

Just because we add more functions and features into CRM software packages with AI/ML approaches does not mean Sales Reps will spend time to absorb and apply the analytical power they will have at their fingertips.

To go beyond only analytical visualization approaches, SalesChoice has just released Selly Says™, our Secret Sales Intelligent Agent that will learn about a sales reps’ activities and pipeline health and will give them coaching 7×24 in full textual messages leveraging iconic symbol queues on what is healthy or not healthy in their sales pipeline activities.  Eventually like SIRI did, we will have speech coaching as well. Selly Says™ will always be there, always on, always learning and will be fun, friendly and more often right than wrong.

We have concluded that as we look at our company’s future product roadmap, we must look more closely at NLP and Speech Recognition as additional vision pathways to create the world’s smartest Sales GPS Guidance System for Sales Professionals.

Benefits of Using Cognitive Approaches 

The three main benefits for software companies that have integrated cognitive technologies into their products will be:

  • Improving core functionality. Cognitive technologies will be used to improve the performance of existing software by doing the same things, only better. For example, we use machine learning to predict sales forecasts and give sales professionals the ability to predict when an opportunity will most likely close in which quarter for outlook purposes. In other words, we help companies see more to win more.  Another example is CheckPoint’s Cognitive Threat Analytics software that relies on advanced statistical modeling and machine learning to independently identify new web security threats, learn from what it sees, and adapt over time.
  • Generating new insights. Machine learning and other advanced analytical technologies will likely make it possible to uncover previously inaccessible insights that were hidden in large data sets or obscured by the unstructured format of the data. We provide reasons for wins and losses of sales cycles that allow a company to see new patterns underlying their sales cycles that they cannot easily see.
  • Automation. Cognitive technologies make it possible to automate tasks formerly done by people. We prioritize all sales cycles to make it easier for sales professionals to do their jobs more efficiently and effectively.


Many top software companies have already discovered the potential for cognitive technologies to enhance their products, create value for customers and improve business operations. There is strong support from venture capital investors is helping to further commercialize enterprise applications of cognitive technologies.

As companies consider how best to apply Cognitive Computing solutions into their enterprise or into their software strategies, I found Accenture’s Cognitive Computing Framework most helpful as it provides guidance on two criteria: first, the complexity of the work that is being done and, second, the complexity of the data and information being worked with.

On the one hand, the work itself may be routine, predictable and rule-based— clerical work and claims processing, for example, or credit decisions. At the other end of the spectrum, activities might be more ad hoc and unpredictable and require the application of human judgment—the work of research scientists, for example, or architects, financial advisors and consultants.  They also look at this in terms of data complexity, data is sometimes quite structured, stable and low- volume—for example, budget data or sales data. In other cases, at the other end of that spectrum, data can be unstructured, volatile and high-volume—social media, multimedia data, sensor data and so forth.

The framework that results from this dual analysis: work complexity and data complexity then position four primary types of activity models: (1) Efficiency (2) Expert (3) Effectiveness and (4) Innovation.

This is an excellent framework and can be found here:

What is clear is Cognitive Computing Approaches are not going away. It is time for all business leaders to have a strategy in their organization to take stock of their position on Becoming more Cognitive Ready. The world is dynamic, fast changing and countries are betting on leapfrogging with advanced AI approaches across all infrastructure platforms. Countries to watch external to North America in AI, are Singapore, particularly the National University of Singapore with their research on robotics as digital receptionists. Nadine is a robot receptionist at the Nanyang Technological University (NTU), is staring at the visitor in front of her. “I remember you,” she says. “You were here last Saturday.” The long-haired, uncannily human-looking robot pauses as her software runs through past interactions to figure out the most appropriate thing to say. Finally, she settles on: “We talked about your job.”

At SalesChoice, we are experimenting in the future world of Sales Professionals using AI/ML methods and we are 100% confident the Death of the Salesman without AI is already here. With Salesforce’s global announcement of Einstein, the world of Sales will never be the same. Every company in the CRM and BI ecosystem needs predictive and prescriptive data sciences teams to keep relevant currency in their investments.

We welcome the world to visit us at SalesChoice, as Selly Says™, our intelligent ML learning agent is now being officially launched and the unisex secret sales agent will tell your sales reps when they have promising sales cycles blooming at the right pace with winning patterns or flagging them when they are on the edge of losing certain deals, and even alerting them to wasting company resources as they are sandbagging and holding back knowledge which is a forbidden fruit in the garden of CRM.

There are no end of possibilities of creating an intelligent robo advisor for a Sales Professional that becomes a future B2B Sales Professional handling all the inside sales activities of call centers, and enabling higher order/judgement functions to be handled by experts where more solutioning complexity resides.

Creating the Office of The Future should be a strategic plan for every Fortune 500 company as the Organization of the Future will have cognitive and AI /ML capabilities interlaced in everything.

Book your DEMO of SalesChoice and see Selly Says™ in Action, only available to companies using Salesforce at this time. SalesChoice is a certified ISV Partner of Salesforce.

Book a Training Session for your Executives, and I would be delighted to visit your company. With 14 books written in the Future of Technology Innovations, Big Data, Social Media and increasingly my research is in the Field of AI/ML, I welcome a opportunity to share our experiences and help spark future possibilities. Reach me at cindy@saleschoice.com

End Notes:

  1. Devin Coldeway, IBM and MIT partner up to create AI that understands sight and sound the way we do Sept 20th 2016 https://techcrunch.com/2016/09/20/ibm-and-mit-partner-up-to-create-ai-that-understands-sight-and-sound-like-we-do/
  2. Derrick Harris, “How Intel is betting on big data to add tens of millions to its bottom line, GigaOm, November 18, 2013, <https://gigaom.com/2013/11/18/how-intel-is-betting-on-big-data-to-add-tens-of-millions-to-its-bottom- line/>, accessed October 14, 2014.
  3. Paul Lee, Duncan Stewart Cognitive technologies enhance enterprise software TMT Predictions 2016http://www2.deloitte.com/global/en/pages/technology-media-and-telecommunications/articles/tmt-pred16-tech-cognitive-technologies-enterprise-software.html#full-report
  4. For an introduction to the concept of cognitive technologies, their improving performance, efforts to commercialize them, and the growing impact we expect them to have, see David Schatsky, Craig Muraskin, and Ragu Gurumurthy, Demystifying artificial intelligence: What business leaders need to know about cognitive technologies, Deloitte University Press, November 4, 2014, <http://dupress.com/articles/what-is-cognitive-technology/>, accessed November 9, 2014

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