“How do we expect to handle the overflow of data that is headed our way?” asks James MacLennan (@jpmacl), Senior Vice President & Chief Information Officer at IDEX Corporation. “Why are we so excited at the prospect of a tidal wave of sensor and social data when we’ve spent much of the last 10 years complaining about information overload in the Internet era. The inconsistency is humorous, but there it is — what can we possibly do with all this data?” What indeed. MacLennan believes that machine learning (a branch of artificial intelligence) is the answer. I’ll go a step further and predict that cognitive computing will answer his questions. So do the analysts at Accenture. In Accenture’s latest technology vision entitled “From Digitally Disrupted to Digital Disrupter,” Accenture analysts state that cognitive computing will provide the “ultimate long-term solution” for many business challenges. Jorge Garcia (@), Senior BI and Data Management Analyst at Technology Evaluation Centers, agrees with that assessment. “Machine learning,” he writes, “along with many other disciplines within the field of artificial intelligence and cognitive systems, is gaining popularity, and it may in the not so distant future have a colossal impact on the software industry.” The software industry is not the only economic sector that will feel the impact of cognitive computing — virtually every sector will.
DeLoitte analysts, Rajeev Ronanki (@) and David Steier (@), answer MacLennan’s questions this way: “Cognitive analytics offers a way to bridge the gap between big data and the reality of practical decision making.” They explain:
“For the first time in computing history, it’s possible for machines to learn from experience and penetrate the complexity of data to identify associations. The field is called cognitive analytics™ — inspired by how the human brain processes information, draws conclusions, and codifies instincts and experience into learning. Instead of depending on predefined rules and structured queries to uncover answers, cognitive analytics relies on technology systems to generate hypotheses, drawing from a wide variety of potentially relevant information and connections. Possible answers are expressed as recommendations, along with the system’s self-assessed ranking of how confident it is in the accuracy of the response. Unlike in traditional analysis, the more data fed to a machine learning system, the more it can learn, resulting in higher-quality insights.”
There are a number of cognitive computing systems currently touting their capabilities. The most famous, of course, is IBM’s Watson. My company, Enterra Solutions®, also has a platform we call the Enterra Enterprise Cognitive System™ (ECS), a system that can Sense, Think, Act, and Learn®. Although most cognitive computing systems use a combination of machine learning, mathematics, and natural language processing, there are differences. Watson basically uses a brute force approach to cognitive analytics. It analyzes massive amounts of data and provides a “best guess” answer (IBM calls it a “confidence-weighted response”) based on what it finds. That’s how Watson beat human champions on the game show Jeopardy! This brute force approach is often called deep learning.
At Enterra®, we take a different approach. The ECS uses various techniques to overcome challenges associated with most deep learning systems. Like deep learning systems, the ECS gets smarter over time and self-tunes by automatically detecting correct and incorrect decision patterns; but, the ECS also bridges the gap between a pure mathematical technique and semantic understanding. The ECS has the ability to do math, but also understands and reasons about what was discovered. Marrying advanced mathematics with a semantic understanding is critical — we call this “Cognitive Reasoning.” The Enterra Enterprise Cognitive System approach — one that utilizes the best (and multiple) solvers based on the challenge to be solved and has the ability to interpret those results semantically — is a superior approach for many of the challenges to which deep learning is now being applied. Ronanki and Steier add, “Cognitive analytics can push past the limitations of human cognition, allowing us to process and understand big data in real time, undaunted by exploding volumes of data or wild fluctuations in form, structure, and quality.”
Jennifer Zaino (@) asserts, “Cognitive Computing increasingly will be put to work in practical, real-world applications. The industries that are adopting it are not all operating at the same maturity levels; there remain some challenges to conquer. The wheels are very much in motion to make cognitive-driven Artificial Intelligence (AI) applications a key piece of enterprise toolsets.” Zaino reports that at a conference held earlier this year, Matt Sanchez, founder and CTO of CognitiveScale, told the audience that companies “must leverage Cognitive Computing to bring contextual insights and advice right to the knowledge workers when it is needed so they can take action.” Because cognitive computing systems use natural language processing, they make analyses available to all those who need that capability and they don’t need to be data scientists to benefit. In fact, Ben Rossi (@) asserts that one of the most important benefits of cognitive computing is that it makes smart people even smarter. “In the past,” he writes, “change has typically been based on technologies that make us faster and more efficient. We’re now entering a time of change where intelligent technologies are going to make us smarter.” Rossi notes that cognitive computing systems “empower business users with the ability to ask questions and immediately get answers without ever having to think about what the machine is actually doing.”
Zaino indicates that one of the concerns potential cognitive computer users have is trust. She explains:
“Another thing to contend with in driving a growing use of cognitive-based applications is the issue of trust. How can businesses and their end users be assured that the cognitive system they are relying on can be trusted to recommend appropriate options or answers to them, or even that the systems are autonomously acting correctly on their own initiatives?”
That’s a concern because some cognitive computer systems conduct “black box” analyses (i.e., they provide insights and recommendations but explain how they reached those conclusions). The Enterra ECS, on the other, can show you the reasoning behind its recommendations and insights. That helps users gain trust in the system. Rossi observes that when cognitive computing systems are implemented correctly, they create a beautiful partnership with humans. “The machine is doing what it does best: reviewing massive data sets and finding patterns that indicate different activities and situations. And, humans are doing what they do best: looking at the situation, fitting it into a larger context and then responding to it appropriately.” IBM Fellow Robert H. High Jr. told a conference audience earlier this year that “business computing will change from transaction processing to cognition processing” over the next decade. If you’re company isn’t already considering how cognitive computing can help usher you into the next decade, you might already be behind your competition.
 James MacLennan, “The Next Next Big Thing,” Smart Data Collective, 3 November 2014.
 Jorge Garcia, “Machine Learning and Cognitive Systems: The Next Evolution of Enterprise Intelligence (Part I),” Wired, July 2014.
 Rajeev Ronanki and David Steier, “Cognitive Analytics,” Deloitte University Press, 21 February 2014.
 Jennifer Zaino, “Cognitive Computing, Artificial Intelligence Apps Have Big Future in the Enterprise,” Dataversity, 17 September 2015.
 Ben Rossi, “How artificial intelligence will make humans smarter,” Information Age, 25 November 2014.
 Frank Tobe, “Cognitive computing will be to computing what transaction processing is today,” The Robot Report, 9 October 2015.