For the past few years, one of the biggest buzzwords in the business arena has been artificial intelligence (AI). Unfortunately, AI is an imprecise term that refers to a number of cognitive technologies. Most often when people use the term artificial intelligence, they are really talking about machine learning (ML). Ronald Schmelzer (@rschmelzer), Managing Partner and Principal Analyst at Cognilytica, correctly notes, “AI isn’t a discrete technology. Rather it’s a series of technologies, concepts, and approaches all aligning towards the quest for the intelligent machine.” Cognitive Computing is another frequently used term associated with AI. Although cognitive computing is not an umbrella term like AI, it normally encompasses a number of technologies — the most common of which are machine learning and natural language processing. The term was originally coined by IBM, but was rapidly adopted by other companies, including Enterra Solutions®.
Definitions of cognitive computing
Cognitive computing systems, like the Enterra Cognitive Core™, can take a few different forms depending on the technologies and analytics they embrace. The definition used by the Cognitive Computing Consortium was developed in mid-2014 by a cross-disciplinary group of experts. According to the Consortium, “Cognitive computing makes a new class of problems computable. It addresses complex situations that are characterized by ambiguity and uncertainty; in other words, it handles human kinds of problems.” The Consortium continues:
“In these dynamic, information-rich, and shifting situations, data tends to change frequently, and it is often conflicting. The goals of users evolve as they learn more and redefine their objectives. To respond to the fluid nature of users’ understanding of their problems, the cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights. To do this, systems often need to weigh conflicting evidence and suggest an answer that is ‘best’ rather than ‘right’. Cognitive computing systems make context computable. They identify and extract context features such as hour, location, task, history or profile to present an information set that is appropriate for an individual or for a dependent application engaged in a specific process at a specific time and place. They provide machine-aided serendipity by wading through massive collections of diverse information to find patterns and then apply those patterns to respond to the needs of the moment. Cognitive computing systems redefine the nature of the relationship between people and their increasingly pervasive digital environment. They may play the role of assistant or coach for the user, and they may act virtually autonomously in many problem-solving situations. The boundaries of the processes and domains these systems will affect are still elastic and emergent. Their output may be prescriptive, suggestive, instructive, or simply entertaining.”
The Enterra Cognitive Core — a system that can Sense, Think, Act, and Learn® — is an actualization of the Consortium’s explanation. To help people better understand cognitive computing, Enterra® defines it as the inter-combination of semantics and computational intelligence (i.e., machine learning). Semantics, in this case, refers to having a symbolic representation of the knowledge domain’s concepts, interrelationships, and rules, which we model within a technology called a Rule-based Ontology. The ontology we use allows our cognitive computing system to learn generalizations, encode learnings as rules, and contextualize numerical values (e.g., 100 is not just a number, but the Celsius temperature at which water boils, etc.).
According to the Consortium, “In order to achieve this new level of computing, cognitive systems must be:
- Adaptive (they must learn as information changes, and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time, or near real time).
- Interactive (they must interact easily with users so that those users can define their needs comfortably. They may also interact with other processors, devices, and Cloud services, as well as with people).
- Iterative and stateful (they must aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They must ‘remember’ previous interactions in a process and return information that is suitable for the specific application at that point in time).
- Contextual (they must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs).
The Consortium concludes, “Cognitive systems differ from current computing applications in that they move beyond tabulating and calculating based on preconfigured rules and programs. Although they are capable of basic computing, they can also infer and even reason based on broad objectives.”
Cognitive computing in the business world
Whereas the ultimate goal of many AI researchers is to develop machines that think and make decisions on their own, cognitive computing was originally developed to augment, rather than replace, human decision-making. That’s one reason decision-makers appreciate the value of cognitive computing for their business. Matt Brown, President of Americas & Asia-Pacific at Signal AI, explains, “AI allows businesses to surface levels of external data, information and insight that would have traditionally been inaccessible. From analyzing complex content and sentiment to categorizing subject matter within documents, AI helps organize information in an understandable format, within seconds of the content being published. This is an impossible task for a human to do without technology, and eminently more effective when combined with human intelligence. Some of the most powerful and impactful AI tools are those that help or augment the human experience. The idea of AI systems that augment human capability is known as Augmented Intelligence.”
Former IBM executive Irving Wladawsky-Berger writes, “Increasingly powerful and inexpensive computers, advanced machine-learning algorithms, and the explosive growth of big data have enabled us to extract insights from all that data and turn them into valuable predictions.” As beneficial as cognitive technologies are, humans still play an important role in decision-making. In an essay entitled “A responsibility to judge carefully in the era of
prediction decision machines,” Harvard University Professor David Parkes wrote, “Machines need to be able to predict to decide, but decision making requires much more. Decision making requires bringing together and reconciling multiple points of view. Decision making requires leadership in advocating and explaining a path forward. Decision making requires dialogue. It is decisions, not predictions, that have consequences.” Augmented Intelligence, via a cognitive computing system, becomes an important part of that dialogue, but only a part of it. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), explain, “The best way to understand any company’s operations is to view them as a series of decisions.” They add, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”
Professor Parke concludes, “As machines come to make decisions on our behalf, on behalf of the companies for whom we work, the organizations to which we contribute our time, our role must shift again. The judgment we will provide, as managers, as volunteers, as citizens, will be about the values that should be enshrined in our systems. Our role will not be one of judgment in the small, but rather judgment in the large, not one of making a point decision at a point in time, but in shepherding the development and deployment of entire systems whose purpose is to make decisions, continuously and automatically.” We are not there yet; however, business leaders are beginning to have a fuller appreciation for how cognitive computing can improve their decision-making.
 Ronald Schmelzer, “Going Beyond Machine Learning To Machine Reasoning,” Forbes, 9 January 2020.
 Staff, “Cognitive Computing Definition,” Cognitive Computing Consortium.
 Matt Brown, “All hail the advent of Augmented Intelligence,” Business Chief, 27 June 2020.
 Irving Wladawsky-Berger, “The Coming Era of Decision Machines,” The Wall Street Journal, 27 March 2020.
 David Parkes, “A responsibility to judge carefully in the era of decision machines,” Harvard University Digital Initiative, 2 December 2019.
 Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.