Gartner analysts predict “smart machines will enter mainstream adoption by 2021 with 30 percent adoption by large companies. This will include cognitive computing, artificial intelligence, intelligent automation, machine learning, and deep learning.”[1] Susan Tan (@SusanSJTan), a research Vice President at Gartner asserts, “The use of smart machines by enterprises can be transformative and disruptive. Smart machines will profoundly change the way work is done and how value is created. From dynamic pricing models and fraud detection, to predictive policing and robotics, smart machines have broad applicability in all industries.”

Cognitive Computing Hype and Reality

Jenna Hogue writes, “The phrase ‘Cognitive Computing’ has been slowly, but prominently, edging its way into the mainstream business culture. … As a result, the ‘hype’ around the subject has grown tremendously and caused a mixture of reactions ranging from enthusiastic to skeptical.”[2] Much of the skepticism involves the term “cognitive,” which, for some people, implies a system is sentient. Clearly, we are nowhere near producing a sentient AI platform. There is, however, a difference between sentience (i.e., self-awareness) and cognition (i.e., sensing, interacting, and learning). Today’s cognitive computing systems are strong artificial intelligence (AI) platforms designed to tackle a number of various challenges; but, they are not sentient. Referring to a presentation given by Sue Feldman and Hadley Reynolds, from the Cognitive Computing Consortium, Hogue writes:

“Although the use of the Cognitive Computing can be extremely beneficial, it’s important to know that there is both a time and a place for it. Cognitive Computing extends computing to whole new range of different problems said Reynolds. The types of problems … tend to be much more complex and human-like than the average non-cognitive system. These problems tend to comprise multiple different variables included, shifting data elements, and an ambiguous nature. ‘There isn’t just one right answer that can be given,’ said Reynolds. ‘In fact, multiple answers are preferred when using Cognitive Computing applications. The right answers then depend on much more accurate variable related to the context, what its purpose is, and who is presenting it.’ All of those things are factored that need to be looked at when considering whether or not to use Cognitive Computing. If the results needed are repeatable and predictable then it’s probably best to consider other options. This also stands true if all the data is structured and numeric, when interaction isn’t actually necessary, and when a probabilistic approach is not desirable. If the current transactional systems are fully adequate and doing their jobs efficiently, said Reynolds, the use of Cloud Computing should potentially be reconsidered. ‘The value really needs to be justified in terms of cost, competition, and or improved productivity.'”

Reynolds is correct about the types of problems best-suited for cognitive computing, but he misses an important point about the role a cognitive computing platform can play in helping an industrial age organization transform into a digital enterprise. Digital transformation requires a holistic approach that integrates both structured and unstructured data as well as helping align processes across an organization. A well-designed cognitive computing platform can help speed up digital transformation because it can handle all types of data, deal with ambiguity, integrate and automate processes, and provide actionable insights to decision makers. Hogue concludes, “As Cognitive Computing continues to expand, it will undoubtedly cause a lot of fear and unrest. The future is unknown when it comes to the impact this type of computing will have. Many individuals are concerned about their jobs and worry about the disruption this will cause. … A lot will be questioned in terms of humans being replaced as a whole by robots. These are some questions and concerns the world just isn’t ready to face or answer quite yet. Cognitive Computing has the potential to unleash a lot of benefits in society. However, we must keep in mind that there is always going to be a time, place, and industry to use it in.”

The Importance of Context

SAS’ Alison Bolen (@alisonbolen) adds, “From self-driving cars to personal assistants, we’ve seen that machines can already read, write, speak, see, hear and learn. But the big question in cognitive computing is: Can they understand? For a machine to be truly intelligent, it’s not enough for it simply to know the words you’ve said. It needs to know what you want and be able to provide assistance in context.”[3] Enterra Solutions’ definition of cognitive computing is a system that combines Semantic Intelligence (i.e., artificial intelligence (including machine learning), natural language processing, and ontologies) and Computational Intelligence (i.e., advanced mathematics). The use of ontologies helps provide the context for which Bolen is looking. Bolen indicates there are a few key points executives should know about cognitive computing, including:

• Self-learning means the system receives initial instructions, but after that it pretty much learns on its own based on the data you continue to feed it.
• Machine-learning techniques automate model building to iteratively learn from data and to find hidden insights without being explicitly programmed where to look.
• Specific, human-like tasks means the system can classify and understand objects and recognize human languages, but the tasks it performs are highly specialized. A system that is designed to drive your car cannot change your oil or clean your garage.
• In an intelligent way describes how the system is able not only to understand input such as text, voice or video, but also to reason and create output consumable by humans.

She adds, “Because they can be programmed to learn and solve problems, cognitive computing systems are disruptive to many industries, including legal, health care, financial services, marketing and customer intelligence.”

Where is Cognitive Computing Most Useful?

As Feldman and Hadley noted, cognitive computing can be useful when confronted by an ambiguous problem. Bolen adds, “In many ways, cognitive computing is a natural extension of existing analytics projects. The challenge for business leaders will be to look for areas where cognitive computing can be applied to business problems. To find areas in your organization that could benefit from cognitive computing, consider where you have a lot of data, where you might need more automated decisions, or where you might need more personalized interactions with fewer business rules. … The biggest areas of assistance may come from assisting your employees, not your customers.” Jessica Goepfert, Program Director for Customer Insights and Analysis at IDC explains, “The potential use cases for cognitive systems are as wide, varied, and rich as the imagination. Automated threat intelligence, for instance, is helping organizations connect the dots between pieces of information to improve security, while in healthcare, cognitive systems are improving the quality of people’s lives by assisting in diagnosis and treatment at the individual patient level. Wherever cognitive systems are in play, workers and organizations can expect to be impacted by the power of more information, intelligence, and automation.”[4] The Cognitive Computing Consortium explains:

“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. … 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.”[5]

The Consortium goes on to note, in order to achieve this new level of computing, cognitive systems must be: Adaptive, Interactive, and Contextual. The conclude, “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.”

Footnotes
[1] Gartner, “Smart Machines will enter mainstream adoption by 2021: Gartner,” Digit, 16 December 2016.
[2] Jenna Hogue, “Cognitive Computing: The Hype, the Reality,” Dataversity, 12 January 2017.
[3] Alison Bolen, “An executive’s guide to cognitive computing,” SAS, January 2017.
[4] Press Release, “Worldwide Spending on Cognitive Systems Forecast to Soar to More Than $31 Billion in 2019, According to a New IDC Spending Guide,” IDC, 8 March 2016.
[5] “Cognitive Computing Defined,” Cognitive Computing Consortium.