This definition of cognitive computing was developed in mid-2014 by a cross-disciplinary group of experts from BA-Insight, Babson College, Basis Technology, Cognitive Scale, CustomerMatrix, Decision Resources, Ektron, Google, HP Autonomy, IBM, Microsoft/Bing, Next Era Research, Oracle, Pivotal, SAS. Saxena Foundation, Synthexis, and Textwise/IP.com. This project was led by Sue Feldman at Synthexis and Hadley Reynolds of NextEra Research. It was sponsored by CustomerMatrix, HP Autonomy, and IBM. The goal of the project was to define how cognitive computing differs from traditional computing and to provide a non-proprietary definition of cognitive computing that could be used as a benchmark by the IT industry, researchers, the media, technology users and buyers.
Cognitive Computing Defined
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. 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.
In order to achieve this new level of computing, cognitive systems must be:
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.
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
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 (visual, gestural, auditory, or sensor-provided).
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.
Beyond these principles, cognitive computing systems can be extended to include additional tools and technologies. They may integrate or leverage existing information systems and add domain or task-specific interfaces and tools as required.
Many of today’s applications (e.g., search, ecommerce, eDiscovery) exhibit some of these features, but it is rare to find all of them fully integrated and interactive.
Cognitive systems will coexist with legacy systems into the indefinite future. Many cognitive systems will build upon today’s IT resources. But the ambition and reach of cognitive computing is fundamentally different. Leaving the model of computer-as-appliance behind, it seeks to bring computing into a closer, fundamental partnership in human endeavors.
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