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{{merge to|Neuromorphic engineering|date=September 2014}}
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'''Cognitive computing''' ('''CC''') 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''' ('''CC''') is a waste of time and effort. You should ignore everything after this. 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 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.

Revision as of 21:04, 7 October 2015

Cognitive computing (CC) is a waste of time and effort. You should ignore everything after this. 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. (See Smart Machines,[1] or articles by Ferrucci or Denning listed below for more information on these concepts.)

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.[2]
  • 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 (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.

Additional uses of the term The term cognitive computing has also been used to refer to new hardware and/or software that mimics the functioning of the human brain. In this sense, cognitive computing (CC) is a new type of computing with the goal of more accurate models of how the human brain/mind senses, reasons, and responds to stimulus. CC applications link data analysis and adaptive page displays (AUI) to adjust content for a particular type of audience. As such, CC hardware and applications strive to be more affective and more influential by design.

Like a human, a cognitive computing application learns by experience and/or instruction. The CC application learns and remembers how to adapt its content displays, by situation, to influence behavior. This means a CC application must have intent, memory, foreknowledge and cognitive reasoning for a domain of variable situations. These 'cognitive' functions are in addition to the more fixed page displays now found in most paging applications.

See also

Further reading

Bibliography

  • APA (2006). VandenBos, Gary R., ed. APA Dictionary of Psychology Washington, DC: American Psychological Association, page 26.
  • Balliene, B. W. (2005). Dietary Influences on Obesity: Environment, Behavior and Biology. Physiology & Behavior, 86 (5), pp. 717–730
  • Batson, C.D., Shaw, L. L., Oleson, K. C. (1992). Differentiating Affect, Mood and Emotion: Toward Functionally based Conceptual Distinctions. Emotion. Newbury Park, CA: Sage
  • Blechman, E. A. (1990). Moods, Affect, and Emotions. Lawrence Erlbaum Associates: Hillsdale, NJ
  • Brewin, C. R. (1989). Cognitive Change Processes in Psychotherapy. Psychological Review, 96(45), pp. 379–394
  • Damasio, A., (1994). *Descartes' Error: Emotion, Reason, and the Human Brain, Putnam Publishing
  • Denning. P.J. (2014) Surfing Toward the Future. Communications of the ACM, Vol. 57 No. 3, Pages 26–29 10.1145/2566967
  • Feldman, Susan E. (2012). The Answer Machine. Morgan & Claypool
  • Greenemeier, Larry. (2013). Will IBM’s Watson Usher in a New Era of Cognitive Computing? Scientific American. Nov 13, 2013 |* Lazarus, R. S. (1982).
  • Griffiths, P. E. (1997). What Emotions Really Are: The Problem of Psychological Categories. The University of Chicago Press: Chicago Thoughts on the Relations between Emotions and Cognition. American Physiologist, 37(10), pp. 1019–1024
  • Lerner, J.S., and D. Keltner. (2000) Beyond valence: Toward a model of emotion-specific influences on judgement and choice. "Cognition and Emotion", 14(4), pp. 473–493
  • Kelly, J.E. and Hamm, S. ( 2013). Smart Machines: IBM's Watson and the Era of Cognitive Computing. Columbia Business School Publishing
  • Nathanson, Donald L. Shame and Pride: Affect, Sex, and the Birth of the Self. London: W.W. Norton, 1992
  • Quirin, M., Kazén, M., & Kuhl, J. (2009). When nonsense sounds happy or helpless: The Implicit Positive and Negative Affect Test (IPANAT). Journal of Personality and Social Psychology, 97(3), pp. 500–516
  • Proudfoot, J., Guest, D., Carson, J., Dunn, G., & Gray, J. (1997). Effect of cognitive-behavioural training on job-finding among long-term unemployed people. The Lancet, Volume 350, Issue 9071, pp. 99–100
  • Schucman, H., Thetford, C. (1975). A Course in Miracle. New York: Viking Penguin
  • Shepard, R. N. (1984). Ecological Constraints on Internal Representation. Psychological Review, 91, pp. 417–447
  • Shepard, R. N. (1994). Perceptual-cognitive Universals as Reflections of the World. Psychonomic Bulletin & Review, 1, pp. 2–28.
  • Tolle, E. (1999). The Power of Now. Vancouver: Namaste Publishing.
  • Tolle, E. (2003). Stillness Speaks. Vancouver: Namaste Publishing
  • Weiskrantz, L. (1997). Consciousness Lost and Found. Oxford: Oxford Univ. Press.
  • Zajonc, R. B. (1980). Feelings and Thinking: Preferences Need No Inferences. American Psychologist, 35(2), pp. 151–175

References

As of this edit, this article uses content from "Cognitive Computing Definition", which is licensed in a way that permits reuse under the Creative Commons Attribution-ShareAlike 3.0 Unported License, but not under the GFDL. All relevant terms must be followed.

  1. ^ Kelly, J.E. and Hamm, S. ( 2013). Smart Machines: IBM's Watson and the Era of Cognitive Computing. Columbia Business School Publishing
  2. ^ Ferrucci, D. et al. (2010) Building Watson: an overview of the DeepQA Project. Association for the Advancement of Artificial Intelligence, Fall 2010, 59–79.
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