Cognitive computing systems (CCS) is the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use  data mining, pattern recognition and  natural language processing  to mimic the way the human brain works. The goal of cognitive computing is to create  automated IT systems  that are capable of solving problems without requiring human assistance.

Cognitive computing systems use  machine learning algorithms. Such systems continually acquire knowledge from the data fed into them by mining data for information. The systems refine the way they look for patterns and as well as the way they process data so they become capable of anticipating new problems and modeling possible solutions. Cognitive computing is used in numerous artificial intelligence (AI) applications, including expert systems, natural language programming, neural networksrobotics and virtual reality. The term cognitive computing is closely associated with IBM’s cognitive computer system, Watson.  (Source:

Here are some basic features that cognitive systems may express:

  1. Adaptiveness: CCS may learn as information changes, and as goals and requirements evolve. They may resolve ambiguity and tolerate unpredictability and be engineered to feed on dynamic data in real time, or near real time.

  2. Interactivity: CCS may interact easily with users so that those users can define their needs. They may also interact with other devices, Cloud services, processors and people.

  3. Iterative and Statefulness: CCS may assist in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They may "remember" previous interactions in a process and return information that is suitable for the specific application at that point in time. The careful application of the data quality and validation methodologies ensures that the CCS is always provided with enough information and that the data sources it operates on delivers reliable and up-to-date input.

  4. Contextuality: CCS may 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).


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  1. Human-Robot Interaction: The study of interactions between humans and robots. It is often referred as HRI by researchers.

  1. Smart Cities & IOT:

  2. An urban development vision to integrate information and communication technology (ICT) and Internet of things (IoT) technology in a secure fashion to manage a city's assets.

  3. Self-Driving Vehicles:

  4. Vehicles that are capable of sensing its environment and navigating without human input.

  1. Cognitive Computing:

  2. The simulation of human thought processes in a computerized model involving self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works.


  1. Self-Driving Cars:

  2. How It Will Work

  3. HRI: Yaskama Motomon/WatsonPaths

  4. Smart Cities’ Efforts


  2. AI Matters: Features an article about the panel.

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