Cognitive computing

Last updated

Cognitive computing (CC) describes technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, among other technologies. [1] [2]



At present, there is no widely agreed upon definition for cognitive computing in either academia or industry. [1] [3] [4]

In general, the term cognitive computing has been used to refer to new hardware and/or software that mimics the functioning of the human brain [5] [6] [7] [8] [9] (2004) and helps to improve human decision-making. [10] In this sense, 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.

Some features that cognitive systems may express are:

They may learn as information changes, and as goals and requirements evolve. They may resolve ambiguity and tolerate unpredictability. They may be engineered to feed on dynamic data in real time, or near real time. [11]
They may 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 may aid 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.
They 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). [12]

Use cases

Cognitive analytics

Cognitive computing-branded technology platforms typically specialize in the processing and analysis of large, unstructured datasets [13] .

Word processing documents, emails, videos, images, audio files, presentations, webpages, social media and many other data formats often need to be manually tagged with metadata before they can be fed to a computer for analysis and insight generation. The principal benefit of utilizing cognitive analytics over traditional big data analytics is that such datasets do not need to be pre-tagged.

Other characteristics of a cognitive analytics system include:


Even if Cognitive Computing can not take the place of teachers, it can still be a heavy driving force in the education of students. Cognitive Computing being used in the classroom is applied by essentially having an assistant that is personalized for each individual student. This cognitive assistant can relieve the stress that teachers face while teaching students, while also enhancing the student’s learning experience over all. [14] Teachers may not be able to pay each and every student individual attention, this being the place that cognitive computers fill the gap. Some students may need a little more help with a particular subject. For many students, Human interaction between student and teacher can cause anxiety and can be uncomfortable. With the help of Cognitive Computer tutors, students will not have to face their uneasiness and can gain the confidence to learn and do well in the classroom. [15] . While a student is in class with their personalized assistant, this assistant can develop various techniques, like creating lesson plans, to tailor and aid the student and their needs.
Numerous tech companies are in the process of developing technology that involves Cognitive Computing that can be used in the medical field. The ability to classify and identify is one of the main goals of these cognitive devices. [16] This trait can be very helpful in the study of identifying carcinogens. This cognitive system that can detect would be able to assist the examiner in interpreting countless numbers of documents in a lesser amount of time than if they did not use Cognitive Computer technology. This technology can also evaluate information about the patient, looking through every medical record in depth, searching for indications that can be the source of their problems.

Industry work

Cognitive Computing in conjunction with big data and algorithms that comprehend customer needs, can be a major advantage in economic decision making.

The powers of Cognitive Computing and AI hold the potential to affect almost every task that humans are capable of performing. This can negatively affect employment for humans, as there would be no such need for human labor anymore. It would also increase the inequality of wealth; the people at the head of the Cognitive Computing industry would grow significantly richer, while workers who are not getting employed anymore would be getting poorer. [17]

The more industries start to utilize Cognitive Computing, the more difficult it will be for humans to compete. [17] Increased use of the technology will also increase the amount of work that AI-driven robots and machines can perform. Only extraordinarily talented, capable and motivated humans would be able to keep up with the machines. The influence of competitive individuals in conjunction with AI/CC with has the potential to change the course of humankind. [18]

See also

Related Research Articles

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".

Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.

Neuromorphic engineering, also known as neuromorphic computing, is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, and transistors.

The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.

A cognitive architecture refers to both a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science. One of the main goals of a cognitive architecture is to summarize the various results of cognitive psychology in a comprehensive computer model. However, the results need to be formalized so far as they can be the basis of a computer program. The formalized models can be used to further refine a comprehensive theory of cognition, and more immediately, as a commercially usable model. Successful cognitive architectures include ACT-R and SOAR.

An artificial brain is software and hardware with cognitive abilities similar to those of the animal or human brain.

An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one, personalized tutoring, in contexts where students would otherwise have access to one-to-many instruction from a single teacher, or no teacher at all. ITSs are often designed with the goal of providing access to high quality education to each and every student.

Intelligence amplification automation of insight-finding by incorporating natural language processing and machine learning to create better data.

Intelligence amplification (IA) refers to the effective use of information technology in augmenting human intelligence. The idea was first proposed in the 1950s and 1960s by cybernetics and early computer pioneers.

The following outline is provided as an overview of and topical guide to artificial intelligence:

Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI, the form of AI where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading platforms, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more.

Research in artificial intelligence (AI) is known to have impacted medical diagnosis, stock trading, robot control, and several other fields. Perhaps less popular is the contribution of AI in the field of music. Nevertheless, artificial intelligence and music (AIM) has, for a long time, been a common subject in several conferences and workshops, including the International Computer Music Conference, the Computing Society Conference and the International Joint Conference on Artificial Intelligence. In fact, the first International Computer Music Conference was the ICMC 1974, Michigan State University, East Lansing, USA Current research includes the application of AI in music composition, performance, theory and digital sound processing.

Watson (computer) Artificial intelligence computer system made by IBM

Watson is a question-answering computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's founder and first CEO, industrialist Thomas J. Watson.

Pedagogical agent concept borrowed from computer science and artificial intelligence and applied to education

A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education, usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content, in an educational environment. A pedagogical agent is designed to model the type of interactions between a student and another person. Mabanza and de Wet define it as "as a character enacted by a computer that interacts with the user in a socially engaging manner". A pedagogical agent can be assigned different roles in the learning environment, such as tutor or co-learner, depending on the desired purpose of the agent. "A tutor agent plays the role of a teacher, while a co-learner agent plays the role of a learning companion".

A cognitive computer combines artificial intelligence and machine-learning algorithms, in an approach which attempts to reproduce the behaviour of the human brain. It generally adopts a Neuromorphic engineering approach. An example of a cognitive computer implemented by using neural networks and deep learning is provided by the IBM company's Watson machine. A subsequent development by IBM is the TrueNorth microchip architecture, which is designed to be closer in structure to the human brain than the von Neumann architecture used in conventional computers. In 2017 Intel announced its own version of a cognitive chip in "Loihi", which will be available to university and research labs in 2018. Intel, Qualcomm, and others are improving neuromorphic processors steadily, Intel with its Pohoiki Beach and Springs systems

Enterprise cognitive systems (ECS) are part of a broader shift in computing, from a programmatic to a probabilistic approach, called cognitive computing. An Enterprise Cognitive System makes a new class of complex decision support problems computable, where the business context is ambiguous, multi-faceted, and fast-evolving, and what to do in such a situation is usually assessed today by the business user. An ECS is designed to synthesize a business context and link it to the desired outcome. It recommends evidence-based actions to help the end-user achieve the desired outcome. It does so by finding past situations similar to the current situation, and extracting the repeated actions that best influence the desired outcome.

This glossary of artificial intelligence terms is about artificial intelligence, its sub-disciplines, and related fields.

Artificial intelligence in healthcare use of complex algorithms and software to estimate human cognition in the analysis of complicated medical data

Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input.

Artificial intelligence in heavy industry

Artificial intelligence, in modern terms, generally refers to computer systems that mimic human cognitive functions. It encompasses independent learning and problem-solving. While this type of general artificial intelligence has not been achieved yet, most contemporary artificial intelligence projects are currently better understood as types of machine-learning algorithms, that can be integrated with existing data to understand, categorize, and adapt sets of data without the need for explicit programming.

IBM Watson Health American multinational technology and consulting corporation

IBM Watson Health is a division of the International Business Machines Corporation, (IBM), an American multinational information technology company headquartered in Armonk, New York. It helps clients facilitate medical research, clinical research, and healthcare solutions, through the use of artificial intelligence, data, analytics, cloud computing, and other advanced information technology.


  1. 1 2 Kelly III, Dr. John (2015). "Computing, cognition and the future of knowing" (PDF). IBM Research: Cognitive Computing. IBM Corporation. Retrieved February 9, 2016.
  2. Augmented intelligence, helping humans make smarter decisions. Hewlett Packard Enterprise.
  3. "Cognitive Computing".
  4. Gutierrez-Garcia, J. Octavio; López-Neri, Emmanuel (November 30, 2015). "Cognitive Computing: A Brief Survey and Open Research Challenges". 2015 3rd International Conference on Applied Computing and Information Technology/2nd International Conference on Computational Science and Intelligence: 328–333. doi:10.1109/ACIT-CSI.2015.64. ISBN   978-1-4673-9642-4.
  5. Terdiman, Daniel (2014) .IBM's TrueNorth processor mimics the human brain.
  6. Knight, Shawn (2011). IBM unveils cognitive computing chips that mimic human brain TechSpot: August 18, 2011, 12:00 PM
  7. Hamill, Jasper (2013). Cognitive computing: IBM unveils software for its brain-like SyNAPSE chips The Register: August 8, 2013
  8. Denning. P.J. (2014). "Surfing Toward the Future". Communications of the ACM. 57 (3): 26–29. doi:10.1145/2566967.
  9. Dr. Lars Ludwig (2013). "Extended Artificial Memory. Toward an integral cognitive theory of memory and technology" (pdf). Technical University of Kaiserslautern. Retrieved February 7, 2017.Cite journal requires |journal= (help)
  10. "Automate Complex Workflows Using Tactical Cognitive Computing: Coseer". Retrieved July 31, 2017.
  11. Ferrucci, D. et al. (2010) Building Watson: an overview of the DeepQA Project. Association for the Advancement of Artificial Intelligence, Fall 2010, 59–79.
  12. Deanfelis, Stephen (2014). Will 2014 Be the Year You Fall in Love With Cognitive Computing? Wired: 2014-04-21
  13. "Cognitive analytics - The three-minute guide" (PDF). 2014. Retrieved August 18, 2017.
  14. Sears, Alec (April 14, 2018). "The Role Of Artificial Intelligence In The Classroom". ElearningIndustry. Retrieved April 11, 2019.
  15. Coccoli, M., Maresca, P. & Stanganelli, L. (2016). Cognitive computing in education. Journal of e-Learning and Knowledge Society, 12(2),. Italian e-Learning Association. Retrieved February 14, 2019 from
  16. Dobrescu, E. M., & Dobrescu, E. M. (2018). ARTIFICIAL INTELLIGENCE (AI) - THE TECHNOLOGY THAT SHAPES THE WORLD. Global Economic Observer, 6(2), 71-81. Retrieved from
  17. 1 2 Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.
  18. West, D. (2018). The Future of Work: Robots, AI, and Automation. Washington, D.C.: Brookings Institution Press. Retrieved from

Further reading