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.
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 (2004) and helps to improve human decision-making. 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.
IBM describes the components used to develop, and behaviors resulting from, "systems that learn at scale, reason with purpose and interact with humans naturally". According to them, while sharing many attributes with the field of artificial intelligence, it differentiates itself via the complex interplay of disparate components, each of which comprise their own individual mature disciplines.
Some features that cognitive systems may express are:
- Adaptive: 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.
- Interactive: 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.
- Contextual: 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).
Cognitive computing has been subject to a great deal of marketing hype over the years and there continues to be a struggle with finding a non-proprietary definition, but as cognitive computing platforms have emerged and become commercially available, evidence of real-world applications are starting to surface. Organizations that adopt and use these cognitive computing platforms, purpose-build applications to address specific use cases that are relevant to their internal and external users, with each application using some combination of available functionality necessary for the use case.
Examples of such real-world use cases include the following:
- Speech recognition
- Sentiment analysis
- Face detection
- Risk Assessment
- Fraud Detection
- Behavioral Recommendations
- Cognitive computing
- Affective computing
- Artificial neural network
- Cognitive computer
- Cognitive reasoning
- Enterprise cognitive system
- Social neuroscience
- Synthetic intelligence
- Kelly III, Dr. John (2015). "Computing, cognition and the future of knowing" (PDF). IBM Research: Cognitive Computing. IBM Corporation. Retrieved February 9, 2016.
- Augmented intelligence, helping humans make smarter decisions. Hewlett Packard Enterprise. http://h20195.www2.hpe.com/V2/GetPDF.aspx/4AA6-4478ENW.pdf
- "IBM Research: Cognitive Computing".
- "Cognitive Computing".
- "Hewlett Packard Labs".
- Terdiman, Daniel (2014) .IBM's TrueNorth processor mimics the human brain.http://www.cnet.com/news/ibms-truenorth-processor-mimics-the-human-brain/
- Knight, Shawn (2011). IBM unveils cognitive computing chips that mimic human brain TechSpot: August 18, 2011, 12:00 PM
- Hamill, Jasper (2013). Cognitive computing: IBM unveils software for its brain-like SyNAPSE chips The Register: August 8, 2013
- Denning. P.J. (2014). "Surfing Toward the Future". Communications of the ACM. 57 (3): 26–29. doi:10.1145/2566967.
- Dr. Lars Ludwig (2013). "Extended Artificial Memory. Toward an integral cognitive theory of memory and technology." (pdf). Technical University of Kaiserslautern. Retrieved 2017-02-07.
- "Research at HP Labs".
- Kelly, J.E. and Hamm, S. ( 2013). Smart Machines: IBM's Watson and the Era of Cognitive Computing. Columbia Business School Publishing
- Ferrucci, D. et al. (2010) Building Watson: an overview of the DeepQA Project. Association for the Advancement of Artificial Intelligence, Fall 2010, 59–79.
- Deanfelis, Stephen (2014). Will 2014 Be the Year You Fall in Love With Cognitive Computing? Wired: 2014-04-21
- Examples of cognitive computing
- Russell, John (2016-02-15). "Mapping Out a New Role for Cognitive Computing in Science". HPCwire. Retrieved 2016-04-21.
In 2003 Xuelong Li has designed and delivered a new course named 'Cognitive Computing' (Module Code: COM847M1) at the School of Computing and Intelligent Systems of the Ulster University. This could be for the first time to set up a 'Cognitive Computing' course in an IT department. After having moved to the University of London in 2004, Xuelong Li proposed to found the technical committee of Cognitive Computing with the IEEE SMC Society in January 2008, and Xuelong Li defined the scope of 'Cognitive Computing' (in 2008) as 'Cognitive Computing breaks the traditional boundary between neuroscience and computer science, and paves the way for machines that will have reasoning abilities analogous to a human brain. It's an interdisciplinary research and application field, and uses methods from psychology, biology, signal processing, physics, information theory, mathematics, and statistics. The development of Cognitive Computing will cross-fertilize these other research areas with which it interacts. There are many open problems to be addressed and to be defined. This technical committee tackles these problems in both academia and industry, and focuses on new foundations/technologies that are intrinsic to Cognitive Computing.'