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
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 pretagged.
Other characteristics of a cognitive analytics system include:
- Adaptability: cognitive analytics systems can use machine learning to adapt to different contexts with minimal human supervision
- Natural language interaction: cognitive analytics systems can be equipped with a chatbot or search assistant that understands queries, explains data insights and interacts with humans in natural language.
- Affective computing
- Artificial neural network
- Cognitive computer
- Cognitive reasoning
- Enterprise cognitive system
- Social neuroscience
- Synthetic intelligence
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