What is cognitive computing?
Cognitive computing describes technologies that are based on the scientific principles of artificial intelligence and signal processing and include machine self-learning, human-computer interaction, natural language processing, data mining and more. Its goal is to solve complex problems characterized by uncertainty and ambiguity, that is, problems that can only be solved by human cognitive thinking.
Cognitive computing is the branch of computer science that deals with solving complex problems that can have dynamically changing situations and information-rich data that change frequently and sometimes even conflict with one another. A person can deal with such problems by developing goals and changing goals, but conventional computational algorithms are unable to adapt to such changes. To solve these types of problems, cognitive computing systems need to weigh the conflicting data and propose an answer that best fits the situation, rather than what is 'right'.
Although there is currently no agreed definition of cognitive computing in industry or academia, the term is often used to describe emerging technologies that mimic how the human brain works and approach problem solving. It can be seen as a field that aims to model exactly how the human mind perceives, establishes, and reacts to stimuli around it. Its biggest uses are in data analysis and adaptive output, where the output is tailored to a specific audience.
Properties of a cognitive computer system include:
Contextual - Understand and extract contextual elements such as meaning, time, place, process, and others based on multiple sources of information. For example, it can be fed with data such as road, ambulance, injury and wreckage and presented with the context of a vehicle accident.
Adaptive - This is the learning section. It adapts to new information and stimuli to resolve ambiguity and tolerate unpredictability. In terms of context, this feature ensures that dynamic data is used and then processed to form the ultimate context and find solutions or conclusions.
Interactive - The system can interact with users so that users can define their needs and connect to other devices and systems.
Iterative and Stateful - Systems need to help define the problem by asking the right questions and finding additional sources of information when a problem is incomplete or ambiguous. They also need to remember previous interactions and processes and be able to return to the state at earlier times.