Cognitive Computing

Cognitive computing algorithms try to mimic a human brain by analyzing text, speech, images and objects in a manner that a human does and tries to give the desired output

Cognitive computing (CC) refers to 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 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 or mind senses, reasons, and responds to stimuli. 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 effective and more influential by design.

Cognitive computing systems can synthesize data from various information sources while weighing context and conflicting evidence to suggest the best possible answers. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition, and natural language processing (NLP) to mimic the way the human brain works.

Using computer systems to solve the types of problems that humans are typically tasked with requires vast amounts of structured and unstructured data, fed to machine learning algorithms. Over time, cognitive systems are able to refine the way they identify patterns and the way they process data to become capable of anticipating new problems and model possible solutions.

To achieve those capabilities, cognitive computing systems must have five key attributes, as listed by the Cognitive Computing Consortium.

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