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What is Cognitive Computing? Explained.

Mohammed NaserMohammed Naser

Cognitive computing uses computerized models to simulate the human thought process in complex situations. Read on to learn more.

Cognitive computing is a tool used to assist humans in decision-making. Some examples of cognitive applications include supporting the medical industry in treatments and the creation of advanced algorithms to support research and growth in artificial intelligence. However, many have questions on what cognitive computing is and how it works. Here is a look.

What is Cognitive Computing?

Cognitive computing uses computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. It often coincides with AI and involves many related technologies to power applications, including robotics, neural networks, expert systems, and virtual and augmented reality systems.

Even though Cognitive computing is often used interchangeably with AI, there are nuances between the two terms. With AI, data is fed into the algorithm over a significant period of time to learn variables and predict outcomes. Cognitive computing is a term typically used to describe AI systems that try and simulate human thought.

How Does IT Work?

Using computers to solve the various problems that humans are typically tasked with needs a vast quantity of structured and unstructured data fed to machine learning algorithms. Cognitive computing systems synthesize information from multiple sources while carrying out weightage of context and analysis to suggest the best answers. To achieve this, such systems include self-learning technologies that utilize data mining and natural language processing, and pattern recognition to mimic how the human brain functions.

Over time, cognitive systems refine the way they perform pattern identification and data processing to anticipate new problems and form possible solutions.

Cognitive computing systems are usually expected to have the following key attributes to achieve those capabilities.

  • Being adaptive to changes and goals
  • Being interactive with users, other machines, and processors
  • Able to identify and solve problems
  • Understand contextual data

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