Artificial intelligence and related disciplines have grown in breadth and reach in recent years. With the rise in popularity of artificial intelligence, there has been a lot of criticism of the technical language that surrounds it. Deep learning, deep learning, voice recognition, text analytics, cognitive computing, and neural networks are just a few terms that spring to mind while thinking about deep learning.

Despite the fact that these terms are commonly used interchangeably, their methodologies and goals are vastly different. Cognitive computing is an example of a technology that is sometimes confused with AI but is actually considerably different. However, while both technologies represent the next big thing in supercomputing, they have different implications when used in practise.

Let’s get down to business: cognitive computing vs. artificial intelligence.

What is AI?

Artificial intelligence (AI) is made up of algorithms that have been trained to find the best way to perform a task or make a decision given a set of constraints, and then take the appropriate action based on their results. AI, like human intelligence, learns from its environment and analyses what it learns in order to decide the best course of action, solution to a problem, or technique of accomplishing a task like identification or speech recognition. AI systems, sometimes known as bots or digital assistants, execute tasks that would ordinarily need human intelligence.

What is Cognitive Computing?

Cognitive computing systems are cognitive decision-making tools at their core. They’re designed to provide data to decision-makers so they can make better data-driven decisions. Cognitive computing systems can handle vast amounts of data (which humans cannot) and do extensive iterative analysis while updating their results as new data arrives.

Self-learning algorithms based on AI technologies such as data mining, picture identification, voice recognition, and natural language processing are used by cognitive computing systems to handle complex issues (NLP). These systems can learn, think, and connect with humans as if they were humans. They have the same ability to cope with symbols and concepts as humans.

Cognitive Computing vs AI

Interaction with Humans

Cognitive computing systems are systems that collaborate with humans to assist them make better decisions by reasoning, analysing, and memorising. Its findings are intended for human consumption. By applying the best algorithm, AI tries to generate the most accurate result or action.

Contextual Solutions

Cognitive computing can take into consideration conflicting and shifting information that is contextually relevant to the situation at hand. Rather than pre-trained algorithms, the conclusions are based on predictive and prescriptive analytics. For example, if a woman in her sixties wanted to know which muscle-building programme she should follow, AI would suggest the best programme available. When it comes to programme updates, cognitive computing, on the other hand, considers her age and competence. Finally, AI solves problems using algorithms to arrive at a final judgement; cognitive computing delivers the relevant facts that helps humans to make the right call for themselves.

Use Cases of Cognitive Computing

Enterprises that require a lot of analysis are more likely to use cognitive computing apps. Listed below are a few examples:

  • Cognitive computing is supporting physicians in making more accurate diagnoses and personalising treatment decisions in the realm of medicine. Thanks to cognitive computing’s ability to access datasets from all over the world via the cloud, doctors now have access to medicines and diagnoses that they would not have had otherwise. Human radiologists frequently ignore nuances revealed by cognitive technologies that read patient pictures.
  • Financial services companies use cognitive computing’s analytic capabilities to find the finest products for their customers. If a product recognised by the system is not already accessible, companies are leveraging the data to create more personalised services. By combining market trends with consumer behaviour data, cognitive computing assists financial institutions in assessing investment risk.
  • Retailers are turning to cognitive computing to provide customers with a personalised online shopping experience that makes finding what they want easier.
  • Manufacturers use cognitive computing technologies to maintain and repair their machinery and equipment, identify defective components, save manufacturing times, and optimise parts management.

AI has applications in all of these disciplines, but its output, as seen in chatbots, virtual assistants, and smart advisers, is aimed toward automating procedures rather than decision assistance. For example, an AI virtual assistant can provide a doctor a specific treatment recommendation, whereas cognitive computing will generate a number of plausible therapy possibilities and leave it up to the doctor to choose the best one for the patient.


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