Cognitive Computing Vs AI

Table of Contents

Artificial intelligence (AI) is a superset of the technologies enabling machines to think and act like humans. It includes machine learning (ML), deep learning, visual recognition, neural networks, and natural language processing (NLP). As cognitive computing also uses the same technologies, it is often confused with AI. However, the output of these two systems and their interactions with humans is quite different.

So what is cognitive computing and how does it differ from AI? Read on to find out. 

Cognitive computing definition

There are lots of talks about data analytics, big data, and cognitive computing. But what it means is unclear to most people. So here’s the cognitive computing definition in simple words: 

Cognitive computing is based on self-learning systems using machine-learning techniques to perform specific, human-like tasks in an intelligent way.

Key differences between cognitive computing and AI

Cognitive computing learns and imitates the human thought process

Unlike AI systems that just take care of a given problem, cognitive computing studies patterns and suggests humans take appropriate actions based on their understanding. The system takes full control of a process in the case of AI and takes steps to complete a task or avoid a scenario using a pre-defined algorithm. Whereas cognitive computing is a different field altogether. It serves as an assistant instead of completing the task. In this manner, cognitive computing provides humans with the power of faster and more accurate data analysis without worrying about the wrong decisions taken by the ML system.

But that doesn’t mean that cognitive computing throws humans out of the picture. Its aim is to assist humans in decision-making. This gives humans superior-grade precision in analysis while also ensuring that everything is in their control. 

Let’s take the example of AI in the healthcare system. An AI-backed system will make all decisions regarding treatment without consulting with a human doctor. On the other hand, cognitive computing would supplement human diagnosis with its own set of data and analysis. This would help in improving the quality of decisions while also adding a human touch to critical processes.

Going Cognitive: Advantages of Cognitive Computing

The modern computing system is all set to revolutionize the current and legacy systems in the field of automation. According to research, cognitive computing is going to disrupt the digital sphere unlike other technologies introduced in the last 20 years. By analyzing and processing large amounts of volumetric data, cognitive computing employs a computing system for relevant real-life systems. It has a host of benefits including the following:

Improved customer interaction

Robots provide contextual information without needing to interact with other members. Cognitive computing can be used to enhance customer interactions with the help of robotic process automation. Cognitive computing makes it possible to provide relevant, contextual, and valuable information to customers. thus improving customer experience and making customers satisfied and more engaged with a business.

Accurate data analysis

Cognitive computing is highly-efficient in collecting and cross-referencing information to analyze a situation effectively. In the case of the healthcare sector, cognitive computing systems like IBM Watson helps physicians to collect and analyze data from different sources such as medical journals, previous medical reports, diagnostic tools, and past data from the medical fraternity thereby assisting doctors in providing a data-backed treatment recommendation benefiting the patient and the doctor. Cognitive computing employs robotic process automation to speed up data analysis instead of replacing doctors.

Leaner and more efficient business processes

Cognitive computing analyzes emerging patterns, spots business opportunities, and takes care of critical process-centric issues in real-time. By examining huge amounts of data, cognitive computing systems like Watson can simplify processes, reduce risk, and pivot according to changing circumstances. This helps in preparing businesses to build a proper response to uncontrollable factors and to create lean business processes.

H2: Uses of cognitive computing

  • In the healthcare sector, cognitive computing helps doctors make better diagnoses & individualize their treatment decisions.
  • Finance companies use cognitive computing analytics to find the right products to meet their client’s requirements. 
  • Manufacturers use cognitive computing to maintain and repair their equipment, identify defective parts, and reduce production times. 
  • Retail companies use cognitive computing to provide their customers with a personalized online shopper capability, making it easier to find what they are looking for online.

H2: Conclusion

AI uses algorithms to make business decisions on its own and empowers machines to think intelligently. On the other hand, cognitive computing uses NLP and data mining to simulate the human thought process and reasoning to come up with pertinent recommendations, that can be used by humans to solve complex problems. Cognitive computing has found a lot of use cases in analysis-intensive industries like finance, healthcare, manufacturing, and retail. 

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