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Cognitive Computing vs. Artificial Intelligence | Which One Is Superior?

By Nivin Biswas Category Artificial Intelligence Reading time 9-10 mins Published on Feb 15, 2023

What Are the Differences Between Cognitive Computing and AI?

There is a lot of lexical overlap between the phrases deep learning, voice recognition, text analytics, cognitive computing, and neural networks. But their procedures and methods differ a bit, sometimes a lot. These are quite known facts. But when it comes to artificial intelligence and cognitive computing, most people get confused about these two terms.

So are they running into a completely different orientation, or are they lexical too?

One such technology that is occasionally mistaken for AI technology is cognitive computing, which is fundamentally different. Although these technologies are the newest and greatest in supercomputing, they have other implications when used in actual applications. Let's now discuss the primary debate between AI and cognitive computing.

How does AI work?

AI employs algorithms that are created to assist it in determining the most effective way to carry out a task or come to a decision. The AI then takes the right action based on what it has learned.

Similar to human intelligence, AI is continually absorbing information from its surroundings and analyzing it to find the best answer to a question or the best way to carry out a task, like speech recognition or image recognition.

Human intelligence is rooted and grounded in detecting the environment, learning from the environment, and digesting the information from the environment; if we can agree on that, AI tends to do the same:-

  • Replicate the human brain and senses to perform tasks.

  • ML and deep learning techniques help in simulating the learning and computation process.

  • Robotic models of social cognition.

An illustration shows a guy sitting in a chair in front of three screens while a robotic machine packs a box in an accelerating tray.

What is Cognitive Computing?

Decision-makers can benefit from cognitive computing systems by getting data-driven insights from them. These systems are capable of processing massive amounts of data and intricate iterative analysis, continuously revising their conclusions as new data becomes available.

Cognitive computing systems employ self-learning algorithms based on AI methods, including data mining, image recognition, voice recognition, and Natural Language Processing (NLP), to solve complex problems. Cognitive computing systems use self-learning algorithms to learn from the user and provide a more individualized experience.

In short, cognitive computing means:

  • Comprehending and multiplying logical reasoning

  • Comprehending and modeling human behavior

  • With the use of cognitive computing, we can readily produce erroneous decisions

The key distinction between Cognitive Computing and AI

1. Complex interactions with humans:-

Cognitive computing systems are designed to evaluate, explain, and memorize alongside people in order to provide advice and insights when making decisions. The AI system tends to deliver the best algorithm to provide more efficient results without the need for human intervention, whereas the cognitive computing system relies on ideas for human use.

2. Contextual solutions:-

Cognitive computing can consider contradictory and fluctuating information pertinent to the current circumstance. It focuses its forecasts and suggestions on analytics rather than pre-programmed algorithms.

Let's use an example to better appreciate this: Imagine a person in his 80s who wants to learn and implement advice on how to build muscle strength. As a result, AI development will be the most outstanding program focused on arithmetic. Conversely, cognitive computing would modify the software by taking into account aspects such as age and disabilities.

Ultimately, we can say that the data provided by AI can be programs used to form a final decision. However, cognitive computing does not make decisions for people; rather, it gives them the pertinent information they need to make such decisions for themselves.

Here is a small chart that will surely help you to understand better their distinct features:-

Cognitive Computing Vs. Artificial Intelligence

Cognitive Computing       Artificial Intelligence (AI)
Deep learning rules are used in robotics and sentiment analysis by cognitive AI. Furthermore, AI has become more involved in the application of ML, neural networks, and Natural Language Processing (NLP).
It aids users in making better decisions by replicating human thought processes. Big data analysis is aided by AI, which also uncovers hidden data and makes complex problem solutions simple to implement.
We can employ cognitive skills to evaluate how people think. We can improve the application of artificial intelligence in automated processes.

An illuminated hand is pointing towards the logo of the health sector, finance/banking, and ecommerce.

Applications of Cognitive Computing:-

Cognitive computing application cases are typically concentrated in sectors requiring much analysis. Several instances include:-

Health sector:-

The healthcare industry has been significantly impacted by cognitive computing. As an alternative, they support patients in choosing more precise diagnoses and personalized treatments by providing them with access to global info and the capability to comprehend patient images. Patients now have access to treatments and diagnoses that they previously lacked.

Finance sector:-

Companies that provide financial services are utilizing cognitive computing to assess investment risks by fusing market trends with data on consumer behavior. This enables them to offer more specialized services and identify the ideal products to satisfy the needs of their customers.

Many businesses are still recovering from security and fraud issues, but creative computing can undoubtedly be helpful in this situation. Cognitive computing can make analyzing the diverse historical data from the far-reaching transformation easier.

For instance, to reduce the risks and claims related to insurance, insurance companies use cognitive computing. IoT devices record their clients' driving behaviors, and auto insurance companies use cognitive analysis to adjust premiums in response.


Because of cognitive computing, online shopping experiences are becoming more personalized, which is very beneficial for retailers because it makes it easier for customers to easily find the products they want. By using cognitive computing, retailers can provide their customers with a more customized and convenient online shopping experience, which will keep them coming back for more.


Manufacturers can benefit from cognitive computing as well. They can quickly streamline their equipment and machinery, find defective components, shorten manufacturing times, and improve parts management. It tends to improve the efficiency of manufacturing lines by producing profitable output.

An illustration shows a man seated on a sofa conversing with a robot standing in front of him. The robot is coming from the callout of the a tab display.

Key attributes of Cognitive Computing:-

The following characteristics of cognitive computing are necessary for it to achieve its capabilities:-


The ability to adapt is essential for cognitive systems. They need to be adaptable enough to comprehend information changes and capable of processing dynamic data in real-time. At the same time, they must be pro in making adjustments as the data and surroundings change.


Interacting with both humans and computers is a key component of cognitive computing. As their demands change, users can alter the machines' behavior and engage with them. Other processors, gadgets, and cloud computing platforms should be able to communicate with the technologies utilized in cognitive systems.

Iterative and stateful:-

In iterative and stateful systems, problems are typically identified by asking questions or extracting further data if the problem isn't complete. These systems work on situations that have already occurred, allowing users to identify patterns and solve problems more effectively.


Cognitive systems need to be able to comprehend, recognize, and retrieve pertinent facts from their operational context in order to be effective.

Contextual information may include things like syntax, time, place, domain, specifications, a particular user's profile, tasks, or objectives. It frequently uses a variety of information sources, including both organized and unstructured data, as well as visual, auditory, or sensor-based data, to accomplish this efficiently.


We can conclude that while cognitive computing and artificial intelligence have similar theoretical underpinnings, they take different approaches to imitating human intelligence. Software developers and business executives need to be aware that cognitive computing includes both natural and artificial intelligence.

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