AI/ML, Cloud Computing

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Cognitive Computing: Amplifying Human Decision-Making with Intelligent Support


A cognitive computer or system utilizes conscious thinking, learns globally, and traditionally connects with people. Instead of being purposefully designed, these systems learn and reason by relationships between people and what they have encountered in their surroundings. Cognitive computing and artificial intelligence share similarities, and the technology behind cognitive applications is similar.

What is Cognitive Computing?

In complex circumstances where the solutions may be vague and uncertain, cognitive computing uses computational models to imitate the human reasoning process.

Computers are quicker than people in processing and calculating, but they are still not very good at understanding spoken language or identifying objects in pictures. Cognitive computing aims to have computers behave cognitively like how people think.

Artificial intelligence (AI) and other underlying technologies are used by cognitive computing to do this, including expert systems, neural networks, machine learning, deep learning, natural language processing, speech recognition, object recognition, robotics, etc.

Cognitive computing combines these techniques with self-learning algorithms, data analysis, and pattern recognition to train computing systems. Speech recognition, sentiment analysis, risk evaluations, face detection, and other tasks can be handled by learning technologies. It is also very helpful in industries like healthcare, banking, finance, and retail.

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Working of Cognitive Computing

Cognitive computing technologies combine several knowledge sources to offer the best solutions, balancing contextual and naturalistic explanations. Cognitive systems use identity techniques that include data mining, pattern recognition, and natural language processing (NLP) to understand how and why the individual brain operates to achieve.

Addressing issues that humans should handle using technology tools requires a lot of structured and unstructured data. When machines get better at pattern recognition and data processing via training, cognitive systems develop the capacity to anticipate new problems and simulate potential solutions.

For instance, an AI system can be trained to recognize images of cats by keeping hundreds of them in a database. A system can learn more and become more accurate over time as it is exposed to more data.

To have such capabilities, cognitive computing systems need to have the following qualities:

  • Adaptive: These systems must be adaptable enough to pick up new information as goals and objectives change. They need to process dynamic data instantly and evolve as the surroundings and data do.
  • Interactive: A crucial element of cognitive systems is human-computer interaction. Cognitive machines must enable users to communicate with them and articulate their changing demands. Additionally, the technologies must be able to communicate with other devices, platforms, and processors.
  • Stateful and iterative: These systems must also be able to identify problems and alert users by raising questions or demanding more details if the problem hasn’t been entirely fixed. These computers achieve this by maintaining a database of prior, comparable events.
  • Contextual: Cognitive systems must identify, understand, and use relevant data such as terminology, time, place, topic, specifications, and a specific person’s identity, responsibilities, or goals. Many data gathering techniques may be used, including visual, auditory, or sensor readings and structured and unstructured data.

Use cases for Cognitive Computing

The following are some examples of how cognitive computing is utilized in various industries:

  • Healthcare: Large amounts of unstructured healthcare data, including patient histories, diagnoses, illnesses, and journal research papers, can be handled by cognitive computing to generate recommendations for medical practitioners. To assist doctors in making wiser treatment choices, this is done. A doctor’s capacities are increased, and cognitive technologies aid decision-making.
  • Retail: These systems examine fundamental consumer data and specifics of the product the customer is viewing in retail settings. The system then gives the customer individualized recommendations.
  • Money and Banking: To learn more about clients, the banking and finance sector uses cognitive computing to analyze unstructured data from many sources. Chatbots that communicate with clients are developed using NLP. Both operational effectiveness and consumer engagement increase as a result.
  • Logistics: Cognitive computing makes IoT devices, networking, and warehouse management easier.

Advantages of Cognitive Computing

Cognitive computing has positive effects in the following areas, among others:

  • Analytical precision: Cognitive computing successfully compares and cross-references structured and unstructured data.
  • Efficiency of business processes: Cognitive technologies can spot patterns while examining massive data sets.
  • Customer interaction and experience: The contextual and appropriate information that cognitive computing provides consumers through tools like chatbots enhances customer interactions. The customer experience is improved by combining cognitive assistants, customized recommendations, and behavior predictions.
  • Service excellence and worker productivity: Employees can examine organized and unstructured data using cognitive technologies to spot trends and patterns.

Limitations of Cognitive Computing

Additionally, there are drawbacks to cognitive technology, such as:

  • Issues with Security: Cognitive systems require a lot of data to learn from. Organizations must appropriately protect the data by employing the technologies, especially concerning customer, health, or other types of personal information.
  • Extended development cycle: To create software for these systems, specialized development teams must invest much time and effort. The systems require in-depth training using enormous data sets to comprehend specific jobs and processes.
  • Gradual adoption: Slow adoption rates have several causes, including a slow development lifecycle. Smaller firms could avoid cognitive systems because of the difficulty in deploying them.
  • Negative effects on the Ecosystem: Training cognitive systems and neural networks uses a lot of energy and produces a lot of carbon dioxide.

Services using Cognitive Computing in AWS

AWS offers several services that use cognitive computing, including:

  1. Amazon Rekognition: A service that uses deep learning algorithms to analyze images and videos and recognize faces, objects, scenes,                            and text.
  2. Amazon Polly: A service that turns text into lifelike speech, using advanced deep learning technologies to synthesize natural-sounding voices.
  3. Amazon Lex: a service that integrates speech and text-based conversational interfaces into any application.
  4. Amazon Comprehend: A service that combines machine learning and natural language processing (NLP) to find patterns and connections in text.
  5. Amazon Translate: A service that uses deep learning to provide high-quality language translation between different languages.
  6. Amazon Transcribe: A service that uses machine learning to convert speech to text, making adding speech-to-text capabilities to your applications easy.
  7. Amazon SageMaker: A wholly-managed machine learning platform that allows programmers and data scientists to create, train, and use machine learning models at scale.

These services can help businesses and developers to build intelligent applications that can understand, reason, and learn from data to make better decisions and provide better customer experiences.


Cognitive computing is employed to support human decision-making. AI uses algorithms to solve issues or find patterns in massive amounts of data. To solve problems as the data and the problems change, cognitive computing systems have the higher and better goal of developing algorithms that imitate the human brain’s deductive reasoning process.

Drop a query if you have any questions regarding Cognitive computing and we will get back to you quickly.

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1. What is Cognitive Computing?

ANS: – Cognitive computing uses computerized models to imitate human mental processes in complex circumstances where the solutions may be vague and uncertain.

2. What is the difference between AI and cognitive AI?

ANS: – In a nutshell, AI’s goal is to think independently and make decisions independently, whereas Cognitive Computing’s goal is to mimic and support human thinking and decision-making.

3. What is an example of a Cognitive system?

ANS: – There are now cognitive systems that can read, write, speak, hear, see, and learn. Examples include brain-machine interfaces, robotic orthotics, prostheses, prosthetics for the senses and cognition, software and robotic assistants, autonomous vehicles, autonomous weaponry, and more.


Aritra Das works as a Research Associate at CloudThat. He is highly skilled in the backend and has good practical knowledge of various skills like Python, Java, Azure Services, and AWS Services. Aritra is trying to improve his technical skills and his passion for learning more about his existing skills and is also passionate about AI and Machine Learning. Aritra is very interested in sharing his knowledge with others to improve their skills.



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