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Cognitive computing is the application of reasoning, natural language processing, machine learning, and human capabilities to help regular computing solve problems and analyze data more effectively. A computer system can handle complex decision-making processes by learning patterns and behaviors, making it more intelligent. It is used to solve day-to-day problems.
According to IBM, cognitive computing is an advanced system that learns at scale, reasons with purpose, and interacts with humans in a natural form. The purpose of cognitive computing is to create a computerized model that simulates human thought processes. The computer can mimic the way the human brain works by using self-learning algorithms based on data mining and pattern recognition. It is an area to properly model how the human mind works and responds to stimuli in its environment. Its most popular uses could be found in data analysis and adaptive output, which adjusts output to fit a specific audience.

Attributes of Cognitive Computing

The attributes of cognitive computing include the following:

  • Iterative and stateful: To identify or clarify a problem, cognitive computing technologies may ask questions and gather additional data. They must be stately in that they retain information about previous similar situations.
  • Contextual: Understanding context is crucial in thought patterns. Contextual data such as time, location, domain, requirements, tasks, and goals must be understood, identified, and mined by cognitive systems. For example, it could be fed data like road, ambulance, and damage to determine the context of a car accident.
  • Adaptive: The systems have to be dynamic enough to adapt as information and goals change. They must process dynamic data in real-time and make adjustments as the data and environment change.
  • Interactive: Human-computer interaction is essential in cognitive systems. Users need to be able to engage with cognitive machines and describe their demands as they change. Additionally, the technologies must be able to communicate with other processors, devices, and cloud platforms.

Examples of Cognitive Computing

Cognitive computing now affects every aspect of our lives, from transportation, sporting activities, and recreation to fitness, health, and wellness. Start-ups and organizations from a broad range of industries have already developed cognitive-based products and services. Their business models are excellent examples of how these applications will improve our lives soon. Below are companies engaged in cognitive computing and what they do:

  • Vantage Software: In real-time, cognitive computing can evaluate new patterns, identify new business models, and handle critical process-centric issues. A cognitive computing system, such as Watson – from IBM – can simplify procedures, minimize risk, and adjust in response to changing circumstances by analyzing massive amounts of data. While this helps businesses prepare for uncontrollable factors, it also aids in the creation of lean business processes.
  • Welltok: Every day, it appears that new – and frequently contradictory – health research is published. As a result, many people struggle to get accurate responses to their health inquiries. Welltok provides CaféWell Concierge, a cognitive-powered tool that can instantly process massive amounts of data to answer questions and provide intelligent, personalized suggestions.
  • LifeLearn: Cognitive computing is not only benefiting humans. It also helps veterinarians in providing better care to the animals that visit their clinics. Sofie, a clinical decision-support tool from LifeLearn, analyses a plethora of veterinary medical resources and recommends appropriate, evidence-based options for treatment. Sofie instantly provides resources and recommendations, allowing busy veterinarians to save time while providing quality care to their patients.
  • Wayblazer: WayBlazer, a cognitive-powered personal travel concierge, makes the task of planning your trip more manageable and enjoyable. The platform allows travelers to ask questions in natural language regarding travel. It also provides customized responses based on a plethora of travel data and insights gathered about individual vacationers’ preferences. Travel providers are already using the tool to boost reservations and enhance customer satisfaction.

Advantages of Cognitive Computing

  • Improved Customer Satisfaction & Retention: With the help of robotic automation, the system can be used to improve customer interactions. Customers can get contextual information from robots without having to interact with other employees. Because cognitive computing enables businesses to provide only relevant, contextual, and valuable data to customers. It also enhances the customer experience, making customers happier and more engaged with a company.
  • More Efficient Business Processes: In real-time, cognitive computing can evaluate new patterns, identify new business models, and handle critical process-centric issues. A cognitive computing system, such as Watson – from IBM – can simplify procedures, minimize risk, and adjust in response to changing circumstances by analyzing massive amounts of data. While this helps businesses prepare for uncontrollable factors, it also aids in the creation of lean business processes.
  • Accurate Analysis: Cognitive systems are extremely efficient at gathering, comparing, and cross-referencing data to effectively analyze a situation. In the healthcare industry, cognitive systems like IBM Watson help doctors in gathering and analyzing information from various sources such as previous medical reports, medical journals, diagnostic tools, and assisting doctors in providing a data-backed treatment recommendation that benefits both the patient and the doctor. Cognitive computing, rather than replacing doctors, use automated robotic processes to accelerate data analysis.

Disadvantages of Cognitive Computing

  • Security concerns: To learn cognitive systems require a large amount of data. Organizations that use these systems must properly protect the data, especially if it contains health, customer, or any other type of personal information. When digital devices handle sensitive information, the issue of security naturally arises. With the ability to handle and analyze massive amounts of data, cognitive computing faces significant challenges in terms of data encryption and security. As more connected devices come into play, cognitive computing will need to consider the issues associated with a security flaw by developing a full-proof security strategy that also includes a mechanism to detect suspicious activity to promote data security.
  • A long development Cycle: To develop software for these systems, talented project members and a significant amount of time are required. To understand given tasks and processes, the systems themselves require extensive and in-depth training with large amounts of data.
  • Adoption is slow: Slow adoption is one of the most significant barriers to any new technology. A reason for low adoption rates is the long development cycle. Smaller organizations may find it more difficult to implement cognitive systems due to their cost and other factors and as a result, avoid them.

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