Cognitive Computing

Although Artificial Intelligence (AI) has been a distant goal since the concept of computing, it seems to be getting closer every day with new cognitive computing models.

Cognitive computing, born from the fusion of cognitive science, is based on the simulation of human thought processes. Its concepts and applications must greatly impact our personal lives and industries, such as medical and insurance. Undoubtedly, the benefits of cognitive technology go a step beyond traditional AI systems.

According to David Kenney, general manager of IBM Watson, a state-of-the-art cognitive computing framework, "AI will be as smart as the person who teaches it." The latest cognitive revolution is not the same.

Cognitive computing combines Artificial intelligence, Neural Networks, Machine Learning (ML), Natural Language Processing (NLP), Emotion Analysis, and Contextual Awareness to solve everyday problems as people do. Cognitive computing is an advanced system that learns, makes purposeful inferences, and naturally interacts with humans.

How Cognitive Computing Works

The systems used in cognitive science combine data from various sources, taking context and conflicting evidence into account and proposing the best possible answer. To achieve this, cognitive systems include data mining, pattern recognition, and self-learning techniques that use NLP to mimic human intelligence.

Using computer systems to solve problems that people normally have to deal with requires a huge amount of structured and unstructured data supplied to machine learning algorithms. Over time, the cognitive system can refine how patterns are identified and processed. You can then predict new problems and model possible solutions.

For example, if you store thousands of photos of dogs in a database, you can teach an AI system how to identify them. The more you interact with the data, the better you will learn and the more accurate you will be over time.

To achieve this, a cognitive computing system must have the following characteristics:

Adaptability

These systems need the flexibility to learn as information changes and goals evolve. Dynamic data must also be digested in real-time and adjusted to change data and environment.

Interactive

Human-computer interaction is an important element in cognitive systems. Users must be able to interact with cognitive machines and define their needs as needs change. It must also be able to interact with other processors, devices, and cloud platforms.

Iterative and stateful

Cognitive computing technology can identify and articulate problems by asking questions and capturing additional data. It also holds information about similar situations that have occurred in the past, so it must be stateful.

Contextual

Understanding context is very important in the thought process. Cognitive systems need to understand, identify, and contextual data such as syntax, time, location, domain, requirements, user profiles, tasks, and goals. The system can utilize multiple sources of information, including structured, unstructured, visual, auditory, and sensor data.

Scope of Cognitive Computing

For decades, computers were faster than humans at performing calculations and processing. Still, they failed to perform tasks humans take for granted, such as understanding natural language and recognizing objects unique to images. However, achieving tasks humans take for granted, such as understanding natural language and recognizing unique objects in images, is disastrous. So cognitive technology makes it possible to calculate these new classes of problems. It can deal with complex situations characterized by ambiguity and can broadly impact personal life, medical care, business, etc.

According to IBM Institute for Business Value's Your Cognitive Future, the cognitive computing spectrum consists of engagement, decision, and Discovery. These three abilities are related to how people think and demonstrate their cognitive abilities in daily life.

Engagement

Cognitive systems have a vast repository of structured and unstructured data. These can deepen insight into the deep areas and provide expert support. The models constructed by these systems include contextual relationships between various entities in the systems world, allowing them to form hypotheses and arguments. These models can adjust ambiguous or even self-contradictory data. In this way, these systems can have a deep dialog with humans. Chatbot technology is a good example of an engagement model. Many AI chatbots are pre-trained with domain knowledge to adapt to different business-specific applications quickly.

Decision making

It is one step ahead of the engagement system and a decision-making system. These systems are modeled using reinforcement learning. Decision-making by cognitive systems continuously evolves based on new information, results, and behaviors. Autonomous decision-making depends on the ability to track why a particular decision was made and to change the reliability score of the system's response. A common use case for this model is IBM Watson in the medical field. The system can collate and analyze data such as patient history and diagnosis results. The solution makes recommendations based on the ability to interpret the meaning and analyze queries in the context of complex medical data and natural language, including physician memos, patient records, medical annotations, and clinical feedback. As the solution learns, its accuracy increases. By providing decision support and reducing paperwork, clinicians can spend more time with their patients.

Discovery

Discovery is the most advanced area of cognitive computing. You'll discover insights, understand them, and develop your skills with Discovery. These models are built on deep learning and unsupervised machine learning. As the volume of data continues to grow, it is clear that there is a need for a system to leverage information more effectively than human beings can do independently. Although it is yet in its infancy, several discovery features have already appeared, and value proposals for future applications are compelling. The Cognitive Information Management (CIM) shell from Louisiana State University (LSU) is one of the cognitive solutions. The model's distributed intelligent agents build interactive sensing, inspection, and visualization systems that collect streaming data, such as text and video, for real-time monitoring and analysis. The CIM shell not only sends warnings but also isolates important events and reconfigures them on the spot to correct the failure.

Benefits of Cognitive Computing

Modern computing systems are said to revolutionize current and legacy systems in the process automation field. According to Gartner, cognitive computing, unlike other technologies introduced in the past 20 years, destroys the digital realm. Cognitive computing, capable of analyzing and processing large amounts of data, is useful when adopting computing systems in related real-life systems. Cognitive computing offers the following benefits:

Accurate data analysis

Cognitive systems are highly efficient in collecting, juxtaposing, and cross-referencing information, enabling you to analyze situations effectively. Taking the healthcare industry as an example, cognitive systems help physicians collect and analyze data from a variety of sources, including past medical reports, medical journals, diagnostic tools, and medical data from the medical community, and provide recommendations for treatments based on data that benefits both patients and physicians. Cognitive computing uses robotic process automation to accelerate data analysis rather than replacing physicians.

Enable more efficient business processes

In real-time, cognitive computing can analyze emerging patterns, identify opportunities, and handle critical process-centric issues. Cognitive computing systems can investigate huge amounts of data to simplify processes, reduce risks, and pivot as the situation changes. This helps you build lean business processes while preparing you to respond appropriately to uncontrollable factors.

Improve customer interaction

With Robotic Process Automation (RPA), you can enhance your interaction with your customers. The robot can provide context-sensitive information to customers without interacting with other staff. Cognitive computing enables customers only to provide relevant, contextual, and valuable information, thereby improving customer experience, satisfying customers, and getting more involved in their business.

Conclusion

As part of the digital evolution cycle, the adoption of cognitive technology begins by identifying manual processes that can be automated through technology. Many companies are already breaking new ground in cognitive technology and bringing excitement to truly digital organizations worldwide.

Over time, more data can be analyzed, insights into past events can be gained, and processes for the present and future can be improved. Cognitive technology is useful for past analysis and enables predictive analysis to predict future events better.

This robust and agile technology is immeasurable in the future potential and path in both the B2B and B2C segments. In the future, it is thought that such technologies will enable people to become more efficient than before, to be entrusted with common analysis, and to focus on creative work.