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Cognitive System-Mimicking Human Understanding

With advancements in artificial intelligence algorithms, it's possible for machines to mimic human understanding. They are able to analyze and interpret information, make deductions and identify patterns from the information sets analogous to human brain. These new generation of machines are categorized as cognitive systems. These systems aggregate machine intelligence, predictive analytics, machines learning, natural language engines and image/video/text analytics to enhance human-machine interaction.

Evolution of Cognitive System

The evolution of cognitive systems can be classified into three types:

  • Cognitive Systems for Process Automation
  • Cognitive System for deriving Insights
  • Cognitive System for Engagement

Cognitive Systems for Process Automation

The first phase focusses on the various machine learning and robotic process automation applications to develop substantial domain insights of particular processes and aim to automate them. This phase is aimed at automating repetitive, mundane and low intelligent jobs that employs highly trained human manpower. An example of it is the character recognition and handwriting detection tools deployed by various banks and financial institutions in middle and back office operations to reduce risk and cost. Another example is implementation of chatbots in customer service fields where the bot is able to answer general customer enquires with regards to account balance, credit card offers, utility bill payment questions etc. Cognitive automation helps in improving efficiency as greater volume of data can be processed at a faster rate while improving the compliance capabilities and reducing errors.

Cognitive Systems for deriving Insights

This phase of cognitive evolution encompasses extraction of meaningful insights and relationships from a myriad of data streams comprising of both structured and unstructured data. This phase is evolutionary in nature as the accuracy of the insights and observations improves as the system processes increased amount of data. Cognitive insights have capabilities of providing actionable understandings into possible future events by sensing and analyzing past and present events. This would enable leaders to plan and prioritize future strategies and roadmaps, and augment enterprise capabilities with changing market dynamics.  An example of cognitive insights could be implementing deep learning networks to understand credit card usage patterns among working class customers aged between 25 years and 40 years. Such tailored and actionable insight would help the bank to create hyper-personalized offerings which would be beneficial for the customers and in turn would improve customer loyalty.

Cognitive Systems for Engagement

The final phase of cognitive evolution is intelligent agents that interact and engage with customers using cognitive capabilities. An example of cognitive engagement could be the deployment of voice assisted virtual agents (like Alexa, Siri etc.) to interact with human for performing certain specific tasks. Customers can book an appointment with a fund manager through an Alexa enabled interface or employees' can clarify doubts with regards to the HR policies in an organization through a voice enabled assistant. Cognitive systems have the capabilities to unlock the power of unstructured information flowing through various digital engagement channels and other sources, leveraging image/ video or text analytics to generate actionable insights and helping the bank to develop personalized relationship with the customer.

Characteristics of Cognitive System

Cognitive systems are characterized by their capabilities to understand and extract context from data and learn from patterns to generate future predictions. Some of the characteristics of cognitive systems are:

  • Recognize and Understand: Cognitive Systems have the capabilities to extract information from handwritten text, image, voice and video by utilizing natural language processing, machine learning and predictive algorithms
  • Identifying Contexts: Cognitive Systems are capable of contextualizing information extracted from various data sources and deduce understanding based on context. As such, these systems are able to process information that are situation aware and build suitable data relationship models.
  • Decision Making: Because of its capabilities to identify and establish context, cognitive systems are able to reason and enable decision making based on real-time environment variables.
  • Learn and Improvise: cognitive systems are designed to continuously learn from data inputs and improvise its decision making capabilities based on previous results and feedback received.

Conclusion

Cognitive systems have the potential to help enterprises to optimize various business processes, infer insights from seemingly inconsequential unstructured data sets and create personalized engagement models. Even though the field of cognitive system is ever-evolving, enterprises should undertake a strategic view point over the long term benefits which could help the organizations to maintain their competitive advantage.

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