Organizations should be aware of upcoming trends of cognitive computing before 2022
Cognitive computing is the amalgamation of cognitive science and is based on the basic premise of simulating the basic thought process. It is a combination of disruptive technologies like AI and machine learning with sentiment analysis and contextual awareness to solve daily problems, just like humans. It is used in different industries like healthcare, insurance and more.
The goal of cognitive computing is to simulate human thought processes in a computerized model. Implementing self-learning algorithms that use data mining, pattern recognition and natural language processing, the machines can mimic the way human brains function.
Cognitive systems like IBM’s Watson, rely on deep learning algorithms and neural networks to process information by comparing it to a set of data. The more the data exposed to the system, the faster it learns and provides accurate results overtime. These systems can be applied in other areas of business including consumer behavior analysis, personal shopping bots, travel agents, tutors, security and diagnosis.
Trends of cognitive computing:
Chat-bots: Cognitive computing allows chat-bots to have certain level of intelligence about human communication. It understands the users’ needs based on past communication, suggestions, and more and reacts accordingly.
Sentiment Analysis: To enable machines to understand human communications, the user needs to input data into the machine and analyze the accuracy of the results. This technology is generally used to analyze social media tweets, replies, reviews, and others.
Risk Assessment: Cognitive computing helps combine behavioral data and market trends to generate insights. Implementing only big data analytics is not enough, enhancing the intelligence of the algorithms using cognitive computing proves effective in risk assessment.
Fraud Detection: Fraud detection is another application of cognitive computing in finance. It is a type of anomaly detection. Different data analysis techniques like logistic regression, clustering, and more can be used to detect these anomalies.
Face Detection: Face detection is the advanced level of image analytics. A cognitive system uses data like face structure, contours, eye color, etc. to differentiate one face from another.
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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.