Applications Of Cognitive Computing Technology
Cognitive computing is a self-learning system that uses Machine Learning techniques to perform specific human-related tasks intelligently. This technology helps to create automated IT models that are capable of solving problems without human assistance. The result is cognitive computing, which is a combination of computer science and cognitive science.
There are many applications of cognitive computing. It can quickly handle a complex set of tasks involving logical reasoning. Here are some applications of cognitive computing in business:
Sentiment analysis: Sentiment analysis is a science of understanding emotions or feelings conveyed in a communication. It is easy for humans to understand tone, intent, and so on in a conversation, but it is complicated for machines. To enable machines to understand human communication, we need to train the model with data of human conversations and then analyze the accuracy of the analysis. Sentiment analysis is majorly used to investigate the interactions in social media, such as tweets, comments, reviews, complaints, and so on.
Chatbots: Chatbots are programs that can replicate a human conversation by understanding the rules of communication. To make this possible, a machine learning technique called NLP is used. NLP allows applications to take inputs from humans (voice or text), analyze them, and then provide logical answers. Cognitive computing enables chatbots to have a certain level of intelligence in communication, like understanding users’ needs based on past conversations, giving suggestions, and so on.
Risk assessment: Risk management requires analysts to go through market trends and historical data to predict the uncertainty involved in an investment. This analysis is related to data trends, guilty feelings, and behavior analytics. As intuition and experience are needed to predict the market’s performance in the future, it is necessary to make algorithms intelligent. Cognitive computing helps to combine behavioral data and market trends to generate different insights. These insights can be evaluated by experienced analysts for further analysis and predictions.
Face detection: Face detection is a detailed analysis of an image. A cognitive system uses data like structure, contours, eye color of the face to differentiate it from other images. After the generation of a facial image, it can be easily used to identify a face in an image.
Fraud detection: Fraud detection is another application of cognitive computing in finance. Here, the system is used to detect anomalies. The goal of fraud detection is to identify transactions that don’t seem to be healthy (defects).
The cognitive computing landscape is influenced by large companies like Microsoft, IBM, and Google. They invested billions of dollars in Big Data and real data analytics and now spent an estimated one-third of their R&D budget on the development of cognitive computing technology. Now, even new companies are investing heavily in this technology to develop better products.
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