ons as well. For example, machines will be able to make payment after processing the image. Not only that, computers will give a response to its users based upon their conversation with the user and can modify their response taking the emotional cue from the user. This means computers are likely to learn from the voice and emotion on the spot and modify their responses accordingly. Take another simple example in daily life- millions of students are being taught by teachers in schools or educational institutions. Today with the changing world, the education sector has also changed. Teachers, unlike their previous generations, are playing many roles- roles of educator, mentor, counselor, analyst, setting question papers, preparing worksheets, taking tests or examinations, evaluating answer papers, preparing lesson plans, etc. Though all of these cannot be taken up by machines or robots, some of these tasks like say preparing lesson plans, preparing time tables, evaluating papers can be automated through machine learning. Needless to say, starting from logistics, home education, recruitment, feedback on client communication, health sector and many more, AI technologies are constantly penetrating into a lot of areas.
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Machine learning has wide applications in the banking and finance sector. Several banks and financial institutions around the world such as Wells Fargo, PayPal, and Citibank are using machine learning which helps to identify most valuable customers or influential customers, preventing fraudulent transactions, money laundering. Machine learning analyses huge volumes of real-time transactions per second to analyze and detect unusual behavior patterns and identify fraud transactions following which alert is triggered to customers. Taking a little deeper look, researchers are combining both Supervised and Unsupervised learning models in AI applications for fraud preventions that can detect a previously unseen event or abnormal behavior pattern.