Machine Learning for efficiency and transparency

Machine Learning

Artificial intelligence comes in different variants. Machine learning is one of these variants. The term machine learning as an AI technology means that the machines or the computers are programmed with the ability to self-learn and improve their performance for a particular task. This is likely to result in better and error-free performance on a specific task in comparison to its human counterpart. In fact, reputed organizations and business entities like Amazon have already started to implement this strategy. In the past few years, it has introduced into its supply chain around 80,000 AI-powered robots across many of its distribution centers. This has helped them in gaining efficiency in the work and also saving the cost. If you speak of Google, well known worldwide for its highly efficient search engine, has now integrated voice search into its search facility which is one of the applications of AI technology today. In the future, not only computers or machines will be able to process voice or sound, but they will also be able to process the image and to some extent emotions 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.


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