The future is here
By the above statements, it is clear that data proliferation will never end and this necessitates use of data related technologies like Data Science and Big Data, increasing day by day. Data Science makes use of several statistical procedures. These procedures range from data transformations, data modeling, statistical operations and machine learning modeling. Data science is an interdisciplinary field that uses techniques such as machine learning and artificial intelligence to extract meaningful information and to predict future patterns and behaviors. It is a general process and method that analyze and manipulate data. Also, enables to find meaning and appropriate information from large volumes of data. This makes it possible for us to use data for making key decisions in business, science, technology, and even politics.
AI (artificial intelligence) is reproducing human intelligence in machines, especially computer systems through learning, reasoning and self-correction, and that’s how for example one gets suggestion according to their taste and choice. While Machine learning (ML) is a subset of Artificial Intelligence, it helps in in teaching computers how to learn and make predictions from data without necessarily being programmed. For example, ML helps you get filtered mails. Deep learning is a subset of machine learning in which data goes through multiple number of non-linear transformations to obtain an output. With deep learning, systems are learning to mimic human voices to the point where it is hard to distinguish between a human and a computer voice-over.
While many might think that AI is a long way in future, it is already here. With technology giants like Facebook and Google, placing huge bets on AI and ML by already incorporating it into their products, AI and ML are certainly here to stay.
Machine learning and artificial intelligence are often used interchangeably. These are the trending buzzwords of the technology industry. Both AI and ML fall under the bigger world of data science that deals with processes or systems used to extract knowledge from large amounts of data.
But the question arises how AI and ML can significantly help enhance e-governance?
The answer is quite clear. The application of AI in governance, such as in security, finance, manufacturing, e-commerce, voice recognition and transportation, provides an opportunity for India for leapfrog developmental. Large data sets and better analytic tools allow for better design of policies.
AI and ML techniques can be applied in large-scale public initiatives ranging from crop insurance schemes to tax fraud detection to enhancing our security strategy. Here are some example:
However, the essential aspect of using both ML and AI is accurate algorithms which should be relevant, stable, and effective. Public sector entities are required to pay close attention to the implementation of these algorithms without which it would not be possible to minimize risks.
Find out more about how we can help
your organization break the thought barriers.