Artificial intelligence (AI), machine learning and deep learning are growing trends being developed across various magnitudes encompassing tremendous amounts of data. This collation of data has made impossible things possible, improbable things positive, and many things available to individuals with a few clicks. With this data growth, we are progressing towards a world that rests on mathematical probabilities.
Take the probability that a rat might die if it doesn’t escape the cat vis-à-vis the possibility that the cat might catch him before he escapes. In order to assist itself, the rat requires a lot of data to analyze the probability of survival, how to effectively extract the data to understand its opponent, and all of this through de-codifying the algorithms created by its community.
The energy propelling all these probabilities is “big data”, often referred to as high-volume, high-velocity and high-variety information, and its effective analysis. These applications with machine learning tools are recording every bit of information fed into their systems with or without knowledge from the users, as there is so much information, with limited ability to utilize it to our advantage.
Authored by Pravin Anand and Mrinali Menon.
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