The usage of machine learning and artificial intelligence (AI) is ubiquitous in the digital world and paved the way to realization of smart products, smart medicine, and diagnosis of diseases.
August 14, 2020
This article was co-authored by Dr. Anil Kumar Munipalli.
The connection between artificial intelligence at macro level and the Gaussian distribution of the features or training parameters at grassroots level is ubiquitous. To start with, most of the natural and physical processes are random (stochastic) in nature and are studied using random variables. The behavior or trend associated with such processes follows a Gaussian distribution that arises from the popularly known “central limit theorem and the law of large numbers.”  In AI-related problems, for example, voice recognition in speech analysis, each training parameter, when estimated over a large dataset, follows a Gaussian distribution. The sum and product of many such training parameters influencing the net result also becomes a Gaussian distribution, which is characterized by its mean (µ) and variance (σ). The understanding of these two measures are key in AI-related problems.