The Humanization of Machine Learning in Our Daily Lives

FREMONT, CA: Recently, machine learning has become a major buzzword, despite being around for decades. Businesses are struggling to comprehend the intrinsic value of machine learning and the methods for successfully implementing it, following in the footsteps of 'digital transformation'.

The machines know us better

Social media: Machine learning is being used by social media platforms for their own and users' benefit, from personalizing news feed to better ad targeting. One must have seen, used, and loved these amazing social media features without realizing that they were all ML applications.

People you may know: Machine learning relies on a simple concept, understanding through experience. In addition to the friends that you connect with, the profiles that you visit very frequently, the interests you share, your workplace, or a group that you share with someone, Facebook always keeps track of these things.

Similar pins: Using machine learning, we can extract useful information from images and videos using Computer Vision. Accordingly, Pinterest recommends similar pins based on the objects in the images.

Timely travel: GPS navigation services have been used by all of us. A central server is managing traffic while we are doing so, saving our current locations and velocities. A traffic map is then constructed from this data. The problem lies in the fact that there are not enough cars equipped with GPS to prevent traffic and analyze congestion. On the basis of daily experiences, machine learning can be used to estimate regions with congestion.

Smart banking: When an individual applies for a credit card or a loan, financial bodies use machine learning to make an effective decision on its approval. In order to do this, financial institutions use machine learning algorithms to assess the risk of each individual user separately and determine whether to admit the request or not depending on the result.  AML also predicts certain requirements like interest rate, credit line amount, etc., that are necessary for an offer.

ML also keeps track of the thousands of transactions, countless bank account holders, and the number of credit cards that are in circulation.

Online fraud deduction: Tracking online monetary fraud is an example of how machine learning can help improve cyberspace's security. Money laundering can be prevented using ML, for example, by Paypal. By comparing millions of transactions taking place, the company can identify which transactions are legitimate or illegitimate.

The impact that machine learning has on society and the issues it addresses can be seen by screening out various applications of ML.