While companies like Apple, Facebook and Tesla rollout ground-breaking updates and revolutionary changes to how we interact with machine learning technology, many of us are still clueless on just how Artificial Intelligence is being used today by businesses both big and small. How much of an effect will this technology have on our future lives and what other ways will it seep into day-to-day life? When A.I. really blossoms, how much of an improvement will it have on the current iterations of this so-called technology?

Applications of Artificial Intelligence In Use Today

…today’s so-called Artificial Intelligence systems are merely advanced machine learning software with extensive behavioral algorithms that adapt themselves to our likes and dislikes. While extremely useful, these machines aren’t getting smarter in the existential sense, but they are improving their skills and usefulness based on a large dataset.

Cogito

Originally co-founded by CEO, Joshua Feast and, Dr. Sandy PentlandCogito is quite possibly one of the most powerful examples of behavioral adaptation to improve the emotional intelligence of customer support representatives that exists on the market today. The company is a fusion of machine learning and behavioral science to improve the customer interaction for phone professionals. This applies to millions upon millions of voice calls that are occurring on a daily basis.

Boxever

Boxever, co-founded by CEO, Dave O’Flanagan, is a company that leans heavily on machine learning to improve the customer’s experience in the travel industry and deliver ‘micro-moments,’ or experiences that delight the customers along the way. It’s through machine learning and the usage of A.I. that the company has dominated the playing field, helping its customers to find new ways to engage their clients in their travel journeys.

Netflix

Netflix provides highly accurate predictive technology based on customer’s reactions to films. It analyzes billions of records to suggest films that you might like based on your previous reactions and choices of films. This tech is getting smarter and smarter by the year as the dataset grows. However, the tech’s only drawback is that most small-labeled movies go unnoticed while big-named movies grow and balloon on the platform.

 

This article originally appeared in chiefexecutive.net.  To read the full article, click here.