Machine Learning has been around since the early days of computer science and has gained notable traction as more & more people begin to realize how advanced it’s becoming.
Today, Machine Learning algorithms apply to various fields, including some of the most common problems. For example, internet-related areas like data mining, content filtering, and product recommendations.
As per Statista, the most wide-scale application of AI & ML in 2021 lies in enhancing the customer experience with a popularity of 57%. It gets followed by ‘generating customer insights’ with 50% favor.
AI & ML remains at the top of the most disruptive technologies worldwide. Moreover, with new & innovative applications, we are witnessing large-scale adoption of these technologies.
Do you know the top AI & ML use cases for enterprises worldwide?
Here’s what research says:
Even though you might not understand all the technical details behind these applications today, you’ve come across multiple of them in your daily life. Yes! not one, but many of them have become an integral part of your everyday life.
Let’s take a look at seven stunning real-life examples of machine learning applications in today’s society.
1) Amazon Using Lex
Amazon has gotten better at understanding what shoppers want before they do. The company in 2017 introduced a new service called Amazon Lex. It uses artificial intelligence to make it easier for companies to add Alexa-like conversational interfaces to their apps and devices.
Lex does all sorts of clever things like understanding natural language questions, detecting sentiment in responses, and deriving meaning from incomplete queries. It’s now convenient than ever for users to add smarts like voice control or even AI assistants into their products via Lex.
2) Facebook Optimizing Content, etc.
Facebook uses artificial intelligence to process and analyze photos, videos, & text. Facebook’s AI analyzes each post you share on your timeline by looking at several factors, including whether you are in any photos or videos in which you get tagged.
For example, suppose you tag yourself in a photo of your friend’s new baby. In that case, Facebook will assume that you like babies because there is an 85% chance that most people who enjoy babies also enjoy seeing other people’s new baby pictures.
This type of analysis helps enhance the user experience. For example, future parents find friends with kids nearby or allow companies to advertise their products based on their friends or family members’ likes.