10 Incredible Applications of Machine Learning That Can Change Our Lives

Machine Learning or ML is a subset of Artificial Intelligence. A basic explanation that gives you a solid idea of what Machine Learning is would be as follows: Machine Learning is a study of algorithms and patterns performed by computers to understand the workings of a system and to perform certain tasks without using explicit instructions. These computer systems rely on patterns found from the data they study which is centered around the task at hand.

In this article, we’re going to be looking at 10 interesting ways in which machine learning will change our lives in the near future.

10 Applications of Machine Learning That Can Change Our Lives

1. Voice Assistants

One technological advancement that has changed our lives completely already is the advent of the voice assistant. There has never been a time in all of human history that it has been this easy to accomplish tasks. You no longer need to set alarms by yourself, you no longer need to send messages on your own, you don’t need to hail a cab by yourself.

You don’t even need to turn on your lights by yourself. Alexa, Siri, and Google will take care of all that. The way these voice assistants use machine learning is that they decide upon our future actions based on all the things we’ve already asked then to do. You could simply speak into your phone’s microphone and say ‘Hey Siri, I’m going home.’, and Siri will pull up a map of your way home and send a text to your family informing them of the same.

2. Facial Recognition

Another great technological advancement that has changed our lives quite significantly in a short amount of time is the Face Unlock feature that has been recently introduced by Apple and a few other smartphone companies. Face Unlock is efficient and saves you time. The way Face Unlock integrates Machine Learning is that it recognizes your face on the basis of Machine Learning.

Every time you unlock your phone from a slightly different angle, your phone registers that information and uses it to make it easier for you to unlock your phone and harder for others to do so. Apart from being used in smartphones, facial recognition is being used by social media companies to identify people in photos. It is also being used by law enforcement to identify criminals.

3. Dynamic Pricing

Imagine this. You’re starting a new ride-hailing company, similar to Uber. You need to figure out how to price your rides. One way to do this would be to price your ride depending on the distance, and although this is not a bad way to price your ride, it doesn’t take into account many other factors.
These factors include weather conditions, customer demand, location, time of day, etc. All of these factors allow you to price your ride more efficiently, leading to more profit for you and your company. But how does all of this take place? You guessed it; machine learning. Machine Learning allows you to collect data from various sources that lead to dynamic pricing. Hence it would be a great boost to your career if you learn ML from institutes like ActiveWizards.

4. Map

Keeping with the theme of transport, online map services such as Google Maps deeply integrate Machine Learning to help their application work effectively. From figuring out the amount of traffic to the estimated time of arrival, these applications use machine learning to collect data and provide up to date information to their users.

5. Google Search

The most popular thing on all of the internet is undoubtedly Google Search. Used by millions every single day, Google Search allows you to procure any information from anywhere on the web instantly.

Although it is hard to point out how exactly Google integrates machine learning into Google Search, it is quite evident that they do so and quite effectively as well. There are trillions of terabytes of data uploaded to the internet on a daily basis. But Google does the job for you and brings you the most popular and reliable information at the click of a button; all because of machine learning.

6. Translation

Translation engines use machine learning quite a bit and it’s easy to tell how they do it. In the early days of online translation, the sentences would come out broken, making little sense. But as time passed, these engines used machine learning to understand the language being translated and the language it was being translated to give users effective translations. This is why services such as Google Translate are so big now.

7. Recommendation Engines

When watching Netflix, we often surf through the ‘Recommended For You’ tab. How does Netflix figure out what we want to watch? That’s right. Machine Learning.
Based on what you’ve watched or bought before, companies such as Netflix and Amazon recommend to you other such products that you may like.

8. Video Surveillance

Gone are the days when the video from surveillance cameras needed to be manually searched through to find the perpetrator of a crime. These days, through machine learning, surveillance systems can track a person’s facial features and using the criminal database of the country, figure out who the person is.

This technology can and has been used wrongly as well, with some countries using it to track its citizens, leaving them with little to no privacy.

9. Healthcare

The healthcare sector has benefited immensely from ML. Sensors attached to a patient can now check their condition, heart rate, blood pressure, temperature, etc. and predict if the patient is in need of care or will be in the future based on its vast collection of data.

This helps doctors learn about fatal situations in advance, helping them save more patients than they would’ve been able to without machine learning.

10. Social Media

As mentioned earlier, social media companies use machine learning in a variety of different ways.
From figuring out who you might want to follow or be friends with to sending specific ads your way based on your searches, social media companies are using ML to the fullest. They can recognize people in pictures and also recognize if a picture or video goes against their guidelines using machine learning.

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