Blog: Importance of Machine Learning in present-Day Mobile Applications
Machine learning has quickly become important topic in information technology. And, although it’s changing the game in a big way right now, it’s actually been kicking around in the tech and innovation scene for several years. Apple, for example, first brought Siri into the light in 2011 but, years earlier, had first begun experimenting with consumer-driven machine learning.
The iPhone and Machine Learning
What’s Next for Mobile Machine Learning
Historically, machine learning requires a tremendous amount of power that mobile devices simply didn’t have. However, businesses can now install special chips in drones, automobiles and smartphones enabling them to consume 90 percent less power. As a result, mobile devices, even without an internet connection can perform a variety of once-complex tasks, including:
- Voice Recognition
- Language Translation
- Virtual / Augmented Reality
- Smarter Camera Functionalities
- Improved Device Security
Machine learning is changing the way we interact with our mobile devices. Our phones and tablets are now powerful enough to run software that can learn and react in real-time. This has opened up the door to some cool applications.
Startups and tech giants are all starting to use machine learning in mobile app development, and they’ve come up some interesting ideas. Let’s take a look at 10 examples you can download to your phone right now.
People want their experience to be totally personalized today. So it is not enough to build a good quality app. You need to make them stick with your mobile app. But how? Let machine learning do the job. Machine learning technology can transform your app into an app of your users’ dream.
Sounds tempting? Believe me, not until you’ve seen it with your own eyes. In this article, you’ll find seven mobile applications that once used machine learning and have never looked back since.
Netflix is one of the most obvious examples of Machine Learning in mobile apps. Today, everyone knows what it is.
The reason is, it just knows what you want to watch, before you want to watch it!
First, you may think, ‘Magic?’
Well, not exactly, but a few decades ago it could have been considered magic! As we know, the magic behind this trick is machine learning.
Netflix has grown from a DVD rental website to a global streaming service. And it has everything to do with machine learning!
At Netflix, they use Linear regression, Logistic regression, and other machine learning algorithms. All these scary words mean that Netflix has perfected its personalized recommendations by means of ML.
Netflix’s content is classified by genre, actors, reviews, length, year and more. All these data go into machine learning algorithms.
ML algorithms at Netflix learn from a user’s actions. For example, yesterday I watched a new TV show, and, in my opinion, it wasn’t really good, so I will not watch the second episode. Based on my behavior, Netflix understands that I do not appreciate this kind of TV shows, and puts it far away from my recommendations. The same thing happens when you watch just a trailer, give a bad review, or if you pick the seventh recommendation instead of the first. Machine learning algorithms adapt to a user’s behavior to provide extremely personalized content.
Snapchat started taking machine learning seriously when they acquired the Ukrainian computer vision company Looksery for $150 million. They use Looksery’s clever facial tracking algorithm to find your face in your snaps and add things like glasses, hats and doggy ears.
Recognizing a face is easy for humans but difficult for computers. Explicitly programming a computer to recognize a face is almost impossible. Instead, Snapchat has its algorithm look at thousands of faces to slowly learn what a face looks like. Each picture has all facial features such as eyes and nose marked by humans.
Take Tinder. Everybody knows it as a dating app that finds your soulmate. The thing is, to find you a perfect match, Tinder uses all kinds of love spells and potions, and one of them is machine learning. The potion is called ‘Smart Photos’ and it increases a user’s chances of finding a match.
But, how is it possible?
With the help of machine learning, this feature shows a random order of your profile photos to people and analyzes how often they’re swiped right or left. This knowledge allows Tinder to reorder your photos by putting most popular ones first. This system is honing itself constantly and the level of improvement depends on the input — the more the better.
This way, you’ll get better results and find your soulmate in no time.
SUCCESS STORIES OF MOBILE APPS BASED ON MACHINE LEARNING
Our friends, who are not in IT, have long puzzled over how this application works. That’s because Shazam is based on the principle of machine learning. By converting the music into digital data, Shazam compares it to all the tracks that were previously analyzed in the learning stage.
The basis for this mobile application is a usual keyboard with an interesting design on Google Play. As far as it gains experience while working with its owner, this keyboard becomes capable of generating frequently used phrases or correct written words, just like the notorious T9.
These days, there is no person familiar with mobile devices based on iOS, who would not know about Siri. This is one of the most successful variations of machine learning iOS apps, presented as a personal assistant based on artificial intelligence. Siri not only helps its users to simplify the everyday tasks, but can brighten up the moments when there is no one to communicate with anyone else, in a way.
Seer Predictive Speed Dialer
This mobile application from Google Play allows you to reduce the use of a phonebook, enabling you to call the most active contacts directly from the home screen. Based on self-learning routines and predictive analysis of user behavior, the application changes the selection of contacts available on the speed-dial widget, depending on current location and previous calls.
Available on iOS and Android platforms, this application, based on the Healint machine learning service, serves as an aide to patients with chronic migraines. Migraine Buddy conducts extensive research of the user’s lifestyle and everyday habits, and takes into account such factors as local weather, characteristics of previous seizures, drugs taken, health conditions, etc., to forecast the possibility of a subsequent headache and ways to prevent it.
Now, you know a brief understanding of how popular mobile apps make use of machine learning. It takes a while for an app to work to learn your preferences and adapt accordingly. Hope that this will motivate you to develop intriguing mobile apps leveraging machine learning.