Top Machine Learning Mobile Apps
App Builder Appy Pie, April 04, 2018: The concept of Machine Learning may not be brand new, but in the recent times the way this technology has grown is astounding. Couple machine learning with mobile apps and you have cutting edge technology in your hands. Machine learning has in fact managed to change the whole way we interact with and use our mobile devices.
The mobile devices we use today, including mobile phones and tablets have become powerful enough to run software that can learn & react in real-time. It is for this reason that we are witnessing some of the most amazing mobile apps today. But before we get into that, let’s begin by understanding what Machine Learning is all about?
What is Machine Learning?
Machine Learning is a field of Computer Science which lends the computer an ability to “learn” with data, without having to explicitly program it. It is essentially an application of Artificial Intelligence. Machine Learning focuses on developing computer programs that accesses data and use it to learn themselves.
Examples of Top Machine Learning Mobile Apps
Machine Learning has managed to transform the expectations of the mobile device users and the way they interact with these devices. Most of the Startups and the tech giants are now beginning to see the benefits and scope offered by Machine Learning in mobile app development. It is this realization that have led to them coming up with some truly unique ideas. Here’s a list of top examples of machine learning mobile apps compiled by Appy Pie.
The filters by Snapchat are an amazing combination of augmented reality and machine learning algorithms for computer vision. Snapchat seriously considered the importance of Machine Learning only after their acquisition of Looksery, the Ukrainian computer vision company for a whopping $150 million. Snapchat makes use of the unique facial tracking algorithm from Looksery to find faces in the snaps and add fun elements like glasses, hats, dog ears, and more!
Snapchat first detects a face, by looking at a photo as a set of data for the color value of each individual pixel. The program looks for areas of contrasts between light and dark parts of the image to determine which part of the picture is a face. Though the algorithm here doesn’t work when tilt your face acutely or face sideways but works really accurately for frontal faces.
Programming a computer explicitly to recognize a face is near impossible, because recognizing a face is very “human” ability, tough for a machine to emulate. The algorithm used in Snapchat looks at thousands of faces to slowly learn what a face looks like.
2) Oval Money
Oval Money is one of the greatest examples of a finance related mobile app that implements machine learning. The app, in fact uses machine learning to help you save money. Now, did we get your attention? The app combines machine learning with the lessons that the users can teach one another in order to build collective intelligence. Oval keeps an eye on your spending habits and combines it with the collective knowledge from all other users and creates a smart and convenient money saving strategy customized especially for you.
The app is the superhero your finances need. It is only through Machine Learning that the app gains the ability to analyze the previous spending habits of the users and the transactional behavior of other users on the app. The strategies that the app suggests its users are based on this analysis and offers to its users, solutions in formats that are easy to follow in order to avoid spending extravagantly.
Pinterest is one of those interesting apps that have caught the attention of almost everyone who has ever dabbled with the social media, even if you are not a hardcore pinner. The core functionality here is to curate the content that exists on this platform, it is only natural that they’d prioritize investing in technologies that would make this process more effective.
In 2015, Pinterest acquired a Machine Learning company Kosei, and that led the company to leverage the potential of this promising technology. Kosei specialized in the commercial application of Machine Learning particular content discovery and recommendation algorithm, which made Kosei & Pinterest an ideal collaboration.
Since this collaboration, Machine Learning has managed to cast a positive impact on almost every aspect of the business operations of Pinterest, whether it is spam moderation, content discovery, advertising monetization, or reducing churn of email newsletter subscribers.
The app that is known for finding you your soulmate – Tinder. How do they do it? Among all the spells and love potions, Tinder has been using one of the most cutting-edge technologies known to mankind today – Machine Learning. The particular element or application of Machine Learning that powers the matchmaking app Tinder is called ‘Smart Photos’. This feature is responsible for increasing the chances of a user finding themselves a match.
The feature ‘Smart Photos’ with the help of Machine Learning technology, shows to people your photos in a random order and then analyzes the frequency of them being swiped right or left i.e. accepted or rejected. After a certain period of time, Tinder learns from these analyses which photos are more popular than the others and then reorders the photos to put popular photos first. This system of reordering photos hones itself over time and the extent of improvement that the system goes through is dependent on the input. This means the more input you feed into it, the better the system gets.
This effectively means that you would get better results and find your perfect match sooner than you think.
Don’t we all love Shazam? The app can simply hear a song and then tell you the artist and title! LeafSnap does the same for trees. The app is designed with the intent to help the botanists determine the species of the tree from a mere photo of a leaf. Leaves are one of the most common types of fossils, and it is quite a tough job to determine the species of this fossil. It is only after looking at thousands of photos of leaves, the algorithm of the app has learned to identify quite a few of them. Though Machine Learning has made it possible to identify thousands of leaves, however, the accuracy is still under scrutiny as mistakes are known to happen. It might not be 100% accurate, but LeafSnap is good enough to be a respectable and valuable tool for the scientists and is only improving with usage and time.
Machine Learning, however is not all about science and such uninteresting things. There are apps like Dango help you in finding the perfect emoji when you want it. Dango is not just any run of the mill app, it is, in fact a floating assistant that can be integrated into a number of different messengers.
Once you integrate Dango into your messenger apps, it can predict the emojis, GIFs & stickers on the basis of what you are writing. In order to accomplish this, Dango needs to understand what you are trying to say through the words you are writing. Dango makes use of deep learning to train the neural networks so that it can understand the words. This training of the app begins with exploring millions of real-life examples of emoji usage and exposing the neural networks to them.
To begin with, all the neural networks can do is randomly take guesses at identifying emojis, however, with the passage of time and long-term use of the app, it develops the ability of matching millions of values becoming better in all its subsequent uses. As the app completes its training of the neural networks, it understands the emotions & jokes a lot better. This means that the more you use it, the more meaningful would be the suggestions for emojis and GIFs that would be offered to you.
7) Sea Hero Quest
Sea Hero Quest is an entirely unique app that serves an entirely different purpose as compared to the rest of the apps on this list. The other apps use Machine Learning to power a cool feature, but Sea Hero Quest collects data from its users. This data that is collected from the users is then used by the scientists in order to machine learning software in order to further the research on Dementia.
The app makes use of a game format to make it interesting for the user. It is designed in such a manner that quite cleverly tests the spatial awareness of the player or the app user. Whenever you play t he game, your scores and other information is relayed anonymously to the experts in order to gain a better understanding of the complex yet intriguing human brain. In case you didn’t get the repercussion, let us tell you that all you have to do is play this fabulously addictive game and do your bit in contributing towards furthering research into a condition that afflicts millions of people the world over.
8) Aipoly Vision
Human vision is massively superior to computer vision and there is no doubt about it. However, in the recent times computer vision has improved to a great degree and it is majorly due to the advancement in the application of Machine Learning in building mobile apps. Today Google Photos and other such apps have the ability to analyze an image and recognize what or who features in it, before tagging people.
Aipoly is, however, a whole lot more ambitious. This unique mobile app can recognize the objects that appear on your camera screen in real time. All you have to do is point your camera to an object and Aipoly would then tell you what the object is according to it. The idea behind building the app was to assist those who are visually impaired in accomplishing their everyday tasks with a relative ease. The app may not be even near perfect, but it is gradually getting a lot better and quite like the other machine learning mobile apps will continue getting better as people keep using it.
One of the most popular and obvious examples of Machine Learning is the mobile app for Netflix. The app is famous for knowing what you want to watch even before you know that you want to watch. It might seem magical, but is it? It is Machine Learning that has enabled Netflix to grow from a DVD rental website to a global streaming device.
Netflix has managed to perfect its personalized recommendations by means of machine learning algorithms including Linear Regression, Logistic Regression, & more. The content on the app is categorized on the basis of genre, actors, reviews, length, year, etc. and all this usage data goes into machine learning algorithms. The machine learning algorithms learn from the users’ behaviors in order to come up with highly personalized recommendations and suggestions.
Twitter might have been chastised by the users for their decision to round out everyone’s avatars or the change in way people were tagged in replies, but one change that has managed to capture the attention of the users is their progression towards an algorithmic feed.
In your Twitter feed you might prefer looking at the ‘best Tweets’ first or go through the Tweets in a logical, chronological manner, what you see is essentially being decided through machine learning. The Machine Learning algorithm evaluates each Tweet being posted on the app and uses a variety of metrics in order to score it.
Eventually the point of this whole exercise is to display tweets that are expected to drive a higher engagement. This decision is taken on an individual basis as the machine learning technology behind this new feature in Twitter makes these decisions based on the individual preferences of the users. This is what creates the new algorithmic feed.
11) Google Maps
How many times have you struggled to find a parking spot for yourself? I am guessing it is more than once! Google Maps the app has come up with a way to help ease this pain and help you find a parking spot. How do they do it? Simple – through analysis of data that the app has gathered. The researchers at Google have gathered and analyzed data from more than 100,000 people by asking questions like “How long did it take you to find parking?” & more.
Further, in order to create training models, anonymous aggregated information is used from the users who have agreed to share their location data. This means that if such a user keeps circling around, after reaching their destination, it indicates that finding a parking spot in that area is tough. The app makes use of a standard logistic regression model to design features that then predict the difficulty or ease in finding an empty parking spot.
So, you tried the new restaurant in town, and hated it, what do you do? You go to Yelp and vent out in a review about them. To begin with, Yelp might not have been considered as one of the tech companies, but in the recent years, it has managed to apply Machine Learning in interesting ways and enhanced user experiences.
It was only recently that Yelp got into machine learning by implementing picture classification technology. It is through machine learning that the human employees at Yelp can accumulate, classify, and label images efficiently and with great ease. This is a huge help considering that you would have to deal with millions and millions of images.
13) Carat App
The battery life of our mobile devices has been a cause of worry and grief for most of us at some point of time. There are a whole lot of developments taking place in this contemporary world, but power storage doesn’t seem to be an area that is keeping pace with the rest. Hence, some of the app developers began looking at other ways of enhancing the battery life of the devices.
The obvious suggestions like “Turn down screen brightness” just do not do enough. Carat is a Machine Learning based app that monitors all kinds of activities that you use your mobile device for, and then offers you suggestions on ways to reduce the power usage. The app was developed by Ph.D. students uses machine learning to learn how you use your phone to be able to alert you whenever there is a problem. In addition to giving you power saving suggestions, the app also notifies you when an app is broken & needs to be downloaded yet again & alerting you when your phone needs to be restarted.
When you are fighting your habit of procrastination, ImprompDo can actually do wonders! The app helps people get things done without a demanding schedule. The app is great at finding that coveted balance which harmonizes between your to-do list and all the time that you have, finding you a suitable moment for you to accomplish the important tasks.
It takes a while for the app to work perfectly so that it has the time to learn how you manage your time efficiently and monitors your location while you are doing it. This can further help the app in prioritizing your to-do list for you!