Machine Learning General Overview
The world of computing has undergone significant changes in the last ten years. The arrival of powerful personal computers and smartphones ushered in a new era of communication, scientific and mathematical research, and applications in the fields that are not associated with computing at all.
Now, although a device cannot actually see or hear the world around, it is capable of performing detailed calculations that would have been considered impossible in the past. All these features are attributed to Machine Learning (ML) technologies.
How does Machine Learning work? It is a methodology for training devices to learn, improve, and predict outcomes without being explicitly programmed. Machine Learning helps develop the so-called “smarter” software, which can eventually become more powerful than any algorithm that programmers could create on their own.
ML is at its peak today, and most likely, this trend is not going to change in the near future. Let’s dive deeper into the benefits of Machine Learning technology for businesses and explore how ML simplifies people’s lives contributing to mobile application development.
Benefits of Machine Learning in Mobile App Development
Obviously, the mobile industry is the one that benefits most from Machine Learning. Mobile app development has gained more importance among businesses because the mobile industry is the most used and trusted channel. Mobile applications have revolutionized the way businesses communicate and conduct their day-to-day activities.
When it comes to mobile application development, Machine Learning brings in a universal approach to resolving web issues. Based on continuous learning, Machine Learning algorithms have enabled mobile app developers to create better targeted offers, quality customer service, and more accurate personalization and recommendations for the users. Leading mobile app development companies tend to involve Machine Learning mechanisms in mobile app development so that the end-users could get the best user experience with their apps. And here is why:
Personalized services inside the apps
It doesn’t matter how complicated functionality has been developed, the final user should be able to find the necessary options and information easily. The high usability of applications makes the users loyal to the software. Artificial intelligence algorithms empower digital products with strong user personalization that make them more tailored to the customer needs. Needless to say, enhancing the personalized experience is the main feature of Machine Learning.
Algorithms analyze the information available in social media seamlessly, and as soon as the user starts searching, relevant results appear one by one. In other words, it assists in understanding user activity and reduces audacity downtime.
With a wide variety of applications, there is a need for simpler authentication. Nobody wants to remember complicated passwords and go through a long login process every time the app is used. Personality recognition with audiovisual data has made it possible to log in to favorite applications much faster.
On-device ML implementation
Another benefit of Machine Learning is its ability to perform operations on the device itself. Modern hardware is enhanced with absolutely sophisticated Neural Processing Units that help to run processes on the device. Mobile application developers achieve a great benefit from this solution. No need remains for data exchange between the device and the cloud. On-device Machine Learning solutions allow businesses to avoid cost-expensive building and maintenance of cloud infrastructure. Thus, a smaller development team could be engaged in the development process with a focus on the features and advantages of the software itself.
Overall, on-device machine learning greatly simplifies the user experience. With only the most pertinent data loaded on the phone, the system can perform complex computations without the burden of network latency or any data transfer, making operations simple.
Industry-specific Application Areas of Machine Learning
Many companies heavily invest in Machine Learning nowadays. From autonomous cars to bots for digital services, no industry has been spared from the Machine Learning wave. It’s a phenomenon with a significant impact on the economy. According to a study, the global machine learning market size is expected to reach USD 96.7 billion by 2025. Below you will find a review of several cases of the Machine Learning use in industry-specific mobile applications.
Machine Learning and VR industry. Use Case
Machine Learning is being widely used in VR development services. The major areas of the Machine Learning implementation are the content creation for mobile game apps and the embodied interaction.
The environment inside the mobile app while playing a game means a lot for its players. Here, the environment implies the app characters, the decorations inside the mobile app, and other graphical objects or textures used in the game. But could you imagine how much time it takes the artist to draw even a small piece in the game app like a character’s armor? Using Machine Learning algorithms to automatically generate content (Procedural Content Generation) can compensate for many work hours, optimizing small teams to produce big mobile game environments and content-rich app experiences. A good sample is the InCell mobile game app for iOS. This game app takes place in a unique micro-world, which recreates the anatomy of a human cell in a very detailed way, giving it an educational angle. Players ride along capillaries and connections as they take in the inner space.
Another use case of Machine Learning is the embodied interaction. People are very complex creatures, so are their bodies. Many human actions are intuitive. The way one moves, uses the fork, or hits the ball is super easy to show but super difficult to explain. It is very difficult to program computers in such a way to recognize movements because people themselves don’t know the mechanisms that define these movements. This is where Machine Learning comes into play. Using Machine Learning, we can program movement interaction systems by giving the examples of movement rather than coding. As a result, natural interactions can be retrieved. This solution is evidenced in the collaboration of the Gucci brand and a fashion-tech studio that specializes in VR and AR technologies. Together they created Gucci’s first virtual sneakers. Now the users could try them on in the Gucci app or in the Wanna Kick AR sneaker app in real time. Isn’t it cool?
Machine Learning and Augmented Reality. Use Case
Augmented Reality (AR) and Machine Learning (ML) are two big players in the tech world nowadays. Both of them are advancing at a rapid pace in an attempt to fuel the tech needs in their respective fields. Integrating Machine Learning and Augmented Reality during the development process can expand the range of AR experiences and add more personal touch that users are yearning for.
The opportunities provided by Machine Learning for AR apps are endless. With the ability to track and understand the 3D world, Machine Learning adds extra detail to AR and can be used to build more immersive experiences that are tailored to the individual needs. When people use AR on their phones, the apps generally require the camera to be open. This feature could be used to collect image data via ML, track objects, and then display information with the added objects from AR. And this is actually how the Houzz app works. AR technology places products into a photo of the user’s home using 3D technology, so the resulting image is lifelike. Besides, the app also offers e-commerce functionality. Thus, people can literally shop for a new couch from their old couch.
IoT and Machine Learning. Use case
IoT, or the Internet of Things, is a platform where everyday things embedded with electronics, software, and sensors are connected to the internet enabling it to collect and exchange data. Almost all devices, sensors, and software are connected to each other. The ability to access these devices remotely through a smartphone is actually what IoT stands for.
Now, how is ML used in IoT? Machine Learning takes large amounts of data and generates useful insights for the users. This means that the machine learning algorithm can make predictions, see what actually happens, compare it against predictions, then make adjustments to become more accurate. This ML predictive analytics is extremely important for IoT applications. The Nest Thermostat is a great example, as it uses Machine Learning to learn your preferences for heating and cooling, ensuring that the house has the right temperature when you get home from work or when you wake up in the morning.
Machine Learning in Other Industries
In addition, Machine Learning brings its priceless commitment into the banking and financial sectors. It assists with tracking the user’s transaction history, credit history, social media activity, and more. That’s why Machine Learning methods are preferred among modern fintech businesses.
Machine Learning can also be used to identify objects. In an example of a shopping app, a user can simply point to an item, and the app will find matching results online. The technology could be implemented by any company that sells goods.
Numerous health-related mobile applications help users to keep track of their diseases. Due to Machine Learning algorithms, such apps analyze user input, predict the possibility of one or other conditions, and notify the patient in advance that some actions are required.
ML-based Mobile Apps Examples
More and more businesses involve Machine Learning experts while developing their iOS and Android applications. There are many ways to use Machine Learning with Android and iOS. Business owners have to ensure that their developers apply the best practices for learning using Data Science Platforms to get the most from Machine Learning systems. Some popular tools for learning are Google’s TensorFlow, AWS SageMaker, CNTK, and Microsoft’s Azure ML. We’d like to highlight a few applications that took the best out of Machine Learning to improve their interaction with the users.
A good example of the Machine Learning use is Google Lens – Google’s translation app. It enables users to scan foreign signs and translates these signs instantaneously.
IKEA Place is another great example, as this app allows users to purchase furniture without leaving their houses. The ability to see how a bed would look in your bedroom is quite a straightforward idea, but a revolutionary use of AR and ML enhances the experience even more. It does not just remember the items you purchased before but also offers other items based on the style of furniture you’ve chosen before. Besides, it could match items with furniture and decor you already have at your place.
How could we forget about Snapchat! The app uses Machine Learning algorithms for computer vision. It recognizes faces in photos and adds different objects and elements such as mustaches, hats, funny ears, and more.
Another use case is the application of ML in dating apps such as Tinder or Badoo. These apps use an algorithm to help the users find their perfect matches. The logic is the following: the app shows photos of other users in a random order. Then, Machine Learning analyzes how many and which swipes each image receives. This way, the dating app learns which photos are more attractive for each user. The algorithm also reorders the users’ photos to put relevant images first.
Overall, with all the perks that make the app development process easier and cheaper, Machine Learning is going to be a standard feature in mobile application development for a long time.
The Future of Machine Learning in Mobile App Development
According to Gartner, Artificial Intelligence and Machine Learning might enter the top 10 revenue drivers for mobile app companies. And as more mobile users are expected to become immersed in Machine Learning, the number of apps that can leverage machine learning for a competitive advantage will increase exponentially.
The growth of demand for Machine Learning experts is already evident since many companies have realized the increasing need for artificial intelligence and automation technologies to optimize and extend their business processes.
In mobile app development, the dev team can help find patterns in the big data collected from the target users. Machine Learning algorithms can make use of the unstructured info and provide valuable insights into user behavior due to this data. Eventually, more and more clients are asking for tools from software developers that leverage Machine Learning to improve the offered services, such as learning about what users are interacting with and what has proved to be a sore point for them.
The Machine Learning market is growing rapidly, as the addition of ML greatly contributes to the operations of every business. As the number of mobile devices increases, the number of apps also goes up. The expansion of the app market, in turn, boosts the complexity of app development. Since the complexity of mobile app development is also rising day by day, businesses will now have to be extra careful to create a better product while serving their target audience.