Many of your favorite mobile apps are currently using some form of neural networks and/or machine learning techniques to personalize your experience. For example, Spotify and Apple Music, leaders in the music industry, use AI-powered recommendations for generating music suggestions. Another shining example of the power of AI in apps is voice assistant tools such as Siri, Google Assistant, and Alexa. First, let’s define what mobile machine learning is and how it’s different than web-based ML.

So What is Mobile Machine Learning?

Mobile machine learning is a growing field of ML that doesn’t involve data centers and giant clusters of high-powered GPU machines. Instead, we can now run machine learning operations on mobile devices to avoid the network bottleneck.

Most of the above mentioned applications use a combination of on-device network and cloud services to generate results. However, on-device deep learning techniques have significantly evolved over the past two years, and they now cover a lot of everyday use cases without making any network calls. These include speech recognition, image recognition, object detection, gesture recognition, translation and text classification.

Choosing a deep learning strategy for mobile applications might be harder because of the lack of relevant resources to get started. In this article, we’ll talk about some of the common challenges you might face while integrating AI elements into your mobile application.

Finding Mobile App Features that are Good Choices for AI

Understandably, introducing your app to AI involves a bit of hard work in the beginning. Most organizations need to start by looking at the best options that will help them adapt to a digital-first, rapidly changing market. This includes deciding on a long-term plan for the stack that you’re going to use and determining whether or not it’s possible to apply your model on a device.

They also need to deal efficiently with tactical areas like securing mobile access to data, integrating the apps’ backend to existing legacy systems, implementing API-based architectures, and adopting agile development methods. However, once this planning stage is completed, positive results should soon follow. Here are some of the features that modern applications rely on AI to employ.

Automated Reasoning

Automated reasoning is the science of enabling computers to apply logical reasoning while solving problems, like proving theorems and solving puzzles. It’s this technology that allows AI-powered machines to defeat human beings at games like chess or Jeopardy and industry tasks like market stock trading.

Services like Uber use similar algorithms to calculate millions of data points from Uber drivers who have driven similar routes in the past. The app then factors this information into making predictions like time to destination, estimated fare, etc.

Recommendation Systems

Recommendation systems are perhaps the most effective and most straightforward application of AI in mobile apps. They can be used for almost any given solution. One of the primary reasons a lot of apps fail within a year of their launch is because they are unable to continuously supply the user with content that’s relevant. As such, they fail to keep the user engaged.

While these services may be providing fresh and updated content at regular intervals, if the content isn’t relevant to the user at the time, it will fail to pique their interest and keep them engaged. On-device AI can help in monitoring users’ choices and then using this information to continuously update a deep learning algorithm with new data points. This ensures the recommendations made by the app are what the user will most likely love.

Learning Behavioral Patterns

Most AI-based platforms are now capable of learning from a user’s behavioral patterns and using this information to make the next session more intuitive and seamless. Let’s take Snaptravel as an example.

Snaptravel is a part-bot, part-human hotel booking service. It makes use of a combination of natural language processing and machine learning to have realistic-sounding conversations with users and visitors based on their preferences. In case the bot is unable to understand and answer a particular question, a human agent takes over. The bot adds this question and answer puzzle to its information base to not make the same mistake again.

Computer Vision

Computer vision is an excellent use case for machine learning, and lots of mobile applications use this for a wide range of purposes. Computer vision is what powers Apple’s Face ID algorithm to verify whether it’s you when you point the camera at your face. It’s also the same technology that lets Snapchat add filters like glasses, hats, and doggy ears into your Snaps.

From an AI perspective, the most important part of computer vision is the recognition and classification of images. There are a few APIs that you can use like Google’s Vision API. However, offloading the image processing to a remote server just isn’t good enough for certain use cases.

But here’s the good news—there are pre-trained models that you can use within your app that handle some of these difficult AI tasks. For instance, you can use a library like OpenCV to implement image tracking, image recognition, or programmatic image filtering. You can integrate OpenCV with your Android, iOS and, Cordova platforms. You might also find popular open source implementations on GitHub.