In today’s world, people spend a lot of time on smartphones. Almost all of that time is spent on apps. If you’re not catching up on your “mobile first” strategies, then your app might as well not exist. Fortunately, this digital revolution has also brought with it the power to save money and increase efficiency with the use of artificial intelligence. Artificial intelligence (AI) is basically software that allows computers to think like humans.
Through machine learning, AI computers can analyze patterns and respond to new situations quickly without needing human assistance. The best way to use this power? In mobile apps! This post will explore some of the most useful ways to incorporate Machine Learning in Mobile Apps design projects.
Chatbot with Machine Learning in Mobile Apps
If you’ve ever used chatbots, then you know how useful they can be for your app users. They can take the place of repetitive tasks, answer user questions, and even perform actions for them. For example, if someone messages your chatbot to find out the weather in their area, the chatbot can then automatically search for the information it needs and send a response.
Chatbots are extremely popular right now, especially on messaging apps like Facebook Messenger and WeChat. And businesses are starting to see the value as well: Uber launched a bot to make it easier for taxi riders to find nearby drivers, while e-commerce company Cdiscount has a bot to help customers find products.
Photo App with Machine Learning in Mobile Apps
Photo apps are incredibly popular, especially among younger users. The basic functionality of these apps is to let users browse and search photos, view filters, share photos, and rate photos. Photo apps can also incorporate other functionality, like letting users create albums and apply creative effects.
The photo app is a classic example of how AI can be used in mobile apps. AI can analyze photos and detect specific details, like facial expressions or objects in a photo. This can be used to create photo filters, create photo albums, and create photo effects.
Voice Assistant with Machine Learning in Mobile Apps
If you’re building a voice-enabled app, then you have probably come across the term “voice assistant” a few times. Like chatbots, voice assistants are a type of AI that lets computers understand human language. They can let app users navigate apps without touching anything, let users create documents and other items, and let users search for things like restaurants, flight times, news stories, and more.
Voice assistants are starting to be integrated with more apps. In fact, Google Assistant, Alexa, and Siri are integrated with almost every single app. That’s why you can ask your Alexa app to play music from Spotify, ask your Siri app to create a calendar event, or find out the latest news from Google News.
Virtual personal assistant with Machine Learning in Mobile Apps
Virtual personal assistants are similar to the popular virtual assistant AI. Instead of creating an assistant that works inside an app, though, you’re building a virtual assistant that works across multiple apps. For example, if you’re an accountant who uses a lot of different apps, a virtual assistant that works across all your apps can save you a lot of time.
Users can create a virtual assistant that is designed to follow and help users navigate through all their different apps. This can be very useful in building virtual assistants that work best with different kinds of apps, like accounting.
Automated task execution service with Machine Learning in Mobile Apps
AI can be used to create automated task execution services. These services let you schedule tasks to be run automatically at certain times, like every day at midnight or every week on a particular day. This can be useful for scheduling automated data transfers, like importing data from a spreadsheet into a database. To create an automated task execution service, you can use tools like Zapier or IFTTT. You can also build your own custom tools to schedule tasks.
Notifications with Machine Learning in Mobile Apps
Mobile apps can take advantage of machine learning to improve the notification experience by learning your preferences over time. When you receive a lot of notifications, it can be annoying to read each one individually. Machine learning can help your app by predicting what you’re likely to be interested in. It can also predict which notifications you may have missed, so it can give you more contextual suggestions when you receive them. For instance, if you frequently receive shopping notifications from a certain brand, the app can use ML to suggest new items you may like. Or it can recommend you your next flight based on the flight number you received.
Augmented reality with Machine Learning in Mobile Apps
The future of AR is here, and it’s ready to change the way you work and play. This is thanks to the power of machine learning, which can help bring AR to life. The camera limits the implementation of AR on your phone. This means that it’s not yet possible to have a true AR experience on mobile. With a lot of research and development, it should be possible to bring the power of AR to mobile devices.
It will then allow you to put apps, menus, and navigation information in real space. You can create AR experiences that can enhance your work and play. AR can be used to show you menus and navigation options inside apps, so you don’t have to dig around for them. You can also create AR experiences that let you interact with information, such as interacting with your calendar or seeing technical details about items.
Personal assistance with Machine Learning in Mobile Apps
Machine learning can be used to build more helpful apps and chatbots. The technology can be used to help people with a wide range of needs. It can be used to provide better assistance with public transportation options, such as train schedules and bus routes. It can also help people to find shorter routes around cities.
Smart navigation with Machine Learning in Mobile Apps
Machine learning can be used to build smarter navigation options inside apps. It can be used to determine the best route for you, based on the routes you’ve taken before, traffic information, and the time of day.
Code-based testing for quality assurance with Machine Learning in Mobile Apps
With the increased focus on security and privacy in the last few years, more app developers are looking to use machine learning to protect their apps. This type of technology can be used to test apps for security vulnerabilities. It can also be used to build tools to help with app quality assurance.
Conclusion
Artificial intelligence technologies can be powerful tools for improving the functionality of your mobile apps. By applying these technologies, you can boost the user experience and make your apps more useful for your users. There are many ways to implement AI in your app design projects, including building chatbots, photo apps, voice assistants, virtual personal assistants, and more. We’ve explored these techniques in this article, and we hope you’ll find them useful.
