How Will Machine Learning Impact Mobile Apps

Machine learning algorithms are helping developers in developing apps to offer more personalized services. But what other impacts are there? Let’s have a look.

Influence of Machine Learning Algorithms on Mobile Apps

If you are a regular user of platforms like Netflix, you might have seen their recommendations on your home screen. These recommendations are shortlisted by the AI and ML processes. Throughout the entire process, three main steps are taken. 

  • Collection of Big Data
  • Using Deep Learning techniques to feed the data to machines
  • Machine learning, the automation process but with the least possible human intervention

Today, machine learning has joined many industries to provide more efficient processes. From smart appliances to large industrial machines, machine learning is pushing AI tools to their best potential. However, we are still not at the full potential of machine learning. 

In this blog, we are talking about a few possible machine learning trends that we can witness in the near future. We will also discuss how these evolving trends are going to impact our lives. 

So, if you find this discussion interesting, stay with us until the end of this blog!

  • Better personalization

As we mentioned in the example of Netflix above, machine learning is now becoming common among such platforms. Other platforms like Spotify, Coursera, and many more are also utilizing this technology to make their services more efficient and customized. 

In terms of education apps, machine learning is capable of helping AI in identifying the weaknesses and strengths of the user. Accordingly, these educational platforms provide users with learning tips and tricks to overcome their weaknesses. Same way, Spotify uses machine learning to recommend you music that you might like. In short, mobile apps are already customizing their services for each user, in the future, the adaptation of ML for almost all genres of mobile apps will increase.

Machine learning algorithms are capable of monitoring networks and databases to find possibilities of cyber threats by detecting anomalies. Not only that, modern smartphones and apps are also capable of detecting hardware failures that might occur in the future due to a component of the device. In cases like attempts of cyberthreats, machine learning can help in generating emergency alerts to warn authorities of possible attempts of attacks. Mainly, fintech apps are using these algorithms to make user data and payment processing more secure.

  • Improved responsiveness

With the adoption of many types of machine learning algorithms, devices such as Alexa are capable of offering better responsiveness. These devices are able to understand more languages, dialects, productions, and more to improve their service quality. It does not only makes the usability more efficient but also improves the user satisfaction level. Also, these Internet of Things (IoT) devices are able to use machine learning for better services like home security, smart appliances, and more.

  • Better healthcare systems

IoT devices have already made healthcare systems more accessible for common users. With machine learning, these devices and mobile apps are able to behave like a personal assistants providing alerts for medicines, keeping a track of health factors like pulse rates, blood pressure, and more. 

The system has become such efficient due to machine learning algorithms which allow programs to think and react according to situations. We already have healthcare apps using IoT sensors to identify if patients living alone are in some trouble to generate emergency alerts. As lifesaving tools, machine learning can be really useful in upcoming times to save more lives by responding better and faster.

  • Innovative dating apps

Apps such as OkCupid are already using machine learning algorithms to recommend you profiles and matches that can be suitable for you. To shortlist such matches, OkCupid has approximately 150 questions to answer. More questions you answer, better recommendations you get. Machine learning algorithms in the virtual dating industry can not help with finding perfect matches, but also help developers in getting rid of fake profiles. 

With machine learning algorithms, automating the process of identifying and filtering fake profiles can be automated. Top machine learning firms involved in the development of dating apps are also using machine learning to verify profiles through pictures uploaded by users. On Bumble, users who verify pictures successfully get a blue symbol along with their name or the initial of the profile as a token of their authenticity. It does not only make the virtual dating environment safer but also helps users in avoiding possible scams.

  • Improved smartphone gaming

Mobile games like PUBG are using ML and AI algorithms to detect cheaters to filter them out from servers. On multiplayer games such as PUBG and Call of Duty: Mobile, it is common for hackers to use tools to tweak resources for better advantages against enemy players. ML and AI tools help game developers in identifying such cheaters in bulk to ensure a better player experience for users who are not using such tools. 

Machine learning is also helping developers in planting Non-playable Characters  (NPCs) that do not require specific programming for each action or reaction. With machine learning, these NPCs can be deployed into the gaming environment in bulk to provide a better gameplay experience. It also reduces the game development time and lets developers focus on other factors like graphics, interactions, navigation, and more.


Machine learning algorithms are proven to behave like better tools for various processes. There are many examples that we can take- firewalls using machine learning are more secure, UI using machine learning offer better user experience, and data analytics using machine learning offer better accuracy. Thus, innovations of machine learning are not going to stop for any industry, be it mobile app development or any other and it will be exciting to see how developers find new ways to use machine learning at its full potential.

In the end, we hope this blog was informative for you. Until we meet with a new topic, Godspeed!