How Machine Learning is Transforming Mobile App Development?

How Machine Learning is Transforming Mobile App Development?



How Machine Learning is Transforming Mobile App Development?

Artificial intelligence and Machine learning have indeed been in the buzz for some time now. Every day almost a new breakthrough is announced where a computer manages to beat previous records. Machines are becoming increasingly better at solving previously impossible computational problems and beating world champions at their very own game.

But where does this relentless and exponential push for better, faster, and smarter algorithms lead to? The ultimate aim would be to have AI and ML models assist us in our everyday tasks; and in a lot of ways, this dream is coming to fruition in the realm of mobile app development. Although far apart from each other, mobile app development has recently discovered some novel applications for cutting-edge machine learning models.

How exactly can machine learning benefit mobile apps? In what ways is the mobile industry fueling R&D for AI? And what common future do these two technologies hold? Are some of the key questions both AI enthusiasts and mobile developers should be asking.

Also Read – Python Programming Language Prominence in Machine Learning

What is Machine Learning?

For us to understand better how these technologies can benefit each other, lets us try to understand what Machine learning is. Put in simple terms; machine learning is training algorithms to recognize patterns in raw data. It’s about computers ‘learning’ to understand and interpret data. One Key characteristic of ML algorithms is the feedback loop that powers them; it means that the algorithm learns from its results.

Changes are made to the neural network based on how good (or bad) its results are, leading to a slightly better algorithm with every iteration. There are mainly three approaches to ML: Supervised learning, Unsupervised Learning, and Reinforcement Learning.

The differences between them are simple; supervised learning requires that the data being fed to the algorithm be labeled. This is how the algorithm understands what pattern it is looking for. While in the case of unsupervised learning, the algorithm simply tries to find whatever pattern it can from the raw data. And reinforcement learning is all about ‘rewarding’ and ‘punishing’ the algorithm as it tries to maximize reward.

How Machine Learning Benefits Mobile Apps?

But how does any of this apply to mobile applications? Well, any time an app can benefit from learning something on its own, it can deploy machine learning to its advantage. This frees up apps from being ‘hard-coded’ and they can adapt to user preferences as they learn what particular users want.

It is the reason why Netflix recommendations have gotten so good lately and why it is so difficult to get out of the usual youtube rabbit hole; the algorithms have got really good at serving us what we want. Every time we click on a video or like a post with a particular hashtag, we are feeding the algorithm data about what we like and want to see more of.

Machine learning has tons of applications for the mobile world beyond just helping apps personalize the user experience. This is why  mobile app development companies USA and worldwide are pouring a lot of cash into developing viable algorithms for their mobile apps.

Groundbreaking Machine learning applications within Mobile apps –

  • Image recognition and processing –

Computer Vision as an area of research has recently exploded with better Machine Learning algorithms and more capable chips and cameras. Image Segmentation, object recognition, facial recognition, style transfer, etc, are a few key applications of ML algorithms when it comes to image processing. These algorithms allow applications like Aipoly Vision to identify objects in milliseconds, Snapchat to apply face lenses on your snaps seamlessly, and Leapsnap to identify plant species in a snap.

According to a report, the image recognition market size is estimated to reach around $109.4 billion by 2027. Given the fact that a significant chunk of this revenue will come in through mobile apps, companies simply cannot ignore the huge potential ML-based algorithms offer to image recognition.

  • Data Mining –

We live in a time where data is often called the new oil. Each day the average user generates tons and tons of user data that companies are constantly competing to understand and take advantage of. But with big data comes the need for better technologies to make sense out of it. This is an area where machine learning especially excels since, in some way, the whole point of ML is to find hidden patterns in large chunks of data that would otherwise be unnoticeable

While data mining and machine learning are often confused to be the same thing, they are fundamentally very different from each other. Data mining is a much more manual process where big chunks of data are analyzed to discover insights. Using machine learning principles, companies can train algorithms that are much faster, accurate, and insightful at processing data than humans will ever be.

  • AR and VR Applications –

With an estimated revenue of around $18 billion in 2020, the AR & VR industry is undoubtedly a huge sector that is closely related to mobile apps. It is also worth noting that AI and ML have some significant implications for the world of AR and VR. AR and VR are already on the path to revolutionize sectors like education, healthcare, gaming, etc., and ML can be the catalyst for this growth.

This can be achieved by combining cutting-edge computer vision breakthroughs with modern AR and VR technologies. Some fruitful products of this union are seen in the form of product visualization, enhanced learning, remote assistance, and a lot more.

  • NLP –

And finally, one of the most significant applications of ML to the world of mobile apps has been through the advancements made in the area of Natural Language Processing. When explained in simple terms, NLP or Natural Language Processing is all about teaching computers how to understand human language better. And algorithms have got really really good at it.

There are many applications of NLP when it comes to mobile apps. One good example is text classification, which is all about understanding the core meaning and sentiment of a text to be able to use and organize it better. To see it in action, look no further than Google’s AI-based suggestions within Gboard. And speaking of Gboard, features like Smart auto-fill and translate are also fueled by NLP.

These are simply scratching the surface when it comes to discussing the potential impact of NLP. Since most of our communication these days is facilitated by our smartphones, ML-based mobile apps hold the potential to completely transform how we interact with each other.

Conclusion –

We’ve discussed so far that only a select few examples of what machine learning can do for mobile apps. Being a relatively new and growing field of research, ML still has a lot to offer to the mobile industry. Mobile app development companies need to recognize this in order to be able to take advantage of this inevitable union.

If you want to develop a mobile app, it might be worth considering how you can apply machine learning techniques to improve your overall product. For those wanting to build an app but lacking the necessary expertise to do so, Goodfirms has curated a list of top mobile app development companies that you can outsource your projects to.

How do you think machine learning will impact the development landscape of mobile development?

Author Bio –

Darren Mathew is passionate about Tech, Business, and the evolving relationship between the two. He is a blogger at GoodFirms – a leading review and rating platform that lets consumers compare and choose the right service provider for their needs.

Also Read – How Machine Learning Can Lead to Better Product Design?


Post Comment