How is AI Changing Mobile App Development?

Hardik Shah
July 14, 2020

Artificial intelligence seemed like a buzzword that Sci-Fi enthusiasts and marketers would get touted until it served their purpose. But researchers and educators like Andrew Ng made it mainstream for all the product engineers and developers globally.

Now, AI is being deployed to solve some of the most complex problems we have faced in the history of mankind. But before we delve into exploring how AI is changing mobile app development services forever, it will make sense to take a detour and intuitively understand the meaning of AI.

The Intuitive Approach to Understanding AI

Artificial Intelligence, as a concept, is not difficult to understand. If you take an evolutionary angle to comprehend it, the meaning becomes more apparent. At the turn of the century, macros and VBA were increasingly used by businesses to automate simple tasks. Once the script was run, the system would perform exactly as the script directed it. Then, Robotic Process Automation started coming into the picture. RPA, as it was popularly called, became the go-to technology stack used for automating backend processes.

The advent of AI became conceptualized and tangible when engineering and academic circles decided to replicate human intelligence using neural networks. In the simplest terms, neural networks execute functions at the level of each neuron. However, whether a neuron is activated or not is decided by the coded rules. Every set of neurons represents a particular set of functions, but there are several possible combinations of data flow patterns. This vast paradigm of data processing and analytics gives neural networks their prowess.

AI is divided into three key buckets – Deep Learning, Computer Vision, and Natural Language Processing. Together, they form the central tenets of AI that help deliver intelligent outcomes in mobile app development services, much like or even beyond the human processing power.

AI and App Development

In the very process of mobile app development, AI is yet to create revolutions. But its deployment has opened up new doors of features, use-cases, functionalities for its users. Here are some of the leading ones:

1. Chatbots

Chatbots have become a crucial part of the interface between digital businesses and customers. Much like RPA, they were introduced with rule-based response generation mechanisms. A chatbot would appear on the web site’s interface, give the user a set of options to choose from, and then give pre-recorded responses to solve the query. While this form of information exchange was more accessible for users than searching in the website’s search bar, it did not represent a ‘human conversation.’

The functionality of chatting got instilled in chatbots when Natural Language Processing became an approachable technology. By leveraging the impact of neural networks, chatbots are not dependent on a fixed set of scripts for understanding the human inputs in queries. They can understand error-prone and incomplete information at a level more sophisticated than Google Search functionality.

2. Predictive Analytics

Predictive Analytics on structured data has been used for decades now. Basic statistical modeling that performs multivariate regression analysis can easily highlight the strength of relationships between different variables, and hence, predict the result of action based on how the variables related to it have been recorded while providing mobile app development services.

With AI at its core, Predictive Analytics has grown as a practice. Neural Networks can now easily detect patterns that statistical analysis processes do. But their effect comes into visibility when they are able to predict behavior even using weak correlations.

For instance, some of the common experiments using predictive analytics include forecasting the future position of a cursor based on structured data of past movements. The predictive analytics algorithms are now used to forecast consumer behavior in apps based on product updates, app features, and UI changes.

Some mobile app development services use-cases even include taking up the user data on the app and creating virtual user profiles using this data to get an idea of how the audience would react to an app update. From smart search queries to dynamic UX – predictive analytics has been so deeply embedded in apps that everyone expects smart recommendations now.

3. Virtual Product Trials

Computer vision is the technological front of AI, which allows a machine to understand data inputs that explain a visual medium, like a video or a photograph. Up until recently, computer vision allowed machines to understand images as a human brain would. With faster handling and a greater amount of data availability, machines can now use the same computer vision platform to process images near real-time.

The remarkable speed can help users try products in fashion, accessories, apparel, and even makeup & beauty categories. Many e-commerce companies in the luxury fashion and eyewear space are now providing virtual trials within their apps. Many medical schools are now deploying the same computer vision technology to process medical images for a more accurate diagnosis. As a matter of fact – even automated cars are dependent on such computer vision advancements.

4. Natural Language Generation

NLG is an after-result of Natural Language Processing. Now that most algorithms have been trained on several years’ worth of data for human-language analysis, they are ready to generate compelling language outputs.

News agencies like Associated Press have been using such technology for years now. However, platforms like Automated Insights, Attivio, and Cambridge Semantics have made content generation easier for any app developer.

Content generation can be of particular help to apps that are pushing out notifications, content, or updates very frequently. If your app sends out rudimentary analysis or reports, NLG can be used there as well.

5. Image Recognition

While computer vision and predictive analytics make virtual product trials easier, image recognition has several use-cases beyond that. Google Reverse Image search was one of the initial projects in the space. While it ran basic image recognition processes to identify similar images when a user uploaded an image in the search engine, several other apps are now taking that approach to the next level.

Apps like CalorieMama allow users to get calorie counts of their food as soon as they click a picture. ScreenShopIt helps you find identical products online using just the image you click. CamFindApp uses image recognition on a wider scale and helps you understand what the object in the image is.

In Conclusion

AI is now being deployed on the backend process engineering side in mobile app development services and as the core feature of the apps. While Deep Learning, NLP, and Computer Vision may seem like only three branches of AI, they form some of the most enticing features available across the spectrum of apps. As a developer, you can now focus on each set of users, find the most pressing issues they are facing, and then formulate solutions across those problems using AI. As far as you have structured data, processing capabilities, and a use-case in mind, AI can be leveraged in practically any context.

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