Artificial Intelligence and Machine Learning are everywhere – from Business Intelligence to social media, it’s a technological advancement that has influenced many worldly aspects. This is all for good reason, of course, owing to its capabilities of accurately forecasting outcomes or suggesting what’s useful based on past behaviour. Likewise, it’s no surprise that AI and ML have also made it into the world of mobile app development. With the current business landscape fierce in terms of competition, building products which truly satisfy the consumer while being customised to individual needs is what will keep brands thriving.
Mobile app development companies in Sri Lanka are no different, especially since many offer their services to overseas organisations. The need to deliver products that are of an international standard are practically a must – but it isn’t solely foreign clientele that inculcate such a competency. Once again, customer-centricity becomes the focal point here; with local audiences being more tech-savvy than ever before, catering to the same crowd also requires top-notch standards.
There’s no doubt that the demand for intuitive digital products is everywhere. From family enterprises to multinational corporations, every business owner wants to meet the demands of an audience whose needs are constantly evolving – and what better way to do that, than a mobile app? Just a conventional mobile app won’t do, though. Henceforth, that’s where AI and ML enter the picture to help businesses predict consumer behaviour before their competitors do – exactly the manner in which a human being would.
How is AI and ML used for mobile app development?
Some of the most popular features available in mobile apps today are all made possible by AI and ML. These include (complete with real-life use cases):
Snapchat is a prime example of stellar face recognition, which then takes it a step further with the assortment of face swaps and filters it provides. While this is purely for entertainment, it is a great use case to marvel over the high potential that AI-based technologies can have, given the accuracy of such capabilities.
When it comes to voice recognition, Alexa, Siri, OK Google, Cortana and many others are up there as competent voice assistant platforms. While these voice assistants deliver what is asked, Machine Learning functionalities enable them to get the gist of different accents, while also recognising familiar voices at the same time.
Google Text-to-Speech is one of the most popular options by far, especially since it comes in-built with all Android smartphones nowadays. If you’re looking to listen to that document instead of reading it, Google Text-to-Speech is your answer. By using natural voice synthesizers, a tap or two is all it takes to get started.
Alternatively, your mobile OS and even third-party apps can detect speech, to then transfer that into words. While this can assist with transcription for official purposes, it can also be a convenient option for day-to-day text messaging.
Augmented Reality is an app that integrates with Google Maps to offer directions and suggestions that are overlapped across a real-world field of vision. The app combines all the features of Google Maps together with its AR capabilities to make this a possibility, giving you all the assistance you need when you’re out and about.
On the other hand, SketchAR is another great use case for Augmented Reality that falls outside the navigation sphere. Assisting users who are keen on drawing but have no prior skill on how to do so, the app provides a screen-to-paper journey that users can follow to make their sketch come to life.
Facebook has been doing this for a while, by automatically adding labels as soon as images are uploaded. This has some rather indirect advantages – those that aren’t apparent at very first glance. For one, the image text becomes increasingly useful when there is a delay in loading images (as is the case when internet connectivity is poor, for example) because the text is then laid out together with the skeletal framework, nonetheless briefly notifying the user on what the content is about.
In addition to that, the image text also simultaneously works as an alt tag (a piece of HTML text that is used by search engine crawlers). In turn, this can benefit your brand in terms of SEO, as search engine algorithms can then assist to display your image when someone keys in similar terms that match your product.
Instagram has been offering name tags by means of QR codes, which can then be scanned by others who wish to follow those accounts. They’re versatile enough to appear anywhere – over another social media account over the desktop, or offline on a pamphlet. All that needs to be done is a quick scan with your smartphone camera, and you’re good to start following that account without having to key in or remember usernames.
Another relatively less common but equally resourceful use for QR codes is to facilitate transactions. Many apps, most of which are concentrated to a single country or region use QR codes to carry out transactions. Apps that offer discounts at nearby places use QR codes that the customer can scan at the merchant outlet, to obtain their deal. Likewise, many payment apps also require customers to scan specific QR codes at the counter, in order to do a transaction that’s cashless, cardless and is complete with a few taps.
Google Translate is capable of this, and is the go-to source for language translation the world over. Whether it’s for asking directions in a new country or to simply understand a phrase from a language you’ve never heard of, automatic language translation has become an immensely useful tool for both personal and commercial use.
But translation capabilities aren’t solely meant for applications that are built to fulfil the very same objective. Automatically translating an app’s interface based on the language selected, for instance, is another common use case. In fact, this is the manner in which most mobile apps make use of the automatic translation feature, besides a platform that is solely dedicated to translate.
Gmail has been one of the most recent email providers to use AI for smart reply purposes. The algorithm helps determine which responses are viable, based on the contents of the email or language patterns regularly adopted by the user. The capability may still not be completely accurate, but it has been received well by the masses and is bound to get more fine-tuned for the future.
Smart reply in email providers also utilise natural language processing, which is another subset of AI-based applications for linguistic purposes. Depending on the words used and the way they are structured, gauging the sentiment behind the content is also equally essential in order to make the right suggestions.
The last, but probably the most important out of all AI-based requisites for mobile app development. Notice how searching for a particular product or service on Google leads to several ads or suggestions that follow you around online? This is made possible by Machine Learning algorithms that are able to determine similar items you may like, based on your searching and even spending habits.
Eventually, this is what makes your mobile app unique to each user, as their interface will only be filled with products, services and suggestions that truly matter to them.
The resources that developers need to make AI-based mobile app development a reality.
AI and ML toolkits for developers are available for easy integration and customisation, thereby making the supply of any business demand a possibility. Google and AWS are the most dominant toolkits by far, offering everything from predetermined models to customisation via models from open-source platforms such as TensorFlow.
The exact AI technologies that are used in the platforms maintained by Google and Amazon per se are provided to developers via the toolkits. These capabilities are then updated as and when the technology is upgraded, making such toolkits an option that’s more viable than a proprietary one. Apart from being guaranteed only the very latest in AI and ML technologies, these toolkits (particularly AWS) comes complete with security features that further protect your final product from a variety of cyber threats.
Mobile app development through intelligent, AI-powered analysis.
A lesser known constituent of AI-backed mobile app development, cutting-edge Business Intelligence is also one of the primary ways in determining what your future mobile app needs. While AI-based features such as face recognition and translation are a means to execute personalised tasks, a digital product that’s truly user-centric will require a foundation that can only be achieved via accurate analyses of consumer thought patterns and behaviour.
Let’s not underestimate the power of Business Intelligence, Big Data and the technology that connects the two.
This is where AI-powered Business Intelligence comes in. While BI platforms have been around for decades, they’ve only just begun to unravel their true potential through AI and ML. Before, key personnel in organisations would discuss metrics they would prefer to measure – which were then coded into the BI system with the assistance of an IT specialist. The data was then inputted to reveal the metrics required. While this provided organisations the analyses they needed, the BI system couldn’t exceed this otherwise finite limit.
With the rise of the digital age, vast amounts of data were now being collected by multiple systems within an interconnected network – particularly social media. Also known as big data, these figures were mere repositories, until new-age BI platforms were connected to make better sense of the numbers. However, the addition of AI further rendered both BI and big data structures more resourceful, as deeper integration led to a streamlining of data analysis in general.
Now, neither of these systems work in silos, or remain stagnant; through AI, every number that comes in is evaluated to provide statistics from multiple angles, instead of the finite perspectives only considered by members from your organisation. In turn, this provides insights you may never have foreseen as a business owner – thereby further enabling your team to take action such that it produces user-centric and lucrative results.
The applications for these statistics are numerous – and determining the next course of action for your mobile app is one of them. Understanding how your consumers are using your app, what they like/dislike and how they would prefer to interact are essential questions that can be answered via AI-powered analytics.
Every aspect of the technology world is dominated by AI – and mobile app development is no exception to this rule. Thanks to leading digital service giants such as Google and AWS, integrating Artificial Intelligence and Machine Learning into any mobile app to fulfil any demand is now made easier than ever, thanks to predetermined models or easy customisation through open-source platforms such as TensorFlow. While AI makes functionalities such as face recognition and text-to-speech possible, it also customises your app such that it is unique to each user, based on their specific interests and requirements.
On top of that, AI-powered analyses through Business Intelligence solutions also provide both business owners and developers the insight they need to gauge the sentiments behind their product, and decide how they should aim to improve services instead. The BI platforms and big data repositories of yesteryear have now been transformed into analytical sources that provide limitless potential. This is mainly by integrating AI and ML capabilities that help make sense of data better – and dissect statistics from varying angles than previously considered by your team. These statistics can then be used to determine what works with your audience and vice versa, in order to tweak your app accordingly.
Whether one chooses to incorporate AI and ML into their mobile app development endeavours or not isn’t the question – it’s when. Considering the competitive business landscape out there, no organisation can afford to lose out to competition that provides a service that’s more accessible, affordable and intuitive.
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