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The best applications of AI in mobile applications:

924 Views Thursday, May 17th, 2018 at 3:23 pm   (4 years ago)   Feature Image, Technology

Artificial Intelligence (AI) has made awfully a long way in a different sector of present world very effectively.

The use of AI can be highly observed in a different field like education, business, and medical science and among others. With all these things AI has been playing a very significant role in mobile apps.

Some of the example that shows the best use of AI in mobile apps are as follows:

Hound Voice search and assistant:

Hound takes speed and accuracy to a new level thanks to our powerful Houndify platform, combining Speech Recognition and Natural Language Understanding into a single step. We call it Speech-to-Meaning (TM). Other assistants simply translate words into text, and the do a voice search the old way. But just recognizing words isn’t fast enough! Hound, powered by Houndify, is the fastest and most accurate independent voice assistant in the market today.

Robin – AI Voice Assistant

Robin, by Audioburst, is your voice assistant on the road, bringing you texting by voice, local information, GPS navigation and even jokes, while keeping your eyes on the road. Voice in, voice out. Plus, Robin has more personality than other voice search products, assistants, chat bots or messenger bots. Robin gives your Smartphone a smarter character.

Cortana Digital Assistant

Microsoft Cortana is your free smart digital assistant. It can support you by giving you reminders, keeping your notes and lists, taking care of tasks and helping manage your calendar.


It wasn’t long ago when both Google and Microsoft added neural networks to their translation apps. Spotify is challenging Apple Music app claiming to use AI-powered recommendations. Period & Ovulation Tracker Flo uses a neural network to outperform the competitors while predicting women’s cycles and ovulation dates. One more breakthrough example of applying AI in mobile apps, called Prisma, uses this complex technology that has been around for quite a while to help users turn their photos and videos into art. For these apps, AI became a reality thanks to recent technological advancements in natural language processing, machine learning, predictive modeling, sensors, and cloud solutions. [upwork]

According to Harry Shum, EVP of AI and Research at Microsoft, AI will amplify human ingenuity making people the heroes and driving the healthy connection between humans and machines


Automated reasoning – it is the art and science of getting computers to apply logical reasoning to solve problems, for example, to prove theorems and solve puzzles. This way AI machines beat humans at chess, stock trading and Jeopardy. Uber uses automated reasoning in order to optimize routes and get the riders to their destinations faster. The algorithm takes millions of bits of data from Uber Drivers who have traveled similar routes and learns from their trips.

Recommendation services –  this is the simplest and most effective application of AI in mobile apps that can be used in almost any solution. The reason why most apps fail within a year of launch is that they fail to provide relevant content to continuously engage users. You may be providing fresh content regularly, but if it isn’t something that is interesting to the end user than it isn’t worth the time you spend creating it. By monitoring the choices users make and inserting them into a learning algorithm, apps make recommendations that users are likely to be interested in. This is a powerful source of revenue for such entertainment app like Netflix. Yet any business that upsells or cross-sells content can utilize this type of AI, even if it’s currently a manual process handled by the sales or marketing team.

Learning behavior patterns – most platforms have the capability to learn users’ behavior patterns in order to make the next session more seamless. For example, Snaptravel is a half-bot, half-human hotel booking service. It uses natural language processing and machine learning to have realistic conversations with users suited to their preferences. If a user stumps the both with a request, a human agent intervenes and teaches the bot how to not make the same mistake next time. Another classic example of AI learning your behavior is fraud detection for online payments. Pattern-detecting algorithms go through your credit card statements and purchases as they happen, and can detect if you’ve made a recent purchase out of the norm of your behavior. [Upwork]


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