Introduction:
Artificial Intelligence or AI, in short, is a buzz word in the category of upcoming or emerging technologies like LBS, AR, VR, IoT, and wearable applications. AI technologies existing since a long in human history, but it has grabbed the attention of the mass when Apple has released its iOS devices, iPhone in particular with a sweet virtual assistant named ‘Siri’ and gulp a big revenue stream from the mobile market.What Is Artificial Intelligence?
In fact, AI is an umbrella name of various underlying technologies such as Natural Language Processing, Deep Learning, Machine Learning, and much more in due course to form an artificial neural network that simulating biological neural networks neurons in CNS of a human being to accomplish intelligence-related tasks beautifully. Just like human CNS, AI system also capable of learning things quickly and accurately when required feeds/data had provided.
Do Mobile App Developer Create AI Engine?
Unfortunately, developing an Artificial Intelligence Engine with the desired characteristic is a job of super-computers and writing binary codes is possible within giant organizations like IBM, Microsoft, Google, Apple, and the large government research laboratories. It means neither a tiny mobile device bear AI capabilities nor mobile app developers are capable of writing code to create an AI engine by themselves.Have you read? Emerging Trends of Blockchain a Worthy Asset for Mobile App Developers
How Do Mobile App Developers add AI Capabilities in Mobile Apps?
So, why the market is buzzing things about AI-powered mobile app development? Yes, it is AI-powered means APIs from various AI Engines are available to integrate with mobile app back-end and leverage their specific offerings in your mobile app development to create AI-based features and functionality. For instance, Amazon AI, IBM Watson, Api.ai, Wit.ai, Tensorflow, and Clarifai are popular APIs for AI integration. Before the selection of any AI-based API, you must have known that what is specified for, and where you can leverage it. Let’s take a glimpse of it all.Amazon AI – API
A known AI platform using NLU, ASR, TTL, and other mechanisms to identify the visual objects, human voice, and execute DML (Deep Machine Learning) technology. Since it is a cloud-based solution, it enables developers to create and run small size applications because data and processes remain on the cloud and only real-time communication is essentiality. Therefore, mobile application developers prefer the integration of API of Amazon AI the most, as they have tiny clients but almost non-stop connectivity for the apps on mobiles.IBM Watson – API
It is another best bet for AI-based API integration when you have to run voice search on the web. It is because it has capacities to translate voice data into textual data and based on it run the search queries on the web. The next features of the AI engine of IBM are its filtering engine and multitasking abilities. Therefore, high scale app processing mobile apps become a possibility and developers are now more inclined to integrate it when multitasking is essentiality at AI part.Wit.ai – API
To obtain the AI analysis of your input data based on your existing training model, Wit.ai platform is an excellent choice. Thus, creating simple AI applications like Cortana or Siri is easy with this API. It is an excellent AI engine working on query-base solutions. It recognizes the central/core object of the query and determine the object of the query and process the query to find the solutions with the help of these two mechanisms.Api.ai – API
Its working mechanism is the same as Wit.ai engine, and the Google Developers develop it. It has a precise entity identification mechanism and colossal knowledge base. These both traits are useful in the development of educational AI software.Tensorflow – API
It is a project of Google developers and working on artificial neural network graph generation principles, so it is a complex library and demands highly experienced developers in AI field to work with it.Clarifai – API
It uses captive and complicated AI mechanisms, so the resulting applications can fully adapt to offer personalization experiences. Therefore, developers use it to create AI assistant based apps using REST API.Which Use Cases When AI Is Best for Apps Mobile App Developers?
We have seen the capacities of different available AI engines and their APIs to integrate with mobile or other software applications. Based on that knowledge, we can conclude that there are some main use cases when mobile app developers can look at the available AI engines and their APIs to integrate, and those are:- Automated Reasoning
- Recommendation Services
- Learning Behavior Patterns
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- NLP (Natural Language Processing) – Use in market intelligence like customer services, report generations, and in marketing campaigns.
- Speech Recognition AI Software – In voice-based search engines to create interactive voice response devices and apps.
- Virtual Agent – Chatbot is an ideal example of it that acts as a virtual agent in customer service apps.
- Machine Learning – ML used in the creation of prediction apps, classification or segregation apps, in the Big Data analytics apps, BI for internal business processes, and in digital ads apps.
- AI Optimized Hardware – To make hardware user-friendly and to create graphics and processing units to support AI tasks.
- AI–Based Decision Making – For maintenance, installation, and adjustments in a system or operations for businesses. In execution of automated decisions like for ads campaign, investment, and other profit generating decision for organizations.
- Deep Learning – To imitate the human brains in various functions including identification, decision making and much more in the field of robotics.
- Biometrics Applications – To create secure access system based on impressions of body parts, recognition of body parts and learning body language for various commercial and security applications.
- Robotics Apps – To create computer-based sales processes, AI marketing, and saving on repetitive tasks in a company or organization.
Conclusion:
Integration of AI capabilities in mobile applications demands hard endeavors from the mobile application developers and the entire mobile app development team to tackle many tactical issues such as data access security, legacy systems, API-based architecture, and working in agile development environment. Therefore, highly experienced and expert team of Mobile AI App Developers is mandatory to make your AI-based app project a success legend, and SysBunny is a right destination to access that team.About Post Author
