Must To Know AI Tools For Mobile App Developers

With the growing advancement in the Artificial Intelligence - (AI) and Machine Learning - (ML), there has been the huge evolution in mobile app and mobile application development.
The AI renders the capability to recognize speech or image or gesture to the apps. The advancement also encourages voice translation with exceptional success.
The huge number of mobile apps hit the app store every day, thus it becomes mandatory for them to stand apart from the rest to beat the competition. And for this, it is essential to meet the growing standard of the consumer. Thus, for maintaining relevancy and updated algorithm, keeping in pace with advancing AI has become the need of the hour for any mobile app development company.
Well, with the soaring popularity, AI and Machine learning there come some set of tools and software available for building apps that are highly engaging and dynamic. Moreover, the cloud-based and device-based artificial intelligence tools render unique feature for the app developers to power efficiency and excellence.
In this article let's explore some of the tools and how it is beneficial for app developers to build excellent apps leveraging AI and machine learning.Caffe2:
The successor of Caffe, this is lightweight, modular and flexible deep learning framework - introduced by Facebook. The main aim behind its development was for production use cases and mobile app development. The developers can enjoy great flexibility in creating products that can perform efficiently.
This framework offers deep-learning experiment with ease and also allows to utilize the large community coupled with advancing models and algorithm. Further, as it cross-platform and can be integrated with Visual Studio, Android Studio and Xcode for app development.
Example:
Caffe2 is being used by the Facebook to benefit tech researchers and developers to train machine learning model and for delivering AI on the smartphone devices. This will enhance the efficiency and quality of machine translation system. Therefore, all machine interpretation at Facebook has been transitioned from the phrase-based technique to native standards for all languages.
Features:
Allow Automation
Image Processing
Perform Object Discovery
Operations based on Statistics and Mathematics
Supports distributed training to enable quick scale up and down.
TensorFlow Lite and Mobile
It is a flexible open source machine learning framework that allows simple model deployment for the different platform from desktops to mobile and edge devices.
TensorFlow offers two solutions for deploying machine learning model on the mobile device; TensorFlow Mobile and TensorFlow Lite.
Well, TensorFlow is the upgraded variant of TensorFlow Mobile and extends great performance and small app size. Moreover, it holds fewer dependencies compared to tensorFlow, and this is the reason it can be built and hosted on the simplistic constrained device.
TensorFlow Lite maintains hardware acceleration with the Android Neural Network API.
Whereas, TensorFlow Mobile allows customization to add extra operators
Example:
The Alibaba's team make use of TensorFlow to execute and optimize speaker recognition on the client side. Further, Google also practices TensorFlow for superior machine learning paradigm.
Features:
Speech recognition
Image recognition
Object localization
Gesture recognition
Optical character recognition
Translation
Text classification
Voice synthesisAccord.NET
Accord.NET is a machine learning framework that is combined with audio and image processing libraries written in C#.The framework is exclusively created for developers to design applications such as pattern recognition, computer vision, computer audition (or machine listening) and signal processing for business use.
The framework is divided into multiple libraries for users that include scientific computing, signal and image processing, and support libraries, with features like, natural learning algorithms, real-time face detection and more.
Example:
Panorama - picture made by combining a series of photos into single picture. Here, it is now possible to make a whole new view of an area or location that cannot fit in a single shot, by just combining a series of photos.
Features:
natural learning algorithms
real-time face detection
Pattern Recognition
Computer Audition
OpenCV
OpenCV- (Open Source Computer Vision Library) generally used to power apps with vision. It is a collection of programming functions for real-time computer vision and machine learning. Having C++, Python, and Java interfaces and it supports Windows, Linux, Mac OS, iOS and Android. Moreover, it also supports the deep learning frameworks TensorFlow and PyTorch.
The library holds 500 optimized algorithms including both classic and state-of-the-art computer vision algorithms and can take advantage of multi-core processing as it is Written natively in C/C++Example:
Plickers (uses OpenCV as graphics and Video SDK) - is an assessment tool, that lets you poll your class for free, without the need for student devices.
Features:
To detect and recognize faces
Identify objects
Classify human actions in videos
Track camera movements and moving objects
Extract 3D models of objects
Produce 3D point clouds from stereo cameras
Couple images together to make a high-resolution image of an entire scene
Dialogflow
This Natural language Understanding platform provides a unique way of interaction with products. With this platform, developers find ease in designing and integrating conversational user interface into mobile apps, web apps, devices, and bots. For instance; one can integrate it on Alexa, Cortana, Facebook Messenger and etc.
Moreover, Dialogflow allows building interfaces like Chatbots and conventional IVR enabling natural interaction taking place between users and business. This is available in two types, namely Dialogflow standard Edition and Dialogflow Enterprise Edition
Example:
Dominos facilitate the easy process of ordering pizza using Dialogflow's conversational Technology. Domino's employ both the large customer service knowledge and Dialogflow's NLU inclinations to develop both simple customer interaction and frequently complex ordering scenarios.
Features:
Render customer support
One-click integration on 14+ platforms
Supports multilingual replies
Update NLU feature by training with negative examples
Debug using more penetration and diagnostics
Microsoft
Microsoft offers three different AI tools for developers; 1) Custom Speech Service, 2) Content Moderator, and 3) Bing Speech APIs - to make AI 'accessible for all'. Azure Machine Learning Studio, lets developers drag and drop datasets and use predictive analytics. Microsoft also provides two open sources of AI tools: the Computational Network Toolkit (CNTK) and the Distributed Machine Learning Toolkit (DMTK).
The Azure Machine Learning Experimentation service, the Azure Machine Learning Workbench and the Azure Machine Learning Model Management service.
These are generally aim to make developers capable to build their own AI agents or build-upon existing models.
Example:
As a safety against fraud, Uber employs the Face API, part of Microsoft Cognitive Services, to ensure the driver using the app matches the account on file.
Cardinal Blue developed an app called PicCollage, a mobile app that allows users to integrate photos, videos, captions, stickers, and special effects altogether to create unique collages.
Features:
Provides capabilities such as image analysis, ocular character recognition in images, face detection and etc.
Allows speech processing capabilities to Integrate into your app or services such as text-to-speech, speech-to-text, speaker recognition, and speech translation.
With the use of Search API, it becomes easy to find exact requirement across billions of web pages.Conclusion:
Choosing the right AI tool fastens the development process, however, apart from this, you must consider other factors affecting the app performance. And, the factors are dependent on the precision of the machine learning model, as this can be influenced by bias and variation, using right datasets for training, seamless user interplay, and resource optimization.
Moreover, while building any intelligent app, the mobile app developers should keep in mind that the AI in your app is solving a problem because of its ability to provide accuracy, analytics, based on data gathered. A great AI app let's user to avail data faster and at the same time enable them to do something beforehand of users.
The growing popularity and speeding up the development of intelligent apps, today, many mobile app development companies have come up to provide AI solutions, ranging from huge tech giants to startups.