Top 5 Artificial Intelligence Tools for Mobile App Development

Even since its inception, AI (Artificial intelligence) has proven to be a wonderful technology that helps startups and businesses outshine their competitors, especially in the mobile app development world.

The versatile nature and trending AI features in mobile app development have opened the doors to countless innovations and possibilities for several sectors to take advantage of — whether in terms of improving existing services or opening new lines of business.

Today, almost all multi-billion brands are leveraging AI machine learning modules in their apps and software to enter users’ lives via their smartphones. The AI tool implementation for app development is helping these businesses take personalization to the next level through its subfields like Machine Learning (ML), deep learning, and predictive analysis.

But, is it that easy to integrate AI mobile app development into your mobile application to enhance the user experience it provides? Well, it is. Thinking, how?

To use these emerging technologies you don’t have to spend a fortune today. With AI and ML  becoming increasingly popular, there are many tools available in the market using which app developers can take advantage of AI-based app development without inventing everything from scratch.

Partnering with a leading mobile application development company can give you access to these tools and expertise, providing budding entrepreneurs with a way to power their applications with wonderful features that users love. In this article, we are going to talk about tools used to build smart apps using artificial intelligence.

Let’s get started with some numbers first.

Why You Can’t Ignore AI Tool Implementation for App Development in 2024 and Years Beyond?

Well, if you aspire to be a successful tech entrepreneur or you want to launch an app that stands out among more than 5.4 million apps available in the Google Play Store and Apple App Store combined, you can not ignore Artificial Intelligence.

AI comes with several benefits that you can use to develop mobile apps offering highly personalized experiences. Here are some of the benefits of using AI:

• Automated business processes
• Insights on user behavior patterns
• Seamless customer service through Chatbots
• App security and user authentication
• Real-time language translation
• Depicting human emotions

These abilities of AI can help you develop wonderful apps that users would love. Thus, as a forward-thinking brand, you should find ways to capitalize on these as AI continues to connect customers to brands in ways never possible. In addition, the global market revenue for AI is expected to grow significantly from 2022 to 2030, as per various studies.

Here are some numbers to make you believe why investing in AI won’t hurt later:

• The worldwide AI market will reach over half a trillion U.S. dollars in size by 2024, as per IDC.
• Research by Precedence says the market will grow to over $1.5 trillion by 2030.
• The worldwide startup funding of AI has exponentially grown from $670 million in 2011 to $36 billion in 2020.
• AI startups received approximately $50 billion in funding in 2023.
• The number of AI acquisitions has grown continuously every year since 2010, peaking in 2019 at 231.

Source

Due to its capability to learn, AI is being used almost everywhere today. Now that you know the scope and benefits of Artificial Intelligence for mobile apps, let’s check out some of the AI tools in mobile application development that can help you improve your app’s experience without spending a fortune on developing them.

Artificial Intelligence Tools: Top 5 AI Tools to Build Smart Applications

From the day of its innovation until now, Artificial Intelligence has been improving and evolving fast. That’s why it is one of the most popular Mobile App Development Trends for 2023 and years beyond. Here are the top AI tools for mobile app development to increase your mobile app’s efficiency.

1. Core ML: One of the Best Artificial Intelligence Tools By Apple

Launched in June 2017, Core ML is Apple’s machine learning framework that was developed to increase user privacy by focusing on the device’s in-built ML over other cloud-based solutions. It is a powerful machine learning framework with an easy-to-utilize drag-and-drop interface and world-class features:

You can use this amazing tool to build intelligent iOS applications that have functionalities that were almost unimaginable a few years ago. You can use this artificial intelligence tool in two ways – using top-level APIs or by integrating its features directly into your app.

With just a few lines of code, you can use the following APIs to use the capabilities of CoreML in your iOS application:

• Natural Language Framework lets you analyze natural language text, sentences, or words, segmenting it into paras, and tag information about those segments.
• Sound Analysis Framework lets you analyze audio and utilize it to differentiate between sounds like singing birds or traffic noise.
• Speech Framework allows you to recognize speech for several languages on live as well as pre-recorded videos.

From text detection and barcode detection, to face detection, language, and script identification – there are several functionalities of Core ML that you can use to make your iOS mobile application better and more intelligent.

2. ML Kit: A Top-Notch Artificial Intelligence Tool By Google

ML Kit is a mobile SDK that helps developers use Google’s on-device ML expertise to build intelligent iOS and Android applications. You can utilize its easy-to-use and powerful Vision and Natural Language APIs to tackle many common problems and challenges in apps or to build seamless user experiences – all free of cost and powered by Google’s world-class machine learning models.

App developers can also launch it using Firebase. As the solution provides you with cloud and as well as on-device APIs in a simple interface, you are free to choose whatever fits your requirements in the best possible manner.

ML Kit by Google has features that can be divided into three parts:

• Vision: These are image and video analysis APIs to detect barcodes,
• selfie segmentation, pose detection, label images, and detect faces, object detection & tracking, and texts.
• Natural languages: These APIs are used to identify and translate text in 58 languages and offer reply suggestions.
• Custom development: You can build your models using AutoML Vision Edge or you can also bring your own models powered by TensorFlow Lite, perform experiments, and then deploy them with ML Kit. You will have full flexibility.

Basically, ML kit makes adding machine learning to mobile apps simple and easy – all you need is to pass data through an API and wait for the SDK to send a response. Here are some of its pros and cons:

3. TensorFlow

TensorFlow is an open-source ML platform that is based on deep-learning neural networks. Basically, it combines deep learning and machine learning algorithms and utilizes them through a common symbol. Tensorflow is Python-based and you can execute mobile apps in C++. Its simplified design allows more people to build intelligent AI-based mobile apps.

Here Natural Language Processing can perform advanced things that include recognizing people, items, or texts in images.

Once ready for deployment, it can be used in a web browser, on-device, or in-cloud offering flexibility to developers as per ML model requirements and needs. Interestingly, developers in TensorFlow can train ML without impacting the speed and performance of the process. 

Airbnb, Coca-Cola, and Intel are some of the most popular brands implementing AI technologies built using TensorFlow. It is an open-source AI library that uses data flow graphs for model creation. Tensorflow can be used for understanding, prediction, classification, discovery, creation, and perception.

This framework has an architecture that permits computation on any GPU or CPU. it is compatible with a server, desktop, or even mobile device. Google also utilizes TensorFlow for complex ML models like RankBrain and Google Translate.

Also Read: Why To Develop Chatbots For Banking And How Much Will It Cost?

4. OpenCV

You can embed OpenCV in Android as well as iOS applications. Basically, it is an open-source cross-platform library that can be used to develop real-time computer vision apps. In simple words, it means that with the help of computer vision, computers can identify and understand people and objects in images and videos. OpenCV is also one of the sub-fields of Artificial intelligence.

Originally developed in C++, Java, and Python bindings were also provided with OpenCV. It can run on several operating systems like Linux, Windows, OSx, Net BSD, etc. The tool’s main focus is on video capture, analysis, and image processing with features like object, gesture, and face detection. OpenCV library also supports Poarch and Tensorflow deep learning frameworks.

Note that OpenCV is an open-source library, which signifies that you can use and modify the code in the library freely. 

The main objective of OpenCV is to provide a standard infrastructure for applications using computer vision and accelerate the adoption of machine learning. Let’s check out the advantages and disadvantages of OpenCV:

5. Caffe2

The modified version of Caffe, Caffe 2 is a modular, lightweight, and flexible deep learning framework that was introduced by Facebook. The main goal behind developing the Caffe framework was to make AI and ML-based mobile app development easy and straightforward.

Caffe2 lets developers do deep learning experiments without any complexities and because it is cross-platform, you can integrate it with Android Studio, Xcode, and Visual Studio for developing robust and intelligent applications.

Facebook uses Caffe2 to help developers and tech researchers train ML models and for delivering artificial intelligence on mobile devices.

The platform is mainly known for its impressive architecture that encourages innovation. Optimizations and models are defined without any hard coding using configuration. You can switch between GPU and CPU by changing a single flag to train on a GPU machine. It is a suitable framework for industry deployments and research experiments as it can process more than 60M images in a day with a single NVIDIA K40 GPU.