Artificial Intelligence a disruptive form of technology has brought all other systems to their knees. With its impact seen in every field, we are completely awe-struck by the all-encompassing nature of AI.
Especially when building smart mobile applications, we can see the use of Machine Learning, an important derivative of Artificial Intelligence. Before we continue, let’s first understand what is Artificial Intelligence and Machine Learning.
What is Artificial Intelligence?
The domain of Artificial Intelligence (AI) encompasses a spectrum of technologies that bestow computational systems with the capability to execute a multitude of complex tasks.
These tasks include visual perception, comprehension, and translation of human languages in both written and spoken forms. In addition to that, AI also helps with the analysis of vast datasets and the generation of insightful recommendations, with the potential for further advancements.
What is Machine Learning?
Whereas Machine learning (ML) constitutes a subfield within artificial intelligence, characterized by its ability to assist autonomous learning in machines.
This empowers them to extract knowledge from data and past interactions, analyzing and understanding underlying patterns to generate predictions with minimal human intervention and utmost accuracy.
Why is it important to Incorporate ML?
Depending on what the mobile application is achieving, several factors are greatly enhanced if we incorporate ML techniques into mobile app development.
These factors include:
1. Enhanced User Experience
ML uplifts user experience by personalizing the application based on the user’s preferences. It also helps with a predictive experience where the application anticipates the user’s next move.
2. Performance Optimization
Through performance data analysis by ML models, we can identify problems and help developers be more efficient in fixing the errors, whereas in other cases, they would have to detect the problems manually. This could be tiring and strenuous.
3. Improved Efficiency
ML algorithms can automate tasks, optimize processes, and offer informed recommendations, thereby reducing the time and effort expended by users.
4. Competitive Advantage
Incorporating Predictive Analysis, which is an application of ML, can differentiate your app from competitors by assessing user trends and predicting future customer demands.
5. Adaptability and Scalability
ML models in mobile applications can adapt to continuous changes, such as user behavior and preferences, and handle increasing amounts of data efficiently. This gives mobile applications an edge over other competing applications that do not incorporate ML into their scope of applications and processes.
Where can ML be incorporated in mobile applications?
After the arrival of AI, many mobile applications were driven by ML models to boost their performance and user experience. Streaming Services such as Netflix and social media applications such as Facebook and Snapchat all use ML to help with data-driven tasks. ML can be used in different ways depending on the type of application being developed.
Here are some examples of ML techniques that can be employed in an application.
1. Image Recognition
A mobile application focused on identifying certain aspects of an image can employ image processing models to discern certain objects, landmarks, or people in an image.
2. Natural Language Processing (NLP)
Commonly used in chatbots and smart assistant applications, NLP is most effective when applied to tasks involving natural language input or output. These include voice commands, text messages, or feedback. It also benefits tasks that rely on extracting topics or generating summaries.
3. Predictive Analytics
As discussed above, this ML technique helps to assess user behavior and preferences, as a result, allowing the application to recommend relevant content, products, or features. This renders a hyper-personalized experience for individual user.
4. Gesture Recognition
Mobile applications can use ML models to recognize certain user inputs and interpret them accordingly. Working with ML can increase the accuracy of recognition significantly, resulting in a better user experience.
An AR (Augmented Reality) focused mobile application can greatly benefit from the use of gesture recognition ML models, as users can interact with application features via gestures.
How to Incorporate ML into your application?
Before diving into ML integration, it is better to understand where ML can improve the application. You must be vigilant regarding the following steps:
- Identify key features of the application that can work better with particular ML algorithms.
- Ensure that data is well organized and secure for the ML models to use.
- After choosing the right AI tools that best synergize with what the application wants to do, proceed with training the AI models and their integration.
Several tools help facilitate developers in integrating ML into their applications.
1. Tensor Flow Lite
Commonly used in Android applications, Tensor Flow Lite is a mobile library for deploying (newly trained or existing) on mobile (and other devices).
2. ML Kit
ML Kit, a mobile SDK developed by Google, is designed to incorporate ML capabilities into iOS and Android applications seamlessly. Unlike cloud-based ML tools, ML Kit offers on-device processing, that enhances the speed and enables real-time applications such as camera input processing.
Its offline functionality allows for image and text processing directly on the device, thus facilitating tasks that require data to remain locally stored.
3. Pytorch Mobile
The beta release of PyTorch Mobile runtime offers a smooth transition from model training to deployment, all within the PyTorch ecosystem.
It streamlines the process from research to production on mobile devices while providing a comprehensive workflow. Moreover, it sets the stage for privacy-enhancing functionalities through federated learning methods.
4. Core ML
Apple’s Core ML empowers developers to seamlessly weave machine learning models into their applications across various platforms, including iOS, macOS, watchOS, and tvOS.
This framework streamlines the deployment of pre-trained models directly onto Apple devices, which unlocks functionalities like image recognition, natural language processing, and a vast array of possibilities.
Core ML ensures optimized performance and efficiency by intelligently leveraging hardware acceleration whenever available.
Lastly,
Choosing which tools to employ ML models depends on the objective the mobile application is trying to achieve. Factors such as device (CPU, chipset, storage), cost, and dataset (training and deployment time) will decide the difficulty and time it will take to integrate ML into the application.
Hence, it is important to plan everything and then work on it later. It is also important to note that in the future, in order to keep up with the latest technology and trends, incorporating AI will become a requirement for mobile applications soon.
Mastery of the tools mentioned above will benefit developers in the future when new tools are deployed.