How to Implement Artificial Intelligence in Mobile Application Development
Artificial intelligence used to be regarded as the most complex technologies
Photo by becominghuman.ai
When we talk about the needs of the world today, we do not realize that we are discussing the future of yesterday. One such futuristic cutting-edge technology to talk about is how to implement and deploy artificial intelligence in both existing and currently running applications.
Through this article, you will learn more about the role of artificial intelligence in the mobile application development industry and how to use and avail benefits from them.
Today, we live in a world driven by machines and their ability to understand human behaviors. Machines are learning our behavior, accessing our data patterns and habits, and making our lives easier.
In our daily lives, we can see how our mobile phone can recognize our speech, make certain decisions, translate one language into another one, and more. Our food delivery application can offer the details of the restaurant that provides a food basis on our recent search patterns.
Taxi applications like Uber or Ola can show us the real-time location of the ride and offer us the nearest available taxi based on our current location.
All such changes are based on the use of artificial intelligence, as the methodology of computer systems has been changed to think-and-learn mode these days.
The focus has shifted to developing smart programs based on research and knowledge of the mental potential and capabilities of the end users, and then are being implemented as built-in intellectual functions in various applications, including mobile apps.
Artificial Intelligence’s Impact
Artificial intelligence used to be regarded as the most complex technologies; however, now it has become a part of our daily routine without even realizing its existence. Check the statistics and they will show us how popular artificial intelligence is and what could be its potential impact on our lives.
- As per research firm Markets and Markets, AI has the potential to become a $190 billion industry by 2025.
- As per renowned research firm IDC, global spending on AI systems could reach $57.6 billion by 2021.
- IDC reports that by 2019, more than 40% of digital transformation initiatives were using AI as an assisting technology.
- IDC forecasts that by 2021, 75% of commercial enterprise applications will use AI at some level.
- More than 80% of technology and enterprise leaders expressed that AI will help them boost productivity and create millions of jobs.
- An overwhelming 80% of marketing and sales experts say that chatbot software has revolutionized the experience of their consumers and they are planning to implement such tools by 2020.
- More than 40% of mobile interactions will be powered by cloud-based neural networks by 2020, reported by Gartner.
Types of Artificial Intelligence
With technological progress in the field of intelligence, data modelling, and machine learning and cloud solutions, combining artificial intelligence with mobile has become a reality. There are broadly three types of artificial intelligence that can be used in mobile applications.
1. NAI (Narrow AI)
NAI is used for systems that are specified to handle a singular or limited task. It offers an algorithm that brings the functions of machine learning to mobile platforms. Examples include Google's Translation Engine, Amazon’s Alexa, or iOS Siri for the iPhone.
2. AIG (Artificial General Intelligence)
AIG is an algorithm that helps us utilize the hypothetical intelligence of a machine that could understand or learn any intellectual task that a human can do. Examples are facial recognition tools, Snapchat filters, or Amazon.com recommendations.
3. ASI (Artificial Superintelligence)
ASI is a software-based system that has artificial intellectual powers across a comprehensive range of activities where human intelligence can be outperformed. Examples are IBM Watson, Google Rankbrain, or Microsoft Cortana.
AI Technologies That Can Be Used in a Mobile Application
AI has the ability to improve user experience significantly and is a tool to solve a wide range of problems and tasks. Here are some practical AI technologies that can be integrated to improve the mobile application development process.
Speech Recognition Technology
Speech recognition technology is one of the most popular technologies used in mobile phones these days. One can easily access voice control systems in mobile applications.
For example, the Alexa, Siri, or Cortana systems observe, decode, and transform human speech into a computer-understandable format. We can express our requirements via voice commands and this technology can help mobile applications take the reactive actions.
Natural Language Generation
NLG is a software process that transforms structured data into natural language. It is used to develop long-form content for organizations to automate their custom reports and produce content for a web or mobile application. This is a big boon for customer service applications. It helps us in creating reports and market reviews.
Biometrics technology allows us to identify, analyze, and measure human behavior along with the physical aspects of shape and structure of the body.
This technology works with images, recognition sensors, voice, and gesture control. It is used for access control, as well; we see this technology in smartphones where users can lock/unlock the phone with face recognition. It is also used to perform marketing research.
Text Analytics and Natural Language Processing
This technology helps us find the required information on search engines, then generate news or stories with the help of structured solid text.
The most common usage of this technology is with GPS navigation applications like Google Maps, where one can verbally request directions to a destination of their choice. This technology understands the request and then processes it further to offer desired results. It is also used for security and fraud detection systems.
Virtual Assistants and Chatbots
This is again the most common technology integrated into mobile applications. This is an important tool for and business applications in that it can interact with users online. It is used to get feedback from the user or the online community. Chatbots and virtual assistants are the front face of an organization and they can enhance the experience of the end-user.
Image Recognition Technology
Image recognition technology can help us process the object and function in a digital image or a video. The most common usage in a mobile application can be seen in IR code scanners or license plate scanners. It can be used to diagnose diseases as well as analyze and verify users by their faces.
It can also be used for the analysis of payments and images for checking credit cards and other payment options.
Emotion recognition is one of the most interesting and commonly uses technology in Mobile applications these days. This technology allows the software to observe and assess the emotions from the human face, by using an advanced image processing or audio data.
It captures the human senses and then helps mobile applications to make changes in faces, gestures, or voice.
Machine Learning Platforms
We have adequate algorithms, tools, and APIs to design and deploy models into mobile applications based on machine learning platforms and models. We have a wide variety of mobile and enterprise applications, mostly used for prediction or classification.
Though the use of AI for mobile hardware is still in the initial phases, we can see the advent of advanced AI especially in computational photography. Several mobile manufacturers are designing AI-optimized GPUs and other appliances to run AI-oriented computational functions with ease.
Several big mobile manufacturers like Samsung and Apple are working with major chipmakers to optimize the hardware to suit their AI-specific needs along with executing the deep learning methods. The renowned chipmakers Qualcomm and Nvidia have launched AI-optimized hardware that can run machine learning workloads on smartphones.
This is one of the most important aspects of AI. Organizations are developing engines that can inject logic and rules into an artificial intelligence system and help enterprises and users make decisions via its automated decision making prowess.
Why to Use Artificial Intelligence in Mobile Applications: An Organization’s Perspective
If we talk from a business’s perspective, then following are the basic benefits that an enterprise or a startup can use if they implement AI in their mobile applications.
- Boosts sales and marketing: Using artificial intelligence in sales and marketing operations helps improve other statistics significantly.
- Easy project funding: Startups with AI implemented are getting funding easily nowadays. So, expect increased competition.
- Enhanced user experience: Artificial intelligence improves the application’s capabilities and improves user experience.
- Enhanced security: Artificial intelligence can help us improve end-user security and detect malicious transactions.
- Manpower utilization: AI can help us reduce repetitive tasks and optimize manpower utilization, which in the process helps us contain the cost.
How to Integrate AI and Develop a Mobile Application
You can use the following steps to implement and integrate artificial intelligence in your mobile application projects.
1. Recognize the Problems You Want to Address
We can apply AI as one or multiple features in a mobile application. However, it is advised to implement AI in one go to prevent complexities and exploit all the benefits of AI.
It has been observed that complexities may increase when AI is implemented in phases. We should recognize the problems we want to solve using AI with our proposed mobile application, and the return on investment and service value should be assessed before starting the integrations process.
Know What Improvements You Expect From AI
There is no doubt that AI is an extremely influential technology and it can certainly improve your existing application and take it to the next level.
However, it is important to understand the potential of AI and the kind of improvements we are seeking from it. We can assess the capabilities of our existing application and note the features or abilities we could add in our proposed application to take the benefits of AI.
A thorough market analysis could be performed to know if we have a similar implementation already in the market, and if yes, then what difference we can offer to consumers.
We must analyze if AI, machine learning, image processing, and pattern recognition are indeed required and what the benefits and ROI we can expect are. This is a foundational step to identify the problems and the scope of its implementation.
Understand the Nature of Data in Your Mobile App
Data is the basis of artificial intelligence, and it is very important for us to learn the flow of the data. The AI operates on the data and its mode of functioning changes with the transition of the nature of data in the mobile application. Once the data flow is assessed, we must think about the refinement of the data. We must ensure that our AI module is fed with clean, informative, and non-repeating data.
Look Beyond the AI-Based APIs
There are multiple AI-related APIs in the market, but using an AI-based API can’t guarantee a full-fledged AI-based solution. We should engage our resources to work on data modelling and other related aspects of artificial intelligence to develop a system that is truly intelligent.
Feasibility Study of AI Upgrade
Now, as we have finished the basic pre-requisites, it is time to execute a thorough feasibility test that may help us understand if our AI implementation is going to improve the end-user experience and increase the user engagement.
It is a rule of thumb that a successful upgrade or migration is the one that makes end-users happy and attracts more potential users to our mobile application.
We need an honest assessment here; if a proposed upgrade cannot increase the efficiency and capability of our mobile application, then there is no point of investing money and effort for that. We also need to analyze if our current resources are able to pull off and deliver the expected AI implementation,
or if we need external resources to enhance our capability. If that is required, then we should remain open to hiring new resources or outsourcing the work to any renowned and reliable resources in the market.
Engage AI-ML Experts and make Implementation strategy – Once the initial analysis is completed and we are aware of our Technological requirements, it is time to engage the AI/ML experts to start the application development.
It is important to delegate the development work to the right resources who have the AI expertise and experience, then only we can achieve the product we are aiming for. The development team should consist of Software consultants, Designers and AI experts and Project Management resources, who can help us strategizing the project and work as per the defined plan.
They should perform user behaviour analysis, application expectations, and analysis of the level of personalization required.
Data Integration and Security
It is extremely important to have a definite data organization model. We must ensure the proper management of our existing data, which, if not organized properly, may impact the efficiency and effectiveness of our AI deployment. It is crucial to focus on our datasets and database and their structures.
Well-structured and organized data and its integration will only enhance the performance of our application and could ensure high quality in the future. Data security is another important aspect that should be a priority and can be ignored only at the cost of a security failure. We need to have the right strategy to integrate the current security standards to make the application intrusion-proof and secured enough to handle security challenges.
Once all the pre-requisite analysis and other tasks are completed, we can proceed with the development work. It is important to carefully test and deploy the implementations before bringing them to the live environment. It is always advised to have a strong analytics system in place before integrating AI capabilities to the proposed mobile application. It will only help you to analyze the pros and cons of AI integration and provides insight that may help us make better decisions in the future.
Use Correct Aiding Technologies
We must select the right set of technology and tools to develop the desired digital solution. To keep our mobile application consistent, our compute services, cloud infrastructure, data storage, development stack, database, backup software, security tools, and other optimization solutions need to be reliable, robust, and future-proof. Not choosing the right set of technology and tools may prove detrimental for the performance of our application in future.
Enable Metrics to Assess the Effectiveness of AI Integration
Assessing the performance of AI integration is indeed an important job, and performance could be assessed only if we have the right metrics in place. We can define the performance baseline, and on that basis, we can review and assess the performance of our AI integration and take appropriate decisions if required.
Engage Data Scientists
A data scientist can help us refine the data and address our data management requirements. Data plays the most important role in AI implementation, and we need the right expertise to fulfil those requirements.
Data augmentation helps developers manage the diversity of data without collecting new data. Several data augmentation techniques such as padding, flipping, and cropping can be used to process large neural networks for various kinds of data. We must make enough provisions in the system to ensure it functions well with the variety of data and environments.
Tools That Can Be Used
We can use the following tools to implement AI along with machine learning in our mobile application project:
- IBM Watson: Helps us develop and train artificial intelligence models. Offers the ability to prepare and analyze data in an integrated environment.
- TensorFlow: Open-source platform for AI and ML that offers a flexible ecosystem of resources that can help developers build AI/ML-powered mobile applications.
- Azure: Core AI Cloud service that lets us build and deploy AI/ML models and solutions.
- API.ai: Uses AI/ML algorithms to assess and match user requirements and help us develop dynamic AI/ML models accordingly.
- Clarifai: Enterprise platform for AI that uses advanced ML to understand text, images, and videos.
- WIT.ai: Facebook-owned chatbot framework and natural language interface that lets developers use its natural language processing capabilities to turn sentences into structured data.
- Amazon AI: Offers several AI services that provide intelligence for your applications. AI services can easily be integrated with your applications to address complex problems.
The primary objective of artificial intelligence is to make computer network’s and devices smart and ensure they coordinate among themselves in an optimal way. It provides us a method to increase human activity. The current era has brought the revolution in human lives with the evolution of artificial intelligence and related technologies.
Implementing artificial intelligence into your business application can bring you benefits in multiple ways. It helps us understand end-user behavior and provides better capabilities for search, digital marketing,
and personalization to say the least. The rise of blockchain and cryptocurrencies has made it necessary for banking and financial applications to utilize AI and ML to understand user behavior and enhance security.
In this article, we explained the importance of artificial intelligence and offered a couple of useful tips of how to integrate AI into your mobile applications and what role AI can play for mobile applications. We hope this will help you to decide the right method to integrate the AI with your mobile application.
Satyam Chaturvedi is a Digital Marketing Strategist at Arka Softwares. He has more than four years of experience in Digital Marketing and loves to read and write about AI, Machine Learning, DevOps and much more about the emerging technologies.