In this digital era of cutting-edge technologies there are many technical tools and devices available in the global market. Mobile devices such as smartphones are popular with billions of people around the globe using them on a daily basis. Apple’s iPhone and Google’s Android are the most widely used smartphones with their combined market share close to 99%. Both Apple and Google are continuously working to bring more innovations and new features and functionalities into their smartphones.
When thinking about artificial intelligence (AI), the first thing that tends to come to mind is Apple’s Siri intelligent assistant and also Alexa or Cortana. The role and importance of Artificial Intelligence in mobile applications has added more fuel to these companies to bring advanced updates to their platforms. McKinsey Global Institute’s study report shows that Google and Baidu are spending huge amounts of money on AI. AI is beneficial for both developers as well as users. By implementing AI features into mobile app development, smartphones will start to perform many of the trending advanced technology functions like text recognition, face recognition, voice recognition, barcode scanning and image labelling. Artificial Intelligence can bring exceptional user experience and simplify work processes.
The idea of a smart assistant like Alexa or Cortana that can solve everyday human tasks attracts millions of users in all business circles, such as healthcare, finance and education. AI isn’t limited to smart assistance.
We have seen many trending technologies for various industries such as Augmented Reality (AR) video games, the medical sector, and smart cars. Social media platforms are active in Machine Learning (ML) and Artificial Intelligence development services.
AI has become more creative than human intelligence. The Director of Engineering in Google has revealed his plan that Artificial Intelligence will touch the human intellect by 2029. It shows robots being smarter thinkers than humans.
According to one of the researchers from Stanford, John McCarthy,
“Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
AI’s goal is to make computers or machine programs smart enough to imitate human mind behaviour.
Knowledge about engineering is an essential part of AI research. Machines and computer programs need to have bountiful information related to the world to behave like human beings. AI must have access to categories, properties, objects, and relations between all of these factors to implement knowledge engineering. AI initiates common sense, analytical reasoning problem-solving, and power in machines, which is a difficult and monotonous job for a person to undertake.
Machine Learning (ML) is an application and subset of Artificial Intelligence. ML is the science of designing and applying computer programs or algorithms that are able to learn things from past cases.
ML can be implemented to solve challenging issues such as credit card fraud detection, face detection and recognition and to enable self-driving cars. ML uses complex computer programs and algorithms that constantly go over large data sets, analysing the patterns in data and facilitating machines to respond to different situations for which they have not been explicitly programmed. The machines learn from past activities or history to produce reliable results. ML algorithms use Computer Science and Statistics to predict rational outputs.
ML is part of computer science where it develops Computer intelligence to understand and work on its own, without being specifically programmed. It is also known as Self-Learning. Machine learning is a process of teaching Networks to advance predictions based on some past data.
Unsupervised Learning algorithms are powerful because the data to be filled is connected to the dataset. Unsupervised machine learning algorithms understand replica from a dataset without connecting to a known set. Unlike supervised machine learning, unsupervised machine learning methods cannot be directly employed to a distribution problem. This makes it impossible to train the program codes that normally perform. Unsupervised learning can be employed for determining the underlying building of the data.
Supervised learning is the pursuit of learning a goal from structured training data. It is based on data from past activities. Supervised learning includes a lot of information and data along with the identified correct products.
Established on the particular results the information is analysed. Depending on the difference between the two signals, an error value is calculated and an algorithm is selected to determine the mapping function from the input to the output.
The purpose is to estimate the mapping role so that when we allow new input data, we can assume the creation variables for that data. It is similar to the work process of a teacher overseeing the learning mode. The learning stops when the algorithm produces an ‘enough’ zone of functioning.
Re-inforcement Learning is a part of machine learning that is involved with estimating the impacts of certain activities to maximise the outputs.
It includes delivering an effective charge with developing its modern body politic in a digital ecosystem and taking actions that maximise the collective of a long-term reward.
The tool will consider the effects and opt for the best approaches to help or support the development to reach the desired goal.
Artificial Intelligence is a special computer system that can do things normally done by a human. The AI technologies offer a high level of intelligence by analysing real-time information in relation to normal human actions as well as behaviour.
Taking into account AI’s abilities and scope, there is no need for typing the commands; we can do everything with the voice. AI can process nearly every command. Take Apple’s Siri for example, a smart assistant can write a note, send a message, and pave the shortest route for you. All you need to do is to say “Hi, Siri!” and AI will do everything else for you within minutes.
AI possibilities have no limits. In mobile applications, AI could be executing in the form of chatbots or any other context-aware sensors. Many organisations select an AI component in their mobile applications to engage and retain their users.
AI is often associated with machine learning and deep learning, but these things are entirely different. Machine learning is a sub-discipline that applies AI possibilities to enable the machines with the ability to learn, and deep learning is a branch of machine learning aimed at mimicking the human brain in data processing. AI builds a technological base for machine learning as well as deep learning.
Since the beginning of AI, the way applications collect, sort, and store data has changed. App developers now need to spend more time finding the way to sustain the information into the machine learning algorithm to make applications smarter. This helps to deepen engagement with customers providing better incentive to use their services. Mobile devices are perfect platforms for AI-based applications.
Modern devices offer a wide range of features and functionalities to unlock the potential of AI such as cameras, microphones and GPS. Apple has recently revealed the iPhone X has a new bionic chip that opens up a new skyline for developers. This bionic chip features a neural engine that allows Face ID scanning. Combing built-in features with AI makes applications more relevant and personalised.
AI is used in a variety of circles, especially in retail. Amazon and eBay eCommerce platforms have proved that AI is worth investing in. Many retail applications feature specific AI-based algorithms that can adapt and adjust based on human behaviour.
These AI algorithms collect data, find market trends and adjust the app to offer a more personalised experience. For example, the Starbucks application collects the user’s preferences to find out what the user wants and to place the order. The AI-powered applications can perform daily tasks without human intervention, thus making life easier for people.
There are no limits for AI, and Alexa is an example of how AI can help gear routine housework. Alexa is a smart voice-controlled home assistant developed by Amazon and.
There are still lots of ways to capitalise on the advantages AI offers as it connects users to the brands.
The mobile app development companies can integrate AI that will result in higher customer satisfaction. AI helps to determine customer behaviour and helps organisations to drive insights to improve a user’s engagement in the application. It helps users to find precise information in the mobile app and enhance their satisfaction.
Mobile apps used to take input from the user either in the form of voice or text. So to search for something, you needed to know its keywords or description related to it. What if the user does not know how to describe what they are looking for or have no idea about what it is called? Now with the use of AI in mobile app development, this issue can be easily solved.
With the use of AI, the technique of visual search can be integrated into the mobile app during its development. Visual search is an AI-powered technique or method that recognises images or visuals in context with the location of the device to provide the search result to the user. Mobile apps that are developed to support visual recognition, in addition to voice recognition, increase the conversion rate.
For example, the user searches for a product which they would love to buy, but doesn’t know what it is called. The user can click a picture of the product and use it to search for that product. This image is recognised and processed by the AI-powered visual search in context with the location of the device. Relevant search results are shown to the user. Google lens is one of the best examples of a visual search engine.
Automated Logical Reasoning is the ability of the machines to solve complex problems. The use of AI in the development of mobile apps can develop this ability in the machines. Machines are capable of not only analysing the user’s preferences, likes, and dislikes but also of finding a good solution. Such an application makes life easier for users.
Using AI, mobile app developers can save on the cost of hiring development teams that are deployed for doing tasks that are repetitive as well as time-consuming. AI helps in automating the HR recruitment tasks that can be completed without any human input. This will make the process of developing mobile apps cost-effective, quick, and less liable to human errors.
Internet of things (IoT) is the network of interconnected devices. Mobile phones, fitness bands, smartwatches, and smart gadgets are some examples. In the digital world, a user is continuously connected to all such smart devices. These devices are functioning through a mobile app that uses AI-powered sensors and chips to recognise the daily routine of the users. During the process of mobile app development, linking AI with IoT can result in maximum utilisation of existing resources.
The mobile app developers can incorporate an auto-reply feature powered by AI in the application. A mobile app not powered by AI cannot communicate with the users, but the AI-powered mobile app can. It is communication between the user and the device. When the user sends a message, the AI-powered device understands it and responds appropriately. For example, Google’s automatic reply features in the Gmail application. It is known as a smart reply.
The mobile app developers can incorporate the AI-powered translator in the mobile apps. This will eliminate the requirement of a separate app for language translation. No matter the language the text input is given, the mobile app will be able to understand it. The language input text is translated as soon as it is entered without any delay. The incorporation of such a language-translation tool in the mobile app will increase its reach across the globe.
Facial recognition techniques powered by AI are a security feature that is incorporated in nearly all the mobile apps that are being developed. AI-powered face recognition used by Apple is so advanced and trending that it can identify and recognise the correct face even with some facial changes, such as a beard. In the future of mobile app development, face recognition technology will be used for acquiring access and also for the following:
Facial recognition technology can be used to make sure that children are restricted from seeing unsuitable advertisements.
Facial recognition technology can be used in the medical field to scan the faces of the patient to recognise the symptoms and diagnosing the disease.
Facial recognition can be used for fraud detection. As this becomes more common, it will substitute the use of traditional login id and password for gaining access.
Chatbots can be defined as robots for a chat. Chatbots are used to chat with the users in case of a query or help needed for a problem. Developers should include chatbots in mobile apps to save on the cost of hiring a team of customer service executives. There are many repetitive questions that are asked by the users such as: is this item in stock, what is the opening time of the store, do you deliver the product to a particular address, etc. It makes no sense to hire someone to answer these simple questions daily. Instead, an AI-powered chatbot can be included in mobile apps to answer such questions. This will help to reduce the response time and generally delivers a great user experience.
Mobile apps that are developed with the use of AI are more productive, efficient, quick to respond and lag-free. Many AI-powered productivity tools, such as Trevor AI, Otter.ai, Microsoft 365, are used to deliver efficient and fast performance.
The increased role of AI in mobile applications has shown its value in business growth as well as user engagement.
Artificial Intelligence can collect and store user data by analysing the behaviour and how the users interact with the application.
AI collects the necessary data such as daily actions, location, and contacts, to serve the users better.
AI-Powered products gear up user engagement and involvement.
Artificial Intelligence and Machine Learning help in creating and innovating business opportunities for all industries including mobile app development companies. Mobile Application development companies utilise AI and ML that allow users to make a search option in an effective manner. Companies can benefit by applying AI in mobile applications where users can search the chosen items and can see the results quickly.
There are many mobile app development firms that can help a business to increase sales and productivity by providing critical insights about business operations.
The arrival of Artificial Intelligence has driven a new class of mobile apps. AI has been influencing mobile application development since the introduction of Apple’s “Siri” voice assistant, but its potential has not been reached.
In the coming years, AI will become an integral part of any mobile application. Consumers will push the app developers to find ways for a more intuitive user experience. The consumers will move away from overloaded websites and will integrate the new technologies into their lives seamlessly with smart home systems, smartwatches and voice assistants
Want to add some intelligence to your mobile app? Contact us and we’ll talk to you about making your mobile apps smarter than ever before.