Next generation mobile experiences using AI/ML

With more and more developments in the technological field, everything is changing at a rapid speed. From understanding planetary behavior to learning more about mobile technology, developments have now become a constant. Out of several technologies that have emerged in the last decade, artificial intelligence (AI) and machine learning (ML) are the two most revolutionary ones. The worldwide artificial intelligence market was worth USD 93.5 billion in 2021, and it is expected to increase at a 38.1% compound annual growth rate (CAGR) from 2022 to 2030.

While they might sound different from each other, both AI and ML are interrelated. They changed the way computers used to operate, but they also transformed the mobile experiences. The following article will talk about how artificial intelligence and machine learning have changed the mobile world and made it possible for users to experience something more immersive and newer.

What is artificial intelligence?

Our human brains work in a much more different manner than anything else. We can easily assess what someone else has to say even before the person completes the sentence. Similarly, our brain can understand our lifestyle and change its functioning according to our habits. For instance, if you suddenly start going to sleep late at night for a couple of days, you will see that sleep won’t come till late at night even if you hit the bed early. This is the magic that our brain can do.

It can truly render someone speechless. This is what artificial intelligence is all about. Here, computers adapt themselves to behave like the human mind. Before we move into the technical details, let us present here an example. When you open the same application for a couple of days, you will also find your computer giving the same suggestion the next day. It remembered what actions you took and responded similarly.

Artificial intelligence is rendering human intelligence and implementing the same for devices, robots, applications, and more. It is pretty hard to define what AI is in the literal term. It is a broad concept that harbors subgroups of technologies and concepts. For example, natural language processing or NLP and machine learning or ML behave as the cores of artificial intelligence. You won’t be able to implement the concepts of AI without using these concepts.

Types of artificial intelligence

There are four ways to implement the concept of artificial intelligence. In the below section, we have briefly discussed these ways before proceeding further.

• Reactive machine

  From the name itself, you can understand that a machine is reactive when it understands the actions and responds accordingly. The response will completely depend on the actions being taken. So, reactive machines are based on the very basic concept of artificial intelligence.

• Limited memory

  Based on complex scenarios, the limited memory machines record past experiences and search the stored data to decide the upcoming actions. These machines connect the past with the present and bring forth a more complex system.

• Theory of mind

  While the concept of the theory of mind has not been implemented on a huge scale, the experimental data says that a machine would work on human emotions and thoughts. It would read how a human is feeling or assess the emotional state. Based on the combined knowledge, the machine will give you the response as per the demand.

• Self-awareness

  This is the final stage of artificial intelligence, where the machines will perceive data from external sources and evaluate them to decide what actions must be taken. However, the decisions won’t be influenced by records or human emotions. Instead, the machine will be able to decide the needful based on a stimulus.

Features of artificial intelligence

There are three main features of artificial intelligence that you should learn without fail to revolutionize the mobile experience for your users. By 2027, the AI chip industry is estimated to be worth over $83.25 billion. Bearing this in mind, we have explained these features so that you can better understand them before we move forward.

• Deep learning

  Deep learning is indeed related to machine learning, with the only difference being that it allows a machine to think just like a human. The brain works with a human intelligencer and delivers the same performance more efficiently and accurately. Multiple layers are present between the output and input that process data differently according to the concerning attributes. You can use Flutter for MVP to focus on adding and optimizing this AI/ML feature.

• Artificial neural networks (AIN)

  Just like the human brain conducts messages in the form of electrical impulses through neurons, a machine integrated with AI will be able to transfer data through an artificial neural network. Each neuron receives a non-linear function which it then evaluates. The resultant is a real number function that defines how the AI system will respond.

• Feature engineering

  Feature engineering is a process through which the machine scans a complete dataset and finds the right information based on some pre-selected attribute. Artificial intelligence plays a role in helping the machine choose attributes that are more relevant to the input actions.

What is machine learning?

Machine learning is a practice in which algorithms and programs are designed to train machines. Based on this, they can search for different attribute values, predictions, mined data units, etc. Machine learning is indeed the core of artificial intelligence.

The process of machine learning comprises three crucial steps:

1. Decision process: Here, the algorithm can compare or identify different key metrics and values. These data units are then processed so that proper actions can be undertaken easily.
2. Error function: Error functions are used when the accurate output of any model is known beforehand. The output is then used to assess whether the given set of attributes has given the correct output or not. If not, then the model is further checked to find the error.
3. Model estimate optimization: Once the gap between actual findings and the estimated model output is discovered, proper steps are taken to reduce the discrepancies. This makes the future model outputs close to accuracy.

There are three types of methods by which machine learning can be processed. In the below section, we have explained what these processes are and how they fit into real life.

Supervised machine learning

From the name itself, it is clear that the supervised machine learning procedure requires a prior dataset for training the algorithm to give the input. Based on the differences between predictions and actual results, the model will optimize its evaluation process to bridge the gap as much as possible.

Unsupervised machine learning

This is one of the best ways to work on unlabeled or unsorted datasets. The algorithms identify the patterns in the given datasets and further arrange them in clusters such that each group can easily delineate what they represent. This particular strategy is mainly used for data analysis, customer segmentation, cross-selling strategies, etc.

Semisupervised machine learning

When the available labeled dataset is small in dimension, it is used as a catalyst for finding the right pattern in the larger data sets. Once the algorithm is properly trained with the smaller available dataset, it can work on the unsorted data collection to create the groups.

Features of machine learning

At present, every business needs to deal with a huge database. These datasets need to be evaluated to understand the customer and market expectations or trends. This is where machine learning comes into picture. It can be used to visualize relationships and trends in the datasets and make predictions as per the requirements.

• Automation

  One of the main characteristics of machine learning is its ability to automate several types of repetitive tasks. With the help of this feature, organizations can easily increase their overall performance and productivity with ease. For example, the collection and evaluation of sales data can be easily automated in the financial sector so that the business can know more about the progress.

• Data analysis

  Another important feature of machine learning is its ability to process data analysis. It doesn’t matter whether the algorithms will use any labeled dataset to identify the trends in datasets or make predictions. Once the unsorted datasets are classified into different groups, the algorithms make relatable predictions by analyzing the data. Prioritize this during the MVP development of your app if you are adding ML tech stack.

• Business intelligence

  While the term “business intelligence” is somewhat new to the market, we cannot deny the usefulness of this concept. Business intelligence or BI requires the evaluated data to process the analytical techniques and ensure that proper predictions can be made concerning the business expectations.

Relationship between artificial intelligence and machine learning

To understand how artificial intelligence and machine learning have changed the mobile experience of different users, you first need to learn the relationship between these two technologies. To explain how these are related, the following illustrations will make it clearer for you to understand.

• Both AI and ML focus on intelligent tasks.

  One of the major ways both artificial intelligence and machine learning work together is by solving different types of complex tasks. The outputs are generated accurately, with maximum level of accuracy. They can easily utilize different algorithms to perform just like a human brain.

• Both AI and ML depend on data greatly

  It doesn’t matter what kind of data the artificial intelligence and machine learning models are considered; they use different data types to extract information and features. The data is used to understand the market and customer behavior while taking actions according to the current scenarios.

Implementation of AI and ML into mobile technology

With more and more users adopting mobile as the main device to connect with online businesses or access the internet, it has become imperative to understand how applications can be developed. Users are demanding more personalized experiences, be it searching for the best type of products on an ecommerce website or analyzing concerned datasets to make the proper predictions.

This is where artificial intelligence and machine learning concepts come into the roleplay. Both AI and ML are used to automate different processes and programs to ensure that productivity can be improved further. The algorithms are properly trained to evaluate different types of datasets or any random inputs.

Once the evaluation is complete, the algorithms will ensure that the results can help businesses fulfill customer expectations. With artificial intelligence and machine learning implementation, mobile applications can understand human actions and behavior to generate the required results.

How AI and ML have changed the mobile experience?

Artificial intelligence and machine learning have changed the mobile app development processes and introduced features that can further enhance the personalized experience. From emotion sensing to voice search, most of the modern-day features that you can see in the applications are based on these two concepts. In this below section, we have discussed the most popular applications of machine learning and artificial intelligence for your better understanding.

• Real-time translations

  One of the best applications of artificial intelligence and machine learning in mobile app development and enhancement is through real-time translations. The apps use natural language processing or NLP through machine learning. It understands what the user is speaking, identifies the language, and then translates it into the desired language.

• Privacy and security

  In today’s time, privacy and security have become more important for users of all ages and backgrounds. Artificial intelligence and machine learning play a big role in bringing revolutions in mobile security. One example is the way how algorithms can easily track your actions and remember whether the responses had any malicious intent or not.

  If you again try to take the same action, your mobile will show a warning message as the algorithm takes reference from the past and displays the related response.

• Facial lock systems

  One of the major applications of machine learning and artificial intelligence is implementing a facial recognition system. Most mobiles come with a facial unlock system where you can access the camera and show your face. The algorithm will map the coordinates to form a facial structure. Once it matches the data present as the labeled input, your phone will be unlocked.

• Chatbots

  
  Another prominent example of the application of artificial intelligence and machine learning is in the form of chatbots. While working on an application, you can often see that a sudden window will appear with someone messaging you about what you need or how you can perform in the best way. These chatbots analyze your actions and display the responses in terms of suggestions or questions. Businesses use chatbots as a virtual spokesperson to offer the ultimate user experience.

• Speech recognition

  You already know how devices like Alexa and Siri can identify your voice, process the command through natural language processing, and take concerted action. The same concept is also applicable to mobile applications. You can speak a statement, and the application algorithms will use ML and NLP to evaluate the same meaning. Once done, the concerned response will be given by displaying search results for your voice query.

• Implementation of IoT

  IoT, or the Internet of Things, is another major area where artificial intelligence and machine learning concepts are implemented. Here, the concepts connect multiple devices working on the IoT technology on a similar network. If you take action on one appliance, you will be able to witness the impacts on others also, no matter how meager it is.

• Smart keyword inputs

  While writing a text, you will often correct the errors automatically, or the right suggestion is displayed. We know this as a smart keyboard where the application uses natural language processing and artificial intelligence for the desired results.

• Intelligent mapping

  Several robots connected with your mobile can easily travel and learn how to create a map on your phone. For instance, a robot vacuum uses artificial intelligence to learn about your house layout and create the same pattern on your phone as a map.

How can we help in changing the experience of your targeted mobile users?

Designing and developing mobile applications based on artificial intelligence and machine learning concepts is not something any professional can achieve. It would help if you had skilled and trained professionals who are experts in this field. This is where SolGuruz comes into play! We will ensure that the mobile applications feature different characteristics that utilize human intelligence and evaluate responses based on the necessary actions.

Furthermore, in the United States, businesses with more than 100 employees are embracing cloud-based apps. Nearly 35% of SMBs in the nation have already used cloud solutions, according to estimates.

Conclusion

Artificial intelligence and machine learning are playing a crucial role in revolutionizing the best way to enhance mobile experiences for your users. From introducing a personalized shopping experience to processing voice search algorithms, several ways are there to implement AI and ML into mobile app development. These will take your application to a new stage and ensure that you can reach out to your customers in a far better manner.

However, to do so without any discrepancy, you need expert solutions. SolGuruz will help you achieve the best results and ensure the mobile applications can track the actions of the users and predict the responses.