What is Mobile Machine Learning (MML)?

Imagine a mobile app intelligent enough to modify itself according to user’s needs without your permanent control.

Machine learning algorithms let a mobile app analyze big sets of different information (text, visual, audio, biometric) in order to make decisions, which are the most suitable for this certain user.

Machine learning, which is the subfield of artificial intelligence, can tailor your app according to personal needs of every single user. By adding machine learning to an app, you will attract new users and build more stable and strong connection with old ones.

But how does it actually work? Machine learning algorithms let a mobile app analyze big sets of different information (text, visual, audio, biometric) in order to make decisions, which are the most suitable for this certain user. After investigating his behavior and other activity, a mobile app can improve its effectiveness by predicting subsequent user’s actions.

How Does Machine Learning Work?

Machine learning refers to technology that allows a computer (or another device with computing powers, such as a mobile device) to process data, identify trends or patterns and then take actions to help fulfill a specific objective.

Machine learning can solve a wide range of tasks and significantly improve the user experience.

REFERENCE:Machine learning on mobile devices: 3 steps for deploying ML in your apps

How Are Developers Using Machine Learning for Mobile Apps?

Exploring the realm of machine learning for mobile apps is interesting because there are a plethora of potential uses for this technology. Here is a look at a few approaches that mobile developers are currently utilizing:

Machine Learning for Artificial Intelligence: Machine learning is a vital component of artificial intelligence technology. In fact, you might think of machine learning as the “brain” of a device with artificial intelligence capabilities. For instance, the machine learning interface would analyze data streams, then algorithm updates would be implemented in order to achieve an overarching objective. This technology allows artificial intelligence to adapt and respond to new scenarios that developers did not account for.

Machine Learning for Predictive Analytics: Machine learning is quite powerful when paired with a predictive analytics engine. Predictive analytics engines process large volumes of data, which are then used to make predictions or recommendations. Shopping recommendations on ecommerce apps are a wonderful example of this technology at work. But alone, PA technology is not capable of handling the unexpected. It’s simply not feasible or practical for developers to program the PA engine in a manner that accounts for all possible conditions and predictions. By adding machine learning, the predictive analytics interface becomes adaptable and flexible in a manner that can dramatically improve its overall efficacy, accuracy and utility.

Machine Learning for Filtering and Security: Machine learning technology is extremely effective for applications that require some form of filtering or protection in response to ever-changing input. For example, machine learning can identify suspicious activity within an app, even though the nature of that activity may be constantly evolving. Similarly, machine learning is effective when applied to email and forum filtering functions. The spammer’s IP address and email address is always in flux, and without machine learning capabilities, a person would need to manually identify spammers and then add an email or IP address to a blacklist for filtering or blocking. But machine learning automates this process, with spammers being identified and blocked without a developer’s explicit programming instructions.

These three methods are just a few of the many potential uses of machine learning for mobile apps. Even something as seemingly simple as optical character recognition (OCR) can benefit from machine learning capabilities since it’s conceivable that a developer may omit some of the countless possible variations in character shape from the original algorithm. Machine learning would give an OCR app the power to identify (and “remember”) characters that are written in a new manner. This app could also theoretically identify new characters or symbols that developers had not considered. The same concept is true for natural language processing (NLP) apps. This is good news for companies that want an app developed, as machine learning will reduce the amount of time spent updating and fine-tuning a number of different algorithm elements.

Integrating Machine Learning Capabilities in Your Mobile App

Virtually all cutting-edge technologies can benefit from machine learning. This includes everything from predictive analytics and natural language processing to augmented reality, virtual reality and artificial intelligence.

Machine Learning and Mobile: Deploying Models on The Edge

The use of machine learning in e-commerce mobile apps can provide relevant information to users while they search products. With its help, the app can recommend them the right products based on their interests, and even analyze the fashion trends and sales information and give predictions in real-time.

ML has started from the computer, but the emerging trend shows that machine learning mobile app development is the next big thing. The modern mobile devices show the high productive capacity level that is enough to perform appropriate tasks to the same degree as traditional computers do.

Strategic Implications of Machine Learning and Mobile

Here are five key forecasts about the future of ML thru Mobiles.

Improved unsupervised algorithms

In machine learning, unsupervised algorithms are employed to make predictions from datasets when only input data is available without corresponding output variables.

Whereas in supervised learning the output of the algorithm is already known, its unsupervised counterpart is closely associated with true artificial intelligence—the concept that a machine can learn to identify complicated processes and patterns without any direct human intervention.

When algorithms are left alone to scour and present the interesting patterns in a dataset, hidden patterns or groupings can be discovered, which could have been difficult to get using supervised methods.

In the coming years, we are likely to see improvements in unsupervised machine learning algorithms. The advancements in developing better algorithms will result in faster and more accurate machine learning predictions.

Enhanced personalization

Machine learning personalization algorithms are used to offer recommendations to users and entice them to complete certain actions.

With such algorithms, you can synthesize the information in a data and make appropriate conclusions, such as a person’s interests.

For example, an algorithm can deduce from a person’s browsing activity on an online retail website and discover that he is interested in purchasing a mower for his garden.

Without that insight, the buyer could have left the website minus making a purchase.

Currently, some of such recommendations are inaccurate and annoying, which cripple users’ experiences. However, in the future, the personalization algorithms are likely to be fine-tuned, leading to far more beneficial and successful experiences.

Increased adoption of quantum computing

Quantum machine learning algorithms have the potential of transforming the field of machine learning. For example, these algorithms can utilize the benefits of quantum computation to enhance the capabilities of classical techniques in machine learning.

If quantum computers are integrated into machine learning, it could lead to faster processing of data, which could accelerate the ability to synthesize information and draw insights—and that’s is what the future holds for us.

Quantum-powered systems will provide a much faster and more heavy-duty computation to both supervised and unsupervised algorithms.

The increased performance will unlock amazing machine learning capabilities, which may not have been realized using classical computers.

Improved cognitive services

Cognitive services consist of a set of machine learning SDKs, APIs, and services, which allow developers to include intelligent capabilities into their applications.

With such services, developers can empower their applications to carry out various duties, such as vision recognition, speech detection, and speech understanding.

As this technology is continuing to evolve, we are likely to witness the development of highly intelligent applications that can increasingly speak, hear, see, and even reason with their surroundings.

Therefore, developers will be able to build more engaging and discoverable applications that can effectively interpret users’ needs based on natural communication techniques.

Rise of robots

As machine learning is becoming more sophisticated, we’ll see increased usage of robots. Robotization depends on machine learning for accomplishing various purposes, including robot vision, self-supervised learning, and multi-agent learning.

Machine Learning is Only as Good as its Training Data

To make a machine learning model you need three things, in order of importance:

1. Training Data: Data which has been tagged, categorized, or otherwise sorted by humans.
2. Software: The software library which builds the machine learning models by evaluating training data.
3. Hardware: The CPUs and GPUs which run the software’s calculations.

On the whole, scenario of latest technologies in mobile app development and ML has created a lot of new changes in the thinking process of various businesses, startups and mobile app development companies. Nowadays businesses are focusing on improving the user experience on one channel to provide smart user experience across various range of channels thru ML. Surely technologies like cloud computing, AI, ML, cloud computing, data analysis, IoT, and much more will make it to the list of best trending technologies. However, currently the focus of businesses is to provide the best user and customer experience.