Machine Learning In Mobile Applications
AI
17Jan 2019
Machine learning is a programming method in which the computer itself generates an algorithm of actions based on the model and data that a person loads. Training is based on the search for patterns. People, by the way, learn in this way.
Machine learning today serves the benefit of humanity and helps to analyze data, build forecasts, and optimize business processes. The more data a man accumulates, the more productive the algorithms will be and the wider the scope.
When Google began to apply machine learning in their projects, businesses quickly realized that it was necessary to pick up a new trend in mobile development. Appear smart fitness trackers, image recognition systems, neural networks that process photos began to appear.
Artificial Intelligence optimizes the consumption of terminal resources by learning from the user automatically. Machine learning improves the performance of the device. Through all the sensors that are incorporated, smartphones can understand better and know the behavior of the user. They can know when to use an application and when not, and thus keep frequently used applications running in the background for a quick re-launch or they can turn off unused applications and thus save memory and battery life.
What real applications are there now of technology?
You will make more quality photographs
The neural network processing unit (NPU) allows the recognition of objects and scenes with artificial intelligence. What is this for? It allows you to identify different scenarios and objects, as well as choosing the right photographic utilities for the right time. These phones also have a 4D predictive focus. With this function, the camera predicts the movement of objects and focuses them with extreme efficiency to capture the details of moving objects. It also incorporates the function of Assisted Composition by the AI, which provides intelligent suggestions for framing shots of groups and landscapes.
Recognize of objects and sounds
Users can learn details about objects and locations, discover how to buy items they see in the real world and translate languages, among many others. Users can use the voice assistant function to perform hands-free functions, such as opening an application or setting an alarm. and extract information.
Greater security in mobile devices
The Gartner consultancy highlighted that simple password-based authentication is becoming too complex and increasingly ineffective, resulting in poor security and user experience. The phones with Artificial Intelligence will be able to capture not only the face but to know the behavior of a user, their patterns when they walk, slide, apply pressure to the phone, move and write, without the need of passwords or active authentications.
The Face ID application that, for example, incorporates the iPhone X already allows user authentication through a sophisticated TrueDepth camera system consisting of a spot projector, an infrared camera, and an IR illuminator, which also uses an A11 Bionic chip to Create a map and recognize a face accurately. This advanced depth detection technology combines to safely perform many tasks, such as unlocking the iPhone, enabling Apple Pay and accessing protected apps.
Face ID projects more than 30,000 invisible IR points. The IR image and the dot pattern go through the neural networks to create a mathematical model of the face, and the data is sent to a Secure Enclave to confirm the coincidence, while the automatic learning adapts to the physical changes in the appearance that may occur over time All stored facial information is protected in the Secure Enclave to ensure security, while processing is doesn’t occur in the cloud but on the device, to keep the privacy of the user. The iPhone X is unlocks when the user looks at the Face ID, while neural networks prevent intrusion with photos or masks.
This same technology allows the creation of Animojis, emojis animated in 3D based on the drawings of traditional emojis that are fixed in your expression and mood and act accordingly.
Who can use this technology
- Internet companies: Mail services use machine learning algorithms to filter spam. Social networks learn to show only interesting news and try to create a “perfect” news feed.
- Security Services: Access systems are based on a photo or biometric recognition algorithms. Road services use automatic data processing to track violators.
- Cybersecurity companies: They are developing systems to protect against hacking of mobile devices using machine learning. A vivid example is Qualcomm's Snapdragon.
- Retailers: Mobile applications of retail chains can study customer data to create personalized shopping lists, increasing customer loyalty. Another smart application can advise products that are of interest to a particular person.
- Financial institutions: Banking applications study user behavior and offer products and services based on customer features.
- Smart homes: A machine-based application will analyze a person’s actions and propose solutions. For example, if it's cold outside, a kettle is boiling, and if friends ring the intercom, the app orders a pizza.
- Medical institutions: Clinics will be able to monitor patients who are outside the hospital. Tracking the performance of the body and physical activity, the algorithm will offer to make an appointment with a doctor or go on a diet. If you show the algorithm a million tomographic images with tumors, the system with high accuracy can predict cancer at an early stage.
So, what is next?
Users will receive new opportunities to solve their problems, and the experience of using mobile applications will become more personal and enjoyable. Cars without drivers and augmented reality will become a commonplace, and artificial intelligence will change our lives.
Machine learning technologies attract customers, analyze large amounts of data and make predictions. Based on Machine Learning, you can build a mobile application that will make life easier for you and your customers. In addition, it will become a competitive advantage of your business.
Recognizing objects, making translations in real time, taking images with higher quality and making your mobile go faster and the battery lasts longer are some of the advantages of machine learning. You should meet with mobile app development company to integrate machine learning into your mobile app.
Harnil Oza is the CEO & Founder of Hyperlink InfoSystem. With a passion for technology and an immaculate drive for entrepreneurship, Harnil has propelled Hyperlink InfoSystem to become a global pioneer in the world of innovative IT solutions. His exceptional leadership has inspired a multiverse of tech enthusiasts and also enabled thriving business expansion. His vision has helped the company achieve widespread respect for its remarkable track record of delivering beautifully constructed mobile apps, websites, and other products using every emerging technology. Outside his duties at Hyperlink InfoSystem, Harnil has earned a reputation for his conceptual leadership and initiatives in the tech industry. He is driven to impart expertise and insights to the forthcoming cohort of tech innovators. Harnil continues to champion growth, quality, and client satisfaction by fostering innovation and collaboration.
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