How to Use Machine Learning in Mobile Apps
How Does Machine Learning Work?
Machine learning is based on the implementation of artificial neural networks, which are actively used both in applications for everyday life (for example, those that recognize human speech) and in scientific software. These allow for conducting diagnostic tests or exploring various biological and synthetic materials. In turn, artificial neural networks are equivalent to real neurons and CNS of a human being. It can be a bit difficult to understand, so let’s find out how the human brain processes memorization and recognition.
Unlike machines (even those possessing enormous memory resources), the human brain is far more powerful. We are able to easily identify human faces, even in low light conditions and in the presence of several extraneous objects.
And what about computers? They do a lesser job at solving similar tasks today. Our brain is different and much more reliable: it does not stop its work while ill, as happens with computers that are damaged at some vital point (hard disk or CPU, for example). Moreover, we don’t need any special firmware, software, or OS to assimilate new information.
With computers, things are much more complicated, which is why scientists are still in active search for algorithms that replicate the work of the central nervous system. There is, however, nothing complicated at all. In order to transmit information to the brain, neural networks generate electrical impulses transmitting the simplest signals from one neuron to another.
On the basis of this concept, the first artificial neuron was created in 1943. It would seem that the mystery of the human brain was solved, but that was not the case. A few decades later, scientists proved that only a small group of linearly separable problems can be solved on the basis of human memory mechanism imitation and analysis. That is, those tasks whose expected result can be systematized and predetermined, in advance.
In order to implement this process, the researchers had to constantly replenish the memory of the learning machine with new examples that would clearly illustrate the solution of the same tasks. This approach works in most cases, but not all the tasks are linearly separable. So, now we’ll talk about what kinds of machine learning are used to solve specific types of problems.
Types of Machine Learning
The controlled or supervised variety of machine learning is based on the analysis of a large number of examples, and is used in the vast majority of modern public platforms for AI-based app development.
In order to implement the learning process, a certain set of examples, which are subsequently analyzed, is loaded into the machine. For this purpose, various accompanying information is included: geolocation data, dates, and specific characteristics of the object analysis itself. Then, a number of templates are generated on their basis. The algorithm finds the best template that corresponds to most of the related information. In turn, the machine gets the actual information (for example, that there is a cat on the photo) or even predictive information (such as forecasting the dollar’s future rate).
In a supervised machine learning environment, there are three standard algorithms:
•classification (when the expected result is an object);
• regression (used to predict prices and other numerical data); and,
• anomaly detection (often used in commercial areas, for example in e-commerce or in finance industries. It is also used in data mining to detect hacker software intervention).
Thus, this algorithm is suitable for eCommerce machine learning applications.
With uncontrolled or unsupervised learning, the machine either does not receive any specific input data or it fails to detect a certain pattern in the user’s request, independently. The simplest example of an application that processes such requests is with mobile-based booking services that find restaurants with a truly delicious cuisine near the owner of the device. What happens with artificial intelligence? During training, it does not receive any information from outside, namely on which samples are correct and which ones are not. Its activity is based solely on the experience of working with a particular user.
Training with reinforcement includes abstracting from the knowledge provided by the teacher and through one’s own experience. For example, you can create an auto-management machine that analyzes user reviews (or records whether or not they leave tips, for example) and improves the quality of service. A similar algorithm is used in robotics and for IoT apps. Today, learning algorithms with reinforcement are the most difficult to implement and, therefore, rarely used in software platforms to create AI-based applications.