Whether you know it or not, machine learning is an integral part of your life. It is used every day, and it has become so normal that we barely notice it anymore. An example, of course, is the smartphone text autocorrector. The system can guess what we mean to write, even if we have typed the wrong keys, and it can even anticipate the words which we mean to follow what we have just written.
How does it do this?
It’s as a result of machine learning that records the terms we use most often, in order to propose them again when we find ourselves talking about similar topics.
What machine learning is and how it works
Machine learning can be translated as automatic learning. It is a vast branch of computer science, and, in fact, it is impossible to find a single definition, due to the variety of different applications and functions.
However, it can be said that machine learning is the set of mechanisms that allows an intelligent machine to improve its capabilities and performance over time. Thanks to machine learning, a machine is able to learn how to do jobs, improving with experience. At the base of machine learning, there are a series of algorithms that, starting from primitive notions, can make a specific decision over another one, or carry out actions learned over time.
The different types of automatic learning
Intelligent machines are distinguished according to the algorithms used, but also according to the purpose for which they were made. Taking this last aspect into consideration, we can differentiate between three types of automatic learning systems: supervised, unsupervised and by reinforcement.
Supervised learning consists in providing the computer system of the machine with a series of specific and coded notions, that is models and examples that allow one to build a real database of information and experiences. In this way, when the machine is faced with a problem, it will only draw on the experiences included in its system, analyze them, and decide which answer to give on the basis of already codified experiences. In this type of learning, the machine chooses the best response to the stimulus given to it.
Unsupervised learning consists of conditions where the information entered into the machine is not encoded, that is the machine has the ability to tap into certain information without having any example use cases and, therefore, without having knowledge of the expected results depending on the choice made. The machine itself must therefore catalog all the information in its possession, organize it and learn its meaning, its use and, above all, the result to which it leads.
Reinforcement learning represents the most complex learning system, which requires the machine to be equipped with systems and tools capable of improving its learning and understanding the characteristics of the surrounding environment. In this case, therefore, the machine is supplied with a series of support elements, such as cameras or GPS, which allows it to detect what is happening in the surrounding environment and make choices for better adaptation to the environment. This type of learning is typical of unmanned cars, which, thanks to a complex system of support sensors, are able to travel along streets, recognizing any obstacles, following road signs and much more.
But what is the use of machine learning in everyday life?
When we talk about machine learning, we often think of sectors in which ordinary people are excluded. Yet this is a common mistake, as machine learning has many applications in everyday life.
A classic application of machine learning is the voice recognition that many smartphones are equipped with, and home automation applications, which allow you to activate commands via your voice.
Another use of machine learning is that it allows companies to make tracker advertisements. This means that, depending on the internet user, advertising proposals are made strictly linked to the interests of the user, whose needs and tastes are recognized through the analysis of their research as carried out on the net.
Another common example is related to email spam filters based on machine learning systems that continually learn both to intercept suspicious or fraudulent email messages and to act accordingly, for example by eliminating them. Systems of this type are, for instance, also used in the finance sector for the prevention of fraud, such as the cloning of credit cards, data and identity theft.
It is even present in the so-called recommendation systems that take advantage of machine learning by learning from the behavior and preferences of users who browse websites, platforms or mobile applications. Examples are those that we are commonly used to seeing and using on e-commerce platforms, such as Amazon, or on entertainment and content platforms, such as Netflix or Spotify.
For any company where technology and innovation are its cornerstones, it is imperative to stay at the forefront and seek ways to achieve better performance, integrating technology and human skills.
The question now is: do you really want to be left behind?
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