Some technologies become part of our daily lives in such a silent way that we hardly notice them. One example is machine learning, which can be translated as machine learning or machine learning: this is a concept associated with artificial intelligence, which is why it is increasingly highlighted by the media. Despite this, few people understand the idea.

If this is your case, don't worry: in the next few lines, you will discover what machine learning is and learn about some applications that already adopt technologies of this type. Difference between machine learning and artificial intelligence

Let's start by clarifying a detail that causes confusion: many people think that machine learning and artificial intelligence mean the same thing, but that's not the case. In fact, artificial intelligence is a broad concept that includes machine learning as one of its features.

But then, what is artificial intelligence? There are several definitions for the idea. One that is widely accepted is that artificial intelligence consists of computational mechanisms that are based on human behavior to solve problems. In other words, technology makes the computer “think” like a person to perform tasks.

It is true that this is a simplistic explanation, after all, an intelligent system does not necessarily need to be something close to human reasoning. In any case, there is still some logic there. We humans are able to analyze data, find patterns or trends in it, do more in-depth analysis from there, and then use the conclusions to make decisions. In a way, artificial intelligence follows this same principle. Data analysis - illustration

Typically, the more we perform a task, the more skilled we become at it. This is a result of our ability to learn. Repetition or frequent execution of related procedures acts as training for us. Something similar occurs in artificial intelligence systems: publicly available data (from the web, for example) or recorded on proprietary platforms serve as training for artificial intelligence algorithms.

How is this training done? There is no one way, there are several algorithms for this purpose. It all depends on the application and the organizations or people behind them. Here, what matters most is knowing that this is when machine learning starts to make sense. What is machine learning, then?

Machine learning is also a concept with several possible definitions. Here's one that allows us to assimilate its essence well: machine learning is a system that can autonomously modify its behavior based on its own experience — the training we discussed previously. Human interference here is minimal.

Such behavioral modification basically consists of establishing logical rules, so to speak, that aim to improve the performance of a task or, depending on the application, make the most appropriate decision for the context. These rules are generated based on pattern recognition within the analyzed data.

Think of a person who types the word brave into a search engine. The service needs to analyze a series of parameters to decide whether to display results equivalent to enraged or courageous, two possible meanings. Among the numerous parameters available is the user's search history: if minutes before he searched for courage, for example, the second meaning is the most likely. Usage - smartphone

This is a very simple example, but it illustrates some important aspects of machine learning. To begin with, it is important that systems of this type carry out analyzes based on a significant amount of data, something that search engines have in abundance due to the millions of hits they receive and which, consequently, serve as training.

Another aspect illustrated there is the constant input of data that favors the identification of new patterns. Suppose the word brave becomes slang associated with a cultural movement. With machine learning, the search engine will be able to identify patterns that point to the new meaning of the term and, after some time, will be able to consider it in search results.

There are several approaches to machine learning. A well-known one is called deep learning. In it, large amounts of data are processed through several layers of artificial neural networks (algorithms inspired by the structure of neurons in the brain) that solve very complex problems, such as object recognition in images. Examples of using machine learning

The use of machine learning in the most diverse applications only tends to grow. It's not on a whim, but out of necessity: many technological resources we have today only work or are viable because of artificial intelligence.

Examples of using machine learning

The use of machine learning in the most diverse applications only tends to grow. It's not on a whim, but out of necessity: many technological resources we have today only work or are viable because of artificial intelligence. Here are some examples:

– Autonomous database: with the help of machine learning, autonomous databases automatically handle various tasks previously performed by an administrator (DBA), allowing the professional to take care of other activities and reducing the risk of application unavailability due to human error;

– Combating fraud in payment systems: several fraud attempts with credit cards and other payment methods are generated every second around the world; Fortunately, machine learning has allowed fraud-fighting systems to block most of these actions; Usage - credit card

– Translation of texts: a translation can never be done literally; it is necessary to take into account contexts, regional expressions and other parameters. Thanks to machine learning, automatic translators are becoming increasingly accurate;

– Content recommendation: video and audio streaming platforms use machine learning to analyze the history of content played or rejected by the user to give them recommendations consistent with their tastes.

Although the concepts of artificial intelligence and machine learning emerged a long time ago, it is only now that we are seeing large-scale use of these technologies.