We are sure that you have heard the term “Artificial Intelligence” before, but do you really know what it is? At CloudLabs, we want to tell you a little bit about the concept and the developments that have arisen from it, beginning with what Machine Learning is.

Let’s start by defining Artificial Intelligence.

Elements that seem to come out of a science fiction movie, such as self-driving cars, shopping suggestions (personal shopper), assistants that quickly translate from one language to another, or virtual voice assistants, are all examples of the scope that Artificial Intelligence (A.I.) has reached today.

Since the 1950s, the pioneers of Artificial Intelligence dreamed of creating machines so complex that they could learn and resemble human intelligence. Today, the idea of building and programming something that emulates, even partially, the functioning of the human mind is still far away. However, great advances and approaches have been observed.

So we can say that this technological advance seeks to develop and create systems capable of learning in the same way that humans do. However, it is important to note that this differs from the field of robotics, as it focuses on mimicking the way of thinking rather than a humanoid hardware system that physically acts like one.

In this modern world, highly mediated by technology, we can find Artificial Intelligence in many of the objects of our daily lives. In our homes, for example, we have home automation; on our cell phones and computers, we have personalized ads within search engines and social networks. After this brief explanation of what artificial intelligence is and some of its applications, we will tell you about Machine Learning.

Let’s talk about Machine Learning

As we have already mentioned, Machine Learning is an element that belongs to the field of Artificial Intelligence in which a system can detect massive data patterns and make predictions using algorithms. This is how computers can perform specific tasks autonomously.

These algorithms are grouped into three categories:

– Supervised learning: it consists of prior learning based on a system of labels associated with data that support prediction and decision-making. An example of this algorithm is the spam detector, where an email is categorized as spam depending on the history of mail movements.

– Unsupervised learning: it focuses on algorithms that have no prior information in order to generate automatic groups. This type of algorithm is widely used in marketing to create highly segmented advertising campaigns.

– Reinforcement learning: its purpose is that the algorithm learns from its own experience. That is, the algorithm is expected to make the best decision in different situations through a trial-and-error approach. This algorithm, for example, is employed to enable facial recognition.

This type of technology is used in industries that work with a large amount of data as it allows them to be more efficient by obtaining a broad view of the data, almost always in real time.

Machine learning and education

Machine learning has been of great importance in the educational field since it speeds up and facilitates students’ progress, which in turn allows teachers’ management in relation to the process of monitoring, traceability, and evaluation to be more objective according to the needs of the current educational environment. As a result, benefits for both teachers and students are highlighted below.

It allows students to develop self-training processes since they will be able to carry out activities, solve challenges, and perform evaluations using technological resources that facilitate their learning process.

It allows teachers to identify behaviors and potential solutions based on the predictions generated by the machine learning system. This will impact the design of precise learning paths according to the educational level or performance level of their students, enhancing the learning process.

This technological trend is already part of modern educational processes, and it has become a reference to understanding the dynamics of learning today. At CloudLabs, we have been making progress in the development of new features associated with artificial intelligence to generate better resources for teachers that allow accurate and objective evaluations. This type of evaluation not only accounts for students’ progress but it also identifies students’ educational levels and the best route to learn, encouraging the development of scientific skills of young people in this new era.