Machine learning algorithms and artificial intelligence

Machine learning algorithms

The Automatic Learning (AL) is the branch of Artificial Intelligence whose main objective is to develop techniques that allow computers to learn, yes, to learn and to reserve information. More specifically, it is about creating algorithms capable of generalizing behaviors and recognizing patterns from information supplied in the form of already established examples. It is, therefore, a process of induction of knowledge, that is, in simpler words, a method that allows to obtain by generalization a statement or general information from statements and examples that describe particular cases.

The automatic learning process is similar to that of data mining. Both systems search through data already supplied to find patterns. However, instead of extracting data for human understanding, such as data mining applications, automated learning uses that data to detect patterns in the data and adjust program actions accordingly.

Algorithms that use automatic learning are often classified as supervised or unsupervised. Supervised algorithms can be applied to what has been learned in the past, that is, to new data supplied, and unsupervised algorithms can extract inferences from data sets.

Machine learning vs artificial intelligence

Automatic learning and artificial intelligence have a close relationship because they are based on large-scale data processing, but their level of complexity is not comparable. In broad outline, it can be said that Artificial Intelligence is the most "basic" technology, since it is the one that always responds to the same parameters, in which it has already evolved in Automatic Learning, who is capable of self learning and correcting errors.

To understand it in a simpler way, it is necessary to do it with an example: one has to imagine that, it is wanted to identify the mark and the model of all the vehicles that pass by a street. On one side would be the images and sound of the cameras and on the other a huge database with the shape, technical characteristics or the engine sound of thousands of cars.

A system that uses artificial intelligence would identify the cars of the images by its approach to the data that already knows; but not always correct, since there are cars with very similar but different characteristics. A system that uses automatic learning could literally "learn" the data and classify vehicles more accurately.

Artificial intelligence is a general concept, encompassing automatic or machine learning, so a first approximation to both terms must be understood in a context of subordination, which in no way implies inferiority.

Despite their differences, both are artificial intelligence systems, and as such pursue a single purpose: the creation of devices or, if applicable, algorithms, which omit or replace the human being by simulating their cognitive functions. Basically, it's about turning machines into smarter devices to better interact with humans.

This is extremely important, because it gives the human being a foothold so that he can perform his daily tasks in a simpler way, without so much effort, so that he can take more advantage of his day, therefore, the saving of the time is indispensable in this new era.