What Is Machine Learning and What Is It For?

These advances coexist within the same field, that of artificial intelligence, although they're not exactly the same thing. In this article we review their points in common, the differences between the concepts, plus their respective advantages.


Artificial intelligence: definitions, examples, its relevance and versatility


Developments of diverse kinds based on artificial intelligence (AI) systems achieved great visibility in the last decade. In summarized terms, AI is a discipline within computer science whose general purpose is for computers to replicate the processes of thought.


In other words, AI proposes a synthesis and automation of intellectual tasks historically associated with human beings, beyond the mere calculation with which computers were always at ease.


The advances in the area aren't exactly new. The term “artificial intelligence” was coined at a conference held in 1956, although it's estimated that developments in the sector began a little earlier, after the Second World War at the start of that decade.


As we noted, in recent years the fame of some AI systems has been increasing, and now they're part of the daily life of digital-tool users. To mention some examples, we've seen machines that beat world chess champions, instant translators and facial recognition mechanisms, also AI-based developments designed for the early detection of diseases, and even technologies inscribed in that terrain that dare to carry out creative tasks, for instance writing poems or painting pictures without direct assistance.


Such is the magnitude of AI's impact on contemporary society that experts have warned about the potential dangers posed by the vertiginous deployment of these technologies. It's not about omitting their benefits, which are multiple, but about establishing regulations to govern their advance.


At the end of 2021, professor Stuart Russell, founder of the Center for Human-Compatible Artificial Intelligence, called for progress on regulations for these developments. As the site La Vanguardia pointed out, Russell stressed the importance of reviewing the form that “ensures that humans will always keep under control a technology that is increasingly intelligent and powerful.”


Other figures, among them businessman Elon Musk and Microsoft's president, Brad Smith, sounded the alarms regarding the unrestricted advance of AI-based technologies.


With its charming promises and, at the same time, the need for rules that help take the best of these advances; the truth is that the spectrum of artificial intelligence is really versatile and, based on that vastness, terminologies are sometimes used erroneously as synonyms. A classic example is the use of machine learning and deep learning as if they were the same and identical matter, when in reality that's not so. Below we'll review what they are, in each case, plus the uses and advantages they offer.  


5 facts about machine learning


Into Spanish it's translated as “aprendizaje automático.”


It's a branch of artificial intelligence.


The concept refers to the software that uses algorithms to find common patterns in the data they're trained with.


Automation of the processes is one of the fundamental characteristics of these developments. In general terms, the tasks they perform are executed without specific programming and without depending at that moment on manual operation (by a human). Usually, they're used for predictions or suggestions.


Among the advantages that machine learning offers, the fast and automatic production of models capable of analyzing large packages of data stands out, producing more precise and faster results. This translates into benefits in multiple sectors, from large industries, the business world, government management, to entertainment and home technology systems.


Examples of machine learning


While the concept of machine learning might a priori seem more typical of scientific laboratories than of our homes, the truth is that the advances in the sector are present in systems we use daily. Below we share some examples that, with lesser or greater complexity, are applications with machine learning.


The automatic detection of junk mail (spam) is a relatively simple case: it's an algorithm designed to make its identifications automatically based on a series of common patterns in those emails.


The recommendations on streaming platforms, like Netflix or Spotify. How do they know what series or music you might like? Behind these services operate algorithms that analyze information and produce their content advice.


Mudafy: A success story with machine learning


One of the fields in which the use of machine learning is fundamental is the real estate market. Thanks to this technology, each user can be given a better experience through the learning of their needs and desires. Federico Aragón, a member of Mudafy's technology team, shares the possibilities that machine learning provides to benefit users and real estate agencies:


It prioritizes properties on the portal.It provides suggestions or alternatives that fit the interests.It facilitates the search for properties.It favors the friendly experience within the portal.It lets you understand data from the real estate partnerships and use it to improve the joint work process.








Do you want to know more about how Mudafy applies machine learning in the real estate market? Read the article and find out everything. 

If you'd like to keep exploring this topic, you can also read how to use AI to boost your professional profile.

Recommended Coderhouse courses

If you want to understand and apply artificial intelligence in your work, Coderhouse has programs for all levels:

• Introduction to Artificial Intelligence Course: to understand how AI models work and start applying them from scratch.

• AI Automation Course: to automate workflows with tools like n8n and Make, without needing to code.

• AI Engineering Course: for developers who want to integrate language models into real applications.