Machine Learning is one of the technologies getting the most attention right now, and not only in technical circles: it is also being discussed in sectors as far removed from computing as industry and manufacturing. To understand why, you first need to know what Machine Learning is, what it is used for, and how it works under the hood. Below, we review the most important points without unnecessary jargon.
Within Artificial Intelligence there are several branches or approaches. One of them is Machine Learning, which in Spanish is translated as “aprendizaje automático.” It has been theorized and developed for decades, but only in recent years has it become popular enough to move beyond the purely scientific sphere and enter business language and mainstream media.
Part of that popularity comes from the fact that Machine Learning is already part of everyday life for almost everyone. Many product and service companies boast about using it as a competitive advantage, and that has helped make the term familiar even to people who have never written a line of code.
Machine Learning is much closer than it seems. Here are some of the most common examples:
It is also behind less visible but equally important uses: early disease detection, financial, weather, or sports forecasting, and increasingly, software modernization that needs some kind of experience-based automation.
There is a lot of theory written about this branch of computing: since the 1950s, the foundation that makes all this possible has been taking shape. However, practical applications were very limited until recent decades, when increased processing power and, above all, lower barriers to access, made a real leap possible.
A good example is internet search engines. In the early 1990s, they were little more than hand-built directories. As the web grew, that stopped being viable, and crawlers began to appear: bots that scanned and indexed content. Google was the first to successfully rank that content by relevance with PageRank, laying the foundations of what we now take for granted when searching online.
Today, search engines use Artificial Intelligence to identify what a page is about by analyzing its actual content, something that used to be done through tags filled in by the website creator themselves and which turned out to be easy to manipulate. They also learn from user behavior: if someone opens the first result and immediately goes back, the system takes note to improve next time.
There are different algorithmic techniques that allow a machine to learn, but most methods start from Bayes’ theorem: the ability to calculate the conditional probability of a given event occurring. In practice, almost everything comes down to trial and error.
The machine analyzes the available data and looks for correlations that, for a human analyst, might not make any sense. It then tests that hypothesis against another data set to check whether it performs better than chance. By repeating this process over and over, the algorithm refines the correlations that work best. In more advanced cases, it is not even necessary to build the algorithm from fixed conclusions: the system feeds back into itself and adjusts over time.
It is a branch of Artificial Intelligence, also called “apprentissage automatique” in Spanish, in which a machine learns from data and examples instead of following fixed rules written by a person.
Netflix recommendations, Gmail text suggestions, mobile virtual assistants, captcha systems, and facial recognition are examples that almost everyone uses every day without thinking about the technology behind them.
The theory goes back to the 1950s, but practical applications were very limited until processing power increased and access to technology became cheaper, well into the current century.
By analyzing data and looking for correlations through trial and error. It compares those correlations against new data to see whether they outperform chance, and repeats the process until it fine-tunes an algorithm that makes reliable predictions.
Not exactly. Artificial Intelligence is the broader field, and Machine Learning is one of its branches: the one focused on systems learning from data instead of following explicitly programmed instructions.
Yes, especially when software needs some kind of automation based on accumulated experience: pattern detection, predictions, or recommendations that used to depend entirely on fixed rules or on a person’s intuition.
