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What is machine learning and what are its current uses?

The future of technology in society

Reading time: 5 min

Your favorite music app suggests songs that match your tastes; the platform where you watch TV shows and films makes suggestions that you might like; and your favourite online clothing shop recommends clothes and accessories that match your style. How can we explain this? Are machines capable of reading your mind? They don't go that far yet. Behind these recommendations is Machine Learning, a branch of artificial intelligence that allows computers to learn autonomously and perform actions without needing to be programmed. 

What is Machine Learning?

Machine Learning is a branch of artificial intelligence that enables machines to learn autonomously without needing to be specifically programmed to do so.

It is not a new concept. The history of machine learning started with an IBM employee, Arthur Samuel, who coined the term in 1959. Samuel was a pioneer in the field of computer games and achieved one of the most famous machine learning stories to date. He managed to get an IBM 701 computer to beat a human at checkers. In the 1990s, especially with the development of the Internet and the increase in the amount of data that could be accessed, Machine Learning experienced a remarkable growth. Among other achievements, a computer called Deep Blue was able to beat Gary Kasparov himself at chess in 1996. 

Today, it is used in a wide variety of fields, such as facial recognition through software, spam detection by email providers, and voice recognition, among others.

Differences between Machine Learning and Deep Learning

Machine Learning and Deep Learning are both branches of artificial intelligence. Although both concepts are sometimes used interchangeably, Deep Learning is a subtype of Machine Learning. 

Very simply, they are learning algorithms (regression or classification) whose objective is to obtain a result dependent on the input variables (data). Conceptually it would be something similar to: if we have "a" and "b", then the result is "x", but if we have "a" and "b" plus "c", then the result is "y".

The fundamental difference between the two concepts lies in the way the respective algorithms learn. 

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Machine Learning

Human assistance is needed to tell the algorithms how to 'learn' to determine what input features must be given to obtain a particular result and, in the process of learning, it needs more labelled data (which refer to already known results).

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Deep Learning

The extraction of features from the process for learning by the algorithm is largely automated, which implies a much lower degree of human intervention. It also enables much larger datasets to be used. 

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Reinforcement Learning

Algorithms are pitted against one another so that one learns from the progress of the other (via trial and error), obtaining more complex and optimal results, leading to generative methods aimed at the automatic generation of objects and experiences from samples.

Methods and types of Machine Learning

There are three main methods of Machine Learning, based on the algorithms they employ (i.e., sets of operations that are systematically executed and perform calculations to find the solution to a problem): 

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  • Supervised learning: algorithms deduce information from data that have been assigned values. This data is used to train a model, while a second set of test data is used to determine how effective the created model is. An example would be the calculation of the price of a house from its characteristics. It has similarities with a new method called "reinforcement learning". In this type, the system learns from the mistakes it makes until it finds the optimal way to perform a task. 
  • Unsupervised learning: Training data is used without labelling. Algorithms detect clusters of data or hidden patterns without requiring human intervention. They are very useful for discovering similarities and differences in information, so they can be applied to processes such as customer segmentation or image recognition.
  • Semi-supervised learning: training data with and without labels is used; typically the labelled data set is much smaller. They are used, for example, to analyze conversations in a call center to easily deduce the characteristics of the callers and their mood, among other aspects.

Uses and applications of Machine Learning

Nowadays, there are more and more fields in which Machine Learning (with all its sub-disciplines) can be used. Let's take a look at some of the most well-known ones:

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Finances

Automatically detect fraudulent practices, such as money laundering. Optimize administrative procedures or interact with customers via chatbots, as well as predict how stocks will perform in the stock market.

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Health

More and more diseases, such as certain types of cancer, can be detected early. In addition, it is possible to predict the possibility of a patient's readmission or to prescribe the most appropriate treatment.

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Transport and mobility

Predict which routes are fastest, where traffic jams are most frequent, or which route is the most fuel-efficient to offer the most efficient option. In addition, the development of autonomous driving systems is also linked to machine learning. 

Marketing, communication, and networking

Reaching more customers, segmenting existing ones, offering the most suitable products for each user, or suggesting who are the most compatible people in dating applications. Other very practical applications are voice recognition or automatic translation. 

Technology

Machine Learning makes it possible, for example, to recognize the face of the owner of a phone so that only that person can unlock it. It also makes it possible to easily detect spam, malware, and viruses. 

Repsol and Machine Learning

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At Repsol, we are firmly committed to the digital transformation. That is why we have committed to the development of ARiA (Advanced Repsol Intelligence & Analytics), a cloud-based data and analytics platform. 

This platform, created in collaboration with Accenture, includes Machine Learning models, configuration alternatives in the Microsoft Azure cloud, tools for data governance from its development laboratory, and solutions for data ingestion. 

It enables employees to make data-driven decisions with self-discovery capabilities, regardless of their analytical skills.