In his book “On Intelligence,” published in 2004, Jeff Hawkins defined intelligence as the ability to predict the future, such as the weight of a glass we are about to lift or the reaction of others to our actions, based on patterns stored in memory (the memory-prediction framework). This same principle underlies Machine Learning (ML).
What is ML?
Machine Learning is a discipline within the field of Artificial Intelligence that, through algorithms, equips computers with the ability to identify patterns in massive data and make predictions (predictive analysis). This learning allows computers to perform specific tasks autonomously, without the need for programming.
The term “machine learning” was first used in the 1950s. Arthur Samuel. He was a researcher at IBM, that developed chess and checkers programs that used learning algorithms to improve performance. In 1959, he wrote a famous article titled “Some Studies in Machine Learning Using the Game of Checkers,” in which he first introduced the term “machine learning.” He described the system he was using to teach computers to play checkers autonomously.
The term has gained relevance in recent years due to increased computing power and the data boom. Machine learning techniques are, in fact, a fundamental part of Big Data.
What Are the Types of ML Algorithms?
We can highlight three main categories, depending on the expected output and input type.
- Supervised ML: These algorithms have prior learning based on a system of labels associated with data, allowing them to make decisions or predictions. An example is a spam detector that labels an email as spam or not based on patterns learned from the email history (sender, text/image ratio, keywords in the subject, etc.).
- Unsupervised ML: These algorithms have no prior knowledge. They face the data chaos with the goal of finding patterns that allow them to organize it in some way. For example, in the field of marketing, they are used to extract patterns from massive social media data and create highly segmented advertising campaigns.
- Reinforcement ML: Their goal is for an algorithm to learn from its own experience. In other words, it should be able to make the best decision in different situations through a trial-and-error process in which correct decisions are rewarded. It is currently used for facial recognition, medical diagnoses, or classifying DNA sequences.
What Are the Practical Applications of ML?
ML is one of the cornerstones of digital transformation. It is currently being used to find new solutions in various fields, including:
- Multimedia and Entertainment: Machine learning algorithms are implemented to offer consumers personalized content recommendations and even streamline production. For example, Spotify uses intelligent processing engines to predict users’ musical tastes and generate playlists automatically so you can always find the music you’re looking for.
- Social Networks: Twitter, for instance, employs MLg algorithms to significantly reduce spam on the platform. Facebook also uses it to detect fake news and prohibited content in live streams, automatically blocking them.
- Medicine and Biological Sciences: ML researchers develop solutions that detect cancerous tumors or eye diseases, increasing the chances of a cure.
- Cybersecurity: New antivirus and malware detection engines already use machine learning to enhance scanning, accelerate detection, and improve anomaly recognition.
- Manufacturing Processes: ML can support predictive maintenance, quality control, and innovative research in the manufacturing sector. For example, NotCo is a company dedicated to the production and sale of 100% vegan products. They work with a machine learning algorithm known as Giuseppe, which seeks the best combination of ingredients to satisfy even the most demanding palates, replicating the taste, texture, and smell of animal-origin products.
Machine Learning has proven to be a powerful tool in the digital age, with a wide scope and limitless possibilities for future innovations.
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