What is machine learning
Discover the main benefits of Machine Learning
Self-driving cars, assistants that translate instantly from one language to another or personalized purchase suggestions. Complex tasks that were once a distant dream are now possible thanks to Machine Learning, a discipline that enables computers to learn by themselves and perform tasks autonomously without the need for explicit programming.
In his book On Intelligence, published in 2004, Jeff Hawkins defined intelligence as the ability to predict the future, for example, the weight of a glass we are going to lift or the reaction of others to our actions, based on patterns stored in the memory (the memory-prediction framework). This same principle is behind Machine Learning (ML), also known as automatic learning.
What is machine learning and what is it for?
Machine Learning is a discipline within the field of Artificial Intelligence which, by means of algorithms, provides computers with the ability to identify patterns from mass data in order and to make predictions (predictive analytics). This learning method allows computers to perform specific tasks autonomously, that is, without the need to be programmed.
The term was first used in 1959. It has, however, gained relevance in recent years due to the increase in computing capacities and the huge increase in data. Automatic learning techniques are, in fact, a fundamental part of Big Data.
This discipline, increasingly advanced thanks to the development of Artificial Intelligence, has raised a range of ethical challenges to ensure it is applied for the benefit of society. In this context, the concept of responsible AI has emerged – an approach to governance adopted by technology companies and institutions focused on transparency and accountability to consumers.
At the same time, Europe already has a legal framework aimed at regulating the misuse of this technology, classifying AI into three risk categories: applications and systems that pose an unacceptable risk, high-risk applications such as CV screening tools that classify job applicants, and applications that are neither explicitly prohibited nor classified as high risk, which are subject to more flexible regulation.
Different machine learning algorithms
Machine Learning algorithms are divided into three categories, the first two being the most common:
- Supervised learning: these algorithms have prior learning incorporated in them and are based on a tag system associated with data that allow them to make decisions or make predictions. An example is a spam detector which tags an e-mail as spam or not, depending on the patterns it has learned from the history of e-mails (sender, text/image ratio, subject key words, etc.).
- Unsupervised learning: these algorithms do not have previous knowledge. They face a data chaos with the objective of finding patterns that somehow allow the organisation thereof. For example, in the field of marketing they are used to extract patterns from mass data obtained from social networks and to create highly segmented publicity campaigns.
- Reinforced learning: its objective is for an algorithm to learn from its own experience. In other words, it will be able to make the best decision in different situations according to a trial and error process in which the correct decisions are awarded. It is currently being used to enable facial recognition, make medical diagnoses or classify DNA sequences.
What is AI?
Are we aware of the challenges and main applications of AI?
Evolution of Artificial Intelligence
Find out how AI has evolved over the years.
Types of AI Algorithms
Learn about the types of algorithms used by Artificial Intelligence.
Benefits of ‘Machine Learning’ for the business world
By analysing purchasing habits, it can predict which products will be more in demand and when it is a good time to raise or lower prices.
Thanks to the mass of data that it is capable of analysing, automatic learning assists in the search for new solutions.
Unsupervised learning algorithms f ind patterns in the information on consumers that is collected by companies.
ML automates tasks to save on human capital or optimise online stores and shopping centres using browsing data and customer flows.
ML algorithms can predict which content is more effective for each target and which time of year and medium are more appropriate.
Chatbots answer customers 24 hours a day, seven days a week and collect data to increase knowledge of consumers.
Practical machine learning applications
Machine Learning is one of the pillars on which digital transformation is based. At present, it is already being used to find new solutions in different fields, of which the following are worth highlighting:
Recommendations
It allows making tailor made purchase suggestions on online platforms or can recommend songs. In its most basic form, it analyses the user's purchase and view history and compares it to what other users with similar trends or spending habits have done. Spotify, YouTube and major streaming platforms use it to recommend new content to keep the user on the page for longer, for example.
Intelligent vehicles
Intelligent vehicles are already with us, with several being tested on the roads. Thanks to automatic learning, these vehicles will be able to adjust the internal settings (temperature, music, backrest inclination etc.) according to the driver's preferences and even move the steering wheel on their own to react to the environment.
Social networks
X, for example, uses Machine Learning algorithms to greatly reduce spam published on this social network while Facebook, in turn, uses it to detect both fake news and content not allowed in live broadcasts that it automatically blocks.
Natural Language Processing (NLP)
By understanding the human language, virtual assistants like Alexa or Siri can instantly translate from one language to another, recognise the user's voice and even analyse the user's feelings. On the other hand, NLP is also used for other complex tasks such as to translate the legal jargon of contracts to simple language and help lawyers sort large volumes of information regarding a case.
Search
Search engines use automatic learning to optimize their results according to their effectiveness, measuring the latter through the user's clicks.
Medicine
Researchers at the Massachusetts Institute of Technology (MIT) already use Machine Learning to for the early detection of breast cancer, which is vitally important because the early detection of cancer increases the chances of curing it. It is also highly effective in detecting pneumonia and diseases of the retina that can lead to blindness.
Cybersecurity
New antivirus and malware detection engines already use automatic learning to boost scanning, accelerate detection and improve the ability to recognise anomalies.
Agentic AI with Machine Learning
New AI agent models can plan, make decisions and execute complex tasks, using Machine Learning as a foundation to generate predictions and operate efficiently. Examples include AI assistants for coding or task execution, such as Claude.
Smart grids
Thanks to Machine Learning and AI-driven data processing, the resilience of smart grids can be strengthened against extreme events associated with climate change, such as storms, heatwaves or flooding. In addition, it can analyse large volumes of historical data and consumption patterns, making it possible to supply electricity to more people without the need to build new infrastructure. One example is LiDAR (Light Detection and Ranging), a data system capable of creating 3D models of all distribution and transmission lines and their surrounding environment, enabling optimised maintenance and inspection.
Machine Learning in the energy sector
Machine learning and artificial intelligence (AI) are transforming the electricity sector, making grids more efficient, secure and sustainable. Thanks to AI, companies can anticipate problems, optimise resource use and ensure that electricity is delivered in the most reliable and cost-effective way. Furthermore, AI is a useful tool in the global fight against climate change, as it helps with adaptation to its impacts, such as anticipating the risks arising from natural disasters, optimising electricity systems, improving energy efficiency and facilitating the large-scale integration of renewable energy.
The solutions and innovations offered by this technology are key to the Iberdrola Group’s objective of reducing its carbon emissions, as they enable electricity to be supplied to a greater number of people without the need to build new infrastructure. Furthermore, by analysing satellite imagery and sensor data, AI algorithms help to identify priority areas for electrification and to design microgrids tailored to local needs, playing a vital role in ensuring access to energy.