31 posts found
How to prepare your data to work with artificial intelligence tools from a legal point of view
The idea of conceiving artificial intelligence (AI) as a service for immediate consumption or utility, under the premise that it is enough to "buy an application and start using it", is gaining more and more ground. However, getting on board with AI isn't like buying conventional software and gettin…
Data governance in smart grids: from the meter to the energy strategy
Energy is the engine of our society, a vital resource that powers our lives and the global economy. However, the traditional energy model faces monumental challenges: growing demand, climate urgency, and the prevailing need for a transition to cleaner and more sustainable sources. In this panorama o…
Federated machine learning: generating value from shared data while maintaining privacy
Data is a fundamental resource for improving our quality of life because it enables better decision-making processes to create personalised products and services, both in the public and private sectors. In contexts such as health, mobility, energy or education, the use of data facilitates more effic…
Industrial data governance: the basis for more efficient and sustainable production
Today's industry is facing one of the biggest challenges in its recent history. Market demands, pressure to meet climate targets, consumer demand for transparency and technological acceleration are converging in a profound transformation of the production model. This transformation is not only aimed…
Computer use: the AI that learns how to operate your computer
The evolution of generative AI has been dizzying: from the first great language models that impressed us with their ability to reproduce human reading and writing, through the advanced RAG (Retrieval-Augmented Generation) techniques that quantitatively improved the quality of the responses provided…
Explainable artificial intelligence (XAI): how open data can help understand algorithms
The increasing adoption of artificial intelligence (AI) systems in critical areas such as public administration, financial services or healthcare has brought the need for algorithmic transparency to the forefront. The complexity of AI models used to make decisions such as granting credit or making a…
Data Mesh and Data Fabric: New Perspectives in Enterprise Data Architectures
Over the last decade, the amount of data that organisations generate and need to manage has grown exponentially. With the rise of the cloud, Internet of Things (IoT), edge computing and artificial intelligence (AI), enterprises face the challenge of integrating and governing data from multiple sourc…
The role of open data in the evolution of SLM and LLM: efficiency vs. power
Language models are at the epicentre of the technological paradigm shift that has been taking place in generative artificial intelligence (AI) over the last two years. From the tools with which we interact in natural language to generate text, images or videos and which we use to create creativ…
Common mistakes in the development of a data strategy
In an increasingly data-driven world, all organisations, both private companies and public bodies, are looking to leverage their information to make better decisions, improve the efficiency of their processes and meet their strategic objectives. However, creating an effective data strategy is a chal…
SLM, LLM, RAG and Fine-tuning: Pillars of Modern Generative AI
In the fast-paced world of Generative Artificial Intelligence (AI), there are several concepts that have become fundamental to understanding and harnessing the potential of this technology. Today we focus on four: Small Language Models(SLM), Large Language Models(LLM), Retrieval Augmented Generation…
