23 posts found
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…
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…
Changes to the INSPIRE cchemes: What do they mean and how to adapt?
In February 2024, the European geospatial community took a major step forward with the first major update of the INSPIRE implementation schemes in almost a decade. This update, which generates version 5.0 of the schemas, introduces changes that affect the way spatial data are harmonised, transformed…
GeoPackage in INSPIRE: efficiency and usability for geospatial data geospatial data.
In the field of geospatial data, encoding and standardisation play a key role in ensuring interoperability between systems and improving accessibility to information.
The INSPIRE Directive (Infrastructure for Spatial Information in Europe) determines the general rules for the establishment of an Inf…
Understanding Word Embeddings: how machines learn the meaning of words
Natural language processing (NLP) is a branch of artificial intelligence that allows machines to understand and manipulate human language. At the core of many modern applications, such as virtual assistants, machine translation and chatbots, are word embeddings. But what exactly are they and why are…
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…
RAG techniques: how they work and examples of use cases
In recent months we have seen how the large language models (LLMs ) that enable Generative Artificial Intelligence (GenAI) applications have been improving in terms of accuracy and reliability. RAG (Retrieval Augmented Generation) techniques have allowed us to use the full power of n…
Complying with Europe. The Mobility High Value Datasets Regulation
Spain, as part of the European Union, is committed to the implementation of the European directives on open data and re-use of public sector information. This includes the adoption of initiatives such as the Implementing Regulation (EU) 2023/138 issued by the European Commission, whic…
Complying with Europe. The High Value Sites of Earth Observation and Environment Regulation
The European Commission Implementing Regulation (EU) 2023/138 sets clear guidelines for public bodies on the availability of high-value datasets within 16 months from 20 January 2023. These high-value high value datasets (High value datasets or HVD) are grouped into the following themes, which were…
Linguistic corpora: the knowledge engine for AI
The transfer of human knowledge to machine learning models is the basis of all current artificial intelligence. If we want AI models to be able to solve tasks, we first have to encode and transmit solved tasks to them in a formal language that they can process. We understand as a solved task informa…
