A more effective vision of machine learning systems entails tools that are able to improve task after task and to reuse the patterns and knowledge that are acquired previously for future tasks. This incremental, long-life ...

[ES] La extracción de datos bibliográficos como publicaciones científicas provenientes de distintos orígenes de datos, y el posterior análisis de los resultados obtenidos, es un método muy utilizado por investigadores ...

While some computational models of intelligence test problems were proposed throughout the second half of the XXth century, in the first years of the XXIst century we have seen an increasing number of computer systems ...

[ES] La movilidad urbana eficiente y limpia es un factor clave de la calidad de vida y la sostenibilidad de las ciudades. El objetivo de este trabajo se centra en el estudio, análisis y utilización de datos abiertos acerca ...

[EN] We address the novel question of determining which kind of machine learning model is behind the predictions when we interact with a black-box model. This may allow us to identify families of techniques whose models ...

[EN] AI systems are usually evaluated on a range of problem instances and compared to other AI systems that use different strategies. These instances are rarely independent. Machine learning, and supervised learning in ...

In this paper, we push forward the idea of machine learning systems for which the operators can be modi ed and netuned for each problem. This allows us to propose a learning paradigm where users can write (or adapt) ...

[Otros] The current analysis in the AI safety literature usually combines a risk or safety issue (e.g., interruptibility) with a particular paradigm for an AI agent (e.g., reinforcement learning). However, there is currently ...