The deployment of autonomous systems has experienced remarkable growth in recent years, driven by their integration into sectors such as industry, medicine, logistics, and domestic environments. This expansion is accompanied by a series of security issues that entail significant risks due to the critical nature of autonomous systems, especially those operating in human-interaction environments. Furthermore, technological advancement and the high operational and architectural complexity of autonomous systems have resulted in an increased attack surface. This article presents a specific security auditing procedure for autonomous systems, based on a layer-structured methodology, a threat taxonomy adapted to the robotic context, and a set of concrete mitigation measures. The validity of the proposed approach is demonstrated through four practical case studies applied to representative robotic platforms: the Vision 60 military quadruped from Ghost Robotics, the A1 robot from Unitree Robotics, the UR3 collaborative arm from Universal Robots, and the Pepper social robot from Aldebaran Robotics.

This study presents the Jusbrasil Study on the Use of General-Purpose AIs in Law, proposing an experimental evaluation protocol combining legal theory, such as material correctness, systematic coherence, and argumentative integrity, with empirical assessment by 48 legal professionals. Four systems (JusIA, ChatGPT Free, ChatGPT Pro, and Gemini) were tested in tasks simulating lawyers' daily work. JusIA, a domain-specialized model, consistently outperformed the general-purpose systems, showing that both domain specialization and a theoretically grounded evaluation are essential for reliable legal AI outputs.

This short essay is a reworking of the answers offered by the author at the Debate Session of the AIHUB (CSIC) and EduCaixa Summer School, organized by Marta Garcia-Matos and Lissette Lemus, and coordinated by Albert Sabater (OEIAC, UG), with the participation of Vanina Martinez-Posse (IIIA-CSIC), Eulalia Soler (Eurecat) and Pompeu Casanovas (IIIA-CSIC) on July 4th 2025. Albert Sabater posed three questions: (1) How can regulatory frameworks priori-tise the protection of fundamental rights (privacy, non-discrimination, autonomy, etc.) in the development of AI, without falling into the false dichotomy between regulation and innova-tion? (2) Given the risks of AI (bias, mass surveillance, manipulation), what examples of regu-lations or policies have demonstrated that it is possible to foster responsible innovation, putting the public interest before profitability, without giving in to competitive pressure from actors such as China or the US? (3) In a scenario where the US prioritizes flexibility, what mecha-nisms could ensure that international cooperation in AI does not become a race to the bottom in rights, but rather a global standard of accountability? The article attempts to answer these three questions and concludes with some reflections on the relevance of the answers for education and research.

Training large-scale deep learning models has become a key challenge for the scientific community and industry. While the massive use of GPUs can significantly speed up training times, this approach has a negative impact on efficiency. In this article, we present a detailed analysis of the times reported by MLPerf Training v4.1 on four workloads: BERT, Llama2 LoRA, RetinaNet, and Stable Diffusion, showing that there are configurations that optimise the relationship between performance, GPU usage, and efficiency. The results point to a break-even point that allows training times to be reduced while maximising efficiency.

Facial recognition powered by Artificial Intelligence has achieved high accuracy in specific scenarios and applications. Nevertheless, it faces significant challenges regarding privacy and identity management, particularly when unknown individuals appear in the operational context. This paper presents the design, implementation, and evaluation of a facial recognition system within a federated learning framework tailored to open-set scenarios. The proposed approach integrates the OpenMax algorithm into federated learning, leveraging the exchange of mean activation vectors and local distance measures to reliably distinguish between known and unknown subjects. Experimental results validate the effectiveness of the proposed solution, demonstrating its potential for enhancing privacy-aware and robust facial recognition in distributed environments. -- El reconocimiento facial impulsado por Inteligencia Artificial ha demostrado una alta precisión en algunos escenarios y aplicaciones. Sin embargo, presenta desafíos relacionados con la privacidad y la identificación de personas, especialmente considerando que pueden aparecer sujetos desconocidos para el sistema que lo implementa. En este trabajo, se propone el diseño, implementación y evaluación de un sistema de reconocimiento facial en un escenario de aprendizaje federado, orientado a conjuntos abiertos. Concretamente, se diseña una solución basada en el algoritmo OpenMax para escenarios de aprendizaje federado. La propuesta emplea el intercambio de los vectores de activación promedio y distancias locales para identificar de manera eficaz tanto personas conocidas como desconocidas. Los experimentos realizados demuestran la implementación efectiva de la solución propuesta.

The growing availability of music on streaming platforms has led to information overload for users. To address this issue and enhance the user experience, increasingly sophisticated recommendation systems have been proposed. This work investigates the use of Large Language Models (LLMs) from the Gemini and LLaMA families, combined with intelligent agents, in a multi-agent personalized music recommendation system. The results are compared with a traditional content-based recommendation model, considering user satisfaction, novelty, and computational efficiency. LLMs achieved satisfaction rates of up to \textit{89{,}32\%}, indicating their promising potential in music recommendation systems.

The production of digital documents has been growing rapidly in academic, business, and health environments, presenting new challenges in the efficient extraction and analysis of unstructured information. This work investigates the use of RAG (Retrieval-Augmented Generation) architectures combined with Large-Scale Language Models (LLMs) to automate the analysis of documents in PDF format. The proposal integrates vector search techniques by embeddings, semantic data extraction and generation of contextualized natural language responses. To validate the approach, we conducted experiments with drug package inserts extracted from official public sources. The semantic queries applied were evaluated by metrics such as accuracy, completeness, response speed and consistency. The results indicate that the combination of RAG with LLMs offers significant gains in intelligent information retrieval and interpretation of unstructured technical texts.

Cybersecurity in robotics stands out as a key aspect within Regulation (EU) 2024/1689, also known as the Artificial Intelligence Act, which establishes specific guidelines for intelligent and automated systems. A fundamental distinction in this regulatory framework is the difference between robots with Artificial Intelligence (AI) and those that operate through automation systems without AI, since the former are subject to stricter security requirements due to their learning and autonomy capabilities. This work analyzes cybersecurity tools applicable to advanced robotic systems, with special emphasis on the protection of knowledge bases in cognitive architectures. Furthermore, a list of basic tools is proposed to guarantee the security, integrity, and resilience of these systems, and a practical case is presented, focused on the analysis of robot knowledge management, where ten evaluation criteria are defined to ensure compliance with the regulation and reduce risks in human-robot interaction (HRI) environments.

The rise of artificial intelligence (AI) and the aggravating biodiversity crisis have resulted in a research area where AI-based computational methods are being developed to act as allies in conservation, and the sustainable use and management of natural resources. While important general guidelines have been established globally regarding the opportunities and challenges that this interdisciplinary research offers, it is essential to generate local reflections from the specific contexts and realities of each region. Hence, this document aims to analyze the scope of this research area from a perspective focused on Colombia and the Neotropics. In this paper, we summarize the main experiences and debates that took place at the Humboldt Institute between 2023 and 2024 in Colombia. To illustrate the variety of promising opportunities, we present current uses such as automatic species identification from images and recordings, species modeling, and in silico bioprospecting, among others. From the experiences described above, we highlight limitations, challenges, and opportunities for in order to successfully implementate AI in conservation efforts and sustainable management of biological resources in the Neotropics. The result aims to be a guide for researchers, decision makers, and biodiversity managers, facilitating the understanding of how artificial intelligence can be effectively integrated into conservation and sustainable use strategies. Furthermore, it also seeks to open a space for dialogue on the development of policies that promote the responsible and ethical adoption of AI in local contexts, ensuring that its benefits are harnessed without compromising biodiversity or the cultural and ecosystemic values inherent in Colombia and the Neotropics.

SAPPO is a high-precision, low-cost and highly scalable indoor localization system. The system is designed using modified HC-SR04 ultrasound transducers as a base to be used as distance meters between beacons and mobile robots. Additionally, it has a very unusual arrangement of its elements, such that the beacons and the array of transmitters of the mobile robot are located in very close planes, in a horizontal emission arrangement, parallel to the ground, achieving a range per transducer of almost 12 meters. SAPPO represents a significant leap forward in ultrasound localization systems, in terms of reducing the density of beacons while maintaining average precision in the millimeter range.