The study of art provides results that indicate the absence of involvement of Spanish language with AI and all the subareas, which consequently adversely affect to the education of future professionals.
The Commission is proposing the first ever legal framework on AI, which addresses the risks of AI and positions Europe to play a leading role globally.
Proposal for a Regulation on a European approach for Artificial Intelligence Leer más »
We present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model.
El estudio arroja resultados que indican la falta de involucración del Español con la IA así como de todas las subáreas, afectando negativamente a la formación de futuros profesionales.
Topological Data Analysis (TDA) is an emergent field that aims to discover topological information hidden in a dataset. TDA tools have been commonly used to create filters and topological descriptors to improve Machine Learning (ML) methods. This paper proposes an algorithm that applies TDA directly to multi-class classification problems, even imbalanced datasets, without any further ML stage
Classification based on Topological Data Analysis Leer más »
We introduce S++, a simple, robust, and deployable framework for training a neural network (NN) using private data from multiple sources, using secret-shared secure function evaluation. In short, consider a virtual third party to whom every data-holder sends their inputs, and which computes the neural network: in our case, this virtual third party is actually a set of servers which individually learn nothing, even with a malicious (but non-colluding) adversary.
Steganography is the science of hiding a secret message within an ordinary public message. Over the years, steganography has been used to encode a lower resolution image into a higher resolution image by simple methods like LSB manipulation. We aim to utilize deep neural networks for the encoding and decoding of multiple secret images inside a single cover image of the same resolution.
Multi-Image Steganography Using Deep Neural Networks Leer más »
Documentation is key – design decisions in AI development must be documented in detail, potentially taking inspiration from the field of risk management. There is a need to develop a framework for large-scale testing of AI effects, beginning with public tests of AI systems, and moving towards real-time validation and monitoring. Governance frameworks for decisions in AI development need to be clarified, including the questions of post-market surveillance of product or system performance. Certification of AI ethics expertise would be helpful to support professionalism in AI development teams. Distributed responsibility should be a goal, resulting in a clear definition of roles and responsibilities as well as clear incentive structures for taking in to account broader ethical concerns in the development of AI systems.
Addressing Ethical Dilemmas in AI: Listening to Engineers Leer más »
El principal objetivo de este documento es construir un glosario, a partir de las propuestas léxicas realizadas por los diferentes entes tecnológicos (ISO, IEEE, Wikipedia y Oxford University Press). Adicionalmente, el glosario estará estructurado según las ramas de conocimiento de esta área de trabajo, determinando exhaustiva y detalladamente las características de los términos que se incluirán en él para así facilitar una lectura amigable a la par que eficiente al usuario.
El arte de la Inteligencia Artificial desde una perspectiva léxica Leer más »
We summarize current datasets and metrics for evaluating GNN explainability. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations.
Explainability in Graph Neural Networks: A Taxonomic Survey Leer más »