IBERAMIA Channel  is the free and open access channel through which the Ibero-American Society of Artificial Intelligence (IBERAMIA) transmits conferences, presentations and system demonstrations, related to all Artificial Intelligence topics, and carried out by relevant international researchers in the area.

All researchers, professionals and students are welcome.

All presentations are broadcast live and remain recorded on our IBERAMIA youtube channel

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Next Presentations

Agentes Inteligentes Afectivos

Short Bio: General Director at valgrAI – Valencian Graduate School and Research Network of Artificial Intelligence. PhD in Computer Science. Full Professor at the Universitat Politècnica de València (UPV). Founder and Head of ‘Grupo de Investigación en Tecnología Informática e Inteligencia Artificial and the Valencian Research Institute for Artificial Intelligence. Pioneer of Artificial Intelligence, Multi-Agent Systems, and Agreement Technologies in Spain and Europe, co-founder of the Agreement Technologies area. His activity in new technologies focused mainly on artificial Intelligence, Manufacturing Intelligent Systems, Multi-agent Systems, Real-Time Intelligent Agents, Agreement Technologies, and Affective Computing. Among its main research results, it is worth mentioning: Multi-agent Methodology for Holonic Manufacturing Systems ANEMONA, Real-Time Intelligent Agent Architecture ARTIS, Architecture for Virtual Organizations THOMAS, Normative Multi-agent Systems MaNEA, Organizational-oriented Methodologies for Open Multi-agent Systems ROMAS, Automated Argumentation Methods, Human-Centered Systems and Agent-based Simulation Models of Human Organizations Based on Realistic Agents REALISMO. Awarded the 2005 Prize of Research of the Spanish Association for Artificial Intelligence, 2017 fellow of the European Association for Artificial Intelligence, and 2018 Informaticéis Spanish National Prize José García Santesmases. Emeritus member of the board of the European Association for Multi-Agent Systems (EURAMAS); EURAMAS Treasurer; EURAMAS Founding Partner; member of the management board of the Spanish Association on Artificial Intelligence, Vice-Rector for Development of ICT Technologies at the UPV; director of Departament de Sistemes Informàtics i Computació, Deputy Director of the Facultat d’Informàtica at UPV, Deputy Director of the Escola Universitaria d’Enginyeria Informàtica at UPV; leader of UPV[X] project and UPValenciaX Project, the MOOC UPV Project in edX.

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La regulación de la Inteligencia Artificial: dilemas éticos y jurídicos

Short Bio: Doctora Sobresaliente Cum Laude por la Universidad Complutense de Madrid,especializada en Derecho Digital. Personal Docente e Investigador (PDI) de la UCM. Miembro del Consejo del Instituto de Tecnología del Conocimiento (ITC) de la misma Universidad. Editora de INTELÉTICA. Revista de Inteligencia Artificial, Ética y Sociedad. Socia de OdiseIA (Observatorio de Impacto Social y Ético de la Inteligencia
Artificial). Miembro del Proyecto “Derechos y garantías frente a las decisiones automatizadas en entornos de inteligencia artificial, IoT, big data y robótica”, financiado por el Ministerio de Ciencia e Innovación de España. Autora de alrededor de 70 contribuciones académicas en revistas arbitradas y en editoriales de prestigio (Q1 y Q2) y Directora y autora de diversos libros publicados en editoriales del primer cuartil (Q1). Ha dictado más de 60 conferencias en distintos países. Ha sido Colaboradora Honorífica del Departamento de Derecho Constitucional de la UCM, investigadora invitada en el Centro de Estudios Políticos y Constitucionales (CEPC) del Ministerio de la Presidencia de España y de la Universidad CEU San Pablo de Madrid en su Departamento de Derecho Público. También fue investigadora en el Centro de Investigación e Innovación en TIC Infotec. Asimismo, ha dirigido distintos proyectos de consultoría, entre ellos, algunos financiados con fondos del Banco Mundial y otras importantes instituciones

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Stay tuned for the next presentations!

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Past Presentations 

Deploying Real Systems to Counter Misinformation in Brazil

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Abstract:  The political debate and electoral dispute in the online space during the 2018 Brazilian elections marked the beginning of a major information war in Brazil. This war has become part of our daily lives and one of the most challenging problems in our society. In order to mitigate the problem, we created the project “Elections without Fake” (www.eleicoes-sem-fake.dcc.ufmg.br), and we deployed technological solutions capable of monitoring and exposing the actions of different political campaigns in the online space. Examples of systems include a monitor for advertisements on Facebook and monitors for public groups focused on political discussion on WhatsApp and Telegram. Our systems have proven to be fundamental for fact-checking, investigative journalism, and becoming a partner of the Superior Electoral Court as part of the national front to combat misinformation. This talk summarizes a few lessons learned from the deployment of these systems and points to future directions for combating misinformation.

Short Bio: Fabrício Benevenuto is an associate professor in the Computer Science Department of the Federal University at Minas Gerais (UFMG). He obtained his PhD. in 2010, and his thesis won the CAPES Thesis Award, the most important Brazilian award of its kind. Fabrício is a former member of the Brazilian Academy of Science (2013-2017). In 2017, he received a Humboldt fellowship through which he was a visiting faculty at Max Planck Institute, Germany. Fabrício has been receiving a CNPq scholarship for Productivity in Research since 2013. Fabrício is the author of widely cited and awarded scientific studies, including the prestigious test-of-time award from the International Conference on Web and Social Media (ICWSM) and the best nominee at WWW, both in 2020. Currently, Fabrício leads a series of projects dedicated to deploying real systems to counter misinformation in digital platforms. 

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A Brazilian AI? Yes, we have. But what crucial notes do we still need to complete this symphony?

Abstract: Although artificial intelligence first emerged around the 1950s, it was only in recent years that its potential to become an integral part of everyday human life has gained worldwide attention. However, like previous technological advancements, countries in the Global South often find themselves merely observing as wealthier nations drive the widespread adoption of AI technologies. This situation leads to the underrepresentation and undervaluation of our unique problems, culture, and languages. Despite these ongoing issues, Brazil boasts a vibrant AI community that has been actively involved in AI for decades, with its main AI academic conference happening for over 30 years. Yet, AI developed in Brazil is frequently overlooked, with many of our innovations remaining in the shadow of globally recognized AI technologies. In this talk, we will explore AI research and technology developed in Brazil, emphasizing the benefits of embracing our diverse and unique cultural and environmental landscape—and, yes, even our complex challenges. We will also explore how the Ibero-American nations can collaborate to share resources and technological advancements, particularly considering their historical, cultural, legal, and political systems, socioeconomic challenges, and linguistic roots. Finally, we will discuss the ongoing challenges in developing AI that truly reflects Brazil’s strengths and needs.

Short Bio: Aline Paes is an Associate Professor at the Institute of Computing at the Federal Fluminense University (UFF). She earned both her master’s and doctorate in Systems and Computing Engineering, specializing in Artificial Intelligence, from COPPE-Systems at UFRJ. She also spent a year as a visiting doctoral student at Imperial College London, UK. Currently, she has a Young Scientist of Rio de Janeiro State fellowship from FAPERJ and a productivity scholarship from CNPq. Aline Paes’s research in Artificial Intelligence spans several areas: machine learning integrated with neural, statistical, and logical techniques; natural language representation learning; model adaptation; transfer learning; explainable AI; and AI for positive social impact. She serves on the editorial boards of the Machine Learning Journal, the Ibero-American Journal of Artificial Intelligence, and the Journal of the Brazilian Computer Society. She was recently awarded the inaugural FAPERJ research project for Young Women Scientists. Since 2020, she has been a Brazilian Women in NLP member (Brasileiras em PLN). In 2023, she was a visiting professor in the NLP Group at the University of Sheffield, supported by a scholarship from CAPES.

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Artificial Intelligence – The importance of contextual knowledge

Luís M.P. Correia – Department of Informatics of Faculdade de Ciências of Universidade de Lisboa

Abstract: The debate on whether Artificial Intelligence (AI) is capable of producing intelligent machines is at least as old as AI itself. It is evident that AI has obtained striking results in specific areas (games, data mining, etc.). However, each of these systems is limited to solve the specific problem for which it was created. A central aspect in AI limitations is its limited coupling between symbolic knowledge and reasoning on the one hand, and data based knowledge and uncertainty on the other. Currently this is perhaps the focus of most expected significant advances in AI. Notwithstanding, AI has always offered a variety of cases where these two types of knowledge coexist, from search in state space problems to recent machine learning techniques, such as reinforcement learning and natural language models. We will approach some of these examples showing tha this coupling exists even if in a primary stage when compared to natural intelligence.

Mini-bio:
Luís M.P. Correia is professor at the Department of Informatics of Faculdade de Ciências of Universidade de Lisboa, Portugal. Currently he is a researcher at LASIGE, ULisboa. His research interests are artificial life, self-organisation, multi-agent systems, autonomous robots, and data mining. He lectures in the three cycles of Informatics at FCUL, and also in the Cognitive Science and in the Complexity Sciences post-graduation programmes of U. Lisboa.

Music Creation with Deep Learning Techniques: Achievements and challenges

Jean–Pierre Briot – LIP6, Sorbonne Université, Paris, France

Abstract: A growing application area for the current wave of deep learning (the return of artificial neural networks on steroids) is the generation of creative content, notably the case of music (and also images and text). The motivation is in using machine learning techniques to automatically learn musical styles from arbitrary musical corpora and then to generate musical samples from the estimated distribution, with some degree of control over the generation. In this talk, we will survey some recent achievements in deep-learning-based music generation, using recent and dedicated generative architectures such as VAE, GAN and Transformer, analyze principles, successes as well as challenges, including the limits of automated generation versus providing assistance to human musicians.

Mini-Bio:
Jean-Pierre Briot is a senior researcher (research director) in computer science at LIP6, joint computer science research lab of CNRS (Centre National de la Recherche Scientifique) and Sorbonne Université in Paris, France. He is also permanent visiting professor at PUC-Rio in Rio de Janeiro, Brazil. His general research interests are about the design of intelligent adaptive and cooperative software, at the crossing of artificial intelligence, distributed systems and software engineering, with various application fields such as internet of things, decision support systems and computer music. His current interest is focused on the use of AI techniques (notably deep learning-based) within music creation processes. He is the main author of a recent reference book about deep learning techniques for music generation (Springer, 2020). https://link.springer.com/book/10.1007/978-3-319-70163-9

Jean-Pierre Briot holds a masters in mathematics (1980), a doctorship (PhD) in computer science (1984) and an “habilitation à diriger des recherches” in computer science (1989), all from Université Pierre et Marie Curie (aka Paris VI, since 2018 renamed/merged as Sorbonne Université). He also holds degrees in music, music acoustics and Japanese language. He has been visiting Professor or visiting researcher in various institutions such as: Federal University of the State of Rio de Janeiro (UNIRIO), Kyoto University (Kyodai), Pontifical University Catholic of Rio de Janeiro (PUC-Rio), Tokyo Institute of Technology (TIT), University of Illinois at Urbana-Champaign (UIUC), University of Southern California (USC) and University of Tokyo (Todai). He has advised or co-advised about 30 PhD students and about 20 master students. He has edited 12 books or journal special issues. In 2010, he has created the CNRS permanent representation office in Rio de Janeiro, for scientific cooperation with Southern America, that he has directed for 5 years.

For more details (including access to publications), please see http://webia.lip6.fr/~briot/cv/

Explainable Artificial Intelligence

Jose Molina López – Universidad Carlos III de Madrid

Short bio:

Jose Manuel Molina Lopez received a degree in Telecommunication Engineering from the Universidad Politecnica de Madrid in 1993 and a Ph.D. degree from the same university in 1997. He  joined the Universidad Carlos III de Madrid in 1993 where, actually, he is Full Professor at Computer Science Department. Currently he leads the Applied Artificial Intelligence Group (GIAA, http://www.giaa.inf.uc3m.es) involved in several research projects related with ambient intelligence, surveillance systems and context based computing. His current research focuses in the application of soft computing techniques (Multiagents Systems, Evolutionary Computation, Fuzzy Systems) to Data Fusion, Data Mining, Surveillance Systems (radar, Video, etc..), Ambient Intelligence and Air/Maritime Traffic Management.

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 Ethics in AI: A Challenging Task

Ricardo Baeza-Yates, Institute for Experiential AI @ Northeastern University

Abstract:
In the first part we cover five current specific challenges through examples: (1) discrimination (e.g., facial recognition, justice, sharing economy, language models); (2) phrenology (e.g., biometric based predictions); (3) unfair digital commerce (e.g., exposure and popularity bias); (4) stupid models (e.g., Signal, minimal adversarial AI) and (5) indiscriminated use of computing resources (e.g., large language models). These examples do have a personal bias but set the context for the second part where we address four generic challenges: (1) too many principles (e.g., principles vs. techniques), (2) cultural differences (e.g., Christian vs. Muslim); (3) regulation (e.g., privacy, antitrust) and (4) our cognitive biases. We finish discussing what we can do to address these challenges in the near future.

Short bio:
Ricardo Baeza-Yates is Director of Research at the Institute for Experiential AI of Northeastern University. He is also part-time professor at Universitat Pompeu Fabra in Barcelona and Universidad de Chile in Santiago. Before, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from 2006 to 2016. He is co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 1999 and 2011 (2nd ed), that won the ASIST 2012 Book of the Year award. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected for the ACM Council. Since 2010 is a founding member of the Chilean Academy of Engineering. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989, and his areas of expertise are web search and data mining, information retrieval, bias on AI, data science and algorithms in general.

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OcéanIA: AI and machine learning for understanding the ocean and climate change.

Sao Paulo: 14:00,   New York 12:00,    Los Angeles: 9:00,    Londres: 17:00
Madrid: 18:00,     Mexico: 11:00,   Buenos Aires:  14:00,    Santiago; 14;00,
Bogotá: 12:00,    Lima: 12;00   Tokio: 02:00,    Moscu: 20:00

Short bio invited speaker:

Dr. Luis Martí is currently the scientific director of Inria in Chile, His research focuses in artificial intelligence, and, in particular, machine learning, neural networks, evolutionary computation, optimization, hybrid systems. His work has encompassed energy sector applications, in particular in renewable energies and more recently to green computing.

Nayat Sanchez-Pi pursued Master (2007) and Ph.D. (2011) degrees in Computer Science from the Universidad Carlos III de Madrid (UC3M), receiving the Extraordinary Ph.D. Thesis Award. Before that, she got a degree in Computer Science from the Universidad de La Habana. Since 2015, she is a Professor of Artificial Intelligence and Human-Computer Interaction at the Universidade do Estado do Rio de Janeiro (UERJ), co-leading the Research Group on Intelligence and Optimization (RIO Group). In 2018, she joined Inria working at TAU project team in Inria Saclay research centre. Nayat Sanchez-Pi has also been a Senior Researcher at Instituto de Lógica Filosofia e Teoria da Ciência (Rio de Janeiro) acting as Chief Science Officer at ADDLabs/UFF (2012-2015), an assistant professor at UC3M (2006-2012), visiting researcher at the Universidade de Lisboa (2009) and the University College in Dublin (2010) and a postdoctoral researcher at the Universidade Federal Fluminense (2011-2012). Her research interests range from Artificial Intelligence, Machine Learning, Internet of Things, Ambient Intelligence and Human-Computer Interaction developing real-world applications in several R&D projects with top-level industry and academic partners. She has been distinguished with a Prociência Project-Fellowship Award and a Young Scientist of the State of Rio de Janeiro Chair.

Abstract: There is strong scientific evidence about the effects of climate change on the global ocean. These changes will have a drastic impact on almost all forms of life in the ocean with further consequences on food security, ecosystem services in coastal and inland communities. Despite these impacts, scientific data and infrastructures are still lacking to better understand and quantify the consequence of these perturbations on the marine ecosystem.

The OcéanIA project has the goal of developing new AI and mathematical modelling tools to contribute to the understanding of the structure, functioning, and underlying mechanisms and dynamics of the global ocean symbiome. These actions are essential to gain a better understanding of the oceans and their role in regulating and sustaining the biosphere. This is also an opportunity to dive into the connections of AI and biodiversity, which can be a major achievement for the sustainability of human societies on the blue part of the planet.

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Neuro-Symnbolic AI

Luis C. Lamb

Short bio invited speaker:

Luis C. Lamb is a Full Professor and Secretary of Innovation, Science and Technology of the State of Rio Grande do Sul, Brazil. He was formerly Vice President for Research (2016-2018) and Dean of the Institute of Informatics (2011-2016) at the Federal University of Rio Grande do Sul (UFRGS), Brazil. He holds both the Ph.D. in Computer Science from Imperial College London (2000) and the Diploma of the Imperial College, MSc by research (1995) and BSc in Computer Science (1992) from UFRGS, Brazil. His research interests include neural-symbolic computing, the integration of learning and reasoning, and ethics in AI.

He co-authored two research monographs: Neural-Symbolic Cognitive Reasoning, with Garcez and Gabbay (Springer, 2009) and Compiled Labelled Deductive Systems, with Broda, Gabbay, and Russo (IoP, 2004). His research has led to publications at flagship journals, AI and neural computation conferences. He was co-organizer of two Dagstuhl Seminars on Neuro-symbolic AI: the Dagstuhl Seminar 14381: Neural-Symbolic Learning and Reasoning (2014) and Dagstuhl Seminar 17192: Human-Like Neural-Symbolic Computing (2017) and several workshops on neural-symbolic learning and reasoning at AAAI and IJCAI.