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 and students are welcome.

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

Next Presentations

Upcoming conferences in May – June 2024. Stay tuned for the news!

Past Presentations 

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.

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

JeanPierre 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.

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.

 Ethics in AI: A Challenging Task

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

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.

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.

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.