Complex networks are ubiquitous to represent real systems in many contexts, such as social networks, computer networks, or biological networks, among others. Most of the real-world networks exhibit non-trivial topological features, and the interest in analyzing their properties has resulted in the emergence of random models to generate them. Probabilistic models are, in general, based on the probability of each edge to occur, and the topology of the network is the consequence of such a probability distribution. In deep generative approaches, a model is trained to learn the features of a training set of examples and generate new networks with similar properties. In this seminar we will review a (non-exhaustive) list of random models of complex networks generation, and analyze how these models can be applied to another challenging problem: the generation of realistic random SAT instances.

Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this talk I present an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. The study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.

Natural Language Understanding (NLU) is the broad research area in Natural Language Processing (NLP) that develops methods to analyze natural language and understand its meaning. It is a key component of any AI system that aims at truly interacting with humans. It is also a key component for automatic systems that do machine reading of the web and social media, which, given the current volumes of information, is the only practical way to access this content.

First I will give a brief overview of Natural Processing Processing tasks, and the evolution of machine learning approaches in recent years. Natural language is structured, very rich, ambiguous, and offers limitless ability to say new things. Because of this, the desire is to have machine learning algorithms that learn hidden-state compositional models of language, and answer questions such as: what are the units and parts of a language? what is the meaning of each part? how do we compose parts into bigger parts? what is the meaning of a composed expression? how do we use these models to solve specific needs? 

Deep learning has made great progress on these questions. Today we have giant neural models like BERT or GPT-3 that are trained at worldwide scale, and are found useful for virtually any empirical NLP task. However, it's largely unclear what these models are learning, and what is their capacity to generalize (as opposed to memorizing data). Also, the costs of learning these models is huge. 

In the second part of this talk, I will focus on compositional models of language that take the form of weighted automata, which are a restricted class of recurrent neural networks. I will describe Spectral Learning algorithms, a family of learning algorithms that reduces the problem of learning a weighted automata to some form of matrix learning. This reduction is based on theoretical connections between formal languages and distributions over the strings they generate. I will highlight several good properties of this family of techniques, and contrast them with deep learning approaches.

Finally, I will describe some research lines on unsupervised spectral learning of natural language grammars that I will pursue in the next few years. 

Classical solution concepts in game theory, such as the Nash equilibrium and the subgame-perfect equilibrium, are based on the assumption that players make their choices on a purely individual basis and that they are not able to coordinate their actions through binding agreements. This sometimes yields counter-intuitive results, such as in the Prisoner's Dilemma. In most real-world situations that are similar to the Prisoner's dilemma, people can negotiate and jointly agree to choose their actions in a way that prevents them from hurting each other. If necessary, with the help of legally binding contracts.

In this talk I will therefore introduce a new game-theoretical solution concept that does take into account the possibility for the players to make binding agreements about their actions. I will use a classical text-book game known as the Centipede Game as an example, and show how this new solution concept prescribes a more satisfactory outcome than the classical subgame-perfect equilibrium. Furthermore, I will present experimental results obtained with a negotiation algorithm based on Monte Carlo Tree Search.  

In this talk I will first give a short overview on optimization and on the related topics that have been subject of our work during the last years. In the second part of the talk I will report on an industrial project that we conducted in 2020 in cooperation with IKERLAN S. Coop. in the context of the optimization of safety-critical systems.

Computer Vision has become one of the most relevant fields of work in AI. During recent years, and with the explosion of Deep Learning and the possibility to have access to massive data sets, Computer Vision itself has also become one of the main driving forces of the AI market, with multiple applications in different areas of social impact such as autonomous mobility, health and well-being, intelligent media analysis, industry 4.0, etc. Tools such as Convolutional Neural Networks have become prominent and omnipresent in approaches tackling both general and specific problems, and the pace at which these new solutions are appearing, day after day, is changing the Computer Vision research scenario dramatically. In this seminar, Prof. Fernando Vilariño (Associate Director and Group Responsible for Research Projects at Computer Vision Centre (CVC) (http://www.cvc.uab.es/)) will provide an introduction to the main areas of impact tackled by the Computer Vision Center, by introducing a number of paradigmatic examples of Computer Vision-based projects, putting emphasis on the specific techniques used. The presentation will have a very practical approach and will allow those interested in deepening in the Computer Vision field to have a set of pointers to dig in, both from a purely scientific or a more implementation-oriented perspective.

Presentation of the High-Performance Cluster for Artificial Intelligence of the IIIA: Technical characteristics, rules of use and operation, available software and mini user guide.

[This is an internal webinar for people working at the IIIA-CSIC]

We will provide a brief overview of the CorporIS project, funded by Spain’s Ministerio de Ciencia e Innovación. The project aims at contributing to the conceptual and theoretical foundations for a mathematical and computational model of embodied conceptualisation, driven by its potential deployment and application in cognitive musicology and musical creativity.

Prof. Mark d'Inverno: "Using a piano (I hope), AI software and a few videos I will aim to try and answer this question from the perspectives of researcher, musician and lecturer."

Bio:

Mark d'Inverno has spent the last 20 years undertaking cutting-edge research at the frontiers of AI, creativity and learning –luckily for him that much has been with colleagues at IIIA - asking how they relate to each other and how the different academic disciplines can provide us with insights into the role we want AI to play in learning, in creative practice and in society in general. Mark's PhD from UCL investigated the concepts of agency and autonomy in artificial systems, and since then he has published around peer-reviewed 200 articles and several books (including the edited book "Computers and Creativity"). Mark was formerly Pro-Warden (Pro-Vice-Chancellor) at Goldsmiths, University of London - known for an array of alumni who have contributed to the creative and cultural industries nationally and internationally - where he has led on developing the College's international profile and engagement and before that led the research and enterprise brief. He is a critically acclaimed jazz pianist (Guardian, Observer, BBC) and for nearly 40 years has led a variety of successful bands in a range of different musical genres.

Professor Gopal Ramchurn from the University of Southampton will give us a brief overview of some of the latest research he has carried out in the area of human-agent collectives and will articulate some of the key challenges that arise when building AI needs to be trustworthy by design and trusted in practice. Then he will detail the programme of the UKRI Trustworthy Autonomous Systems Hub (www.tas.ac.uk), which is a newly funded £12m programme to coordinate a portfolio of £21m of research projects across multiple universities in the UK.

Speaker: Filippo Bistaffa - researcher at the IIIA-CSIC. 

Filippo will present an approach that allows one to approximate every characteristic function games (CFG) as an induced subgraph game (ISG), a succinct game representation that is based on a weighted graph among the agents. The proposal outperforms existing CSG approaches for ISGs by using off-the-shelf optimisation solvers.

Marta R. Costa-jussà, a Ramon y Cajal Researcher at the Universitat Politècnica de Catalunya (UPC, Barcelona), will be giving some deep insights into (spoken) multilingual language translation pursuing similar quality for all languages. Also, we are going to discuss how we can efficiently add new languages in a highly multilingual system. Finally, we are going to give details on the fairness challenge, why neutral words as “doctor” tend to infer the “male” gender when translated into a language that requires gender flexion for this word?

Professor Juan Antonio Rodríguez will present to us the AI4EU project. AI4EU is the European Union’s landmark Artificial Intelligence project, which seeks to develop a European AI ecosystem, bringing together the knowledge, algorithms, tools and resources available and making it a compelling solution for users. Involving 80 partners, covering 21 countries, the €20m project kicked off in January 2019 and will run for three years. 

Professor Cecilio Angulo will present us the IDEAI-UPC (https://ideai.upc.edu/en) research lab. Cecilio is the current director of the IDEAI-UPC. He will present some of the projects that are being carried out at the IDEAI and that perhaps may open up new collaboration lines between the two institutes.

The Doctoral Consortium will take place on July 21 and 22. Due to concerns regarding COVID-19 the DC2020 will be held online.

Crowdsourcing is often associated with the darker side of Artificial Intelligence...

This year's Christmas Concert coincides with the 25th anniversary of the institute.

La jornada consistirà en un parell de taules rodones sobre la història i el futur del IIIA i una conferència a càrrec del Professor Luc Steels, seguit d’un dinar.

Amb el motiu de la commemoració dels 25 anys de l'Institut d'Investigació en Intel·ligència Artificial (IIIA-CSIC), ens complau convirdar-vos a la jornada "25 anys fent IA: El Repte d'Innovar" que tindrà lloc el proper 18 de desembre al mateix institut.

Estas jornadas se enmarcan dentro de las acciones iniciales de AIHUB.CSIC y tienen el objetivo de completar el mapa de competencias del CSIC reuniendo a representantes y miembros de los grupos activos en inteligencia artificial.

El Dr. Ramon López de Mántaras (expert en Intel·ligència Artificial) i el Dr. Jordi Isern (expert en Ciències de l'Espai) parlaran sobre l'impacte de la intel·ligència artificial en el marc dels viatges interplanetaris tripulats, i també sobre els límits ètics i les implicacions per a l'ésser humà. Un diàleg a la frontera entre ciència i filosofia, per plantejar reptes futurs ara que fa 50 anys que vam trepitjar la Lluna i que es plantegen els viatges a Mart.

The Doctoral Consortium will take place on July 16 and 17.

The Doctoral Consortium will take place on July 17 and 18.

The Doctoral Consortium will take place on July 18 and 19.

The Doctoral Consortium will take place on July 21 and 22.

The Doctoral Consortium will take place on July 15.

The Doctoral Consortium will take place on July 16.

The Doctoral Consortium will take place on July 22 and 23.

The Doctoral Consortium will take place on June 19, 20 and 21.

The Doctoral Consortium will take place on June 20 and 21.