Vaishak Belle

University of Edinburgh

Journées d'Intelligence Artificielle Fondamentale (JIAF)

Titre : Neuro-symbolic Systems for Responsible AI: Challenges and Opportunities

Résumé : Machine learning (ML) techniques have become pervasive across a range of different applications, and are now widely used in areas as disparate as recidivism prediction, consumer credit-risk analysis, and insurance pricing. Likewise, in the physical world, ML models are critical components in autonomous agents such as robotic surgeons and self-driving cars. Among the many dimensions that arise in the use of ML technology in such applications, analysing properties such as correctness, fairness, and so on is both immediate and profound. In this talk, we advocate for a two-pronged approach to ethical/resposible decision-making enabled using rich models of autonomous agency: on the one hand, we need to draw on philosophical notions of such as beliefs, causes, effects and intentions, and on the other, we consider the problem of practical and effective knowledge acquisition and learning

Biographie : Dr Vaishak Belle (he/him) is Reader at the University of Edinburgh, an Alan Turing Fellow, and a Royal Society University Research Fellow. He has made a career out of doing research on the science and technology of AI. He has published close to 120 peer-reviewed articles, won best paper awards, and consulted with banks on explainability. As PI and CoI, he has secured a grant income of close to 8 million pounds. more

Rémi Flamary

École Polytechnique

Conférence sur l'Apprentissage Automatique (CAP)

Titre : À venir

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Biographie : À venir

Luis Galárraga

INRIA/IRISA

Rencontres des Jeunes Chercheurs en Intelligence Artificielle (RJCIA)

Titre : À venir

Résumé : À venir

Biographie : À venir

Christian Hennig

University of Bologna

Rencontres de la Société Francophone de Classification (SFC)

Titre : On decision making in cluster analysis

Résumé : There are many different approaches to cluster analysis, and when applied to the same data, different methods will pften produce quite different clusterings. Data analysts do not only have to choose a clustering method, also pre- and postprocessing decisions need to be made, such as selection and transformation of features and the number of clusters.

Making all the required decisions is very difficult. As there is no unique definition of the clustering problem, neither is there a unique or "optimal" way to measure the quality of a clustering, and the data alone do not hold all the information required to make these decisions. This is a big challenge for automatising cluster analysis for machine learning in particular.

I will discuss some of the required decisions and quality criteria, illustrating problems with automated decision making, and how background knowledge and techniques such as data visualisation can help.

Biographie : Christian Hennig is Full Professor at the Department for Statistical Science "Paolo Fortunati", University of Bologna, Italy. Before, he was Senior Lecturer at University College London and Lecturer at the University of Hamburg and ETH Zürich.

He did statistical advisory for more than 100 clients. He is author of the popular foc and prabclus R-packages. He is first editor of the Handbook of Cluster Analysis and has written key references on robustness in cluster analysis, cluster stability, and choosing the number of clusters. Other work concerns the philosophical foundation of statistical modelling, objectivity and subjectivity in statistics, identifiability, and the role of model assumptions.

Simon Lucas

Queen Mary, University of London

Conférence Nationale en Intelligence Artificielle (CNIA)

Titre : À venir

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Biographie : À venir

Nardine Z Osman

Artificial Intelligence Research Institute (IIIA-CSIC)

Journées Francophones sur les Systèmes Multi-Agents (JFSMA)

Titre : À venir

Résumé : À venir

Biographie : À venir

Louis-Martin Rousseau

Polytechnique Montréal, Canada

Journées Francophones de Programmation par Contraintes (JFPC)

Titre : En route vers des solveurs neuro-symbolique en programmation par contrainte.

Résumé : La présentation examine comment l’apprentissage automatique peut améliorer la programmation par contraintes (CP), en mettant en avant l’intégration de techniques d’apprentissage pour optimiser la recherche et la propagation dans les solveurs de CP. Elle s’appuie sur les avancées récentes en programmation mathématique pour démontrer comment l’apprentissage peut renforcer les heuristiques de sélection des valeurs et des variables, notamment à travers l’apprentissage par renforcement et les réseaux de neurones graphiques. Une attention particulière est accordée à l’amélioration des bornes duales grâce à la relaxation lagrangienne et à la décomposition, illustrant comment l’apprentissage peut accélérer ces méthodes tout en augmentant leur efficacité. Enfin, la présentation insiste sur l’importance d’une approche hybride qui allie intelligence artificielle et raisonnement logique, afin de doter les solveurs de capacités accrues et d’améliorer la résolution des problèmes combinatoires de manière plus performante et adaptable.

Biographie :Louis-Martin Rousseau est professeur au département de mathématiques et de génie industriel à Polytechnique Montréal depuis plus de 20 ans. Spécialiste de l’intelligence artificielle, de la recherche opérationnelle et de la science de la gestion, il est reconnu internationalement pour ses contributions aux problèmes d’optimisation combinatoire, notamment dans les domaines de la génération de colonnes, de la logistique du transport, de l’ordonnancement et de l’optimisation des ressources en santé. Il est titulaire de la Chaire de recherche du Canada en logistique des soins de santé (HANALOG) depuis 2016 grace à laquelle il mène des recherches visant à améliorer la planification et l’efficacité des services hospitaliers.

Marieke van Erp

KNAW Humanities Cluster

Ingénierie des Connaissances (IC)

Titre : Layering Knowledge to Unpack the Layers of Meaning in Historical Texts

Résumé : Historical texts present computational analyses with many different challenges: digitisation artefacts, segmentation, language evolution, and changing societal values. In this talk, I will present various interdisciplinary projects that my team has worked and is working on that address these challenges. Our use cases range from government and company records, to literature, letters, newspapers, and cookbooks - all spanning centuries. The approaches we use depend on the best tool for the job: rules, machine learning, prompt engineering - but all informed by domain expertise leading to applications that are used by researchers and the general public to make sense of big historical data.

Biographie : Marieke van Erp is a Language Technology and Semantic Web expert engaged in interdisciplinary research. She holds a PhD in computational linguistics from Tilburg University and has worked on many (inter)national interdisciplinary projects. Since 2017, she has been leading the Digital Humanities Research Lab at the Royal Netherlands Academy of Arts and Sciences Humanities Cluster. She is one of the founders and scientific directors of the Cultural AI Lab, a collaboration between 8 research and cultural heritage institutions in the Netherlands aimed at the study, design and development of socio-technological AI systems that are aware of the subtle and subjective complexity of human culture. In January 2023, she was awarded an ERC Consolidator project that will investigate how language and semantic web technologies can improve the creation of knowledge graphs supporting humanities research.

Chi Wang

Google DeepMind

Conférence Nationale sur les Applications Pratiques de l'Intelligence Artificielle (APIA)

Titre : AG2: Open-Source AgentOS for Agentic AI

Résumé : This presentation will address the future landscape of AI applications and the ways in which we can enable every developer to create them. It will examine the trend of agentic AI and the fundamental design considerations for agentic AI operating systems. Subsequently, it will explore a pioneering initiative, AG2, outlining the primary concepts and its application across a diverse range of tasks and industries, achieving top rankings in challenging benchmarks, and leading research advancements. The talk will conclude with open questions.

Biographie : Chi is founder of AG2 (formerly known as AutoGen), the open-source AgentOS to support agentic AI, and its parent open-source project FLAML, a fast library for AutoML & tuning. He has received multiple awards such as best paper of ICLR’24 LLM Agents Workshop, Open100, and SIGKDD Data Science/Data Mining PhD Dissertation Award. Chi runs the AG2 community with 20K+ members. He has 15+ years of research experience in Computer Science and work experience in Google DeepMind, Microsoft Research and Meta.