At the core of MIAI

Future AI has to get off the cloud, in order to meet its users, and overcome problems related to communication overload and data privacy. Hardware architectures for AI (i.e. Neuro Processing Unit - NPU) is a key topic to address new applications, embedded in low power, low latency apparatus (cars, healthcare wearable devices or event smart sensors). At the same time, a new IT paradigm, mixing Edge/Fog/Cloud computing and IoT, requires advanced resource management. Distributed Intelligence is an emerging topic, which will allow optimizing distributed applications, including distributed learning. The following chairs address these issues related to embedded and distributed AI and hardware architecture for AI.

  • Lorena Anghel & Alexandre Valentian - Hardware for spike-coded neural networks exploiting hybrid CMOS non-volatile technologies
  • Frédéric Pétrot - Digital Hardware AI Architectures 
  • Denis Trystram - Edge intelligence 
Automatic decision systems are currently deployed at large scale. They are already affecting the life of citizens, and their impact is expected to grow. Often based on complex data-driven machine learning models, these systems raise many scientific challenges regarding safety, robustness, privacy, fairness, and data efficiency when massively annotated data are not available. The Grenoble ecosystem possesses many assets to solve these challenges by combining the perspectives of various scientific fields, both on the academic and industrial sides. There is indeed a long tradition of research in optimisation, statistics, symbolic AI in Grenoble, as well as a more recent one in machine learning. Our strategy in MIAI consists in fostering interactions between these different disciplines, in order to make fundamental contributions to machine learning and reasoning through the chairs below
  • Sophie Achard & Martial Mermillod - Towards Robust and Understandable Neuromorphic Systems
  • Diane Larlus - Lifelong Representation Learning 
  • Julien Mairal - Towards More Data Efficiency in Machine Learning 
  • Romain Couillet - Large Dimensional Statistics for AI 
  • Anatoli Juditsky & Arkadi Nemirovski (International) - High-dimensional Inference by Convex Optimisation 
  • Jérôme Malick & Yurii Nesterov (International) - Optimisation & Learning 
  • Patrick Loiseau & Marie-Christine Rousset - Explainable and Responsible AI 
  • Jérôme Euzenat - Knowledge communication and evolution 
A major objective of artificial intelligence is to enhance the abilities of humans to interact with their environment. This involves the resolution of various problems, including perceiving, analysing and learning the informational structure of this environment, and acting on it in an adequate and efficient way. Importantly, the human environment is also composed of other humans, which raises specific questions about the automatic analysis of human behavior and the design of efficient systems for enhanced interaction between humans. The Grenoble teams have a long-standing competence on human-machine and human-human interaction, with an increasing use of machine learning techniques and maintaining at the same time ancient and strong links between computer science and cognitive psychology. The chairs bellow, addresses separately the questions of visual analysis of the external world, interaction with humans and objects in the sensory-motor framework associated to robotics, and communicating with humans by speech and language. The following chairs address these issues related to perception and interaction.
  • Xavier Alameda-Pineda & Radu Horaud - Audio-visual machine perception and interaction for companion robots 
  • Gérard Bailly & James Crowley - Collaborative Intelligent Systems 
  • Christophe Prieur - AI and dynamical systems: new paradigms for control and robots 
  • François Portet & Laurent Besacier - Artificial Intelligence & Language 
  • Pascal Perrier - Bayesian Cognition and Machine Learning for Speech Communication 
  • Edmond Boyer - Data Driven 3D Vision 
Artificial Intelligence offers great opportunities for elaborating innovative solutions to improve people’s life and their social environment. The integration of AI into society affects most areas of private and social lives, at the collective and individual levels. In response, individuals, groups and institutions implement regulatory processes to address the real or imagined risks resulting from AI. To avoid both disaster scenarios and the dangers of wilful blindness, social and computer scientists should join forces to implement research on the actual societal impact of AI. Moreover, reasoned regulation of AI requires not only an understanding of algorithms and technologies, but also the study of the social value and meaning that users attribute to them and the understanding of the society where they are deployed. Grenoble is particularly well prepared to meet this challenge. For several years, the Univ. Grenoble Alpes IDEX program has been promoting the development of humanities and social sciences, fostering interdisciplinary work in the digital field. Our objective within MIAI is to build on these foundations and change scale by implementing chairs dealing with the issues of: Integration of AI into society and regulation of AI by society. The following chairs address these issues related to AI and society.
  • Théodore Christakis - Legal and regulatory implications of artificial intelligence 
  • Thierry Ménissier - Ethics and AI 
  • Sihem Amer-Yahia - Contextual Recommendations in Action - Bridging AI and Real-Life Economics
  • Gilles Bastin - Algorithmic society 
Published on  December 5, 2023
Updated on December 5, 2023