Artificial intelligence and data science have been at the heart of the LIRMM’s activities for several decades. The scientific and societal importance of these topics is undeniable today.
Our research teams contribute to the methodological aspects of symbolic and sub-symbolic AI, with a recent development of work in the field of statistical machine learning. They aim to bridge the gap between our well-established research activities in symbolic AI and the more recent work in numerical AI that is increasingly being adopted in our work. They also target the efficient and robust implementation of embedded AI thanks to emerging computing paradigms, architectures and technologies (neuromorphic computing, resistive memories, …). Finally, they study numerous applications of AI in many fields, such as robotics and microelectronics. Our teams also work on modelling and data processing, as well as knowledge engineering in relation to numerous application fields, for example the environment, agronomy and health. Current applications concern a wide range of fields, such as the environment, agronomy and biology (with CIRAD, INRAE and IRD), health (CHU, IDESP), remote sensing (Espace Dev, TETIS), human and social sciences, microelectronics and robotics.
– Trusted AI: explainability of black-box AI systems in general, argumentation and human/machine dialogue, robustness of hardware architectures dedicated to AI;
– Mathematical foundations of AI: logical approaches and algorithmic and complexity studies, dynamic neural networks taking into account the suppression of inductive biases via so-called “Transformers” models.
– Embedded AI and frugal AI: energy efficiency through the design and programming of low-power hardware accelerator architectures and neuro-inspired architectures. The architectures studied in this way target embedded systems, for example in a distributed context such as edge computing. On the other hand, we are interested in AI methods applicable to the parsimonious management of frugal data lakes storing masses of data while waiting for their exploitation.
List of teams involved in the theme
Specific transverse action
– HAIR (Human, AI & Robotics). This is a transverse action, conducted jointly by teams from the Computer Science and Robotics departments. The objective of this action is to study the multidisciplinary aspects of human-robot interaction. Led by a computer scientist (M. Croitoru) and a roboticist with a background in neuroscience (G. Gowrishankar), the action regularly organises meetings between LIRMM members, has obtained INS2I funding to study artificial mediation by robots (Master’s thesis of S. Victor) and organises an international workshop each year (HRI@Montpellier).
Collaborations and visibility
– Local partnerships: IMAG (specialist in digital AI techniques), ESPACE DEV (working on ontologies and constraints), TETIS (specialist in geo-spatial data)
– Projects: ANR (https://www.lirmm.fr/projets-anr/) and EU (https://www.lirmm.fr/projet-europeen/)
International workshop within the transverse action HAIR: “AI for improving machine interactions with humans and the environment”, 27 – 28 October 2021, Montpellier https://sites.google.com/view/hrimontpellier/home