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.

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). A specific program will address related research topics. 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. Building upon the scientific effort presented in the machine learning & reasoning program, both research topics will make use of on-line, unsupervised, incremental, and “under constraint” learning in order to provide adaptivity to the environment, customization to the users, and system efficiency.

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. This leads to three programmes addressing 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.