With the ever-increasing use of internet-connected devices, such as computers, smart grids, IoT appliances and GPS-enabled equipments, personal data are collected in larger and larger amounts, and then stored and manipulated for the most diverse purposes. Undeniably, the big-data technology provides enormous benefits to industry, individuals and society, ranging from improving business strategies and boosting quality of service to enhancing scientific progress. On the other hand, however, the collection and manipulation of personal data raises alarming privacy issues. Both the experts and the population at large are becoming increasingly aware of the risks, due to the repeated cases of violations and leaks that keep hitting the headlines. The objective of this project is to develop the theoretical foundations, methods and tools to protect the privacy of the individuals while letting their data to be collected and used for statistical purposes. We aim in particular at developing mechanisms that: (1) can be applied and controlled directly by the user, thus avoiding the need of a trusted party, (2) are robust with respect to combination of information from different sources, and (3) provide an optimal trade-off between privacy and utility. We intend to pursue these goals by developing a new framework for privacy based on the addition of controlled noise to individual data, and associated methods to recover the useful statistical information, and to protect the quality of service.

The objective of VHIALab is the development and commercialization of software packages enabling a robot companion to robustly interact with multiple users. VHIALab builds on the scientific findings of ERC VHIA (February 2014 - January 2019). Solving the problems of audio-visual analysis and interaction opens the door to multi-party and multi-modal human-robot interaction (HRI). In contrast to well investigated single-user spoken dialog systems, these problems are extremely challenging because of noise, interferences and reverberation present in far-field acoustic signals, overlap of speech signals from two or more different speakers, visual clutter due to complex situations, people appearing and disappearing over time, speakers turning their faces away from the robot, etc. For these reasons, today's companion robots have extremely limited capacities to naturally interact with a group of people. Current vision and speech technologies only enable single-user face-to-face interaction with a robot, benefitting from recent advances in speech recognition, face recognition, and lip reading based on close-field microphones and cameras facing the user. As a consequence, although companion robots have an enormous commercialization potential, they are not yet available on the consumer market. The goal of VHIALab is to further reduce the gap between VHIA's research activities and the commercialization of companion robots with HRI capabilities. We propose to concentrate onto the problem of audio-visual detection and tracking of several speakers, to develop an associated software platform, to interface this software with a commercially available companion robot, and to demonstrate the project achievements based on challenging practical scenarios.

Building machines that interact with their world, discover interesting interactions and learn open-ended repertoires of skills is a long-standing goal in AI. This project aims at tackling the limits of current AI systems by building on three families of methods: Bayesian program induction, intrinsically motivated learning and human-machine linguistic interactions. It targets three objectives: 1) building autonomous agents that learn to generate programs to solve problems with occasional human guidance; 2) studying linguistic interactions between humans and machines via web-based experiments (e.g. properties of human guidance, its impact on learning, human subjective evaluations); and 3) scaling the approach to the generation of constructions in Minecraft, guided by real players. The researcher will collaborate with scientific pioneers and experts in the key fields and methods supporting the project. This includes supervisors Joshua Tenenbaum (program synthesis, MIT) and Pierre-Yves Oudeyer (autonomous learning, Inria); diverse collaborators, and an advisory board composed of an entrepreneur and leading scientists in developmental psychology and human-robot interactions. The 3rd objective will be pursued via a secondment with Thomas Wolf (CSO) at HuggingFace, a world-leading company in the open source development of natural language processing methods and their transfer to the industry. By enabling users to participate in the training of artificial agents, the project aims to open research avenues for more interpretable, performant and adaptive AI systems. This will result in scientific (e.g. interactive program synthesis approaches), societal (e.g. democratized AI training) and economic impacts (e.g. adaptive AI assistants). The dissemination, communication and exploitation plans support these objectives by targeting scientific (AI, cognitive science), industrial (video games, smart homes) and larger communities (gamers, software engineers, large public).