Domaines de recherche
Exploratory actions: targeting new research pathways
© Goodluz - Fotolia
Inria’s exploratory actions programme was set up to promote the emergence of new research themes, giving scientists the means with which to test original ideas with a view towards forming Inria project teams.
Exploratory actions give us an opportunity to put our trust in the intuition of the members of our research teams. The aim of this scheme is to focus resources around highly innovative subjects, targeting approaches that are “off the beaten track”, risky and/or which represent a disruption in relation to traditional approaches.
The programme will make it possible to delve deeper into certain subjects and to prove their relevance from a scientific perspective, a vital stage in the build-up to forming a project team. This may also involve exploring themes that are unusual or marginal for Inria, such as subjects in the social sciences or in the legal domain.
Providing a greater incentive for risk-taking in science
What makes a proposal “exploratory” is not easy to sum up in a few words, given that we could reasonably class all research as exploratory. Irrespective of the research domain, however, certain subjects or approaches are clearly better established, either through their lifespan or the number of researchers. Subjects may be applied or theoretical, rooted in digital or in interdisciplinarity, provided they remain faithful to the underlying principle for all Inria research: to focus on the impact the results could have in the long-term, either in computational science or in the fields in which they are used.
In 2019, two exploratory actions from the Inria Lille - Nord Europe centre were selected within the context of the programme.
The ETHICAM project, the Internet of Everything for communication
Valéria Loscri - Inria / Photo C. Morel
Following on from the Internet of Things (IoT) comes the Internet of Everything. This was the subject of the exploratory action undertaken by Valeria Loscri. More specifically, the project was focused on going beyond the limits of existing means of communication in order to develop connected, autonomous objects capable of communicating with each other effectively. We might be close to reaching maximum potential in terms of radio frequencies, but requirements in relation to flow, speed and the communication spectrum continue to grow. With this in mind, our researcher was keen to explore new avenues and to introduce new paradigms. She had the idea of facilitating information exchanges starting from nanodevices, objects, materials and even biological systems, focusing, for example, on phonons, which are to sound what photons are to light. Developing a fuller understanding of the properties of materials and such particles could one day enable her to devise a more effective way of transmitting information. It has to be said, however, that molecular communication remains largely unknown territory. For this reason alone, Valeria Loscri’s project very much meets the criteria of exploratory actions, with its basis on risky, fundamental and innovative research. The project, organised over three years, should enable collaborative partnerships to be formed with other researchers in the field of materials or physics, laying the foundations for interdisciplinary research with a view towards establishing new communication paradigms. The end goal will be to imagine innovations such as intelligent materials capable of communicating with each other and which could, for example, be used in the future in autonomous vehicles.
The SR4SG project - restoring the societal aspect to sequential learning
Odalric-Ambrym Maillard - © Inria
At this moment in time, sequential learning is primarily used to display targeted adverts on the internet. As far as Odalric-Ambrym Maillard is concerned, however, this is far from satisfactory, which is what guided him towards undertaking an exploratory action aimed at restoring the societal dimension to this sub-domain of machine learning. The aim of this innovation was to combine sequential learning, sustainable agriculture, soil preservation and biodiversity. In a concrete sense, this involved building a platform based on recommendation algorithms in order to encourage good agricultural practice to be shared in a collaborative way. Various different groups, such as the Museum of natural history, the Cirad (the French Agricultural Research Centre for International Development) and Inra (Institut national de la recherche agronomique) are now gathering observation data on garden spaces. However, they do not possess the requisite machine learning skills to draw personalised recommendations from this data. The primary objective of this exploratory action, therefore, is to bring specialists in biodiversity and good agricultural practice together with Inria researchers in the field of artificial intelligence in order to foster the emergence of coherent research problems. The goal will then be to work on collecting data. However, the aim is not simply to make observations, but rather to develop a timelines of the steps taken and the impact they had, something which is essential when it comes to enabling sequential learning through algorithms. This stage will result in the creation of an application enabling gardeners, whether experienced or just starting out, to share their observations, the measures they have introduced and the effects observed. One final objective of this exploratory action will be to fine-tune the algorithms that will use this data to learn and to make tailored recommendations for each plant in each garden, depending on the context. Given the scope of the project, nobody is expecting a perfect model of the application to be deployed worldwide within the next four years, but Odalric-Ambrym Maillard hopes to have at his disposal a platform prototype bringing together a diverse scientific community that will enable him to obtain funding from an institution such as the ANR, for example, in order to continue the research. Maillard is confident that this exploratory action will help lay the foundations for the tools of the future for good agricultural practice.