Using AI competitions to advance science and attract talent

Humans like to compete, not only in sports but also in computer science. This makes data competitions an excellent way of reaching very diverse goals and interest groups. The universal language of competitions allows to showcase what is possible and to create incentives for applicable and experimental research. It is also used to attract talent.

The competitions put forward by the TAILOR network cover various fields from charging station infrastructure to neuroscience, controlling the power grid, developing numerical twins or creating stable fusion reactors. An example of such a TAILOR challenge was the Smarter Mobility Data Challenge, developed in partnership with the industrial groups behind the Manifeste IA* together with INRIA Saclay scientists Marc Schoenauer and Sebastien Treguer.

– We believe that this most human on instincts can help further the development of AI. Since the dawn of time, we have been driven to compete. From the Olympic Games to the original space race and on to hot dog eating contests and now machine learning, we have always enjoyed competing and it has made us all better, says Marc Schoenauer.

The Smarter Mobility Data Challenge was open to all European students and had them develop machine learning models that could forecast the states of car charging stations around Paris, at several levels of geographical resolutions, based on historical data.

The competition sat at the intersection of public interest, industry and academia and was meant to showcase the possibilities of AI when it comes to making any complex system more efficient. The participants were asked not only to deliver access to their code but also explain their approach and explainability of the final model was taken into account by the jury. The best teams received job propositions by some of the industrial partners of the “Manifeste IA” .


AI competitions are used by participants to learn more applied knowledge by practice, gain experience, develop their skills and sharpen their tooling, build a team for future projects, and in some cases make a name for oneself within the machine learning and AI space. Industry uses them as equal parts to evaluate the benefits they can derive from AI applied to their real-world problem, develop proofs of concept, new talents into the field. Scientists use competitions to federate participants from various backgrounds to collaboratively explore new ways to advance science and solve various problems whether on fundamental research subjects or more applied topics and share new common building blocks, such as scientific papers and open-source code.

– I can see this technology having a profound impact on society, and as long as we can make sure that the system is trustworthy and that it is aligned with our common goals, I think it will add enormous value. Getting there and finding the best algorithms will take some trial and error though, and I think competitions are the perfect place to do so, concludes Marc.

About the Researchers:
Marc Schoenauer
Principal Senior Researcher (DR0) with INRIA Saclay – Île-de-France
Sebastien Treguer
Research & Development in Machine Learning/Deep Learning/AI
at INRIA – LISN – TAU Team