Big data & Artificial intelligence

Every day, companies create and manipulate a considerable amount of data. This data constitutes a veritable treasure trove, previously largely under-exploited by organizations. But to take advantage of it, you need to be able to collect, visualize and analyze this enormous volume of information. The term "big data" was coined to reflect the colossal amount of data circulating in our digital world.

To meet this challenge, advances in data science are essential, in terms of scientific concepts, digital infrastructures (computing and cloud) and deployment and industrialization methods. The ambitions are many: to improve the quality of the data collected, to develop methods for visualizing and analyzing large quantities of information, to optimize flows within companies... For applications in absolutely all sectors concerned by major transitions: digital, industrial, environmental, societal...

This field of research is often associated with artificial intelligence. Indeed, AI opens up new prospects for the automatic analysis of data and knowledge. Although the first work in this field dates back to the 1950s, with the first artificial neural networks, it is recent technological advances that have enabled the development of data science and AI. Today, machine learning techniques such as machine learning and deep learning are a reality in many application sectors. However, there are still many problems to be solved. Which algorithms should be selected for which situations? How can learned models be generalized and used in different contexts? How can the results of these different models be made intelligible and explained? How can we integrate machine learning techniques with symbolic reasoning and knowledge graph techniques to build hybrid systems? How can artificial intelligence be integrated into edge computing systems? How can we optimize computing power, and hence energy consumption?

But today's AI challenges go beyond scientific and technological issues. We need to consider the impact of these new technologies on society, and think about how an "intelligent" machine can really help people. The human and social sciences therefore have an essential role to play here, with a view to successful integration with users.

This is why the Carnot Télécom et Société Numérique's multi-disciplinary approach, based on Institut Mines-Télécom's flagship Data analytics & AI theme, is a major asset. Our researchers cover a wide range of skills around big data and artificial intelligence: machine learning and data science, signal and image processing, symbolic reasoning and knowledge representation, autonomous decisions, autonomous agents and assistants, optimization and constraint satisfaction, data-driven modeling, responsible AI and the impacts of AI, including the law, economics and sociology of AI.... And their results are enabling many companies in a wide range of sectors (from healthcare to industry 4.0, not forgetting transport and energy) to regain control of their data, improve its understanding and truly benefit from it.