Energy and ecology

What impact will artificial intelligence have on the environment? How can it redefine energy challenges? How can it benefit these fields?

The world of the environment and energy is undergoing profound changes these days: the industrial world is expanding exponentially with consequences for the environment, demand for electricity and other energy sources is increasing, to name but a few. Progress and nature are seen as enemies, but they are not always opposed. 

AI is making a positive contribution to the challenges of energy and ecology.

It enables the rational exploitation of agriculture, the optimization of pollution-reduction processes such as recycling, or participation in research to combat plastic pollution of the oceans. In this context, the DATAIA Institute is funding two research projects, PEPER and Warm Rules, aimed respectively at optimizing the use of renewable energies and studying the adaptation of organisms to climate change.

e-Science

The revolution in experimental techniques has opened the door to very large datasets with a limited number of observations. A key challenge is to support this change by developing a culture of data expertise.

Scientific disciplines generate large amounts of data through experiments. A large part of a scientist's work consists in exploiting this data: storing it, representing it, extracting useful or important information from it, then modeling it, developing statistical models, making predictions, testing it and so on. As this data becomes increasingly complex, not all scientists are necessarily equipped to meet the challenges posed by this data, which is becoming more and more technical as time goes by.

The DATAIA Institute offers tools to make data analysis easier for scientists.

To create new collaborations and energize data analysis, one new approach is to support the creation of challenges to identify the best strategies for a given problem, and combine them to arrive at a more effective overall model. As part of the Center for Data Science, researchers and engineers from the Parietal team at the Inria Saclay - Île-de-France center, launched a challenge on autism in 2018 based on images of the brains of children, autistic or not.  In this way, learning specialists are developing predictive models for diagnosing the disease. This challenge has advanced the prediction score and boosted the quality of autism diagnosis by 10%.Within the e-sciences framework, another type of approach involves establishing the causal structure of phenomena rather than prediction: this is a very active branch of learning.

Industry

Artificial intelligence is seen as the strategic feature of the future, but its development is only possible through collaboration between research and application. The DATAIA Institute is committed to strengthening these relationships.

Artificial intelligence is seen as the strategic feature of the future, but its development is only possible through collaboration between research and application. The DATAIA Institute is committed to strengthening these relationships. Research is focused on the development of analytical tools capable of interrogating large databases. However, industries are the subjects that store the most information. On the other hand, to make the most of these large databases, companies need the right tools to interrogate and manipulate them.

The development of AI in the industrial field is made possible by prior academic research.

Numerous initiatives have been taken to date to strengthen collaboration between these two players. As part of the government's "AI for Humanity" initiative, a manifesto for AI at the service of industry has been signed. Its aim is to coordinate an action plan with the French ecosystem to define the challenges linked to the development of AI and prioritize research themes. The DATAIA Institute is promoting an Industrial Affiliation Plan, a partnership aimed at enriching the development of academic research on AI, as well as providing advantages for companies.

Networks of people and objects

Social networks are networks of influence and value production. While most messages are currently generated by users, sensors are beginning to produce an additional, continuous flow of information. A new form of communication and information exchange is therefore emerging, and it is essential to understand how our behavior and social interactions are influenced.

To do this, we need to develop new methodologies that enable us to adequately analyze the various impacts linked to these evolutions.

Health and well-being

One of the potentials of AI in the healthcare environment lies in its ability to recognize and correlate biological or physiological factors in large quantities of data.

By extracting information from a wide range of clinical practices and medical research, AI can help establish personalized risk correlations or provide doctors with optimal decision support.

Access to healthcare data is a major issue for the development of AI in France.

There is no simple framework for accelerating data availability. In France, in June 2018, the Ministry of Health launched a mission, the "Health Data Hub", to facilitate the use of data for research and innovation. To address this issue, the DATAIA Institute funded the "MissingBigData" research project in 2018, which aims to approach the problem of missing data from a different angle and propose new, more powerful models using larger data samples to impute missing values.

Urbanization 4.0 and transportation

Intelligent and cooperative transportation systems, based on traffic modeling, distributed and interactive traffic management and optimal decision-making, are among our fields of application.

The collaborative exploitation of distributed information between (semi) autonomous vehicles will promote significant progress in terms of safety and traffic. Also concerned are interconnected cyber-physical systems, which require the development of solutions for optimal distributed decision-making in a context of uncertainty. More generally, intelligent urbanization by integrating distributed sensor information sources for land-use planning, resource management, transport and mobility.

The success of AI in transport and mobility largely depends on the quality of driving situation data and the ability to process it.

Today, this labeling has to be done manually, which means that the desired performance cannot be achieved quickly. There is therefore a lack of available expertise and large-scale experimentation. What's more, private players are finding it difficult to set up projects in collaboration with researchers. A medium-term partnership scheme has been launched by the DATAIA Institute. The Industrial Affiliation Program, in fact, aims to promote collaboration between academia and industry by fostering contact and exchanges between them.