The aim of this workshop is gather specialists from the Machine Learning community, Chemists and Nuclear Physicists around the big picture that Machine Learning may strongly impact our capabilities to tackle the theoretical description of Quantum Many Body systems. Acknowledging that the development of weak Artificial Intelligence is a disruptive technology, we intend to grasp a global understanding of the implications of these new developments. For this reason, we plan to invite sociologists and philosophers to discuss a risk benefit analysis of the development of Artificial Intelligence, and which role the scientists might play on these questions.
The main goals of the workshop are:
1. To establish a global overview of state-of-the art Machine Learning Techniques, which may lead to progress in our field of research;
2. To initiate a large scale effort towards applications of Neural Networks to the nuclear Many Body Problem;
3. To establish a “task-force” to promote these approaches and study their epistemological implications;
4. To give epistemological and societal insights of the massive development of Artificial Intelligence.
For further information, please contact the organizers of the project:
R.-D. Lasseri (CEA, ESNT, contact), D. Regnier (CEA DAM); J.-P. Ebran (CEA/DAM), G. Hupin (IPNO).
This workshop session is organized in the framework of the "Espace de Structure et de réactions Nucléaires Théorique" (ESNT). See the web page for details about the updated program : http://esnt.cea.fr >> Ateliers 2020