Overview
Artificial Intelligence (AI) has gained significant attention for its applications in science. This workshop will focus on its potential use beyond primary scientific data analysis pipelines, hence the "meta" in AI for meta-science.
This workshop is mainly funded by AISSAI, CNRS's interdisciplinary centre for AI, and is hosted by Institut Pascal, Université Paris-Saclay.
The workshop is structured to favour discussions. Each theme will be covered in half a day to a full day, featuring 45-minute keynote talks and a series of short contributed talks, with ample time for discussion, which is well-suited to Institut Pascal. Each participant will have a desk in shared offices with access to meeting rooms (and, of course, free coffee machines) and will be provided with lunches, a workshop dinner on Thursday, and additional social events.
Attendance is limited to 66 scientists, by invitation or upon application till 23rd September (menu on the left); applications will be evaluated based on applicants’ expertise in at least one of the topics covered, as well as the goal of ensuring a diverse group of participants. Young researchers are especially encouraged to apply.
Two hackathons will be run in parallel for two half-days. The call for hackathon proposals is open (see menu on the left) till 30th September. Accommodation and travel expenses will be provided for up to two organisers per hackathon. The hackathons will have defined inputs, deliverables and a procedure (what participants are supposed to do). A hackathon could be a meta-hackathon, a hackathon to define a hackathon, for example, a hackathon to lay out a scientific competition with a dataset, a platform and a figure of merit.
Themes
Several key areas have been identified:
- AI for managing the operation of very large scientific apparatuses, such as particle accelerators, telescopes or large gravitational wave detectors. Can these facilities be made fully autonomous in terms of control and monitoring?
- AI for optimising large computing resource infrastructures by intelligently pre-loading data and scheduling data processing tasks to maximise throughput.
- AI for enhancing experiment design, including the use of fully differentiable simulators to optimise experiment sensitivity and its integration with generative design techniques in engineering. Do experiments to be analysed by AI algorithms need to be designed differently?
- Large Language Models (LLMs) for managing, processing, and making available the internal documentation associated with large-scale scientific experiments, including, but not limited to, the software codebase.
- AI for epistemology: can an LLM not only extract the most up-to-date information but also analyse the dynamics of scientific thought by tracking the emergence and decline of ideas through the study of scientific peer-review papers, as well as drafts and discussion documents?
- AI for scientific discovery: to what extent can AI be used to become a major player in the scientific process, by making hypotheses, connecting insights and designing experiments
- Last but not least: what does it mean to do a PhD in such an environment? How should future scientists be trained?
Contact: David Rousseau, IJCLab, rousseau@ijclab.in2p3.fr