Overview
Hackathons are announced, registration has reopened
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.
Two hackathons will be run in parallel for two half-days (Tuesday morning and Thursday morning), Mad Scientist, and From Literature to Benchmark (see below).
Attendance is limited to 70 scientists, by invitation or upon application till 6th Nov (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 including PhD students are especially encouraged to apply.
Confirmed speakers
Confirmed speakers include so far:
- Mateusz Bawaj (U Perugia) AI control of Gravitation Wave interferometer
- David Chamont (IJCLab) Labobots school
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Adnan Ghribi (GANIL) Summary of accelerator data hackathon
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Damien Gratadour (CNRS) AI control of telescopes
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Verena Kain (CERN) AI control of accelerators
- Francois Lanusse (CEA) LightCone : agentic AI assisted science
- Pierre Manil (CEA) AI and mechanical design
- Claire Nedellec (INRAE) AI for knowledge extraction for life science
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Pietro Vischia (Oviedo) AI assisted experiment co design
Mad Scientist hackathon is organised by Savannah Thais (Hunter, NY) and Nathan Suri (Yale)
From Literature to benchmark hackathon is organised by Joao Goncalves (U Bonn) and Romina Raoofian (U Bonn)
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?
Hackathons
Participants to the workshop will pick one of the hackathon. They will participate through their own LLM API or using an API provided by ILaaS a French higher education federation.
Mad Scientist
Mad Scientist, the first red-teaming effort built for AI agents for scientific research. As a hackathon, participants will be tasked with designing "attacks" to deliberately misalign an honest agentic pipeline created to search for new particles at the Large Hadron Collider. Attacks can be in the form of input poisoning, agent misalignment, or even multi-agent collusion. The aim of the exercise is to use our knowledge as science practitioners to intuit where an agent could fail in a way that would pass standard control structures. These attacks can then be used to build more robust and safe scientific agents as well as inform scientists who collaborate with these agents on critical failure modes to watch out for.
From Literature to Benchmark
A new major goal of th AI and physics community is to develop Foundation Physics Model. These models trained on very large multimodal datasets would be capable of performing tasks like particle identification, inference or generation. The purpose of this hackathon is to define automatic system capable to parse the litterature and identify the most relevant benchmarks for foundation models.
Contact: David Rousseau, IJCLab, rousseau@ijclab.in2p3.fr