In the era of AI-driven transformations, foundation models (FMs), like large-scale language and vision models, have become pivotal in various applications, from natural language processing to computer vision. These models, with their immense capabilities, reshape the future of scientific research and the broader human society, but also introduce challenges in their in-the-wild deployments. The Workshop on FMs in the wild delves into the urgent need for these models to be useful when applied to our societies. The significance of this topic cannot be overstated, as the real-world implications of these models impact everything from daily information access to critical decision-making in fields like medicine and finance. Stakeholders, from developers to end-users, care deeply about this because the successful integration of FMs into in-the-wild frameworks necessitates a careful consideration of adaptivity, reliability and efficiency. Some of the fundamental questions that this workshop aims to address are:
We invite submissions from researchers in the fields of machine learning pertaining to foundation models and its in-the wild applications. Additionally, we welcome contributions from scholars in the natural sciences (such as physics, chemistry, and biology) and social sciences (including pedagogy and sociology) that necessitate the use of foundation models. In summary, our topics of interest include, but are not limited to:
For any questions, please contact us at fmwild2024@googlegroups.com.
Submission deadline: June 7, 2024, AOE (EXTENDED)
Notification to authors: July 3, 2024, AOE (EXTENDED)
Camera-ready deadline: July 17, 2024, AOE (EXTENDED)
Workshop Data: July 26, 2024, AOE
This is the tentative schedule of the workshop. All slots are provided in Central European Summer Time (CEST).
08:50 - 09:00 | Introduction and opening remarks |
09:00 - 09:30 | Invited Talk 1: Dakuo Wang |
09:30 - 10:00 | Invited Talk 2: David Alvarez-Melis |
10:00 - 10:15 | Oral Presentation 1: Parameter-Efficient Quantized MoE Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis |
10:15 - 11:15 | Poster Session 1 |
11:15 - 11:45 | Invited Talk 3: Boran Han |
11:45 - 12:00 | Oral Presentation 2: RouteFinder: Towards Foundation Models for Vehicle Routing Problems |
12:00 - 12:30 | Invited Talk 4: Hannah Kerner |
12:30 - 13:30 | Break |
13:30 - 13:45 | UK AI Safety Institute: Empirically Assessing AI's Risks & Advancing Systemic Safety |
13:45 - 14:00 | Oral Presentation 3: DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning |
14:00 - 14:30 | Invited Talk 5: Steven Wu |
14:30 - 15:30 | Poster Session 2 |
15:30 - 16:00 | Invited Talk 6: Pang Wei Koh |
16:00 - 16:30 | Invited Talk 7: Jimeng Sun |
16:30 - 17:00 | Invited Talk 8: Sheng Wang |
Northeastern University
Harvard University
AWS AI Research
University of Washington
University of Illinois Urbana-Champaign
University of Washington
Arizona State University
Carnegie Mellon University
Carnegie Mellon University
Meta AI
University of California San Diego
Carnegie Mellon University & Meta AI
Carnegie Mellon University
University of Oxford & Meta AI
New York University
University of Oxford
Rutgers University
Massachusetts Institute of Technology
UNC-Chapel Hill
Stanford University