NSF directors
Margaret Martonosi (Assistant Director, CISE)
Michael Littman (Division Director, CISE/IIS)
Nina Amla (Senior Science Advisor, CISE)
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Abstract: Large pre-trained language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology, has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases...
In this short talk I will discuss our recent work on an approach to introducing connections between the Fokker-Planck equation and learning algorithms for dynamical systems that follow Markov Decision Processes
There is an increasing consensus in the wider scientific community that AI is poised to disrupt science by unlocking entirely new approaches, driving new scientific inquiry, and enabling greater scientific leaps with far-reaching societal consequences. In addition, challenges unique to scientific problems offer an opportunity to dramatically advance AI. However, there are substantial barriers...
Developments in modern computation and instrumentation have led to the possibility of recording enormous amounts of data, the data revolution. Along with this incredible data flow, a new demand has emerged for algorithms that can run on all this data to “Harness the Data Revolution.” Large datasets are rapidly encompassing many scientific domains, including high-energy physics, Astronomy,...
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project aims to overcome major obstacles limiting our understanding of the fundamental properties of the Universe by (1) providing thousands of state-of-the-art hydrodynamic simulations of cosmological structure formation covering a broad range of sub-grid models for the physics of galaxy formation and (2) developing...
In recent years, the fields of natural language processing and computer vision have been revolutionized by the success of large models pretrained with task-agnostic objectives on massive, diverse datasets. This has, in part, been driven by the use of self-supervised pretraining methods which allow models to utilize far more training data than would be accessible with supervised training. These...
In this lightning talk, we will provide an overview of NOAA Center for AI's approach to foster an open community discussion that gather members from academic researchers, industry leaders, and government researchers and managers around the topics of AI development in environmental sciences. Since 2022, the annual NOAA AI workshop transitioned into an open community forum where all interested...
This presentation briefly summarizes a workshop convened by the National Academies of Sciences, Engineering, and Medicine on February 7, 10, and 11, 2022, on the opportunities and challenges of using ML/AI to advance Earth system science, including their ethical development and use. The workshop explored how ML/AI approaches can contribute to improving understanding, analysis, modeling,...
New techniques in AI are rapidly being developed, extended and applied to challenging problems in biology. At the same time, as new assays, new data efforts, and greater understanding is developed in biology, the class and scope of problems that are amendable to AI approaches is growing. In order to survey the current frontier of the interface between AI methodology and biology and to chart...
In this short lightning talk I will discuss the Acceleration Consortium's annual Accelerate conference, which we ran in 2022 and 2023 in Toronto and are in the early stages of planning 2024 in a different host city. Accelerate spans the entire field of accelerated discovery with AI and automation: computational tools, high-throughput and autonomous experimentation, the ethics of accelerated...
A Research Roadmap for the Next Pandemic PREPARE (Pandemic Research for Preparedness and Resilience) is an NSF CISE-sponsored virtual organization tasked with fostering research collaborations and synthesizing critical pandemic-related computing research into a roadmap to help inform NSF funding opportunities that will aid our nation’s effective response to the next pandemic. Since we started...
With the quickly growing quantity and variety of transportation data, Artificial intelligence (AI) technologies are revolutionizing transportation research from system management to automated vehicle and infrastructure control. Emerging AI technologies combined with other analytical methods will lead to improved scientific understandings, transformative methods, and innovative, proactive...
Baskar Ganapathysubramanian (Iowa State)
Each room picks 2 or 3 topics to discuss Barrier, Challenge, Opportunities and Recommendations.
Room1 AI-advanced Science & Science-informed AI: Xia Ning (Moderator) Wei Ding (Scribe) note1
Room2 LLM and Continuous ML: Jing Gao (Moderator) Joshua Agar (Scribe) <a...
Moderator: Jennifer Dy (NEU)
HCI: Marti Hearst (Berkeley)
Data, AI and Machine Learning: Aidong Zhang (UVA), Shih-Chieh Hsu (UW)
Digital Twins: Omar Ghattas (UTexas)
Smart Sensing and Analytics: Mingyi Hong (UMichigan)
Rigorous & Reproducible Reasoning: Rajagopalan Balaji (Colorado)
Programmable/Self-Driving Labs: TBC
Lai-Yung (Ruby) Leung (PNNL)
