The Department of Energy has announced a $67 million investment in several artificial intelligence projects as part of its AI for Science initiative, with six projects led by Oak Ridge National Laboratory receiving funding. The goal of this funding is to establish foundational models in research areas such as scientific machine learning, large language models for high-performance computing, and automating laboratory workflow.
Key individuals involved in the work include William Godoy, senior computer scientist at ORNL, who will use the new funding to work alongside experts from Lawrence Livermore National Laboratory, the University of Maryland, and Northeastern University to identify strategies for creating large language models designed specifically for high-performance computing.
Pedro Valero Lara, a senior computer scientist at ORNL, is also involved in the project, which aims to create large language models that can be used for programming language translation, such as translating legacy HPC Fortran codes into more modern and capable C++ codes.
Advancing Artificial Intelligence for Scientific Research
The Department of Energy (DOE) has taken a significant step in establishing artificial intelligence (AI) as a priority in scientific research by investing $67 million in several AI projects from institutions in both government and academia. This investment is part of the DOE’s AI for Science initiative, which aims to establish foundational models in research areas such as scientific machine learning, large language models for high-performance computing, and automating laboratory workflow.
Six projects led or co-led by Oak Ridge National Laboratory (ORNL) received funding, including ENGAGE, DyGenAI, SciGPT, Productive AI-Assisted HPC Software Ecosystem, Privacy-Preserving Federated Learning for Science, and Durban. These projects were chosen through a competitive peer-review process under the DOE Funding Opportunity Announcement (FOA) for Advancements in Artificial Intelligence for Science.
Large Language Models for High-Performance Computing
One of the key areas of focus is the development of large language models (LLMs) designed specifically for high-performance computing (HPC). William Godoy, senior computer scientist at ORNL, will use the new funding to work alongside his contemporaries at Lawrence Livermore National Laboratory, as well as HPC and AI experts from the University of Maryland and Northeastern University, to identify the best strategies for creating LLMs designed specifically for HPC.
These LLMs can also be used for programming language translation, such as translating legacy HPC Fortran codes into more modern and capable C++ codes. Pedro Valero Lara, a senior computer scientist who works with Godoy, explained that building this capability in LLMs for specific HPC targets was part of the larger goal to support HPC more broadly.
Privacy-Preserving Federated Learning for Science
Another project, led by Olivera Kotevska, a research scientist in the Computer Science and Mathematics Division at ORNL, focuses on developing privacy-preserving federated learning for science. This project aims to advance cutting-edge research in privacy-preserving AI, crucial for safeguarding sensitive scientific data while fostering collaboration across institutions.
Kotevska emphasized that this support enables her team to develop sustainable, trustworthy AI solutions that can have a wide-reaching impact on scientific discovery and national security. Additionally, it strengthens ORNL’s leadership in building trustworthy AI systems for science, benefiting both the lab and the broader scientific community.
ORNL’s Leadership in AI Research
Prasanna Balaprakash, director of AI programs at ORNL, praised ORNL’s vast capabilities and deep history in AI research. The six awards cover all five areas of the FOA, a unique distinction for ORNL. These awards are a testament to ORNL’s AI expertise and capabilities, solidifying its position as a major leader in AI for science.
Several of the projects have been supported by ORNL’s AI Initiative — a lab-directed research and development investment focused on developing secure, trustworthy, and energy-efficient AI solutions to address problems of national importance.
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