2024-20676. Notice of Request for Information (RFI) on Frontiers in AI for Science, Security, and Technology (FASST) Initiative

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Department of Energy

AGENCY:

Office of Critical and Emerging Technologies, Department of Energy.

ACTION:

Request for information (RFI).

SUMMARY:

The Department of Energy's Office of Critical and Emerging Technologies (CET) seeks public comment to inform how DOE and its 17 national laboratories can leverage existing assets to provide a national AI capability for the public interest.

DATES:

Responses to the RFI are requested by November 11, 2024.

ADDRESSES:

Interested parties may submit comments electronically to [e-mail] and include “FASST RFI” in the subject line of the email.

FOR FURTHER INFORMATION CONTACT:

Further questions may be addressed to Charles Yang through [e-mail] or [phone].

SUPPLEMENTARY INFORMATION:

I. Background

This is an RFI issued by the U.S. Department of Energy's (DOE) Office of Critical and Emerging Technologies (CET). This RFI seeks public input to inform our ongoing work and DOE's proposed Frontiers in AI for Science, Security, and Technology (FASST) initiative,^[1] which seeks to build the world's most powerful, integrated scientific AI models for scientific discovery, applied energy deployment, and national security applications.

DOE seeks input from:

• Academic institutions interested in partnering with DOE to leverage AI for scientific research
• For-profit and non-profit AI developers and research labs
• Data center and compute infrastructure providers
• Startups and investors
• Small businesses involved in the development or provision of AI technologies and services
• Civil society organizations potentially impacted by AI
• Labor training and technical workforce development organizations
• Think tanks and research organizations
• And other interested entities

II. Purpose

FASST is DOE's proposed initiative to build the world's most powerful, integrated scientific AI systems. This initiative leverages DOE's demonstrated history of capability building for the U.S. government, as well as key enabling infrastructure already housed at the DOE's Office of Science and Applied Energy facilities, and facilities operated by National Nuclear Security Administration (NNSA), including:

• Data: DOE is the leading generator of classified and unclassified scientific data through the world's largest collection of advanced experimental facilities, including particle accelerators, powerful light sources, specialized facilities for genomics and nanoscience, and neutron scattering sources.
• Computing Infrastructure: For decades, DOE has built and operated the world's fastest, most powerful, and highly energy efficient supercomputers. These supercomputers are strategic components of the nation's defensive capabilities, drive innovation through open access to the scientific community, and are the basis upon which to build safe and trustworthy AI capability for the nation.
• Workforce: DOE and its national labs host over 40,000 physicists, chemists, biologists, materials scientists, and computer scientists, who tackle some of the most urgent challenges in the national interest.
• Partnerships: DOE has unparalleled experience in mission-driven public-private collaborations. Through the Exascale Computing Project, DOE worked with industry partners to co-design and develop critical components of the computer chips that power today's leading AI models and partnered with leading academic institutions to develop scalable high-performance software libraries.

This RFI seeks public input to inform how DOE can partner with outside institutions and leverage its assets to implement and develop the roadmap for FASST, based on the four pillars of FASST: AI-ready data; Frontier-Scale AI Computing Infrastructure and Platforms; Safe, Secure, and Trustworthy AI Models and Systems; and AI Applications; as well as considerations for workforce and FASST governance. ( print page 74269)

III. Questions

1. Data

(a) What kinds of data governance practices, risks, and opportunities should DOE take into consideration, particularly for open sourcing scientific corpuses to the community or interested parties?

(b) What types of scientific and energy data should DOE prioritize for large-scale tokenization?

(c) Are there partner organizations with relevant scientific or energy-related data that DOE should work with?

(d) What are additional data-related tools and technologies DOE should invest in to promote AI-ready data and fuel continued US leadership in AI?

2. Compute

(a) How can DOE ensure FASST investments support a competitive hardware ecosystem and maintain American leadership in AI compute, including through DOE's existing AI and high-performance-computing testbeds? ^[2]

(b) How can DOE improve awareness of existing allocation processes for DOE's AI-capable supercomputers and AI testbeds for smaller companies and newer research teams? ^[3] How should DOE evaluate compute resource allocation strategies for large-scale foundation-model training and/or other AI use cases?

(c) How can DOE continue to support development of energy-efficient AI hardware, algorithms, and platforms?

(d) How can DOE continue to support the development of AI hardware, algorithms, and platforms tailored for science and engineering applications in cases where the needs of those applications differ from the needs of commodity AI applications? How can DOE partner with other compute capability providers, including both on-premises and cloud solution providers, to support various hardware technologies and provide a portfolio of compute capabilities for its mission areas?

3. Models

(a) How should DOE consider the benefits of open sourcing of scientific and applied energy AI models for the scientific community while fully addressing research security and other national-security concerns?

(b) How can DOE support investment and innovation in energy efficient AI model architectures and deployment, including potentially through prize-based competitions?

(c) What considerations should inform DOE's ongoing AI red-teaming and safety tests, particularly for Chemical, Biological, Radiological and Nuclear (CBRN) risks?

4. Applications

(a) What are application areas in science, applied energy, and national security that are primed for AI breakthroughs?

(b) How can DOE ensure foundation AI models are effectively developed to realize breakthrough applications, in partnership with industry, academia, and other agencies?

5. Workforce

(a) DOE has an inventory of AI workforce training programs underway through our national labs.^[4] What other partnerships or convenings could DOE host or develop to support an AI ready scientific workforce in the United States?

6. Governance

(a) How can DOE effectively engage and partner with industry and civil society? What are convenings, organizational structures, and engagement mechanisms that DOE should consider for FASST?

(b) What role should public-private partnerships play in FASST? What problems or topics should be the focus of these partnerships?

IV. Response Guidelines

Commenters are welcome to comment on any question. RFI responses shall include:

1. RFI title;

2. Name(s), phone number(s), and email address(es) for the principal point(s) of contact;

3. Institution or organization affiliation and postal address; and

4. Clear indication of the specific question(s) to which you are responding.

Responses to this RFI must be submitted electronically to [e-mail] with the subject line “FASST RFI” no later than 5:00 p.m. (ET) on November 11, 2024. Responses must be provided as attachments to an email. It is recommended that attachments with file sizes exceeding 25 MB be compressed ( i.e., zipped) to ensure message delivery. Responses must be provided as a Microsoft Word (*.docx) or Adobe Acrobat (*.pdf) attachment to the email and should be no more than 15 pages in length, 12-point font, 1-inch margins. Only electronic responses will be accepted. Only one response per individual or organization will be accepted.

A response to this RFI will not be viewed as a binding commitment to develop or pursue the project or ideas discussed. DOE may engage in post-response conversations with interested parties.

Confidential Business Information

Because information received in response to this RFI may be used to structure future programs and/or otherwise be made available to the public, respondents are strongly advised NOT to include any information in their responses that might be considered business sensitive, proprietary, or otherwise confidential.

Pursuant to 10 CFR 1004.11, any person submitting information that he or she believes to be confidential and exempt by law from public disclosure should submit via email two well-marked copies: one copy of the document marked “confidential” including all the information believed to be confidential, and one copy of the document marked “non-confidential” with the information believed to be confidential deleted. Failure to comply with these marking requirements may result in the disclosure of the unmarked information under the Freedom of Information Act or otherwise. The U.S. Government is not liable for the disclosure or use of unmarked information and may use or disclose such information for any purpose. If your response contains confidential, proprietary, or privileged information, you must include a cover sheet marked as follows identifying the specific pages containing confidential, proprietary, or privileged information:

Notice of Restriction on Disclosure and Use of Data

Pages [list applicable pages] of this response may contain confidential, proprietary, or privileged information that is exempt from public disclosure. Such information shall be used or disclosed only for the purposes described in this RFI. The Government may use or disclose any information that is not appropriately marked or otherwise restricted, regardless of source.

In addition, (1) the header and footer of every page that contains confidential, proprietary, or privileged information must be marked as follows: “Contains, Confidential, Proprietary, or Privileged Information Exempt from Public Disclosure” and (2) every line and ( print page 74270) paragraph containing proprietary, privileged, or trade secret information must be clearly marked with [[double brackets]] or highlighting. Submissions containing CBI should be sent to: [e-mail].

Signing Authority

This document of the Department of Energy was signed on September 6, 2024, by Helena Fu, Director, Office of Critical and Emerging Technologies, pursuant to delegated authority from the Secretary of Energy. That document with the original signature and date is maintained by DOE. For administrative purposes only, and in compliance with requirements of the Office of the Federal Register, the undersigned DOE Federal Register Liaison Officer has been authorized to sign and submit the document in electronic format for publication, as an official document of the Department of Energy. This administrative process in no way alters the legal effect of this document upon publication in the Federal Register .

Signed in Washington, DC, on September 9, 2024.

Treena V. Garrett,

Federal Register Liaison Officer, U.S. Department of Energy.

Footnotes

1.   www.energy.gov/​fasst.

2.   www.energy.gov/​cet/​artificial-intelligence-testbeds-doe.

3.   https://science.osti.gov/​ascr/​Facilities/​Accessing-ASCR-Facilities.

4.   www.energy.gov/​cet/​supercharging-americas-ai-workforce.