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DOE Labs Pitching Major AI R&D Initiative to Congress

AUG 11, 2023
Department of Energy national labs are laying groundwork for a potential multi-billion dollar initiative to develop artificial intelligence tools for scientific and security applications, leveraging its advanced computing capabilities.
Jacob Taylor headshot
Senior Editor for Science Policy, FYI American Institute of Physics
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The Frontier supercomputer at Oak Ridge National Lab. Frontier is the first to come online of the three exascale machines produced by DOE’s Exascale Computing Project.

As lawmakers scramble to respond to the emergence of artificial intelligence, the Department of Energy is making the case for them to assign it a lead role in developing AI tools for fundamental research, energy technology development, and national security. Staff from DOE national labs have presented an initial vision for an AI initiative in the form of a 200-page report published this summer titled, “Advanced Research Directions on AI for Science, Energy, and Security”.

DOE staff have drawn from the report in congressional briefings, including a Senate-wide briefing last month organized by Senate Majority Leader Chuck Schumer (D-NY), who is preparing to advance major legislation on AI. The initiative proposed in the report is designed in part as a follow-on to DOE’s $1.8 billion Exascale Computing Project (ECP), which is about to conclude following the deployment last spring of the first U.S. exascale computer at Oak Ridge National Lab and the planned deployment of additional machines at Argonne and Lawrence Livermore National Labs.

The report paints a picture of AI as a potential way to dramatically accelerate the pace of R&D and enable previously impossible scientific capabilities. It also casts AI as a strategically essential technology that will have multiplicative effects and determine the leaders and followers in other technological arenas. “Establishing leadership in AI and in the underlying capabilities, including high performance computing, will be intimately tied to the nation’s future and its role in the global order,” it argues.

Six main thrusts of effort proposed

The national labs’ report proposes six main areas where AI could be applied across DOE’s mission areas: foundation models for scientific discovery and synthesis; surrogate models for scientific computing; property inference and inverse design; design, prediction, and control of complex engineered systems; autonomous discovery; and software engineering. It also details “grand challenges” associated with each area.

AI foundation models are general-purpose models that can be applied to a wide range of tasks, such as the language models and image generators that have recently grabbed public attention. The report imagines creating new foundation models that are dedicated to scientific and national security problems, while acknowledging the task is daunting enough to require a “moonshot” level of effort. The report envisions for instance that foundation models could help synthesize research relevant to fundamental scientific questions such as how clouds affect the Earth’s climate or how vortices evolve in fusion plasmas.

“Regardless of the specific problem being studied, a frequent challenge is the vast amount of existing knowledge that could potentially be relevant to its solution — a quantity that typically far exceeds the cognitive capacity of any one individual or even team. The recent and considerable successes achieved with large language models suggest that a transformative solution may be on the horizon,” it notes.

The report describes surrogate models as “simpler yet faithful” representations of complex, real-life systems, with the models themselves trained on the outputs of other computational models. It recommends launching pilot programs to develop surrogate models of plasma turbulence and the Earth’s oceans, among other systems. DOE’s Energy Exascale Earth System Model is flagged as an existing project that could be made much less computationally demanding by applying surrogate models.

AI could also be used to infer the properties of specific materials or to identify materials that might meet certain criteria, according to the report. Such inference models could be applied at vastly different scales. “Inference models describing the nuclear stockpile or microelectronics cover length scales from atoms to the whole system and timescales from milliseconds to days,” it states.

The report further envisions using AI to help control complex machines and experiments, such as DOE’s various user facilities. For example, AI could be used to tune particle accelerators in real time or calibrate the diagnostic systems of laser experiments and nuclear reactors. Such efforts are currently time-consuming and some rely on massive conventional computing systems. The report also imagines using AI-controlled robots to help automate the process of scientific discovery, such as by autonomously manufacturing specialized materials and using AI-generated designs as a starting point for new nuclear weapons systems.

Among the overarching challenges identified is that many AI tools suffer from an inability to determine how they arrived at particular conclusions. “The ‘black box’ nature of AI models confounds our ability to validate the results, hindering adoption,” the report observes.

DOE seeks a lead role in AI R&D

Among the report’s lead organizers is the head of Argonne’s Computing, Environment, and Life Sciences Directorate, Rick Stevens, who participated in the Senate-wide briefing. Stevens offered details on his discussions with Congress at a recent meeting of the advisory committee for DOE’s Advanced Scientific Computing Research (ASCR) program, which is co-leading the ECP with the National Nuclear Security Administration.

“The pitch that we’re making is that only DOE and NNSA can really advance this responsible codesign of AI R&D with a strong focus on science, energy, and national security,” he said, while observing that the department “is probably not the right organization to be the lead on monitoring and regulation.”

Stevens explained how DOE could leverage its experience organizing large interdisciplinary teams and running exascale computers to spearhead an even larger project focused on developing AI tools relevant across the entire department. “This is not going to be a small initiative. This could be several times or more the scale of what we have been doing with ECP,” he said.

Asked about the prospects for launching such an effort given the recent budget caps set by Congress, Stevens expressed hope that it could receive funding as early as fiscal year 2024 through a special appropriation, perhaps via a follow-on to the CHIPS and Science Act. Schumer has said he wants to develop a bill that would include direct funding for technology areas beyond semiconductors.

The chair of the advisory committee, Dan Reed, emphasized the likely need for a special appropriation. “With the budget caps in place, we’re talking about potentially a 1% increase [in overall federal discretionary spending]. With inflation running what it is, that’s a de facto budget cut. The only viable strategy is a version of what Rick articulated: it will require emergency appropriations outside the regular order.”

Under the spending caps, the House and Senate have proposed to cut ASCR’s regular annual budget by 5% to just over $1 billion for fiscal year 2024. Asked about these proposals, acting ASCR head Ceren Susut told FYI, “The numbers are what they are, and it remains to be seen what we end up with at the end of the day. I think what we can do from our point of view is to prepare for a big initiative and also respond to the congressional staffers’ questions about what we can do, what are the concerns about AI, what are the opportunities about AI, and how DOE can take a leadership role.”

She noted DOE is in the early stages of planning for such an initiative and that the AI report is a major input that will be supplemented with additional ideas from stakeholders.

“The case that we’re trying to make is that we have a unique role to play here,” Susut said.

Concerns mounting over global competition in supercomputing

The report emphasizes the current international race to develop AI technology is closely connected to competition to develop ever-faster supercomputers. “Progress in designing and deploying supercomputers in China, Japan, Europe, and other nations has resulted in a competitive AI position that cannot be ignored,” it states.

This sentiment is echoed in a separate report recently published by the advisory committee for ASCR titled, “Can the United States Maintain Its Leadership in High-Performance Computing?” The committee expresses deep concern about the outlook for supercomputer development in the U.S. It argues for instance that uncertainty about what will come after the ECP is “generating much anxiety” among staff members working on the project, with the risk many may leave the national labs to work in the private sector.

The committee concludes that “U.S. leadership in HPC has eroded” despite the impending completion of the three exascale machines produced by ECP, adding that “it seems clear that China has at least matched U.S. HPC capabilities.” It notes China reportedly plans to deploy 10 exascale machines by 2025.

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