AI transcript
0:00:10 [MUSIC]
0:00:13 >> Hello, and welcome to the NVIDIA AI podcast.
0:00:15 I’m your host, Noah Kravitz.
0:00:18 This past October, leaders from the public sector
0:00:22 joined NVIDIA customers and partners at AI Summit in Washington, D.C.
0:00:24 for three days of connection and discussion
0:00:28 around all of the innovative and meaningful work being done with AI.
0:00:32 One of the panels at the summit, AI for Science, Energy, and Security,
0:00:36 focused on how artificial intelligence is transforming scientific discovery,
0:00:38 economic growth, and national security,
0:00:40 and how the US government can lead the way
0:00:43 in developing safe and trustworthy AI
0:00:45 to address national and global challenges.
0:00:49 It’s a great discussion, and I encourage you to watch it via NVIDIA on demand.
0:00:51 But even better than that,
0:00:54 we’re fortunate enough to have the chance to delve a bit deeper into the role
0:00:57 the US Department of Energy is playing in AI development.
0:01:00 Spoiler alert, they’re doing a lot more than you might think.
0:01:04 With us now is Halina Fu, Director of the Office of Critical and Emerging Technology,
0:01:07 CET, at the US Department of Energy.
0:01:11 Director Fu previously served as Director for Technology and National Security
0:01:13 at the White House National Security Council
0:01:17 as Director of International Science and Technology Cooperation
0:01:20 and Trusted Research for the Office of Science at the Department of Energy
0:01:24 and at the White House Office of Science and Technology Policy.
0:01:27 Director Fu’s credentials extend much further than that,
0:01:29 but I’ll leave it there so we can welcome her onto the show.
0:01:34 Halina Fu, thank you so much for taking the time to join the NVIDIA AI podcast.
0:01:36 Great to be here, Noah. Thanks for inviting me.
0:01:41 So perhaps we can start with the CET and what your role is as Director.
0:01:43 Tell us a bit about that.
0:01:48 Sure. So the Office of Critical and Emerging Technologies, or CET,
0:01:51 because we love a good acronym, is a new office at the department.
0:01:54 It was launched in December of 2023.
0:01:58 So we’re less than a year old, coming on that one-year anniversary.
0:02:02 And our senior leadership at the department
0:02:05 really wanted to have a central node within the department
0:02:09 that was focused on leveraging capabilities and expertise
0:02:12 across the department and at 17 national labs
0:02:15 in specific areas of critical and emerging technologies.
0:02:20 And our office focuses on four specific technology areas.
0:02:24 AI, microelectronics, quantum information science,
0:02:25 and biotechnology.
0:02:29 And these four we really see as foundational technologies
0:02:33 that enable so many other critical and emerging technologies.
0:02:34 Absolutely.
0:02:37 And so how does that fit into the broader scope?
0:02:39 We were chatting for a second before we started recording.
0:02:41 I was watching the panel you were on,
0:02:44 and there were opening remarks from the Secretary of Energy as well.
0:02:48 So again, it’s a great panel, and I learned a lot by watching it.
0:02:49 And in particular, I was struck.
0:02:53 I think there was a joke made on the panel about calling it Department of Everything.
0:02:57 I was struck by how much the DOE broadly does.
0:02:59 So maybe you could take a second, if you would,
0:03:01 and kind of speak to a little bit to that.
0:03:04 Yeah, I think that’s a joke that our Secretary made.
0:03:08 But actually, many Secretaries of Energy have made this joke
0:03:11 because as everyone comes into the department,
0:03:15 they see the vast scope of what we actually do here at DOE.
0:03:20 So as you said, many people think about DOE as the clean energy department.
0:03:23 And we are, but we do do so much more.
0:03:27 We fund basic scientific discovery and research.
0:03:31 We steward scientific infrastructure all across the country.
0:03:35 And these are some of the world’s most powerful x-rays, particle accelerators,
0:03:38 colliders, sort of really, really big science.
0:03:41 We advance energy research applications,
0:03:46 which is the part that I think many people know and touch when it comes to DOE,
0:03:51 as well as, you know, we’re the sector risk management agency for the electricity sector.
0:03:54 But we also play a key role in the national security space
0:03:57 through the National Nuclear Security Administration.
0:04:00 And on top of that, we steward the 17 national labs,
0:04:03 which are located all across the country.
0:04:06 Many of them were birthed out of the Manhattan Project,
0:04:12 but today they serve as that scientific infrastructure backbone of the United States
0:04:17 and the capability not just for DOE, but for the broader U.S. government.
0:04:21 And so I talked a little bit about that scientific infrastructure.
0:04:24 DOE has 34 scientific user facilities.
0:04:28 Now, these are places where scientists within the United States,
0:04:32 from academia, from industry, as well as international scientists,
0:04:37 they can access these facilities on a peer-reviewed basis, of course.
0:04:41 These are the kinds of particle accelerators, molecular foundries,
0:04:45 supercomputers, that the department stewards for the country.
0:04:52 So when I think about, and I think a lot of the conversations that we’ve been having on the podcast,
0:04:56 when I think about the relationship between AI-accelerated computing,
0:05:00 these other advanced technologies and emerging technologies and energy,
0:05:02 I sort of think of two different things.
0:05:08 And I’m curious how you think about it, if you think about it in similar buckets or not.
0:05:14 One is compute uses energy. AI, all this stuff, the bigger, the faster,
0:05:16 the more powerful, the more energy. So there’s that.
0:05:20 And there’s been talk lately about, is nuclear going to play a role?
0:05:23 All these different things, how does clean energy or renewable energy,
0:05:27 how does all of this relate to powering the GPs, the data centers, all that stuff?
0:05:35 And then the other side of it is, how can all of the technology inform finding new sources of renewable energy,
0:05:41 making it more efficient, all of that good stuff to make the whole process of creating and using energy
0:05:46 better for everybody? I can’t even imagine how complex these things are when you think about them.
0:05:50 But how do you describe the relationship between energy and specifically what you’re doing,
0:05:55 CET and DOE, and the relationship to all of this technology stuff that we’re talking about?
0:06:00 I think that’s a great question. And really, the Department of Energy is sitting at that intersection.
0:06:08 This intersection of AI as applied to the energy sector and the energy availability for AI to power
0:06:15 the data centers that these AI models are trained on. And so we are absolutely laser focused on both
0:06:21 of these issues. And I think one really concrete example of where this all comes together really
0:06:27 is in some of the partnerships that we’ve been able to develop with industry to drive energy
0:06:34 efficiency of computing. This is the story behind the Exascale Computing Project and the initiative
0:06:40 that DOE has been very, very involved in over the last decade. And the very beginnings of that
0:06:48 was this presupposition that to actually get to Exascale Computing, you would need to make huge
0:06:54 advances in energy efficiency. Can I stop you for a second and just ask you to define Exascale
0:07:00 Computing for everyone or the project, I should say? Sure, sure. So this is something that was
0:07:06 really done in deep partnership between the Office of Science at the Department of Energy
0:07:12 and the National Nuclear Security Administration. Okay. So this is a really a committed partnership
0:07:20 that was begun back in 2016 and running through 2024. And this was really about how we could build
0:07:27 some of the most powerful and fastest supercomputers in the world, right? And so Exascale is really
0:07:33 referring to how quickly how many floating point operations per second could actually be performed,
0:07:38 right? And so that is one quintillion floating point operations per second or one exathlon.
0:07:43 I realize I said define as in like, what did I mean? But really, I just wanted you to talk
0:07:49 about the project, but that’s a great definition. So thank you. Back at that time, DOE was already
0:07:55 investing in advanced supercomputing, but we recognized that to really push the boundaries
0:08:01 of science, energy applications and national security, we would need even faster and more
0:08:07 powerful supercomputers to do the kinds of exquisite modeling and simulation that would be needed for
0:08:14 the nuclear stockpile, but also model the climate. And so there were so many different applications
0:08:18 in that space. But really, at the time, it was an energy efficiency question. How could we build
0:08:25 these kinds of huge, huge supercomputers? And how would we manage the power envelope and the cost
0:08:31 associated with power in these supercomputers? And so way back during that time, the goals that
0:08:38 were set for Exascale were not set by what was technically possible at the time, but were really
0:08:44 set by just a number. How much we were willing to pay over the life of the system, and then just
0:08:51 divided it by five. And that was the goal. And really, at the time, I think many folks thought
0:08:58 we were crazy, but I think that’s really the magic of DOE. I don’t want to call it magic,
0:09:03 it’s not magic, it’s just the ability of the department to partner deeply with industry,
0:09:10 to accomplish things at scale. I think that is really how I would think about where DOE fits
0:09:15 in the larger AI ecosystem. I think one other thing that would be really helpful to kind of
0:09:22 weigh out. So I talked about AI and energy and energy for AI as one intersection where DOE sits.
0:09:29 I think the other one that is not very widely known, but kind of goes to all of the different
0:09:36 parts of DOE, is that we sit at this intersection of open science and classified science. So open
0:09:44 science and national security. I’m imagining you walking a tightrope with the balance beam thing,
0:09:50 just saying classified science kind of brings that area. There’s many national security applications
0:09:56 of science, of course. And much of that really is dependent on advances in open science. And I
0:10:05 think the fact of DOE as a mission-driven R&D agency that is able to bridge these two
0:10:12 arenas and to do that at scale, I think scale, again, is an important demarcator there, is really
0:10:17 what makes working at DOE so exciting. And frankly, as we bring it back to our Office of
0:10:23 Critical Immersion Technologies, the four technology areas I mentioned, AI, microelectronics,
0:10:29 quantum information science, and biotechnology, they’re all dual-use technologies. So I think really,
0:10:35 as we think about both opportunities to advance these technologies and opportunities to manage
0:10:42 the risks of those technologies, you really do need that duality of approach where the science
0:10:47 is going and what the national security implications are going to be to address this effectively.
0:10:55 Are there specific AI-related initiatives going on within DOE, within CET, that you can talk about?
0:11:01 Sure, yes. And we’re very excited about the opportunity space of AIS, if you haven’t caught
0:11:09 on already. We do have a very exciting proposed initiative called FAST, Frontiers in AI for
0:11:18 Science, Security and Technology, which we see as a real opportunity space for a step change in U.S.
0:11:25 government capability in AI. We all see the advancements that industry is making. We need to
0:11:31 be able to harness those advancements for our mission space. And I think the recently issued
0:11:38 national security memorandum on AI just last week really directs departments and agencies in the
0:11:44 national security space to do just this. But we also think that there’s even more to do, right?
0:11:52 So FAST is sort of organized in four general buckets, one around data, one around advancing
0:11:59 compute, one on developing new kinds of models, and the fourth around the application space. So how
0:12:05 do we actually use these models to solve things in the real world? Yes, there’s been, I actually was
0:12:10 recording a podcast earlier this week with somebody and used this same line. So forgive me, listeners.
0:12:17 But there’s kind of this sense that 2022, 2023, in the generative AI space in particular, we’re
0:12:23 sort of the years that LLMs and the idea of models kind of exploded into the mainstream. And businesses
0:12:28 and other organizations started to take notice and really think about what we need to do, invest,
0:12:34 we need to do things. And then 2024 now has been the year where folks in the application space
0:12:40 are really starting to hone in on, okay, how do we take all this power and translate it into
0:12:45 something that is, you know, practical, useful, and easy for an end user to kind of pick up and go
0:12:51 with. Is there a similar sort of timeline or are you kind of in the same sort of headspace
0:12:57 with relative to building the applications and that focus now in your government work or is it
0:13:02 a different, are you in a different spot? So I think that’s a really interesting question because
0:13:09 when it comes to agency deployment of industry models, many of these are
0:13:15 large language models, right? And so we see tremendous efficiency, potential efficiencies
0:13:20 within the department on utilizing these. And we have some things that we’re beta testing now
0:13:26 and excited about them. But what we’re talking about in FAST is really around how we harness
0:13:34 the scientific data that DOE generates, which is some of the most unclassified and classified
0:13:40 scientific experimental data in the world. You can’t even imagine, yeah. How we really advance
0:13:47 computing in this space in a way that we were able to do through the exascale computing project,
0:13:54 but really think further beyond the horizon. Yeah. And then the models themselves, we really think
0:13:58 that much of industry obviously is focused on large language models. And there’s been a lot of
0:14:04 exciting developments. And even those models are becoming much better at scientific reasoning.
0:14:09 Really, we’re seeing tremendous advances in that space. And that really excites us because that
0:14:15 means the time to discovery, whether it’s for in discovery science or in the energy applications
0:14:21 or in national security, that time is going to be cut so significant. But we also think
0:14:26 that there’s going to need to be new kinds of models, additional kinds of models that are not
0:14:31 language-based, but are graph neural networks or physics-informed models
0:14:37 that are the kinds of models that need to be able to learn from the kinds of data that we hold.
0:14:44 And that can be then shown to translate into the physical world, which is why we have laboratories.
0:14:50 So I think this is where we get so excited because we think the opportunities for partnership
0:14:57 with industry are really enormous. And a place where there hasn’t yet been the same kind of
0:15:03 attention and focus more broadly. Because frankly, these are things that are in the realm of
0:15:07 science, right? Like government investment in science. That’s why there is that sort of
0:15:12 public-private partnership opportunity. If I could just add one thing though.
0:15:17 Please. Because there’s been a lot of attention when we talk about AI for science. There’s been
0:15:23 a lot of attention, for example, on AlphaFold. And I think what it’s important to note there
0:15:31 is AlphaFold’s statistical models, they’re trained on data from experimentally determined protein
0:15:37 structures that were only made possible by the use of DOE’s unique large-scale light sources.
0:15:45 And the protein structure data that was used and that is free and open to the public
0:15:51 are stored on DOE-funded protein data bank, which gets funding from other U.S. government
0:15:56 departments and agencies. But I think that’s an example of where there is that public-private
0:16:02 dimension to even industry advancements. My guest today is Helena Fu. Helena is the
0:16:07 director of the Office of Critical and Emerging Technologies at the U.S. Department of Energy.
0:16:11 And as we’ve been talking about, the Department of Energy, the work that they’re doing, the work
0:16:16 that director Fu’s team is doing as well, touches so many different parts of private life, public
0:16:22 life, different parts of the government working with science and industry and everything else.
0:16:28 So many different places. Let’s kind of land on a note of opportunity, and I’m not going to ask
0:16:33 you to predict the future. But what are some of the spaces that you in particular, your teams,
0:16:39 are really excited about when it comes to the applications of large language models, AI, any
0:16:44 of the technology we’ve been talking about, or other things that we haven’t gotten to yet?
0:16:50 Thanks for that question. I think I might answer in two timescales. The first is the near term.
0:16:55 So we have a number of activities already underway that are really seeking to harness
0:17:02 these powerful tools. One example here is really around smart grids and using AI. For example,
0:17:08 our grid deployment office funded Portland General Electric to deploy utility data smart meters,
0:17:14 which have GPUs inside of them to customers. And we think that there’s an opportunity also for
0:17:20 local models that help disaggregate loads like electric vehicles, heat pumps, and that provide
0:17:28 utilities insight while also protecting customer privacy. Our Office of Policy and Pacific Northwest
0:17:34 National Lab are also leading this voltaic initiative, which is really around how we can harness
0:17:39 large language models for permitting and how we find efficiencies in that process.
0:17:43 Permitting as in granting permits to citizens to do things?
0:17:51 Permitting process like environmental permitting reviews, NEPA. And in fact, one of the things
0:17:58 that we were able to do just earlier this year was to take the entire corpus of NEPA data over the
0:18:06 last decade or so and make that AI ready and available and is now open and available to
0:18:12 the public for researchers to work on for industry to develop new tools around. We are working with
0:18:18 other agencies in the Federal Permitting Council to see how they can potentially use this tool
0:18:22 for their permitting processes. So that’s exciting. And that’s at the federal level,
0:18:27 but the voltaic initiative is actually also focused on expanding to state and local
0:18:32 permitting because there’s even more variants across state and local ordinances, for example,
0:18:40 for EB charging or for any other kind of siting. Yeah, the mention of the grid and the smart grid
0:18:46 meters with the GPUs and then you’d be able to compute on the fly. I mean, that hits home for me.
0:18:51 I’m kind of the stereotypical Northern California. I’ve got an EV, the whole thing,
0:18:55 but it really makes me think about all of the opportunities in the physical world
0:19:01 that if the infrastructure is in place and you’ve got this data to work with, which obviously is
0:19:06 huge and I can’t even imagine the troves of data that are around waiting to be processed and put
0:19:13 to use, but you can deploy things at the edge like that to a customer’s, in my case, to where
0:19:19 I live and a residential person can have that smart meter or can have the ability to shift
0:19:25 the loads locally. And I can only imagine the possibilities that opens up for grid resilience
0:19:32 and all of those things. That’s exactly right. And I think moving beyond the short term and into
0:19:38 that medium and long-term time scale, we’re so excited about the opportunity of AI applied
0:19:43 to materials discovery, which I think has applications across science, energy and national
0:19:49 security, right? It’s enormous, yeah. We often talk about this example from PNNL and Microsoft
0:19:54 to look at the universe of potential materials and then fabricate a battery that uses 70%
0:20:01 less lithium. But I think that’s just only the tip of the story, right? Obviously, battery materials,
0:20:07 carbon capture sorbents, hydrogen catalyst, there’s so much opportunity to discover new materials
0:20:15 that will have both impacts on like, abundance and affordability of energy, but also for strategic
0:20:22 technologies like critical minerals and hypersonics, right? There’s many, many applications across
0:20:28 the spectrum. Similarly, in the physical sciences, right, there’s obviously a lot of work already
0:20:34 underway on physics-informed models for climate. We need to be able to bridge climate and weather
0:20:39 across multiple timescales. And that will have huge implications for disaster response and
0:20:45 preparedness and just climate modeling more generally. Yes, yeah. As you said, that’s just
0:20:51 kind of the tip of the iceberg. And the idea of 70% more efficient car batteries built from
0:20:56 renewable energy is sort of mind-blowing to me anyway, but that really is just kind of the
0:21:01 beginning of the story, right? We’re in the early innings here in general. Before we wrap up,
0:21:07 any closing thoughts, people listening out there, perhaps there are organizational leaders,
0:21:13 IT leaders, people in there, whether it’s private or public sector, who are really starting to work
0:21:19 on, they understand the opportunity, they’ve used some AI tools, they’re thinking in that same way,
0:21:24 right, that kind of short and kind of midterm timescales. Advice you would give to somebody who
0:21:30 is, whether their organization is small or governmental scale, who’s trying to kind of
0:21:37 lead that initiative to take advantage of AI in a thoughtful and sort of sustainably minded way.
0:21:43 Any words of advice from your experience? Yeah, I am also the acting chief AI officer
0:21:48 for the Department of Energy. As I think about how the Department of Energy is going to utilize
0:21:54 AI and work very closely with our chief information office on this, we think really,
0:22:01 and this goes for within DOE, also companies, as they think about adopting AI, we really need
0:22:05 to innovate up that ladder of trust. So we really need to think about, I mean, when I think about
0:22:10 this for DOE, I think about what are the immediate use cases? How do we make sure that they’re not
0:22:16 rights and safety impacting? What are the processes we have in place to manage those risks?
0:22:22 But I think the overall advice here really would be, this is a transformative tool.
0:22:28 It is a tool in the tool bell. It’s very powerful. We should all figure out how to use this to the
0:22:34 best of our ability to amplify and augment the work that we’re doing. So we are thinking about
0:22:39 this day in, day out at DOE. We think that partnerships are going to be essential to our
0:22:46 success when it comes to applying AI for our broad science, energy, and national security mission.
0:22:53 Fantastic. For listeners who would like to learn more about any of the many things that you covered,
0:22:56 what the Office of Critical and Emerging Technologies is doing, the Department of
0:23:02 Energy is doing, all of that good work. Where are some places they can go online to get started?
0:23:05 Yeah, well, the Office of Critical and Emerging Technologies has a website,
0:23:10 and that is a place where we really look to point to all of the different and amazing
0:23:15 activities happening across the department and the national laboratory. So that’s a really good
0:23:21 resource to learn more about our Frontiers in AI for Science, Security, and Technology initiative.
0:23:28 And also, TESPEs that are available at DOE, training opportunities at the national laboratories,
0:23:35 foundation models that we’ve already developed in partnership with others, and so much more.
0:23:40 Fantastic. Well, again, thank you so much for taking the time, Director Fu, to come on the podcast
0:23:46 and kind of extend the conversation that you started at the AI Summit, and hopefully we can do it
0:23:50 again down the line. In the meantime, I’m really excited to follow the work that you and your teams
0:23:55 and the rest of the folks in the government are doing to make life better for all of us. So thank you.
0:24:07 Thank you. Thanks so much.
0:24:18 [Music]
0:24:46 [Music]
0:24:56 [BLANK_AUDIO]
0:00:13 >> Hello, and welcome to the NVIDIA AI podcast.
0:00:15 I’m your host, Noah Kravitz.
0:00:18 This past October, leaders from the public sector
0:00:22 joined NVIDIA customers and partners at AI Summit in Washington, D.C.
0:00:24 for three days of connection and discussion
0:00:28 around all of the innovative and meaningful work being done with AI.
0:00:32 One of the panels at the summit, AI for Science, Energy, and Security,
0:00:36 focused on how artificial intelligence is transforming scientific discovery,
0:00:38 economic growth, and national security,
0:00:40 and how the US government can lead the way
0:00:43 in developing safe and trustworthy AI
0:00:45 to address national and global challenges.
0:00:49 It’s a great discussion, and I encourage you to watch it via NVIDIA on demand.
0:00:51 But even better than that,
0:00:54 we’re fortunate enough to have the chance to delve a bit deeper into the role
0:00:57 the US Department of Energy is playing in AI development.
0:01:00 Spoiler alert, they’re doing a lot more than you might think.
0:01:04 With us now is Halina Fu, Director of the Office of Critical and Emerging Technology,
0:01:07 CET, at the US Department of Energy.
0:01:11 Director Fu previously served as Director for Technology and National Security
0:01:13 at the White House National Security Council
0:01:17 as Director of International Science and Technology Cooperation
0:01:20 and Trusted Research for the Office of Science at the Department of Energy
0:01:24 and at the White House Office of Science and Technology Policy.
0:01:27 Director Fu’s credentials extend much further than that,
0:01:29 but I’ll leave it there so we can welcome her onto the show.
0:01:34 Halina Fu, thank you so much for taking the time to join the NVIDIA AI podcast.
0:01:36 Great to be here, Noah. Thanks for inviting me.
0:01:41 So perhaps we can start with the CET and what your role is as Director.
0:01:43 Tell us a bit about that.
0:01:48 Sure. So the Office of Critical and Emerging Technologies, or CET,
0:01:51 because we love a good acronym, is a new office at the department.
0:01:54 It was launched in December of 2023.
0:01:58 So we’re less than a year old, coming on that one-year anniversary.
0:02:02 And our senior leadership at the department
0:02:05 really wanted to have a central node within the department
0:02:09 that was focused on leveraging capabilities and expertise
0:02:12 across the department and at 17 national labs
0:02:15 in specific areas of critical and emerging technologies.
0:02:20 And our office focuses on four specific technology areas.
0:02:24 AI, microelectronics, quantum information science,
0:02:25 and biotechnology.
0:02:29 And these four we really see as foundational technologies
0:02:33 that enable so many other critical and emerging technologies.
0:02:34 Absolutely.
0:02:37 And so how does that fit into the broader scope?
0:02:39 We were chatting for a second before we started recording.
0:02:41 I was watching the panel you were on,
0:02:44 and there were opening remarks from the Secretary of Energy as well.
0:02:48 So again, it’s a great panel, and I learned a lot by watching it.
0:02:49 And in particular, I was struck.
0:02:53 I think there was a joke made on the panel about calling it Department of Everything.
0:02:57 I was struck by how much the DOE broadly does.
0:02:59 So maybe you could take a second, if you would,
0:03:01 and kind of speak to a little bit to that.
0:03:04 Yeah, I think that’s a joke that our Secretary made.
0:03:08 But actually, many Secretaries of Energy have made this joke
0:03:11 because as everyone comes into the department,
0:03:15 they see the vast scope of what we actually do here at DOE.
0:03:20 So as you said, many people think about DOE as the clean energy department.
0:03:23 And we are, but we do do so much more.
0:03:27 We fund basic scientific discovery and research.
0:03:31 We steward scientific infrastructure all across the country.
0:03:35 And these are some of the world’s most powerful x-rays, particle accelerators,
0:03:38 colliders, sort of really, really big science.
0:03:41 We advance energy research applications,
0:03:46 which is the part that I think many people know and touch when it comes to DOE,
0:03:51 as well as, you know, we’re the sector risk management agency for the electricity sector.
0:03:54 But we also play a key role in the national security space
0:03:57 through the National Nuclear Security Administration.
0:04:00 And on top of that, we steward the 17 national labs,
0:04:03 which are located all across the country.
0:04:06 Many of them were birthed out of the Manhattan Project,
0:04:12 but today they serve as that scientific infrastructure backbone of the United States
0:04:17 and the capability not just for DOE, but for the broader U.S. government.
0:04:21 And so I talked a little bit about that scientific infrastructure.
0:04:24 DOE has 34 scientific user facilities.
0:04:28 Now, these are places where scientists within the United States,
0:04:32 from academia, from industry, as well as international scientists,
0:04:37 they can access these facilities on a peer-reviewed basis, of course.
0:04:41 These are the kinds of particle accelerators, molecular foundries,
0:04:45 supercomputers, that the department stewards for the country.
0:04:52 So when I think about, and I think a lot of the conversations that we’ve been having on the podcast,
0:04:56 when I think about the relationship between AI-accelerated computing,
0:05:00 these other advanced technologies and emerging technologies and energy,
0:05:02 I sort of think of two different things.
0:05:08 And I’m curious how you think about it, if you think about it in similar buckets or not.
0:05:14 One is compute uses energy. AI, all this stuff, the bigger, the faster,
0:05:16 the more powerful, the more energy. So there’s that.
0:05:20 And there’s been talk lately about, is nuclear going to play a role?
0:05:23 All these different things, how does clean energy or renewable energy,
0:05:27 how does all of this relate to powering the GPs, the data centers, all that stuff?
0:05:35 And then the other side of it is, how can all of the technology inform finding new sources of renewable energy,
0:05:41 making it more efficient, all of that good stuff to make the whole process of creating and using energy
0:05:46 better for everybody? I can’t even imagine how complex these things are when you think about them.
0:05:50 But how do you describe the relationship between energy and specifically what you’re doing,
0:05:55 CET and DOE, and the relationship to all of this technology stuff that we’re talking about?
0:06:00 I think that’s a great question. And really, the Department of Energy is sitting at that intersection.
0:06:08 This intersection of AI as applied to the energy sector and the energy availability for AI to power
0:06:15 the data centers that these AI models are trained on. And so we are absolutely laser focused on both
0:06:21 of these issues. And I think one really concrete example of where this all comes together really
0:06:27 is in some of the partnerships that we’ve been able to develop with industry to drive energy
0:06:34 efficiency of computing. This is the story behind the Exascale Computing Project and the initiative
0:06:40 that DOE has been very, very involved in over the last decade. And the very beginnings of that
0:06:48 was this presupposition that to actually get to Exascale Computing, you would need to make huge
0:06:54 advances in energy efficiency. Can I stop you for a second and just ask you to define Exascale
0:07:00 Computing for everyone or the project, I should say? Sure, sure. So this is something that was
0:07:06 really done in deep partnership between the Office of Science at the Department of Energy
0:07:12 and the National Nuclear Security Administration. Okay. So this is a really a committed partnership
0:07:20 that was begun back in 2016 and running through 2024. And this was really about how we could build
0:07:27 some of the most powerful and fastest supercomputers in the world, right? And so Exascale is really
0:07:33 referring to how quickly how many floating point operations per second could actually be performed,
0:07:38 right? And so that is one quintillion floating point operations per second or one exathlon.
0:07:43 I realize I said define as in like, what did I mean? But really, I just wanted you to talk
0:07:49 about the project, but that’s a great definition. So thank you. Back at that time, DOE was already
0:07:55 investing in advanced supercomputing, but we recognized that to really push the boundaries
0:08:01 of science, energy applications and national security, we would need even faster and more
0:08:07 powerful supercomputers to do the kinds of exquisite modeling and simulation that would be needed for
0:08:14 the nuclear stockpile, but also model the climate. And so there were so many different applications
0:08:18 in that space. But really, at the time, it was an energy efficiency question. How could we build
0:08:25 these kinds of huge, huge supercomputers? And how would we manage the power envelope and the cost
0:08:31 associated with power in these supercomputers? And so way back during that time, the goals that
0:08:38 were set for Exascale were not set by what was technically possible at the time, but were really
0:08:44 set by just a number. How much we were willing to pay over the life of the system, and then just
0:08:51 divided it by five. And that was the goal. And really, at the time, I think many folks thought
0:08:58 we were crazy, but I think that’s really the magic of DOE. I don’t want to call it magic,
0:09:03 it’s not magic, it’s just the ability of the department to partner deeply with industry,
0:09:10 to accomplish things at scale. I think that is really how I would think about where DOE fits
0:09:15 in the larger AI ecosystem. I think one other thing that would be really helpful to kind of
0:09:22 weigh out. So I talked about AI and energy and energy for AI as one intersection where DOE sits.
0:09:29 I think the other one that is not very widely known, but kind of goes to all of the different
0:09:36 parts of DOE, is that we sit at this intersection of open science and classified science. So open
0:09:44 science and national security. I’m imagining you walking a tightrope with the balance beam thing,
0:09:50 just saying classified science kind of brings that area. There’s many national security applications
0:09:56 of science, of course. And much of that really is dependent on advances in open science. And I
0:10:05 think the fact of DOE as a mission-driven R&D agency that is able to bridge these two
0:10:12 arenas and to do that at scale, I think scale, again, is an important demarcator there, is really
0:10:17 what makes working at DOE so exciting. And frankly, as we bring it back to our Office of
0:10:23 Critical Immersion Technologies, the four technology areas I mentioned, AI, microelectronics,
0:10:29 quantum information science, and biotechnology, they’re all dual-use technologies. So I think really,
0:10:35 as we think about both opportunities to advance these technologies and opportunities to manage
0:10:42 the risks of those technologies, you really do need that duality of approach where the science
0:10:47 is going and what the national security implications are going to be to address this effectively.
0:10:55 Are there specific AI-related initiatives going on within DOE, within CET, that you can talk about?
0:11:01 Sure, yes. And we’re very excited about the opportunity space of AIS, if you haven’t caught
0:11:09 on already. We do have a very exciting proposed initiative called FAST, Frontiers in AI for
0:11:18 Science, Security and Technology, which we see as a real opportunity space for a step change in U.S.
0:11:25 government capability in AI. We all see the advancements that industry is making. We need to
0:11:31 be able to harness those advancements for our mission space. And I think the recently issued
0:11:38 national security memorandum on AI just last week really directs departments and agencies in the
0:11:44 national security space to do just this. But we also think that there’s even more to do, right?
0:11:52 So FAST is sort of organized in four general buckets, one around data, one around advancing
0:11:59 compute, one on developing new kinds of models, and the fourth around the application space. So how
0:12:05 do we actually use these models to solve things in the real world? Yes, there’s been, I actually was
0:12:10 recording a podcast earlier this week with somebody and used this same line. So forgive me, listeners.
0:12:17 But there’s kind of this sense that 2022, 2023, in the generative AI space in particular, we’re
0:12:23 sort of the years that LLMs and the idea of models kind of exploded into the mainstream. And businesses
0:12:28 and other organizations started to take notice and really think about what we need to do, invest,
0:12:34 we need to do things. And then 2024 now has been the year where folks in the application space
0:12:40 are really starting to hone in on, okay, how do we take all this power and translate it into
0:12:45 something that is, you know, practical, useful, and easy for an end user to kind of pick up and go
0:12:51 with. Is there a similar sort of timeline or are you kind of in the same sort of headspace
0:12:57 with relative to building the applications and that focus now in your government work or is it
0:13:02 a different, are you in a different spot? So I think that’s a really interesting question because
0:13:09 when it comes to agency deployment of industry models, many of these are
0:13:15 large language models, right? And so we see tremendous efficiency, potential efficiencies
0:13:20 within the department on utilizing these. And we have some things that we’re beta testing now
0:13:26 and excited about them. But what we’re talking about in FAST is really around how we harness
0:13:34 the scientific data that DOE generates, which is some of the most unclassified and classified
0:13:40 scientific experimental data in the world. You can’t even imagine, yeah. How we really advance
0:13:47 computing in this space in a way that we were able to do through the exascale computing project,
0:13:54 but really think further beyond the horizon. Yeah. And then the models themselves, we really think
0:13:58 that much of industry obviously is focused on large language models. And there’s been a lot of
0:14:04 exciting developments. And even those models are becoming much better at scientific reasoning.
0:14:09 Really, we’re seeing tremendous advances in that space. And that really excites us because that
0:14:15 means the time to discovery, whether it’s for in discovery science or in the energy applications
0:14:21 or in national security, that time is going to be cut so significant. But we also think
0:14:26 that there’s going to need to be new kinds of models, additional kinds of models that are not
0:14:31 language-based, but are graph neural networks or physics-informed models
0:14:37 that are the kinds of models that need to be able to learn from the kinds of data that we hold.
0:14:44 And that can be then shown to translate into the physical world, which is why we have laboratories.
0:14:50 So I think this is where we get so excited because we think the opportunities for partnership
0:14:57 with industry are really enormous. And a place where there hasn’t yet been the same kind of
0:15:03 attention and focus more broadly. Because frankly, these are things that are in the realm of
0:15:07 science, right? Like government investment in science. That’s why there is that sort of
0:15:12 public-private partnership opportunity. If I could just add one thing though.
0:15:17 Please. Because there’s been a lot of attention when we talk about AI for science. There’s been
0:15:23 a lot of attention, for example, on AlphaFold. And I think what it’s important to note there
0:15:31 is AlphaFold’s statistical models, they’re trained on data from experimentally determined protein
0:15:37 structures that were only made possible by the use of DOE’s unique large-scale light sources.
0:15:45 And the protein structure data that was used and that is free and open to the public
0:15:51 are stored on DOE-funded protein data bank, which gets funding from other U.S. government
0:15:56 departments and agencies. But I think that’s an example of where there is that public-private
0:16:02 dimension to even industry advancements. My guest today is Helena Fu. Helena is the
0:16:07 director of the Office of Critical and Emerging Technologies at the U.S. Department of Energy.
0:16:11 And as we’ve been talking about, the Department of Energy, the work that they’re doing, the work
0:16:16 that director Fu’s team is doing as well, touches so many different parts of private life, public
0:16:22 life, different parts of the government working with science and industry and everything else.
0:16:28 So many different places. Let’s kind of land on a note of opportunity, and I’m not going to ask
0:16:33 you to predict the future. But what are some of the spaces that you in particular, your teams,
0:16:39 are really excited about when it comes to the applications of large language models, AI, any
0:16:44 of the technology we’ve been talking about, or other things that we haven’t gotten to yet?
0:16:50 Thanks for that question. I think I might answer in two timescales. The first is the near term.
0:16:55 So we have a number of activities already underway that are really seeking to harness
0:17:02 these powerful tools. One example here is really around smart grids and using AI. For example,
0:17:08 our grid deployment office funded Portland General Electric to deploy utility data smart meters,
0:17:14 which have GPUs inside of them to customers. And we think that there’s an opportunity also for
0:17:20 local models that help disaggregate loads like electric vehicles, heat pumps, and that provide
0:17:28 utilities insight while also protecting customer privacy. Our Office of Policy and Pacific Northwest
0:17:34 National Lab are also leading this voltaic initiative, which is really around how we can harness
0:17:39 large language models for permitting and how we find efficiencies in that process.
0:17:43 Permitting as in granting permits to citizens to do things?
0:17:51 Permitting process like environmental permitting reviews, NEPA. And in fact, one of the things
0:17:58 that we were able to do just earlier this year was to take the entire corpus of NEPA data over the
0:18:06 last decade or so and make that AI ready and available and is now open and available to
0:18:12 the public for researchers to work on for industry to develop new tools around. We are working with
0:18:18 other agencies in the Federal Permitting Council to see how they can potentially use this tool
0:18:22 for their permitting processes. So that’s exciting. And that’s at the federal level,
0:18:27 but the voltaic initiative is actually also focused on expanding to state and local
0:18:32 permitting because there’s even more variants across state and local ordinances, for example,
0:18:40 for EB charging or for any other kind of siting. Yeah, the mention of the grid and the smart grid
0:18:46 meters with the GPUs and then you’d be able to compute on the fly. I mean, that hits home for me.
0:18:51 I’m kind of the stereotypical Northern California. I’ve got an EV, the whole thing,
0:18:55 but it really makes me think about all of the opportunities in the physical world
0:19:01 that if the infrastructure is in place and you’ve got this data to work with, which obviously is
0:19:06 huge and I can’t even imagine the troves of data that are around waiting to be processed and put
0:19:13 to use, but you can deploy things at the edge like that to a customer’s, in my case, to where
0:19:19 I live and a residential person can have that smart meter or can have the ability to shift
0:19:25 the loads locally. And I can only imagine the possibilities that opens up for grid resilience
0:19:32 and all of those things. That’s exactly right. And I think moving beyond the short term and into
0:19:38 that medium and long-term time scale, we’re so excited about the opportunity of AI applied
0:19:43 to materials discovery, which I think has applications across science, energy and national
0:19:49 security, right? It’s enormous, yeah. We often talk about this example from PNNL and Microsoft
0:19:54 to look at the universe of potential materials and then fabricate a battery that uses 70%
0:20:01 less lithium. But I think that’s just only the tip of the story, right? Obviously, battery materials,
0:20:07 carbon capture sorbents, hydrogen catalyst, there’s so much opportunity to discover new materials
0:20:15 that will have both impacts on like, abundance and affordability of energy, but also for strategic
0:20:22 technologies like critical minerals and hypersonics, right? There’s many, many applications across
0:20:28 the spectrum. Similarly, in the physical sciences, right, there’s obviously a lot of work already
0:20:34 underway on physics-informed models for climate. We need to be able to bridge climate and weather
0:20:39 across multiple timescales. And that will have huge implications for disaster response and
0:20:45 preparedness and just climate modeling more generally. Yes, yeah. As you said, that’s just
0:20:51 kind of the tip of the iceberg. And the idea of 70% more efficient car batteries built from
0:20:56 renewable energy is sort of mind-blowing to me anyway, but that really is just kind of the
0:21:01 beginning of the story, right? We’re in the early innings here in general. Before we wrap up,
0:21:07 any closing thoughts, people listening out there, perhaps there are organizational leaders,
0:21:13 IT leaders, people in there, whether it’s private or public sector, who are really starting to work
0:21:19 on, they understand the opportunity, they’ve used some AI tools, they’re thinking in that same way,
0:21:24 right, that kind of short and kind of midterm timescales. Advice you would give to somebody who
0:21:30 is, whether their organization is small or governmental scale, who’s trying to kind of
0:21:37 lead that initiative to take advantage of AI in a thoughtful and sort of sustainably minded way.
0:21:43 Any words of advice from your experience? Yeah, I am also the acting chief AI officer
0:21:48 for the Department of Energy. As I think about how the Department of Energy is going to utilize
0:21:54 AI and work very closely with our chief information office on this, we think really,
0:22:01 and this goes for within DOE, also companies, as they think about adopting AI, we really need
0:22:05 to innovate up that ladder of trust. So we really need to think about, I mean, when I think about
0:22:10 this for DOE, I think about what are the immediate use cases? How do we make sure that they’re not
0:22:16 rights and safety impacting? What are the processes we have in place to manage those risks?
0:22:22 But I think the overall advice here really would be, this is a transformative tool.
0:22:28 It is a tool in the tool bell. It’s very powerful. We should all figure out how to use this to the
0:22:34 best of our ability to amplify and augment the work that we’re doing. So we are thinking about
0:22:39 this day in, day out at DOE. We think that partnerships are going to be essential to our
0:22:46 success when it comes to applying AI for our broad science, energy, and national security mission.
0:22:53 Fantastic. For listeners who would like to learn more about any of the many things that you covered,
0:22:56 what the Office of Critical and Emerging Technologies is doing, the Department of
0:23:02 Energy is doing, all of that good work. Where are some places they can go online to get started?
0:23:05 Yeah, well, the Office of Critical and Emerging Technologies has a website,
0:23:10 and that is a place where we really look to point to all of the different and amazing
0:23:15 activities happening across the department and the national laboratory. So that’s a really good
0:23:21 resource to learn more about our Frontiers in AI for Science, Security, and Technology initiative.
0:23:28 And also, TESPEs that are available at DOE, training opportunities at the national laboratories,
0:23:35 foundation models that we’ve already developed in partnership with others, and so much more.
0:23:40 Fantastic. Well, again, thank you so much for taking the time, Director Fu, to come on the podcast
0:23:46 and kind of extend the conversation that you started at the AI Summit, and hopefully we can do it
0:23:50 again down the line. In the meantime, I’m really excited to follow the work that you and your teams
0:23:55 and the rest of the folks in the government are doing to make life better for all of us. So thank you.
0:24:07 Thank you. Thanks so much.
0:24:18 [Music]
0:24:46 [Music]
0:24:56 [BLANK_AUDIO]
Helena Fu, director of the DOE’s Office of Critical and Emerging Technologies (CET) and DOE’s chief AI officer, discusses the latest groundbreaking efforts with AI that are transforming national security, infrastructure, and scientific discovery. With oversight of 17 national labs and 34 facilities, the DOE is at the forefront of AI research and development.