Generative AI could enhance scientific discovery by supporting knowledge workers in science organizations. However, the real-world applications and perceived concerns of generative AI use in these organizations are uncertain. In this paper, we report on a collaborative study with a US national laboratory with employees spanning Science and Operations about their use of generative AI tools. We surveyed 66 employees, interviewed a subset (N=22), and measured early adoption of an internal generative AI interface called Argo lab-wide. We have four findings: (1) Argo usage data shows small but increasing use by Science and Operations employees; Common current and envisioned use cases for generative AI in this context conceptually fall into either a (2) copilot or (3) workflow agent modality; and (4) Concerns include sensitive data security, academic publishing, and job impacts. Based on our findings, we make recommendations for generative AI use in science and other organizations.

ABSTRACT Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signalto-noise Figure 1: Surface representations of the 2D Brownian surface ratios inherent within non-vision signal processing noise injected into our Neural SDE tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical confidence of neural networks in their responses. Such confidence infrastructure domain, such as smart-grid sensing, anomaly metrics will enable human Subject Matter Experts to detection, and non-intrusive load monitoring. Currently, build a relationship of trust with robust neural networks that these models can be brittle, which makes them susceptible to have a history of credible and correctly calibrated responses.

The report describes the discussions from the Workshop on Mathematical Opportunities in Digital Twins (MATH-DT) from December 11-13, 2023, George Mason University. It illustrates that foundational Mathematical advances are required for Digital Twins (DTs) that are different from traditional approaches. A traditional model, in biology, physics, engineering or medicine, starts with a generic physical law (e.g., equations) and is often a simplification of reality. A DT starts with a specific ecosystem, object or person (e.g., personalized care) representing reality, requiring multi -scale, -physics modeling and coupling. Thus, these processes begin at opposite ends of the simulation and modeling pipeline, requiring different reliability criteria and uncertainty assessments. Additionally, unlike existing approaches, a DT assists humans to make decisions for the physical system, which (via sensors) in turn feeds data into the DT, and operates for the life of the physical system. While some of the foundational mathematical research can be done without a specific application context, one must also keep specific applications in mind for DTs. E.g., modeling a bridge or a biological system (a patient), or a socio-technical system (a city) is very different. The models range from differential equations (deterministic/uncertain) in engineering, to stochastic in biology, including agent-based. These are multi-scale hybrid models or large scale (multi-objective) optimization problems under uncertainty. There are no universal models or approaches. For e.g., Kalman filters for forecasting might work in engineering, but can fail in biomedical domain. Ad hoc studies, with limited systematic work, have shown that AI/ML methods can fail for simple engineering systems and can work well for biomedical problems. A list of `Mathematical Opportunities and Challenges' concludes the report.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' The top Republican on the Senate Energy Committee will warn Thursday against allowing U.S. artificial intelligence capabilities to fall into China's hands when the panel meets for a hearing on the topic. Senators returned to Capitol Hill just days ago after spending the month of August in their home states. AI is expected to be a prominent topic for lawmakers as they race to get ahead of the rapidly advancing technology. It's also the topic at the heart of Thursday's hearing led by Energy Committee Chair Joe Manchin, D-W.Va., and ranking member John Barrasso, R-Wyo., that aims to examine how AI has affected the U.S. energy sector and how the federal government can stay competitive in that lane.

This is the third in a series of posts about how international competition could reshape the U.S. economy. As you might expect from the picture at the top of this post, I'm a little ambivalent when it comes to breathless reports that America is falling behind its rivals technologically. That picture is from the movie Dr. Strangelove, which (among other things) lampooned America's Cold War obsession with the "missile gap". On one hand, in a geopolitical contest such as the one we now find ourselves in with Russia and China, we need to prioritize which battles to concentrate resources on -- a point made very convincingly by Hal Brands and Michael Beckley's new book Danger Zone. But on the other hand, our obsession with the "missile gap" gave us the space race and the moon landings and all the technological spinoffs from those, plus a boost to our semiconductor industry.

Less than a half-year into its existence, the Energy Department's new Artificial Intelligence and Technology Office officially unveiled its leader. According to a Thursday announcement, the agency's undersecretary for science officially swore in Cheryl Ingstad as the inaugural director of AITO on Feb. 4. "AI technologies will be as transformative to our daily lives as electricity was to American society more than a century ago, and as a world-leading AI enterprise, [Energy] has the power and the obligation to ensure that it is used as a force for good," Energy Secretary Dan Brouillette said in a statement. He added that Ingstad's "proven public and private sector leadership in driving crosscutting, breakthrough technologies at the nexus of energy and national security" made her the agency's "ideal candidate" for the role. According to her LinkedIn profile, Ingstad is an Army veteran and has experience in the Defense Department's cyber operations. The agency also said she was an "early leader" in the Defense Intelligence Agency's Information Operations Branch.

We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspective of Fermilab, America's premier national laboratory for High Energy Physics (HEP). We believe the NAIRDSP should be extended in light of the rapid pace of development and innovation in the field of Artificial Intelligence (AI) since 2016, and present our recommendations below. AI has profoundly impacted many areas of human life, promising to dramatically reshape society --- e.g., economy, education, science --- in the coming years. We are still early in this process. It is critical to invest now in this technology to ensure it is safe and deployed ethically. Science and society both have a strong need for accuracy, efficiency, transparency, and accountability in algorithms, making investments in scientific AI particularly valuable. Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership. Moreover, investments in AI will be important for maintaining US leadership in the physical sciences.

Introduced by Conner Prochaska, Chief Commercialization Officer, U.S. Department of Energy This panel highlights the breadth of research in AI across the national laboratory complex and discusses how this research fits into the recently announced National AI Initiative; several notable collaborations in AI between companies and national labs are discussed. Applications of AI to solving core problems in energy grid optimization are discussed. In particular, AI approaches to energy grid security, flexibility, and reliability in light of future energy demands are highlighted. Panel participants include representatives from major utilities. State of applications of AI to transformative problems in drug discovery are discussed; ATOM Partnership is highlighted; future opportunities across disease areas are discussed with participants from life sciences companies.

The Energy Department has a new chief information officer and a plan to bring its infrastructure into the modern age. DOE CIO Chris "Rocky" Campione outlined four primary objectives to modernize and rationalize the IT infrastructure that supports his agency's wide scope of programs. In fiscal 2020, operational visibility, delivery excellence, delivering innovation and workforce development will be the name of the game. "How do we make sure -- and that's everything from making sure we're reskilling and retraining the federal workforce," Campione said of the fourth objective, on Federal Monthly Insights -- A New Approach in IT Modernization. "But's also looking at how do we provide the information to our workforce so that we can make good decisions?"

ALBUQUERQUE, N.M. (KRQE) - New Mexico has a long legacy of high tech projects coming out of our national labs. Now, tech experts from around the state are brainstorming ways to keep New Mexico at the forefront of developing the next wave of technological advances. Leaders say New Mexico has the potential to lead the charge in artificial intelligence--from military defense to health care and agriculture. "A-I is going to touch every portion of our lives. It's going to affect the kinds of foods we buy, what we put into our body, how medicine works, how we find information, how we educate our children," says Mark Johnson.