Over the past decade, artificial intelligence (AI) has experienced a renaissance. AI enables machines to learn and make decisions without being explicitly programmed.

AI has enabled a new generation of applications, opening the door to breakthroughs in many aspects of daily life. From situational awareness to threat analysis and detection, online signals to assurance of high consequence systems, Pacific Northwest National Laboratory (PNNL) is advancing the frontiers of scientific research and national security by applying AI to scientific problems.

The recently established Center for AI @PNNL is driving a research agenda that explores the foundations and emerging frontiers of AI, combining capability development and application to mission areas in science, security, and energy resilience. 

Recently the Center for AI led PNNL’s participation, along with nine national laboratories, in the Department of Energy’s 1,000 Scientist AI Jam Session. Scientists explored advanced AI models to understand and explore the potential impact of AI reasoning models on national security and science, especially how AI models may accelerate discoveries.  

Starting with the right environment

For machine learning models, domain-specific knowledge can enhance domain-agnostic data in terms of accuracy, interpretability, and defensibility. Our AI research has been applied across a variety of domain areas such as national security to the electric grid and Earth systems. Leveraging a deep expertise in the power grid domain, PNNL’s DeepGrid open-source platform uses deep reinforcement learning to help power system operators create more robust emergency control protocols—the safety net of our electric grid.

Researchers are also harnessing the power of generative AI to drive innovation across the science, energy, and security research domains. Leveraging PNNL’s expertise in foundational and applied AI, the Generative AI investment aims to push the discovery of new AI methodologies and technologies. 

Building stronger AI systems

PNNL takes a holistic approach to research focused on assuring the safety, security, interpretability, explainability, and general robustness of AI-enabled systems deployed in the real world. This research includes understanding and mitigating system failures caused by design and development flaws, as well as the malicious activities of adversaries.

Revealing the reasoning behind deep-learning-based decisions is a critical component of assuring safety, security, and robustness. This reasoning allows our researchers to assess complex systems from the perspective of digital and physical system security, as well as from development and operational perspectives.

Forecasting real-world events

PNNL’s research in content intelligence focuses on the development of novel AI models to explain and predict social systems and behaviors related to national security challenges in the human domain. Experts with PNNL’s Northwest Regional Technology Center are exploring the potential of leveraging AI for emergency management—researching opportunities, applications, and existing implementations. 

PNNL’s interactive tools like CrossCheck, ESTEEM, and ErrFilter not only ensure we develop robust and generalizable AI models, but also advance understanding and effective reasoning about extreme volumes of dynamic, multilingual, and diverse real-world data.

Integrating across missions

Data engineering is foundational to data science, focusing on information flow from data sources to application. Combining this capability—including expertise in data architectures and pipelines, data collection, and validation—with AI enables cross-functional teams to provide optimal solutions to critical mission spaces.

Sponsored by DOE’s Advanced Scientific Computing Research program, the Advanced Memory to Support AI for Science project is focused on new memory systems pertinent to scientific computational research. With thrust areas ranging from performance analysis to theory, the research aims to enable the convergence of first-principles scientific modeling and simulation with AI data-driven science. 

PNNL is partnering with Oak Ridge National Laboratory, the University of Arizona, and the University of California, Santa Barbara, to develop mathematical foundations for data-driven decision control for complex systems, such as high-energy physics facilities and smart buildings.

Further, our researchers integrate current approaches for scientific high-performance computing, deep learning, and graph analytics computing paradigms into a converged, coherent computing capability to accelerate scientific discovery.

Gaining great insights from small datasets

While almost all research on few-shot learning is done exclusively on images, researchers at PNNL have shown success in other data types, including text, audio, and video. This has greatly expanded our AI capabilities beyond traditional, publicly available image datasets and allows researchers to quickly build machine learning models using small amounts of user-classified training examples

Sharkzor, for example, combines human interaction with machine learning techniques to allow classification using just five to 10 images—far fewer than the hundreds or thousands needed for traditional deep learning.

Learn more about how Sharkzor can be applied to nuclear forensics analysis.