The U.S. Department of Energy (DOE) and its National Laboratories maintain an innovation ecosystem to fulfill their missions in science, engineering, and national security. This ecosystem is expanding and improving AI systems, tools, methods, algorithms, and applications.
Artificial intelligence (AI) describes the capability of machines to rapidly learn from large data sets, solve problems, and continuously adapt to new data without human intervention. These machine systems leverage computer science, data science, and mathematics to deliver insight at data rates and scales incomprehensible to humans. AI functions include image and natural language processing, sensor networks, predictive planning, and decision support.
AI research, development, and demonstration (RD&D) has grown rapidly over the last 20 years, largely driven by advances in processors and big data. AI is now used in nearly every part of the economy to transform data stores into useful knowledge. Examples include targeted ads, face recognition, digital assistants, and self-driving vehicles. Global spending on AI systems is projected to reach $98 billion in 2023, up from the $37.5 billion forecast for 2019.
Source: International Data Corporation
To accelerate AI innovation and partnerships, DOE established the Artificial Intelligence and Technology Office (AITO). AITO serves as DOE's hub for coordinating the agency's efforts as a world-leading enterprise in scientific and technological discovery and accelerates the development, delivery, and adoption of AI.
DOE focuses its efforts on early-stage research to advance technologies and develop the next-generation computing capabilities, infrastructure, and tools that the nation needs but industry is unlikely to develop on its own.
Strategy 1: Make long-term investments in AI research Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI.
Strategy 2: Develop effective methods for human-AI collaboration Increase understanding of how to create AI systems that effectively complement and augment human capabilities.
Strategy 3: Understand and address the ethical, legal, and societal implications of AI Research AI systems that incorporate ethical, legal, and societal concerns through technical mechanisms.
Strategy 4: Ensure the safety and security of AI systems Advance knowledge of how to design AI systems that are reliable, dependable, safe, and trustworthy.
Strategy 5: Develop shared public datasets and environments for AI training and testing Develop and enable access to high-quality datasets and environments, as well as to testing and training resources.
Strategy 6: Measure and evaluate AI technologies through standards and benchmarks Develop a broad spectrum of evaluative techniques for AI, including technical standards and benchmarks.
Strategy 7: Better understand the national AI R&D workforce needs Improve opportunities for R&D workforce development to strategically foster an AI-ready workforce.
Strategy 8: Expand public-private partnerships to accelerate advances in AI Promote opportunities for sustained investment in AI R&D and for transitioning advances into practical capabilities.
AI research at DOE is enabling solutions to critical issues in many economic sectors. Leading AI applications include grid optimization for renewable energy sources, transportation network efficiency, quality healthcare access, and advanced manufacturing processes.
AI Use in Science (Office of Science) The Office of Science announced $13 million in funding to develop AI as a tool for scientific investigation and prediction.
AI as a Tool to Improve Grid Resiliency (Office of Electricity) The Office of Electricity announced $7 million in federal funding for several AI-related projects to support faster grid analytics and modeling.
Advancing the State of the Art through Strategic Investments in AI (ARPA-E) ARPA-E announced up to $20 million for projects as part of the DIFFERENTIATE program, applying AI and machine learning to energy applications.
AI will facilitate grid modernization through autonomous systems optimization. The long-term goal is a fully Autonomous Energy Grid (AEG) that is:
AI can help to increase the generation and use of renewable energy in many ways:
Geothermal: DOE research is applying AI to the exploration and production of geothermal resources. Successful implementation of machine learning methods could facilitate the discovery of geothermal wells, increase drilling accuracy, and reduce costs.
Biomass: Scientists at Idaho National Laboratory are using AI analysis of biorefinery processing data to guide operational adjustments that maximize output while mitigating system damage.
DOE and its National Laboratories use AI and machine learning to better understand and optimize transportation systems and urban mobility. These powerful tools are helping to improve safety and mobility while reducing congestion and energy use.
Benefits of Automation:
Advances in AI are unlocking new applications and approaches to healthcare. By applying machine learning, DOE's National Labs are improving the interpretation of medical and biological data and enabling advancements in diagnostics, drug discovery, and treatment.
CANDLE (Cancer Distributed Learning Environment): A partnership between DOE and the National Cancer Institute developing an open-source software platform using deep learning methodologies to explore cancer causes, treatment, and methods to improve patient outcomes.
DOE National Laboratories are leveraging AI to advance the state of manufacturing and materials research:
DOE hosts four of the ten fastest supercomputers in the world:
Summit (ORNL): Currently the fastest computer, performing 200,000 trillion calculations per second (200 petaflops). Has enabled scientists to apply advanced machine learning to multiple topics.
Sierra (LLNL): At 125 petaflops, the world's second most powerful computer. Allows NNSA to run simulations on nuclear weapons in lieu of underground testing.
Trinity (SNL, LANL): Capable of operating at 41 Petaflops for maintaining a safe, reliable, and secure national nuclear stockpile.
Exascale computing—able to do a quintillion computations per second—promises unprecedented breakthroughs in AI and machine learning.
2021 Scheduled Completions:
2022 Expected:
Exascale Computing Project (ECP): Driving the development of future exascale supercomputers and architectures through collaboration between DOE's Office of Science and National Nuclear Security Administration.
CANDLE: Collaboratively developed open-source software platform providing deep learning methodologies for cancer research.
HPC4 Energy Initiative: Facilitates partnerships between industry and national labs using DOE's high-performance computing resources for energy challenges in manufacturing, materials, and mobility.
ATOM (Accelerating Therapeutics for Opportunities in Medicine): Consortium to accelerate drug discovery using high-performance computing, biological data, and new biotechnologies.
The document includes over 20 detailed success stories demonstrating AI applications across DOE and its National Laboratories, including:
This Spotlight document from the DOE Office of Technology Transitions demonstrates the breadth and depth of AI research across the Department of Energy's National Laboratory system and its critical importance to U.S. competitiveness and national security.
