The event brought together 35 University of California students—ranging from undergraduates to graduate-level students from a diversity of majors—to work in groups to solve four key tasks, using actual electrocardiogram data to predict heart health.
Using explainable artificial intelligence techniques can help increase the reach of machine learning applications in materials science, making the process of designing new materials much more efficient.
The “crystal ball” that provided increased pre-shot confidence in LLNL's fusion ignition breakthrough involved a combination of detailed HPC design and a suite of methods combining physics-based simulation with machine learning—called cognitive simulation, or CogSim.
The report lays out a comprehensive vision for the DOE Office of Science and NNSA to expand their work in scientific use of AI by building on existing strengths in world-leading high performance computing systems and data infrastructure.
The new model addresses a problem in simulating RAS behavior, where conventional methods come up short of reaching the time- and length-scales needed to observe biological processes of RAS-related cancers.
Women data scientists, Lab employees, and other attendees interested in the field gathered at the Livermore Valley Open Campus for the annual Livermore Women in Data Science (WiDS) regional event held in conjunction with the global WiDS conference.