At a conference featuring the most advanced computing hardware and software, ML in its various guises was on full display and highlighted by Nathan Baker’s featured invited presentation.
Advancements such as LEDs have changed consumers’ experience with lighting. Whereas there was once a simple choice of how much light a consumer desired, there’s now a variety of choices to be made about the appearance of light.
Scientists at PNNL are bringing artificial intelligence into the quest to see whether computers can help humans sift through a sea of experimental data.
In today’s digital age, the rabbit hole of connected information can be not only a time sink, but downright overwhelming. Even for high-performance computers.
Twenty-four analysts from U.S. intelligence organizations met in August for a machine learning activity with PNNL researchers Nicole Nichols, Jeremiah Rounds, Lawrence Phillips, and Brian Kritzstein.
Trouble on the electric grid might start with something relatively small: a downed power line, or a lightning strike at a substation. What happens next?
Pacific Northwest National Laboratory is leading efforts to address next-generation computing’s critical role in protecting the nation from cybersecurity threats.
A staple in horror movies, flickering lights can also summon potential human health and productivity concerns. PNNL studied hand-held meters that measure flicker, and the results could improve future measurement and lighting strategies.
When two powerful earthquakes rocked southern California earlier this month, officials’ attention focused, understandably, on safety. How many people were injured? Were buildings up to code? How good are we at predicting earthquakes?
PNNL’s Janet Jansson is part of an international team of scientists warning scientists of the urgency to pay more attention to the role of microorganisms in our climate.
PNNL scientist Wei-Jun Qian and colleagues have contributed to a study that offers clues for delaying or even preventing the autoimmune attack that’s at the core of type-1 diabetes.
Researchers at PNNL are applying deep learning techniques to learn more about neutrinos, part of a worldwide network of researchers trying to understand one of the universe’s most elusive particles.