Researchers from institutions including ORNL have created a new method for statistically analyzing climate models that projects future conditions with more fidelity.
ORNL has joined a global consortium of scientists from federal laboratories, research institutes, academia and industry to address the challenges of building large-scale artificial intelligence systems and advancing trustworthy and reliable AI for
Scientists at ORNL used their expertise in quantum biology, artificial intelligence and bioengineering to improve how CRISPR Cas9 genome editing tools work on organisms like microbes that can be modified to produce renewable fuels and chemicals.
ORNL has been selected to lead an Energy Earthshot Research Center, or EERC, focused on developing chemical processes that use sustainable methods instead of burning fossil fuels to radically reduce industrial greenhouse gas emissions to stem climate change and limit the crisis of a rapidly warming planet.
The Department of Energy’s Oak Ridge National Laboratory announced the establishment of the Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making.
Innovations in artificial intelligence are rapidly shaping our world, from virtual assistants and chatbots to self-driving cars and automated manufacturing.
An innovative and sustainable chemistry developed at ORNL for capturing carbon dioxide has been licensed to Holocene, a Knoxville-based startup focused on designing and building plants that remove carbon dioxide
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.
Paul Langan will join ORNL in the spring as associate laboratory director for the Biological and Environmental Systems Science Directorate.
While studying how bio-inspired materials might inform the design of next-generation computers, scientists at ORNL achieved a first-of-its-kind result that could have big implications for both edge computing and human health.