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Media Contacts
We have a data problem. Humanity is now generating more data than it can handle; more sensors, smartphones, and devices of all types are coming online every day and contributing to the ever-growing global dataset.
Each year, approximately 6 billion gallons of fuel are wasted as vehicles wait at stop lights or sit in dense traffic with engines idling, according to US Department of Energy estimates.
As the second-leading cause of death in the United States, cancer is a public health crisis that afflicts nearly one in two people during their lifetime.
A novel approach developed by scientists at ORNL can scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days.
A typhoon strikes an island in the Pacific Ocean, downing power lines and cell towers. An earthquake hits a remote mountainous region, destroying structures and leaving no communication infrastructure behind.
Researchers across the scientific spectrum crave data, as it is essential to understanding the natural world and, by extension, accelerating scientific progress.
For nearly three decades, scientists and engineers across the globe have worked on the Square Kilometre Array (SKA), a project focused on designing and building the world’s largest radio telescope. Although the SKA will collect enormous amounts of precise astronomical data in record time, scientific breakthroughs will only be possible with systems able to efficiently process that data.
Students often participate in internships and receive formal training in their chosen career fields during college, but some pursue professional development opportunities even earlier.
Processes like manufacturing aircraft parts, analyzing data from doctors’ notes and identifying national security threats may seem unrelated, but at the U.S. Department of Energy’s Oak Ridge National Laboratory, artificial intelligence is improving all of these tasks.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.