Defense Advanced Research Projects AgencyTagged Content List

Fundamental Physical Science

Pushing the boundaries of knowledge of the physical sciences

Showing 110 results for Fundamentals RSS
02/24/2016
This year, 2016, is a banner year for advocates of the power of ten. It is the 150th anniversary of Congress passing Public Law 39-183, otherwise known as the Metric Act of 1866, which for the first time made it legal to use the metric system for weights and measures in the United States. 2016 also marks the 100th anniversary of the American Metric Association (renamed the U.S. Metric Association in 1974), a non-profit organization created to advocate adoption of the metric system in U.S. commerce and education. American appreciation of the metric system has been less than avid but has gradually grown, in large part because of leadership from science and engineering communities.
03/04/2016
The explosive growth of global digital connectivity has opened new possibilities for designing and conducting social science research. Once limited by practical constraints to experiments involving just a few dozen participants—often university students or other easily available groups—or to correlational studies of large datasets without any opportunity for determining causation, scientists can now engage thousands of diverse volunteers online and explore an expanded range of important topics and questions.
03/16/2016
The body’s branching network of peripheral nerves connects neurons in the brain and spinal cord to organs, skin, and muscles, regulating a host of biological functions from digestion to sensation to locomotion.
05/18/2016
DARPA’s Defense Sciences Office (DSO) is sponsoring a two-day Proposers Day, June 22-23, to provide information to potential proposers on the objectives of the DSO Office-wide Broad Agency Announcement (BAA): http://go.usa.gov/cStaQ. Attendees may register to attend in person or via webinar.
05/26/2016
It’s not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren’t easily generalized to solve related problems or that can’t be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently—but how much so?