June 7, 2019 | International, Aerospace, C4ISR
Lockheed Martin (NYSE: LMT) develops satellite imagery recognition system using open-source deep learning libraries to quickly identify and classify objects or targets in large areas across the world, potentially saving image analysts countless hours manually categorizing and labeling items within an image. Global Automated Target Recognition runs in the cloud, using Maxar's Geospatial Big Data platform (GBDX) to access Maxar's 100 petabyte satellite imagery library and millions of curated data labels across dozens of categories that expedite the training of deep learning algorithms. Fast GPU's let GATR scan a large area very quickly, while deep learning methods automate object recognition and reduce the need for extensive algorithm training.
GATR teaches itself what the identifying characteristics of an object area or target, for example, learning how to distinguish between a cargo plane and a military transport jet. The system scales quickly to scan large areas, including entire countries. GATR uses deep learning techniques common in the commercial sector and can identify ships, airplanes, buildings, seaports, and many other categories.
“There's more commercial satellite data than ever available today, and up until now, identifying objects has been a largely manual process,” said Maria Demaree, vice president and general manager of Lockheed Martin Space Mission Solutions. “Artificial Intelligence models like GATR keep analysts in control while letting them focus on higher-level tasks.”
GATR has a high accuracy rate, well over 90% on the models we've tested so far. It only took two hours to search the entire state of Pennsylvania for fracking sites – that's 120,000 square kilometers
“I'm not an expert on what oil production sites are, and I don't have to be,” said Mark Pritt, senior fellow at Lockheed Martin and principle investigator for GATR. “This system teaches itself the defining characteristics of an object, saving valuable time training an algorithm and ultimately letting an image analyst focus more on their mission.”
GATR builds on research Pritt's team pioneered during a Intelligence Advanced Research Projects Activity (IARPA) challenge, called the “Functional Map of the World.” The Lockheed Martin team was the only team from a company who placed in the top five.
November 29, 2022 | International, Naval
The Navy is nearing delivery of an extra large UUV test vehicle, and will soon field a new torpedo tube launch and recovery capability for the medium UUV.
July 6, 2020 | International, Aerospace, Naval, Land, C4ISR, Security
Byron Callan June 30, 2020 The COVID-19 pandemic has stoked consternation that U.S. defense spending is going to be significantly pressured in the 2020s. Congress will likely stick to the $740.5 billion defense discretionary top line agreed to in last year's budget deal for fiscal 2021. But the combination of trillions more in federal debt from higher spending and lower tax receipts this year and next and the probability that there will be future federal spending to better prepare for pandemics raise a higher probability of defense spending pressure. “Flat” was already the new “up,” but “flat” now may be a budget that does not keep pace with annual inflation. The fears may be that defense spending will decline in the 2020s after a couple of good years of largesse from Congress and the White House. Despite trillions in additional deficits and federal borrowing in 2020-21, there is one bright spot that indicates less dire defense spending pressures than now perceived—the interest on the federal debt. U.S. federal debt is comprised of debt held by the public and intragovernmental debt, which is owned by different federal trust funds, the largest of which is Social Security. As of May, total debt held by the public was $19.8 trillion, and intragovernmental debt was another $6 trillion. Often, these two sums are lumped together, but they should be treated separately. The interest paid on debt held by the public is dispersed by the Treasury in the form of outlays to the owners of that debt. The interest paid on intragovernmental debt is, in essence, interest the federal government pays itself. The Office of Management and Budget (OMB), in its annual projections of outlays, breaks out these two components of interest outlays to show net interest outlays. This is mandatory spending, and so it has been paid along with the other mandatory and discretionary funding the U.S. federal government provides. One of the silver linings of the pandemic has been the Federal Reserve's aggressive lowering of interest rates. This makes federal debt more affordable, much in the way that a lower interest rate on a home mortgage can make a place to live more affordable. The OMB projections released in February showed net interest outlays of $378 billion for fiscal 2021 rising to $665 billion by 2030. One could take issue with the deficit projections behind these outlay projects, as they may have rested on GDP growth expectations that were too optimistic and nondefense spending cuts that were not going to be realized. However, dividing interest outlays on debt held by the public by debt projections implied an interest rate of 3% or more over the forecast period. The pandemic has trashed those rate projections. Federal debt held by the public is offered in different maturities. Treasury bills, which mature in a year or less as of May, were 23% of the total debt held by the public. Treasury notes that mature in 1-10 years were 51%, and bonds that mature in 10-30 years were 12%. (There is another 10% of other Treasury instruments.) Rates now are much lower, although clearly that would only matter for new debt that is issued by the Treasury. The rate on a 90-day Treasury bill is currently 0.13%. On a five-year note, it is 0.33%, and on the 10-year note, 0.69%. The 30-year note rate is 1.4%. This implies that interest outlay projections should be declining, although new projections may have to wait until the White House releases its 2022 fiscal budget request and out-year projections, presumably in February-March 2021. Net interest outlays could be at least $100 billion less in 2022-23 than the February 2020 projections on higher debt but lower rates. In the scheme of total federal outlays, which the OMB projected to be $4.8 trillion for 2021, $100 billion is not a lot, but it indicates there is a bit more headroom for defense spending and other nondefense discretionary spending than a focus on federal debt alone might suggest. Federal infrastructure spending could be one area of more traction in the 2020s, and the issue of social justice may also spur more demand for federal resources. One outcome of the pandemic, however, will be to make defense expectations more sensitive to interest rate expectations. It is not too difficult to project scenarios with rising debt and interest rates that increase to more “normal” levels. The pandemic also underscores that the unthinkable should be given a bit more room on long-term projections. It is quite conceivable that a major military conflict, a massive natural disaster or another economic contraction could further add to federal debt in the 2020s. https://aviationweek.com/defense-space/budget-policy-operations/opinion-why-interest-federal-debt-matters-defense
September 10, 2020 | International, Aerospace, C4ISR
Aaron Mehta and Andrew Eversden WASHINGTON — U.S. Defense Secretary Mark Esper announced Wednesday that the Pentagon intends to conduct live trials pitting tactical aircraft controlled by artificial intelligence against human pilots in 2024. The announcement comes three weeks after an AI algorithm defeated a human pilot in a simulated dogfight between F-16s, something Esper described as an example of the “tectonic impact of machine learning” for the Defense Department's future. “The AI agent's resounding victory demonstrated the ability of advanced algorithms to outperform humans in virtual dogfights. These simulations will culminate in a real-world competition involving full-scale tactical aircraft in 2024,” Esper said in prepared remarks delivered to the department's Artificial Intelligence Symposium. The Aug. 20 test was the finale of the Pentagon research agency's AI air combat competition. The algorithm, developed by Heron Systems, easily defeated the fighter pilot in all five rounds that capped off a yearlong competition hosted by the Defense Advanced Research Projects Agency. Heron's AI system gained notoriety throughout the competition for its aggressiveness and the accuracy of its shot. But the system wasn't perfect. Heron often made an error in basic fighter maneuvers by turning away from enemy aircraft to where the AI thought the other aircraft would go. It was then unable to recover throughout the fights. “There are a lot caveats and disclaimers to add in here,” Col. Dan Javorsek, program manager in DARPA's Strategic Technology Office, said after the test, including that the AI had significant information that might not be available in an actual combat scenario. Military officials have long eyed the potential for AI to control aircraft, whether as part of a “loyal wingman” setup where a number of systems are controlled by one pilot, or through taking existing systems and making them optionally manned. https://www.c4isrnet.com/artificial-intelligence/2020/09/09/dod-to-pit-ai-vs-human-pilots-in-live-fighter-trials-by-2024/