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Accelerating The Pace Of Machine Learning - AI Summary

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But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. In the paper "Distributed Learning With Sparsified Gradient Differences," published in a special ML-focused issue of the IEEE Journal of Selected Topics in Signal Processing, Blum and collaborators propose the use of "Gradient Descent method with Sparsification and Error Correction," or GD-SEC, to improve the communications efficiency of machine learning conducted in a "worker-server" wireless architecture. "Various distributed optimization algorithms have been developed to solve this problem," he continues,"and one primary method is to employ classical GD in a worker-server architecture. "Current methods create a situation where each worker has expensive computational cost; GD-SEC is relatively cheap where only one GD step is needed at each round," says Blum. Professor Blum's collaborators on this project include his former student Yicheng Chen '19G '21PhD, now a software engineer with LinkedIn; Martin Takác, an associate professor at the Mohamed bin Zayed University of Artificial Intelligence; and Brian M. Sadler, a Life Fellow of the IEEE, U.S. Army Senior Scientist for Intelligent Systems, and Fellow of the Army Research Laboratory. But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. In the paper "Distributed Learning With Sparsified Gradient Differences," published in a special ML-focused issue of the IEEE Journal of Selected Topics in Signal Processing, Blum and collaborators propose the use of "Gradient Descent method with Sparsification and Error Correction," or GD-SEC, to improve the communications efficiency of machine learning conducted in a "worker-server" wireless architecture. "Various distributed optimization algorithms have been developed to solve this problem," he continues,"and one primary method is to employ classical GD in a worker-server architecture.


Improved MIDAS C-UAS Successfully Tested

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Aurora Flight Sciences, a Boeing Company, recently completed a project to advance the capabilities of its Modular Intercept Drone Avionics Set (MIDAS) counter-unmanned aircraft system (C-UAS). Aurora engineers designed, implemented, and tested improvements to the drone engagement device (DED) and onboard autonomy, as well as to the speed and maneuverability of the vehicle platform. Using similar test parameters to last spring's demonstration for the Joint Counter-sUAS Office (JCO) and the Army Rapid Capabilities and Critical Technologies Office (RCCTO), MIDAS autonomously defeated 83% of small UAS targets. "Since our successful customer demos last year, we've continued to improve hardware and software systems on MIDAS to ensure we are prepared to meet the needs of future counter-sUAS programs," said Jason Grzywna, director of small UAS programs at Aurora. "The vehicle we used for the most recent tests included enhancements in agility and autonomy, which improved target acquisition, and in the bolos fired by the DED, which proved more effective in completely disabling the target."


How Facial Recognition Tech Made Its Way to the Battlefield in Ukraine

Slate

When the Russian warship Moskva sank in the Black Sea south of Ukraine, some 500 crew members were reportedly on board. The Russian state held a big ceremony for the surviving sailors and officers who were on the ship. But, considering Russia's history of being not exactly truthful when it comes to events like this, many people wondered whether these were actual sailors from Moskva. Toler is director of research and training for Bellingcat, the group that specializes in open-source and social media investigations. He used facial recognition software to identify the men in the video through images in Russian social media, and found that most of the men were indeed sailors from Sevastopol, the town the ship was operating out of.


Artificial Intelligence and the Future of War

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Consider an alternative history for the war in Ukraine. Intrepid Ukrainian Army units mount an effort to pick off Russian supply convoys. But rather than rely on sporadic air cover, the Russian convoys travel under a blanket of cheap drones. The armed drones carry relatively simple artificial intelligence (AI) that can identify human forms and target them with missiles. The tactic claims many innocent civilians, as the drones kill nearly anyone close enough to the convoys to threaten them with anti-tank weapons.


As Russia Plots Its Next Move, an AI Listens to the Chatter

WIRED

A radio transmission between several Russian soldiers in Ukraine in early March, captured from an unencrypted channel, reveals panicked and confused comrades retreating after coming under artillery fire. "Vostok, I am Sneg 02. On the highway we have to turn left, fuck," one of the soldiers says in Russian using code names meaning "East" and "Snow 02." No need to move further. Later, a third soldier tries to make contact with another codenamed "South 95": "Yug 95, do you have contact with a senior? The third Russian soldier continues, becoming increasingly agitated: "Get on the radio.


Artificial Intelligence and the Future of War

#artificialintelligence

Consider an alternative history for the war in Ukraine. Intrepid Ukrainian Army units mount an effort to pick off Russian supply convoys. But rather than rely on sporadic air cover, the Russian convoys travel under a blanket of cheap drones. The armed drones carry relatively simple artificial intelligence (AI) that can identify human forms and target them with missiles. The tactic claims many innocent civilians, as the drones kill nearly anyone close enough to the convoys to threaten them with anti-tank weapons.


AI Algorithms Could Rapidly Deploy to the Battlefield Under New Initiative

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The Pentagon's Joint Artificial Intelligence Center recently started building a joint operating system and integration layer that combatant commands and other military components could eventually use to rapidly make and field artificial intelligence algorithms. This work is one key piece of the center's new Artificial Intelligence and Data Accelerator, or AIDA, JAIC Director Lt. Gen. Michael Groen confirmed this week during the NDIA 2022 Expeditionary Warfare Conference. "AIDA brings us, in small teams, out to the combatant commanders--now, for those of you who have been in combatant commands, or you're familiar with that environment--combat commanders have all of the challenges, all the problems and only some capability, right? And so what we're trying to do from an information advantage perspective is bring them the advantages of good data and good artificial intelligence-generating insights," he explained. Launched last year by Deputy Defense Secretary Kathleen Hicks, AIDA marks a broad initiative to boost data-based decision-making across the military's 11 combatant commands.


Army Buys Artificial Intelligence-Infused Folding Quadcopters For Battlefield Use – The Drive

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Skydio says the X2D offers up to 35 minutes of flight time, can operate in both day or night, and features artificial intelligence tools that enable …


Army Buys Artificial Intelligence-Infused Folding Quadcopters For Battlefield Use

#artificialintelligence

Those requirements could have played a part in the selection process for the Army's SRR program, as the service has previously had to halt the use of foreign-made drones. In 2017, the U.S. Army banned the use of all drones made by Chinese drone manufacturer DaJiang Innovations, or DJI, which supplies over half of all drones sold in the U.S. The company eventually created a U.S. government model in an attempt to allay these concerns. Still, in January 2021, the White House signed an Executive Order that instructed executive branch departments and agencies to examine all of their current drone technologies for potential threats and banned drones and drone subsystems from adversary countries defined as Iran, North Korea, Russia, and China. The Associated Press later reported in June 2021 that Pentagon had cleared some Chinese-made DJI drones for government use, but that report was quickly deemed inaccurate by the Department of Defense (DOD). "This report was inaccurate and uncoordinated, and its unauthorized release is currently under review by the department," the DOD said in a statement in response to the AP report.


Machine learning fine-tunes graphene synthesis

AIHub

Rice University chemists are employing machine learning to fine-tune its flash Joule heating process to make graphene. A flash signifies the creation of graphene from waste. Rice University scientists are using machine learning techniques to streamline the process of synthesizing graphene from waste through flash Joule heating. This flash Joule process has expanded beyond making graphene from various carbon sources, to extracting other materials, like metals, from urban waste. The technique is the same for all of the above: blasting a jolt of high energy through the source material to eliminate all but the desired product.