Dimitris A. Pados, Ph.D., principal investigator, a professor in the Department of Computer and Electrical Engineering and Computer Science, a fellow of FAU's Institute for Sensing and Embedded Network Systems Engineering (I-SENSE), the Charles E. Schmidt Eminent Scholar in Engineering and Computer Science, and director of the Center for Connected Autonomy and Artificial Intelligence. Ensuring data quality is critical for artificial intelligence (AI) machines to learn effectively and operate efficiently and safely. Researchers from Florida Atlantic University's College of Engineering and Computer Science have received a three-year, $653,393 grant from the United States Air Force Office of Scientific Research (AFOSR) for a project titled, "Data Analytics and Data Conformity Evaluation with L1-norm Principal Components." For the project, researchers will develop new theory and methods to curate training data sets for AI learning and screen real-time operational data for AI field deployment. The project team is spearheaded by Dimitris A. Pados, Ph.D., principal investigator, a professor in the Department of Computer and Electrical Engineering and Computer Science, a fellow of FAU's Institute for Sensing and Embedded Network Systems Engineering (I-SENSE), the Charles E. Schmidt Eminent Scholar in Engineering and Computer Science, and director of the Center for Connected Autonomy and Artificial Intelligence (ca-ai.fau.edu)
Amateurs talk strategy, professionals talk logistics. In the 2020s, that old chestnut should probably be updated: "Professionals talk about the network." And boy are they talking. The U.S. government will be one of the biggest spenders on private 5G infrastructure, and the Department of Defense leads the pack. DoD's growing network demands include connecting in-field technology as well as supporting day-to-day base operations and force training.
These risk estimates are from the World Economic Forum, the Intergovernmental Panel on Climate Change, the Chicago Actuarial Association, the Global Challenges Foundation, Bethan Harris at the University of Reading, and David Morrison at NASA, with advice from Phil Torres at the Institute for Ethics and Emerging Technologies, author of Human Extinction: A Short History. Fully autonomous weapons don't exist yet, but advances in drone technology and AI make them likely. Rogue code and irresponsible use could lead to mass violence on a scale and speed we don't understand today. Hacking the transport system or a central bank would wreak havoc and threaten public safety. Prevention relies on educating people about cybersecurity.
Fluree is not a very straightforward product to get. To some extent, that goes for all data management systems. Fluree combines a graph database with blockchain. And it just switched to open source, after scoring a $1.5M seed extension round as part of its DoD contract. ZDNet caught up with Brian Platz, Fluree co-founder and co-CEO, to try and unpack all that.
Artificial intelligence is the new oil, and the governments or the countries that get the best datasets will unquestionably develop the best AI, the Joint Artificial Intelligence Center's chief technology officer said Oct. 15. Speaking on a panel about AI superpowers at the Politico AI Summit, Nand Mulchandani said AI is a very large technology and industry. "It's not a single, monolithic technology," he said. "It's a collection of algorithms, technologies, etc., all cobbled together to call AI." The United States has access to global datasets, and that's why global partnerships are so incredibly important, he said, noting the Defense Department launched the AI partnership for defense at the JAIC recently to have access to global datasets with partners, which gives DOD a natural advantage in building these systems at scale.
U.S. Soldiers, assigned to the 1st Squadron, 2d Cavalry Regiment, scan sectors of fire during the AH-64 Apache helicopter gunnery training in Grafenwoehr Training Area, Germany, July 15, 2020. The gunnery training concluded with the squadron's table VIII and IX live-fire certifications. LaShic Patterson) For future battlefield operations, speed is the name of the game. The side that can make decisions faster has the advantage.A new, AI-enabled effort by the U.S. Army can give operators the ability to detect, identify, process and engage targets quicker than ever and at longer ranges than before. The Tactical Intelligence Targeting Access Node, or TITAN, offers frontline forces, as well as headquarter commanders, a resilient tactical ground station capable of rapidly sifting through massive amounts of incoming sensor data to find and track potential threats.
With the addition of artificial intelligence and machine learning, the aim is to make every soldier, regardless of job specialty, capable of identifying and knocking down threatening drones. While much of that mission used to reside mostly in the air defense community, those attacks can strike any infantry squad or tank battalion. The goal is to reduce cognitive burden and operator stress when dealing with an array of aerial threats that now plague units of any size, in any theater. "Everyone is counter-UAS," said Col. Marc Pelini, division chief for capabilities and requirements at the Joint Counter-Unmanned Aircraft Systems Office, or JCO. Army units aren't ready to defeat aerial drones, the study shows.
Last month, an artificial intelligence agent defeated human F-16 pilots in a Defense Advanced Research Projects Agency challenge, reigniting discussions about lethal AI and whether it can be trusted. Allies, non-government organizations, and even the U.S. Defense Department have weighed in on whether AI systems can be trusted. But why is the U.S. military worried about trusting algorithms when it does not even trust its AI developers? Any organization's adoption of AI and machine learning requires three technical tools: usable digital data that machine learning algorithms learn from, computational capabilities to power the learning process, and the development environment that engineers use to code. However, the military's precious few uniformed data scientists, machine learning engineers, and data engineers who create AI-enabled applications are currently hamstrung by a lack of access to these tools.
While the U.S. has lacked central organizing of its AI, it has an advantage in its flexible tech industry, said Nand Mulchandani, the acting director of the U.S. Department of Defense Joint Artificial Intelligence Center. Mulchandani is skeptical of China's efforts at "civil-military fusion," saying that governments are rarely able to direct early stage technology development. Tensions over how to accelerate AI are driven by the prospect of a tech cold war between the U.S. and China, amid improving Chinese innovation and access to both capital and top foreign researchers. "They've learned by studying our playbook," said Elsa B. Kania of the Center for a New American Security. "Many commentators in Washington and Beijing have accepted the fact that we are in a new type of Cold War," said Ulrik Vestergaard Knudsen, deputy secretary general of Organization for Economic Cooperation and Development (OECD), which is leading efforts to develop global AI cooperation.