The U.S. and India have agreed to engage in new talks about artificial intelligence and its use in matters of national security, an outgrowth of the nations' deepening relationship at a time of sharpened Indo-Pacific focus. News of the inaugural Defense Artificial Intelligence Dialogue came after Secretary of Defense Lloyd Austin and Secretary of State Antony Blinken met with their Indian counterparts, Minister of Defense Rajnath Singh and Minister of External Affairs Dr. S. Jaishankar, April 11. Both the Defense and State departments acknowledged the topic in their accounts of the international get-together. "The United States and India signed a Space Situational Awareness arrangement, which lays the groundwork for more advanced cooperation in space," the Pentagon said in a readout. "They also agreed to launch an inaugural Defense Artificial Intelligence Dialogue, while expanding joint cyber training and exercises."

RAMSTEIN AIR BASE, Germany – The United States marshaled 40 allies on Tuesday to furnish Ukraine with long-term military aid in what could become a protracted battle against the Russian invasion, and Germany said it would send dozens of armored anti-aircraft vehicles. It was a major policy shift for a country that had wavered over fear of provoking Russia. The announcement by Germany, Europe's biggest economy and one of Russia's most important Western trading partners, was among many signals on Tuesday pointing to further escalation in the war and disappointment for diplomacy. Germany's shift on weapons also was seen as a strong affirmation of a toughened message by the administration of U.S. President Joe Biden, which has said it wants to see Russia not only defeated in Ukraine but seriously weakened from the conflict that Russian President Vladimir Putin began two months ago. The increasing flow of Western weapons into Ukraine -- including howitzers, armed drones, tanks and ammunition -- also amounted to another sign that a war Putin had expected would divide his Western adversaries had instead drawn them much closer together.

With the end of Moore's law approaching and Dennard scaling ending, the computing community is increasingly looking at new technologies to enable continued performance improvements. Neuromorphic computers are one such new computing technology. The term neuromorphic was coined by Carver Mead in the late 1980s1,2, and at that time primarily referred to mixed analogue–digital implementations of brain-inspired computing; however, as the field has continued to evolve and with the advent of large-scale funding opportunities for brain-inspired computing systems such as the DARPA Synapse project and the European Union's Human Brain Project, the term neuromorphic has come to encompass a wider variety of hardware implementations. We define neuromorphic computers as non-von Neumann computers whose structure and function are inspired by brains and that are composed of neurons and synapses. Von Neumann computers are composed of separate CPUs and memory units, where data and instructions are stored in the latter.

One-hour flights anywhere may be some way off yet, but artificial intelligence could play a massive part in speeding up the development of hypersonic airliners. U.S.-based Argonne National Laboratory announced a partnership with NASA to boost hypersonic flight research and make vastly shorter travel times a reality with the help of AI-enhanced computer simulations, a press statement reveals. Hypersonic flight is achieved at a speed of Mach 5, or five times the speed of sound at sea level -- sound travels differently at different altitudes and on different planets. Argonne will bring its supercomputing capacity to the table to help NASA develop its hypersonic testing systems, including experimental aircraft such as its X-43A scramjet-powered aircraft, built as part of its Hyper-X program. The company uses computer fluid dynamics (CDF) to model and predict how an aircraft will react to the forces around it at hypersonic speeds.

DJI, the world's largest drone manufacturer, has announced it is temporarily halting operations in Russia and Ukraine, in a rare example of a Chinese firm suspending business in response to the war in Ukraine. The Shenzhen-headquartered company said on Wednesday it would suspend its business in the two countries while "internally reassessing compliance requirements in various jurisdictions". DJI, which was founded in Hong Kong in 2006, added it was "engaging with customers, partners and other stakeholders regarding the temporary suspension," according to a company statement. Adam Lisberg, DJI's director of corporate communications for North America, told Al Jazeera the company had taken the action "not to make a statement about any country, but to make a statement about our principles". "DJI abhors any use of our drones to cause harm, and we are temporarily suspending sales in these countries in order to help ensure no one uses our drones in combat," Lisberg said.

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."

Image: DroneShield RfPatrol body-worn C-UAS device with enrolled firmware upgrades. DroneShield has announced it has commenced a release of a ground-breaking software update across the global fleet of its C-UAS portable, vehicle/ship based and fixed site devices, deployed with military, intelligence community, Homeland Security, law enforcement, critical infrastructure and other users. Enrolled devices receive quarterly firmware updates of the proprietary DroneShield RFAI Artificial Intelligence engine, with periodic quarters being major enhancements, such as this 2Q22 release. Angus Bean, DroneShield Chief Technology Officer, commented, "DroneShield offers unparalleled C-UAS performance as the original pioneer in this sector. Ongoing R&D programs sustain the cutting-edge nature of our products, protecting and serving our user community. We are excited about the enhancements to the performance of our deployed fleet of devices, developed, field-tested, and rolled out in a highly expedient manner."

In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and Hinf-norm based correction for a linear Gaussian system. As the dimension of state or parameter space grows, performing the full Kalman update with the dense covariance matrix for a large scale system requires increased storage and computational complexity, making it impractical. The VIF approach, based on mean-field Gaussian variational inference, reduces this burden through the variational approximation to the covariance usually in the form of a diagonal covariance approximation. The challenge is to retain convergence and correct for biases introduced by the sequential VIF steps. We desire a framework that improves feasibility while still maintaining reasonable proximity to the optimal Kalman filter as data is assimilated. To accomplish this goal, a Hinf-norm based optimization perturbs the VIF covariance matrix to improve robustness. This yields a novel VIF- Hinf recursion that employs consecutive variational inference and Hinf based optimization steps. We explore the development of this method and investigate a numerical example to illustrate the effectiveness of the proposed filter.

Differentially private (DP) synthetic data generation is a practical method for improving access to data as a means to encourage productive partnerships. One issue inherent to DP is that the "privacy budget" is generally "spent" evenly across features in the data set. This leads to good statistical parity with the real data, but can undervalue the conditional probabilities and marginals that are critical for predictive quality of synthetic data. Further, loss of predictive quality may be non-uniform across the data set, with subsets that correspond to minority groups potentially suffering a higher loss. In this paper, we develop ensemble methods that distribute the privacy budget "wisely" to maximize predictive accuracy of models trained on DP data, and "fairly" to bound potential disparities in accuracy across groups and reduce inequality. Our methods are based on the insights that feature importance can inform how privacy budget is allocated, and, further, that per-group feature importance and fairness-related performance objectives can be incorporated in the allocation. These insights make our methods tunable to social contexts, allowing data owners to produce balanced synthetic data for predictive analysis.

Biofourmis, a startup developing digital therapeutics and artificial intelligence to remotely monitor patients, said its valuation hit $1.3 billion after raising a $300 million Series D funding round led by General Atlantic. The Boston-based company said CVS Health joined the round, along with existing investors, and also announced Omar Ishrak, chairperson of Intel and former CEO of Medtronic, will chair its board. Biofourmis had previously raised a $100 million round in September 2020 led by SoftBank Investment Advisers at an undisclosed valuation. This Series D round brings the company's total funding to $445 million. Originally based in Singapore, Biofourmis moved its headquarters to the United States in 2019.