1 février 2023 | International, C4ISR

Defense Innovation Board launches survey to boost private partnerships

The survey will inform a broader study that considers how DoD can better mobilize capital investment toward critical defense technology areas.


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  • Webinar: The Future Of U.S. Foreign Military Sales

    19 janvier 2021 | International, Aérospatial, Naval, Terrestre, C4ISR, Sécurité

    Webinar: The Future Of U.S. Foreign Military Sales

    This webinar took place January 15, 2021 and sponsored by Makino. In the final months of President Donald Trump's presidency, his administration notified Congress of billions in potential Foreign Military Sales – including the possibility of the UAE purchasing F-35s and MQ-9 Reapers, Taiwan to buy guided cruise missiles and reconnaissance pods for its F-16 fighters. In all, Congress was notified of sales worth up to $175 billion in 2020, though actual completed sales for the year totaled just $50.8 billion – a three-year low. Join us as we discuss: What the fate of these proposed transactions might be How a President Joe Biden administration may change course How the COVID-19 pandemic may influence sales How companies can navigate this changing environment Watch the full recording above. Panelists: Steve Trimble, Defense Editor, Aviation Week Network Lee Hudson, Pentagon Editor, Aviation Week Network Michael Bruno, Senior Business Editor, Aviation Week Network Moderated by Jen DiMascio, Executive Editor, Defense & Space, Aviation Week Network https://aviationweek.com/defense-space/z/webinar-future-us-foreign-military-sales

  • DARPA: Defending Against Adversarial Artificial Intelligence

    7 février 2019 | International, C4ISR

    DARPA: Defending Against Adversarial Artificial Intelligence

    Today, machine learning (ML) is coming into its own, ready to serve mankind in a diverse array of applications – from highly efficient manufacturing, medicine and massive information analysis to self-driving transportation, and beyond. However, if misapplied, misused or subverted, ML holds the potential for great harm – this is the double-edged sword of machine learning. “Over the last decade, researchers have focused on realizing practical ML capable of accomplishing real-world tasks and making them more efficient,” said Dr. Hava Siegelmann, program manager in DARPA's Information Innovation Office (I2O). “We're already benefitting from that work, and rapidly incorporating ML into a number of enterprises. But, in a very real way, we've rushed ahead, paying little attention to vulnerabilities inherent in ML platforms – particularly in terms of altering, corrupting or deceiving these systems.” In a commonly cited example, ML used by a self-driving car was tricked by visual alterations to a stop sign. While a human viewing the altered sign would have no difficulty interpreting its meaning, the ML erroneously interpreted the stop sign as a 45 mph speed limit posting. In a real-world attack like this, the self-driving car would accelerate through the stop sign, potentially causing a disastrous outcome. This is just one of many recently discovered attacks applicable to virtually any ML application. To get ahead of this acute safety challenge, DARPA created the Guaranteeing AI Robustness against Deception (GARD) program. GARD aims to develop a new generation of defenses against adversarial deception attacks on ML models. Current defense efforts were designed to protect against specific, pre-defined adversarial attacks and, remained vulnerable to attacks outside their design parameters when tested. GARD seeks to approach ML defense differently – by developing broad-based defenses that address the numerous possible attacks in a given scenario. “There is a critical need for ML defense as the technology is increasingly incorporated into some of our most critical infrastructure. The GARD program seeks to prevent the chaos that could ensue in the near future when attack methodologies, now in their infancy, have matured to a more destructive level. We must ensure ML is safe and incapable of being deceived,” stated Siegelmann. GARD's novel response to adversarial AI will focus on three main objectives: 1) the development of theoretical foundations for defensible ML and a lexicon of new defense mechanisms based on them; 2) the creation and testing of defensible systems in a diverse range of settings; and 3) the construction of a new testbed for characterizing ML defensibility relative to threat scenarios. Through these interdependent program elements, GARD aims to create deception-resistant ML technologies with stringent criteria for evaluating their robustness. GARD will explore many research directions for potential defenses, including biology. “The kind of broad scenario-based defense we're looking to generate can be seen, for example, in the immune system, which identifies attacks, wins and remembers the attack to create a more effective response during future engagements,” said Siegelmann. GARD will work on addressing present needs, but is keeping future challenges in mind as well. The program will initially concentrate on state-of-the-art image-based ML, then progress to video, audio and more complex systems – including multi-sensor and multi-modality variations. It will also seek to address ML capable of predictions, decisions and adapting during its lifetime. A Proposers Day will be held on February 6, 2019, from 9:00 AM to 2:00 PM (EST) at the DARPA Conference Center, located at 675 N. Randolph Street, Arlington, Virginia, 22203 to provide greater detail about the GARD program's technical goals and challenges. Additional information will be available in the forthcoming Broad Agency Announcement, which will be posted to www.fbo.gov. https://www.darpa.mil/news-events/2019-02-06

  • Dassault Aviation poursuit sa collaboration avec l'ISAE-SUPAERO sur la chaire de recherche « Conception et Architecture de Systèmes Aériens Cognitifs »

    23 mars 2022 | International, Aérospatial, C4ISR

    Dassault Aviation poursuit sa collaboration avec l'ISAE-SUPAERO sur la chaire de recherche « Conception et Architecture de Systèmes Aériens Cognitifs »

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