Gripen E/F le dernier appareil suédois ?

Sur le même sujet

• 

  18 juillet 2018 | International, Aérospatial, C4ISR

  US Air Force looks to accelerate artificial intelligence contracts

  FARNBOROUGH, England — The Air Force is still not moving fast enough to recruit the software talent that it needs to harness emerging technologies like artificial intelligence, its acquisition head said Tuesday. "I don't think we're attracting enough people. Whether they're the right people or not, I think that's a separate question. I'm not sure that we'll be able to answer that until we're working with a broader set of the industry base that's working AI,” Will Roper, the Air Force's undersecretary for acquisition, technology and logstics, told reporters at Farnborough Airshow. “I contend that the companies driving AI are a different breed of company than those who drive evolution in hardware, especially companies that drove hardware that have gotten us to today's military. The paces are faster, turnovers are quicker. Software is done in month cycles not year cycles.” Over the past year, the Air Force has charted some successes and some failures in its attempts to integrate tech like AI and big data analytics with legacy hardware systems like fighter jets or air operations center. It has established the Kessel Run Experimentation Lab, a group of industry and airmen in Boston that are iterating new capabilities for air operations centers. Instead of rolling out a large software package, the coders focus on app-like updates that can more rapidly insert new functionality into the AOC. But it's also suffered setbacks — most notably, Google's stated intention to withdraw from future Defense Department projects after some employees objected to the company's work on Project Maven, a program would allow the Pentagon to use AI to review footage from drones. Some have worried that could have a chilling effect on future efforts. Roper said that a big focus of his job is changing how the Air Force approaches software. In the past, software was a product that could be bought in cycles, just like a physical product like a missile or aircraft. Now, it's a service that must be reworked constantly, he said. “You get a good set of coders in, they can push out so much code per month. You put them with the user that's going to use the code and together they're able to collaborate to make sure that the developer is creating something that the operator is using,” he said. “That's working very well for us in Boston, and we're looking to expand that. That's the basic mechanism to move towards AI. We're going to need people that are working with us that are software people that are working, tweaking algorithms with the users that use them, and it's probably a different company than have worked with us over the past 10 years.” The Air Force has to get those companies under contract faster, in weeks instead of months, Roper said. It's looking for opportunities to use contract vehicles specifically delegated for small businesses and to use AFWERX — its outreach arm to nontraditional contractors who are creating promising commercial technologies — to introduce startups to the service. But Roper acknowledged there was no easy answer to the problem. One possible way to inject AI into Air Force programs — although a mundane one — is to use it for predictive maintenance technologies that use sensors to forecast when a component will break, said Air Force Under Secretary Matt Donovan. “It's very exciting for us and I think it holds a lot of potential to reduce our sustainment costs,” he said, noting that sustainment makes up a whopping 70 percent of the life-cycle cost of any given product. Roper agreed that sustainment was a great area to begin employing AI, and that experience could help the Air Force begin to figure out how to use the technology for classified applications. https://www.defensenews.com/digital-show-dailies/farnborough/2018/07/17/air-force-looks-to-accelerate-artificial-intelligence-contracts

• 

  2 janvier 2019 | International, Terrestre

  SAIC Strikes Out On Mobile Protected Firepower, Plans To Remain In Combat Vehicle Market

  Even though SAIC [SAIC] was eliminated from the Army's new light tank program, the company's second major vehicle loss in the last year, the company will continue exploring combat vehicle opportunities, according to a company spokeswoman.Lauren Presti said... http://www.defensedaily.com/saic-strikes-mobile-protected-firepower-plans-remain-combat-vehicle-market

• 

  18 février 2019 | International, C4ISR

  DARPA: Teaching AI Systems to Adapt to Dynamic Environments

  Current AI systems excel at tasks defined by rigid rules – such as mastering the board games Go and chess with proficiency surpassing world-class human players. However, AI systems aren't very good at adapting to constantly changing conditions commonly faced by troops in the real world – from reacting to an adversary's surprise actions, to fluctuating weather, to operating in unfamiliar terrain. For AI systems to effectively partner with humans across a spectrum of military applications, intelligent machines need to graduate from closed-world problem solving within confined boundaries to open-world challenges characterized by fluid and novel situations. To attempt this leap, DARPA today announced the Science of Artificial Intelligence and Learning for Open-world Novelty (SAIL-ON) program. SAIL-ON intends to research and develop the underlying scientific principles and general engineering techniques and algorithms needed to create AI systems that act appropriately and effectively in novel situations that occur in open worlds. The program's goals are to develop scientific principles to quantify and characterize novelty in open-world domains, create AI systems that react to novelty in those domains, and to demonstrate and evaluate these systems in a selected DoD domain. A Proposers Day for interested proposers is scheduled for March 5, 2019, in Arlington, Virginia: https://go.usa.gov/xEUWh “Imagine if the rules for chess were changed mid-game,” said Ted Senator, program manager in DARPA's Defense Sciences Office. “How would an AI system know if the board had become larger, or if the object of the game was no longer to checkmate your opponent's king but to capture all his pawns? Or what if rooks could now move like bishops? Would the AI be able to figure out what had changed and be able to adapt to it?” Existing AI systems become ineffective and are unable to adapt when something significant and unexpected occurs. Unlike people, who recognize new experiences and adjust their behavior accordingly, machines continue to apply outmoded techniques until they are retrained. Given enough data, machines are able to do statistical reasoning well, such as classifying images for face-recognition, Senator said. Another example is DARPA's AI push in self-driving cars in the early 2000s, which led to the current revolution in autonomous vehicles. Thanks to massive amounts of data that include rare-event experiences collected from tens of millions of autonomous miles, self-driving technology is coming into its own. But the available data is specific to generally well-defined environments with known rules of the road. “It wouldn't be practical to try to generate a similar data set of millions of self-driving miles for military ground systems that travel off-road, in hostile environments and constantly face novel conditions with high stakes, let alone for autonomous military systems operating in the air and on sea,” Senator said. If successful, SAIL-ON would teach an AI system how to learn and react appropriately without needing to be retrained on a large data set. The program seeks to lay the technical foundation that would empower machines, regardless of the domain, to go through the military OODA loop process themselves – observe the situation, orient to what they observe, decide the best course of action, and then act. “The first thing an AI system has to do is recognize the world has changed. The second thing it needs to do is characterize how the world changed. The third thing it needs to do is adapt its response appropriately,” Senator said. “The fourth thing, once it learns to adapt, is for it to update its model of the world.” SAIL-ON will require performers and teams to characterize and quantify types and degrees of novelty in open worlds, to construct software that generates novel situations at distinct levels of a novelty hierarchy in selected domains, and to develop algorithms and systems that are capable of identifying and responding to novelty in multiple open-world domains. SAIL-ON seeks expertise in multiple subfields of AI, including machine learning, plan recognition, knowledge representation, anomaly detection, fault diagnosis and recovery, probabilistic programming, and others. A Broad Agency Announcement (BAA) solicitation is expected to be posted in the near future and will be available on DARPA's FedBizOpps page: http://go.usa.gov/Dom https://www.darpa.mil/news-events/2019-02-14