21 juillet 2020 | International, Technologies propres, Méga données et intelligence artificielle, Fabrication avancée 4.0, Systèmes autonomes (Drones / E-VTOL), Conception et essais virtuels, Fabrication additive

Startups Need Free Data To Work With Army: Venture Capitalists

Startups Need Free Data To Work With Army: Venture Capitalists

Because open-source software lacks the same kind of cyber certification that comes with more sensitive information, it is fertile ground for start-ups looking to work on military data, provided each service makes an open-source library available.

By on July 20, 2020 at 7:01 AM

ALBUQUERQUE: Venture capitalists want the Pentagon to be a good market. But for an industry that makes many unsuccessful bets in the promise that just a few pan out spectacularly, marketing software exclusively to the Pentagon poses an almost unacceptable risk. To ease startups into contracting, investors suggest the Army should provide unclassified, open-source data as the Air Force already does.

Near the top of his investors' wishlist, says Stu Solomon, CTO of intelligence provider Recorded Future, is removing “a lot of the friction necessary to get innovation into the government without having to be directly aligned or affiliated with the big solution integrators.”

Hitching new technology to a company already firmly ingrained in the Pentagon's ecosystem is a popular way to shepherd new software through the acquisitions process. It is also partly explains how, despite hundreds of millions of dollars in military contracts going to Silicon Valley companies, tech adoption seems as slow from the Valley as elsewhere.

Solomon's remarks came during a panel at AFCEA's 2020 AFCEA Army Signal conference. Recorded Future was founded in 2008, received early funding from IN-Q-TEL, received a contract from DIU in 2017, and a contract from Cyber Command in 2020. Much of Recorded Future's product is built on ingesting open-source information and offering analysis. As a feature, that meant the company could sustain itself in the commercial market, selling enterprise software, while still planning long-term to contract with the military, DHS, and intelligence services.

“If you think this is eventually going to be a market that matters to you, you're not going to be able to wait four years for the procurement process to mature as your product matures,” said Elizabeth Lawler, founder of Founder of AppLand.

If a startup's focus is solely on processing classified data, the capital investors need to be aligned directly with that goal to fund it since getting certified to handle classified material is one of the major sources of cost and friction.

“My current startup, focused on providing real-time up-to-date software images, works on things that are less sensitive as a starting point,” said Lawler, “for example, some of the code bases in the Air Force's open source code repository.”

Because open-source software lacks the same kind of cyber certification that comes with more sensitive information, it is fertile ground for start-ups looking to work on military data, provided the service makes an open-source library available.

“When it comes to this Valley of Death, I really view what we do when we start companies as an awful lot like a really difficult special forces mission,” said Andy Palmer, co-founder and CEO of data management company Tamr. “When you go in, you drop onto the ground to start a company, with a small team of people, and limited resources, and what oftentimes feels like an unreasonable objective. It's hand to hand combat for much of it, it's not pretty. The goal is survival.”

So, if the Army wants to bring new data tools to the battlefields of the future, it could start by creating open-source environments that allow companies to solve problems, at a smaller scale and without the hurdles of classification, suggested several panelists.

https://breakingdefense.com/2020/07/startups-need-free-data-to-work-with-army-venture-capitalists/

Sur le même sujet

  • 3D Printing of Multilayered Materials for Smart Helmets | 3D Printing Progress

    3 août 2021

    3D Printing of Multilayered Materials for Smart Helmets | 3D Printing Progress

    A mechanical and aerospace engineering professor is developing advanced helmets to ensure that members of the military are as protected as possible from blasts and other types of attacks.

  • Deadlines for B-21 Raider and ARCYBER

    3 mars 2021

    Deadlines for B-21 Raider and ARCYBER

    The B-21 Training Systems Innovation Challenge Deadline: WEDNESDAY 3 March 2021 The U.S. Air Force Global Strike Command and Rapid Capabilities Office have launched an innovation challenge on Vulcan focused on the B-21 Raider Training Systems. The results of the challenge will inform USAF decisions on the adoption of innovative solutions that enhance training systems for pilots and maintainers. The challenge is the continuation of a user-centered innovation effort spurred by a multidisciplinary USAF team (incl. end users) and—depending on merit and viability —provides the opportunity to: Engage with a state-of-the-art program and receive direct operator feedback. Be selected for a funded prototype demonstration Be considered for other potential development and/or integration activities beyond initial demonstration. Don't delay your engagement with this challenge. You can continue editing your submission all the way until the deadline next Wednesday 3 March 2021. Submit your innovations in Vulcan U.S. Army Cyber PAI Tools, Analytics, and Visualization Assessment Event (AE) Deadline: TUESDAY 2 March 2021 The U.S. Army Cyber Command (ARCYBER) Technical Warfare Center (TWC), is in search of industry expertise, software integration, analytic development, and data visualization capabilities to allow real-time, near real-time, and historical analysis of publicly available information (PAI). This call is interested in technologies that provide some or all of the following capabilities: Data Acquisition and Storage Data Structuring, Preparation, and Integration Data Analytics Data Visualization Submit your relevant capabilities to ARCYBER Cyber Fusion Innovation Center (CFIC) HERE by next Tuesday 2 March 2021. Review the instructions and be proactive in your submission process in order to increase the effectiveness of your engagement. IT TAKES A NETWORK!

  • Trustworthy AI: A Conversation with NIST's Chuck Romine

    21 janvier 2020

    Trustworthy AI: A Conversation with NIST's Chuck Romine

    By: Charles Romine Artificial Intelligence (AI) promises to grow the economy and improve our lives, but with these benefits, it also brings new risks that society is grappling with. How can we be sure this new technology is not just innovative and helpful, but also trustworthy, unbiased, and resilient in the face of attack? We sat down with NIST Information Technology Lab Director Chuck Romine to learn how measurement science can help provide answers. How would you define artificial intelligence? How is it different from regular computing? One of the challenges with defining artificial intelligence is that if you put 10 people in a room, you get 11 different definitions. It's a moving target. We haven't converged yet on exactly what the definition is, but I think NIST can play an important role here. What we can't do, and what we never do, is go off in a room and think deep thoughts and say we have the definition. We engage the community. That said, we're using a narrow working definition specifically for the satisfaction of the Executive Order on Maintaining American Leadership in Artificial Intelligence, which makes us responsible for providing guidance to the federal government on how it should engage in the standards arena for AI. We acknowledge that there are multiple definitions out there, but from our perspective, an AI system is one that exhibits reasoning and performs some sort of automated decision-making without the interference of a human. There's a lot of talk at NIST about “trustworthy” AI. What is trustworthy AI? Why do we need AI systems to be trustworthy? AI systems will need to exhibit characteristics like resilience, security and privacy if they're going to be useful and people can adopt them without fear. That's what we mean by trustworthy. Our aim is to help ensure these desirable characteristics. We want systems that are capable of either combating cybersecurity attacks, or, perhaps more importantly, at least recognizing when they are being attacked. We need to protect people's privacy. If systems are going to operate in life-or-death type of environments, whether it's in medicine or transportation, people need to be able to trust AI will make the right decisions and not jeopardize their health or well-being. Resilience is important. An artificial intelligence system needs to be able to fail gracefully. For example, let's say you train an artificial intelligence system to operate in a certain environment. Well, what if the system is taken out of its comfort zone, so to speak? One very real possibility is catastrophic failure. That's clearly not desirable, especially if you have the AI deployed in systems that operate critical infrastructure or our transportation systems. So, if the AI is outside of the boundaries of its nominal operating environment, can it fail in such a way that it doesn't cause a disaster, and can it recover from that in a way that allows it to continue to operate? These are the characteristics that we're looking for in a trustworthy artificial intelligence system. NIST is supposed to be helping industry before they even know they needed us to. What are we thinking about in this area that is beyond the present state of development of AI? Industry has a remarkable ability to innovate and to provide new capabilities that people don't even realize that they need or want. And they're doing that now in the AI consumer space. What they don't often do is to combine that push to market with deep thought about how to measure characteristics that are going to be important in the future. And we're talking about, again, privacy, security and resilience ... trustworthiness. Those things are critically important, but many companies that are developing and marketing new AI capabilities and products may not have taken those characteristics into consideration. Ultimately, I think there's a risk of a consumer backlash where people may start saying these things are too easy to compromise and they're betraying too much of my personal information, so get them out of my house. What we can do to help, and the reason that we've prioritized trustworthy AI, is we can provide that foundational work that people in the consumer space need to manage those risks overall. And I think that the drumbeat for that will get increasingly louder as AI systems begin to be marketed for more than entertainment. Especially at the point when they start to operate critical infrastructure, we're going to need a little more assurance. That's where NIST can come together with industry to think about those things, and we've already had some conversations with industry about what trustworthy AI means and how we can get there. I'm often asked, how is it even possible to influence a trillion-dollar, multitrillion-dollar industry on a budget of $150 million? And the answer is, if we were sitting in our offices doing our own work independent of industry, we would never be able to. But that's not what we do. We can work in partnership with industry, and we do that routinely. And they trust us, they're thrilled when we show up, and they're eager to work with us. AI is a scary idea for some people. They've seen “I, Robot,” or “The Matrix,” or “The Terminator.” What would you say to help them allay these fears? I think some of this has been overhyped. At the same time, I think it's important to acknowledge that risks are there, and that they can be pretty high if they're not managed ahead of time. For the foreseeable future, however, these systems are going to be too fragile and too dependent on us to worry about them taking over. I think the biggest revolution is not AI taking over, but AI augmenting human intelligence. We're seeing examples of that now, for instance, in the area of face recognition. The algorithms for face recognition have improved at an astonishing rate over the last seven years. We're now at the point where, under controlled circumstances, the best artificial intelligence algorithms perform on par with the best human face recognizers. A fascinating thing we learned recently, and published in a report, is that if you take two trained human face recognizers and put them together, the dual system doesn't perform appreciably better than either one of them alone. If you take two top-performing algorithms, the combination of the two doesn't really perform much better than either one of them alone. But if you put the best algorithm together with a trained recognizer, that system performs substantially better than either one of them alone. So, I think, human augmentation by AI is going to be the revolution. What's next? I think one of the things that is going to be necessary for us is pulling out the desirable characteristics like usability, interoperability, resilience, security, privacy and all the things that will require a certain amount of care to build into the systems, and get innovators to start incorporating them. Guidance and standards can help to do that. Last year, we published our plan for how the federal government should engage in the AI standards development process. I think there's general agreement that guidance will be needed for interoperability, security, reliability, robustness, these characteristics that we want AI systems to exhibit if they're going to be trusted. https://www.nist.gov/blogs/taking-measure/trustworthy-ai-conversation-nists-chuck-romine

Toutes les nouvelles