March 4, 2021 | International, Aerospace
Top USAF general urges support for Next-Gen fighter - Skies Mag
Air Combat Command chief talks NGAD, Tacair study, and acknowledges current F-35 problems.
July 10, 2018 | International, Naval
Has the US Navy thought this new frigate through? New report raises questions.
By: David B. Larter
WASHINGTON ― The U.S. Navy is rapidly moving toward procuring the first hull in its new class of frigate in 2020, but a new report is raising questions about whether the Navy has done detailed analysis about what it needs out of the ship before barging ahead.
The Navy may not have done an adequate job of analyzing gaps and capabilities shortfalls before it set itself on a fast-track to buying the so-called FFG(X) as an adaptation from a parent design, said influential Navy analyst Ron O'Rourke in a new Congressional Research Service report.
In essence, the CRS report questions whether the Navy looked at what capabilities the service already has in the fleet, what capabilities it's missing and whether the FFG(X) is the optimal solution to address any identified shortfalls.
O'Rourke suggests Congress push the Navy on “whether procuring a new class of FFGs is the best or most promising general approach for addressing the identified capability gaps and mission needs, and whether the Navy has performed a formal, rigorous analysis of this issue, as opposed to relying solely on subjective judgments of Navy or [Defense Department] leaders.”
““Subjective judgments, though helpful, can overlook counter-intuitive results regarding the best or most promising general approach,” the report reads. “Potential alternative general approaches for addressing identified capability gaps and mission needs in this instance include (to cite a few possibilities) modified LCSs, FFs, destroyers, aircraft, unmanned vehicles, or some combination of these platforms.”
The Navy is looking to adapt its FFG(X) from an existing design such as Fincantieri's FREMM, one of the two existing littoral combat ships or the Coast Guard's national security cutter as a means of getting updated capabilities into a small surface combatant and into the fleet quickly.
A better approach, O'Rourke suggests, would be to make a formal, rigorous analysis of alternatives to its current course. Failure to do so has led to a series of setbacks with the Navy's current small surface combatant program, the LCS.
“The Navy did not perform a formal, rigorous analysis of this kind prior to announcing the start of the LCS program in November 2001, and this can be viewed as a root cause of much of the debate and controversy that attended the LCS program, and of the program's ultimate restructurings in February 2014 and December 2015,” O'Rourke writes.
O'Rourke further suggests the Navy is relying too much on subjective opinions of Navy and Defense Department leaders, instead of a legitimate analysis. And indeed, the Navy has made rapid acquisition of the new ship the hallmark of the program.
“Subjective judgments can be helpful, particularly in terms of capturing knowledge and experience that is not easily reduced to numbers, in taking advantage of the ‘wisdom of the crowd,‘ and in coming to conclusions and making decisions quickly,” O'Rourke argues.
“On the other hand, a process that relies heavily on subjective judgments can be vulnerable to group-think, can overlook counter-intuitive results regarding capability gaps and mission needs, and, depending on the leaders involved, can emphasize those leaders' understanding of the Navy's needs.”
Read the full report here.
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March 15, 2021 | International, Aerospace
DoD SBIR/STTR Component BAA Pre-Release: Space Development Agency (SDA) HQ085021S0001
The DoD Small Business and Technology Partnerships Office announces the pre-release of the following Broad Agency Announcement (BAA) topics: Space Development Agency (SDA), HQ085021S0001 SBIR Topic HQ085021S0001-07: “Target Recognition and Acquisition in Complex Environments,” published at: https://beta.sam.gov/opp/8e5e2483aaac4ab9834601e7d2888581/view STTR Topic HQ085021S0001-08: “Target Recognition and Acquisition in Complex Environments,” published at: https://beta.sam.gov/opp/12f9af5e67a34384a5b0bd38dc3c079b/view IMPORTANT DATES: March 25, 2021: Topic Q&A opens; BAA opens, begin submitting proposals in DSIP April 13, 2021: Topic Q&A closes to new questions at 12:00 p.m. ET April 27, 2021: BAA closes, full proposals must be submitted in DSIP no later than 12:00 p.m. ET Full topics and instructions are available at the links provided above. Topic Q&A During pre-release, proposers can contact TPOCs directly. Once DoD begins accepting proposals on March 25, 2021, no further direct contact between proposers and topic authors is allowed. Topic Q&A will be available for proposers to submit technical questions at https://www.dodsbirsttr.mil/submissions/login beginning March 25, 2021. All questions and answers are posted electronically for general viewing. Topic Q&A will close to new questions on April 13, 2021 at 12:00 p.m. ET but will remain active to view questions and answers related to the topics until the BAA close.
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January 21, 2020 | International, C4ISR
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