5 août 2024 | International, Aérospatial
Two key elements of the service's B-52 overhaul effort — the engine replacement and radar modernization programs — have seen cost and schedule growth.
5 novembre 2021 | International, Aérospatial
Airbus and France's Dassault Aviation are still ironing out details of the work sharing agreement they reached last spring for the trinational Future Combat Air System program, according to Airbus Defense and Space CEO Michael Schoellhorn.
30 janvier 2019 | International, C4ISR
By: Mark Pomerleau The intelligence community has unveiled its multi-pronged plan to compete in the increasingly digital and data-centric world. The strategy, titled “Augmenting Intelligence using Machines (AIM)” and released Jan. 16, outlines how the intelligence community will adjust its investments, personnel and practices to better incorporate associated technologies “To meet its vision of ensuring intelligence advantage, the IC must adapt to the rapid global technological democratization in sensing, communications, computing, and machine analysis of data,” Sue Gordon, principal deputy director of national intelligence, said in the document. “These trends threaten to erode what were previously unique [IC] capabilities and advantages; going forward, we must improve our ability to analyze and draw conclusions from IC-wide data collections at scale.” During remarks at an August 2018 conference, Gordon said she wants the strategy to help focus on what the IC is trying to achieve rather than the technologies that they use.She described the need for getting machines as partners to help derive intelligence as one of the two most existential threats facing the community. The strategy lists four primary investment objectives ranked chronologically. They include: - Immediate and ongoing needs: Creating digital foundations, data and science and technical intelligence. - Short-term needs: Adopting commercial and open source narrow AI solutions, which are AI solutions that are designed with very specific tasks and functions. - Medium-term needs: Make investments in the gaps such as AI assurance and multimodal AI. - Long-term needs: Make investments in basic research focused on sense-making. The strategy notes that the “IC must be willing to rethink or abandon processes and mechanisms designed for an earlier era, establish disciplined engineering and operations practices, and maintain an absolute focus on assuring advantage in an intensely competitive global adversarial environment.” However, it also warns that artificial intelligence and associated technologies are not a panacea or a substitute for creating a digital foundation. Instead, the intelligence community must understand how AI algorithms may succeed and fail. The strategy also aims to bring together disparate efforts in artificial intelligence, process automation, and IC officer augmentation as a way to match investments in AI and data from other countries. One example includes the use of such technologies to create forgeries of audio and video, often referred to as deepfakes, which could lead to difficulties in deciphering fact from fiction. Gordon said in August that the National Geospatial-Intelligence Agency is using AI to automate image processing and the National Reconnaissance Office is using artificial intelligence tools to automate analysis of streaming multi-intelligence data for detecting activity and automating tasks of scarce and expensive collection resources. The strategy also focuses on the workforce. It notes that it is much more than technology and implementing the strategy will entail addressing workforce challenges and understanding and shaping the policies and authorities governing how the IC deploys and uses AI. https://www.c4isrnet.com/c2-comms/2019/01/28/how-the-intel-community-could-use-machines-and-ai
19 septembre 2018 | International, C4ISR
By: Daniel Cebul It's well known that satellites and other intelligence, surveillance and reconnaissance platforms collect more data than is possible for humans to analyze. To tackle this problem, the Intelligence Advanced Research Projects Activity, or IARPA, conducted the Functional Map of the World (fMoW) TopCoder challenge from July 2017 through February 2018, inviting researchers in industry and academia to develop deep learning algorithms capable of scanning and identifying different classes of objects in satellite imagery. IARPA curated a dataset of 1 million annotated, high-resolution satellite images aggregated using automated algorithms and crowd sourced images for competitors to train their algorithms to classify objects into 63 classes, such as airports, schools, oil wells, shipyards, or ports. Researchers powered their deep learning algorithms by combining large neural networks, known as convolutional neural networks (CNNs), and computers with large amounts of processing power. The result was a network that, when fed massive amounts of training data, can learn to identify and classify various objects from satellite imagery. By combining a number of these networks into what is called an ensemble, the algorithm can judge the results from each CNN to produce a final, improved result that is more robust than any single CNN. This is how a team from Lockheed Martin, led by Mark Pritt, designed their deep learning algorithm for the challenge. Pritt explained to C4ISRNET, that he and his team developed their CNN using machine learning software and framework from online open source software libraries, such as Tensor Flow. Earning a top five finish, the algorithm designed by Pritt's team achieved a total accuracy of 83 percent, and was able to classify 100 objects per second. Pritt said that with fully functioning algorithm, this software could take an image recognition task that takes a human an hour to complete and reduce the process to a few seconds. The team's algorithm excelled at identifying classes with distinctive features, and successfully matched nuclear power plants, tunnel openings, runways, tool booths, and wind farms with accuracies greater than 95 percent, but struggled with more indiscreet classes such as shipyards and ports, hospitals, office buildings, and police stations. “Usually when you develop an algorithm its nice to see where it succeeds, but you actually learn the most where you look at where the algorithm fails or it doesn't do well,” Pritt said. In trying to decipher why the algorithms struggled, Pritt said the competitors suggested that some objects simply don't have any distinguishing features from the point of view of a satellite image for the algorithms to recognize. “Maybe the most important ingredient you need for these new types of algorithm to work is the dataset because these algorithms require a great amount of data to train on,” Pritt explained. “It's kind of analogous to the way a human will learn in childhood how to recognize things. You need lots of examples of what those things are and then you can start to generalize and make your own judgments,” he said. But even with large amounts of training data that is correctly labeled, it is also possible the deep learning technology of today cannot reach the higher levels of intelligence to recognize nuanced differences. For example, Lockheed Martin's algorithm confused shipyards and ports 56 percent of the time. Pritt said that people “look at an image and they can tell that it's a port or a shipyard, they are usually looking at very subtle things such as if there is a ship in dry dock or if there is a certain type of crane present. They are looking for details in the image that are maybe higher level or more complicated than what these deep learning algorithms can do right now.” However, the fact that these algorithms cannot do everything should not dismiss the significant contribution they could provide to the defense and intelligence community. Hakjae Kim, IARPA's program manager for the fMoW challenge, said the benefits of this technology could extend far beyond faster image processing. “I want to look at it more in the perspective that we can do things we weren't able to do before,” Kim said. “Because its technology that we are now able to do x, y and z, there are more applications you can create because with the human power it is just impossible to do before.” Kim and Pritt stressed managing expectations for CNN-based artificial intelligence. “This is a real technology that will work, but it also has limitations. I don't want to express this technology as a magic box that will just solve everything magically,” Kim said. “I don't want the users in the field to get disappointed by the initial delivery of this technology and say 'Oh, this is another technology that was oversold and this is not something we can use," he added. Part of managing our expectations for AI requires recognizing that although intelligence is in the name, this technology does not think and reason like humans. “A lot of the time we think that because we use the term AI, we tend to think these algorithms are like us, they are intelligent like us,” Pritt said. “And in someways they seem to mimic our intelligence, but when they fail we realize ‘Oh, this algorithm doesn't really know anything, [it] doesn't have any common sense.'” So how are IARPA and Lockheed Martin working to improve their algorithms? For IARPA, Kim's team is working on updating and maintaining their dataset to ensure algorithms have the most up to date information to train on, ultimately making the CNN-based algorithms easier to trust. “[S]ubtle changes in the area mess up the brains of the system and that system will give you a totally wrong answer,” Kim explained. “So we have planned to continuously look over the area and make sure the algorithm we are developing and reassessing for the government to test on and use to be robust enough for their application," he furthered. Work is also underway at American universities. Kim described how a team of researchers at Boston University are using the fMoW dataset and tested algorithms to create heat maps that visualize what part of the image algorithms are using to classify objects. They've found that sometimes it is not the object itself, but clues surrounding the object that aid most in classification. For example a “windmill that actually shows a shadow gives a really good indicator of what that object is,” Kim said. “Shadows show a better view of the object. A shadow is casting the side view of the object over on the ground, so [BU's heat map algorithm] actually points out the shadow is really important and the key feature to make the object identified as a windmill.” But don't expect these algorithms to take away the jobs of analysts any time soon. “I think you still need a human doing the important judgments and kind of higher level thinking,” Pritt said. “I don't think AI will take away our jobs and replace humans, but I think what we have to do is figure out how to use them as a tool and how to use them efficiently, and that of course requires understanding what they do well and what they do poorly," he concluded. https://www.c4isrnet.com/intel-geoint/2018/09/18/differentiating-a-port-from-a-shipyard-is-a-new-kind-of-problem-for-ai