Meta's chief technology officer has called it "the great honor of my life" to be enlisted in a new US army corps that defence chiefs set up to better integrate military and tech industry expertise, including senior figures from top tech firms that also include Palantir and OpenAI. Andrew Bosworth, a long-term lieutenant to Mark Zuckerberg known widely as "Boz", is one of several senior Silicon Valley executives commissioned to the rank of lieutenant colonel in the corps, called Detachment 201, which the US army says will "fuse cutting-edge tech expertise with military innovation". Bosworth, who joined Facebook in 2006, was sworn into the army reserves earlier this month alongside Shyam Sankar, the chief technology officer of Palantir, a technology firm with extensive defence contracts, Kevin Weil, chief product officer of OpenAI, and Bob McGrew, an adviser at Thinking Machines Lab, a 10bn AI company. They wore military fatigues at the swearing-in ceremony but will not be full-time soldiers. The recruitment is a sign of the increasing importance of technology in modern warfare and growing commercial and research links between some of the largest tech firms and the military.

Members of Ukraine's 1st Separate Assault Battalion describe themselves as firemen. Their job is to rapidly deploy to areas along the front that are in danger of collapse. Lately, their service has been in high demand: the front is burning. A large-scale counter-offensive last year failed to achieve meaningful victories, and since then Russia has been on the attack. One of its priorities appears to be Kupyansk, a city in northeastern Ukraine, some twenty miles from the Russian border.

A disc-shaped object longer than the height of the Empire State Building emerged from the horizon of Ukraine's embattled Donetsk province last Friday, hovering eerily still a mile off the ground, a soldier has told DailyMail.com That soldier, a drone operator, had cautiously guided his infrared quadcopter 500-feet for a reconnaissance mission, struggling against high winds, when he suddenly spotted the flat, 1,300-foot-long UFO, which stood motionless despite those winds. In an interview from the warzone, the soldier, who is with the Ukrainian army's 406th Battalion, said he and his fellow servicemen had'never seen things like this before.' 'Initially, I thought that it was something new invented by the Russians,' he added, 'but then I understood... 'No! It might be [a] UFO.''

A disc-shaped, completely silent UFO was caught on camera by Ukrainian troops in the war-torn country, in footage shared exclusively with DailyMail.com. 'What the f-[expletive] is this? Why isn't it moving?' the men with Ukraine's 406th Battalion can be heard debating as they witnessed the deadly calm UFO hovering over their warzone. While the size, altitude, and shape of the object remain a mystery, the drone's own altitude indicates that the apparent object could be a large craft over 30 miles away. The eerie footage was captured by the 406th Battalion this month via one of the over 300 'heat vision' quadcopter drones used by the Ukrainian Armed Forces (UAF) in their effort to defend the nation from a now two-years long invasion by Russia.

Transformer networks have revolutionized NLP representation learning since they were introduced. Though a great effort has been made to explain the representation in transformers, it is widely recognized that our understanding is not sufficient. One important reason is that there lack enough visualization tools for detailed analysis. In this paper, we propose to use dictionary learning to open up these "black boxes" as linear superpositions of transformer factors. Through visualization, we demonstrate the hierarchical semantic structures captured by the transformer factors, e.g., word-level polysemy disambiguation, sentence-level pattern formation, and long-range dependency. While some of these patterns confirm the conventional prior linguistic knowledge, the rest are relatively unexpected, which may provide new insights. We hope this visualization tool can bring further knowledge and a better understanding of how transformer networks work. The code is available at https://github.com/zeyuyun1/TransformerVis

The United States said Tuesday it has "credible information" that Russia may use "chemical agents" in its offensive to take the besieged Ukrainian city of Mariupol, reigniting concerns about the use of such prohibited weapons. While the West and Kyiv have been warning Moscow since the start of its invasion on February 24 against any use of chemical weapons, fears have grown this week after unconfirmed reports emerged that such weapons may have already been deployed. The Organization for the Prohibition of Chemical Weapons (OPCW) said Tuesday that it was "concerned" by allegations that chemical weapons had been used in Mariupol, a strategic port city besieged by Russian forces in the east of Ukraine and the scene of heavy fighting. The OPCW, to which both Russia and Ukraine belong, referred to "accusations leveled by both sides around possible misuse of toxic chemicals." The Ukrainian Azov battalion, which is engaged in the defense of Mariupol, said Monday that a Russian drone had dropped a "poisonous substance" on soldiers and civilians in Mariupol.

Neural text generation models are likely to suffer from the low-diversity problem. Various decoding strategies and training-based methods have been proposed to promote diversity only by exploiting contextual features, but rarely do they consider incorporating syntactic structure clues. In this work, we propose using linguistic annotation, i.e., part-of-speech (POS), to guide the text generation. In detail, we introduce POS Guided Softmax (POSG-Softmax) to explicitly model two posterior probabilities: (i) next-POS, and (ii) next-token from the vocabulary of the target POS. A POS guided sampling strategy is further proposed to address the low-diversity problem by enriching the diversity of POS. Extensive experiments and human evaluations demonstrate that, compared with existing state-of-the-art methods, our proposed methods can generate more diverse text while maintaining comparable quality.

One of the most difficult of tasks on the modern battlefield is predicting where the enemy will attack next. Although the Army has plenty of ways to find the enemy, figuring out his intentions are something else entirely. Now the U.S. Army plans to use drones, target recognition, artificial intelligence, and machine learning to tell the colonels and generals where an attack appears imminent. The Army's Aided Threat Recognition from Mobile Cooperative and Autonomous Sensors (ATR-MCAS) program aims to operate autonomous air and ground drones throughout the battle zone, keeping a continuous watch on the enemy. The drones identify the enemy weapons systems, such as tanks or infantry fighting vehicles, then pass on the sightings to the AI.

Data fusion allows the elaboration and the evaluation of a situation synthesized from low level informations provided by different kinds of sensors. The fusion of the collected data will result in fewer and higher level informations more easily assessed by a human operator and that will assist him effectively in his decision process. In this paper we present the suitability and the advantages of using a Possibilistic Assumption based Truth Maintenance System (0-ATMS) in a data fusion military application. We first describe the problem, the needed knowledge representation formalisms and problem solving paradigms. Then we remind the reader of the basic concepts of ATMSs, Possibilistic Logic and Il-ATMSs. Finally we detail the solution to the given data fusion problem and conclude with the results and comparison with a non-possibilistic solution.

In previous work, we pointed out the limitations of standard Bayesian networks as a modeling framework for large, complex domains. We proposed a new, richly structured modeling language, {em Object-oriented Bayesian Netorks}, that we argued would be able to deal with such domains. However, it turns out that OOBNs are not expressive enough to model many interesting aspects of complex domains: the existence of specific named objects, arbitrary relations between objects, and uncertainty over domain structure. These aspects are crucial in real-world domains such as battlefield awareness. In this paper, we present SPOOK, an implemented system that addresses these limitations. SPOOK implements a more expressive language that allows it to represent the battlespace domain naturally and compactly. We present a new inference algorithm that utilizes the model structure in a fundamental way, and show empirically that it achieves orders of magnitude speedup over existing approaches.