Fox News correspondent Mike Tobin has the latest on tensions amid the Russia-Ukraine war on'Special Report.' EXCLUSIVE: House Foreign Affairs Committee Chairman Michael McCaul called Russia's interception of a U.S. drone a "blatant intimidation tactic," and stressed the need for the United States to continue its support for Ukraine and security efforts in the region. A Russian Su-27 fighter plane collided with a U.S. MQ-9 Reaper drone, which was conducting "routine operations" over the Black Sea on Tuesday. The jet in question was one of two Su-27's flying in tandem when the collision occurred in international airspace over international waters. "To be clear, the Black Sea is not a Russian lake. The U.S. surveillance drone intercepted by a Russian aircraft was operating in international airspace," McCaul told Fox News Digital.

The United States military has released footage it says is of an unsafe intercept of a US drone by a Russian jet over the Black Sea. The US on Tuesday alleged that a Russian Su-27 fighter jet collided with one of its Reaper surveillance drones in international airspace, forcing it to crash into the sea. Russia denied it deliberately brought the unmanned aerial vehicle down. VIDEO: Two #Russian Su-27s conducted an unsafe & unprofessional intercept w/a @usairforce intelligence, surveillance & reconnaissance unmanned MQ-9 operating w/i international airspace over the #BlackSea March 14. https://t.co/gMbKYNtIeQ The declassified 42-second footage released by the US European Command shows the Su-27 fighter jet approaching the back of the MQ-9 drone, the Pentagon said.

U.S. European Command on Thursday released video of a Russian Su-27 fighter jet colliding with a U.S. MQ-9 Reaper drone over the Black Sea, March 14. U.S. officials have released video that shows a Russian Su-27 fighter jet colliding with the propeller of a U.S. MQ-9 Reaper drone. The video release by the U.S. military's European Command on Thursday came amid a race to secure the downed American aircraft. Russian ships are at the MQ-9 drone crash site in the Black Sea, a U.S. defense official told Fox News Thursday. Russia sent ships to search the debris field almost immediately following the crash.

Intensified spying by American drones near Ukraine could lead to an escalation and Russia will respond proportionally to future intelligence-gathering operations, Moscow's defence chief has told his US counterpart. The comments came in a phone conversation on Wednesday between Sergei Shoigu and Pentagon boss Lloyd Austin after the United States accused a Russian Su-27 fighter jet of colliding with one of its Reaper surveillance drones, forcing it to crash into the Black Sea. Russia denied it deliberately brought the unmanned aerial vehicle down. "It was noted that flights by American strategic lethal drones by the Crimea coastline were provocative in nature and created pre-conditions for an escalation of the situation in the Black Sea zone," a defence ministry statement quoted Shoigu as saying. "[Russia] has no interest in such a development, but it will continue to respond proportionately to all provocations."

Hari Ravichandran is the CEO and Founder of Aura, a leading provider of comprehensive digital security solutions for consumers. One of the reasons for the rapid acceleration of cybercrime is the lower barrier to entry for malicious actors. Cybercriminals have evolved their business models, including now offering subscription services and starter kits. The use of large language models (LLMs) like ChatGPT to write malicious code also highlights the potential challenges to cybersecurity. Because of these threats, all business leaders in today's digital world must be knowledgeable about the developments of AI in cybersecurity.

Recovery of a US surveillance drone that crashed after being intercepted by Russian fighter jets would be challenging given the deep waters in the Black Sea, a senior United States general said, as reports emerged of Russian vessels at the crash site. Chairman of the US Joint Chiefs of Staff General Mark Milley said remains of the uncrewed MQ-9 Reaper drone, which the US claims was brought down by one of two Russian Su-27 jets clipping the drone's propeller, sank in waters as deep as 1,219 to 1,524 meters (4,000 to 5,000 feet). "It probably sank to some significant depths, so any recovery operation from a technical standpoint would be very difficult," Milley told reporters on Wednesday. Milley added it would take several days before the US would know for certain the size of the debris field. Moscow – which denies that its jets were in physical contact with the drone – said it would try to retrieve the drone wreckage as reports emerged on Thursday of US officials confirming that Russian ships had reached the crash site.

WASHINGTON/KYIV – Washington's top general has said the crash of a U.S. surveillance drone after being intercepted by Russian jets showed Moscow's increasingly aggressive behavior while Russia warned Washington that flying drones near Crimea risked escalation. A day after the U.S. drone went down over the Black Sea, defense ministers and military chiefs from the U.S. and Russia held rare telephone conversations on Wednesday with relations at their lowest point in decades over Moscow's invasion of Ukraine. Moscow's Defence Minister Sergei Shoigu told his U.S. counterpart, Lloyd Austin, that American drone flights by Crimea's coast "were provocative in nature" and could lead to "an escalation … in the Black Sea zone," a ministry statement said. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

While the majority of autonomous driving research has concentrated on everyday driving scenarios, further safety and performance improvements of autonomous vehicles require a focus on extreme driving conditions. In this context, autonomous racing is a new area of research that has been attracting considerable interest recently. Due to the fact that a vehicle is driven by its perception, planning, and control limits during racing, numerous research and development issues arise. This paper provides a comprehensive overview of the autonomous racing system built by team KAIST for the Indy Autonomous Challenge (IAC). Our autonomy stack consists primarily of a multi-modal perception module, a high-speed overtaking planner, a resilient control stack, and a system status manager. We present the details of all components of our autonomy solution, including algorithms, implementation, and unit test results. In addition, this paper outlines the design principles and the results of a systematical analysis. Even though our design principles are derived from the unique application domain of autonomous racing, they can also be applied to a variety of safety-critical, high-cost-of-failure robotics applications. The proposed system was integrated into a full-scale autonomous race car (Dallara AV-21) and field-tested extensively. As a result, team KAIST was one of three teams who qualified and participated in the official IAC race events without any accidents. Our proposed autonomous system successfully completed all missions, including overtaking at speeds of around $220 km/h$ in the IAC@CES2022, the world's first autonomous 1:1 head-to-head race.

It is likely that AI systems driven by pre-trained language models (PLMs) will increasingly be used to assist humans in high-stakes interactions with other agents, such as negotiation or conflict resolution. Consistent with the goals of Cooperative AI \citep{dafoe_open_2020}, we wish to understand and shape the multi-agent behaviors of PLMs in a pro-social manner. An important first step is the evaluation of model behaviour across diverse cooperation problems. Since desired behaviour in an interaction depends upon precise game-theoretic structure, we focus on generating scenarios with particular structures with both crowdworkers and a language model. Our work proceeds as follows. First, we discuss key methodological issues in the generation of scenarios corresponding to particular game-theoretic structures. Second, we employ both crowdworkers and a language model to generate such scenarios. We find that the quality of generations tends to be mediocre in both cases. We additionally get both crowdworkers and a language model to judge whether given scenarios align with their intended game-theoretic structure, finding mixed results depending on the game. Third, we provide a dataset of scenario based on our data generated. We provide both quantitative and qualitative evaluations of UnifiedQA and GPT-3 on this dataset. We find that instruct-tuned models tend to act in a way that could be perceived as cooperative when scaled up, while other models seemed to have flat scaling trends.

Counterfactual explanations are an increasingly popular form of post hoc explanation due to their (i) applicability across problem domains, (ii) proposed legal compliance (e.g., with GDPR), and (iii) reliance on the contrastive nature of human explanation. Although counterfactual explanations are normally used to explain individual predictive-instances, we explore a novel use case in which groups of similar instances are explained in a collective fashion using ``group counterfactuals'' (e.g., to highlight a repeating pattern of illness in a group of patients). These group counterfactuals meet a human preference for coherent, broad explanations covering multiple events/instances. A novel, group-counterfactual algorithm is proposed to generate high-coverage explanations that are faithful to the to-be-explained model. This explanation strategy is also evaluated in a large, controlled user study (N=207), using objective (i.e., accuracy) and subjective (i.e., confidence, explanation satisfaction, and trust) psychological measures. The results show that group counterfactuals elicit modest but definite improvements in people's understanding of an AI system. The implications of these findings for counterfactual methods and for XAI are discussed.