These days, it's hard to go anywhere without encountering artificial intelligence (AI). Predictive text offers to finish our web searches and our text messages. AI learning-based software can produce everything from research papers to poetry, solving complex math equations to writing computer code. AI can be used to write algorithms, collect data on which areas experience the most gun violence and dictate which neighborhoods receive access to vital resources. This year, five students who make up the 2022-24 Lender Center for Social Justice Fellowship Project will set out to investigate how AI weapons systems transform war and surveillance, and they will also analyze how AI accentuates our social and political vulnerabilities to violence.

A deep reinforcement learning technique is presented for task offloading decision-making algorithms for a multi-access edge computing (MEC) assisted unmanned aerial vehicle (UAV) network in a smart farm Internet of Things (IoT) environment. The task offloading technique uses financial concepts such as cost functions and conditional variable at risk (CVaR) in order to quantify the damage that may be caused by each risky action. The approach was able to quantify potential risks to train the reinforcement learning agent to avoid risky behaviors that will lead to irreversible consequences for the farm. Such consequences include an undetected fire, pest infestation, or a UAV being unusable. The proposed CVaR-based technique was compared to other deep reinforcement learning techniques and two fixed rule-based techniques. The simulation results show that the CVaR-based risk quantifying method eliminated the most dangerous risk, which was exceeding the deadline for a fire detection task. As a result, it reduced the total number of deadline violations with a negligible increase in energy consumption.

Former DHS adviser Charles Marino breaks down the national security risks posed by the U.S. shooting down the third flying object in a week on'Fox News Live.' Iranian President Ebrahim Raisi is expected to pay a state visit to China this week at the request of President Xi Jinping, Beijing confirmed Sunday. The visit, scheduled from Tuesday to Thursday, is Raisi's first to China since 2021 and is intended to strengthen ties between the two countries, both U.S. rivals. FILE: In this file photo released by China's Xinhua News Agency, Iran's President Ebrahim Raisi, right, and Chinese President Xi Jinping pose for a photo on the sidelines of a meeting at the Shanghai Cooperation Organization (SCO) summit in Samarkand, Uzbekistan on Sept. 16, 2022. Raisi will meet with Xi and their delegations will sign cooperation documents, according to Iran's state news agency IRNA. Meeting with Iranian and Chinese business leaders and Iranian expatriates in China is also part of his itinerary, the report added.

Automatic crack detection and segmentation play a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG, and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and its performance of it is compared with other state-of-the-art network architecture. We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark dataset on the internet for crack detection and segmentation, which is open-sourced for the research community. Our feature pyramid crack segmentation network is tested on the benchmark dataset and gives satisfactory segmentation results. A framework for automatic unmanned aerial vehicle inspections is also proposed and will be established for the crack inspection tasks of various concrete structures. All our self-established datasets and codes are open-sourced at: https://github.com/KangchengLiu/Crack-Detection-and-Segmentation-Dataset-for-UAV-Inspection

Iran's supply of drones to Russia for its war effort in Ukraine appears to have escalated in recent months after a study released this week found Tehran has modified its drones to employ maximum damage. A January report by Conflict Armament Research (CAR) released publicly Thursday broke down why the affordably made Iranian Shahed-131 single-use drones have been employed to significant effect in Ukraine. Russia has relied on Iranian-supplied unmanned areial vehicles (UAV) in Ukraine for months to help assist with its diminishing missile stockpiles as the war continues into its 11th month, according to the Pentagon. Multipurpose warhead from a Shahed-131 UAV found in research by Conflict Armament Research on Thursday. Iranian drones have been used to hit civilian structures and target Ukraine's critical infrastructure as Russia looks to bombard the war-torn nation in near-daily strikes.

The US Pentagon is planning a new'weapon of mass destruction' that involves thousands of drones that strike by air, land and water to destroy enemy defenses - but experts fear humans could lose control of the'swarms.' The top-secret project, dubbed AMASS (Autonomous Multi-Domain Adaptive Swarms-of-Swarms), would represent automated warfare on an unprecedented scale. AMASS is still in the planning stages, but DARPA (Defense Advanced Research Project Agency) has been collecting bids from suppliers for the $78 million contract. Small drones would be equipped with weapons and tools for navigation and communication, along with abilities ranging from radar jamming to launching lethal attacks. While the technology would change how the US goes to war, experts in the industry raise concerns.

Elon Musk's SpaceX may be willing to supply Ukraine with Starlink service as it repels the Russian invasion, but it's not thrilled with every use of the satellite internet technology. Operating chief Gwynne Shotwell tells guests at a Federal Aviation Administration conference that SpaceX objects to reported uses of Starlink to control military drones. While the company doesn't mind troops using satellite broadband for communication, it doesn't mean for the platform to be used for "offensive purposes," Shotwell says. The executive adds that SpaceX can limit Ukraine's ability to use Starlink with combat drones, and has already done so. The company hasn't explained how it curbs use in the field.

The age-old question in my industry is, "Where are we in a given hype cycle?" For now, crypto news cycle dominance has, thankfully, died now, largely through its own self-destructive tendencies. FTX obviously served as the most prominent recent example of what happens when the tech community believes its own hype. You so badly wish for the success of a concept that you lose the thread. Sprinkle in legitimately bad actors and platforms that allow such actions to thrive, and you've got a recipe for catastrophic implosion.

An application of stereo thermal vision to perform preliminary inspection operations of electrical power lines by a particular class of small Unmanned Aerial Vehicles (UAVs), aka Micro Unmanned Aerial Vehicles (MAVs), is presented in this paper. The proposed hardware and software setup allows the detection of overheated power equipment, one of the major causes of power outages. The stereo vision complements the GPS information by finely detecting the potential source of damage while also providing a measure of the harm extension. The reduced sizes and the light weight of the vehicle enable to survey areas otherwise difficult to access with standard UAVs. Gazebo simulations and real flight experiments demonstrate the feasibility and effectiveness of the proposed setup.

This article proposes Persistence Administered Collective Navigation (PACNav) as an approach for achieving decentralized collective navigation of Unmanned Aerial Vehicle (UAV) swarms. The technique is based on the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. As global and concurrent information of all swarm members is not available in natural swarms, these systems use local observations to achieve the desired behavior. Similarly, PACNav relies only on local observations of relative positions of UAVs, making it suitable for large swarms deprived of communication capabilities and external localization systems. We introduce the novel concepts of path persistence and path similarity that allow each swarm member to analyze the motion of other members in order to determine its own future motion. PACNav is based on two main principles: (1) UAVs with little variation in motion direction have high path persistence, and are considered by other UAVs to be reliable leaders; (2) groups of UAVs that move in a similar direction have high path similarity, and such groups are assumed to contain a reliable leader. The proposed approach also embeds a reactive collision avoidance mechanism to avoid collisions with swarm members and environmental obstacles. This collision avoidance ensures safety while reducing deviations from the assigned path. Along with several simulated experiments, we present a real-world experiment in a natural forest, showcasing the validity and effectiveness of the proposed collective navigation approach in challenging environments. The source code is released as open-source, making it possible to replicate the obtained results and facilitate the continuation of research by the community.