DJI's drones dominate the market to such an extent that you barely hear any other brands mentioned. They're not quite competing with themselves, but the DJI Air 3 does fill a gap between the professional-grade Mavic series and the pocket-size Mini range. It's the first new Air model since 2021's Air 2S, and DJI has done it again, with a drone that offers enhanced video performance and mercifully stress-free flying. I've been flying the DJI Air 3 for the past few months, comparing it with a range of other drones, including its main competitor, the superb Air 2S. The biggest improvement over its older cousin is a multi-camera setup previously exclusive to the much pricier Mavic models, but there are notable boosts to battery life and flight safety too.

With the launch of the Mini 4 Pro, DJI has nailed the recipe for producing premium-level ultralight drones. It comes just 18 months after the superb Mini 3 Pro, but with significant tweaks to the image processing and obstacle avoidance tech, it's undeniably the one to beat. At under 250 grams in weight, it's free from many of the rules and regulations that apply to larger models. In the USA there's no need to register it with the FAA for recreational use, while in the UK (a notoriously difficult place to fly drones), it can be flown pretty freely in public areas. Here in the UK, finding a sub-250 gram drone is critical for most amateur pilots.

Iran has made it no secret that it plans to invest heavily in artificial intelligence (AI) to help better its military capabilities, but Iranian President Ebrahim Raisi is now turning to Iran's private sector in a move he thinks will boost his crippling economy. On Sunday, Raisi met with private sector companies to announce Tehran's intent to invest in digital businesses. Raisi claimed the move would not only help develop Iran's AI capabilities, but help achieve his goal to grow the economy by 8%, reported pro-government media outlet Tasnim News Agency. However, experts remain skeptical about whether the move will actually fix Iran's economic woes and said they are more concerned by the abilities AI would grant Tehran when it comes to the battlefield. An Iranian-made unmanned aerial vehicle, the Shahed-136, is being displayed at Azadi Square in western Tehran, Iran, on Feb. 11, 2024, during a rally to mark the 45th anniversary of the victory of Iran's 1979 Islamic Revolution.

Olena Rozumovska is at the end of her rope. Her two-bedroom apartment in an Soviet-era concrete building has no electricity or water supply, and the central heating is off after Russian drones and missiles struck Kharkiv, Ukraine's second largest city, on Friday. I want to howl with despair," the 33-year-old, whose husband, Mykhailo, is fighting against Russian forces in southeastern Ukraine, told Al Jazeera over the phone. The outdoor temperatures in Kharkiv barely rose above freezing on Friday, a cold drizzle was falling, and her apartment building "is losing warmth", she said. Early in the morning, she jumped out of bed on hearing the thud of a powerful explosion. More than a dozen heavy, blood-curdling blasts followed as she hid in the frigid basement with her two children, Bohdan, who is seven, and four-year-old Roxana. The children were "hysterical" because they had to leave their Siamese cat behind. Their pet, named Monya, wouldn't come out from under the sofa. What roiled her and millions of Ukrainians was the scope of the bombardment, which became the largest strike on their nation's energy infrastructure since the war began in 2022. "The aim is not just to destroy but to try yet again, like last year, to cause a massive disruption of the energy infrastructure," Energy Minister Herman Halushchenko wrote on Facebook. In the winter of 2022-2023, Moscow switched to massive shelling that targeted energy infrastructure and civilian sites after realising that its blitzkrieg to take over all of Ukraine had failed. Friday's attacks with about 60 drones and 90 missiles killed at least two people, wounded scores, struck Ukraine's largest dam and severed the power supply to the Russia-occupied Zaporizhzhia nuclear plant, officials said. Ukrainian President Volodymyr Zelenskyy rebuked the West for months-long delays in military aid. "Russian missiles have no delays, unlike aid packages for Ukraine.

Unmanned aerial systems (UAS) rely on various avionics systems that are safety-critical and mission-critical. A major requirement of international safety standards is to perform rigorous system-level testing of avionics software systems. The current industrial practice is to manually create test scenarios, manually/automatically execute these scenarios using simulators, and manually evaluate outcomes. The test scenarios typically consist of setting certain flight or environment conditions and testing the system under test in these settings. The state-of-the-art approaches for this purpose also require manual test scenario development and evaluation. In this paper, we propose a novel approach to automate the system-level testing of the UAS. The proposed approach (AITester) utilizes model-based testing and artificial intelligence (AI) techniques to automatically generate, execute, and evaluate various test scenarios. The test scenarios are generated on the fly, i.e., during test execution based on the environmental context at runtime. The approach is supported by a toolset. We empirically evaluate the proposed approach on two core components of UAS, an autopilot system of an unmanned aerial vehicle (UAV) and cockpit display systems (CDS) of the ground control station (GCS). The results show that the AITester effectively generates test scenarios causing deviations from the expected behavior of the UAV autopilot and reveals potential flaws in the GCS-CDS.

Video retrieved from an Israeli drone in Gaza shows the moments four apparently unarmed Palestinians were killed by Israeli air attacks.

The Department of Justice and more than a dozen states have filed a lawsuit against Apple in the US federal court, accusing the company of violating antitrust laws. It says Apple's hardware and software products are largely inaccessible to competitors, making it difficult for rivals to compete and for customers to switch to other companies' products. The lawsuit comes after the European Commission fined Apple 1.8 billion ( 1.95 billion) for stopping music-streaming developers from "informing iOS users about alternative and cheaper music subscription services available" outside the App Store. The DOJ suggests Apple used its control over iOS to block innovative apps and cloud streaming services from the public. The suit also suggests Apple has obstructed rival payment platforms, made it harder for Android messages to appear on iPhones and restricted how competing smartphones integrated with iOS devices.

Potential malicious misuse of civilian artificial intelligence (AI) poses serious threats to security on a national and international level. Besides defining autonomous systems from a technological viewpoint and explaining how AI development is characterized, we show how already existing and openly available AI technology could be misused. To underline this, we developed three exemplary use cases of potentially misused AI that threaten political, digital and physical security. The use cases can be built from existing AI technologies and components from academia, the private sector and the developer-community. This shows how freely available AI can be combined into autonomous weapon systems. Based on the use cases, we deduce points of control and further measures to prevent the potential threat through misused AI. Further, we promote the consideration of malicious misuse of civilian AI systems in the discussion on autonomous weapon systems (AWS).

This paper addresses the critical challenge of vehicle detection in the harsh winter conditions in the Nordic regions, characterized by heavy snowfall, reduced visibility, and low lighting. Due to their susceptibility to environmental distortions and occlusions, traditional vehicle detection methods have struggled in these adverse conditions. The advanced proposed deep learning architectures brought promise, yet the unique difficulties of detecting vehicles in Nordic winters remain inadequately addressed. This study uses the Nordic Vehicle Dataset (NVD), which has UAV images from northern Sweden, to evaluate the performance of state-of-the-art vehicle detection algorithms under challenging weather conditions. Our methodology includes a comprehensive evaluation of single-stage, two-stage, and transformer-based detectors against the NVD. We propose a series of enhancements tailored to each detection framework, including data augmentation, hyperparameter tuning, transfer learning, and novel strategies designed explicitly for the DETR model. Our findings not only highlight the limitations of current detection systems in the Nordic environment but also offer promising directions for enhancing these algorithms for improved robustness and accuracy in vehicle detection amidst the complexities of winter landscapes. The code and the dataset are available at https://nvd.ltu-ai.dev.

This paper addresses the problem of target search and tracking using a fleet of cooperating UAVs evolving in some unknown region of interest containing an a priori unknown number of moving ground targets. Each drone is equipped with an embedded Computer Vision System (CVS), providing an image with labeled pixels and a depth map of the observed part of its environment. Moreover, a box containing the corresponding pixels in the image frame is available when a UAV identifies a target. Hypotheses regarding information provided by the pixel classification, depth map construction, and target identification algorithms are proposed to allow its exploitation by set-membership approaches. A set-membership target location estimator is developed using the information provided by the CVS. Each UAV evaluates sets guaranteed to contain the location of the identified targets and a set possibly containing the locations of targets still to be identified. Then, each UAV uses these sets to search and track targets cooperatively.