Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push the capabilities of AI to the network edge for real-time, efficient and secure intelligent decision-making and computation. However, EI faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EI in the eyes of stakeholders. This survey comprehensively summarizes the characteristics, architecture, technologies, and solutions of trustworthy EI. Specifically, we first emphasize the need for trustworthy EI in the context of the trend toward large models. We then provide an initial definition of trustworthy EI, explore its key characteristics and give a multi-layered architecture for trustworthy EI. Then, we summarize several important issues that hinder the achievement of trustworthy EI. Subsequently, we present enabling technologies for trustworthy EI systems and provide an in-depth literature review of the state-of-the-art solutions for realizing the trustworthiness of EI. Finally, we discuss the corresponding research challenges and open issues.

The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite images, have been linked to severe weather hazards but only relate to a limited portion of the total storm updraft. This study investigates if a machine learning model, namely U-Nets, can skillfully retrieve maximum vertical velocity and its areal extent from 3-dimensional gridded radar reflectivity alone. The machine learning model is trained using simulated radar reflectivity and vertical velocity from the National Severe Storm Laboratory's convection permitting Warn on Forecast System (WoFS). A parametric regression technique using the sinh-arcsinh-normal distribution is adapted to run with U-Nets, allowing for both deterministic and probabilistic predictions of maximum vertical velocity. The best models after hyperparameter search provided less than 50% root mean squared error, a coefficient of determination greater than 0.65 and an intersection over union (IoU) of more than 0.45 on the independent test set composed of WoFS data. Beyond the WoFS analysis, a case study was conducted using real radar data and corresponding dual-Doppler analyses of vertical velocity within a supercell. The U-Net consistently underestimates the dual-Doppler updraft speed estimates by 50$\%$. Meanwhile, the area of the 5 and 10 m s^-1 updraft cores show an IoU of 0.25. While the above statistics are not exceptional, the machine learning model enables quick distillation of 3D radar data that is related to the maximum vertical velocity which could be useful in assessing a storm's severe potential.

Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their inception. Today, AI-Robotics systems have become an integral part of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These systems are built upon three fundamental architectural elements: perception, navigation and planning, and control. However, while the integration of AI-Robotics systems has enhanced the quality our lives, it has also presented a serious problem - these systems are vulnerable to security attacks. The physical components, algorithms, and data that make up AI-Robotics systems can be exploited by malicious actors, potentially leading to dire consequences. Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security. Our goal is to provide users, developers and other stakeholders with a holistic understanding of these areas to enhance the overall AI-Robotics system security. We begin by surveying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems. Besides, emerging trends such as HRI are discussed, considering privacy, integrity, safety, trustworthiness, and explainability concerns. Finally, we present our vision for future research directions in this dynamic and promising field.

With the widespread digitization of finance and the increasing popularity of cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering -- the movement of illicit funds to conceal their origins -- can cross bank and national boundaries, producing complex transaction patterns. The UN estimates 2-5\% of global GDP or \$0.8 - \$2.0 trillion dollars are laundered globally each year. Unfortunately, real data to train machine learning models to detect laundering is generally not available, and previous synthetic data generators have had significant shortcomings. A realistic, standardized, publicly-available benchmark is needed for comparing models and for the advancement of the area. To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets. We have calibrated this agent-based generator to match real transactions as closely as possible and made the datasets public. We describe the generator in detail and demonstrate how the datasets generated can help compare different machine learning models in terms of their AML abilities. In a key way, using synthetic data in these comparisons can be even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected.

In the New Hampshire primary yesterday, a relatively unknown Democratic congressman from Minnesota gained nearly 20 percent of the vote. Dean Phillips, who is challenging Joe Biden for the Democratic nomination, was boosted by rich techies from Silicon Valley hoping to shake up the Democratic primary. Biden wasn't on the ballot in New Hampshire, and hasn't campaigned in the state. The President still won the state by a landslide, gaining more than 50 percent of the vote, thanks to a successful write-in campaign. Despite the meager showing in New Hampshire, Phillips' tech-adjacent supporters have long seen his campaign as a way to disrupt yet another arena: the election industrial complex.

In some ways, it already has - Baltimore union denies school principal went on'ungrateful Black kids' rant, calls it an AI fraud FORMIDABLE WARRIORS: Ukraine's artificial intelligence (AI) development continues at a frightening pace beyond that of even tech giants in the U.S. and China as the war with Russia lurches toward a third year, but experts highlighted America's critical role in helping that rapid advance. VICTOR-AI SECRET: Victoria's Secret & Co. and Google Cloud announced a multi-year partnership that will allow the popular retailer to use Google's artificial intelligence technology to create a personalized shopping experience. TECH THREATS: Concerns about AI interfering with the 2024 elections are well-founded, yet not unprecedented in recent history. In 1975, the Asilomar Conference on Recombinant DNA foreshadowed today's AI concerns. Generative AI tools can help job seekers make their resumes and applications more visual, as well as getting ideas for content.

In some ways, it already has - Experts highlight American role in Ukraine's unbelievable AI military development - Baltimore union denies school principal went on'ungrateful Black kids' rant, calls it an AI fraud ROBOT IQ: The rapid development of artificial intelligence has led some to fear dangerous scenarios where the technology is smarter than the humans who created it, but some experts believe AI has already reached that point in certain ways. FORMIDABLE WARRIORS: Ukraine's artificial intelligence (AI) development continues at a frightening pace beyond that of even tech giants in the U.S. and China as the war with Russia lurches toward a third year, but experts highlighted America's critical role in helping that rapid advance. RUSH TO JUDGMENT?: A Baltimore, Maryland school district has launched an investigation after a high school principal was allegedly recorded making racist comments to students and staff. And AI is being blamed. Baltimore County Public Schools said it launched an internal investigation after an audio recording claiming to capture the principal of Pikesville High School making offensive comments circulated online.

Russia and Ukraine traded deadly aerial attacks on civilian centres in the past week of the war, but Ukraine also scored hits on military and economic infrastructure deep in the Russian heartland, extending its reach to St Petersburg for the first time. Ukrainian military intelligence said it had struck an unspecified military target in St Petersburg on Thursday, using drones launched from Ukrainian soil. Ukrainian strategic industries minister Oleksandr Kamyshin confirmed the attack, telling the World Economic Forum in Davos that the attack was carried out by a Ukrainian-built drone that had travelled 1,250km (780 miles) from Ukrainian soil. Russia's defence ministry said three drones had been launched and it had downed all three over the Gulf of Finland that day, one near an oil terminal. On Sunday, Ukraine attacked again in several locations, and this time, the evidence of its success was clear.

Arthel Neville welcomes former U.S. Defense Intelligence Officer Rebekah Koffler to discuss the massive global cyberattack that had impacted several federal agencies. The next big war may be fought in space. As the Pentagon is gearing up for a future celestial conflict, so are our chief adversaries, China and Russia. Here's why "Star Wars" is no longer merely a topic of science fiction. The best way to avoid space warfare is to be ready for it. On Dec. 28, Elon Musk's Space X launched into space the Pentagon's highly secretive X-37B Orbital Test Vehicle, an unmanned reusable robotic spacecraft operated by the Air Force, in collaboration with Space Force.

Wealthy tech entrepreneurs including Elon Musk launched OpenAI in 2015 as a nonprofit research lab that they said would involve society and the public in the development of powerful AI, unlike Google and other giant tech companies working behind closed doors. In line with that spirit, OpenAI's reports to US tax authorities have from its founding said that any member of the public can review copies of its governing documents, financial statements, and conflict of interest rules. But when WIRED requested those records last month, OpenAI said its policy had changed, and the company provided only a narrow financial statement that omitted the majority of its operations. "We provide financial statements when requested," company spokesperson Niko Felix says. "OpenAI aligns our practices with industry standards, and since 2022 that includes not publicly distributing additional internal documents."