Google has a monopoly, and that's the official line of the US federal government. In fact, it has two of them, losing two separate antitrust cases that threaten to cripple the tech giant. The Department of Justice has proposed forcing Google to sell or otherwise divest itself of the Chrome browser as its first and preferred remedy. But who would buy it? Unsurprisingly, there are beaucoup business beaus lining up around the block for this browser bachelorette.

Microsoft has good news for anyone with corner office ambitions. In the future we're all going to be bosses – of AI employees. The tech company is predicting the rise of a new kind of business, called a "frontier firm", where ultimately a human worker directs autonomous artificial intelligence agents to carry out tasks. Everyone, according to Microsoft, will become an agent boss. "As agents increasingly join the workforce, we'll see the rise of the agent boss: someone who builds, delegates to and manages agents to amplify their impact and take control of their career in the age of AI," wrote Jared Spataro, a Microsoft executive, in a blogpost this week.

United States President Donald Trump's envoy Steve Witkoff has arrived in Moscow for a meeting with President Vladimir Putin, hours after Russia killed at least five people in Ukraine. A child was among three people killed overnight on Friday in Russian drone attacks on central Ukraine's industrial city of Pavlohrad, according to Serhiy Lysak, governor of the Dnipropetrovsk region. He said 14 people were also wounded in the attack on a five-storey building, including a six-year-old boy and teenagers, aged 15 and 17. Five of the wounded remained in hospital, he added. Two more people were killed on Friday morning in Donetsk region's Yarova settlement, where an aerial bomb was dropped on a residential building, according to Donetsk regional prosecutors.

Amidst the chaos and upheaval at the Social Security Administration (SSA) caused by Elon Musk's so-called Department of Government Efficiency (DOGE), employees have now been asked to integrate the use of a generative AI chatbot into their daily work. But before any of them can use it, they all need to watch a four-minute training video featuring an animated, four-fingered woman crudely drawn in a style that would not look out of place on websites created in the early part of this century. Aside from the Web 1.0-era graphics employed, the video also fails at its primary purpose of informing SSA staff about one of the most important aspects of using the chatbot: Do not use any personally identifiable information (PII) when using the assistant. There is nothing wrong with your speakers; WIRED has disabled the sound. "Our apologies for the oversight in our training video," the SSA wrote in a fact sheet about the chatbot that was shared in an email to employees last week.

Elon Musk's artificial intelligence company is stirring controversy in Memphis, Tennessee. That's where he's building a massive supercomputer to power his company xAI. Community residents and environmental activists say that since the supercomputer was fired up last summer it has become one of the biggest air polluters in the county. But some local officials have championed the billionaire, saying he's investing in Memphis. The first public hearing with the health department is scheduled for Friday, where county officials will hear from all sides of the debate.

The House of Representatives, Japan's lower chamber of parliament, passed a bill on Thursday to promote the development of artificial intelligence technology and take steps to mitigate its risks. The legislation is expected to be enacted during the current parliamentary session set to end in June after deliberations at the House of Councilors, the upper chamber. AI "will be the foundation of economic and social development and is an important technology from the viewpoint of security," the bill said.

Autonomous Vehicles (AVs) rely on artificial intelligence (AI) to accurately detect objects and interpret their surroundings. However, even when trained using millions of miles of real-world data, AVs are often unable to detect rare failure modes (RFMs). The problem of RFMs is commonly referred to as the "long-tail challenge", due to the distribution of data including many instances that are very rarely seen. In this paper, we present a novel approach that utilizes advanced generative and explainable AI techniques to aid in understanding RFMs. Our methods can be used to enhance the robustness and reliability of AVs when combined with both downstream model training and testing. We extract segmentation masks for objects of interest (e.g., cars) and invert them to create environmental masks. These masks, combined with carefully crafted text prompts, are fed into a custom diffusion model. We leverage the Stable Diffusion inpainting model guided by adversarial noise optimization to generate images containing diverse environments designed to evade object detection models and expose vulnerabilities in AI systems. Finally, we produce natural language descriptions of the generated RFMs that can guide developers and policymakers to improve the safety and reliability of AV systems.

Petros Georgiou, Aaron Mark Thomas, and Sharu Theresa Jose Department of Computer Science, University of Birmingham, UK Osvaldo Simeone KCLIP Lab Centre for Intelligent Information Processing Systems (CIIPS) Department of Engineering, King's College London, UK (Dated: April 25, 2025) Quantum classifiers are vulnerable to adversarial attacks that manipulate their input classical or quantum data. A promising countermeasure is adversarial training, where quantum classifiers are trained by using an attack-aware, adversarial loss function. This work establishes novel bounds on the generalization error of adversarially trained quantum classifiers when tested in the presence of perturbation-constrained adversaries. The bounds quantify the excess generalization error incurred to ensure robustness to adversarial attacks as scaling with the training sample size m as 1 / m, while yielding insights into the impact of the quantum embedding. For quantum binary classifiers employing rotation embedding, we find that, in the presence of adversarial attacks on classical inputs x, the increase in sample complexity due to adversarial training over conventional training vanishes in the limit of high dimensional inputs x . In contrast, when the adversary can directly attack the quantum state ρ ( x) encoding the input x, the excess generalization error depends on the choice of embedding only through its Hilbert space dimension. The results are also extended to multi-class classifiers. I. INTRODUCTION Context and Motivation: Quantum Machine Learning (QML) aims to leverage quantum computing capabilities to outperform classical ML techniques [1, 2]. Recent studies have highlighted limitations of QML including difficulties in training unstructured QML models [3, 4] and the classical simulability of some structured QML models [5]. Another concern with QML models is the fact that, similar to their classical counterparts, QML models are susceptible to adversarial attacks [6-8]. For instance, a quantum classifier utilizing superconduct-ing qubits to classify MRI images, achieving a test accuracy of 99%, was found to be easily deceived by minor adversarial perturbations [7]. This vulnerability poses another challenge on the way to realizing quantum advantages. To address this problem, recent works [9, 10] have explored efficient strategies to defend quantum classifiers against adversarial attacks, with adversarial training emerging as a promising strategy [6]. Adversarial training replaces the standard classification loss with an attack-aware adversarial loss, accounting for the worst-case effect of adversarial perturbation of the input data. This results in a min-max optimization problem with the classifier attempting to minimize the worst-case adversarial loss. In classical machine learning models it has been observed that adversarially trained classifiers have desirable training performance but a poor performance on pxg402@student.bham.ac.uk Figure 1.

In this paper, we adopt a survivor-centered approach to locate and dissect the role of sociotechnical AI governance in preventing AI-Generated Non-Consensual Intimate Images (AIG-NCII) of adults, colloquially known as "deep fake pornography." We identify a "malicious technical ecosystem" or "MTE," comprising of open-source face-swapping models and nearly 200 "nudifying" software programs that allow non-technical users to create AIG-NCII within minutes. Then, using the National Institute of Standards and Technology (NIST) AI 100-4 report as a reflection of current synthetic content governance methods, we show how the current landscape of practices fails to effectively regulate the MTE for adult AIG-NCII, as well as flawed assumptions explaining these gaps.

The proliferation of abusive language in online communications has posed significant risks to the health and wellbeing of individuals and communities. The growing concern regarding online abuse and its consequences necessitates methods for identifying and mitigating harmful content and facilitating continuous monitoring, moderation, and early intervention. This paper presents a taxonomy for distinguishing key characteristics of abusive language within online text. Our approach uses a systematic method for taxonomy development, integrating classification systems of 18 existing multi-label datasets to capture key characteristics relevant to online abusive language classification. The resulting taxonomy is hierarchical and faceted, comprising 5 categories and 17 dimensions. It classifies various facets of online abuse, including context, target, intensity, directness, and theme of abuse. This shared understanding can lead to more cohesive efforts, facilitate knowledge exchange, and accelerate progress in the field of online abuse detection and mitigation among researchers, policy makers, online platform owners, and other stakeholders.