In recent days, Los Angeles residents have taken to the streets to protest the Trump administration's immigration policies and the increasingly frequent ICE raids. WIRED's senior politics editor Leah Feiger joins Zoë Schiffer, director of business and industry, to discuss the related flood of information on social media, and how AI chatbots like Grok and ChatGPT are delivering incorrect and at times, inflammatory answers. Mentioned in today's episode: AI Chatbots Are Making LA Protest Disinformation Worse by David Gilbert I Joined Every Class Action Lawsuit I Could Find, and So Can You by Andy Vasoyan Vibe Coding Is Coming for Engineering Jobs by Will Knight Write to us at uncannyvalley@wired.com. You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link. Note: This is an automated transcript, which may contain errors.

Breakthroughs, discoveries, and DIY tips sent every weekday. A shipwreck accidentally discovered off France's southeastern coast near Saint-Tropez appears to be a striking well-preserved 16th-century Italian merchant ship. At 8,422 feet below sea level, the vessel is likely the deepest of its kind ever found in French waters, according to the official announcement. But next to scattered ceramics, metal bars, and rigging rests what appear to be jarring reminders of modern life. Earlier this year, French military personnel noticed an odd ping while guiding an underwater drone along a routine surveying expedition. Although intended to monitor potential oceanic resources and deepsea cable routes, the equipment flagged something sizable already laying over 1.5 miles below the surface of the Mediterranean Sea.

The Mossad published footage of its operatives carrying out covert actions inside Iran prior to Israel's preemptive attack. Israel's overnight strike on Iran was not only one of the most ambitious aerial campaigns in recent history, it was the result of years of covert planning, surveillance and infiltration by Israeli intelligence. While dozens of fighter jets bombed nuclear and military targets across Iran early Friday morning, the groundwork had long been laid by Mossad agents working in lockstep with the Israeli military. Code-named "Am Kelavi" (Rising Lion), the preemptive operation was the product of unprecedented coordination between the Israeli air force, the Military Intelligence Directorate, Mossad and the country's defense industries. For years, they worked "shoulder to shoulder" to gather the intelligence files needed to eliminate Iran's most sensitive military and nuclear assets.

On Wednesday, United States Customs and Border Protection confirmed to 404 Media that it has been flying Predator drones over Los Angeles amid the LA protests. The military drones, a CBP statement said, "are supporting our federal law enforcement partners in the Greater Los Angeles area, including Immigration and Customs Enforcement, with aerial support of their operations." State-level law enforcement agencies across the US use various types of drones and other vehicles, like helicopters, to conduct aerial surveillance, and other agencies use drones in their operations as well. For example, the California Department of Forestry and Fire Protection "doubled its use of drones" this year, according to the office of Governor Gavin Newsom, as part of efforts to combat forest fires. However, CBP's MQ-9 Reaper drones, also known as Predator B drones, are military-caliber UAVs used for aerial reconnaissance that can be armed.

After Israel bombed a reported 100 locations in Iran, including residential buildings, Iran has responded by launching a drone swarm towards Israel. Reports indicate neighbouring countries, including Jordan, are shooting them down. Nour Odeh has this update from Amman.

Text-to-image (T2I) models such as Stable Diffusion have advanced rapidly and are now widely used in content creation. However, these models can be misused to generate harmful content, including nudity or violence, posing significant safety risks. While most platforms employ content moderation systems, underlying vulnerabilities can still be exploited by determined adversaries. Recent research on red-teaming and adversarial attacks against T2I models has notable limitations: some studies successfully generate highly toxic images but use adversarial prompts that are easily detected and blocked by safety filters, while others focus on bypassing safety mechanisms but fail to produce genuinely harmful outputs, neglecting the discovery of truly high-risk prompts. Consequently, there remains a lack of reliable tools for evaluating the safety of defended T2I models. To address this gap, we propose GenBreak, a framework that fine-tunes a red-team large language model (LLM) to systematically explore underlying vulnerabilities in T2I generators. Our approach combines supervised fine-tuning on curated datasets with reinforcement learning via interaction with a surrogate T2I model. By integrating multiple reward signals, we guide the LLM to craft adversarial prompts that enhance both evasion capability and image toxicity, while maintaining semantic coherence and diversity. These prompts demonstrate strong effectiveness in black-box attacks against commercial T2I generators, revealing practical and concerning safety weaknesses.

However, existing attacks have been shown to not suit this kind of classifier. In this paper, we discuss the problem of attacking a mixture in a principled way and introduce two desirable properties of attacks based on a geometrical analysis of the problem (effectiveness and maxi-mality). We then show that existing attacks do not meet both of these properties. Finally, we introduce a new attack called lattice climber attack with theoretical guarantees in the binary linear setting, and demonstrate its performance by conducting experiments on synthetic and real datasets. Keywords: adversarial robustness adversarial attacks randomized classifiers mixtures.

Lunar surface operations impose stringent requirements on wireless communication systems, including autonomy, robustness to disruption, and the ability to adapt to environmental and mission-driven context. While Space-O-RAN provides a distributed orchestration model aligned with 3GPP standards, its decision logic is limited to static policies and lacks semantic integration. We propose a novel extension incorporating a semantic agentic layer enabled by the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication protocols, allowing context-aware decision making across real-time, near-real-time, and non-real-time control layers. Distributed cognitive agents deployed in rovers, landers, and lunar base stations implement wireless-aware coordination strategies, including delay-adaptive reasoning and bandwidth-aware semantic compression, while interacting with multiple MCP servers to reason over telemetry, locomotion planning, and mission constraints.

Human capital (HC) is increasingly important to corporate value creation. Unlike other assets, however, HC is not currently subject to well-defined measurement or disclosure rules. We use a machine learning algorithm (word2vec) trained on a confirmed set of HC disclosures to develop a comprehensive list of HC-related keywords classified into five subcategories (DEI; health and safety; labor relations and culture; compensation and benefits; and demographics and other) that capture the multidimensional nature of HC management. We share our lexicon, corporate HC disclosures, and the Python code used to develop the lexicon, and we provide detailed examples of using our data and code, including for fine-tuning a BERT model. Researchers can use our HC lexicon (or modify the code to capture another construct of interest) with their samples of corporate communications to address pertinent HC questions. We close with a discussion of future research opportunities related to HC management and disclosure.

Enzymes are important proteins that catalyze chemical reactions. In recent years, machine learning methods have emerged to predict enzyme function from sequence; however, there are no standardized benchmarks to evaluate these methods. We introduce CARE, a benchmark and dataset suite for the Classification And Retrieval of Enzymes (CARE). CARE centers on two tasks: (1) classification of a protein sequence by its enzyme commission (EC) number and (2) retrieval of an EC number given a chemical reaction. For each task, we design train-test splits to evaluate different kinds of out-of-distribution generalization that are relevant to real use cases. For the classification task, we provide baselines for state-of-the-art methods. Because the retrieval task has not been previously formalized, we propose a method called Contrastive Reaction-EnzymE Pretraining (CREEP) as one of the first baselines for this task and compare it to the recent method, CLIPZyme. CARE is available at https://github.com/jsunn-y/CARE/.