WIRED's Politics Issue Cover Is Coming to a City Near You We're turning our latest cover into posters, billboards, and even a mural in New York, Los Angeles, Austin, San Francisco, and Washington, DC. Here's how to find it. Here at WIRED, we tend to stick to journalism. We talk about our work to anyone who will listen--during podcasts, on social media, over dinner with our politely listening friends--but we tend to confine our bragging to the scoops we get, the stories we write. For our new politics issue, though, we decided to do something different and bring WIRED's work to outside, to you, directly.

What does the White House's new roadmap for AI entail? Digital Dilemma What does the US's AI roadmap entail?

Nissan revamps ProPilot to rival Tesla's driver-assist technology Nissan Motor is on a mission under new CEO Ivan Espinosa to rebuild its business, and while refreshing its lineup is a key part of that, so is winning back customers who demand cutting-edge technology. Leveraging its partnership with Wayve Technologies, a U.K.-based artificial intelligence startup backed by SoftBank Group, Nissan is preparing to launch the newest generation of its ProPilot driver-assistance system during the fiscal year ending March 2028. The automaker says the most advanced iteration of its driver-assist technology will be on par with Tesla's Full Self-Driving, which despite its name requires human supervision and intervention. While the systems still amount to Level 2 autonomy -- meaning a person must always be ready to take over -- ProPilot amounts to Nissan's best foot forward in contending with the U.S. EV giant and Alphabet's Waymo in the race to build self-driving cars. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? A Ukrainian drone attack killed three people and injured 16 near the town of Foros on the Crimean Peninsula, the Russian-appointed head of Crimea, Sergei Aksyonov, wrote in a post on Telegram. Russia's Ministry of Defence said the attack occurred "using strike drones equipped with high-explosive payloads", in a resort area "where there are no military targets whatsoever".

Recommender systems (RS), which are widely deployed across high-stakes domains, are susceptible to biases that can cause large-scale societal impacts. Researchers have proposed methods to measure and mitigate such biases -- but translating academic theory into practice is inherently challenging. RS practitioners must balance the competing interests of diverse stakeholders, including providers and users, and operate in dynamic environments. Through a semi-structured interview study (N=11), we map the RS practitioner workflow within large technology companies, focusing on how technical teams consider fairness internally and in collaboration with other (legal, data, and fairness) teams. We identify key challenges to incorporating fairness into existing RS workflows: defining fairness in RS contexts, particularly when navigating multi-stakeholder and dynamic fairness considerations. We also identify key organization-wide challenges: making time for fairness work and facilitating cross-team communication. Finally, we offer actionable recommendations for the RS community, including HCI researchers and practitioners.

Over the past decade, machine learning has undergone transformative advancements, primarily fueled by the development of sophisticated deep learning architectures and training methodologies. In parallel, Quantum Machine Learning (QML) has emerged as a field dedicated to exploring how quantum algorithms and quantum computing platforms can be utilized to process, model, and extract meaningful insights from data [9, 14, 65], and also generate new data [26, 59]. While efforts in QML primarily focused on leveraging quantum computing to accelerate classical machine learning tasks [19, 34], a significant and increasingly important direction involves the development of quantum models that operate directly on quantum data [7, 9, 41]. These models, tailored specifically to quantum data, are essential for realizing the full potential of quantum technologies, enabling applications in quantum information processing that are intractable with classical methods [25]. A notable model within QML for handling quantum data is the Quantum Autoencoder (QAE), which draws inspiration from its classical counterpart, the Autoen-coder (AE) [5, 58]. QAE has been applied to demonstrate how quantum circuits can be trained to compress quantum states, with applications to quantum simulation and quantum information [13, 29, 42, 44, 57]. Further developments extend these architectures to the denoising of entangled quantum states under realistic noise models [1, 10, 62, 63], along with proposals for error mitigation strategies tailored to Noisy Intermediate-Scale Quantum (NISQ) devices [46, 66]. Practical realizations of QAE in quantum hardware, such as nitrogen-vacancy centers, demonstrated robust compression and the preservation of entanglement, while significantly lengthening the coherence times of Bell states [67]. These two authors contributed equally.

Fine-grained visual classification (FGVC) requires distinguishing between visually similar categories through subtle, localized features - a task that remains challenging due to high intra-class variability and limited inter-class differences. Existing part-based methods often rely on complex localization networks that learn mappings from pixel to sample space, requiring a deep understanding of image content while limiting feature utility for downstream tasks. In addition, sampled points frequently suffer from high spatial redundancy, making it difficult to quantify the optimal number of required parts. Inspired by human saccadic vision, we propose a two-stage process that first extracts peripheral features (coarse view) and generates a sample map, from which fixation patches are sampled and encoded in parallel using a weight-shared encoder. We employ contextualized selective attention to weigh the impact of each fixation patch before fusing peripheral and focus representations. To prevent spatial collapse - a common issue in part-based methods - we utilize non-maximum suppression during fixation sampling to eliminate redundancy. Comprehensive evaluation on standard FGVC benchmarks (CUB-200-2011, NABirds, Food-101 and Stanford-Dogs) and challenging insect datasets (EU-Moths, Ecuador-Moths and AMI-Moths) demonstrates that our method achieves comparable performance to state-of-the-art approaches while consistently outperforming our baseline encoder.

Standard Behavior Cloning (BC) fails to learn multimodal driving decisions, where multiple valid actions exist for the same scenario. W e explore Implicit Behavioral Cloning (IBC) with Energy-Based Models (EBMs) to better capture this multimodality. W e propose Data-Augmented IBC (DA-IBC), which improves learning by perturbing expert actions to form the counterexamples of IBC training and using better initialization for derivative-free inference. Experiments in the CARLA simulator with Bird's-Eye View inputs demonstrate that DA-IBC outperforms standard IBC in urban driving tasks designed to evaluate multimodal behavior learning in a test environment. The learned energy landscapes are able to represent multimodal action distributions, which BC fails to achieve.

Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics (e.g., to serve as risk models or augment survey datasets). However, when should a user have confidence that an LLM will provide high-quality predictions for their particular task? To address this question, we conduct a large-scale empirical study of LLMs' zero-shot predictive capabilities across a wide range of tabular prediction tasks. We find that LLMs' performance is highly variable, both on tasks within the same dataset and across different datasets. However, when the LLM performs well on the base prediction task, its predicted probabilities become a stronger signal for individual-level accuracy. Then, we construct metrics to predict LLMs' performance at the task level, aiming to distinguish between tasks where LLMs may perform well and where they are likely unsuitable. We find that some of these metrics, each of which are assessed without labeled data, yield strong signals of LLMs' predictive performance on new tasks.

Why is Israel still in southern Lebanon? A war to shape Lebanon's future An Israeli drone strike has killed five people, including three children, in the southern Lebanese town of Bint Jbeil, Lebanon's Health Ministry has said, as Israel continues to target its neighbour despite a US-brokered truce that took effect in November. The state-run National News Agency (NNA) reported on Sunday that the strike targeted a motorcycle and a vehicle, and wounded two other people. Why then did Israel attack Syria? The mother of the children was injured in the attack.