A data center for cryptocurrency mining, cloud services, and AI computing in Stutsman County, North Dakota.halbergman/Getty This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. Republicans are pushing to pass a major spending bill that includes provisions to prevent states from enacting regulations on artificial intelligence. Such untamed growth in AI will take a heavy toll upon the world's dangerously overheating climate, experts have warned. About 1 billion tons of planet-heating carbon dioxide are set to be emitted in the US just from AI over the next decade if no restraints are placed on the industry's enormous electricity consumption, according to estimates by researchers at Harvard University and provided to the Guardian.

A deal that had been reached between Sens. Marsha Blackburn, R-Tenn., and Ted Cruz, R-Texas, over how states can regulate artificial intelligence has been pulled from President Donald Trump's "big, beautiful" bill. The collapsed agreement would have required states seeking to access hundreds of millions of dollars in AI infrastructure funding in the "big, beautiful" bill to refrain from adopting new regulations on the technology for five years, a compromise down from the original 10 years. It also included carveouts to regulate child sexual abuse material, unauthorized use of a person's likeness and other deceptive practices. Blackburn announced Monday night that she is withdrawing her support for the agreement. A deal between Sens. Marsha Blackburn and Ted Cruz over how states can regulate AI has been pulled from the "big, beautiful" bill.

Tracking devices inserted under offenders' skin, robots assigned to contain prisoners and driverless vehicles used to transport them were among the measures proposed by technology companies to ministers who are gathering ideas to tackle the crisis in the UK justice system. The proposals were made at a meeting of more than two dozen tech companies in London last month, chaired by the justice secretary, Shabana Mahmood, minutes seen by the Guardian show. Amid an acute shortage of prison places and probation officers under severe strain, ministers told the companies they wanted ideas for using wearable technologies, behaviour monitoring and geolocation to create a "prison outside of prison". Those present included representatives of Google, Amazon, Microsoft and Palantir, which works closely with the US military and has contracts with the NHS. IBM and the private prison operator Serco also attended alongside tagging and biometric companies, according to a response to a freedom of information request.

A Dragons' Den-style event this week, where tech companies will have 20 minutes to pitch ideas for increasing automation in the British justice system, is one of numerous examples of how the cash-strapped Labour government hopes artificial intelligence and data science can save money and improve public services. Amid warnings from critics that Downing Street has been "drinking the Kool-Aid" on AI, the Department of Health and Social Care this week announced an AI early warning system to detect dangerous maternity services after a series of scandals, and Wes Streeting, the health secretary, said he wants one in eight operations to be conducted by a robot within a decade. AI is being used to prioritise actions on the 25,000 pieces of correspondence the Department for Work and Pensions receives each day and to detect potential fraud and error in benefit claims. Ministers even have access to an AI tool that is supposed to provide a "vibe check" on parliamentary opinion to help them weigh the political risks of policy proposals. Again and again, ministers are turning to technology to tackle acute crises that in the past might have been dealt with by employing more staff or investing more money.

As Congress races to pass President Donald Trump's "Big Beautiful Bill," it's also sprinting to placate the many haters of the bill's "AI moratorium" provision which originally required a 10-year pause on state AI regulations. The provision, which was championed by White House AI czar and venture capitalist David Sacks, has proved remarkably unpopular with a diverse contingent of lawmakers ranging from 40 state attorneys general to the ultra-MAGA Representative Marjorie Taylor Greene. Sunday night, Senator Marsha Blackburn and Senator Ted Cruz announced a new version of the AI moratorium, knocking the pause from a full decade down to five years and adding a variety of carve-outs. But after critics attacked the watered-down version of the bill as a "get-out-of-jail free card" for Big Tech, Blackburn reversed course Monday evening. "While I appreciate Chairman Cruz's efforts to find acceptable language that allows states to protect their citizens from the abuses of AI, the current language is not acceptable to those who need these protections the most," Blackburn said in a statement to WIRED.

One of the most difficult challenges in cybersecurity is eliminating Distributed Denial of Service (DDoS) attacks. Automating this task using artificial intelligence is a complex process due to the inherent class imbalance and lack of sufficient labeled samples of real-world datasets. This research investigates the use of Semi-Supervised Learning (SSL) techniques to improve DDoS attack detection when data is imbalanced and partially labeled. In this process, 13 state-of-the-art SSL algorithms are evaluated for detecting DDoS attacks in several scenarios. We evaluate their practical efficacy and shortcomings, including the extent to which they work in extreme environments. The results will offer insight into designing intelligent Intrusion Detection Systems (IDSs) that are robust against class imbalance and handle partially labeled data.

Just like power, water, and transportation systems, wireless networks are a crucial societal infrastructure. As natural and human-induced disruptions continue to grow, wireless networks must be resilient. This requires them to withstand and recover from unexpected adverse conditions, shocks, unmodeled disturbances and cascading failures. Unlike robustness and reliability, resilience is based on the understanding that disruptions will inevitably happen. Resilience, as elasticity, focuses on the ability to bounce back to favorable states, while resilience as plasticity involves agents and networks that can flexibly expand their states and hypotheses through real-time adaptation and reconfiguration. This situational awareness and active preparedness, adapting world models and counterfactually reasoning about potential system failures and the best responses, is a core aspect of resilience. This article will first disambiguate resilience from reliability and robustness, before delving into key mathematical foundations of resilience grounded in abstraction, compositionality and emergence. Subsequently, we focus our attention on a plethora of techniques and methodologies pertaining to the unique characteristics of resilience, as well as their applications through a comprehensive set of use cases. Ultimately, the goal of this paper is to establish a unified foundation for understanding, modeling, and engineering resilience in wireless communication systems, while laying a roadmap for the next-generation of resilient-native and intelligent wireless systems.

Remarkable progress in text-to-image diffusion models has brought a major concern about potentially generating images on inappropriate or trademarked concepts. Concept erasing has been investigated with the goals of deleting target concepts in diffusion models while preserving other concepts with minimal distortion. To achieve these goals, recent concept erasing methods usually fine-tune the cross-attention layers of diffusion models. In this work, we first show that merely updating the cross-attention layers in diffusion models, which is mathematically equivalent to adding \emph{linear} modules to weights, may not be able to preserve diverse remaining concepts. Then, we propose a novel framework, dubbed Concept Pinpoint Eraser (CPE), by adding \emph{nonlinear} Residual Attention Gates (ResAGs) that selectively erase (or cut) target concepts while safeguarding remaining concepts from broad distributions by employing an attention anchoring loss to prevent the forgetting. Moreover, we adversarially train CPE with ResAG and learnable text embeddings in an iterative manner to maximize erasing performance and enhance robustness against adversarial attacks. Extensive experiments on the erasure of celebrities, artistic styles, and explicit contents demonstrated that the proposed CPE outperforms prior arts by keeping diverse remaining concepts while deleting the target concepts with robustness against attack prompts. Code is available at https://github.com/Hyun1A/CPE

Forecasting central bank policy decisions remains a persistent challenge for investors, financial institutions, and policymakers due to the wide-reaching impact of monetary actions. In particular, anticipating shifts in the U.S. federal funds rate is vital for risk management and trading strategies. Traditional methods relying only on structured macroeconomic indicators often fall short in capturing the forward-looking cues embedded in central bank communications. This study examines whether predictive accuracy can be enhanced by integrating structured data with unstructured textual signals from Federal Reserve communications. We adopt a multi-modal framework, comparing traditional machine learning models, transformer-based language models, and deep learning architectures in both unimodal and hybrid settings. Our results show that hybrid models consistently outperform unimodal baselines. The best performance is achieved by combining TF-IDF features of FOMC texts with economic indicators in an XGBoost classifier, reaching a test AUC of 0.83. FinBERT-based sentiment features marginally improve ranking but perform worse in classification, especially under class imbalance. SHAP analysis reveals that sparse, interpretable features align more closely with policy-relevant signals. These findings underscore the importance of integrating textual and structured signals transparently. For monetary policy forecasting, simpler hybrid models can offer both accuracy and interpretability, delivering actionable insights for researchers and decision-makers.

-- Autonomous robots are increasingly playing key roles as support platforms for human operators in high-risk, dangerous applications. T o accomplish challenging tasks, an efficient human-robot cooperation and understanding is required. While typically robotic planning leverages 3D geometric information, human operators are accustomed to a high-level compact representation of the environment, like top-down 2D maps representing the Building Information Model (BIM). In this work, we introduce Pixels-to-Graph (Pix2G), a novel lightweight method to generate structured scene graphs from image pixels and LiDAR maps in real-time for the autonomous exploration of unknown environments on resource-constrained robot platforms. T o satisfy onboard compute constraints, the framework is designed to perform all operation on CPU only. The method output are a de-noised 2D top-down environment map and a structure-segmented 3D pointcloud which are seamlessly connected using a multi-layer graph abstracting information from object-level up to the building-level. The proposed method is quantitatively and qualitatively evaluated during real-world experiments performed using the NASA JPL NeBula-Spot legged robot to autonomously explore and map cluttered garage and urban office like environments in real-time. I. INTRODUCTION Autonomous mobile robots are increasingly utilized for augmenting human actions in everyday operations. Given their maturing abilities to robustly carry out complex tasks in dynamic and challenging environments, they are especially being deployed in dirty and dangerous applications where the risk to human lives is high. Nevertheless, in applications like infrastructure inspection and disaster response, robotic autonomy still needs human operator support for carrying out the complex decision making process. The decision making process is typically guided by the situational awareness provided by the robot and transmitted to human operators: detailed and time-critical situational awareness provision leads to more accurate and efficient mission strategies.