Environment

What GAO Found GAO has previously found that the Department of Energy’s (DOE) Office of Environmental Management (EM) has not followed its standards for defining mission need for some large projects. A mission need statement documents DOE’s identification of a mission-related need and, according to DOE standards, should not identify a particular solution. This ensures that DOE does not limit potential solutions at the project initiation stage. However, the majority of mission need statements tha

Google tries balancing AI data center emissions with clean energy efforts.

What GAO Found The Department of Homeland Security’s (DHS) Federal Emergency Management Agency (FEMA), the Department of Housing and Urban Development (HUD), and the Department of Energy (DOE) have obligated about $14 billion for Puerto Rico’s grid recovery and modernization since 2017, but limited funding has been disbursed. About $2.7 billion of about $11.1 billion obligated by FEMA has been disbursed since 2017, largely for equipment and materials and architecture and engineering. In addition

Carbon emissions drive climate change, and carbon credits mitigate climate deterioration and environmental damage while assisting organizations in managing their carbon footprint. Fully utilizing carbon credits remains challenging. This study enhances understanding of the engineering practices for carbon credits to develop responsible fintech solutions and provide insights for carbon emission management. We review the negative impacts of organizations’ strategy of evading carbon management throu

Analysis and insights for driving a rapid transition to net-zero while building resilience to physical climate impacts ...

Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things (IoT), enterprise, and distributed network environments. However, developing reliable IDS models remains challenging because attack behaviors evolve over time, realistic datasets are difficult to obtain, traffic records may be incomplete, attack classes are often imbalanced, and privacy constraints limit centralized data collection. Rec

Analysis and insights for driving a rapid transition to net-zero while building resilience to physical climate impacts ...

This article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia . As artificial intelligence accelerates global demand for compute, a parallel constraint is emerging with equal urgency: energy. From hyperscale data centers to electrified industries, AI is driving a step change in electricity demand. This is not a future challenge, it is a present, system-level issue requiring coordinated action across energy, infrastructure, and engineering disciplines.

Amazon is developing precise, sector-specific approaches to measuring decarbonization progress — starting with emissions per unit shipped.

Generalizing machine learning models to environments that differ from their training distribution remains a critical hurdle, particularly when data from the target domain is entirely or partially unavailable. We propose Generative Meta-Learning with Human Feedback (GMHF), a novel framework that bridges this domain gap by leveraging expert intuition to guide data synthesis. Grounded in a theoretical analysis of generalization error, we derive bounds demonstrating that aligning the distribution of

NVIDIA and its partners are investing in American manufacturing, supply chains, energy grids and skilled workforces so the U.S. can produce the infrastructure needed for better healthcare, breakthrough scientific discovery, stronger industrial productivity and global technology leadership.

Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging. Meta-learning and transfer learning have been explored to enable deep learning-based beam prediction models to rapidly adapt to unseen environments; however, existing meta-learning approaches adapt the entire network and are trained from random initialization, leading to a large number of updated parameters and a high meta-training cost, while t

“ The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public . Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO,

The Political Economy of Energy Poverty

Northeast India is a cradle of cultural and ecological diversity, where Indigenous communities have cultivated rich traditional knowledge systems across generations—ranging from sustainable agroforestry, medicinal plant use, and sacred landscape management to complex oral histories and language traditions. However, these knowledge systems now face accelerating threats from environmental degradation, climate change, socio-economic shifts, and cultural erosion. This review explores how artificial

Artificial intelligence (AI) systems are routinely modified after deployment through retraining and changes in their environments. These transformations raise a metaphysical question: under what conditions does an AI system remain the same system over time or across deployments? Earlier work formulates synchronic and diachronic identity propositionally, by relating identity within a fixed AI system type to equality of trustworthiness levels. Such criteria specify when identity statements are tru

A local-to-global context mismatch arises when autoregressive traffic simulators trained on ego-centric driving logs are deployed in globally observable closed-loop environments. In such logs, the ego vehicle has rich local observations, while surrounding agents are only partially observed due to perception limits and occlusions. As a result, simulators may learn incomplete context--action mappings that remain hidden in log-based training but emerge during closed-loop rollouts, leading to unreal

Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding environments, robotic systems, security-operation workflows, and autonomous agents that can read private data, call tools, write files, execute code, and act across organizational boundaries. This shift changes the security problem: risks do not arise from the model weights alone, but from the full lifecycle and application stack through which data, promp

Staff absenteeism imposes substantial operational costs in high-demand work environments such as healthcare, emergency services, meat processing, construction, and courier and delivery services, where proactive workforce planning depends on reliable individual-level absence prediction. Existing regression and classification approaches share a structural limitation; they map features observed at time t to labels at the same time t, reproducing already-realised outcomes rather than predicting futu

Reinforcement learning for long-horizon robotic manipulation is often limited by sparse and delayed rewards, while manually designing dense shaping signals is costly and brittle to changes in environments and object configurations. This work proposes Stage-Transition Dense Reward (STDR), a visual reward-learning framework that converts unstructured expert videos into logically grounded dense rewards for training RL agents from scratch. STDR leverages semantic understanding to infer a task's stag

Joint OFDM-RIS optimization for 6G is a mixed-integer nonlinear programming (MINLP) problem covering sum-rate maximization, energy efficiency, max-min fairness, and peak-to-average power ratio (PAPR)-constrained objectives. Seventy-eight joint OFDM-RIS optimization works published between 2021 and 2026 are surveyed. No standardized benchmark exists, and cross-paper comparisons remain infeasible. This survey classifies these works into four paradigms: (I) model-based convex relaxation, (II) heuri

(box) (popup) Human Rights at local and regional levels Human rights and the environment The Case Law of the European Court of Human Rights - Local and Regional Authorities Children's Rights LGBTI ...

3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions. Existing approaches typically perform reasoning over the entire scene, leading to ambiguous predictions and high computational cost, especially in cluttered environments. We observe that many referential expressions rely on local spatial context and often correspond to restricted spatial regions rather than the full scene. Motivated by this insight, we propose PruneGround, an effective pl

Marine and coastal ecosystems are among the least observable yet most rapidly changing environments, where climate impacts, pollution, and biodiversity loss demand monitoring and intervention at scales that manual sampling and single-robot deployments cannot sustain. This paper argues for a conceptual shift in ecological monitoring and restoration toward networked robotic ecosystems, adopting cooperative swarms of autonomous aquatic robots coupled to in-situ digital twins and human-in-the-loop s

The infostealer was delivered via CVE-2026-48558, a critical authentication bypass vulnerability in SimpleHelp, targeting credentials linking development and admin environments to wider enterprise systems.

MEMORANDUM FOR THE ADMINISTRATOR OF THE ENVIRONMENTAL PROTECTION AGENCY By the authority vested in me as President by the Constitution and the laws of the United States of America, I hereby direct: Section 1. Purpose. During the previous administration, crushing environmental regulatory burdens caused the average cost of vehicles to soar. My Administration has therefore […] The post Lowering the Cost of Living by Promoting the Freedom to Fix appeared first on The White House .

Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment. Acting rationally then requires inferring the unobserved quantities that govern it and updating beliefs about them as evidence accumulates. Yet most evaluations only score the model's final-turn answer in a single-turn format, leaving this process unexamined. We ask how closely LLMs' belief updates match those of

Most corporate workplace environments enforce policies and technical controls that limit the storage of sensitive data on client endpoints. Consequently, ransomware operators have evolved variants that expand their attack surface from local systems to network drives and shared storage resources. As traditional endpoint detection mechanisms focus primarily on local system behaviour, a compromised client can impact remote file servers, such as by encrypting shared data, without directly triggering

Consumer confidence is typically modeled as a persistent macroeconomic index, yet its movements arise from households that interpret economic information through heterogeneous constraints, exposures, prior beliefs, and attention. We introduce ConsumerSim, a generative Human--Environment response framework that reconstructs Consumer Confidence Index (CCI) dynamics from a microdata-calibrated synthetic population, time-stamped macroeconomic, financial, policy, and news signals, survey-like respons

Today, you probably asked a question of a large language model, or accepted a connection suggestion on LinkedIn, or watched a recommended video on YouTube, or took a different route to work based on a traffic prediction from Google Maps. In other words, you probably used artificial intelligence. But what you might not know is how much energy that interaction consumed or why. AI requires processing massive amounts of data, which is usually done in large data centers populated by thousands of GPUs

Showcasing the importance of open source innovation in American AI, Palantir’s new intelligent engine — introduced today — uses NVIDIA Nemotron open models to serve the needs of U.S. government agencies. Open source software has long been a pillar of U.S. technology leadership. In 1969, DARPA connected four university computers — from UCLA, Stanford, UCSB […]

AIID editor's note: Please visit the original source for the full article. A group of California motorists has filed a proposed class-action lawsuit alleging that several major fuel retailers and an energy pricing software provider used ar ... (https://incidentdatabase.ai/cite/1559#7473)

Human companionship is an essential capability for mobile robots operating in dynamic, human-centered environments. It enables robots to perform tasks such as guidance, assistance, surveillance, and service delivery across various domains, including healthcare, logistics, and public safety. The recent advances in artificial intelligence (AI), particularly in computer vision, deep learning, and sensor fusion, have significantly improved the reliability, adaptability, and contextual understanding

Mobile robots equipped with projectors enable versatile applications such as multimedia display, interactive communication, and environmental augmentation. However, wall projection, which is required for displaying multimedia content on walls, remains challenging, because it is difficult to autonomously locate a projection space that is both flat and unobstructed. Some existing approaches address wall projection using 2D maps or by considering only large continuous surfaces, but these methods fa

Autonomous vehicles (AVs) are increasingly deployed in urban environments, yet their safety frameworks remain primarily designed around collision avoidance and minimal risk condition (MRC) behaviors such as slowing or stopping when uncertainty arises. Although effective in reducing immediate crash risk, real-world deployments indicate that stopping alone does not guarantee safe integration into human-governed roadway systems. Incidents reported by municipalities and public records show that AV f

This article is the result of a collaboration with The Sunday Times. You can find their corresponding piece here. Every Friday evening, the brochure says, players can compete to win cash prizes in one of the world’s fastest-growing racquet sports. The padel club in Dubai’s west is the picture of modern wellness culture: climate-controlled courts, […] The post Poster Boy: Sanctioned Kinahan Cartel Lieutenant Found Playing Padel in Dubai appeared first on bellingcat .

Large language models are deployed in long-context, emotionally interactive environments like digital humans, AI companions, educational assistants, and counseling systems. Unlike jailbreak attacks with explicit adversarial prompts, these systems interact with emotionally charged narratives involving bullying, betrayal, loneliness, social hostility, and institutional unfairness. This raises an important question: can prolonged narrative exposure reshape the reasoning and alignment stability of L

Embodied agents operating in decentralized and partially observable environments have attracted growing attention in recent years. However, existing large language model (LLM)-based agents often exhibit behaviors that are misaligned with their partners or inconsistent with the environment state, leading to inefficient cooperation and poor task success. To address this challenge, we propose a novel framework, Learning Laws of Cooperation (LLawCo), that enables embodied agents to autonomously alig

Published on May 15, 2024 5:30 PM GMT This is the third in a sequence of posts taken from my recent report: Why Did Environmentalism Become Partisan? Summary Rising partisanship did not make environmentalism more popular or politically effective. Instead, it saw flat or falling overall public opinion, fewer major legislative achievements, and fluctuating executive actions. Public Opinion One hypothesis is that partisanship was useful, or even necessary, for an issue to become popular. Maybe jour

Plus: Fiery resistance to a nuclear AI data center and A24's Google debacle. Welcome to the first episode of BLOOD IN THE MACHINE: THE SHOW, with the great AI and crypto watchdog, Molly White.