Environment

Climate change amplifies natural disasters, posing an existential threat to our society. However, a digital storm is raging on right-wing extremist Telegram channels where climate disinformation works to delegitimize scientific consensus. Investigating the factors that amplify climate disinformation is as critical to combating it as understanding natural disasters and their drivers. We The post Amplifying the storm: Climate disinformation dynamics during natural disasters on right-wing extremist

arXiv:2607.19389v1 Announce Type: new Abstract: As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases have drawn attention. In this paper, we revisit the nuances of long-term `fairness' achievable by an ADM, specifically in the context of a credit lending induced wealth process. The literature on long-term fairness mostly (a) considers passive environments, i.e. the outcome of a predictor does not change

arXiv:2607.19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings. A prior architectural study by the same authors (Tertulino and Alencar, 2026) demonstrated, on a synthetic six-feature benchmark, that server-side adaptive optimization acts as a temporal denoiser for Differential Privacy noise, answ

arXiv:2607.19827v1 Announce Type: cross Abstract: Ensuring safety in Physical AI systems operating in real-world environments is a critical challenge, particularly in hospital wards where vulnerable patients, clinical staff, medical devices, and assistive robots coexist. In this paper, we reinterpret Clinical Pathways as explicit runtime safety specifications for embodied medical AI. We propose a conceptual robotic architecture that integrates wearable sensors, smart medical devices, and assisti

Multi-tier aerial communication networks (MACNs), integrating satellites, high-altitude platforms, and unmanned aerial vehicles, are emerging as a cornerstone of next-generation global connectivity. Their promise of resilient and ubiquitous coverage, however, is hindered by highly dynamic topologies, severe energy and computational constraints, environment-sensitive channels, diverse quality-of-service requirements, and limited real-world validation. Artificial intelligence (AI) has increasingly

Photovoltaic (PV) systems have proven themselves to be a viable alternative energy source; however, there are multiple faults related to PV systems which cause energy losses and low efficiencies. Manual or rule-based algorithms are traditionally used for fault diagnosis, which are not efficient and unsuitable for real-time applications. In this paper, a novel hybrid intelligent classification system for PV fault detection is proposed by integrating Fuzzy C-Means (FCM) clustering and Deep Learnin

There’s no question that seeing Christopher Nolan’s epic “The Odyssey” in 70mm and Imax – or both – is an expansive and breathtaking experience. But for cinephiles who have already checked off those formats on their list, what’s it like to watch the Matt Damon-starring, three-hour blockbuster in 4DX? The seat-rumbling, water-spritzing technology, available in […]

Democratic Texas governor's race nominee Gina Hinojosa is focusing on artificial intelligence data centers as a key issue in her campaign against Republican Gov.

Around 278 teams will gain access to the platform, including agentic frameworks and high-performance computing, among other resources. The post Energy Department demos Genesis Mission platform appeared first on FedScoop .

Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make agents hard to train end-to-end with open infrastructure, whose SFT/RL stacks cannot natively express stateful, multi-process harness inference. To address this, we present OpenForgeRL, an open-source framework for training harness-based agents end-to-end in diverse environments.

Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedback. In-context learning offers a highly sample-efficient way for agents to learn from their own interaction histories, but its gains disappear once that experience is removed from the context. Separately, context distillation provides a mechanism for internalizing contextual information into model weights. However, applying it to agents' interacti

The development of generalizable robotic manipulation policies is inherently bounded by the availability of large-scale, high-fidelity scene data. While recent automated synthesis methods attempt to bridge this gap via text-to-layout hallucination or simplified procedural generation, they frequently suffer from physical implausibility and fail to capture the complex, dense clutter of actual human environments. In this paper, we introduce TableVerse, a fully automated Real2Sim pipeline that shift

Federal agencies have committed more than $5 billion to the Genesis Mission, an Energy Department-led effort to use artificial intelligence to accelerate scientific research, White House science adviser Michael Kratsios announced on Wednesday. The initiative selected 278 projects from more than 5,000 applications, Energy Secretary Chris Wright said at a summit in Washington. More than […] This story continues at The Next Web

OpenAI made a mistake setting up what it called a “highly isolated” testing environment and sandbox. According to cybersecurity experts, that human mistake is what made the AI-powered attack on Hugging Face possible.

Bills aimed at modernizing the nation’s grid for AI data centers advance to the full House

There are now 20 federal agencies on board with the Genesis Mission as a whole-of-government approach takes shape. The post NASA, DOD and others join Energy Department-led Genesis Mission appeared first on FedScoop .

The incident is unique because it was "driven, end to end, by an autonomous AI agent system," according to Hugging Face.

Full-sized humanoid robot capabilities have grown exponentially in recent years, aiming towards general-purpose deployment in human environments. A popular control method used by manufacturers utilizes Virtual Reality for upper-body teleoperation and Reinforcement Learning for lower-body balance and locomotion control. As a result, a single remote operator can see, manipulate, and navigate about a real, distant physical environment. This powerful control stack is often relegated to expensive ful

Winter Storm Uri, the multiday freeze that slammed Texas in February 2021 and pummeled the state’s power grid, has been on Kenneth Kovar’s mind ever since. Though the resident of New Braunfels, northeast of San Antonio, didn’t lose power at the time, he wasn’t able to run his septic tank—it is independent from local systems. He had to fill his toilets with water from his backyard pool. So when Kovar, 64, bought a Ford F-150 last fall, his hope was to be better prepared for any new crisis. “I was

The worm blends in with thousands of other commands occurring daily in any given environment, yet its intent and origins remain unknown. The post Malware is targeting AI tools in software development environments appeared first on CyberScoop .

Closing the gap between benchmark performance and reliable real-world operation remains a central challenge for Vision-Language-Action (VLA) humanoid robots, which must handle execution errors, distribution shifts, and environmental variability. This paper presents DEED (Data-Efficient Post-Training and Experience-Driven Learning), a systems-level approach evaluated on a supermarket chip-restocking task using a Unitree G1-Edu humanoid robot and the GR00T N1.6 foundation model. DEED comprises thr

Thanks for reading this week’s free Where’s Your Ed At newsletter. Friday’s premium newsletter will ask the simple question: Is Oracle dying? It’s been one year since I launched the premium newsletter, and I’ve decided to extend the discount

OpenAI is planning a data center in Georgia called "Project Camellia" with a 3.2-gigawatt power deal from Georgia Power. The company pledged $80 million for the local community and $71 million in Codex credits for students to counter growing opposition to US data centers that many residents see as resource-hungry but job-poor. The article OpenAI's "Project Camellia" in Georgia secures a massive 3.2-gigawatt power deal through 2032 appeared first on The Decoder .

We consider a task planning scenario in which robots sharing a persistent environment are assigned tasks one at a time from a held-out sequence. Standard task planners, lacking foresight of future tasks and inconsiderate of others' constraints, solve each task in isolation, leaving terminal states that increase future cost for all, side effects that compound over lengthy task sequences. To reduce cost over the sequence, a robot must anticipate how its actions now may impact performance on future

Modern power systems increasingly require probabilistic forecasts amid interacting uncertainties from renewable intermittency, flexible demand, market volatility, and weather-dependent generation. However, existing methods often treat multi-scale decomposition, exogenous-variable alignment, and probabilistic output as separate steps, obscuring how predictable structures and uncertainty-bearing fluctuations jointly shape the forecast distribution. This paper proposes a state-space exogenous-conte

Active Inference (AIF) frames adaptive behavior as the minimization of expected free energy (EFE), combining epistemic and pragmatic objectives within a single variational principle. We frame AIF as policy optimization and show that, for closed-loop control policies, EFE minimization can be formulated as a convex Markov decision process (MDP). In this formulation, the pragmatic terms are linear in the predictive state marginals and therefore equivalent to reward maximization in a latent MDP, whi

Biodiversity loss is one of the central challenges of the “triple planetary crisis” alongside climate change and pollution. Scientific assessments suggest that humanity has already transgressed the planetary boundary relating to biosphere integrity and researchers discuss whether we are already within a sixth mass extinction event. This post seeks to initiate a broader discussion on the promises and pitfalls of replicating successful climate advisory opinion initiatives for the integrity of the

Each project chosen by the Department of Energy focuses on advancing a critical scientific area with the help of artificial intelligence.

Can we ever develop and use AI in an environmentally sustainable way? My conversation with Philipp Hacker and Boris | Edition #308

OpenAI announces Project Camellia in Effingham County, Georgia, with commitments to responsible energy, community investment, jobs, and access to Codex.

Vultr, the world’s largest privately-held cloud infrastructure company, and AMD, a leader in high-performance and AI computing, have announced their support for the University of Cambridge's Energy & Environment Group in developing TESSERA, a groundbreaking AI foundation model designed to monitor environmental change internationally at unprecedented scale.

In 2019, the AI researcher Rich Sutton published a short essay called “The Bitter Lesson.” Seventy years of AI research, he argued, kept demonstrating the same thing: The most effective ways to train AI rely on raw, brute-force computing power — simply scaling up — rather than clever approaches built on human insight. It didn’t […]

Anthropic detailed the containment architectures it uses for Claude across its products. It argues that agent safety depends on placing deterministic limits on an agent’s filesystem, network, and execution environment rather than on permission prompts or safeguards. Most notably, it examines failures at trust boundaries and along permitted egress paths that led Anthropic to revise those designs. By Eran Stiller

Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents. This gap is reflected in their limited performance on continual learning benchmarks such as ARC-AGI-3, especially when models are evaluated out of the box. Various agent harnesses have been proposed to close this gap, and each commits to a strategy for handling long sequences of observations, i.e., what information to save from the environment and how

OpenAI outlines its commitment to advancing American science working with the U.S. Department of Energy and national labs to use frontier AI to accelerate discovery.

Microsoft is set to invest $60 million in the Department of Energy’s (DOE) "Genesis Mission," which focuses on accelerating scientific discovery and technological leadership through artificial intelligence. The company’s investment will put $40 million toward Azure cloud and AI computing credits over three years to “support large-scale AI and scientific workloads” while $20 million will...

What GAO Found The Infrastructure Investment and Jobs Act (IIJA) and Inflation Reduction Act (IRA) provided budget authorities for transportation, infrastructure, and energy projects. Together, the four agencies selected for this review—the Environmental Protection Agency (EPA), Department of the Interior (Interior), National Telecommunications and Information Administration (NTIA), and Department of Transportation (DOT)—obligated a majority of their IIJA or IRA funding. IIJA. Of the approximate

An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-off between security and energy use, as strong detection models can drain the battery of devices fast. This work tests different Multi-Layer Perceptron (MLP) model configurations to balance malware detection performance and energy efficiency. In this work, we compared standard FP32 models with optimized INT8 quantized neural networks with different model depths

Dynamic manufacturing environments require multi-agent systems to coordinate effectively under frequent operational disturbances such as machine failures, urgent job arrivals, and processing time variations. Existing multi-agent reinforcement learning approaches treat each disturbance episode independently, discarding valuable coordination experience that could accelerate future adaptation. In this paper, we propose a Graph-Structured Experiential Memory (GSEM) framework for multi-agent coordina

In the face of backlash to concerns the AI boom will increase consumer electricity bills, the largest utility companies and data center developers in the US are now promising to do something about it. The Wall Street Journal reports that nearly 200 organizations have signed President Donald Trump's "rate payer protection pledge" that's meant to […]