The rapid growth of large AI models has raised significant environmental concerns due to their substantial carbon footprint. Existing carbon accounting methods for AI models are fundamentally deterministic and fail to account for inherent uncertainties in embodied and operational carbon emissions. Our work aims to investigate the effect of these uncertainties on embodied and operational carbon footprint estimates for large AI models. We propose a Probabilistic Carbon Accounting Model (PCAM), which quantifies uncertainties in the carbon accounting of large AI models. We develop parameter models to quantify key components (processors, memory, storage) in the carbon footprint of AI models. To characterize the distribution of the parameters, we develop a carbon dataset by aggregating related data from various sources. Then, we generate the probabilistic distribution of the parameters from the collected dataset. We compare the performance of PCAM with LLMCarbon, the state-of-the-art carbon accounting method for large AI models.

While promising, existing methods suffer from two major limitations: 1) reliance on real data from limited domain for knowledge fusion, preventing the target LLM from fully acquiring knowledge across diverse domains, and 2) fixed data allocation proportions across domains, failing to dynamically adjust according to the target LLM's varying capabilities across domains, leading to a capability imbalance. To overcome these limitations, we propose Bohdi, a synthetic-data-only heterogeneous LLM fusion framework. Through the organization of knowledge domains into a hierarchical tree structure, Bohdi enables automatic domain exploration and multi-domain data generation through multimodel collaboration, thereby comprehensively extracting knowledge from source LLMs. By formalizing domain expansion and data sampling proportion allocation on the knowledge tree as a Hierarchical Multi-Armed Bandit problem, Bohdi leverages the designed DynaBranches mechanism to adaptively adjust sampling proportions based on the target LLM's performance feedback across domains. Integrated with our proposed Introspection-Rebirth (IR) mechanism, DynaBranches dynamically tracks capability shifts during target LLM's updates via Sliding Window Binomial Likelihood Ratio Testing (SWBLRT), further enhancing its online adaptation capability. Comparative experimental results on a comprehensive suite of benchmarks demonstrate that Bohdi significantly outperforms existing baselines on multiple target LLMs, exhibits higher data efficiency, and virtually eliminates the imbalance in the target LLM's capabilities. Our code is available at Bohdi.

DOJ Lawyers Argue xAI Is'Vital' for National Security in NAACP Lawsuit In a bid to dismiss a lawsuit over xAI's polluting gas turbines, the Justice Department claimed the company is integral to military operations--including the Iran War. The Department of Justice intervened in a lawsuit over xAI's gas turbines on Monday. In a filing, the agency sided with Elon Musk's company, saying attempts to stop xAI from running the natural gas turbines "threatens American national, economic, and energy security by seeking to shut off the power supply for artificial-intelligence innovation that supports the Department of War's military operations." The DOJ, along with xAI and the state of Mississippi, asked the court to dismiss the suit, filed by the NAACP in April. The NAACP alleges xAI isn't following the Clean Air Act and is endangering public health by running unpermitted natural gas turbines at the site of its second data center in Southaven, Mississippi, dubbed Colossus 2. In May, the NAACP filed a request for a preliminary injunction to stop xAI from running the turbines, alleging that their continued use without a permit "increases risks of asthma attacks and heart disease" in communities with an already heavy pollution burden .

Hidden Markov Models (HMMs) are foundational tools for modeling sequential data with latent Markovian structure, yet fitting them to real-world data remains computationally challenging. In this work, we show that pre-trained large language models (LLMs) can effectively model data generated by HMMs via in-context learning (ICL)--their ability to infer patterns from examples within a prompt. On a diverse set of synthetic HMMs, LLMs achieve predictive accuracy approaching the theoretical optimum. We uncover novel scaling trends influenced by HMM properties, and offer theoretical conjectures for these empirical observations. We also provide practical guidelines for scientists on using ICL as a diagnostic tool for complex data. On real-world animal decision-making tasks, ICL achieves competitive performance with models designed by human experts. To our knowledge, this is the first demonstration that ICL can learn to predict HMM-generated sequences--an advance that deepens our understanding of in-context learning in LLMs and establishes its potential as a powerful tool for uncovering hidden structure in complex scientific data.

Plus: Anthropic has shut down access to its top models after a US directive. That's good for our health, but bad for the planet: it already accounts for 7% of global electricity use and 3% of greenhouse-gas emissions. Feeling the heat, scientists and startups are hoping to amp up solid-state cooling. These systems move heat through conductive materials, which could cool spaces and surfaces with fewer messy side effects. The catch is whether it can match the efficiency of traditional AC. Find out how the unconventional coolers aim to dial down AC emissions .

Facilities can be branded as aligned with Scotland's climate goals despite significant emissions, said APRS. Facilities can be branded as aligned with Scotland's climate goals despite significant emissions, said APRS. Scotland's'green datacentres' policy ignores emissions impact of AI, analysis shows A Scottish government policy designed to encourage datacentres to build in Scotland could lead to a massive volume of carbon emissions being ignored, according to an analysis by a Scottish charity. "Green datacentres" are at the heart of Scotland's ambitions to develop economically. Enshrined in national policy, they are part of a larger, UK-wide effort to attract big AI investment to Scotland.

Datacentres could require at least 6GW of capacity by 2030 under government plans to expand AI infrastructure. Datacentres could require at least 6GW of capacity by 2030 under government plans to expand AI infrastructure. Sun 26 Apr 2026 03.00 EDTLast modified on Sun 26 Apr 2026 03.01 EDT One vision of the UKâ s future involves a decarbonised economy powered by clean, renewable energy. Another involves making the UK an AI superpower. The government departments responsible for these two visions do not appear to have agreed on their numbers.

We develop a semi-parametric state-space model for time-series data with latent regime transitions. Classical Markov-switching models use fixed parametric transition functions, such as logistic or probit links, which restrict flexibility when transitions depend on nonlinear and context-dependent effects. We replace this assumption with learned functions $f_0, f_1 \in \calH$, where $\calH$ is either a reproducing kernel Hilbert space or a spline approximation space, and define transition probabilities as $p_{jk,t} = \sigmoid(f(\bx_{t-1}))$. The transition functions are estimated jointly with emission parameters using a generalized Expectation-Maximization algorithm. The E-step uses the standard forward-backward recursion, while the M-step reduces to a penalized regression problem with weights from smoothed occupation measures. We establish identifiability conditions and provide a consistency argument for the resulting estimators. Experiments on synthetic data show improved recovery of nonlinear transition dynamics compared to parametric baselines. An empirical study on financial time series demonstrates improved regime classification and earlier detection of transition events.

A startup is transforming polluted air into apparel. At least 15 major brands, including H&M and Walmart, are testing new technology for carbon neutral clothing. Breakthroughs, discoveries, and DIY tips sent six days a week. It might not seem like it when you nonchalantly click a Buy Now button while online shopping, but that new t-shirt is part of a complex global web of commerce taking a toll on the environment . Consulting giant McKinsey estimates that the fashion industry alone accounts for as much as 4 percent of total global climate emissions.

Many of those tasks have been addressed on datasets created with single climate models.