Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely focus on univariate forecasting, limiting their applicability in real-world scenarios where multivariate data and covariates play a crucial role. We present Chronos-2, a pretrained model capable of handling univariate, multivariate, and covariate-informed forecasting tasks in a zero-shot manner. Chronos-2 employs a group attention mechanism that facilitates in-context learning (ICL) through efficient information sharing across multiple time series within a group, which may represent sets of related series, variates of a multivariate series, or targets and covariates in a forecasting task. These general capabilities are achieved through training on synthetic datasets that impose diverse multivariate structures on univariate series. Chronos-2 delivers state-of-the-art performance across three comprehensive benchmarks: fev-bench, GIFT-Eval, and Chronos Benchmark II. On fev-bench, which emphasizes multivariate and covariate-informed forecasting, Chronos-2's universal ICL capabilities lead to substantial improvements over existing models. On tasks involving covariates, it consistently outperforms baselines by a wide margin. Case studies in the energy and retail domains further highlight its practical advantages. The in-context learning capabilities of Chronos-2 establish it as a general-purpose forecasting model that can be used "as is" in real-world forecasting pipelines.

State's Republican-led public service commission to decide on power expansion and prices, as Democrats vie for voice Georgia is facing the largest demand for electricity in its history, driven by nation-leading datacenter construction. The Georgia Power company has made an unprecedented bid to the agency that oversees the utility for about 10 additional gigawatts of energy in the coming years - enough to power 8.3m homes, at an estimated cost of nearly $16bn, according to the Southern Environmental Law Center . But those huge numbers are not primarily for homes or local businesses in Georgia . Instead about 80% of the company's ask is driven by datacenters, primarily for artificial intelligence, according to Tom Krause, spokesperson for the state's public service commission, or PSC. It is the largest increase ever considered by the commission in a multiyear plan and comes as the Atlanta metro area led the nation in datacenter construction last year - a phenomenon playing out across the US and increasingly sparking protests and pushback.

Countries have fallen behind on emissions goals, but Hannah Ritchie looks at the numbers and sees real gains. Get your news from a source that's not owned and controlled by oligarchs. It has been 10 years since countries signed on to the Paris Agreement, and emissions and temperatures continue to reach new highs, fueling unprecedented weather disasters around the globe. Meanwhile, the shift to clean energy is facing powerful headwinds in the United States, where climate policies are being reversed and support for clean energy is withdrawn. Yet, while the headlines paint a dismal picture of efforts to rein in climate change, the numbers often tell a different story. That is the assessment of data scientist Hannah Ritchie, a researcher at the University of Oxford and deputy editor of the publication .

Gear Home This 297-piece Kobalt Mechanics Tool Kit is just $99 at Lowe's with an included tool box This kit is typically $150, but it's just $99 at Lowe's, which makes it a fantastic gift for just about anyone. We may earn revenue from the products available on this page and participate in affiliate programs. I truly believe that a big tool kit with a dedicated carrying case is one of the best gifts you can give. It looks really impressive, it's useful for every type of person, and it's easy to wrap because it's usually rectangular (though, I recommend ditching wrapping paper this year). Right now, Lowe's has this 297-piece Mechanics Tool Set for just $99, which is a total sweet spot for gift buying.

With AI datacenters soaring power bills for households, a policy called'demand flexibility' could help ease grid strain A cheap, bipartisan tool could help the US meet increasing energy demand from AI datacenters while also easing soaring power bills for households, preventing deadly blackouts and helping the climate. The policy solution, called "demand flexibility", can be quickly deployed across the US. Demand flexibility essentially means rewarding customers for using less power during times of high demand, reducing strain on the grid or in some cases, selling energy they have captured by solar panels on their homes. Peak power demand is expected to grow by 20% over the next decade - driven by the dramatic rise of AI datacenters, onshoring of manufacturing, increasing use of EVs and growing need for air conditioning amid hotter summers. Increasing energy demand is putting states such as California and Texas at higher risk of life-threatening blackouts in extreme weather.

In this era of AI slop, the idea that generative AI tools like Midjourney and Runway could be used to make art can seem absurd. But amid all the muck, there are people using AI tools with real consideration and intent. Some of them are finding notable success as AI artists: They are gaining huge online followings, selling their work at auction, and even having it exhibited in galleries and museums. This story is from our forthcoming print issue, which is all about the body. Plus, you'll also receive a free digital report on nuclear power. Take our quiz: How much do you know about antimicrobial resistance?

As the Group of 20 leaders gather in Cape Town, clean air features on the agenda as a standalone priority for the first time in the forum's history. The reality, however, is stark. Outdoor air pollution claims 5.7 million lives each year, and a report released last week highlights the lack of international development finance for clean air. Only $3.7bn was spent globally in 2023, representing barely 1 percent of aid, with only a fraction reaching Africa. As the minister chairing the G20's environment workstream this year, I am proud to have worked with member countries and international organisations to place air pollution firmly on the agenda.

The eighth AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) will take place in Madrid, Spain from 20-22 October 2025. The programme will feature keynote talks, two panels, discussions, paper presentations, and poster sessions. There are two keynote talks, scheduled on Tuesday 21 and Wednesday 22. The two panels will take place on Monday 20 and Tuesday 21. Tuesday Panel: Pedagogy Panel: How (and to Whom) Do We Teach AI Ethics?

The Bourbon Industry Is in Turmoil. Could Tech Provide the Shot It Needs? The software-driven approach pioneered by a new Kentucky distillery runs counter to the low-tech methods of whiskey's old guard. Its mix of data and automation might help pave a way forward. Kendra Skeeters, a warehouse operator at Whiskey House, works the barrel-filling stations at the company's facility in Elizabethtown, Kentucky.Photograph: LEANDRO LOZADA Save this storyIn case you missed it, the American whiskey industry is seemingly in free fall. The once untouchable bourbon business has seen many big brands abruptly retreating, with sales of Bulleit down 7 percent and Wild Turkey down 8 percent in the first half of this year.

The optimal power flow (OPF) is a multi-valued, non-convex mapping from loads to dispatch setpoints. The variability of system parameters (e.g., admittances, topology) further contributes to the multiplicity of dispatch setpoints for a given load. Existing deep learning OPF solvers are single-valued and thus fail to capture the variability of system parameters unless fully represented in the feature space, which is prohibitive. To solve this problem, we introduce a diffusion-based OPF solver, termed \textit{DiffOPF}, that treats OPF as a conditional sampling problem. The solver learns the joint distribution of loads and dispatch setpoints from operational history, and returns the marginal dispatch distributions conditioned on loads. Unlike single-valued solvers, DiffOPF enables sampling statistically credible warm starts with favorable cost and constraint satisfaction trade-offs. We explore the sample complexity of DiffOPF to ensure the OPF solution within a prescribed distance from the optimization-based solution, and verify this experimentally on power system benchmarks.