At a crucial moment for climate change, these technologies show us where we're heading. I know it's a bit late to say, but it never quite feels like the year has started until the new edition of our 10 Breakthrough Technologies list comes out. For 25 years, has put together this package, which highlights the technologies that we think are going to matter in the future. This year's version has some stars, including gene resurrection (remember all the dire wolf hype last year?) And of course, the world of climate and energy is represented with sodium-ion batteries, next-generation nuclear, and hyperscale AI data centers . Let's take a look at what ended up on the list, and what it says about this moment for climate tech.

Modeling complex human behavior, such as voter decisions in national elections, is a long-standing challenge for computational social science. Traditional agent-based models (ABMs) are limited by oversimplified rules, while large-scale statistical models often lack interpretability. We introduce FlockVote, a novel framework that uses Large Language Models (LLMs) to build a "computational laboratory" of LLM agents for political simulation. Each agent is instantiated with a high-fidelity demographic profile and dynamic contextual information (e.g. candidate policies), enabling it to perform nuanced, generative reasoning to simulate a voting decision. We deploy this framework as a testbed on the 2024 U.S. Presidential Election, focusing on seven key swing states. Our simulation's macro-level results successfully replicate the real-world outcome, demonstrating the high fidelity of our "virtual society". The primary contribution is not only the prediction, but also the framework's utility as an interpretable research tool. FlockVote moves beyond black-box outputs, allowing researchers to probe agent-level rationale and analyze the stability and sensitivity of LLM-driven social simulations.

The Trump Administration's Data Center Push Could Open the Door for New Forever Chemicals The EPA is prioritizing review of new chemicals to be used in data centers. Experts say this could lead to the fast approval of new types of forever chemicals--with limited oversight. In recent months, the Trump administration has opened a deregulatory floodgate in the name of building more data centers. Among other things, this has involved ordering rollbacks of clean water regulations and opening up public lands to coal mining. Now, it's turning its eye to chemical regulation with a new policy that could, experts say, potentially fast-track the approval of new chemicals for use in the US--including new types of forever chemicals--with limited oversight. In September, the EPA announced it would be prioritizing the regulatory review of new chemicals used in data centers or related projects.

Starting with the Thomspon sampling algorithm, recent years have seen a resurgence of interest in Bayesian algorithms for the Multi-armed Bandit (MAB) problem. These algorithms seek to exploit prior information on arm biases and while several have been shown to be regret optimal, their design has not emerged from a principled approach. In contrast, if one cared about Bayesian regret discounted over an infinite horizon at a fixed, pre-specified rate, the celebrated Gittins index theorem offers an optimal algorithm. Unfortunately, the Gittins analysis does not appear to carry over to minimizing Bayesian regret over all sufficiently large horizons and computing a Gittins index is onerous relative to essentially any incumbent index scheme for the Bayesian MAB problem. The present paper proposes a sequence of'optimistic' approximations to the Gittins index. We show that the use of these approximations in concert with the use of an increasing discount factor appears to offer a compelling alternative to a variety of index schemes proposed for the Bayesian MAB problem in recent years. In addition, we show that the simplest of these approximations yields regret that matches the Lai-Robbins lower bound, including achieving matching constants.

It was October 2024, and Hurricane Helene had just devastated the US Southeast. Representative Marjorie Taylor Greene of Georgia found an abstract target on which to pin the blame: "Yes they can control the weather," she posted on X. "It's ridiculous for anyone to lie and say it can't be done." She was repeating what's by now a pretty familiar and popular conspiracy theory: that shadowy forces are out there, wielding technology to control the weather and wreak havoc on their enemies. This preposterous claim has grown louder and more common in recent years, especially after extreme weather strikes. But here's the thing: While Greene and other believers are not correct, this conspiracy theory--like so many others--holds a kernel of much more modest truth. This story is part of's series "The New Conspiracy Age," on how the present boom in conspiracy theories is reshaping science and technology.

The Tokyo government revised its flood prevention guidelines to implement preventive measures for underground spaces that are considered to be at the highest risk of flooding, such as subway stations. The Tokyo Metropolitan Government has revised its flood prevention guidelines for underground spaces in the capital for the first time in 17 years amid abnormal weather conditions in recent years, with disaster plans updated to include artificial intelligence-based risk assessments. The guidelines were revised earlier this month to implement preventive measures for underground spaces that are considered to be at the highest risk of flooding, such as shopping malls and subway stations, as well as rooms in privately owned buildings and houses that are partially or fully below ground. The revision includes implementing AI technologies to process data on past damage and weather patterns as well as the respective area's topography data to calculate the risk of underground facilities flooding and the best route of evacuation. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Amazon has developed a new warehouse robot that uses touch to rummage around shelves to find the right product to ship to customers. The robot, called Vulcan, is a meaningful step towards making robots less sausage-fingered compared to human beings. Honing robots' tactile abilities further may allow them to take on more fulfillment and manufacturing work in the years ahead. Aaron Parness, Amazon's director of robotics AI who led the development of Vulcan, explains that touch sensing helps the robot push items around on a shelf and identify what it's after. "When you're trying to stow [or pick] items in one of these pods, you can't really do that task without making contact with the other items," he says.

It's one of the most difficult decisions a new parent can make – what shall we call our baby? Now, a huge analysis has revealed that names associated with intelligence are dying out, while those linked to beauty, elegance or strength are on the up. The study, carried out by The Economist, scrutinised the names of nearly 400 million infants born in Britain and the US over the last 143 years. Researchers used a large language model – the type of AI that powers the likes of ChatGPT – for their analysis. They fed it with an enormous amount of text taken from the internet and asked it to identify the five most common terms linked with each name.