Urban causal research is essential for understanding the complex, dynamic processes that shape cities and for informing evidence-based policies. However, current practices are often constrained by inefficient and biased hypothesis formulation, challenges in integrating multimodal data, and fragile experimental methodologies. Imagine a system that automatically estimates the causal impact of congestion pricing on commute times by income group or measures how new green spaces affect asthma rates across neighborhoods using satellite imagery and health reports, and then generates comprehensive, policy-ready outputs, including causal estimates, subgroup analyses, and actionable recommendations. In this Perspective, we propose UrbanCIA, an LLM-driven conceptual framework composed of four distinct modular agents responsible for hypothesis generation, data engineering, experiment design and execution, and results interpretation with policy insights. We begin by examining the current landscape of urban causal research through a structured taxonomy of research topics, data sources, and methodological approaches, revealing systemic limitations across the workflow. Next, we introduce the design principles and technological roadmap for the four modules in the proposed framework. We also propose evaluation criteria to assess the rigor and transparency of these AI-augmented processes. Finally, we reflect on the broader implications for human-AI collaboration, equity, and accountability. We call for a new research agenda that embraces LLM-driven tools as catalysts for more scalable, reproducible, and inclusive urban research.

This work proposes a learning method to accelerate robotic pick-and-place planning by predicting shared grasps. Shared grasps are defined as grasp poses feasible to both the initial and goal object configurations in a pick-and-place task. Traditional analytical methods for solving shared grasps evaluate grasp candidates separately, leading to substantial computational overhead as the candidate set grows. To overcome the limitation, we introduce an Energy-Based Model (EBM) that predicts shared grasps by combining the energies of feasible grasps at both object poses. This formulation enables early identification of promising candidates and significantly reduces the search space. Experiments show that our method improves grasp selection performance, offers higher data efficiency, and generalizes well to unseen grasps and similarly shaped objects.

In this paper, we first propose a novel algorithm for model fusion that leverages Wasserstein barycenters in training a global Deep Neural Network (DNN) in a distributed architecture. To this end, we divide the dataset into equal parts that are fed to "agents" who have identical deep neural networks and train only over the dataset fed to them (known as the local dataset). After some training iterations, we perform an aggregation step where we combine the weight parameters of all neural networks using Wasserstein barycenters. These steps form the proposed algorithm referred to as FedWB. Moreover, we leverage the processes created in the first part of the paper to develop an algorithm to tackle Heterogeneous Federated Reinforcement Learning (HFRL). Our test experiment is the CartPole toy problem, where we vary the lengths of the poles to create heterogeneous environments. We train a deep Q-Network (DQN) in each environment to learn to control each cart, while occasionally performing a global aggregation step to generalize the local models; the end outcome is a global DQN that functions across all environments.

Amazon CEO Andy Jassy speaks during an Amazon Devices launch event in New York City, Feb. 26, 2025. TECH TAKEOVER: Amazon CEO Andy Jassy says artificial intelligence will "change the way" work is done and expects the company's total corporate workforce to be reduced as a result. 'GIANT OFFERS': Meta has allegedly tried to recruit employees from competitor OpenAI by offering bonuses as high as 100 million, OpenAI CEO Sam Altman claimed on a podcast that aired Tuesday. ENERGY OUTLOOK: The rise of artificial intelligence and the increasing popularity of cryptocurrency will continue to push electricity consumption to record highs in 2025 and 2026. POWER DRAIN CRISIS: Every time you ask ChatGPT a question, to generate an image or let artificial intelligence summarize your email, something big is happening behind the scenes.

Advances in the field of computer science, such as very complex simulations, data analysis, or machine learning (ML) in data-driven applications (for example, computational fluid dynamics, large language models) are leading to an increased demand of IT performance and data storage capacity. Therefore, the electricity demands of digital infrastructures in science and industry are increasing. High-performance computing (HPC) has become an enabling technology and a vital tool to greatly reduce the processing and execution time of advanced computing- or data-intensive tasks. An obvious consequence: HPC datacenters (DCs) require an enormous amount of electricity, have volatile demands, and produce notable amounts of waste heat. If not well located, built, and operated, such infrastructures generate a significant CO2 backpack, and the applications and products that use them inherit the backpack from the computing platform.

A growing number of fire departments across the country are turning to artificial intelligence to help detect and respond to wildfires more quickly. Every time you ask ChatGPT a question, to generate an image or let artificial intelligence summarize your email, something big is happening behind the scenes. Not on your device, but in sprawling data centers filled with servers, GPUs and cooling systems that require massive amounts of electricity. The modern AI boom is pushing our power grid to its limits. ChatGPT alone processes roughly 1 billion queries per day, each requiring data center resources far beyond what's on your device.

Japan is encouraging energy importers to secure liquefied natural gas (LNG) past 2050 -- the deadline the second-biggest buyer of the fossil fuel has set itself for net zero emissions. Several of the country's largest LNG buyers are considering 20-year supply deals with projects that would start after 2030, according to people with knowledge of the discussions, who asked not to be named as the negotiations are private. They aim to deploy technology such as carbon capture and storage to mitigate the emissions from burning the super-chilled fossil fuel under Japan's national target. The government expects a boom in artificial intelligence, data centers and semiconductor chip-making factories to revive power demand, which has been tracking a declining population for years. It sees LNG as vital to energy security, even as it works on increasing renewable energy generation and restarting nuclear reactors idled after the 2011 Fukushima No. 1 disaster.

Is this the electric grid of the future? Lincoln Electric System, a publicly owned utility in Nebraska, is used to weathering severe blizzards. But what will happen soon--not only at Lincoln Electric but for all electric utilities--is a challenge of a different order. Utilities must keep the lights on in the face of more extreme and more frequent storms and fires, growing risks of cyberattacks and physical disruptions, and a wildly uncertain policy and regulatory landscape. They must keep prices low amid inflationary costs. And they must adapt to an epochal change in how the grid works, as the industry attempts to transition from power generated with fossil fuels to power generated from renewable sources like solar and wind.

"People are often curious about how much energy a ChatGPT query uses," Sam Altman, the CEO of OpenAI, wrote in an aside in a long blog post last week. The average query, Altman wrote, uses 0.34 watt-hours of energy: "About what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes." For a company with 800 million weekly active users (and growing), the question of how much energy all these searches are using is becoming an increasingly pressing one. But experts say Altman's figure doesn't mean much without much more public context from OpenAI about how it arrived at this calculation--including the definition of what an "average" query is, whether or not it includes image generation, and whether or not Altman is including additional energy use, like from training AI models and cooling OpenAI's servers. As a result, Sasha Luccioni, the climate lead at AI company Hugging Face, doesn't put too much stock in Altman's number.

Breakthroughs, discoveries, and DIY tips sent every weekday. In recent years, researchers and climate advocates have been ringing the alarm about artificial intelligence's impact on the environment. Advanced and increasingly popular large language models (LLMs)--such as those offered by OpenAI and Google--reside in massive data centers that consume significant amounts of electricity and water to cool servers. Every time someone types a question or phrase into one of these platforms, the energy used to generate a response produces a measurable amount of potentially harmful CO₂. But, according to a new research published in Frontiers in Communication, not all of those prompts leave have the same environmental impact.