Kawasaki's CORLEO is a hydrogen-powered, AI-driven rideable robot. Kawasaki Heavy Industries has introduced something that feels straight out of a video game: CORLEO, a hydrogen-powered, four-legged robot prototype designed to be ridden by humans. Unveiled at the Osaka-Kansai Expo 2025, this futuristic machine is built to handle rugged terrain with ease, combining cutting-edge robotics and sustainable energy. Let's take a closer look at what makes CORLEO so cutting-edge. GET SECURITY ALERTS & EXPERT TECH TIPS – SIGN UP FOR KURT'S'THE CYBERGUY REPORT' NOW Instead of wheels, it has four robotic legs that move independently, allowing it to handle uneven ground like rocks, grass and steep inclines.

This story was originally published by WIRED and is reproduced here as part of the Climate Desk collaboration. Last Tuesday, President Donald Trump held a press conference to announce the signing of executive orders intended to shape American energy policy in favor of one particular source: coal, the most carbon-intense fossil fuel. "I call it beautiful, clean coal," President Trump said while flanked by a crowd of miners at the White House. "I tell my people never use the word coal unless you put'beautiful, clean' before it." Trump has talked about saving coal, and coal jobs, for as long as he's been in politics.

Some 22,500 miles above Earth, a spacecraft filled with thruster fuel will gas up two orbiting Space Force assets. The high-altitude endeavor, undertaken by the orbital servicing enterprise Astroscale U.S., is slated to occur in the summer of 2026, the company announced this week. This Department of Defense-funded mission will see Astroscale's 660-pound craft refuel a satellite with the propellant hydrazine, then maneuver to a fueling depot to fill up with more fuel, and then refuel another asset. But it will be the first time a Space Force craft will be refueled in space. Such a fuel shuttle could keep spacecraft in orbit longer and eliminate the need for any craft to suspend its mission to retrieve thruster propellant.

There are reasonable arguments to suggest that AI tools may eventually help reduce emissions, as the IEA report underscores. But what we know for sure is that they're driving up energy demand and emissions today--especially in the regional pockets where data centers are clustering. So far, these facilities, which generally run around the clock, are substantially powered through natural-gas turbines, which produce significant levels of planet-warming emissions. Electricity demands are rising so fast that developers are proposing to build new gas plants and convert retired coal plants to supply the buzzy industry. The other thing we know is that there are better, cleaner ways of powering these facilities already, including geothermal plants, nuclear reactors, hydroelectric power, and wind or solar projects coupled with significant amounts of battery storage. The trade-off is that these facilities may cost more to build or operate, or take longer to get up and running.

A handful of wealthy, politically connected Silicon Valley investors are reportedly eyeing Greenland's icy shores as the site for a techno-utopian "freedom city." That's according to a report from Reuters, which details a proposed effort to establish a new, libertarian-minded municipality characterized by minimal corporate regulation and a focus on accelerating emerging technologies like AI and mini nuclear reactors. Supporters of increased economic development in Greenland argue its frigid climate could naturally cool massive, energy intensive AI data centers. Large deposits of critical and rare earth minerals buried beneath the island's ice sheets could also potentially be used to manufacture consumer electronics. The so-called "start-up city"--which bears similarities to another ongoing venture in California's Solano County--reportedly already has the backing of PayPal founder Peter Thiel and Ken Howery, President Donald Trump's pick for Denmark ambassador.

A pair of studies analyzing the effects of AI on our planet have been released and the news is fairly grim. Greenpeace studied the emissions generated from the production of the semiconductors used in AI chips and found that there was a fourfold increase in 2024. This analysis was completed using publicly available data. Many of the big chipmakers like NVIDIA rely on companies like Taiwan Semiconductor Manufacturing Co and SK Hynix Inc. for the components of GPUs and memory units. Most of this manufacturing happens in Taiwan, South Korea and Japan, where power grids are primarily reliant on fossil fuels.

Black Mirror is more than science fiction – its stories about modernity have become akin to science folklore, shaping our collective view of technology and the future. Each new innovation gets an allegory: smartphones as tools for a new age caste system, robot dogs as overzealous human hunters, drones as a murderous swarm, artificial intelligence as new age necromancy, virtual reality and brain chips as seizure-inducing nightmares, to name a few. It is a must-watch, but must we take it so seriously? Black Mirror fails to consistently explore the duality of technology and our reactions to it. It is a critical deficit.

The global rush to AI technology will require almost as much energy by the end of this decade as Japan uses today, but only about half of the demand is likely to be met from renewable sources. Processing data, mainly for AI, will consume more electricity in the US alone by 2030 than manufacturing steel, cement, chemicals and all other energy-intensive goods combined, according to a report from the International Energy Agency (IEA). AI will be the main driver of that increase, with demand from dedicated AI datacentres alone forecast to more than quadruple. One datacentre today consumes as much electricity as 100,000 households, but some of those currently under construction will require 20 times more. But fears that the rapid adoption of AI will destroy hopes of tackling the climate crisis have been "overstated", according to the report, which was published on Thursday.

Power grid operation is becoming increasingly complex due to the rising integration of renewable energy sources and the need for more adaptive control strategies. Reinforcement Learning (RL) has emerged as a promising approach to power network control (PNC), offering the potential to enhance decision-making in dynamic and uncertain environments. The Learning To Run a Power Network (L2RPN) competitions have played a key role in accelerating research by providing standardized benchmarks and problem formulations, leading to rapid advancements in RL-based methods. This survey provides a comprehensive and structured overview of RL applications for power grid topology optimization, categorizing existing techniques, highlighting key design choices, and identifying gaps in current research. Additionally, we present a comparative numerical study evaluating the impact of commonly applied RL-based methods, offering insights into their practical effectiveness. By consolidating existing research and outlining open challenges, this survey aims to provide a foundation for future advancements in RL-driven power grid optimization.

We propose an innovative design for an all-optical Echo State Network (ESN), an advanced type of reservoir computer known for its universal computational capabilities. Our design enables fully optical implementation of arbitrary ESNs, featuring complete flexibility in optical matrix multiplication and nonlinear activation. Leveraging the nonlinear characteristics of stimulated Brillouin scattering (SBS), the architecture efficiently realizes measurement-free operations crucial for reservoir computing. The approach significantly reduces computational overhead and energy consumption compared to traditional software-based methods. Comprehensive simulations validate the system's memory capacity, nonlinear processing strength, and polynomial algebra capabilities, showcasing performance comparable to software ESNs across key benchmark tasks. Our design establishes a feasible, scalable, and universally applicable framework for optical reservoir computing, suitable for diverse machine learning applications.