Greenland'will stay Greenland', former Trump adviser declares Donald Trump will not be able to force Greenland to change ownership, a former top adviser to the US president has told the BBC. IBM's vice chairman Gary Cohn, who advised Trump on the economy in his first term, said Greenland will stay Greenland and linked the need for access to critical minerals to his former boss's plans for the territory. Cohn is one of America's top tech bosses, a leader in the race to develop AI and quantum computing, and served under Trump as director of the White House National Economic Council. In a sign of how seriously business leaders are taking the crisis, he warned invading an independent country that is part of Nato would be over the edge. He also suggested the president's recent comments about Greenland may be part of a negotiation.

Feedback comes across a YouTuber's efforts to build a large language model in Minecraft and is impressed at the scale of it - even if it doesn't quite live up to its promise to blow your mind in spectacular fashion There are few things Feedback appreciates more than a truly committed hobbyist: someone who happily spends months or even years building something that is of no practical use whatsoever, just to be able to look at it or play with it. For those who might be unfamiliar, Minecraft is an open-world game in which everything is made up of cubical blocks. Players dig into the ground to collect cubes of useful minerals, which they can use to build things. For instance, they might build a house so that the monsters that come out at night can't get them. Or they might go big.

Autonomous Raman instruments on Mars rovers, deep-sea landers, and field robots must interpret raw spectra distorted by fluorescence baselines, peak shifts, and limited ground-truth labels. Using curated subsets of the RRUFF database, we evaluate one-dimensional convolutional neural networks (CNNs) and report four advances: (i) Baseline-independent classification: compact CNNs surpass $k$-nearest-neighbors and support-vector machines on handcrafted features, removing background-correction and peak-picking stages while ensuring reproducibility through released data splits and scripts. (ii) Pooling-controlled robustness: tuning a single pooling parameter accommodates Raman shifts up to $30 \,\mathrm{cm}^{-1}$, balancing translational invariance with spectral resolution. (iii) Label-efficient learning: semi-supervised generative adversarial networks and contrastive pretraining raise accuracy by up to $11\%$ with only $10\%$ labels, valuable for autonomous deployments with scarce annotation. (iv) Constant-time adaptation: freezing the CNN backbone and retraining only the softmax layer transfers models to unseen minerals at $\mathcal{O}(1)$ cost, outperforming Siamese networks on resource-limited processors. This workflow, which involves training on raw spectra, tuning pooling, adding semi-supervision when labels are scarce, and fine-tuning lightly for new targets, provides a practical path toward robust, low-footprint Raman classification in autonomous exploration.

Evaluating large language models (LLMs) in complex decision-making is essential for advancing AI's ability for strategic planning and real-time adaptation. However, existing benchmarks for tasks like StarCraft II fail to capture the game's full complexity, such as its complete game context, diverse action spaces, and all playable races. To address this gap, we present SC2Arena, a benchmark that fully supports all playable races, low-level action spaces, and optimizes text-based observations to tackle spatial reasoning challenges. Complementing this, we introduce StarEvolve, a hierarchical framework that integrates strategic planning with tactical execution, featuring iterative self-correction and continuous improvement via fine-tuning on high-quality game-play data. Its key components include a Planner-Executor-V erifier structure to break down gameplay, and a scoring system for selecting high-quality training samples. Comprehensive analysis using SC2Arena provides valuable insights into developing generalist agents that were not possible with previous benchmarks. Experimental results also demonstrate that our proposed StarEvolve achieves superior performance in strategic planning. Our code, environment, and algorithms are publicly available.

NASA's Curiosity rover snapped a bizarre, coral-shaped rock on the surface of Mars, sparking fresh speculation about signs of ancient life on the Red Planet. The twisted, alien-like formation was sculpted by wind and time, according to NASA, which said it likely formed billions of years ago when water once flowed across the Martian surface. The images have taken the internet by storm, with some users claiming: 'Corals are true signs of ancient life forms along with the ancient rivers. This is a huge discovery!!' Another wrote on X: 'There's your Mars fossilized foreign life material evidence everybody's been asking for. That's obviously been there all along.'

New discoveries in c hemistry and materials science, with increasingly expanding volume of requisite knowledge and experimental workload, provide unique opportunities for machine learning (ML) to take critical roles in accelerat ing research efficiency . Here, we demonstrate (1) the use of large language models (LLMs) for automated literature reviews, and (2) the training of an ML model to predict chemical knowledge (thermodynamic parameters) . Our LLM - based literature review tool (LMExt) successfully extracted chemical information and beyond into a machine - readable structure, including stability constants for metal cation - ligand interactions, thermodynamic properties, and other broader data types ( medical research papers, and financial reports), effectively overcoming the challenges inherent in each domain. Using the autonomous acquisition of thermodynamic data, an ML model was trained using the CatBoost algorithm for accurately predict ing thermodynamic parameters (e.g., enthalpy of formation) of minerals. This work highlights the transformative potential of integrated ML approaches to reshape chemistry and materials science research . Keywords: Thermodynamics, Machine L earning, Large Language Model, D ata M ining, Database Introduction Chemi cal thermodynamics are fundamental for understanding chemical reactions, proposing novel methods to control these reactions, and pred icting chemical equilibria /reactions for new materials. Although scientific breakthroughs occur regularly, contributing to these advances becomes progressively more complex. T ypical research project necessitates a comprehensive literature review that should cover the current state of the field and identify knowledge gaps . Subsequently, rigorous experimentation and modeling are performed to fill such gaps or check hypothesis - driven predictions . Both these steps are essential research steps not unique in chemical research, which however, are inherently mentally - intensive and time - consuming .

It's one of the most profound questions in science – did life ever exist on Mars? Now, experts have unearthed evidence that the Red Planet was once habitable. Scientists have found carbon residue in Martian rocks, indicating that an ancient carbon cycle existed. And it means the Red Planet was likely once warm enough to sustain life. Researchers have long believed that, billions of years ago, Mars had a thick, carbon dioxide-rich atmosphere with liquid water on its surface.

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.

FIRST ON FOX: The Department of Energy, the Department of the Interior and the Environmental Protection Agency are set to announce a bevy of new actions Tuesday afternoon that will "unleash" coal energy following President Donald Trump's expected signature on an executive order reinvigorating "America's beautiful clean coal industry," Fox News Digital learned. "The American people need more energy, and the Department of Energy is helping to meet this demand by unleashing supply of affordable, reliable, secure energy sources -- including coal," Department of Energy Secretary Chris Wright said in a Tuesday statement provided to Fox News Digital. "Coal is essential for generating 24/7 electricity generation that powers American homes and businesses, but misguided policies from previous administrations have stifled this critical American industry," he said. "With President Trump's leadership, we are cutting the red tape and bringing back common sense." Trump is expected to sign an executive order Tuesday afternoon that will cut through red tape surrounding the coal industry, including directing the National Energy Dominance Council to designate coal as a "mineral," end a current pause to coal leasing on federal lands, promote coal and coal technology exports, and encourage the use of coal to power artificial intelligence initiatives, Fox News Digital learned of the upcoming executive order.

This paper presents a fully autonomous robotic system that performs sim-to-real transfer in complex long-horizon tasks involving navigation, recognition, grasping, and stacking in an environment with multiple obstacles. The key feature of the system is the ability to overcome typical sensing and actuation discrepancies during sim-to-real transfer and to achieve consistent performance without any algorithmic modifications. To accomplish this, a lightweight noise-resistant visual perception system and a nonlinearity-robust servo system are adopted. We conduct a series of tests in both simulated and real-world environments. The visual perception system achieves the speed of 11 ms per frame due to its lightweight nature, and the servo system achieves sub-centimeter accuracy with the proposed controller. Both exhibit high consistency during sim-to-real transfer. Benefiting from these, our robotic system took first place in the mineral searching task of the Robotic Sim2Real Challenge hosted at ICRA 2024.