Researchers have succeeded in developing the smallest fully autonomous robot in history. It measures less than 1 millimeter and can swim underwater for months powered only by light. Miniaturization has long been a challenge in the history of robotics . While engineers have made great strides in the miniaturization of electronics in the past few decades, builders of miniature autonomous robots have not been able to meet the goal of getting them under 1 millimeter in size. This is because small arms and legs are fragile and difficult to manufacture.

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The complexity of Structured Query Language (SQL) and the specialized nature of geospatial functions in tools like PostGIS present significant barriers to non-experts seeking to analyze spatial data. While Large Language Models (LLMs) offer promise for translating natural language into SQL (Text-to-SQL), single-agent approaches often struggle with the semantic and syntactic complexities of spatial queries. To address this, we propose a multi-agent framework designed to accurately translate natural language questions into spatial SQL queries. The framework integrates several innovative components, including a knowledge base with programmatic schema profiling and semantic enrichment, embeddings for context retrieval, and a collaborative multi-agent pipeline as its core. This pipeline comprises specialized agents for entity extraction, metadata retrieval, query logic formulation, SQL generation, and a review agent that performs programmatic and semantic validation of the generated SQL to ensure correctness (self-verification). We evaluate our system using both the non-spatial KaggleDBQA benchmark and a new, comprehensive SpatialQueryQA benchmark that includes diverse geometry types, predicates, and three levels of query complexity. On KaggleDBQA, the system achieved an overall accuracy of 81.2% (221 out of 272 questions) after the review agent's review and corrections. For spatial queries, the system achieved an overall accuracy of 87.7% (79 out of 90 questions), compared with 76.7% without the review agent. Beyond accuracy, results also show that in some instances the system generates queries that are more semantically aligned with user intent than those in the benchmarks. This work makes spatial analysis more accessible, and provides a robust, generalizable foundation for spatial Text-to-SQL systems, advancing the development of autonomous GIS.

Researchers are closer to equipping a dog-like robot to conduct science on the surface of Mars after five days of experiments this month at White Sands National Park in New Mexico. The national park is serving as a Mars analog environment and the scientists are conducting field test scenarios to inform future Mars operations with astronauts, dog-like robots known as quadruped robots, rovers and scientists at Mission Control on Earth. The work builds on similar experiments by the team with the same robot on the slopes of Mount Hood in Oregon, which simulated the landscape on the Moon. "Our group is very committed to putting quadrupeds on the Moon and on Mars," said Cristina Wilson, a robotics researcher in the College of Engineering at Oregon State University. "It's the next frontier and takes advantage of the unique capabilities of legged robots."

Prompting methods for language models, such as Chain-of-thought (CoT), present intuitive step-by-step processes for problem solving. These methodologies aim to equip models with a better understanding of the correct procedures for addressing a given task. Despite these advancements, CoT lacks the ability of reflection and error correction, potentially causing a model to perpetuate mistakes and errors. Therefore, inspired by the human ability for said tasks, we propose Error Reflection Prompting (ERP) to further enhance reasoning in language models. Building upon CoT, ERP is a method comprised of an incorrect answer, error recognition, and a correct answer. This process enables the model to recognize types of errors and the steps that lead to incorrect answers, allowing the model to better discern which steps to avoid and which to take. The model is able to generate the error outlines itself with automated ERP generation, allowing for error recognition and correction to be integrated into the reasoning chain and produce scalability and reliability in the process. The results demonstrate that ERP serves as a versatile supplement to conventional CoT, ultimately contributing to more robust and capable reasoning abilities along with increased interpretability in how models ultimately reach their errors.

Google has agreed to secure as much as 3GW of US hydropower in the world's largest corporate clean power pact for hydroelectricity, the company said on Tuesday, as big tech pursues the expansion of energy-hungry datacenters. The deal between Google and Brookfield Asset Management includes initial 20-year power purchase agreements, totaling 3bn, for electricity generated from two hydropower facilities in Pennsylvania. The tech giant will also invest 25bn in datacenters across Pennsylvania and neighboring states over the next two years, Semafor reported on Tuesday. The technology industry is intensifying the hunt for huge amounts of clean electricity to power datacenters needed for artificial intelligence and cloud computing, which has driven US power consumption to record highs after nearly two decades of stagnation. Ruth Porat, president and chief investment officer at Google parent company Alphabet, discussed the news at an AI summit in Pittsburgh.

Donald Trump joined big oil and technology bosses on Tuesday at a major artificial intelligence and energy summit in Pittsburgh, outraging environmentalists and community organizations. The event came weeks after the passage of a mega-bill that experts say could stymy AI growth with its attacks on renewable energy. "We're here today because we believe that America's destiny is to dominate every industry and be the first in every technology, and that includes being the world's number one superpower in artificial intelligence," said Trump. The inaugural Pennsylvania energy and innovation summit, held at Carnegie Mellon University, is an attempt to position the state as an AI leader, showcasing the technological innovation being developed in the city and the widespread availability of fossil fuel reserves to power them. At the gathering, Trump announced 70bn in AI and energy investments for the state, Axios first reported, in a move the event's host, the Republican Pennsylvania senator, Dave McCormick, says will be a boon to local economies.

Fox News chief national security correspondent Jennifer Griffin reports on what the United States and Israel are doing to stay ahead of adversaries in A.I. on'Special Report.' Today, something big and unprecedented is happening in Pittsburgh. The inaugural Pennsylvania Energy and Innovation summit at Carnegie Mellon University is the clearest and most dramatic manifestation yet of President Donald Trump's promises to make America energy dominant, lead in advanced technology, and create jobs and opportunity for working families in Pennsylvania and across America. In 2017, Mr. Trump said he was "elected to represent the citizens of Pittsburgh, not Paris." Today in the Steel City, I am proud to welcome the President and more than 60 CEOs of the world's most important companies and largest investors to my hometown to announce over 50 billion in new investments in energy, artificial intelligence (AI), and workforce development all targeted at making sure Pennsylvania powers the AI revolution.