The carnivores are particularly vulnerable to rising sea temperatures. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The bears are gathering in the Polar Bear Capital of the World. Polar bears () are typically solitary creatures. But in a Canadian town on Hudson Bay, the predators gather along the coast every fall to wait for the ice to freeze.

Owner of US tech giant reveals breach of one of world's most powerful AI models. Reports of unauthorised access to one of the most powerful Artificial Intelligence models yet developed have emerged. Nothing malicious, say the owners - but it has intensified focus on such technology falling into the wrong hands. So, how is AI being controlled globally? Will complex EU loan deal intensify conflict?

Rare polar bear adoption could save cub's life The cubs were born into a well-studied'celebration' of polar bears in Canada. Breakthroughs, discoveries, and DIY tips sent every weekday. Scientists in Churchill, Manitoba, Canada (aka the polar bear capital of the world) have confirmed that a wild female polar bear has adopted a cub that is not her own. This rare behavior was captured on cameras during the polar bear's annual migration along Western Hudson Bay . Researchers from Environment and Climate Change Canada and Polar Bears International spotted the mother bear (designated as bear X33991) during spring 2025, when she came out of her maternity den.

We start by exploring strategies for eliciting explicit, coherent numerical predictive distributions from LLMs.

Sea ice plays a critical role in the global climate system and maritime operations, making timely and accurate classification essential. However, traditional manual methods are time-consuming, costly, and have inherent biases. Automating sea ice type classification addresses these challenges by enabling faster, more consistent, and scalable analysis. While both traditional and deep learning approaches have been explored, deep learning models offer a promising direction for improving efficiency and consistency in sea ice classification. However, the absence of a standardized benchmark and comparative study prevents a clear consensus on the best-performing models. To bridge this gap, we introduce \textit{IceBench}, a comprehensive benchmarking framework for sea ice type classification. Our key contributions are threefold: First, we establish the IceBench benchmarking framework which leverages the existing AI4Arctic Sea Ice Challenge dataset as a standardized dataset, incorporates a comprehensive set of evaluation metrics, and includes representative models from the entire spectrum of sea ice type classification methods categorized in two distinct groups, namely, pixel-based classification methods and patch-based classification methods. IceBench is open-source and allows for convenient integration and evaluation of other sea ice type classification methods; hence, facilitating comparative evaluation of new methods and improving reproducibility in the field. Second, we conduct an in-depth comparative study on representative models to assess their strengths and limitations, providing insights for both practitioners and researchers. Third, we leverage IceBench for systematic experiments addressing key research questions on model transferability across seasons (time) and locations (space), data downscaling, and preprocessing strategies.

In recent research advancements within the community, large language models (LLMs) have sparked great interest in creating autonomous agents. However, current prompt-based agents often heavily rely on large-scale LLMs. Meanwhile, although fine-tuning methods significantly enhance the capabilities of smaller LLMs, the fine-tuned agents often lack the potential for self-reflection and self-improvement. To address these challenges, we introduce a novel agent framework named RetroAct, which is a framework that jointly optimizes both task-planning and self-reflective evolution capabilities in language agents. Specifically, we develop a two-stage joint optimization process that integrates imitation learning and reinforcement learning, and design an off-policy joint policy gradient optimization algorithm with imitation learning regularization to enhance the data efficiency and training stability in agent tasks. RetroAct significantly improves the performance of open-source models, reduces dependency on closed-source LLMs, and enables fine-tuned agents to learn and evolve continuously. We conduct extensive experiments across various testing environments, demonstrating RetroAct has substantial improvements in task performance and decision-making processes.

Can simplifying AI rules in Europe create competition for US and China? Can simplifying AI rules in Europe create competition for US and China? Europe to cut red tape to make artificial intelligence advancements easier.Read more The Artificial Intelligence Action Summit in Paris has drawn nearly 100 world leaders and tech firms, and the consensus is that 2025 is not the year for new AI regulations. France says it is time to simplify the rules in Europe to allow AI advances – or risk being left behind. Which countries have banned DeepSeek and why? list 2 of 3 Elon Musk-led group makes 97.4bn bid for OpenAI list 3 of 3 In January, Chinese start-up DeepSeek disrupted Wall Street and Silicon Valley.