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Glacial Lake Outburst Floods (GLOFs) are rare but destructive hazards in high mountain regions, yet predictive research is hindered by fragmented and unimodal data. Most prior efforts emphasize post-event mapping, whereas forecasting requires harmonized datasets that combine visual indicators with physical precursors. We present GLOFNet, a multimodal dataset for GLOF monitoring and prediction, focused on the Shisper Glacier in the Karakoram. It integrates three complementary sources: Sentinel-2 multispectral imagery for spatial monitoring, NASA ITS_LIVE velocity products for glacier kinematics, and MODIS Land Surface Temperature records spanning over two decades. Preprocessing included cloud masking, quality filtering, normalization, temporal interpolation, augmentation, and cyclical encoding, followed by harmonization across modalities. Exploratory analysis reveals seasonal glacier velocity cycles, long-term warming of ~0.8 K per decade, and spatial heterogeneity in cryospheric conditions. The resulting dataset, GLOFNet, is publicly available to support future research in glacial hazard prediction. By addressing challenges such as class imbalance, cloud contamination, and coarse resolution, GLOFNet provides a structured foundation for benchmarking multimodal deep learning approaches to rare hazard prediction.

While large language models (LLMs) are widely evaluated for reasoning in English, their abilities in Indic languages remain understudied, limiting our understanding of whether these models generalize across languages. To address this gap, we introduce a new Hindi Analogy Test Set (HATS), comprising 405 multiple-choice questions sourced from Indian government exams. We benchmark state-of-the-art multilingual LLMs using various prompting strategies and introduce a grounded Chain of Thought approach that leverages cognitive theories of analogical reasoning. This approach improves model performance on Hindi analogy questions. Our experiments show that models perform best with English prompts, irrespective of the prompting strategy. Our test set addresses the lack of a critical resource to evaluate LLM reasoning capabilities in Hindi. The test set is publicly available for research purposes here https://github.com/Inequilazitive/

Mitigating the hallucinations of Large Language Models (LLMs) and enhancing them is a crucial task. Although some existing methods employ model self-enhancement techniques, they fall short of effectively addressing unknown factual hallucinations. Using Knowledge Graph (KG) enhancement approaches fails to address the generalization across different KG sources and the enhancement of open-ended answer questions simultaneously. To tackle these limitations, there is a framework that combines Pseudo-Graph Generation and Atomic Knowledge Verification proposed. The enhancement of LLM using KG in an open-ended question-answering setting is implemented by leveraging the Pseudo-Graph Generation. Atomic Knowledge Verification utilizes atomic-level knowledge querying and verification to achieve generalizability under different KG sources. Compared to the baseline, this approach yields a minimum improvement of 11.5 in the ROUGE-L score for open-ended questions. For precise questions, we observe a minimum accuracy improvement of 7.5. Moreover, there is also demonstration that this framework exhibits generalizability across different KG sources. In summary, our results pave the way for enhancing LLMs by incorporating Pseudo- and Multisource-KGs, particularly in the context of open-ended questions.

With AskSid's retail AI brain serving business innovation, we have supported a host of global brands including AkzoNobel, Danone, Wolford, Akris, and Himalaya in improving marketing efficacy, launching new product lines, and expanding to new markets, all led by actionable insights. With live implementations across 23 countries and supporting 100 international languages, AskSid embodies the essence of the retail industry and is driving change in the way brands meet their customers going forward. This infographic brings together every milestone, every AI advancement, and piece of news that kept us going in 2021. AskSid is a global conversational solutions company that partners with retail brands to elevate shopping experiences by leveraging the power of Artificial Intelligence. Our capabilities extend beyond support automation, extending to insight extraction, analysis, and business opportunity generation, all by leveraging the power of conversational data.

While most of China was quarantined and Mount Everest was closed to climbers due to COVID-19, a herd of nearly 50 yaks made their way up the snowy north slopes of the world's highest mountain in temperatures that dipped below zero degrees Fahrenheit. On their backs were loads of equipment--metal beams, cables, and solar panels strapped down with cord--that would be used to build 5G antennas on rocky moraines scattered across the mountainside. Chinese tech giant Huawei and state-owned network provider China Mobile teamed up for this project to bring the latest in wireless data to Everest, which previously had very little cell coverage above base camp. Now, data speeds in the "death zone" on Everest, where the altitude is too high and the air is too thin to support life, are faster than in most American neighborhoods. In a press release, Huawei stated that the new super-fast data speeds on Everest will be used for "smart tourism"--with high-definition video streaming and virtual reality experiences for digital tourists to "visit" Everest from anywhere in the world.

Their work is published in Environmental Research Letters. At the southern tip of the Himalayas, farmers in Himachal Pradesh graze cattle among rolling hills and forests. While policies to manage the region's forest have been but in place by the Indian government, the impact that these policies have had remains unclear. In this study, scientists led by Dr. Pushpendra Rana at the University of Illinois applied machine learning algorithms to examine natural resources policy and governance, evaluating how policies actually work on the ground. Using satellite images from NASA, Rana's machine learning algorithm was able to simultaneously evaluate policy effectiveness in over 200 forest management regions in Kangra, covering a 14-year period.