TJS Articles & News

MeteorPred: A Meteorological Multimodal Large Model and Dataset for Severe Weather Event Predictioncs.AI updates on arXiv.org arXiv:2508.06859v2 Announce Type: replace
Abstract: Timely and accurate forecasts of severe weather events are essential for early warning and for constraining downstream analysis and decision-making. Since severe weather events prediction still depends on subjective, time-consuming expert interpretation, end-to-end “AI weather station” systems are emerging but face three major challenges: (1) scarcity of severe weather event samples; (2) imperfect alignment between high-dimensional meteorological data and textual warnings; (3) current multimodal language models cannot effectively process high-dimensional meteorological inputs or capture their complex spatiotemporal dependencies. To address these challenges, we introduce MP-Bench, the first large-scale multimodal dataset for severe weather events prediction, comprising 421,363 pairs of raw multi-year meteorological data and corresponding text caption, covering a wide range of severe weather scenarios. On top of this dataset, we develop a Meteorology Multimodal Large Model (MMLM) that directly ingests 4D meteorological inputs. In addition, it is designed to accommodate the unique characteristics of 4D meteorological data flow, incorporating three plug-and-play adaptive fusion modules that enable dynamic feature extraction and integration across temporal sequences, vertical pressure layers, and spatial dimensions. Extensive experiments on MP-Bench show that MMLM achieves strong performance across multiple tasks, demonstrating effective severe weather understanding and representing a key step toward automated, AI-driven severe weather events forecasting systems. Our source code and dataset will be made publicly available.

 arXiv:2508.06859v2 Announce Type: replace
Abstract: Timely and accurate forecasts of severe weather events are essential for early warning and for constraining downstream analysis and decision-making. Since severe weather events prediction still depends on subjective, time-consuming expert interpretation, end-to-end “AI weather station” systems are emerging but face three major challenges: (1) scarcity of severe weather event samples; (2) imperfect alignment between high-dimensional meteorological data and textual warnings; (3) current multimodal language models cannot effectively process high-dimensional meteorological inputs or capture their complex spatiotemporal dependencies. To address these challenges, we introduce MP-Bench, the first large-scale multimodal dataset for severe weather events prediction, comprising 421,363 pairs of raw multi-year meteorological data and corresponding text caption, covering a wide range of severe weather scenarios. On top of this dataset, we develop a Meteorology Multimodal Large Model (MMLM) that directly ingests 4D meteorological inputs. In addition, it is designed to accommodate the unique characteristics of 4D meteorological data flow, incorporating three plug-and-play adaptive fusion modules that enable dynamic feature extraction and integration across temporal sequences, vertical pressure layers, and spatial dimensions. Extensive experiments on MP-Bench show that MMLM achieves strong performance across multiple tasks, demonstrating effective severe weather understanding and representing a key step toward automated, AI-driven severe weather events forecasting systems. Our source code and dataset will be made publicly available. Read More  

Are AI Browsers Any Good? A Day with Perplexity’s Comet and OpenAI’s AtlasKDnuggets Understanding the underlying technology helps explain why AI browsers exhibit such uneven performance.

 Understanding the underlying technology helps explain why AI browsers exhibit such uneven performance. Read More  

RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture RadarTowards Data Science The high-resolution physics turning microwave echoes into real-time flood intelligence
The post RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar appeared first on Towards Data Science.

 The high-resolution physics turning microwave echoes into real-time flood intelligence
The post RISAT’s Silent Promise: Decoding Disasters with Synthetic Aperture Radar appeared first on Towards Data Science. Read More  

5 Excel AI Lessons I Learned the Hard WayKDnuggets This article transforms the unwelcome experiences into five comprehensive frameworks that will elevate your Excel-based machine learning work.

 This article transforms the unwelcome experiences into five comprehensive frameworks that will elevate your Excel-based machine learning work. Read More  

New Microsoft cloud updates support Indonesia’s long-term AI goalsAI News Indonesia’s push into AI-led growth is gaining momentum as more local organisations look for ways to build their own applications, update their systems, and strengthen data oversight. The country now has broader access to cloud and AI tools after Microsoft expanded the services available in the Indonesia Central cloud region, which first went live six
The post New Microsoft cloud updates support Indonesia’s long-term AI goals appeared first on AI News.

 Indonesia’s push into AI-led growth is gaining momentum as more local organisations look for ways to build their own applications, update their systems, and strengthen data oversight. The country now has broader access to cloud and AI tools after Microsoft expanded the services available in the Indonesia Central cloud region, which first went live six
The post New Microsoft cloud updates support Indonesia’s long-term AI goals appeared first on AI News. Read More  

Black Forest Labs Releases FLUX.2: A 32B Flow Matching Transformer for Production Image PipelinesMarkTechPost Black Forest Labs has released FLUX.2, its second generation image generation and editing system. FLUX.2 targets real world creative workflows such as marketing assets, product photography, design layouts, and complex infographics, with editing support up to 4 megapixels and strong control over layout, logos, and typography. FLUX.2 product family and FLUX.2 [dev] The FLUX.2 family
The post Black Forest Labs Releases FLUX.2: A 32B Flow Matching Transformer for Production Image Pipelines appeared first on MarkTechPost.

 Black Forest Labs has released FLUX.2, its second generation image generation and editing system. FLUX.2 targets real world creative workflows such as marketing assets, product photography, design layouts, and complex infographics, with editing support up to 4 megapixels and strong control over layout, logos, and typography. FLUX.2 product family and FLUX.2 [dev] The FLUX.2 family
The post Black Forest Labs Releases FLUX.2: A 32B Flow Matching Transformer for Production Image Pipelines appeared first on MarkTechPost. Read More  

Estimating LLM Consistency: A User Baseline vs Surrogate Metricscs.AI updates on arXiv.org arXiv:2505.23799v4 Announce Type: replace-cross
Abstract: Large language models (LLMs) are prone to hallucinations and sensitive to prompt perturbations, often resulting in inconsistent or unreliable generated text. Different methods have been proposed to mitigate such hallucinations and fragility, one of which is to measure the consistency of LLM responses — the model’s confidence in the response or likelihood of generating a similar response when resampled. In previous work, measuring LLM response consistency often relied on calculating the probability of a response appearing within a pool of resampled responses, analyzing internal states, or evaluating logits of responses. However, it was not clear how well these approaches approximated users’ perceptions of consistency of LLM responses. To find out, we performed a user study ($n=2,976$) demonstrating that current methods for measuring LLM response consistency typically do not align well with humans’ perceptions of LLM consistency. We propose a logit-based ensemble method for estimating LLM consistency and show that our method matches the performance of the best-performing existing metric in estimating human ratings of LLM consistency. Our results suggest that methods for estimating LLM consistency without human evaluation are sufficiently imperfect to warrant broader use of evaluation with human input; this would avoid misjudging the adequacy of models because of the imperfections of automated consistency metrics.

 arXiv:2505.23799v4 Announce Type: replace-cross
Abstract: Large language models (LLMs) are prone to hallucinations and sensitive to prompt perturbations, often resulting in inconsistent or unreliable generated text. Different methods have been proposed to mitigate such hallucinations and fragility, one of which is to measure the consistency of LLM responses — the model’s confidence in the response or likelihood of generating a similar response when resampled. In previous work, measuring LLM response consistency often relied on calculating the probability of a response appearing within a pool of resampled responses, analyzing internal states, or evaluating logits of responses. However, it was not clear how well these approaches approximated users’ perceptions of consistency of LLM responses. To find out, we performed a user study ($n=2,976$) demonstrating that current methods for measuring LLM response consistency typically do not align well with humans’ perceptions of LLM consistency. We propose a logit-based ensemble method for estimating LLM consistency and show that our method matches the performance of the best-performing existing metric in estimating human ratings of LLM consistency. Our results suggest that methods for estimating LLM consistency without human evaluation are sufficiently imperfect to warrant broader use of evaluation with human input; this would avoid misjudging the adequacy of models because of the imperfections of automated consistency metrics. Read More  

Amazon SageMaker AI introduces EAGLE based adaptive speculative decoding to accelerate generative AI inferenceArtificial Intelligence Amazon SageMaker AI now supports EAGLE-based adaptive speculative decoding, a technique that accelerates large language model inference by up to 2.5x while maintaining output quality. In this post, we explain how to use EAGLE 2 and EAGLE 3 speculative decoding in Amazon SageMaker AI, covering the solution architecture, optimization workflows using your own datasets or SageMaker’s built-in data, and benchmark results demonstrating significant improvements in throughput and latency.

 Amazon SageMaker AI now supports EAGLE-based adaptive speculative decoding, a technique that accelerates large language model inference by up to 2.5x while maintaining output quality. In this post, we explain how to use EAGLE 2 and EAGLE 3 speculative decoding in Amazon SageMaker AI, covering the solution architecture, optimization workflows using your own datasets or SageMaker’s built-in data, and benchmark results demonstrating significant improvements in throughput and latency. Read More  

Manufacturing’s pivot: AI as a strategic driverAI News Manufacturers today are working against rising input costs, labour shortages, supply-chain fragility, and pressure to offer more customised products. AI is becoming an important part of a response to those pressures. When enterprise strategy depends on AI Most manufacturers seek to reduce cost while improving throughput and quality. AI supports these aims by predicting equipment
The post Manufacturing’s pivot: AI as a strategic driver appeared first on AI News.

 Manufacturers today are working against rising input costs, labour shortages, supply-chain fragility, and pressure to offer more customised products. AI is becoming an important part of a response to those pressures. When enterprise strategy depends on AI Most manufacturers seek to reduce cost while improving throughput and quality. AI supports these aims by predicting equipment
The post Manufacturing’s pivot: AI as a strategic driver appeared first on AI News. Read More