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Google researchers release AI-driven framework for mapping nature restoration efforts

Google’s new AI framework aims to simplify nature restoration by translating raw satellite pixels into actionable ecosystem data, though field-scale reliability remains unproven.

#ArtificialIntelligence#EarthScience#ConservationTech

Jun 18, 20261m readAI

The latest research from Google+1

Zhipu AI releases GLM-5.2 with focus on long-horizon reasoning

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🤖 Technology

Zhipu AI releases GLM-5.2 with focus on long-horizon reasoning

Zhipu AI’s new GLM-5.2 model aims to solve long-horizon reasoning challenges, though performance in extended, multi-turn workflows remains an open question for independent testers.

#ArtificialIntelligence#GLM52#MachineLearning

Jun 17, 20261m readAI

Hugging Face - Blog+1

PixelRAG framework enables visual-based indexing for document retrieval

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🤖 Technology

PixelRAG framework enables visual-based indexing for document retrieval

A new pipeline called PixelRAG aims to improve document search by treating files as images, bypassing traditional text-parsing issues but introducing new computational trade-offs.

Fastino releases GLiNER2.5 for optimized entity recognition

Fastino's new GLiNER2.5 aims to lower the computational cost of entity extraction, but real-world performance benchmarks against legacy NER tools are still pending.

Aug 24, 20261m readAI

MarkTechPost

Google releases TimesFM-3, a 330M parameter model for multivariate time series

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🤖 Technology

Google releases TimesFM-3, a 330M parameter model for multivariate time series

Google Research has unveiled TimesFM-3, a 330M parameter model built for multivariate forecasting. We examine how it differs from previous iterations and the performance questions that remain.

Aug 31, 20261m readAI

MarkTechPost+1

LLMs show a consistent, biased preference for the number 42 when prompted

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💼 Business & Finance

LLMs show a consistent, biased preference for the number 42 when prompted

New analysis shows that top AI models consistently favor the number 42 when asked for a random choice, raising questions about training data bias and the limits of machine randomness.

Jul 25, 20261m readAI

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