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Huawei Introduces Pangu Ultra MoE: A 718B-Parameter Sparse Language Model Trained...

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S Pen could lose Bluetooth in the Galaxy S25 Ultra :...

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Nvidia is bringing a new PC generation, and it will run...

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NVIDIA announced that DLSS 4 would be available on all RTX...

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Computer Vision

NEC and Biomy Partner in the Development and Expansion of AI-Based...

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Google DeepMind researchers introduce a new benchmark to improve LLM factuality...

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OpenAI has started building out its robotics teams

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Elon Musk wants the courts to force OpenAI into auctioning off...

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Alibaba Cloud’s Tongyi Lingma Artificial Intelligence Programmer is fully online

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Samsung Galaxy S25 could be subject to an unwelcome increase in...

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News Roundup: Meta’s Content Shakeup, Nvidia Gaming Revolution, and more

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A Coding Implementation of Accelerating Active Learning Annotation with Adala and...

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LightOn AI Released GTE-ModernColBERT-v1: A Scalable Token-Level Semantic Search Model for...

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This AI Paper Introduces Effective State-Size (ESS): A Metric to Quantify...

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Technology

Dream 7B: How Diffusion-Based Reasoning Models Are Reshaping AI

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A Coding Implementation of Accelerating Active Learning Annotation with Adala and...

In this tutorial, we’ll learn how to leverage the framework to build a modular active learning pipeline for medical symptom classification. We begin by installing and verifying Adala alongside required dependencies, then integrate Google Gemini as a custom annotator to categorize symptoms into predefined medical domains. Through a...