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

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Anthropic

Samsung’s 2TB portable SSD is currently 48% off

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Honor integrates DeepSeek into its YOYO Assistant

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Realme GT7 Pro Racing Edition launches in China on February 13

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The RAM, storage and colors of the Xiaomi 15 Ultra global...

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Apple AirPods Pro 2 are at a record low, but stock...

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Super Bowl 2025 Official Ads are on Your TV Screen Today.

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OpenAI CEO Sam Altman admits that AI’s benefits may not be...

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AI Briefing: Big Tech announces another quarter’s earnings from AI

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Can Le Chat, a mobile app from French AI startup Mistral,...

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CourtAvenue’s newest acquisition dives into bots –

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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...