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

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This Week in AI

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DeepSeek R1 and OpenAI Deep Research have redefined AI. RAG, distillation...

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Anthropic

More live photos of Oppo Find N5

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This $100 Android phone reminded me of the Pixel 9 Pro

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Why I prefer these Shokz headphones to the AirPods Pro when...

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SoftBank woos OpenAI for $40B, making Microsoft’s $13B seem quaint

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Safaricom enters AI race with FarmerAI, a new AI chatbot for...

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Hacking of 20 million OpenAI users? Here’s a guide to staying...

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Craft’s latest update may change the way you use AI on...

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AI pioneer Fei Fei Li says AI policies must be based...

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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...