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

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The Download: Google Project Astra and China’s Export Bans

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Google Deepmind’s new forecaster is better than the competition

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Altman admits that ChatGPT Pro is struggling to make a profit...

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Nvidia’s RTX-5090 with 32GB GDDR7 Memory

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Rumors suggest that next-gen RTX50 GPUs will have big jumps in...

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Apple AI Yao Qiu Xi Jie ,Jiu Ji Wei ,7GB Chu...

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Small language models: 10 Breakthrough Technologies by 2025

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GPT-5 has a problem that could slow the advance of Artificial...

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From January One Magyarorszag Zrt. Vodafone Hungary continues to work under...

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Blackwell before the launch: The Geforce RTX 5090 should need 575...

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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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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...