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

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Experts say that Trump revoking Biden’s AI EO will cause chaos...

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ServiceNow launches enterprise AI governance capabilities

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Tips for ChatGPT Voice Mode? What are the best AI uses...

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More and more young people are choosing the agricultural profession, and...

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Top Five Chinese EV startups: Li Auto Leads and Xiaomi Gaining...

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MSI Afterburner prepares for GeForce RTX5080 with expanded support for fan...

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Apple AirDrop for Android? It Sounds Like A Dream That Will...

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Would you like to have Apple AirDrop on your Android phone?...

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The smart glasses can be purchased for as little as $295...

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ChatGPT continues its dominance, but this Google AI Tool is gaining...

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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...