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

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Nvidia’s DLSS 4 may not be what you think. Let’s bust...

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OpenAI is launching a new line of autonomous cars, drones, humanoids,...

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Generative AI should be used to transform society, not put dogs...

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LaCie launches rugged Thunderbolt 5 portable SSDs (

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WhatsApp may allow you to create AI chatbots in the app

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Deals: OnePlus launches 13R while Red Magic 10 Pro is also...

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Nvidia’s AI Empire: A look at the top startup investments

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Anthropic’s Chief Scientist on 5 ways agents will even be better...

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Musk’s Lawsuit Against OpenAI Gets a Boost From Lina Khan’s FTC

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Media agencies are facing the uncertainty of a Trump-2.0 presidency and...

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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...