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

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Midjourney V7: Faster, smarter, more realistic

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NVIDIA just made game physics a playground for everyone

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No rules, just vibes! What is vibe coding?

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AI is pushing the limits of the physical world

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Generative AI is reshaping South Korea’s webcomics industry

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The future of AI processing

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Seeing AI as a collaborator, not a creator

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We need to start thinking of AI as ā€œnormalā€

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The AI Hype Index: AI agent cyberattacks, racing robots, and musical...

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This data set helps researchers spot harmful stereotypes in LLMs

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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...