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MCP and the Innovation Paradox: Why open standards can save AI...

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Character.AI takes teen safety seriously after bots are alleged to have...

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Stable Diffusion 3.5 is now available on Amazon Bedrock. What does...

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Hugging Face shows that test-time scaling can help small language models...

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Hugging Face’s SmolVLM can reduce AI costs by a large margin...

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The excellent isometric RPG Underrail is back

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A new robotic surgery procedure was tested at the University of...

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MediaTek: First information about the next high-end chip

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Education

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...