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

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Character.AI will no longer allow its chatbots to romance teenagers

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

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The excellent isometric RPG Underrail is back

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IT gigantite v’zrazhdat iadrenata energetika

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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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Nvidia AI Blueprint allows developers to easily build automated agents that...

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ByteDance seems to be circumventing US restrictions in order to buy...

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I found an AirTag wallet alternative that is more functional than...

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Apple AirPods Pro 3 monitor heart rate and bring health functions

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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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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...