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

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OpenAI is having a rough week–it could be the start of...

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Sam Altman’s Sister is suing OpenAI CEO for sexual abuse

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This Week in AI

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HONOR Magic7 Lite

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Asus is developing the ROG Flow Z13 to make more sense...

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Nvidia CEO: PC gaming will never be rendered entirely by AI

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Nvidia’s AI Snake is feeding itself. Announces GeForce GTX 5090 GPU....

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Mergers & Acquisitions

Navigating AI M&A: A Comprehensive Guide to Due Diligence in the...

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LG and Samsung will add Microsoft Copilot to their new TVs.

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AI Hardware

HP at CES: The latest Elitebooks powered by Intel’s AI chips

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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...