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

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Anthropic

Apple Intelligence powered ‘Priority Notifications” will be available in iOS 18.4

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Nvidia CEO Jensen Huang says market got it wrong about DeepSeek’s...

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Nvidia GeForce RTX 5070 Ti review: An RTX 4080 for $749,...

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Elon Musk’s AI claimed that he and Trump deserve to die

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OpenAI confirms 400 Million Weekly ChatGPT Users

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Leaked chat logs reveal inner workings of secretive Ransomware group

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OpenAI’s ChatGPT reaches 400M weekly users with GPT-5 in the works

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ChatGPT reaches 400M active users weekly

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Computer Vision

Deep Nanometry reveals hidden nanoparticles

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Anthropic

Ghana’s Oze Raises Financing to Bring AI-Powered Digital Lending Solutions for...

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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...