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

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Rare 1998 Nvidia Riva TNT prototype and signed lunchbox up for...

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Nintendo Switch 2 specs suggest GPU performances similar to a GTX1050...

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This simple trick makes Apple Intelligence Writing Tools more useful on...

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Yolk on you

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OpenAI’s new push for democratic AI: Another marketing gimmick? Key Takeaways:

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Why AI integration is key to maximizing its value

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Google adds on device AI to Chrome in order to catch...

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Experts reveal how “evil AI’ is changing hacking forever at RSA...

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How cloud and AI transform customer experiences

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Multimodal LLMs Without Compromise: Researchers from UCLA, UW–Madison, and Adobe Introduce...

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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...