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

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Breaking: Microsoft pledges 1M Nigerians $1M in AI

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Nvidia 50-series cards no longer support PhysX. This has an impact...

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Ex-OpenAI CTO Mira Murati unveils Thinking Machines: A startup focused on...

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Mira Murati Launches Thinking Machines Lab to Make AI More Accessible

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Elon Musk has released an AI that is smarter than ChatGPT

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The AI app wars

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Humane lost its bet on the iPhone because it was cloaked...

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OpenAI’s study highlights the limitations of LLMs for software engineering

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Meta has scheduled a generative AI event called LlamaCon on April...

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Roundtables on Generative AI Search: The Changing Internet

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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...