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

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Sam Altman: OpenAI to keep nonprofit soul in restructuring

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UAE to teach its children AI

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ServiceNow bets on unified AI to untangle enterprise complexity

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Samsung AI strategy delivers record revenue despite semiconductor headwinds

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Google Launches Gemini 2.5 Pro I/O: Outperforms GPT-4 in Coding, Supports...

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Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

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Repurposing Protein Folding Models for Generation with Latent Diffusion

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Defending against Prompt Injection with Structured Queries (StruQ) and Preference Optimization...

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Updating the Frontier Safety Framework

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Gemini 2.0 is now available to everyone

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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...