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

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Computer Vision

AI tool uses face photos to estimate biological age and predict...

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Google Releases 76-Page Whitepaper on AI Agents: A Deep Technical Dive...

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Implementing an AgentQL Model Context Protocol (MCP) Server

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LLMs Can Now Talk in Real-Time with Minimal Latency: Chinese Researchers...

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Is Automated Hallucination Detection in LLMs Feasible? A Theoretical and Empirical...

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This AI Paper Introduce WebThinker: A Deep Research Agent that Empowers...

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A Step-by-Step Guide to Implement Intelligent Request Routing with Claude

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Researchers from Fudan University Introduce Lorsa: A Sparse Attention Mechanism That...

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

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Hugging Face Releases nanoVLM: A Pure PyTorch Library to Train a...

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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...