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

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Researchers from Fudan University and Shanghai AI Lab Introduces DOLPHIN: A...

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Meta AI Introduces CLUE (Constitutional MLLM JUdgE): An AI Framework Designed...

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Salesforce AI Introduces TACO: A New Family of Multimodal Action Models...

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Meet Search-o1: An AI Framework that Integrates the Agentic Search Workflow...

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InfiGUIAgent: A Novel Multimodal Generalist GUI Agent with Native Reasoning and...

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What is Artificial Intelligence (AI)?

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The Raspberry Pi 5 now comes in a 16GB super-powered model

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Top 10 trending mobile phones of Week 2

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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...