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

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Anthropic

Deals: Galaxy S25 and S25+ get price cuts, OnePlus 13 is...

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Weekly poll: Is the CMF Phone 2 Pro right for you?

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SoundCloud says that it doesn’t use your music to train generative...

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The latest AMD Radeon RX9060 XT information reveals high boost clocks...

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NVIDIA to launch a cut-down H20 Chip for China as soon...

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Apple’s silicon roadmap is sweeping, with the M6, M7 and smart...

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Computer Vision

Fake AI video creators drop new Noodlophile information stealer malware

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A Deep Technical Dive into Next-Generation Interoperability Protocols: Model Context Protocol...

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Enterprise AI Without GPU Burn: Salesforce’s xGen-small Optimizes for Context, Cost,...

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ByteDance Open-Sources DeerFlow: A Modular Multi-Agent Framework for Deep Research Automation

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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...