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

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AI chip restrictions limit Nvidia H20 China exports

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DeepMind

Google DeepMind has added a dial that controls how much a...

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Windsurf: OpenAI could bet $3B to drive the ‘vibe-coding’ movement

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OpenAI pursued the Cursor maker, before entering into negotiations to buy...

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I interviewed Realbotix Aria’s humanoid in order to understand the AI’s...

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AI Hardware

OpenAI launches Flex Processing for cheaper and slower AI tasks

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Anthropic

Opera Mini launches AI-powered update to compete with Google and Microsoft...

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Anthropic

South Africa suspends new SASSA Payment Cards, putting 28 million at...

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Anthropic

I want to upgrade to Windows 11. Microsoft won’t let

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NVIDIA RTX5060 family brings Blackwell power to an affordable price.

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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...