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

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DeepSeek Temporarily Stops User Registrations

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This quantum computer built in server racks paves way for bigger...

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AI Hardware

Microsoft’s results demonstrate cloud AI balancing act.

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How publishers choose which LLMs they will use

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Anthropic

Bento CEO resigns amid EFCC and LIRS probes; gives up equity.

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Anthropic

Uber-backed Moove purchases Brazil’s Kovi in order to continue global expansion.

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Kimi k1.5 -OpenAI Model that can match full-powered O1 performance

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OpenAI’s Sora generates ten videos per second. Here are the top...

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OpenAI and friends aren’t the only Chinese LLM makers to be...

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DeepSeek limits registrations in the wake of large-scale cyberattacks

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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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AI Observer

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...