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

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9to5Neural: DeepSeek explained, deep NVIDIA losses, AI privacy claim debunked

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Nvidia deprecates CUDA support for aging architectures

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Meta

Meta AI now uses your Facebook and Instagram activity for its...

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Anthropic

Bento Africa is under investigation by LIRS & EFCC. CEO Okubanjo...

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Anthropic

South African fintech Stitch purchases ExiPay to expand in-person payments.

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Anthropic

Starlink is now Kenya’s 8th-largest ISP, despite growing regulatory concerns.

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Anthropic

Bolt enters the grocery delivery market while others run to the...

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China’s DeepSeek just dropped a free challenger to OpenAI’s o1

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Elon Musk and Sam Altman Fight on X about Trump’s Data...

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ChatGPT Downtime Leaves User in a Feral state

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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...