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

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Meta

WhatsApp says forcing the blue Meta AI circle to everyone is...

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Ziff Davis and IGN file suit against OpenAI for copyright violations

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TSMC announces plans for giant AI processors to meet the surging...

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How to watch LlamaCon, Meta’s first generative AI Developer Conference

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Anthropic

South Africa’s cybercrime threat level is increasing; here’s why.

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Anthropic

GetEquity achieves profitability after shifting to local debt investments.

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Anthropic

Google’s Gemini smartwatch and car

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Nvidia’s RTX-5060 is reportedly set to launch on May 19, a...

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Chinese tech giants secured NVIDIA H20 shipments worth billions ahead of...

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The new AI calculus

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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...