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

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OpenAI is interested in Chrome if it is going to become...

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Startup challenge seeks sustainable AI energy solutions

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Meta to resume AI training on content shared by Europeans

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Computer Vision

ShengShu launches Vidu Q1, which puts full-stack video and audio in...

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Anthropic

Artificial Intelligence chat: What is it and how can it help?

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Anthropic

Taobao app to be available in Malaysian language

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Anthropic

Reolink security cameras gain ‘Works With Home Assistant” certification

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It costs tens of thousands of dollars to be nice to...

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Adaptive Computer wants non-programmers to code with ‘vibes’ on the PC

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The Washington Post now lets ChatGPT summarize their articles

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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...