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

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Google Challenges Apple on Search Trends

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Apple Developing Chips for AR, AI

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Anthropic

I regret buying RGB for my gaming computer

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Anthropic

This $1,200 PTZ is a glorified Webcam, but gave my creator...

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How creators are using generative AI in podcasts, videos and newsletters...

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ChatGPT Deep research can now connect to GitHub.

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OpenAI to spend $3 billion on AI coding software Windsurf, as...

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OpenAI and Microsoft tell Senate that ‘no single country can win...

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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...