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

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Take a Tour of All the Essential Features in ChatGPT

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Mergers & Acquisitions

OpenAI Backs Down on Restructuring Amid Pushback

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Amazon Has Made a Robot With a Sense of Touch

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OpenAI and the FDA Are Holding Talks About Using AI In...

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Singapore’s Vision for AI Safety Bridges the US-China Divide

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Sam Altman admits ChatGPT personality issue; promises fix

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Meta takes aim at ChatGPT with its new AI app

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OpenAI rolls back GPT-4o update to fix ChatGPT personality issue

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This new relationship tracking ring makes trust issues wearable

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Google wants to bring Gemini to Apple Intelligence

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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...