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MCP and the Innovation Paradox: Why open standards can save AI...

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Top Five Chinese EV startups: Li Auto Leads and Xiaomi Gaining...

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MSI Afterburner prepares for GeForce RTX5080 with expanded support for fan...

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The smart glasses can be purchased for as little as $295...

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ChatGPT continues its dominance, but this Google AI Tool is gaining...

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The Download: Google Project Astra and China’s Export Bans

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Google Deepmind’s new forecaster is better than the competition

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Altman admits that ChatGPT Pro is struggling to make a profit...

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AI Hardware is in its ‘Put up or Shut Up Era’

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Nvidia’s RTX-5090 with 32GB GDDR7 Memory

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Rumors suggest that next-gen RTX50 GPUs will have big jumps in...

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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...