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

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MedVoice AI Delivers Conversational AI Powered Medical Devices.

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The 4 biggest AI stories of 2024 and a key prediction...

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The code whisperer

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The Download: Anduril’s latest humanoid robot project and the most trustworthy...

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Government of Canada announces $2 billion investment in AI Infrastructure

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New Models & Research

Server manufacturers ramp up edge AI efforts

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OneCell Diagnostics receives $16M for AI to limit cancer reoccurrence

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It’s just a matter time before LLMs start supply-chain attack

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The Year of the AI Election Didn’t Go Quite as Everyone...

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Infosec experts divided on AI’s potential to assist red teams

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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...