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

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SpaceX resets the ‘Days Since Starship exploded’ counter to zero.

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GM parks claims that driver location data was given to insurers,...

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Cisco and Nvidia smile at you as your LLM goes off...

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Nvidia claims that over 80% of GeForce RTX owners use DLSS.

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Computer Vision

ADS Transform CV: COTS, Deep Learning based Computer Vision System for...

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Mira Murati’s AI Startup Hires First, Including Former OpenAI Executives

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Mapping Elon Musk’s Global Empire

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AI Hardware

New US Rule Aims to Block China’s Access to AI Chips...

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Machine Learning

Asymmetric Certified Robustness via Feature-Convex Neural Networks

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A Spymaster Sheikh Controls a $1.5 Trillion Fortune. He Wants to...

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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...