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

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Gemini is a good chatbot but it’s not a great assistant.

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Anthropic

Hyundai Insteroid, a rare model we want but may not receive

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Anthropic

Google announces new security requirements for HTTPS providers

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Anthropic

Gartner predicts $644 billion in AI spending by 2025, but few...

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Anthropic

The first 3D-printed train stations in Japan were assembled in record...

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Best Graphics Cards for PC: Nvidia AMD Intel

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OpenAI says it could be a positive thing that ChatGPT has...

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AI agents can make early-stage startups efficient, but developer jobs may...

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The Download: generative AI Therapy, and the Future of 23andMe’s Genetic...

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The Exposed database of an AI Image Generator reveals what people...

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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...