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

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The Download: AI’s coding promise, and OpenAI’s longevity push

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OpenAI’s agent tool may be nearing release

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AI Briefing: Copyright Battles Bring Meta and OpenAI Datasets Under the...

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Anthropic

DDN looks to AI leadership as it secures $300m investment

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Anthropic

AI comes alive: From bartenders, to surgical aides, to puppies, robots...

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Anthropic

AI or Not raises 5M dollars to stop AI fraud, deepfakes,...

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Anthropic

You can now fine tune your own version AI image maker...

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DeepMind

Today’s Android app deals and freebies: Agatha Knife, Miden Tower, Runic...

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AI benchmarking organization criticized for waiting to disclose funding from OpenAI

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The Pentagon says AI is accelerating its ‘killing chain’

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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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AI Observer

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...