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

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UK firms struggle to scale AI across their businesses

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Anthropic

Oracle Cloud security SNAFU: IT giant accused as evidence disappears

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Anthropic

Check Point confirms breach but says it was “old” data and...

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Anthropic

AI datacenters are going nuclear. Too bad they needed this yesterday

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Nvidia’s challenger Cerebras claims it has jumped the Mid-East funding obstacle...

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Microsoft restates commitment towards OpenAI amid analyst notes about datacentre expansion...

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ChatGPT Projects feature brings order to your AI chaos

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Nvidia GPU roadmap confirms that Moore’s Law has died and been...

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Nvidia RTX RTX 5090 with missing RFP intentionally sold as “B...

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Apple

This Android alternative to Apple AirTags has a much better functionality

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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...