News

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

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News

Meta.AI is now available in your AI folder, alongside Gemini, Copilot...

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AI Hardware

As AI lawsuits mount, publishers still struggle to block the bots

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AI Hardware

Agent Pay from Mastercard transforms the way enterprises use AI search

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Anthropic

AI in national security raises privacy and proportionality concerns

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Anthropic

FCA wants to create a’safe space’ for finance firms that want...

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Anthropic

Government receives 200 bids from local authorities who want AI growth...

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Anthropic

Scattered Spider is on the hook for M&S Cyber Attack

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Apple

Researchers find multiple Apple AirPlay vulnerabilities that allow “wormable” exploits to...

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Meta

Meta launches standalone AI chat application based on Llama 4 based...

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News

OpenAI rolls back the update that turned ChatGPT into an ass-kissing...

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Featured

Education

A Coding Implementation of Accelerating Active Learning Annotation with Adala and...

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News

LightOn AI Released GTE-ModernColBERT-v1: A Scalable Token-Level Semantic Search Model for...

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News

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...