News

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

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Anthropic

Anthropic agrees with music publishers to work together to prevent copyright...

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Anthropic

Claude AI and other system could be vulnerable to worrying Command...

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Anthropic

Can AI save the public sector? Will it deliver on its...

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Anthropic

L’Oreal: Making AI worthwhile

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Anthropic

Anthropomorphizing Artificial intelligence: The consequences of mistaking human-like AI for humans...

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News

Microsoft AutoGen v0.4

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FTC says Microsoft-OpenAI partnerships raise antitrust concerns.

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News

The CIA’s first CTO, Nand Mulchandani, prepares for the Trump administration

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AMD

OpenAI announces a new o3 model, but you can’t yet use...

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AMD

Databricks CEO explains his decision to wait to go public.

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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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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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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...