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

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Computer Vision

Uber CEO warns that robotaxis cannot find a quick route to...

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Trace.Space, a startup that uses AI to accelerate product design, raises...

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Anthropic

Cognita.ai raises 15M to fix enterprise AI’s biggest bottleneck : deployment

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Anthropic

ESA announces innovation-focused summit for April 2026.

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Anthropic

Workday dismisses 1,750 employees citing AI demand

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Baidu

DeepSeek Launched by Baidu, Tencent, Alibaba and other platforms

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No need to sign in anymore for ChatGPT Search

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Want to save money on ChatGPT Deep research? This open-source alternative...

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People are more open to AI if they know less about...

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How Technology is Improving Workplace Safety and Health

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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...