AGI? GPUs? Learn the definitions of the most common AI terms to enter our vocabulary

The rise of AI has given us an entirely new vocabulary. Here's a list of the top AI terms you need to learn, in alphabetical ...

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning

Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning ...
IEEE

Comprehensive Framework for Tuberculosis Detection Using Deep Learning and Image Processing in Whole-Slide Images

Abstract: Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTb), remains one of the leading causes of death globally, primarily affecting the lungs. Recent technological advancements have ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
C&EN

Leveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In recent AI-driven disease diagnosis, the success of models has depended mainly on ...
The New York Times

Has America Given Up on Children’s Learning?

Politicians used to care how much students learn. Now, to find a defense of educational excellence, we have to look beyond politics. Credit...Photo illustration by Alex Merto Supported by By Dana ...
Frontiers

Deep learning-driven medical image analysis for computational material science applications

Introduction: Deep learning has significantly advanced medical image analysis, enabling precise feature extraction and pattern recognition. However, its application in computational material science ...
Frontiers

Deep learning for steganalysis: evaluating model robustness against image transformations

This study investigates the robustness of deep learning-based steganalysis models against common image transformations because most literature has not paid enough attention to resilience assessment.