WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

TTT-Discover optimizes GPU kernels 2x faster than human experts — by training during inference

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...
Psychology Today

Why the New Artificial Intelligence Is So Powerful

AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, ...
EurekAlert!

How can computing for AI and other demands be more energy efficient?

The growth and impact of artificial intelligence are limited by the power and energy that it takes to train machine learning models. So how are researchers working to improve computing efficiency to ...
Visual Studio Magazine

Linear Regression with Pseudo-Inverse Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with pseudo-inverse training implemented using JavaScript. Compared to other training techniques, such as ...

AI Tool Uncovers Hundreds of Hidden Cosmic Oddities in Hubble Data

A team of astronomers based at the European Space Agency demonstrated how artificial intelligence technology will alter ...

AI systems learn better when trained with inner speech like humans

Talking to yourself feels deeply human. Inner speech helps you plan, reflect, and solve problems without saying a word.
IEEE

Importance Aware Undervolting for Robust Neural Network Training

Abstract: Convolutional Neural Network (CNN) is a powerful tool that has been extensively applied to many different applications. However, recent developments at CNN have revealed its vulnerability ...
blockchain

DeepMind Reveals 'Grokking' in Neural Networks: Sudden Generalization After Prolonged Training – Implications for AI Model Learning

According to God of Prompt on Twitter, DeepMind researchers have identified a phenomenon called 'Grokking' where neural networks may train for thousands of epochs with little to no improvement, then ...
Science Daily

Scientists discover the moment AI truly understands language

Neural networks first treat sentences like puzzles solved by word order, but once they read enough, a tipping point sends them diving into word meaning instead—an abrupt “phase transition” reminiscent ...
bjo.bmj

Neural network for natural language processing to determine treatment urgency in an ophthalmology emergency department

Background In an ophthalmology emergency department, determining treatment urgency is crucial for patient safety and the efficient use of resources. The aim of this study was to use artificial ...
IEEE

Patch-Based Deep-Learning Model With Limited Training Dataset for Liver Tumor Segmentation in Contrast-Enhanced Hepatic Computed Tomography

Abstract: The automatic segmentation of liver tumors plays an important role in the diagnosis and treatment of liver cancer. While deep convolutional neural network (DCNN) models are widely used for ...