eLife

Adjoint propagation of error signal through modular recurrent neural networks for biologically plausible learning

Biologically plausible learning mechanisms have implications for understanding brain functions and engineering intelligent systems. Inspired by the multi-scale recurrent connectivity in the brain, we ...
ascopubs.org

Using Recurrent Neural Networks to Extract High-Quality Information From Lung Cancer Screening Computerized Tomography Reports for Inter-Radiologist Audit and Feedback Quality ...

Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network–Based Artificial Intelligence Tool The best performing model, a Bi-LSTM NER ...
Ars Technica

Researchers isolate memorization from problem-solving in AI neural networks

When engineers build AI language models like GPT-5 from training data, at least two major processing features emerge: memorization (reciting exact text they’ve seen before, like famous quotes or ...

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.
Electronic Design

A Neural-Network-Based Approach to Smarter DPD Engines

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.
Devdiscourse

Hybrid AI can reduce failures and energy use in manufacturing robots

The research, titled AI-Driven Hybrid Deep Learning and Swarm Intelligence for Predictive Maintenance of Smart Manufacturing Robots in Industry 4.0 and published in Electronics, introduces an AI ...