In bone imaging, machine learning accelerates research progress and uncovers hidden patterns

In a future perhaps not too far away, artificial intelligence and its subfield of machine learning (ML) tools and models, ...
Hackaday

Reverse-Engineering Human Cognition And Decision Making In A Modern Age

Cognitive processes are not something that we generally pay much attention to until something goes wrong, but they cover the ...

From LLMs to hallucinations, here’s a simple guide to common AI terms

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most ...
Tech Xplore

A simple baseline for AI forecasting in machine learning

In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively ...
Scientific Research Publishing

Predicting Stock Price Movements Using Deep Learning Techniques ()

Stock Price Prediction, Deep Learning, LSTM, GRU, Attention Mechanism, Financial Time Series Share and Cite: Kirui, D. (2026) ...
Communications of the ACM

Copiloting the Copilots for Automated Program Repair

Repilot synthesizes a candidate patch through the interaction between an LLM and a completion engine, which prunes away ...
Streaming MediaOpinion

The State of AI In Live Streaming

As with all streaming workflows, AI has steadily crept into the live streaming technology stack. In some cases, the impact is ...
IEEE

GURNet: A Gated U-Shaped Encoder-Decoder Predictor for Reversible Data Hiding

Abstract: The existing deep learning based reversible data hiding (RDH) predictors typically adopt standard convolutions for extracting features, which inherently fails to capture contextual ...

New Children’s Book, ‘Tilly and the Wisdom Trials,’ Combines Fantasy Adventure with Interactive Learning

Prairie City comes alive in David Tra’s one-of-a-kind story and game experience for curious young minds. MD, UNITED ...
IEEE

Anomaly Detection in Medical Images Using Encoder-Attention-2Decoders Reconstruction

Abstract: Anomaly detection (AD) in medical applications is a promising field, offering a cost-effective alternative to labor-intensive abnormal data collection and labeling. However, the success of ...