Annotation automation fails in safety-critical edge cases where human judgment is the only reliable signal While autonomous vehicle programs have matured through standardized sensor configurations and ...

Artificial intelligence systems are only as powerful as the data they are trained on. High-quality labeled datasets determine whether a model performs with precision or fails in production. For ...

Subramaniam Vincent is director of journalism and media ethics at the Markkula Center for Applied Ethics at Santa Clara University. Views are his own. At the core of storytelling for news is sourcing ...

When we talk about artificial intelligence, most people immediately think of futuristic robots and self-driving cars. But here’s the truth I’ve learned over years of working with data and leading ...

mLLMCelltype is a multi-LLM consensus framework for automated cell type annotation in single-cell RNA sequencing (scRNA-seq) data. The framework integrates multiple large language models including ...

Marine scientists have been leveraging supervised machine learning algorithms to analyze image and video data for nearly two decades. There have been many advances, but the cost of generating expert ...

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

Abstract: Automated classification of learner-generated text to identify behavior, emotion, and cognition indicators, collectively known as learning engagement classification (LEC), has received ...

Community driven content discussing all aspects of software development from DevOps to design patterns. Consider a simple example of testing a number-guessing game. If the application generates a ...