The 2,500 questions that make up the exam are specifically designed to probe the outer limits of what today’s AI systems cannot do.

Data is fundamental to hydrological modeling and water resource management; however, it remains a major challenge in many ...

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...

Abstract: This paper describes machine learning approaches for the study of the Chinese Buddhist Canon with bibliographic, quotation, and terminology databases. The use of these techniques brings ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

Abstract: The diagnosis and monitoring of voice-affecting disorders using machine learning (ML) approaches have garnered significant attention globally. This review highlights the current state of ...

Understanding and preventing drug side effects holds a profound influence on drug development and utilization, profoundly impacting patients’ physical and mental well-being. Traditional artificial ...

Introduction to the Author and Book’s Relevance: Hemanth Gadde, a respected authority in AI, machine learning, and data management, has recently published a groundbreaking book, Artificial ...

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integral to database management, driving new levels of automation and intelligence in how data systems are administered. Modern ...

Objective Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. Our study aims to develop and validate predictive ...