Stanford faculty across disciplines are integrating AI into their research, balancing its potential to accelerate analysis against ethical concerns and interpretive limitations.

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results.

Nandita Giri is a senior software engineer with experience at Amazon, Meta, and Microsoft. She recommends job seekers spend ...

Scholars and artists at Sorbonne University trained artificial intelligence to imitate the French playwright’s themes, ...

Discover the top AI certifications for 2026 to boost your skills, impress employers, and prepare for high-demand AI and tech ...

An AI model that learns without human input—by posing interesting queries for itself—might point the way to superintelligence ...

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

The world tried to kill Andy off but he had to stay alive to to talk about what happened with databases in 2025.

Explore five free and low-cost AI certifications that help tech professionals build AI skills across cloud, machine learning, ...

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...

This is a guest post by Tim Allen, principal engineer at Wharton Research Data Services at the University of Pennsylvania, a member of the Readers Council and an organizer of the Philadelphia Python ...

Abstract: In the wave of technological innovation, generative artificial intelligence tools provide college students with personalized learning experience, promote autonomous learning and exploratory ...