A collaborative approach to training AI models can yield better results, but it requires finding partners with data that ...

Theoretical physicists use machine-learning algorithms to speed up difficult calculations and eliminate untenable theories—but could they transform what it means to make discoveries? Theoretical ...

Effective learning isn't just about finding the easiest path—it's about the right kind of challenge. Two prominent theories—Desirable Difficulties (DDF) and Cognitive Load Theory (CLT)—offer valuable ...

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

In education, as in psychology, clarity matters. Yet in everyday conversations about teaching and learning, terms like learning theory and pedagogy are often used interchangeably. Phrases such as “We ...

Forbes contributors publish independent expert analyses and insights. I write on the human/political issues surrounding college admissions. Two young male students investigate a project at a science ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

COMET, a novel machine learning framework, integrates EHR data and omics analyses using transfer learning, significantly enhancing predictive modeling and uncovering biological insights from small ...

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...