Not long ago, science followed a fairly natural rule: to predict a property of a substance or material, you had to understand ...

A new theoretical framework separates computational speed from statistical generalization in quantum machine learning, showing that faster quantum algorithms alone do not guarantee better learning ...

Millions of electric motors run inside the country’s buildings, often inefficiently. Variable frequency drives can slash ...

How do humans make remarkable decisions with limited data and computing power—and what can that efficiency teach us about AI?

The changing use of the word algorithm reflects this enhanced visibility. In fact, the meaning of this word has changed ...

Machine learning operates as the silent engine behind modern digital infrastructure. It filters out malicious traffic, anticipates supply chain bottlenecks, and guides autonomous vehicles. However, ...

IntroductionHello! This is AI Nest Inc.You can read summaries of AI research papers here. 解説記事 | Scholar Compass Scholar ...

A sweeping new review in the Journal of Materials Science shows how synchrotron light, neutron scattering, advanced ...

Susan C. Athey is an economist who has studied ways to analyze digital markets and policy by connecting economic theory, ...

Spatial transcriptomics can reveal where thousands of genes are active across a tissue, creating molecular maps at single-cell resolution. But comparing two such maps is difficult: thin slices of ...

A strong backtest may be evidence of a durable edge, or it may show how thoroughly an algorithm has adapted to historical noise. That distinction ...

Can predicting energy levels from quantum systems be done efficiently on conventional computers? New results demonstrate this ...