This review systematically examines the integration of machine learning (ML) and artificial intelligence (AI) in nanomedicine ...

Inspired by how the human brain consolidates memory, the 'Nested Learning' framework allows different parts of a model to learn and update at different speeds.

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

A new technique enables huge machine-learning models to efficiently generate more accurate quantifications of their uncertainty about certain predictions. This could help practitioners determine ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

Modern large language models (LLMs) might write beautiful sonnets and elegant code, but they lack even a rudimentary ability to learn from experience. Researchers at Massachusetts Institute of ...

To identify and evaluate candidate materials, process engineers must analyze an enormous amount of data. Bulk properties like resistivity or thermal conductivity are a starting point, but these ...

Their study presents a new combination of computational tools that merges the Chinese Pangolin Optimizer (CPO) with the ...

As global participation in digital-asset ecosystems expands and blockchain behaviour becomes increasingly complex, platforms ...