High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of ...

Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow Despite the success of supervised machine learning and deep ...

Welcome to TNW Basics, a collection of tips, guides, and advice on how to easily get the most out of your gadgets, apps, and other stuff. This is also a part of our “Beginner’s guide to AI,” featuring ...

Researchers have developed a new AI algorithm, called Torque Clustering, that significantly improves how AI systems independently learn and uncover patterns in data, without human guidance.

Following up on my last article, “Demystifying machine learning,” it’s clear that the machine learning space has blossomed over the last few years. Machine learning technologies are gaining momentum ...

Signal processing algorithms, architectures, and systems are at the heart of modern technologies that generate, transform, and interpret information across applications as diverse as communications, ...

Self-supervised models generate implicit labels from unstructured data rather than relying on labeled datasets for supervisory signals. Self-supervised learning (SSL), a transformative subset of ...