The AI in use today is actually a group of related technologies, including machine learning, supervised learning, and computer vision that allows companies to create automated tasks on a large scale.

High-dimensional datasets, typified by genomics, proteomics and high-resolution imaging, pose acute challenges due to the vast number of features relative to available labels. Transfer learning ...

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 ...

Our bodies are made up of around 75 billion cells. But what function does each individual cell perform and how greatly do a healthy person's cells differ from those of someone with a disease? To draw ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of payments. We live in a world where machines can understand speech, recognize faces, and even generate ...

David Gerbing from the School of Business at Portland State University introduces lessR, a tool designed to facilitate professional-quality data visualizations and data analysis without programming re ...

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of payments. As machine learning continues to reshape the financial services industry, most headlines are ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...