We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

For about a decade, computer engineer Kerem Çamsari employed a novel approach known as probabilistic computing. Based on probabilistic bits (p-bits), it’s used to solve an array of complex ...

The software tool developed by Stony Brook University uses self-supervised learning to detect long-term solar equipment damage weeks or years before manual inspections find it.

Seeking to improve automatic emotion tracking, which detects and monitors emotions over time, a group of researchers in the field of human-computer interaction decided to approach the task by modeling ...

AI (Artificial Intelligence) is a broad concept and its goal is to create intelligent systems whereas Machine Learning is a specific approach to reach the same goal.

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 ...

Overview: AI-powered credit scoring uses advanced data analysis and machine learning to assess borrower risk more accurately than traditional models.Modern loan ...

There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...