While building machine learning models is fundamental to today’s narrow applications of AI, there are a variety of different ways to go about realizing the same ends. So-called machine learning ...

1. Demand Prediction Engine: A Technological Leap from "Passive Response" to "Active Anticipation"

A team of computer scientists at UC Riverside has developed a method to erase private and copyrighted data from artificial intelligence models—without needing access to the original training data.

Today’s data scientists and machine learning engineers now have a wide range of choices for how they build models to address the various patterns of AI for their particular needs.

The fields of psychology, robotics and machine learning have each been using some version of the concept for decades. You likely have a world model running inside your skull right now — it’s how you know not to step in front of a moving train without needing to run the experiment first.

Machine learning plays a pivotal role in the development of autonomous vehicles and robots. In the realm of mechanical engineering, these technologies rely on machine learning algorithms for navigation, object recognition, and decision-making. This convergence is revolutionizing transportation and manufacturing industries.

Learn more about the relationship between Machine Learning vs Deep Learning vs Foundation Models and how they effect AI models working as a

Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in weather and climate, but a new study by MIT researchers shows that bigger models are not always better.