Soon, researchers may be able to create movies of their favorite protein or virus better and faster than ever before. Researchers at the Department of Energy's SLAC National Accelerator Laboratory ...

As the use of machine learning algorithms in health care continues to expand, there are growing concerns about equity, fairness, and bias in the ways in which machine learning models are developed and ...

Industrial automation is entering a new era with physical AI, where machine learning meets real-world motion control. AI-driven robotics and digital twins are closing the gap ...

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

Researchers aimed to understand the role of AI in detecting Alzheimer disease and related dementias in primary care.

Semi-supervised learning merges supervised and unsupervised methods, enhancing data analysis. This approach uses less labeled data, making it cost-effective yet precise in pattern recognition.

As you begin your hybrid quantum approach, here are the advantages, use cases and limitations to keep in mind.

The project will build upon CSIRO’s expertise in the field of QML to develop new and innovative QML models. QML has the potential to offer enhanced reliability, training speed-up and unique feature ...

In both cases, it would be better to train the machine learning model with a loss function that ignores the human’s objective and then adjust predictions ex post according to that objective. We ...

When it comes to training robots to perform agile, single-task motor skills, such as handstands or backflips, artificial intelligence methods can be very useful. But if you want to train your robot to ...