Overview: Interpretability tools make machine learning models more transparent by displaying how each feature influences ...

Computer vision researchers use machine learning to train computers in visually recognizing objects but very few apply machine learning to mechanical parts, such as gearboxes, bearings, brakes, ...

To form qubit states in semiconductor materials, it requires tuning for numerous parameters. But as the number of qubits increases, the amount of parameters also increases, thereby complicating this ...

[Daniel Geng] and others have an interesting system of generating multi-view optical illusions, or visual anagrams. Such images have more than one “correct” view and visual interpretation. What’s more ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...

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 ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of finance and technology, follow for more. We live in a world where machines can understand speech, ...

(a) The flow to train the estimator. Training data for the CNN was prepared by simulation using the CI model. The researchers simplified the data with pre-processing and then trained the CNN. (b) The ...