Machine learning tagged posts

Computers which are capable of teaching themselves to predict premature death could greatly improve preventative healthcare in the future, suggests a new study by experts at the University of Nottingham. The team of healthcare data scientists and doctors have developed and tested a system of computer-based ‘machine learning’ algorithms to predict the risk of early death due to chronic disease in a large middle-aged population.

They found this AI system was very accurate in its predictions and performed better than the current standard approach to prediction developed by human experts...

This is a Brain Music EEG headset. Credit: Osaka University

Music, more than any art, is a beautiful mix of science and emotion. It follows a set of patterns almost mathematically to extract feelings from its audience. Machines that make music focus on these patterns, but give little consideration to the emotional response of their audience. An international research team led by Osaka University together with Tokyo Metropolitan University, imec in Belgium and Crimson Technology has released a new machine-learning device that detects the emotional state of its listeners to produce new songs that elicit new feelings.

“Most machine songs depend on an automatic composition system,” says Masayuki Numao, professor at Osaka University...

Trapped Ytterbium ions were used as one of the most advanced laboratory quantum systems for this study. Professor Biercuk’s research laboratories are now located in the Sydney Nanoscience Hub, after six years as a visiting scientist at the National Measurement Institute.
Credit: University of Sydney

What if big data could help you see the future and prevent your mobile phone from breaking before it happened? Scientists at the University of Sydney have demonstrated the ability to “see” the future of quantum systems, and used that knowledge to preempt their demise, in a major achievement that could help bring the strange and powerful world of quantum technology closer to reality...

Dan Tamayo is a postdoctoral fellow in the Centre for Planetary Science at U of T Scarborough. Credit: Photo by Ken Jones

Same class of algorithms used by Google and Netflix can also tell us if distant planetary systems are stable or not. Machine learning is a powerful tool used for a variety of tasks in modern life, from fraud detection and sorting spam in Google, to making movie recommendations on Netflix. Now a team of researchers from the University of Toronto Scarborough has developed a novel approach in using it to determine whether planetary systems are stable or not.

“Machine learning offers a powerful way to tackle a problem in astrophysics, and that’s predicting whether planetary systems are stable,” says Dan Tamayo, U of T Scarborough...