Machine learning tagged posts

Seeing the Quantum Future, literally

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

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...

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What do Netflix, Google and Planetary Systems have in common?

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

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...

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