Machine learning tagged posts

Researchers have discovered new properties of tiny magnetic whirlpools called skyrmions. Their pivotal discovery could lead to a new generation of microelectronics for memory storage with vastly improved energy efficiency in high performance computers.

Magnets generate invisible fields that attract certain materials. A common example is fridge magnets. Far more important to our everyday lives, magnets also can store data in computers. Exploiting the direction of the magnetic field (say, up or down), microscopic bar magnets each can store one bit of memory as a zero or a one — the language of computers.

Scientists at the U.S...

Imperial privacy experts have created an AI algorithm that automatically tests privacy-preserving systems for potential data leaks.

This is the first time AI has been used to automatically discover vulnerabilities in this type of system, examples of which are used by Google Maps and Facebook.

The experts, from Imperial’s Computational Privacy Group, looked at attacks on query-based systems (QBS)—controlled interfaces through which analysts can query data to extract useful aggregate information about the world. They then developed a new AI-enabled method called QuerySnout to detect attacks on QBS.

QBS give analysts access to collections of statistics gathered from individual-level data like location and demographics...

Researchers trained a machine learning tool to capture the physics of electrons moving on a lattice using far fewer equations than would typically be required, all without sacrificing accuracy.

Using artificial intelligence, physicists have compressed a daunting quantum problem that until now requir...

Researchers from the McKelvey School of Engineering at Washington University in St. Louis have developed a machine learning algorithm that can create a continuous 3D model of cells from a partial set of 2D images that were taken using the same standard microscopy tools found in many labs today.

Their findings were published Sept. 16 in the journal Nature Machine Intelligence.

“We train the model on the set of digital images to obtain a continuous representation,” said Ulugbek Kamilov, assistant professor of electrical and systems engineering and of computer science and engineering. “Now, I can show it any way I want. I can zoom in smoothly and there is no pixelation.”

The key to this work was the use of a neural field network, a particular kind of machine learning system that...