Researchers at the University of Massachusetts and the Air Force Research Laboratory Information Directorate have recently created a 3D computing circuit that could be used to map and implement complex machine learning algorithms, such convolutional neural networks (CNNs). This 3D circuit, presented in a paper published in Nature Electronics, comprises eight layers of memristors; electrical components that regulate the electrical current flowing in a circuit and directly implement neural network weights in hardware.

“Previously, we developed a very reliable memristive device that meets most requirements of in-memory computing for artificial neural networks, integrated the devices into large 2-D arrays and demonstrated a wide variety of machine intelligence applications,” Prof. Qiangfei Xia, one of the researchers who carried out the study, told TechXplore. “In our recent study, we decided to extend it to the third dimension, exploring the benefit of a rich connectivity in a 3D neural network.”

Essentially, Prof. Xia and his team were able to experimentally demonstrate a 3D computing circuit with eight memristor layers, which can all be engaged in computing processes. Their circuit differs greatly from other previously developed 3D circuits, such as 3D NAND flash, as these systems are usually comprised of layers with different functions (e.g. a sensor layer, a computing layer, a control layer, etc.) stacked or bonded together.

The news: In a fresh spin on manufactured pop, OpenAI has released a neural network called Jukebox that can generate catchy songs in a variety of different styles, from teenybop and country to hip-hop and heavy metal. It even sings—sort of.

How it works: Give it a genre, an artist, and lyrics, and Jukebox will produce a passable pastiche in the style of well-known performers, such as Katy Perry, Elvis Presley or Nas. You can also give it the first few seconds of a song and it will autocomplete the rest.