Authors: George Rajna
Analog machine learning hardware offers a promising alternative to digital counterparts as a more energy efficient and faster platform. Wave physics based on acoustics and optics is a natural candidate to build analog processors for time-varying signals.  Recent advances in optical neural networks, however, are closing that gap by simulating the way neurons respond in the human brain.  An international team of scientists from Eindhoven University of Technology, University of Texas at Austin, and University of Derby, has developed a revolutionary method that quadratically accelerates artificial intelligence (AI) training algorithms.  Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes-he helped develop technology that evolved into predictive texting and Apple's Siri.  Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology.  Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning-and it has already beaten humans at a complex image comprehension test.  A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning.  Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations.  Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts-a finding that will help scientists further develop the quantum versions. 
Comments: 51 Pages.
[v1] 2020-01-11 04:57:31
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