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Many recent computing advances derive their inspiration from models of the human brain. For example, researchers have created a machine-learning model that mimics the brain’s ability to recognize new patterns by recalling previously encountered ones. So far, implementations of “associative memory” have largely involved conventional silicon-chip-based computers. Now, Benjamin Lev of Stanford University and colleagues propose a way of implementing associative memory with multiple Bose-Einstein condensates (BECs) and an optical cavity. The researchers say that their method should be better at learning and recognizing patterns than the standard associative memory design.

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