By MIT Technology Review
One of the great challenges of neuroscience is to understand the short-term working memory in the human brain. At the same time, computer scientists would dearly love to reproduce the same kind of memory in silico.
Today, Google’s secretive DeepMind startup, which it bought for $400 million earlier this year, unveils a prototype computer that attempts to mimic some of the properties of the human brain’s short-term working memory. The new computer is a type of neural network that has been adapted to work with an external memory. The result is a computer that learns as it stores memories and can later retrieve them to perform logical tasks beyond those it has been trained to do.
DeepMind’s breakthrough follows a long history of work on short-term memory. In the 1950s, the American cognitive psychologist George Miller carried out one of the more famous experiments in the history of brain science. Miller was interested in the capacity of the human brain’s working memory and set out to measure it with the help of a large number of students who he asked to carry out simple memory tasks.
Miller’s striking conclusion was that the capacity of short-term memory cannot be defined by the amount of information it contains. Instead Miller concluded that the working memory stores information in the form of “chunks” and that it could hold approximately seven of them.
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