Ask a computer to pick a random number and you’ll probably get a response that isn’t completely unpredictable. Because they are deterministic automatons, computers struggle to generate numbers that are truly random. But a new advance on a method known as a randomness extractor makes it easier for machines to roll the dice, generating truly random numbers by harvesting randomness from the environment.
The method improves on previous randomness extractors because it requires only two sources of randomness, and those sources can be very weak. “It’s a big breakthrough on a fundamental problem,” says computer scientist Dana Moshkovitz of MIT. “It’s a huge improvement over anything that was done before.”
Computer scientists Eshan Chattopadhyay and David Zuckerman of the University of Texas at Austin will present the new randomness extractor June 20 in Cambridge, Mass., at the Symposium on the Theory of Computing.
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