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Researchers from MIT and other institutions have developed a machine learning-based approach to create better hash functions. Hashing is a core operation in most online databases, which generates codes that replace data inputs and makes it easier to find and retrieve the original information. Traditional hash functions generate codes randomly, which can lead to collisions when searching for one item points a user to many pieces of data with the same hash value. Perfect hash functions are designed to sort data in a way that prevents collisions, but they must be specially constructed for each dataset and take more time to compute.
