This paper reports a system with unexpected features. Starting from scratch, it learns to synthesize the function between the input and the output of a black box, which receives N bits and produces one. After being trained with one pass of a random sample of inputs and outputs, the system reproduces the sample completely but, if the sample size is greater than, or equal to some minimum (that depends on the black box function), the system will forecast correctly outside of the learned sample. The system has been tested with functions that range from easy to difficult and it has survived (up to now) all the tests. One of them is here reported.
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The RM-CELL: A set of old ideas that produce new results
de la Pena Casares, G
Follow ACM SIGART Bulletin
, Volume (97)
Association for Computing Machinery – Jul 1, 1986
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