A revised design for an understanding machine

This paper argues that machine translation programs will be able to solve certain problems, e.g., the resolution of polysemy, only by storing the meaning of natural language words in a medium and a format providing properties similar to those of human “understanding”. It also maintains that all human meaning may be exhaustively represented in terms of readings on a practically infinite number of calibrated standards, or, alternatively, by elaborate constellations of readings on a very small number of “element” standards. It is proposed that representing the meanings of natural language words in terms of such constellations is to represent them in a medium appropriate to serve as a mechanical equivalent of human understanding, at least for the purposes of mechanical translation. Such representation of meaning would also permit the overall body of semantic information to be stratified in accord with the dimensional complexity of concepts. This would allow encyclopedic amounts of information about the meaning of each natural language word to be stored in memory for use when a decision dependent on “understanding” arose, while at the same time only very brief summational symbols of this information would ordinarily be adequate as a translation interlingua. Several general characteristics of such representation and storage of semantic information, and some of the standards possibly usable as element standards, are described.