example: (1,0,0) <-- (OR (0,0,1) (0,0,2)). Using this method, one may describe an arbitrarily complex computing substrate as well as the instructions to be executed on it. Natural language messages This research focuses on the event that we receive a signal or message that is either not directed at us (eavesdropping) or one that is in its natural communicative form. To tackle this difficult, but probable scenario, methods are being developed that will detect if a signal has structure indicative of an intelligent source, categorize the type of structure detected, and then decipher its content, from its physical level encoding and patterns to the parts-of-speech that encode internal and external ontologies. Primarily, this structure modeling focuses on the search for generic human and inter-species language universals to devise computational methods by which language may be discriminated from non-language, and core structural syntactic elements of unknown languages may be detected. Aims of this research include contributing to the understanding of language structure and the detection of intelligent language-like features in signals, in order to aid the search for extraterrestrial intelligence. The problem goal is therefore to separate language from non-language without dialogue, and learn something about the structure of language in the passing. The language may not be human (animals, aliens, computers, etc.), the perceptual space may be unknown, and human language structure cannot be presumed, but must begin somewhere. The language signal should be approached from a naive viewpoint, increasing ignorance and assuming as little as possible. If a sequence can be tokenized, that is, separated into "words", an unknown human language may be distinguished from many other data sequences by the frequency distribution of the tokens. Human languages conform to a Zipfian distribution, while many (but not all) other data sequences do not. It has been proposed that an alien language also might conform to such a distribution. When displayed in a log-log graph of frequency vs. rank, this distribution would appear as a somewhat straight line with a slope of approximately -1. SETI scientist Laurance Doyle explains that the slope of a line that represents individual tokens in a stream of tokens may indicate whether the stream contains linguistic or other structured content. If the line angles at 45°, the stream contains such content. If the line is flat, it does not. CETI researchers Frank Drake (SETI Institute): SETI pioneer, composed the Arecibo message. Dr John Elliott: research