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Proposes a “Baby Turing Test” that extends the classic Turing Test to measure conversational intelligence, paired with a controlled language built on an extensible semantic-graph representation. The author argues that combining a measurable quality test with a controllable, human-inspectable language model offers a path to general-purpose conversational systems that avoid both the brittleness of hand-built dialog trees and the opacity of pure neural chat models.
A SingularityNET/Aigents source on interpretable, semantic-graph-based conversational intelligence and controlled-language/interlingua design — relevant to the interpretable-NLP and NL-to-symbolic-structure themes of the Semantic Parsing Deep Dive.